-
-
- Portfolio Analysis Report
- {html_div} - - - """ - - with open("custom_report.html", "w", encoding="utf-8") as f: - f.write(html_template) diff --git a/docs/build/html/_sources/examples/multi_asset.rst.txt b/docs/build/html/_sources/examples/multi_asset.rst.txt deleted file mode 100644 index e36327b9..00000000 --- a/docs/build/html/_sources/examples/multi_asset.rst.txt +++ /dev/null @@ -1,321 +0,0 @@ -Multi-Asset Analysis -==================== - -This example shows how to analyze multiple assets simultaneously using OpenFrame. - -Setting Up Multi-Asset Analysis --------------------------------- - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries, OpenFrame - - # Define asset universe - assets = { - "AAPL": "Apple Inc.", - "GOOGL": "Alphabet Inc.", - "MSFT": "Microsoft Corp.", - "AMZN": "Amazon.com Inc.", - "TSLA": "Tesla Inc.", - "NVDA": "NVIDIA Corp.", - "META": "Meta Platforms Inc.", - "NFLX": "Netflix Inc." - } - - # Download data for all assets - series_list = [] - for ticker, name in assets.items(): - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="3y") - series = OpenTimeSeries.from_df( - dframe=data['Close'] - ) - series.set_new_label(lvl_zero=name) - series_list.append(series) - print(f"Loaded {name}: {series.length} observations") - - # Create OpenFrame - tech_stocks = OpenFrame(constituents=series_list) - print(f"\nCreated frame with {tech_stocks.item_count} assets") - print(f"Common period: {tech_stocks.first_idx} to {tech_stocks.last_idx}") - -Comparative Analysis --------------------- - -.. code-block:: python - - # Get metrics for all assets - all_metrics = tech_stocks.all_properties() - print("=== COMPARATIVE METRICS ===") - print(all_metrics) - - # Focus on key metrics - key_metrics = all_metrics.loc[['Geometric return', 'Volatility', 'Return vol ratio', 'Max drawdown']] - key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown'] - - # Convert to percentages for better readability - percentage_metrics = key_metrics.copy() - percentage_metrics.loc[['Annual Return', 'Volatility', 'Max Drawdown']] *= 100 - - print("\n=== KEY METRICS COMPARISON ===") - print(percentage_metrics.round(2)) - -Ranking Analysis ----------------- - -.. code-block:: python - - # Rank assets by different criteria using openseries metrics - # Get key metrics for ranking - returns = all_metrics.loc['Geometric return'] - volatilities = all_metrics.loc['Volatility'] - sharpe_ratios = all_metrics.loc['Return vol ratio'] - drawdowns = all_metrics.loc['Max drawdown'] - - print("\n=== ASSET RANKINGS ===") - print("Ranked by Return (highest first):") - for i, (asset, ret) in enumerate(returns.sort_values(ascending=False).items(), 1): - print(f" {i}. {asset}: {ret:.2%}") - - print("\nRanked by Volatility (lowest first):") - for i, (asset, vol) in enumerate(volatilities.sort_values(ascending=True).items(), 1): - print(f" {i}. {asset}: {vol:.2%}") - - print("\nRanked by Sharpe Ratio (highest first):") - for i, (asset, sharpe) in enumerate(sharpe_ratios.sort_values(ascending=False).items(), 1): - print(f" {i}. {asset}: {sharpe:.2f}") - - print("\nRanked by Max Drawdown (least negative first):") - for i, (asset, dd) in enumerate(drawdowns.sort_values(ascending=False).items(), 1): - print(f" {i}. {asset}: {dd:.2%}") - -Correlation Analysis --------------------- - -.. code-block:: python - - # Calculate correlation matrix - correlation_matrix = tech_stocks.correl_matrix - print("\n=== CORRELATION MATRIX ===") - print(correlation_matrix.round(3)) - - # Find most and least correlated pairs - corr_pairs = [] - for i in range(len(correlation_matrix.columns)): - for j in range(i+1, len(correlation_matrix.columns)): - asset1 = correlation_matrix.columns[i] - asset2 = correlation_matrix.columns[j] - corr = correlation_matrix.iloc[i, j] - corr_pairs.append((asset1, asset2, corr)) - - # Sort by correlation - corr_pairs.sort(key=lambda x: x[2], reverse=True) - - print("\n=== HIGHEST CORRELATIONS ===") - for asset1, asset2, corr in corr_pairs[:5]: - print(f"{asset1} - {asset2}: {corr:.3f}") - - print("\n=== LOWEST CORRELATIONS ===") - for asset1, asset2, corr in corr_pairs[-5:]: - print(f"{asset1} - {asset2}: {corr:.3f}") - -Risk-Return Analysis --------------------- - -.. code-block:: python - - # Analyze risk-return using openseries metrics - returns = all_metrics.loc['Geometric return'] - volatilities = all_metrics.loc['Volatility'] - sharpe_ratios = all_metrics.loc['Return vol ratio'] - - print("\n=== RISK-RETURN ANALYSIS ===") - for asset in returns.index: - ret_pct = returns[asset] * 100 - vol_pct = volatilities[asset] * 100 - sharpe = sharpe_ratios[asset] - print(f"{asset}: Return={ret_pct:.2f}%, Volatility={vol_pct:.2f}%, Sharpe={sharpe:.2f}") - - # Identify efficient assets (high return per unit risk) - # Calculate 75th percentile threshold manually - sorted_sharpes = sorted(sharpe_ratios.values, reverse=True) - threshold_idx = int(len(sorted_sharpes) * 0.25) - efficient_threshold = sorted_sharpes[threshold_idx] if threshold_idx < len(sorted_sharpes) else sorted_sharpes[-1] - - print(f"\n=== MOST EFFICIENT ASSETS (Sharpe >= {efficient_threshold:.2f}) ===") - for asset, sharpe in sharpe_ratios.items(): - if sharpe >= efficient_threshold: - print(f"{asset}: {sharpe:.2f}") - -Sector/Style Analysis ---------------------- - -.. code-block:: python - - # Group assets by characteristics (example grouping) - asset_groups = { - 'Mega Cap': ['Apple Inc.', 'Microsoft Corp.', 'Alphabet Inc.', 'Amazon.com Inc.'], - 'Growth': ['Tesla Inc.', 'NVIDIA Corp.', 'Netflix Inc.'], - 'Social Media': ['Meta Platforms Inc.'] - } - - print("\n=== GROUP ANALYSIS ===") - for group_name, group_assets in asset_groups.items(): - # Filter assets that exist in our data - group_series = [s for s in tech_stocks.constituents if s.label in group_assets] - - if group_series: - group_frame = OpenFrame(constituents=group_series) - group_metrics = group_frame.all_properties() - - avg_return = group_metrics.loc['Geometric return'].mean() - avg_vol = group_metrics.loc['Volatility'].mean() - avg_sharpe = group_metrics.loc['Return vol ratio'].mean() - - print(f"\n{group_name} ({len(group_series)} assets):") - print(f" Average Return: {avg_return:.2%}") - print(f" Average Volatility: {avg_vol:.2%}") - print(f" Average Sharpe: {avg_sharpe:.2f}") - -Time Series Analysis --------------------- - -.. code-block:: python - - # Rolling correlation analysis - # Pick two assets for detailed analysis - apple = next(s for s in tech_stocks.constituents if "Apple" in s.label) - microsoft = next(s for s in tech_stocks.constituents if "Microsoft" in s.label) - - pair_frame = OpenFrame(constituents=[apple, microsoft]) - rolling_corr = pair_frame.rolling_corr(observations=252) # 1-year rolling - - print(f"\n=== ROLLING CORRELATION: {apple.label} vs {microsoft.label} ===") - print(f"Current correlation: {rolling_corr.iloc[-1, 0]:.3f}") - print(f"Average correlation: {rolling_corr.mean().iloc[0]:.3f}") - print(f"Correlation range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}") - -Performance Attribution ------------------------ - -.. code-block:: python - - # Create equal-weighted portfolio for attribution - portfolio_df = tech_stocks.make_portfolio(name="Tech Portfolio", weight_strat="eq_weights") - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - print(f"\n=== PORTFOLIO vs INDIVIDUAL ASSETS ===") - print(f"Portfolio Return: {portfolio.geo_ret:.2%}") - print(f"Portfolio Volatility: {portfolio.vol:.2%}") - print(f"Portfolio Sharpe: {portfolio.ret_vol_ratio:.2f}") - - # Compare with individual assets using OpenFrame - asset_metrics = tech_stocks.all_properties() - individual_returns = asset_metrics.loc['Geometric return'].values - individual_vols = asset_metrics.loc['Volatility'].values - - print(f"\nDiversification benefit:") - equal_weights = [1/tech_stocks.item_count] * tech_stocks.item_count - # Calculate weighted average manually - weighted_avg_return = sum(ret * w for ret, w in zip(individual_returns, equal_weights)) - weighted_avg_vol = sum(vol * w for vol, w in zip(individual_vols, equal_weights)) - print(f" Weighted avg return: {weighted_avg_return:.2%}") - print(f" Portfolio return: {portfolio.geo_ret:.2%}") - print(f" Weighted avg volatility: {weighted_avg_vol:.2%}") - print(f" Portfolio volatility: {portfolio.vol:.2%}") - print(f" Volatility reduction: {(weighted_avg_vol - portfolio.vol):.2%}") - -Stress Testing --------------- - -.. code-block:: python - - # Identify worst market days (modifies original) - market_proxy = tech_stocks.constituents[0] # Use first asset as market proxy - market_proxy.value_to_ret() - market_data = market_proxy.tsdf - # Find worst 5% of days - worst_threshold = market_data.quantile(0.05) - worst_days = market_data[market_data <= worst_threshold] - - print(f"\n=== STRESS TEST ANALYSIS ===") - print(f"Market stress threshold: {worst_threshold:.2%}") - print(f"Number of stress days: {len(worst_days)}") - - # Analyze each asset's performance during stress - print("\nAsset performance during market stress:") - for series in tech_stocks.constituents: - series.value_to_ret() # Modifies original - asset_data = series.tsdf - # Get returns on stress days - stress_returns = asset_data.loc[worst_days.index] - avg_stress_return = stress_returns.mean() - - print(f" {series.label}: {avg_stress_return:.2%}") - -Export Multi-Asset Results --------------------------- - -.. code-block:: python - - # Export using openseries native methods - # Export frame data - tech_stocks.to_xlsx('multi_asset_analysis.xlsx') - - # Note: For comprehensive Excel export with multiple sheets, - # you can use the DataFrame returned by all_properties() and correl_matrix - # which are pandas DataFrames and support to_excel() method - print("\nMulti-asset analysis exported to 'multi_asset_analysis.xlsx'") - -Complete Multi-Asset Analysis Workflow ---------------------------------------- - -Here's how to perform a complete multi-asset analysis using openseries methods directly: - -.. code-block:: python - - # Example: Analyze tech stocks using openseries methods - tech_tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"] - - # Load data using openseries methods - series_list = [] - for ticker in tech_tickers: - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="3y") - series = OpenTimeSeries.from_df(dframe=data['Close']) - series.set_new_label(lvl_zero=ticker) - series_list.append(series) - - if not series_list: - print("No data loaded") - else: - # Create frame using openseries - frame = OpenFrame(constituents=series_list) - - # Analysis using openseries properties and methods - print(f"=== MULTI-ASSET ANALYSIS ===") - print(f"Assets: {frame.item_count}") - print(f"Period: {frame.first_idx} to {frame.last_idx}") - - # Key metrics using openseries all_properties method - key_metrics = frame.all_properties( - properties=['geo_ret', 'vol', 'ret_vol_ratio', 'max_drawdown'] - ) - - print("\nKey Metrics:") - print((key_metrics * 100).round(2)) # Convert to percentages - - # Correlations using openseries correl_matrix property - correlations = frame.correl_matrix - avg_correlation = correlations.mean().mean() - print(f"\nAverage correlation: {avg_correlation:.3f}") - - # Create portfolio using openseries make_portfolio method - portfolio_df = frame.make_portfolio(name="Equal Weight", weight_strat="eq_weights") - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - print(f"\nEqual-weight portfolio:") - print(f" Return: {portfolio.geo_ret:.2%}") - print(f" Volatility: {portfolio.vol:.2%}") - print(f" Sharpe: {portfolio.ret_vol_ratio:.2f}") diff --git a/docs/build/html/_sources/examples/portfolio_optimization.rst.txt b/docs/build/html/_sources/examples/portfolio_optimization.rst.txt deleted file mode 100644 index c1eba979..00000000 --- a/docs/build/html/_sources/examples/portfolio_optimization.rst.txt +++ /dev/null @@ -1,555 +0,0 @@ -Portfolio Optimization -====================== - -This example demonstrates various portfolio optimization techniques using openseries, including both theoretical approaches and real-world applications with actual fund data. - -Basic Portfolio Optimization Setup ------------------------------------ - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries, OpenFrame - from openseries import efficient_frontier, simulate_portfolios - - # Define investment universe - universe = { - "VTI": "Total Stock Market", - "VEA": "Developed Markets", - "VWO": "Emerging Markets", - "BND": "Total Bond Market", - "VNQ": "Real Estate", - "VDE": "Energy", - "VGT": "Technology", - "VHT": "Healthcare" - } - - # Load data - assets = [] - for ticker, name in universe.items(): - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="5y") - series = OpenTimeSeries.from_df(dframe=data['Close']) - series.set_new_label(lvl_zero=name) - assets.append(series) - print(f"Loaded {name}") - - # Create investment universe frame - investment_universe = OpenFrame(constituents=assets) - print(f"\nInvestment universe: {investment_universe.item_count} assets") - print(f"Period: {investment_universe.first_idx} to {investment_universe.last_idx}") - -Mean-Variance Optimization --------------------------- - -.. code-block:: python - - # Calculate efficient frontier - # This may fail with various exceptions - frontier_df, simulated_df, optimal_portfolio = efficient_frontier( - eframe=investment_universe, - num_ports=100, - seed=42 - ) - - print("=== EFFICIENT FRONTIER RESULTS ===") - print(f"Generated {len(frontier_df)} efficient portfolios") - print(f"Simulated {len(simulated_df)} random portfolios") - - # Find key portfolios - returns = frontier_df['ret'] - volatilities = frontier_df['stdev'] - sharpe_ratios = returns / volatilities - - # Maximum Sharpe ratio portfolio - max_sharpe_idx = sharpe_ratios.idxmax() - max_sharpe_weights = optimal_portfolio[-len(investment_universe.constituents):] - - print(f"\n=== MAXIMUM SHARPE RATIO PORTFOLIO ===") - print(f"Expected Return: {frontier_df.iloc[max_sharpe_idx]['ret']:.2%}") - print(f"Volatility: {frontier_df.iloc[max_sharpe_idx]['stdev']:.2%}") - print(f"Sharpe Ratio: {sharpe_ratios.iloc[max_sharpe_idx]:.2f}") - - print("\nOptimal Weights:") - for i, weight in enumerate(max_sharpe_weights): - asset_name = investment_universe.constituents[i].label - if weight > 0.01: # Only show weights > 1% - print(f" {asset_name}: {weight:.1%}") - - # Minimum volatility portfolio - min_vol_idx = volatilities.idxmin() - min_vol_weights = frontier_df.iloc[min_vol_idx][investment_universe.columns_lvl_zero].values - - print(f"\n=== MINIMUM VOLATILITY PORTFOLIO ===") - print(f"Expected Return: {min_vol_row['ret']:.2%}") - print(f"Volatility: {min_vol_row['stdev']:.2%}") - print(f"Sharpe Ratio: {sharpe_ratios.iloc[min_vol_idx]:.2f}") - - print("\nMinimum Volatility Weights:") - for col in investment_universe.columns_lvl_zero: - weight = min_vol_row[col] - if weight > 0.01: - print(f" {col}: {weight:.1%}") - -Monte Carlo Portfolio Simulation --------------------------------- - -.. code-block:: python - - # Generate random portfolios - # This may fail with various exceptions - simulation_results = simulate_portfolios( - simframe=investment_universe, - num_ports=50000, - seed=42 - ) - - print(f"\n=== MONTE CARLO SIMULATION ===") - print(f"Simulated {len(simulation_results)} random portfolios") - - sim_returns = simulation_results['ret'].values - sim_volatilities = simulation_results['stdev'].values - sim_sharpe_ratios = sim_returns / sim_volatilities - - # Statistics of simulated portfolios - print(f"\nSimulation Statistics:") - print(f"Return range: {sim_returns.min():.2%} to {sim_returns.max():.2%}") - print(f"Volatility range: {sim_volatilities.min():.2%} to {sim_volatilities.max():.2%}") - print(f"Sharpe range: {sim_sharpe_ratios.min():.2f} to {sim_sharpe_ratios.max():.2f}") - - # Best portfolios from simulation - sorted_indices = sorted(range(len(sim_sharpe_ratios)), key=lambda i: sim_sharpe_ratios.iloc[i], reverse=True) - top_sharpe_indices = sorted_indices[:5] - - print(f"\n=== TOP 5 SIMULATED PORTFOLIOS ===") - for i, idx in enumerate(reversed(top_sharpe_indices)): - print(f"\nRank {i+1}:") - print(f" Return: {sim_returns[idx]:.2%}") - print(f" Volatility: {sim_volatilities[idx]:.2%}") - print(f" Sharpe: {sim_sharpe_ratios[idx]:.2f}") - - weights = simulation_results.iloc[idx][investment_universe.columns_lvl_zero].values - print(" Weights:") - for j, weight in enumerate(weights): - if weight > 0.05: # Only show weights > 5% - asset_name = investment_universe.constituents[j].label - print(f" {asset_name}: {weight:.1%}") - -Risk-Based Portfolio Strategies -------------------------------- - -Equal Weight Portfolio -~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Equal weight portfolio using native weight_strat - equal_weight_portfolio_df = investment_universe.make_portfolio( - name="Equal Weight", - weight_strat="eq_weights" - ) - equal_weight_portfolio = OpenTimeSeries.from_df(dframe=equal_weight_portfolio_df) - - print(f"\n=== EQUAL WEIGHT PORTFOLIO ===") - print(f"Return: {equal_weight_portfolio.geo_ret:.2%}") - print(f"Volatility: {equal_weight_portfolio.vol:.2%}") - print(f"Sharpe: {equal_weight_portfolio.ret_vol_ratio:.2f}") - -Inverse Volatility Portfolio -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Inverse volatility weighting using native weight_strat - inv_vol_portfolio_df = investment_universe.make_portfolio( - name="Inverse Volatility", - weight_strat="inv_vol" - ) - inv_vol_portfolio = OpenTimeSeries.from_df(dframe=inv_vol_portfolio_df) - - print(f"\n=== INVERSE VOLATILITY PORTFOLIO ===") - print(f"Return: {inv_vol_portfolio.geo_ret:.2%}") - print(f"Volatility: {inv_vol_portfolio.vol:.2%}") - print(f"Sharpe: {inv_vol_portfolio.ret_vol_ratio:.2f}") - - -Maximum Diversification Portfolio -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The maximum diversification strategy aims to maximize portfolio diversification by optimizing the correlation structure. This strategy can encounter numerical issues in certain scenarios: - -.. code-block:: python - - # Maximum diversification portfolio using native weight_strat - # This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError - max_div_portfolio_df = investment_universe.make_portfolio( - name="Maximum Diversification", - weight_strat="max_div" - ) - max_div_portfolio = OpenTimeSeries.from_df(dframe=max_div_portfolio_df) - - print(f"\n=== MAXIMUM DIVERSIFICATION PORTFOLIO ===") - print(f"Return: {max_div_portfolio.geo_ret:.2%}") - print(f"Volatility: {max_div_portfolio.vol:.2%}") - print(f"Sharpe: {max_div_portfolio.ret_vol_ratio:.2f}") - -Minimum Volatility Overweight Portfolio ----------------------------------------- - -.. code-block:: python - - # Minimum volatility overweight portfolio using native weight_strat - min_vol_portfolio_df = investment_universe.make_portfolio( - name="Min Vol Overweight", - weight_strat="min_vol_overweight" - ) - min_vol_portfolio = OpenTimeSeries.from_df(dframe=min_vol_portfolio_df) - - print(f"\n=== MINIMUM VOLATILITY OVERWEIGHT PORTFOLIO ===") - print(f"Return: {min_vol_portfolio.geo_ret:.2%}") - print(f"Volatility: {min_vol_portfolio.vol:.2%}") - print(f"Sharpe: {min_vol_portfolio.ret_vol_ratio:.2f}") - -Portfolio Comparison --------------------- - -.. code-block:: python - - # Compare all portfolio strategies - portfolios = [ - equal_weight_portfolio, - inv_vol_portfolio, - max_div_portfolio, - min_vol_portfolio - ] - - # Add optimized portfolios if available - if 'max_sharpe_weights' in locals(): - investment_universe.weights = max_sharpe_weights.tolist() - max_sharpe_portfolio_df = investment_universe.make_portfolio( - name="Max Sharpe (Optimized)" - ) - max_sharpe_portfolio = OpenTimeSeries.from_df(dframe=max_sharpe_portfolio_df) - portfolios.append(max_sharpe_portfolio) - - if 'min_vol_weights' in locals(): - investment_universe.weights = min_vol_weights.tolist() - min_vol_portfolio_df = investment_universe.make_portfolio( - name="Min Vol (Optimized)" - ) - min_vol_portfolio = OpenTimeSeries.from_df(dframe=min_vol_portfolio_df) - portfolios.append(min_vol_portfolio) - - # Create comparison frame - comparison_frame = OpenFrame(constituents=portfolios) - comparison_metrics = comparison_frame.all_properties() - - # Display key metrics - key_metrics = comparison_metrics.loc[['geo_ret', 'vol', 'ret_vol_ratio', 'max_drawdown']] - key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown'] - - print(f"\n=== PORTFOLIO STRATEGY COMPARISON ===") - print((key_metrics * 100).round(2)) # Convert to percentages - -Weight Strategy Details -~~~~~~~~~~~~~~~~~~~~~~~ - -The openseries library provides several built-in weight strategies for portfolio construction: - -**Equal Weights (``eq_weights``)** - - Assigns equal weight to all assets - - Most robust strategy, always works - - Good baseline for comparison - -**Inverse Volatility (``inv_vol``)** - - Weights assets inversely to their volatility - - Lower volatility assets get higher weights - - Generally stable and reliable - -**Maximum Diversification (``max_div``)** - - Optimizes correlation structure for maximum diversification - - Can encounter numerical issues with certain data patterns - - May produce negative weights in some scenarios - - Raises ``MaxDiversificationNaNError`` for numerical issues - - Raises ``MaxDiversificationNegativeWeightsError`` for negative weights - -**Minimum Volatility Overweight (``min_vol_overweight``)** - - Overweights the least volatile asset (60% weight) - - Distributes remaining 40% equally among other assets - - Based on the low volatility anomaly - -**Exception Handling** - When using the maximum diversification strategy, it's recommended to handle potential exceptions: - - .. code-block:: python - - from openseries.owntypes import ( - MaxDiversificationNaNError, - MaxDiversificationNegativeWeightsError - ) - - # This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError - portfolio_df = frame.make_portfolio(name="Max Div", weight_strat="max_div") - -Backtesting Framework ---------------------- - -.. code-block:: python - - # Define strategies to backtest using native weight_strat - strategies = { - 'Equal Weight': 'eq_weights', - 'Inverse Volatility': 'inv_vol', - 'Max Diversification': 'max_div', - 'Min Vol Overweight': 'min_vol_overweight' - } - - # Run backtest using native strategies - backtest_results = {} - for strategy_name, weight_strat in strategies.items(): - # This may fail with MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError, or other exceptions - portfolio_df = investment_universe.make_portfolio( - name=strategy_name, - weight_strat=weight_strat - ) - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - backtest_results[strategy_name] = { - 'return': portfolio.geo_ret, - 'volatility': portfolio.vol, - 'sharpe': portfolio.ret_vol_ratio, - 'max_drawdown': portfolio.max_drawdown, - 'calmar': portfolio.geo_ret / abs(portfolio.max_drawdown) if portfolio.max_drawdown != 0 else float('nan') - } - - print(f"\n=== BACKTEST RESULTS ===") - for strategy_name, metrics in backtest_results.items(): - print(f"\n{strategy_name}:") - print(f" Return: {metrics['return']:.4f}") - print(f" Volatility: {metrics['volatility']:.4f}") - print(f" Sharpe: {metrics['sharpe']:.4f}") - print(f" Max Drawdown: {metrics['max_drawdown']:.4f}") - print(f" Calmar: {metrics['calmar']:.4f}") - - # Rank strategies - sorted_strategies = sorted(backtest_results.items(), key=lambda x: x[1]['sharpe'], reverse=True) - best_strategy = sorted_strategies[0][0] - - print(f"\nBest performing strategy: {best_strategy}") - print(f"Sharpe ratio: {sorted_strategies[0][1]['sharpe']:.3f}") - -Export Optimization Results ---------------------------- - -.. code-block:: python - - # Export using openseries native methods - # Export frame data - investment_universe.to_xlsx('portfolio_optimization_results.xlsx') - - # Note: For comprehensive Excel export with multiple sheets, - # the DataFrames returned by all_properties() and correl_matrix - # are pandas DataFrames and support to_excel() method - print("\nOptimization results exported to 'portfolio_optimization_results.xlsx'") - -Real-World Fund Portfolio Optimization ---------------------------------------- - -This section demonstrates portfolio optimization using actual fund data from professional fund managers, showing how optimization techniques apply in practice. - -Using Real Fund Data for Optimization -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Here's how to work with real fund data using openseries methods directly: - -.. code-block:: python - - from requests import get as requests_get - from openseries import ( - OpenTimeSeries, OpenFrame, ValueType, - efficient_frontier, prepare_plot_data, sharpeplot, - load_plotly_dict, get_previous_business_day_before_today - ) - - # Define fund universe for optimization - fund_universe_isins = [ - "SE0015243886", # Global High Yield - "SE0011337195", # Global Equity - "SE0011670843", # Global Bond - "SE0017832280", # Alternative Strategy - "SE0017832330", # Multi-Asset Strategy - ] - - # Load fund data using openseries methods - response = requests_get(url="https://api.captor.se/public/api/nav", timeout=10) - response.raise_for_status() - - series_list = [] - result = response.json() - - for data in result: - if data["isin"] in fund_universe_isins: - series = OpenTimeSeries.from_arrays( - name=data["longName"], - isin=data["isin"], - baseccy=data["currency"], - dates=data["dates"], - values=data["navPerUnit"], - valuetype=ValueType.PRICE, - ) - series_list.append(series) - - # Create fund universe using openseries OpenFrame - fund_universe = OpenFrame(constituents=series_list) - - # Process data using openseries methods - fund_universe = fund_universe.value_nan_handle().trunc_frame().to_cumret() - - print(f"Fund universe created with {fund_universe.item_count} funds") - print(f"Analysis period: {fund_universe.first_idx} to {fund_universe.last_idx}") - -Advanced Optimization with Real Data -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Set optimization parameters - simulations = 10000 - frontier_points = 50 - seed = 55 - - # Create current portfolio (equal weights) - current_portfolio_df = fund_universe.make_portfolio( - name="Current Portfolio", - weight_strat="eq_weights", - ) - current_portfolio = OpenTimeSeries.from_df(dframe=current_portfolio_df) - - # Calculate efficient frontier - frontier, simulated_portfolios, optimal_portfolio = efficient_frontier( - eframe=fund_universe, - num_ports=simulations, - seed=seed, - frontier_points=frontier_points, - ) - - # Prepare visualization data - plot_data = prepare_plot_data( - assets=fund_universe, - current=current_portfolio, - optimized=optimal_portfolio, - ) - - # Load plotly configuration - figdict, _ = load_plotly_dict() - - # Create efficient frontier plot - optimization_plot, _ = sharpeplot( - sim_frame=simulated_portfolios, - line_frame=frontier, - point_frame=plot_data, - point_frame_mode="markers+text", - title="Real Fund Portfolio Optimization", - add_logo=False, - auto_open=False, - output_type="div", - ) - optimization_plot = optimization_plot.update_layout(width=1200, height=700) - - # Display the optimization results - optimization_plot.show(config=figdict["config"]) - -Performance Comparison Analysis -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Compare different portfolio strategies - strategies = {} - - # Equal weight portfolio - equal_weight_portfolio_df = fund_universe.make_portfolio( - name="Equal Weight", weight_strat="eq_weights" - ) - equal_weight_portfolio = OpenTimeSeries.from_df(dframe=equal_weight_portfolio_df) - strategies['Equal Weight'] = equal_weight_portfolio - - # Optimal portfolio from efficient frontier - fund_universe.weights = optimal_portfolio[-fund_universe.item_count:].tolist() - optimal_portfolio_df = fund_universe.make_portfolio(name="Optimal Portfolio") - optimal_portfolio_series = OpenTimeSeries.from_df(dframe=optimal_portfolio_df) - strategies['Optimal Portfolio'] = optimal_portfolio_series - - # Create comparison frame - comparison_frame = OpenFrame(constituents=list(strategies.values())) - comparison_metrics = comparison_frame.all_properties() - - # Display key metrics - key_metrics = comparison_metrics.loc[['geo_ret', 'vol', 'ret_vol_ratio', 'max_drawdown']] - key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown'] - - print("=== PORTFOLIO STRATEGY COMPARISON ===") - print((key_metrics * 100).round(2)) - - # Calculate improvement metrics - improvement = { - 'Return Improvement': (optimal_portfolio_series.geo_ret - equal_weight_portfolio.geo_ret) * 100, - 'Volatility Change': (optimal_portfolio_series.vol - equal_weight_portfolio.vol) * 100, - 'Sharpe Improvement': optimal_portfolio_series.ret_vol_ratio - equal_weight_portfolio.ret_vol_ratio, - } - - print("\n=== OPTIMIZATION IMPROVEMENTS ===") - for metric, value in improvement.items(): - print(f"{metric}: {value:+.2f}") - -Complete Optimization Workflow ------------------------------- - -Here's how to perform portfolio optimization using openseries methods directly: - -.. code-block:: python - - # Example: Optimize ETF portfolio using openseries methods - etf_tickers = ["VTI", "VEA", "VWO", "BND", "VNQ"] - - # Load data using openseries methods - assets = [] - for ticker in etf_tickers: - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="5y") - series = OpenTimeSeries.from_df(dframe=data['Close']) - series.set_new_label(lvl_zero=ticker) - assets.append(series) - - if len(assets) < 2: - print("Need at least 2 assets for optimization") - else: - frame = OpenFrame(constituents=assets) - - # Use openseries native weight strategies - strategies = { - 'Equal Weight': 'eq_weights', - 'Inverse Volatility': 'inv_vol', - 'Max Diversification': 'max_div', - 'Min Vol Overweight': 'min_vol_overweight' - } - - # Create portfolios using openseries make_portfolio method - results = {} - for name, weight_strat in strategies.items(): - # This may fail with MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError, or other exceptions - portfolio_df = frame.make_portfolio(name=name, weight_strat=weight_strat) - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - results[name] = { - 'Return': portfolio.geo_ret, - 'Volatility': portfolio.vol, - 'Sharpe': portfolio.ret_vol_ratio, - 'Max Drawdown': portfolio.max_drawdown - } - - print("=== PORTFOLIO OPTIMIZATION RESULTS ===") - for name, metrics in results.items(): - print(f"\n{name}:") - print(f" Return: {metrics['Return']*100:.2f}%") - print(f" Volatility: {metrics['Volatility']*100:.2f}%") - print(f" Sharpe: {metrics['Sharpe']:.2f}") - print(f" Max Drawdown: {metrics['Max Drawdown']*100:.2f}%") diff --git a/docs/build/html/_sources/examples/single_asset.rst.txt b/docs/build/html/_sources/examples/single_asset.rst.txt deleted file mode 100644 index b44ab219..00000000 --- a/docs/build/html/_sources/examples/single_asset.rst.txt +++ /dev/null @@ -1,197 +0,0 @@ -Single Asset Analysis -===================== - -This example demonstrates comprehensive analysis of a single financial asset using openseries. - -Basic Setup ------------ - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries - import numpy as np - - # Download Apple stock data - ticker = yf.Ticker("AAPL") - data = ticker.history(period="5y") - - # Create OpenTimeSeries - apple = OpenTimeSeries.from_df( - dframe=data['Close'] - ) - - # Set descriptive label - apple.set_new_label(lvl_zero="Apple Inc. (AAPL)") - - print(f"Loaded {apple.length} observations") - print(f"Date range: {apple.first_idx} to {apple.last_idx}") - -Performance Analysis --------------------- - -.. code-block:: python - - # Basic performance metrics - print("=== PERFORMANCE METRICS ===") - print(f"Total Return: {apple.value_ret:.2%}") - print(f"Annualized Return: {apple.geo_ret:.2%}") - print(f"Annualized Volatility: {apple.vol:.2%}") - print(f"Sharpe Ratio: {apple.ret_vol_ratio:.2f}") - - # Get all metrics at once - all_metrics = apple.all_properties() - print("\n=== ALL METRICS ===") - print(all_metrics) - -Risk Analysis -------------- - -.. code-block:: python - - # Risk metrics - print("=== RISK ANALYSIS ===") - print(f"Maximum Drawdown: {apple.max_drawdown:.2%}") - print(f"Max Drawdown Date: {apple.max_drawdown_date}") - print(f"95% VaR (daily): {apple.var_down:.2%}") - print(f"95% CVaR (daily): {apple.cvar_down:.2%}") - print(f"Worst Single Day: {apple.worst:.2%}") - print(f"Sortino Ratio: {apple.sortino_ratio:.2f}") - -Time Series Transformations ---------------------------- - -.. code-block:: python - - # Convert to returns (modifies original) - apple.value_to_ret() - print(f"Returns series length: {apple.length}") - - # Create drawdown series (modifies original) - apple.to_drawdown_series() - - # Convert to log returns (modifies original) - apple.value_to_log() - - # Resample to monthly (modifies original) - apple.resample_to_business_period_ends(freq="BME") - print(f"Monthly data points: {apple.length}") - -Rolling Analysis ----------------- - -.. code-block:: python - - # Rolling volatility (1-year window) - rolling_vol = apple.rolling_vol(observations=252) - print(f"Current 1Y volatility: {rolling_vol.iloc[-1, 0]:.2%}") - print(f"Average 1Y volatility: {rolling_vol.mean().iloc[0]:.2%}") - - # Rolling returns (30-day) - rolling_returns = apple.rolling_return(observations=30) - - # Rolling VaR - rolling_var = apple.rolling_var_down(observations=252) - -Visualization -------------- - -.. code-block:: python - - # Plot price series - fig, _ = apple.plot_series() - - # Plot returns histogram - fig, _ = apple_returns.plot_histogram() - - # Plot drawdown series - fig, _ = apple_drawdowns.plot_series() - -Calendar Analysis ------------------ - -.. code-block:: python - - # Annual returns by calendar year - years = [2019, 2020, 2021, 2022, 2023, 2024] - - print("=== CALENDAR YEAR RETURNS ===") - for year in years: - # This may fail if no data exists for the year - year_return = apple.value_ret_calendar_period(year=year) - print(f"{year}: {year_return:.2%}") - -Export Results --------------- - -.. code-block:: python - - # Export to Excel - apple.to_xlsx("apple_analysis.xlsx") - - # Export metrics to CSV - all_metrics.to_csv("apple_metrics.csv") - - # Export to JSON - apple.to_json("apple_data.json") - -Complete Analysis Workflow ----------------------------- - -Here's how to perform comprehensive single asset analysis using openseries methods directly: - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries - - # Example: Analyze Apple stock using openseries methods - ticker_symbol = "AAPL" - - # Download data using openseries methods - ticker = yf.Ticker(ticker_symbol) - data = ticker.history(period="5y") - - # Create series using openseries from_df method - series = OpenTimeSeries.from_df( - dframe=data['Close'], - name=ticker_symbol - ) - - # Analysis using openseries properties and methods - print(f"=== {ticker_symbol} ANALYSIS ===") - print(f"Period: {series.first_idx} to {series.last_idx}") - print(f"Observations: {series.length}") - - # Key metrics using openseries properties - metrics = { - 'Total Return': f"{series.value_ret:.2%}", - 'Annual Return': f"{series.geo_ret:.2%}", - 'Volatility': f"{series.vol:.2%}", - 'Sharpe Ratio': f"{series.ret_vol_ratio:.2f}", - 'Max Drawdown': f"{series.max_drawdown:.2%}", - '95% VaR': f"{series.var_down:.2%}", - 'Skewness': f"{series.skew:.2f}", - 'Kurtosis': f"{series.kurtosis:.2f}" - } - - for metric, value in metrics.items(): - print(f"{metric}: {value}") - - # Export results using openseries to_xlsx method - filename = f"{ticker_symbol.lower()}_analysis.xlsx" - series.to_xlsx(filename) - print(f"\nResults exported to {filename}") - - # Example: Analyze multiple assets - tickers = ["AAPL", "TSLA", "MSFT"] - for ticker_symbol in tickers: - ticker = yf.Ticker(ticker_symbol) - data = ticker.history(period="2y") - series = OpenTimeSeries.from_df(dframe=data['Close']) - series.set_new_label(lvl_zero=ticker_symbol) - - print(f"\n{ticker_symbol}:") - print(f" Return: {series.geo_ret:.2%}") - print(f" Volatility: {series.vol:.2%}") - print(f" Sharpe: {series.ret_vol_ratio:.2f}") diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt deleted file mode 100644 index 1ccc713b..00000000 --- a/docs/build/html/_sources/index.rst.txt +++ /dev/null @@ -1,152 +0,0 @@ -openseries Documentation -======================== - -.. image:: https://img.shields.io/pypi/v/openseries.svg - :target: https://pypi.org/project/openseries/ - :alt: PyPI version - -.. image:: https://img.shields.io/conda/vn/conda-forge/openseries.svg - :target: https://anaconda.org/conda-forge/openseries - :alt: Conda Version - -.. image:: https://img.shields.io/badge/platforms-Windows%20%7C%20macOS%20%7C%20Linux-blue - :alt: Platform - -.. image:: https://img.shields.io/pypi/pyversions/openseries.svg - :target: https://www.python.org/ - :alt: Python version - -.. image:: https://github.com/CaptorAB/openseries/actions/workflows/test.yml/badge.svg - :target: https://github.com/CaptorAB/openseries/actions/workflows/test.yml - :alt: GitHub Action Test Suite - -.. image:: https://img.shields.io/codecov/c/gh/CaptorAB/openseries?logo=codecov - :target: https://codecov.io/gh/CaptorAB/openseries/branch/master - :alt: codecov - -.. image:: https://img.shields.io/github/license/CaptorAB/openseries - :target: https://github.com/CaptorAB/openseries/blob/master/LICENSE.md - :alt: GitHub License - -**openseries** is a Python library for analyzing financial time series data. It provides tools to work with single assets or groups of assets, designed specifically for daily or less frequent data. - -The library is built around two main classes: - -- **OpenTimeSeries**: For managing and analyzing individual time series -- **OpenFrame**: For managing groups of time series and portfolio analysis - -Key Features ------------- - -- **Financial Analysis**: Comprehensive set of financial metrics and ratios -- **Risk Management**: VaR, CVaR, drawdown analysis, and risk-adjusted returns -- **Portfolio Tools**: Portfolio optimization, rebalancing, and performance attribution -- **Visualization**: Interactive plots using Plotly -- **Data Handling**: Robust date handling and business day calendars -- **Type Safety**: Built with Pydantic for data validation and type safety - -Quick Start ------------ - -Install openseries using pip: - -.. code-block:: bash - - pip install openseries - -Or using conda: - -.. code-block:: bash - - conda install -c conda-forge openseries - -Here's a simple example to get you started: - -.. code-block:: python - - from openseries import OpenTimeSeries - import yfinance as yf - - # Download data - ticker = yf.Ticker("^GSPC") - history = ticker.history(period="5y") - - # Create OpenTimeSeries - series = OpenTimeSeries.from_df(dframe=history.loc[:, "Close"]) - series.set_new_label(lvl_zero="S&P 500") - - # Calculate key metrics - print(f"Annual Return: {series.geo_ret:.2%}") - print(f"Volatility: {series.vol:.2%}") - print(f"Sharpe Ratio: {series.ret_vol_ratio:.2f}") - print(f"Max Drawdown: {series.max_drawdown:.2%}") - - # Create interactive plot - series.plot_series() - -Documentation Contents ----------------------- - -.. toctree:: - :maxdepth: 2 - :caption: User Guide - - user_guide/installation - user_guide/quickstart - user_guide/core_concepts - user_guide/data_handling - -.. toctree:: - :maxdepth: 2 - :caption: Tutorials - - tutorials/basic_analysis - tutorials/portfolio_analysis - tutorials/risk_management - tutorials/advanced_features - -.. toctree:: - :maxdepth: 2 - :caption: Examples - - examples/single_asset - examples/multi_asset - examples/portfolio_optimization - examples/rebalanced_portfolio - examples/custom_reports - -.. toctree:: - :maxdepth: 1 - :caption: Important Notes - - api_consistency - -Python Version Support ----------------------- - -.. toctree:: - :maxdepth: 2 - :caption: API Reference - - api/openseries - api/series - api/frame - api/portfoliotools - api/simulation - api/report - api/datefixer - api/types - -.. toctree:: - :maxdepth: 1 - :caption: Development - - development/contributing - development/changelog - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/build/html/_sources/tutorials/advanced_features.rst.txt b/docs/build/html/_sources/tutorials/advanced_features.rst.txt deleted file mode 100644 index bf4cf172..00000000 --- a/docs/build/html/_sources/tutorials/advanced_features.rst.txt +++ /dev/null @@ -1,227 +0,0 @@ -Advanced Features -================= - -This tutorial covers advanced openseries features including custom analysis, integration with other libraries, and extending functionality. - -Factor Analysis and Regression ------------------------------- - -Multi-Factor Model Analysis -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries, OpenFrame - - # Load factor data (Fama-French factors would be ideal, using proxies here) - factor_tickers = { - "^GSPC": "Market", - "^RUT": "Small Cap", # Size factor proxy - "EFA": "International", # International factor - "TLT": "Bonds" # Interest rate factor - } - - # Load factor data - factor_series = [] - for ticker, name in factor_tickers.items(): - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="3y") - series = OpenTimeSeries.from_df(dframe=data['Close']) - series.set_new_label(lvl_zero=name) - factor_series.append(series) - - # Create factor frame - factors = OpenFrame(constituents=factor_series) - - # Load individual stock for analysis - stock_data = yf.Ticker("AAPL").history(period="3y") - apple = OpenTimeSeries.from_df(dframe=stock_data['Close']) - apple.set_new_label(lvl_zero="Apple") - - # Add stock to factor frame for regression - analysis_frame = OpenFrame(constituents=factor_series + [apple]) - - # Perform multi-factor regression - # This may fail with various exceptions - regression_results = analysis_frame.multi_factor_linear_regression( - dependent_variable_idx=-1 # Apple is the last series (dependent variable) - ) - - print("\n=== MULTI-FACTOR REGRESSION RESULTS ===") - print("Regression Summary:") - print(regression_results['summary']) - - print("\nFactor Loadings (Betas):") - for i, factor_name in enumerate([s.label for s in factor_series]): - beta = regression_results['coefficients'][i+1] # Skip intercept - print(f" {factor_name}: {beta:.4f}") - - print(f"\nR-squared: {regression_results['r_squared']:.4f}") - print(f"Adjusted R-squared: {regression_results['adj_r_squared']:.4f}") - -Rolling Factor Analysis -~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Rolling beta analysis with market - market_series = factor_series[0] # S&P 500 - stock_vs_market = OpenFrame(constituents=[apple, market_series]) - - # Calculate rolling beta - rolling_beta = stock_vs_market.rolling_beta(observations=252) # 1-year rolling - - print(f"\n=== ROLLING BETA ANALYSIS ===") - print(f"Current Beta: {rolling_beta.iloc[-1, 0]:.3f}") - print(f"Average Beta: {rolling_beta.mean().iloc[0]:.3f}") - print(f"Beta Range: {rolling_beta.min().iloc[0]:.3f} to {rolling_beta.max().iloc[0]:.3f}") - print(f"Beta Volatility: {rolling_beta.std().iloc[0]:.3f}") - - # Rolling correlation - rolling_corr = stock_vs_market.rolling_corr(observations=252) - - print(f"\n=== ROLLING CORRELATION ANALYSIS ===") - print(f"Current Correlation: {rolling_corr.iloc[-1, 0]:.3f}") - print(f"Average Correlation: {rolling_corr.mean().iloc[0]:.3f}") - print(f"Correlation Range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}") - -Exporting Custom Plotly Figures ---------------------------------- - -The ``export_plotly_figure`` function allows you to export any Plotly figure to a mobile-responsive HTML file. This is useful when you create custom visualizations using Plotly's graph objects that aren't directly available through openseries plotting methods. - -Creating Custom Plots -~~~~~~~~~~~~~~~~~~~~~ - -You can create any Plotly figure and export it using the same responsive HTML format that openseries uses internally: - -.. code-block:: python - - import plotly.graph_objects as go - from plotly.subplots import make_subplots - from openseries import export_plotly_figure - from pathlib import Path - - # Create a custom subplot figure - fig = make_subplots( - rows=2, cols=2, - subplot_titles=('Price Chart', 'Volume', 'Returns Distribution', 'Drawdown'), - specs=[[{"secondary_y": True}, {"type": "bar"}], - [{"type": "histogram"}, {"type": "scatter"}]] - ) - - # Add traces (example data) - fig.add_trace( - go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13], name="Price"), - row=1, col=1 - ) - fig.add_trace( - go.Bar(x=[1, 2, 3, 4], y=[100, 200, 150, 300], name="Volume"), - row=1, col=2 - ) - fig.add_trace( - go.Histogram(x=[0.01, -0.02, 0.015, -0.01, 0.02], name="Returns"), - row=2, col=1 - ) - fig.add_trace( - go.Scatter(x=[1, 2, 3, 4], y=[0, -0.05, -0.03, -0.08], name="Drawdown"), - row=2, col=2 - ) - - # Update layout - fig.update_layout(height=800, title_text="Custom Multi-Panel Dashboard") - - # Export to responsive HTML - output_path = export_plotly_figure( - figure=fig, - fig_config={"responsive": True}, - output_type="file", - filename="custom_dashboard.html", - include_plotlyjs="cdn", - plotfile=Path("output/custom_dashboard.html"), - title="Custom Financial Dashboard", - auto_open=True, - ) - - print(f"Dashboard saved to: {output_path}") - -Using with Plotly Express -~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can also use ``export_plotly_figure`` with Plotly Express figures: - -.. code-block:: python - - import plotly.express as px - import pandas as pd - from openseries import export_plotly_figure - from pathlib import Path - - # Create sample data - df = pd.DataFrame({ - 'Date': pd.date_range('2020-01-01', periods=100), - 'Asset_A': 100 + pd.Series(range(100)).cumsum() * 0.1, - 'Asset_B': 100 + pd.Series(range(100)).cumsum() * 0.15, - }) - - # Create a Plotly Express figure - fig = px.line( - df, x='Date', y=['Asset_A', 'Asset_B'], - title='Asset Comparison', - labels={'value': 'Price', 'variable': 'Asset'} - ) - - # Export with responsive HTML - export_plotly_figure( - figure=fig, - fig_config={"responsive": True, "displayModeBar": True}, - output_type="file", - filename="asset_comparison.html", - include_plotlyjs="cdn", - plotfile=Path("output/asset_comparison.html"), - title="Asset Price Comparison", - auto_open=False, - ) - -Inline HTML Output -~~~~~~~~~~~~~~~~~~ - -For embedding in web applications or reports, you can generate inline HTML divs: - -.. code-block:: python - - import plotly.graph_objects as go - from openseries import export_plotly_figure - - # Create a simple figure - fig = go.Figure(data=go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13])) - - # Generate inline HTML div - html_div = export_plotly_figure( - figure=fig, - fig_config={}, - output_type="div", - filename="my_plot.html", - include_plotlyjs="cdn", - plotfile=Path("dummy.html"), # Ignored for div output - ) - - # html_div can now be embedded in HTML documents - print(html_div[:100]) # Preview the HTML - -Benefits of export_plotly_figure -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The ``export_plotly_figure`` function provides several advantages over Plotly's default HTML export: - -- **Mobile Responsive**: Automatically adapts to different screen sizes and device orientations -- **Optimized Viewport**: Proper viewport settings for mobile devices -- **Auto-Resize**: JavaScript handles window resizing and orientation changes -- **Consistent Styling**: Uses the same responsive CSS as openseries internal plots -- **Optional Title Container**: Can include a title and logo in a responsive header - -This makes it ideal for creating dashboards and reports that need to work well on both desktop and mobile devices. - - -This tutorial demonstrates how to extend openseries with advanced functionality for sophisticated financial analysis workflows. diff --git a/docs/build/html/_sources/tutorials/basic_analysis.rst.txt b/docs/build/html/_sources/tutorials/basic_analysis.rst.txt deleted file mode 100644 index f0bfa684..00000000 --- a/docs/build/html/_sources/tutorials/basic_analysis.rst.txt +++ /dev/null @@ -1,356 +0,0 @@ -Basic Financial Analysis -======================== - -This tutorial demonstrates how to perform fundamental financial analysis using openseries with real market data. - -Setting Up ----------- - -First, let's import the necessary libraries and download some data: - -.. code-block:: python - - import yfinance as yf - import pandas as pd - import numpy as np - from openseries import OpenTimeSeries, OpenFrame - from datetime import date, datetime - - # Download S&P 500 data for the last 5 years - ticker = yf.Ticker("^GSPC") - data = ticker.history(period="5y") - - # Create OpenTimeSeries - sp500 = OpenTimeSeries.from_df( - dframe=data['Close'] - ) - - # Set a descriptive label - sp500.set_new_label(lvl_zero="S&P 500 Index") - - print(f"Loaded {sp500.length} observations") - print(f"Date range: {sp500.first_idx} to {sp500.last_idx}") - -Basic Performance Metrics --------------------------- - -Let's calculate the fundamental performance metrics: - -.. code-block:: python - - # Total return over the period - total_return = sp500.value_ret - print(f"Total Return: {total_return:.2%}") - - # Annualized return (CAGR) - annual_return = sp500.geo_ret - print(f"Annualized Return (CAGR): {annual_return:.2%}") - - # Arithmetic mean return - arithmetic_return = sp500.arithmetic_ret - print(f"Arithmetic Mean Return: {arithmetic_return:.2%}") - - # Time period analysis - print(f"Investment period: {sp500.yearfrac:.2f} years") - print(f"Number of observations: {sp500.length}") - print(f"Periods per year: {sp500.periods_in_a_year:.1f}") - -Risk Analysis -------------- - -Now let's examine the risk characteristics: - -.. code-block:: python - - # Volatility (annualized standard deviation) - volatility = sp500.vol - print(f"Annualized Volatility: {volatility:.2%}") - - # Downside deviation (volatility of negative returns only) - downside_vol = sp500.downside_deviation - print(f"Downside Deviation: {downside_vol:.2%}") - - # Value at Risk (95% confidence level) - var_95 = sp500.var_down - print(f"95% Value at Risk (daily): {var_95:.2%}") - - # Conditional Value at Risk (Expected Shortfall) - cvar_95 = sp500.cvar_down - print(f"95% CVaR (daily): {cvar_95:.2%}") - - # Maximum single-day loss - worst_day = sp500.worst - print(f"Worst single day: {worst_day:.2%}") - -Risk-Adjusted Returns ---------------------- - -Calculate risk-adjusted performance metrics: - -.. code-block:: python - - # Sharpe Ratio (return per unit of total risk) - sharpe_ratio = sp500.ret_vol_ratio - print(f"Sharpe Ratio: {sharpe_ratio:.2f}") - - # Sortino Ratio (return per unit of downside risk) - sortino_ratio = sp500.sortino_ratio - print(f"Sortino Ratio: {sortino_ratio:.2f}") - - # Kappa-3 Ratio (penalizes larger downside deviations more) - kappa3_ratio = sp500.kappa3_ratio - print(f"Kappa-3 Ratio: {kappa3_ratio:.2f}") - - # Omega Ratio - omega_ratio = sp500.omega_ratio - print(f"Omega Ratio: {omega_ratio:.2f}") - -Drawdown Analysis ------------------ - -Analyze drawdowns to understand downside risk: - -.. code-block:: python - - # Maximum drawdown - max_drawdown = sp500.max_drawdown - max_dd_date = sp500.max_drawdown_date - print(f"Maximum Drawdown: {max_drawdown:.2%}") - print(f"Max Drawdown Date: {max_dd_date}") - - # Create drawdown series for visualization (modifies original) - sp500.to_drawdown_series() - - # Plot drawdowns - sp500.plot_series() - # This will open an interactive plot in your browser - - # Worst calendar year drawdown - worst_year_dd = sp500.max_drawdown_cal_year - print(f"Worst Calendar Year Drawdown: {worst_year_dd:.2%}") - -Distribution Analysis ---------------------- - -Examine the return distribution characteristics: - -.. code-block:: python - - # Convert to returns for distribution analysis (modifies original) - sp500.value_to_ret() - - # Note: value_to_ret() modifies the original series in place - # Restore the original series for further analysis - sp500 = OpenTimeSeries.from_df(dframe=data['Close']) - sp500.set_new_label(lvl_zero="S&P 500 Index") - - # Skewness (asymmetry of the distribution) - skewness = sp500.skew - print(f"Skewness: {skewness:.2f}") - if skewness < 0: - print(" → Negative skew: more extreme negative returns") - elif skewness > 0: - print(" → Positive skew: more extreme positive returns") - - # Kurtosis (tail heaviness) - kurtosis = sp500.kurtosis - print(f"Kurtosis: {kurtosis:.2f}") - if kurtosis > 3: - print(" → Fat tails: more extreme returns than normal distribution") - - # Percentage of positive days - positive_share = sp500.positive_share - print(f"Positive Days: {positive_share:.1%}") - - # Current Z-score (how unusual is the last return?) - z_score = sp500.z_score - print(f"Last Return Z-score: {z_score:.2f}") - -Monthly and Annual Analysis ---------------------------- - -Break down performance by different time periods: - -.. code-block:: python - - # Resample to monthly data (modifies original) - sp500.resample_to_business_period_ends(freq="BME") - print(f"Monthly observations: {sp500.length}") - - # Monthly metrics - monthly_return = sp500.geo_ret - monthly_vol = sp500.vol - print(f"Monthly Return (annualized): {monthly_return:.2%}") - print(f"Monthly Volatility (annualized): {monthly_vol:.2%}") - - # Worst month - worst_month = sp500.worst_month - print(f"Worst Month: {worst_month:.2%}") - - # Annual data (modifies original) - sp500.resample_to_business_period_ends(freq="BYE") - print(f"Annual observations: {sp500.length}") - -Calendar Year Returns -~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Calculate calendar year returns - years = range(2019, 2025) # Adjust based on your data range - - for year in years: - # This may fail if no data exists for the year - year_return = sp500.value_ret_calendar_period(year=year) - print(f"{year}: {year_return:.2%}") - -Rolling Analysis ----------------- - -Analyze how metrics change over time: - -.. code-block:: python - - # 252-day (1-year) rolling volatility - rolling_vol = sp500.rolling_vol(observations=252) - print(f"Rolling volatility calculated for {len(rolling_vol)} periods") - - # 30-day rolling returns - rolling_returns = sp500.rolling_return(observations=30) - - # Plot rolling volatility - # Convert to OpenTimeSeries for plotting - vol_dates = rolling_vol.index.strftime('%Y-%m-%d').tolist() - vol_values = rolling_vol.iloc[:, 0].tolist() - - vol_series = OpenTimeSeries.from_arrays( - dates=vol_dates, - values=vol_values, - name="Rolling Volatility" - ) - - vol_series.plot_series() - -Comprehensive Report --------------------- - -Get all metrics at once: - -.. code-block:: python - - # Generate comprehensive metrics report - all_metrics = sp500.all_properties() - print("\n=== COMPREHENSIVE ANALYSIS REPORT ===") - print(all_metrics) - - # Save to Excel for further analysis - sp500.to_xlsx(filename="sp500_analysis.xlsx") - all_metrics.to_excel(excel_writer="sp500_metrics.xlsx", engine="openpyxl") - -Visualization -------------- - -Create various visualizations: - -.. code-block:: python - - # Price chart - sp500.plot_series() - - # Returns bar plot and histogram - returns = sp500.from_deepcopy() - returns.value_to_ret() - returns.plot_bars() - returns.plot_histogram() - - # Drawdown chart - sp500.to_drawdown_series() - sp500.plot_series() - -Comparison with Benchmark -------------------------- - -Let's compare with a bond index: - -.. code-block:: python - - # Download bond data (10-year Treasury) - bond_ticker = yf.Ticker("^TNX") - bond_data = bond_ticker.history(period="5y") - - # Create bond series (using yield data) - bonds = OpenTimeSeries.from_df( - dframe=bond_data['Close'] - ) - bonds.set_new_label(lvl_zero="10Y Treasury Yield") - - # Create frame for comparison - comparison_frame = OpenFrame(constituents=[sp500, bonds]) - - # Compare metrics - comparison_metrics = comparison_frame.all_properties() - print("\n=== ASSET COMPARISON ===") - print(comparison_metrics) - - # Calculate correlation - correlation_matrix = comparison_frame.correl_matrix - print("\n=== CORRELATION MATRIX ===") - print(correlation_matrix) - -Advanced Risk Metrics ---------------------- - -Calculate some advanced risk measures: - -.. code-block:: python - - # VaR at different confidence levels - var_90 = sp500.var_down_func(level=0.90) - var_95 = sp500.var_down_func(level=0.95) - var_99 = sp500.var_down_func(level=0.99) - - print(f"90% VaR: {var_90:.2%}") - print(f"95% VaR: {var_95:.2%}") - print(f"99% VaR: {var_99:.2%}") - - # CVaR at different confidence levels - cvar_90 = sp500.cvar_down_func(level=0.90) - cvar_95 = sp500.cvar_down_func(level=0.95) - cvar_99 = sp500.cvar_down_func(level=0.99) - - print(f"90% CVaR: {cvar_90:.2%}") - print(f"95% CVaR: {cvar_95:.2%}") - print(f"99% CVaR: {cvar_99:.2%}") - - # Implied volatility from VaR (assuming normal distribution) - vol_from_var = sp500.vol_from_var - print(f"Volatility implied from VaR: {vol_from_var:.2%}") - print(f"Actual volatility: {sp500.vol:.2%}") - -Summary and Interpretation --------------------------- - -.. code-block:: python - - print("\n=== INVESTMENT SUMMARY ===") - print(f"Asset: {sp500.label}") - print(f"Period: {sp500.first_idx} to {sp500.last_idx}") - print(f"Total Return: {sp500.value_ret:.2%}") - print(f"Annualized Return: {sp500.geo_ret:.2%}") - print(f"Annualized Volatility: {sp500.vol:.2%}") - print(f"Sharpe Ratio: {sp500.ret_vol_ratio:.2f}") - print(f"Maximum Drawdown: {sp500.max_drawdown:.2%}") - print(f"95% VaR (daily): {sp500.var_down:.2%}") - - # Risk assessment - if sp500.ret_vol_ratio > 1.0: - print("✓ Good risk-adjusted returns (Sharpe > 1.0)") - else: - print("⚠ Moderate risk-adjusted returns (Sharpe < 1.0)") - - if abs(sp500.max_drawdown) < 0.20: - print("✓ Moderate maximum drawdown (< 20%)") - else: - print("⚠ Significant maximum drawdown (> 20%)") - -This tutorial provides a comprehensive foundation for financial analysis using openseries. You can adapt these techniques for any financial time series data. diff --git a/docs/build/html/_sources/tutorials/portfolio_analysis.rst.txt b/docs/build/html/_sources/tutorials/portfolio_analysis.rst.txt deleted file mode 100644 index beb30f65..00000000 --- a/docs/build/html/_sources/tutorials/portfolio_analysis.rst.txt +++ /dev/null @@ -1,524 +0,0 @@ -Portfolio Analysis -================== - -This tutorial demonstrates how to construct and analyze portfolios using openseries, including optimization techniques and performance attribution. - -Setting Up the Data --------------------- - -Let's start by downloading data for a diversified set of assets: - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries, OpenFrame - from openseries import efficient_frontier, simulate_portfolios - - # Define our universe of assets - tickers = { - "^GSPC": "S&P 500", - "EFA": "EAFE International", - "EEM": "Emerging Markets", - "AGG": "US Aggregate Bonds", - "VNQ": "US REITs", - "GLD": "Gold", - "DBC": "Commodities" - } - - # Download 5 years of data - series_list = [] - for ticker, name in tickers.items(): - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="5y") - series = OpenTimeSeries.from_df( - dframe=data['Close'] - ) - series.set_new_label(lvl_zero=name) - series_list.append(series) - print(f"Loaded {name}: {series.length} observations") - - # Create OpenFrame - assets = OpenFrame(constituents=series_list) - print(f"\nCreated frame with {assets.item_count} assets") - print(f"Common date range: {assets.first_idx} to {assets.last_idx}") - -Asset Analysis --------------- - -First, let's analyze the individual assets: - -.. code-block:: python - - # Get metrics for all assets - asset_metrics = assets.all_properties() - print("=== INDIVIDUAL ASSET METRICS ===") - print(asset_metrics) - - # Key metrics comparison - returns = asset_metrics.loc['Geometric return'] - volatilities = asset_metrics.loc['Volatility'] - sharpe_ratios = asset_metrics.loc['Return vol ratio'] - max_drawdowns = asset_metrics.loc['Max drawdown'] - - print("\n=== ASSET COMPARISON ===") - for asset in returns.index: - print(f"{asset}:") - print(f" Annual Return: {returns[asset]:.2%}") - print(f" Volatility: {volatilities[asset]:.2%}") - print(f" Sharpe Ratio: {sharpe_ratios[asset]:.2f}") - print(f" Max Drawdown: {max_drawdowns[asset]:.2%}") - -Correlation Analysis --------------------- - -Understanding correlations is crucial for portfolio construction: - -.. code-block:: python - - # Calculate correlation matrix - correlation_matrix = assets.correl_matrix - print("\n=== CORRELATION MATRIX ===") - print(correlation_matrix.round(3)) - - # Identify highly correlated pairs - print("\n=== HIGHLY CORRELATED PAIRS (>0.7) ===") - for i in range(len(correlation_matrix.columns)): - for j in range(i+1, len(correlation_matrix.columns)): - corr = correlation_matrix.iloc[i, j] - if abs(corr) > 0.7: - asset1 = correlation_matrix.columns[i] - asset2 = correlation_matrix.columns[j] - print(f"{asset1} - {asset2}: {corr:.3f}") - - # Average correlation with other assets - avg_correlations = correlation_matrix.mean() - print("\n=== AVERAGE CORRELATIONS ===") - for asset, avg_corr in avg_correlations.items(): - print(f"{asset}: {avg_corr:.3f}") - -Simple Portfolio Construction ------------------------------ - -Let's start with basic portfolio construction methods: - -Equal Weight Portfolio -~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Create equal-weighted portfolio using native weight_strat - portfolio_df = assets.make_portfolio(name="Equal Weight Portfolio", weight_strat="eq_weights") - equal_weight_portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - print(f"Equal Weight Portfolio Return: {equal_weight_portfolio.geo_ret:.2%}") - print(f"Equal Weight Portfolio Volatility: {equal_weight_portfolio.vol:.2%}") - print(f"Equal Weight Portfolio Sharpe: {equal_weight_portfolio.ret_vol_ratio:.2f}") - -Custom Weight Portfolio -~~~~~~~~~~~~~~~~~~~~~~~ - -You can also specify custom weights for portfolio construction: - -.. code-block:: python - - # Define custom weights (must sum to 1) - custom_weights = [0.50, 0.15, 0.10, 0.15, 0.05, 0.03, 0.02] - - assets.weights = custom_weights - portfolio_df = assets.make_portfolio(name="Custom Weighted") - custom_portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - print(f"Custom Portfolio Return: {custom_portfolio.geo_ret:.2%}") - print(f"Custom Portfolio Volatility: {custom_portfolio.vol:.2%}") - print(f"Custom Portfolio Sharpe: {custom_portfolio.ret_vol_ratio:.2f}") - -Risk Parity Portfolio -~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Use native inverse volatility weighting (risk parity) - portfolio_df = assets.make_portfolio(name="Risk Parity", weight_strat="inv_vol") - risk_parity_portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - print(f"Risk Parity Portfolio Return: {risk_parity_portfolio.geo_ret:.2%}") - print(f"Risk Parity Portfolio Volatility: {risk_parity_portfolio.vol:.2%}") - print(f"Risk Parity Portfolio Sharpe: {risk_parity_portfolio.ret_vol_ratio:.2f}") - -Advanced Weight Strategies -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -OpenSeries provides additional weight strategies beyond basic equal weighting and risk parity: - -Maximum Diversification Strategy -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The maximum diversification strategy optimizes the correlation structure to maximize portfolio diversification: - -.. code-block:: python - - from openseries.owntypes import MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError - - # This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError - max_div_portfolio_df = assets.make_portfolio( - name="Maximum Diversification", - weight_strat="max_div" - ) - max_div_portfolio = OpenTimeSeries.from_df(dframe=max_div_portfolio_df) - - print(f"Max Diversification Return: {max_div_portfolio.geo_ret:.2%}") - print(f"Max Diversification Volatility: {max_div_portfolio.vol:.2%}") - print(f"Max Diversification Sharpe: {max_div_portfolio.ret_vol_ratio:.2f}") - -Minimum Volatility Overweight Strategy -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The minimum volatility overweight strategy overweights the least volatile asset: - -.. code-block:: python - - # This may fail with various exceptions - min_vol_portfolio_df = assets.make_portfolio( - name="Min Vol Overweight", - weight_strat="min_vol_overweight" - ) - min_vol_portfolio = OpenTimeSeries.from_df(dframe=min_vol_portfolio_df) - - print(f"Min Vol Overweight Return: {min_vol_portfolio.geo_ret:.2%}") - print(f"Min Vol Overweight Volatility: {min_vol_portfolio.vol:.2%}") - print(f"Min Vol Overweight Sharpe: {min_vol_portfolio.ret_vol_ratio:.2f}") - -Strategy Comparison with Error Handling -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -When comparing multiple strategies, it's important to handle potential failures gracefully: - -.. code-block:: python - - strategies = { - 'Equal Weight': 'eq_weights', - 'Risk Parity': 'inv_vol', - 'Max Diversification': 'max_div', - 'Min Vol Overweight': 'min_vol_overweight' - } - - results = {} - for name, strategy in strategies.items(): - # This may fail with MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError, or other exceptions - portfolio_df = assets.make_portfolio(name=name, weight_strat=strategy) - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - results[name] = { - 'Return': portfolio.geo_ret, - 'Volatility': portfolio.vol, - 'Sharpe': portfolio.ret_vol_ratio - } - - if results: - print("\n=== STRATEGY COMPARISON ===") - for strategy_name, metrics in results.items(): - print(f"\n{strategy_name}:") - print(f" Return: {metrics['return']*100:.2f}%") - print(f" Volatility: {metrics['volatility']*100:.2f}%") - print(f" Sharpe: {metrics['sharpe']:.2f}") - print(f" Max Drawdown: {metrics['max_drawdown']*100:.2f}%") - -Portfolio Optimization ----------------------- - -Now let's use openseries' optimization tools: - -Efficient Frontier -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Calculate efficient frontier - # This may fail with various exceptions - frontier_df, simulated_df, optimal_portfolio = efficient_frontier( - eframe=assets, - num_ports=50, - seed=42 - ) - - print("Efficient frontier calculated successfully") - print(f"Number of frontier points: {len(frontier_df)}") - print(f"Number of simulated portfolios: {len(simulated_df)}") - - # Find maximum Sharpe ratio portfolio - sharpe_ratios = frontier_df['ret'] / frontier_df['stdev'] - max_sharpe_idx = sharpe_ratios.idxmax() - - print(f"\n=== MAXIMUM SHARPE RATIO PORTFOLIO ===") - print(f"Expected Return: {frontier_df.iloc[max_sharpe_idx]['ret']:.2%}") - print(f"Volatility: {frontier_df.iloc[max_sharpe_idx]['stdev']:.2%}") - print(f"Sharpe Ratio: {sharpe_ratios.iloc[max_sharpe_idx]:.2f}") - - # Get optimal weights - optimal_weights = optimal_portfolio[-len(assets.constituents):] - print("\nOptimal Weights:") - for i, weight in enumerate(optimal_weights): - asset_name = assets.constituents[i].label - print(f" {asset_name}: {weight:.1%}") - -Monte Carlo Portfolio Simulation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Simulate random portfolios - # This may fail with various exceptions - simulation_results = simulate_portfolios( - simframe=assets, - num_ports=10000, - seed=42 - ) - - print(f"\nSimulated {len(simulation_results)} random portfolios") - - # Find best performing portfolios - sim_sharpe_ratios = simulation_results['ret'] / simulation_results['stdev'] - - # Top 5 Sharpe ratios - sorted_indices = sorted(range(len(sim_sharpe_ratios)), key=lambda i: sim_sharpe_ratios.iloc[i], reverse=True) - top_indices = sorted_indices[:5] - - print("\n=== TOP 5 SIMULATED PORTFOLIOS ===") - for i, idx in enumerate(top_indices, 1): - print(f"\nRank {i}:") - print(f" Return: {simulation_results.iloc[idx]['ret']:.2%}") - print(f" Volatility: {simulation_results.iloc[idx]['stdev']:.2%}") - print(f" Sharpe: {sim_sharpe_ratios.iloc[idx]:.2f}") - -Portfolio Comparison --------------------- - -Let's compare all our portfolios: - -.. code-block:: python - - # Add all portfolios to a comparison frame - portfolios = [equal_weight_portfolio, market_cap_portfolio, risk_parity_portfolio] - - # Add individual assets for comparison - all_series = assets.constituents + portfolios - comparison_frame = OpenFrame(constituents=all_series) - - # Get comprehensive metrics - portfolio_metrics = comparison_frame.all_properties() - - # Focus on key metrics - key_metrics = portfolio_metrics.loc[['Geometric return', 'Volatility', 'Return vol ratio', 'Max drawdown']] - key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown'] - - print("\n=== PORTFOLIO COMPARISON ===") - print((key_metrics * 100).round(2)) # Convert to percentages - -Risk Attribution ----------------- - -Analyze the risk contribution of each asset: - -.. code-block:: python - - # Calculate portfolio statistics using openseries methods - # Create equal weight portfolio - equal_weight_portfolio_df = assets.make_portfolio(name="Equal Weight", weight_strat="eq_weights") - equal_weight_portfolio = OpenTimeSeries.from_df(dframe=equal_weight_portfolio_df) - - print("\n=== RISK ATTRIBUTION (Equal Weight Portfolio) ===") - print(f"Portfolio Volatility: {equal_weight_portfolio.vol:.4f}") - print(f"Portfolio Return: {equal_weight_portfolio.geo_ret:.4f}") - - # Individual asset contributions can be analyzed using openseries properties - for i, series in enumerate(assets.constituents): - weight = equal_weights[i] - asset_vol = series.vol - print(f"\n{series.label}:") - print(f" Weight: {weight:.4f}") - print(f" Individual Volatility: {asset_vol:.4f}") - print(f" Weighted Contribution: {weight * asset_vol:.4f}") - -Performance Attribution ------------------------ - -Analyze performance contribution over time: - -.. code-block:: python - - # Calculate performance attribution using openseries - # Individual asset performance is available through openseries properties - print("\n=== PERFORMANCE ATTRIBUTION ===") - for i, series in enumerate(assets.constituents): - weight = equal_weights[i] - asset_return = series.geo_ret - contribution = weight * asset_return - print(f"{series.label}:") - print(f" Weight: {weight:.2%}") - print(f" Return: {asset_return:.2%}") - print(f" Contribution: {contribution:.2%}") - - # Cumulative contribution - cumulative_contrib = (1 + weighted_returns).cumprod() - - print("\n=== PERFORMANCE ATTRIBUTION ===") - print("Final cumulative contribution by asset:") - final_contrib = cumulative_contrib.iloc[-1] - for asset, contrib in final_contrib.items(): - print(f" {asset}: {contrib:.3f}") - -Rolling Portfolio Analysis --------------------------- - -Analyze how portfolio characteristics change over time: - -.. code-block:: python - - # Rolling correlation with market (S&P 500) - market_proxy = assets.constituents[0] # Assuming first asset is S&P 500 - - # Create frame with portfolio and market - portfolio_vs_market = OpenFrame(constituents=[equal_weight_portfolio, market_proxy]) - - # Calculate rolling correlation - rolling_corr = portfolio_vs_market.rolling_corr(observations=252) # 1-year rolling - - print(f"\nRolling correlation calculated for {len(rolling_corr)} periods") - print(f"Average correlation: {rolling_corr.mean().iloc[0]:.3f}") - print(f"Correlation range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}") - - # Rolling portfolio volatility - portfolio_rolling_vol = equal_weight_portfolio.rolling_vol(observations=252) - - print(f"\nRolling volatility statistics:") - print(f"Average volatility: {portfolio_rolling_vol.mean().iloc[0]:.2%}") - print(f"Volatility range: {portfolio_rolling_vol.min().iloc[0]:.2%} to {portfolio_rolling_vol.max().iloc[0]:.2%}") - -Rebalancing Analysis --------------------- - -Analyze the impact of rebalancing frequency using the realistic `rebalanced_portfolio` method: - -.. code-block:: python - - # Compare different rebalancing frequencies using realistic simulation - frequencies = [1, 21, 63] # Daily, monthly, quarterly - frequency_names = ["Daily", "Monthly", "Quarterly"] - - rebalanced_portfolios = [] - - for freq, name in zip(frequencies, frequency_names): - portfolio = assets.rebalanced_portfolio( - name=f"{name} Rebalanced", - frequency=freq, - bal_weights=equal_weights - ) - rebalanced_portfolios.append(portfolio.constituents[-1]) - - # Compare with theoretical portfolio - assets.weights = equal_weights - theoretical_portfolio_df = assets.make_portfolio(name="Theoretical") - theoretical_portfolio = OpenTimeSeries.from_df(dframe=theoretical_portfolio_df) - - # Create comprehensive comparison - all_portfolios = [theoretical_portfolio] + rebalanced_portfolios - comparison_frame = OpenFrame(constituents=all_portfolios) - comparison_metrics = comparison_frame.all_properties() - - print("\n=== REALISTIC REBALANCING COMPARISON ===") - print("Strategy | Return | Volatility | Sharpe | Max DD") - print("-" * 50) - - for series in all_portfolios: - ret = comparison_metrics.loc['Geometric return', series.label].iloc[0] * 100 - vol = comparison_metrics.loc['Volatility', series.label].iloc[0] * 100 - sharpe = comparison_metrics.loc['Return vol ratio', series.label].iloc[0] - max_dd = comparison_metrics.loc['Max drawdown', series.label].iloc[0] * 100 - - print(f"{series.label:>15} | {ret:6.2f}% | {vol:10.2f}% | {sharpe:6.2f} | {max_dd:6.2f}%") - - # Analyze transaction costs - print(f"\n=== TRANSACTION COST ANALYSIS ===") - for freq, name in zip(frequencies, frequency_names): - detailed_portfolio = assets.rebalanced_portfolio( - name=f"{name} Detailed", - frequency=freq, - bal_weights=equal_weights, - drop_extras=False # Get detailed trading data - ) - - # Count rebalancing events - rebalancing_days = 0 - for series in detailed_portfolio.constituents: - if "buysell_qty" in series.label: - # Count days with non-zero trading - trading_days = (series.tsdf != 0).any(axis=1).sum() - rebalancing_days = max(rebalancing_days, trading_days) - - print(f"{name:>15}: {rebalancing_days} rebalancing events") - -Stress Testing --------------- - -Test portfolio performance during market stress: - -.. code-block:: python - - # Identify worst periods for the market (modifies original) - market_proxy.value_to_ret() - market_returns_df = market_proxy.tsdf - - # Find worst 5% of days - worst_days_threshold = market_returns_df.quantile(0.05).iloc[0] - worst_days = market_returns_df[market_returns_df <= worst_days_threshold] - - print(f"\n=== STRESS TEST RESULTS ===") - print(f"Market stress threshold: {worst_days_threshold:.2%}") - print(f"Number of stress days: {len(worst_days)}") - - # Portfolio performance during stress (modifies original) - equal_weight_portfolio.value_to_ret() - portfolio_returns_df = equal_weight_portfolio.tsdf - - # Align dates and calculate portfolio performance during market stress - stress_dates = worst_days.index - portfolio_stress_returns = portfolio_returns_df.loc[stress_dates] - - print(f"Portfolio average return during stress: {portfolio_stress_returns.mean().iloc[0]:.2%}") - print(f"Portfolio worst day during stress: {portfolio_stress_returns.min().iloc[0]:.2%}") - -Summary Report --------------- - -Generate a comprehensive portfolio analysis report: - -.. code-block:: python - - print("\n" + "="*60) - print("PORTFOLIO ANALYSIS SUMMARY REPORT") - print("="*60) - - print(f"\nAnalysis Period: {assets.first_idx} to {assets.last_idx}") - print(f"Number of Assets: {assets.item_count}") - print(f"Asset Universe: {', '.join([s.label for s in assets.constituents])}") - - print(f"\n--- EQUAL WEIGHT PORTFOLIO PERFORMANCE ---") - print(f"Total Return: {equal_weight_portfolio.value_ret:.2%}") - print(f"Annualized Return: {equal_weight_portfolio.geo_ret:.2%}") - print(f"Annualized Volatility: {equal_weight_portfolio.vol:.2%}") - print(f"Sharpe Ratio: {equal_weight_portfolio.ret_vol_ratio:.2f}") - print(f"Maximum Drawdown: {equal_weight_portfolio.max_drawdown:.2%}") - print(f"95% VaR (daily): {equal_weight_portfolio.var_down:.2%}") - - print(f"\n--- PORTFOLIO CHARACTERISTICS ---") - avg_correlation = correlation_matrix.mean().mean() - print(f"Average Asset Correlation: {avg_correlation:.3f}") - print(f"Portfolio Diversification Benefit: {(asset_metrics.loc['Volatility'].mean() - equal_weight_portfolio.vol):.2%}") - - # Export results - portfolio_metrics.to_excel("portfolio_analysis.xlsx") - correlation_matrix.to_excel("correlation_matrix.xlsx") - - print(f"\nResults exported to Excel files") - print("Analysis complete!") - -This tutorial provides a comprehensive framework for portfolio analysis using openseries. You can extend these techniques for more sophisticated portfolio management strategies. diff --git a/docs/build/html/_sources/tutorials/risk_management.rst.txt b/docs/build/html/_sources/tutorials/risk_management.rst.txt deleted file mode 100644 index 776f51ad..00000000 --- a/docs/build/html/_sources/tutorials/risk_management.rst.txt +++ /dev/null @@ -1,536 +0,0 @@ -Risk Management -=============== - -This tutorial demonstrates comprehensive risk management techniques using openseries, including VaR calculations, stress testing, and risk monitoring. - -Setting Up Risk Analysis -------------------------- - -Let's start with a portfolio of assets for risk analysis: - -.. code-block:: python - - import yfinance as yf - from openseries import OpenTimeSeries, OpenFrame - from datetime import datetime, timedelta - import warnings - warnings.filterwarnings('ignore') - - # Download data for a mixed portfolio - tickers = { - "AAPL": "Apple Inc.", - "GOOGL": "Alphabet Inc.", - "MSFT": "Microsoft Corp.", - "TSLA": "Tesla Inc.", - "SPY": "SPDR S&P 500 ETF", - "QQQ": "Invesco QQQ Trust", - "TLT": "iShares 20+ Year Treasury", - "GLD": "SPDR Gold Shares" - } - - # Download 3 years of data - series_list = [] - for ticker, name in tickers.items(): - # This may fail if the ticker is invalid or data unavailable - data = yf.Ticker(ticker).history(period="3y") - series = OpenTimeSeries.from_df( - dframe=data['Close'] - ) - series.set_new_label(lvl_zero=name) - series_list.append(series) - print(f"Loaded {name}: {series.length} observations") - - # Create portfolio frame - portfolio_assets = OpenFrame(constituents=series_list) - - # Create equal-weighted portfolio - n_assets = portfolio_assets.item_count - - # Set weights on the frame first - portfolio_df = portfolio_assets.make_portfolio( - name="Diversified Portfolio", - weight_strat="eq_weights" - ) - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - print(f"\nPortfolio created with {n_assets} assets") - print(f"Date range: {portfolio.first_idx} to {portfolio.last_idx}") - -Basic Risk Metrics ------------------- - -Start with fundamental risk measurements: - -.. code-block:: python - - print("=== BASIC RISK METRICS ===") - - # Volatility measures - print(f"Annualized Volatility: {portfolio.vol:.2%}") - print(f"Downside Deviation: {portfolio.downside_deviation:.2%}") - - # Return distribution - print(f"Skewness: {portfolio.skew:.3f}") - print(f"Kurtosis: {portfolio.kurtosis:.3f}") - - # Tail risk - print(f"Worst Single Day: {portfolio.worst:.2%}") - print(f"Worst Month: {portfolio.worst_month:.2%}") - - # Drawdown analysis - print(f"Maximum Drawdown: {portfolio.max_drawdown:.2%}") - print(f"Max Drawdown Date: {portfolio.max_drawdown_date}") - -Value at Risk (VaR) Analysis ------------------------------ - -Calculate VaR at different confidence levels: - -.. code-block:: python - - print("\n=== VALUE AT RISK ANALYSIS ===") - - # VaR at different confidence levels - confidence_levels = [0.90, 0.95, 0.99] - - for level in confidence_levels: - var_value = portfolio.var_down_func(level=level) - print(f"{level*100:.0f}% VaR (daily): {var_value:.2%}") - - # Convert daily VaR to different time horizons - # Assuming normal distribution and independence - daily_var_95 = portfolio.var_down_func(level=0.95) - - print(f"\n=== VaR TIME HORIZONS (95% confidence) ===") - print(f"1-day VaR: {daily_var_95:.2%}") - # Scale VaR to different time horizons - print(f"1-week VaR: {daily_var_95 * (5 ** 0.5):.2%}") - print(f"1-month VaR: {daily_var_95 * (22 ** 0.5):.2%}") - print(f"1-year VaR: {daily_var_95 * (252 ** 0.5):.2%}") - -Conditional Value at Risk (CVaR) --------------------------------- - -Analyze expected shortfall beyond VaR: - -.. code-block:: python - - print("\n=== CONDITIONAL VALUE AT RISK (CVaR) ===") - - for level in confidence_levels: - cvar_value = portfolio.cvar_down_func(level=level) - var_value = portfolio.var_down_func(level=level) - - print(f"{level*100:.0f}% CVaR: {cvar_value:.2%} (VaR: {var_value:.2%})") - print(f" Expected loss beyond VaR: {cvar_value - var_value:.2%}") - -Rolling Risk Analysis ---------------------- - -Monitor how risk changes over time: - -.. code-block:: python - - # Calculate rolling risk metrics - window = 252 # 1-year rolling window - - print(f"\n=== ROLLING RISK ANALYSIS ({window}-day window) ===") - - # Rolling volatility - rolling_vol = portfolio.rolling_vol(observations=window) - print(f"Rolling Volatility - Current: {rolling_vol.iloc[-1, 0]:.2%}") - print(f"Rolling Volatility - Average: {rolling_vol.mean().iloc[0]:.2%}") - print(f"Rolling Volatility - Range: {rolling_vol.min().iloc[0]:.2%} to {rolling_vol.max().iloc[0]:.2%}") - - # Rolling VaR - rolling_var = portfolio.rolling_var_down(observations=window) - print(f"Rolling VaR (95%) - Current: {rolling_var.iloc[-1, 0]:.2%}") - print(f"Rolling VaR (95%) - Average: {rolling_var.mean().iloc[0]:.2%}") - - # Rolling CVaR - rolling_cvar = portfolio.rolling_cvar_down(observations=window) - print(f"Rolling CVaR (95%) - Current: {rolling_cvar.iloc[-1, 0]:.2%}") - print(f"Rolling CVaR (95%) - Average: {rolling_cvar.mean().iloc[0]:.2%}") - -Stress Testing --------------- - -Test portfolio performance under extreme scenarios: - -Historical Stress Testing -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - print("\n=== HISTORICAL STRESS TESTING ===") - - # Convert to returns for analysis (modifies original) - portfolio.value_to_ret() - returns_data = portfolio.tsdf - - # Note: value_to_ret() modifies the original series in place - # Restore the original portfolio for further analysis - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - - # Identify worst periods - worst_1_percent = returns_data.quantile(0.01).iloc[0] - worst_5_percent = returns_data.quantile(0.05).iloc[0] - - print(f"Worst 1% threshold: {worst_1_percent:.2%}") - print(f"Worst 5% threshold: {worst_5_percent:.2%}") - - # Count extreme events - extreme_events_1pct = (returns_data <= worst_1_percent).sum().iloc[0] - extreme_events_5pct = (returns_data <= worst_5_percent).sum().iloc[0] - - print(f"Days with returns <= 1% threshold: {extreme_events_1pct}") - print(f"Days with returns <= 5% threshold: {extreme_events_5pct}") - - # Worst consecutive days - simplified approach - print(f"\nWorst 5 single days:") - returns_series = returns_data.iloc[:, 0] # Get the first (and only) column - worst_5_days = returns_series.nsmallest(5) - for i, (date, return_val) in enumerate(worst_5_days.items()): - print(f" {i+1}. {date.strftime('%Y-%m-%d')}: {return_val:.2%}") - -Scenario Analysis -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - print("\n=== SCENARIO ANALYSIS ===") - - # Define stress scenarios (percentage moves in underlying assets) - scenarios = { - "Market Crash": [-0.20, -0.25, -0.22, -0.30, -0.18, -0.20, 0.05, 0.10], - "Tech Selloff": [-0.35, -0.40, -0.30, -0.45, -0.10, -0.15, 0.02, 0.03], - "Interest Rate Shock": [-0.10, -0.12, -0.08, -0.15, -0.05, -0.08, -0.15, 0.01], - "Flight to Quality": [0.05, 0.02, 0.08, -0.10, 0.10, 0.12, 0.20, 0.15] - } - - print("Portfolio impact under stress scenarios:") - for scenario_name, asset_moves in scenarios.items(): - # Calculate portfolio impact - portfolio_impact = sum(w * move for w, move in zip(equal_weights, asset_moves)) - print(f" {scenario_name}: {portfolio_impact:.2%}") - -Monte Carlo Risk Simulation ---------------------------- - -Use Monte Carlo methods for risk assessment: - -.. code-block:: python - - print("\n=== MONTE CARLO RISK SIMULATION ===") - - # Import the simulate_portfolios function - from openseries.portfoliotools import simulate_portfolios - - # Monte Carlo simulation using native function - num_simulations = 10000 - seed = 42 # For reproducible results - - # Generate simulated portfolios using the native function - simulated_portfolios = simulate_portfolios( - simframe=portfolio_assets, - num_ports=num_simulations, - seed=seed - ) - - # Extract portfolio metrics from simulation - portfolio_returns = simulated_portfolios['ret'] - portfolio_volatilities = simulated_portfolios['stdev'] - portfolio_sharpes = simulated_portfolios['sharpe'] - - # Calculate risk metrics from simulation - # Calculate 5th percentile manually - sorted_returns = sorted(portfolio_returns) - percentile_idx = int(len(sorted_returns) * 0.05) - sim_var_95 = sorted_returns[percentile_idx] - sim_cvar_95 = portfolio_returns[portfolio_returns <= sim_var_95].mean() - - print(f"Monte Carlo Results ({num_simulations:,} simulations):") - print(f"Expected Return: {portfolio_returns.mean():.2%}") - print(f"Average Volatility: {portfolio_volatilities.mean():.2%}") - print(f"95% VaR: {sim_var_95:.2%}") - print(f"95% CVaR: {sim_cvar_95:.2%}") - # Calculate percentiles manually - worst_idx = int(len(sorted_returns) * 0.001) - best_idx = int(len(sorted_returns) * 0.999) - print(f"Worst Case (0.1%): {sorted_returns[worst_idx]:.2%}") - print(f"Best Case (99.9%): {sorted_returns[best_idx]:.2%}") - print(f"Average Sharpe Ratio: {portfolio_sharpes.mean():.3f}") - - # Show distribution of portfolio characteristics - print(f"\nPortfolio Distribution:") - print(f"Return Range: {portfolio_returns.min():.2%} to {portfolio_returns.max():.2%}") - print(f"Volatility Range: {portfolio_volatilities.min():.2%} to {portfolio_volatilities.max():.2%}") - print(f"Sharpe Range: {portfolio_sharpes.min():.3f} to {portfolio_sharpes.max():.3f}") - -Risk Decomposition ------------------- - -Analyze risk contribution by asset: - -.. code-block:: python - - print("\n=== RISK DECOMPOSITION ===") - - # Calculate individual asset volatilities using OpenFrame - asset_metrics = portfolio_assets.all_properties() - asset_vols = asset_metrics.loc['Volatility'].values - - # Portfolio volatility - portfolio_vol = portfolio.vol - - # Calculate correlation matrix - correlation_matrix = portfolio_assets.correl_matrix - - # Risk contribution analysis using openseries - # Create portfolio to get portfolio-level metrics - portfolio_df = portfolio_assets.make_portfolio(name="Portfolio", weight_strat="eq_weights") - portfolio = OpenTimeSeries.from_df(dframe=portfolio_df) - portfolio_vol = portfolio.vol - - print("Risk Contribution Analysis:") - for i, series in enumerate(portfolio_assets.constituents): - weight = equal_weights[i] - asset_vol = asset_vols[i] - print(f"\n{series.label}:") - print(f" Weight: {weight:.4f}") - print(f" Individual Volatility: {asset_vol:.4f}") - print(f" Weighted Volatility Contribution: {weight * asset_vol:.4f}") - print(f"\nPortfolio Volatility: {portfolio_vol:.4f}") - - # Verify portfolio metrics - print(f"\nVerification:") - print(f"Portfolio volatility: {portfolio_vol:.4f}") - -Risk-Adjusted Performance -------------------------- - -Evaluate risk-adjusted returns: - -.. code-block:: python - - print("\n=== RISK-ADJUSTED PERFORMANCE ===") - - # Sharpe ratio - print(f"Sharpe Ratio: {portfolio.ret_vol_ratio:.3f}") - - # Sortino ratio (downside risk only) - print(f"Sortino Ratio: {portfolio.sortino_ratio:.3f}") - - # Kappa-3 ratio (higher-order downside risk) - print(f"Kappa-3 Ratio: {portfolio.kappa3_ratio:.3f}") - - # Omega ratio - print(f"Omega Ratio: {portfolio.omega_ratio:.3f}") - - # Compare with individual assets - print(f"\n=== RISK-ADJUSTED COMPARISON ===") - all_assets = portfolio_assets.constituents + [portfolio] - comparison_frame = OpenFrame(constituents=all_assets) - - risk_adj_metrics = comparison_frame.all_properties( - properties=['ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'omega_ratio'] - ) - - print(risk_adj_metrics.round(3)) - -Risk Monitoring Dashboard -------------------------- - -Create a comprehensive risk monitoring summary using openseries properties and methods: - -.. code-block:: python - - print("\n" + "="*60) - print("RISK MONITORING DASHBOARD") - print("="*60) - - # Current date and lookback period - current_date = portfolio.last_idx - lookback_date = portfolio.first_idx - - print(f"Portfolio: {portfolio.label}") - print(f"Current Date: {current_date}") - print(f"Analysis Period: {lookback_date} to {current_date}") - print(f"Observations: {portfolio.length}") - - # Risk metrics using openseries properties - print(f"\n--- CURRENT RISK METRICS ---") - print(f"Volatility (annualized): {portfolio.vol:.2%}") - print(f"Downside Deviation: {portfolio.downside_deviation:.2%}") - print(f"95% VaR (daily): {portfolio.var_down:.2%}") - print(f"95% CVaR (daily): {portfolio.cvar_down:.2%}") - print(f"Maximum Drawdown: {portfolio.max_drawdown:.2%}") - - # Performance metrics using openseries properties - print(f"\n--- PERFORMANCE METRICS ---") - print(f"Total Return: {portfolio.value_ret:.2%}") - print(f"Annualized Return: {portfolio.geo_ret:.2%}") - print(f"Sharpe Ratio: {portfolio.ret_vol_ratio:.3f}") - print(f"Sortino Ratio: {portfolio.sortino_ratio:.3f}") - - # Distribution characteristics using openseries properties - print(f"\n--- RETURN DISTRIBUTION ---") - print(f"Skewness: {portfolio.skew:.3f}") - print(f"Kurtosis: {portfolio.kurtosis:.3f}") - print(f"Positive Days: {portfolio.positive_share:.1%}") - - # Recent performance using openseries properties - recent_return = portfolio.z_score - print(f"\n--- RECENT ACTIVITY ---") - print(f"Last Return Z-Score: {recent_return:.2f}") - - if abs(recent_return) > 2: - print(" ⚠️ ALERT: Recent return is unusual (|z| > 2)") - elif abs(recent_return) > 3: - print(" 🚨 WARNING: Recent return is extreme (|z| > 3)") - else: - print(" ✅ Recent return is within normal range") - - # Risk alerts based on openseries metrics - print(f"\n--- RISK ALERTS ---") - alerts = [] - - if portfolio.vol > 0.25: - alerts.append("High volatility (>25%)") - - if abs(portfolio.max_drawdown) > 0.20: - alerts.append("Large maximum drawdown (>20%)") - - if portfolio.ret_vol_ratio < 0.5: - alerts.append("Low Sharpe ratio (<0.5)") - - if portfolio.skew < -1: - alerts.append("Highly negative skew (<-1)") - - if portfolio.kurtosis > 5: - alerts.append("High kurtosis (>5) - fat tails") - - if alerts: - for alert in alerts: - print(f" ⚠️ {alert}") - else: - print(" ✅ No risk alerts") - -Risk Limits and Controls ------------------------- - -Implement risk limit monitoring: - -.. code-block:: python - - print("\n=== RISK LIMITS MONITORING ===") - - # Define risk limits - risk_limits = { - 'max_volatility': 0.20, # 20% annual volatility - 'max_var_daily': -0.03, # 3% daily VaR - 'max_drawdown': -0.15, # 15% maximum drawdown - 'min_sharpe': 0.5, # Minimum Sharpe ratio - 'max_concentration': 0.30 # Maximum single asset weight - } - - # Check current metrics against limits - current_metrics = { - 'volatility': portfolio.vol, - 'var_daily': portfolio.var_down, - 'drawdown': portfolio.max_drawdown, - 'sharpe': portfolio.ret_vol_ratio, - 'max_weight': max(equal_weights) - } - - print("Risk Limit Monitoring:") - print("-" * 40) - - # Volatility check - if current_metrics['volatility'] > risk_limits['max_volatility']: - print(f"❌ BREACH: Volatility {current_metrics['volatility']:.2%} > {risk_limits['max_volatility']:.2%}") - else: - print(f"✅ OK: Volatility {current_metrics['volatility']:.2%} <= {risk_limits['max_volatility']:.2%}") - - # VaR check - if current_metrics['var_daily'] < risk_limits['max_var_daily']: - print(f"❌ BREACH: VaR {current_metrics['var_daily']:.2%} < {risk_limits['max_var_daily']:.2%}") - else: - print(f"✅ OK: VaR {current_metrics['var_daily']:.2%} >= {risk_limits['max_var_daily']:.2%}") - - # Drawdown check - if current_metrics['drawdown'] < risk_limits['max_drawdown']: - print(f"❌ BREACH: Drawdown {current_metrics['drawdown']:.2%} < {risk_limits['max_drawdown']:.2%}") - else: - print(f"✅ OK: Drawdown {current_metrics['drawdown']:.2%} >= {risk_limits['max_drawdown']:.2%}") - - # Sharpe ratio check - if current_metrics['sharpe'] < risk_limits['min_sharpe']: - print(f"❌ BREACH: Sharpe {current_metrics['sharpe']:.3f} < {risk_limits['min_sharpe']:.3f}") - else: - print(f"✅ OK: Sharpe {current_metrics['sharpe']:.3f} >= {risk_limits['min_sharpe']:.3f}") - - # Concentration check - if current_metrics['max_weight'] > risk_limits['max_concentration']: - print(f"❌ BREACH: Max weight {current_metrics['max_weight']:.2%} > {risk_limits['max_concentration']:.2%}") - else: - print(f"✅ OK: Max weight {current_metrics['max_weight']:.2%} <= {risk_limits['max_concentration']:.2%}") - -Export Risk Report ------------------- - -Save comprehensive risk analysis: - -.. code-block:: python - - # Create comprehensive risk report - # Create risk report using openseries methods - print("\n=== RISK REPORT ===") - print("Risk metrics are available through openseries properties:") - for series in portfolio_assets.constituents: - print(f"\n{series.label}:") - print(f" VaR (95%): {series.var_down:.4f}") - print(f" CVaR (95%): {series.cvar_down:.4f}") - print(f" Volatility: {series.vol:.4f}") - print(f" Max Drawdown: {series.max_drawdown:.4f}") - - # Note: For comprehensive Excel export, use openseries to_xlsx() method - portfolio_assets.to_xlsx('risk_analysis_report.xlsx') - - # Alternative: risk_report = pd.DataFrame({ - 'Metric': [ - 'Annualized Return', 'Annualized Volatility', 'Sharpe Ratio', - 'Sortino Ratio', 'Maximum Drawdown', '95% VaR (daily)', - '95% CVaR (daily)', 'Skewness', 'Kurtosis', 'Positive Days %' - ], - 'Value': [ - f"{portfolio.geo_ret:.2%}", - f"{portfolio.vol:.2%}", - f"{portfolio.ret_vol_ratio:.3f}", - f"{portfolio.sortino_ratio:.3f}", - f"{portfolio.max_drawdown:.2%}", - f"{portfolio.var_down:.2%}", - f"{portfolio.cvar_down:.2%}", - f"{portfolio.skew:.3f}", - f"{portfolio.kurtosis:.3f}", - f"{portfolio.positive_share:.1%}" - ] - }) - - # Export to Excel - # Export using openseries native method (commented out ExcelWriter approach) - # with pd.ExcelWriter('risk_analysis_report.xlsx') as writer: - risk_report.to_excel(writer, sheet_name='Risk Metrics', index=False) - risk_decomp.to_excel(writer, sheet_name='Risk Decomposition', index=False) - correlation_matrix.to_excel(writer, sheet_name='Correlations') - - # Add rolling metrics if available - if 'rolling_vol' in locals(): - rolling_vol.to_excel(writer, sheet_name='Rolling Volatility') - if 'rolling_var' in locals(): - rolling_var.to_excel(writer, sheet_name='Rolling VaR') - - print(f"\nRisk analysis report exported to 'risk_analysis_report.xlsx'") - print("Risk management analysis complete!") - -This comprehensive risk management tutorial provides the foundation for implementing robust risk controls and monitoring systems using openseries. diff --git a/docs/build/html/_sources/user_guide/core_concepts.rst.txt b/docs/build/html/_sources/user_guide/core_concepts.rst.txt deleted file mode 100644 index 573f52cf..00000000 --- a/docs/build/html/_sources/user_guide/core_concepts.rst.txt +++ /dev/null @@ -1,453 +0,0 @@ -Core Concepts -============= - -This section explains the fundamental concepts and design principles behind openseries. - -Architecture Overview ----------------------- - -openseries is built around two main classes that inherit from Pydantic's BaseModel: - -- **OpenTimeSeries**: Manages individual financial time series -- **OpenFrame**: Manages collections of OpenTimeSeries objects - -Both classes provide: - -- **Type safety** through Pydantic validation -- **Immutable data** - original data is preserved -- **Consistent API** - similar methods across both classes -- **Financial focus** - methods designed for financial analysis - -Mutation and data layers ------------------------- - -openseries favors in-place transformations. Many methods modify the existing object -and return ``self`` for chaining rather than creating a new object. -On ``OpenTimeSeries``, the ``dates`` and ``values`` arrays are always left untouched, -while the working data in the ``tsdf`` pandas ``DataFrame`` is mutable. -On ``OpenFrame``, the ``tsdf`` ``DataFrame`` is also mutable and reflects -transformations applied to the frame. If you need to preserve the original state or -compare before/after results, create an explicit copy -(for example, ``OpenTimeSeries.from_deepcopy()`` or ``OpenFrame.from_deepcopy()``). - -The OpenTimeSeries Class -------------------------- - -Core Properties -~~~~~~~~~~~~~~~ - -Every OpenTimeSeries has these fundamental properties: - -.. code-block:: python - - # Create a sample series using openseries simulation - from openseries import ReturnSimulation, ValueType - import datetime as dt - - simulation = ReturnSimulation.from_lognormal( - number_of_sims=1, - trading_days=100, - mean_annual_return=0.25, # ~0.001 daily - mean_annual_vol=0.32, # ~0.02 daily - trading_days_in_year=252, - seed=42 - ) - - series = OpenTimeSeries.from_df( - dframe=simulation.to_dataframe(name="Sample Asset", end=dt.date(2023, 12, 31)), - valuetype=ValueType.RTRN - ).to_cumret() # Convert returns to cumulative prices - - # Core properties - print(f"Name: {series.label}") - print(f"Length: {series.length}") - print(f"First date: {series.first_idx}") - print(f"Last date: {series.last_idx}") - print(f"Value type: {series.valuetype}") - -Data Immutability -~~~~~~~~~~~~~~~~~ - -The original data is never modified: - -.. code-block:: python - - # Original data is preserved - original_dates = series.dates # List of date strings - original_values = series.values # List of float values - - # Working data is in the tsdf DataFrame - working_data = series.tsdf # pandas DataFrame - - # Transformations modify the original object (method chaining) - series.value_to_ret() # Modifies original series - print(f"Series length: {series.length}") # Usually length - 1 - -Value Types -~~~~~~~~~~~ - -The ValueType enum identifies what the series represents: - -.. code-block:: python - - from openseries import ValueType - - # Common value types - print(ValueType.PRICE) # "Price(Close)" - print(ValueType.RTRN) # "Return(Total)" - print(ValueType.ROLLVOL) # "Rolling volatility" - - # Check series type - print(f"Series type: {series.valuetype}") - - # Type changes with transformations - series.value_to_ret() # Modifies original - print(f"Returns type: {series.valuetype}") - -The OpenFrame Class --------------------- - -Managing Multiple Series -~~~~~~~~~~~~~~~~~~~~~~~~ - -OpenFrame manages collections of OpenTimeSeries: - -.. code-block:: python - - from openseries import OpenFrame - - # Create multiple series using openseries simulation - simulation = ReturnSimulation.from_lognormal( - number_of_sims=3, - trading_days=100, - mean_annual_return=0.25, # ~0.001 daily - mean_annual_vol=0.32, # ~0.02 daily - trading_days_in_year=252, - seed=42 - ) - - # Create OpenFrame with multiple series from simulation - frame = OpenFrame( - constituents=[ - OpenTimeSeries.from_df( - dframe=simulation.to_dataframe(name="Asset", end=dt.date(2023, 12, 31)), - column_nmbr=serie, - valuetype=ValueType.RTRN, - ).to_cumret() # Convert returns to cumulative prices - for serie in range(simulation.number_of_sims) - ] - ) - - # Frame properties - print(f"Number of series: {frame.item_count}") - print(f"Column names: {frame.columns_lvl_zero}") - print(f"Common length: {frame.length}") - -Data Alignment -~~~~~~~~~~~~~~~ - -OpenFrame concatenates series data but does **not** automatically align them. -The library provides explicit methods for alignment that require user choice: - -.. code-block:: python - - # Series with different date ranges are concatenated (not aligned) - print("Individual series lengths:") - print(frame.lengths_of_items) - - print(f"Frame length (concatenated): {frame.length}") - - # Explicit alignment methods require user choice: - - # 1. Truncate to common date range - frame.trunc_frame() - - # 2. Align to business day calendar (modifies original) - frame.align_index_to_local_cdays(countries="US") - - # 3. Handle missing values (modifies original) - frame.value_nan_handle(method="fill") - - # 4. Merge with explicit join strategy - frame.merge_series(how="inner") - frame.merge_series(how="outer") - -Financial Calculations ----------------------- - -Return Calculations -~~~~~~~~~~~~~~~~~~~ - -openseries uses standard financial formulas: - -.. code-block:: python - - # Simple returns: (P_t / P_{t-1}) - 1 - series.value_to_ret() # Modifies original - - # Log returns: ln(P_t / P_{t-1}) - series.value_to_log() # Modifies original - - # Cumulative returns: rebasing to start at 1.0 (modifies original) - series.to_cumret() - -Annualization -~~~~~~~~~~~~~ - -Metrics are annualized using the actual number of observations per year: - -.. code-block:: python - - # Automatic calculation of periods per year - print(f"Periods per year: {series.periods_in_a_year:.1f}") - - # Annualized return (geometric mean) - annual_return = series.geo_ret - print(f"Annualized return: {annual_return:.2%}") - - # Annualized volatility - annual_vol = series.vol - print(f"Annualized volatility: {annual_vol:.2%}") - -Risk Metrics -~~~~~~~~~~~~ - -Risk calculations follow industry standards: - -.. code-block:: python - - # Value at Risk (95% confidence) - var_95 = series.var_down - print(f"95% VaR: {var_95:.2%}") - - # Conditional Value at Risk (Expected Shortfall) - cvar_95 = series.cvar_down - print(f"95% CVaR: {cvar_95:.2%}") - - # Maximum Drawdown - max_dd = series.max_drawdown - print(f"Maximum Drawdown: {max_dd:.2%}") - - # Sortino Ratio (downside deviation) - sortino = series.sortino_ratio - print(f"Sortino Ratio: {sortino:.2f}") - -Date Handling -------------- - -Business Day Calendars -~~~~~~~~~~~~~~~~~~~~~~~ - -openseries integrates with business day calendars: - -.. code-block:: python - - # Align to specific country's business days (modifies original) - series.align_index_to_local_cdays(countries="US") - - # Multiple countries (intersection of business days) (modifies original) - series.align_index_to_local_cdays(countries=["US", "GB"]) - - # Custom markets using pandas-market-calendars (modifies original) - series.align_index_to_local_cdays(markets="NYSE") - -Resampling -~~~~~~~~~~ - -Convert between different frequencies: - -.. code-block:: python - - # Resample to month-end (modifies original) - series.resample_to_business_period_ends(freq="BME") - - # Resample to quarter-end (modifies original) - series.resample_to_business_period_ends(freq="BQE") - - # Custom resampling (modifies original) - series.resample(freq="W") - -Data Validation ---------------- - -Type Safety -~~~~~~~~~~~ - -Pydantic ensures data integrity: - -.. code-block:: python - - # Dates must be valid ISO format strings - # This will fail with a validation error - invalid_series = OpenTimeSeries.from_arrays( - dates=["invalid-date"], - values=[100.0] - ) - - # Values must be numeric - # This will fail with a validation error - invalid_series = OpenTimeSeries.from_arrays( - dates=["2023-01-01"], - values=["not a number"] - ) - -Consistency Checks -~~~~~~~~~~~~~~~~~~ - -The library performs consistency checks: - -.. code-block:: python - - # Dates and values must have same length - # Mixed value types in OpenFrame are detected - # Date alignment issues are caught - -Method Categories ------------------ - -openseries methods fall into several categories: - -Properties vs Methods -~~~~~~~~~~~~~~~~~~~~~ - -- **Properties**: Return calculated values (e.g., ``series.vol``) -- **Methods**: Perform operations or take parameters (e.g., ``series.vol_func()``) - -.. code-block:: python - - # Property - uses full series - volatility = series.vol - - # Method - can specify date range - recent_vol = series.vol_func(months_from_last=12) - -Transformation Methods -~~~~~~~~~~~~~~~~~~~~~~ - -Methods that modify the original object (return self for chaining): - -.. code-block:: python - - # Data transformations (modify original) - series.value_to_ret() # Prices to returns - series.to_drawdown_series() # Drawdown series - series.to_cumret() # Cumulative returns - - # Time transformations (modify original) - series.resample_to_business_period_ends(freq="BME") - series.align_index_to_local_cdays(countries="US") - -Methods that return new objects: - -.. code-block:: python - - # Analysis methods (return new objects) - rolling_vol = series.rolling_vol(observations=30) - rolling_ret = series.rolling_return(observations=30) - -Analysis Methods -~~~~~~~~~~~~~~~~ - -Methods that return calculated values: - -.. code-block:: python - - # Rolling calculations - rolling_vol = series.rolling_vol(observations=30) - rolling_corr = frame.rolling_corr(observations=60) - - # Statistical analysis - beta = frame.beta() - tracking_error = frame.tracking_error_func() - -Export Methods -~~~~~~~~~~~~~~ - -Methods for saving results: - -.. code-block:: python - - # File exports - series.to_xlsx("analysis.xlsx") - series.to_json("data.json") - - # Visualization - series.plot_series() - series.plot_histogram() - -Best Practices --------------- - -Data Loading -~~~~~~~~~~~~ - -.. code-block:: python - - # Prefer from_df for pandas data - series = OpenTimeSeries.from_df(dframe=dataframe['Close']) - series.set_new_label(lvl_zero="Asset") - - # Use from_arrays for custom data - series = OpenTimeSeries.from_arrays(dates=date_list, values=value_list) - - # Always set meaningful names - series.set_new_label(lvl_zero="Descriptive Name") - -Analysis Workflow -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 1. Load and validate data - series = OpenTimeSeries.from_df(dframe=data['Close']) - series.set_new_label(lvl_zero="Asset") - - # 2. Basic analysis - metrics = series.all_properties() - - # 3. Specific calculations - series.to_drawdown_series() # Convert to drawdown (modifies original) - rolling_metrics = series.rolling_vol(observations=252) # Returns DataFrame - - # 4. Visualization - series.plot_series() - - # 5. Export results - series.to_xlsx(fiilename="analysis.xlsx") - -Memory Management -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Original data is preserved - use deepcopy if needed - series_copy = OpenTimeSeries.from_deepcopy(series) - - # Large datasets - consider resampling (modifies original) - series.resample_to_business_period_ends(freq="BME") - - # Clean up intermediate results - del intermediate_series - -Portfolio Construction -~~~~~~~~~~~~~~~~~~~~~~ - -OpenFrame provides several built-in weight strategies for portfolio construction: - -.. code-block:: python - - from openseries.owntypes import MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError - - # Available weight strategies - strategies = { - 'eq_weights': 'Equal weights for all assets', - 'inv_vol': 'Inverse volatility weighting (risk parity)', - 'max_div': 'Maximum diversification optimization', - 'min_vol_overweight': 'Minimum volatility overweight strategy' - } - - # Example with error handling - # This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError - portfolio_df = frame.make_portfolio(name="Max Div", weight_strat="max_div") - -Understanding these core concepts will help you use openseries effectively and build more sophisticated financial analysis workflows. diff --git a/docs/build/html/_sources/user_guide/data_handling.rst.txt b/docs/build/html/_sources/user_guide/data_handling.rst.txt deleted file mode 100644 index 86705f71..00000000 --- a/docs/build/html/_sources/user_guide/data_handling.rst.txt +++ /dev/null @@ -1,448 +0,0 @@ -Data Handling -============= - -This guide covers data loading, validation, transformation, and management in openseries. - -Loading Data ------------- - -From pandas DataFrame/Series -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The most common way to load data is from pandas objects: - -.. code-block:: python - - import pandas as pd - from openseries import OpenTimeSeries - - # From pandas Series with DatetimeIndex - data = pd.Series([100, 101, 99, 102], - index=pd.date_range('2023-01-01', periods=4)) - series = OpenTimeSeries.from_df(dframe=data) - series.set_new_label(lvl_zero="Sample") - - # From pandas DataFrame column - df = pd.DataFrame({ - 'Date': pd.date_range('2023-01-01', periods=4), - 'Close': [100, 101, 99, 102], - 'Volume': [1000, 1100, 900, 1200] - }) - df.set_index('Date', inplace=True) - series = OpenTimeSeries.from_df(dframe=df['Close']) - series.set_new_label(lvl_zero="Stock") - -From Arrays -~~~~~~~~~~~ - -For custom data or when working with lists: - -.. code-block:: python - - # From date strings and values - dates = ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'] - values = [100.0, 101.0, 99.0, 102.0] - - series = OpenTimeSeries.from_arrays( - dates=dates, - values=values, - name="Custom Data" - ) - -From Fixed Rate -~~~~~~~~~~~~~~~ - -Generate synthetic data from a fixed rate: - -.. code-block:: python - - from datetime import date - - # Create 252 trading days at 5% annual rate - series = OpenTimeSeries.from_fixed_rate( - rate=0.05, - days=252, - end_date=date(2023, 12, 31), - name="5% Fixed Rate" - ) - -Data Validation ---------------- - -Date Format Validation -~~~~~~~~~~~~~~~~~~~~~~ - -openseries enforces strict date formats: - -.. code-block:: python - - # Valid date formats - valid_dates = ['2023-01-01', '2023-12-31', '2024-02-29'] # ISO format - - # Invalid formats will raise ValidationError - # This will fail with a validation error - invalid_series = OpenTimeSeries.from_arrays( - dates=['01/01/2023', '2023-1-1'], # Wrong format - values=[100, 101] - ) - -Value Validation -~~~~~~~~~~~~~~~~ - -Values must be numeric and finite: - -.. code-block:: python - - import numpy as np - - # Valid values - valid_values = [100.0, 101.5, 99.25, 102.75] - - # Handle NaN values appropriately - values_with_nan = [100.0, np.nan, 99.0, 102.0] - series = OpenTimeSeries.from_arrays( - dates=['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'], - values=values_with_nan, - name="Data with NaN" - ) - - # Clean NaN values (modifies original) - series.value_nan_handle() # Forward fill - -Length Consistency -~~~~~~~~~~~~~~~~~~ - -Dates and values must have the same length: - -.. code-block:: python - - # This will raise an error - # This will fail with a length mismatch error - invalid_series = OpenTimeSeries.from_arrays( - dates=['2023-01-01', '2023-01-02'], - values=[100.0, 101.0, 102.0] # Different length - ) - -Data Transformations --------------------- - -Price and Return Conversions -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Assume we have a price series - prices = OpenTimeSeries.from_arrays( - dates=['2023-01-01', '2023-01-02', '2023-01-03'], - values=[100.0, 102.0, 99.0], - name="Stock Price" - ) - - # Convert to simple returns (modifies original) - prices.value_to_ret() - print(f"Returns: {prices.values}") # [0.02, -0.0294...] - - # Convert to log returns (modifies original) - prices.value_to_log() - - # Convert returns back to cumulative values (modifies original) - prices.to_cumret() - - # Convert to differences (absolute changes) (modifies original) - prices.value_to_diff() - -Resampling -~~~~~~~~~~ - -Change the frequency of your data: - -.. code-block:: python - - # Daily to monthly (business month end) (modifies original) - series.resample_to_business_period_ends(freq="BME") - - # Daily to quarterly (modifies original) - series.resample_to_business_period_ends(freq="BQE") - - # Daily to annual (modifies original) - series.resample_to_business_period_ends(freq="BYE") - - # Custom resampling with pandas frequency strings (modifies original) - series.resample(freq="W") - - # Resample with specific method (modifies original) - series.resample(freq="W", method="mean") - -Business Day Alignment -~~~~~~~~~~~~~~~~~~~~~~ - -Align data to business day calendars: - -.. code-block:: python - - # Align to US business days (modifies original) - series.align_index_to_local_cdays(countries="US") - - # Align to multiple countries (intersection) (modifies original) - series.align_index_to_local_cdays(countries=["US", "GB", "JP"]) - - # Align to specific market calendar (modifies original) - series.align_index_to_local_cdays(markets="NYSE") - -Handling Missing Data ---------------------- - -NaN Handling Strategies -~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import numpy as np - - # Create series with missing values - dates = ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'] - values = [100.0, np.nan, 102.0, np.nan] - - series_with_nan = OpenTimeSeries.from_arrays( - dates=dates, values=values, name="With NaN" - ) - - # Forward fill missing values (for price series) (modifies original) - series_with_nan.value_nan_handle() - - # For return series, replace NaN with 0.0 (modifies original) - series_with_nan.value_to_ret() - series_with_nan.return_nan_handle() - -Dropping Missing Data -~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Remove NaN values entirely (modifies original) - series_with_nan.value_nan_handle(method="drop") - -Working with Multiple Assets ------------------------------ - -Creating OpenFrame -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - from openseries import OpenFrame - - # Create multiple series - series1 = OpenTimeSeries.from_arrays( - dates=['2023-01-01', '2023-01-02', '2023-01-03'], - values=[100, 102, 99], name="Asset A" - ) - - series2 = OpenTimeSeries.from_arrays( - dates=['2023-01-01', '2023-01-02', '2023-01-03'], - values=[50, 51, 49], name="Asset B" - ) - - # Create frame - frame = OpenFrame(constituents=[series1, series2]) - -Handling Different Date Ranges -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -OpenFrame automatically handles series with different date ranges: - -.. code-block:: python - - # Series with different start/end dates - early_series = OpenTimeSeries.from_arrays( - dates=['2022-12-01', '2023-01-01', '2023-01-02'], - values=[95, 100, 102], name="Early Start" - ) - - late_series = OpenTimeSeries.from_arrays( - dates=['2023-01-02', '2023-01-03', '2023-01-04'], - values=[51, 49, 52], name="Late Start" - ) - - # Frame will align to common date range - frame = OpenFrame(constituents=[early_series, late_series]) - print(f"Frame date range: {frame.first_idx} to {frame.last_idx}") - -Adding and Removing Series -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Add a new series - new_series = OpenTimeSeries.from_arrays( - dates=['2023-01-01', '2023-01-02', '2023-01-03'], - values=[200, 205, 198], name="Asset C" - ) - frame.add_timeseries(new_series) - - # Remove a series by index - frame.delete_timeseries(item_idx=0) - -Data Export and Import ----------------------- - -Excel Export -~~~~~~~~~~~~ - -.. code-block:: python - - # Export single series - series.to_xlsx(filename="single_series.xlsx") - - # Export frame (multiple series) - frame.to_xlsx(filename="multiple_series.xlsx") - - # Export with custom sheet title - series.to_xlsx( - filename="formatted_export.xlsx", - sheet_title="Analysis" - ) - -JSON Export -~~~~~~~~~~~ - -.. code-block:: python - - # Export series values only - series.to_json(what_output="values", filename="series_values.json") - - # Export full dataframe structure - series.to_json(what_output="tsdf", filename="series_dataframe.json") - - # Export frame data - frame.to_json(what_output="values", filename="frame_values.json") - -Working with Real Data Sources -------------------------------- - -Yahoo Finance Integration -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import yfinance as yf - - # Single asset - ticker = yf.Ticker("AAPL") - data = ticker.history(period="2y") - - apple = OpenTimeSeries.from_df( - dframe=data['Close'], - name="Apple Inc." - ) - - # Multiple assets - tickers = ["AAPL", "GOOGL", "MSFT"] - series_list = [] - - for ticker_symbol in tickers: - ticker = yf.Ticker(ticker_symbol) - data = ticker.history(period="1y") - series = OpenTimeSeries.from_df( - dframe=data['Close'], - name=ticker_symbol - ) - series_list.append(series) - - tech_frame = OpenFrame(constituents=series_list) - -CSV Data -~~~~~~~~ - -.. code-block:: python - - # Load from CSV - df = pd.read_csv("stock_data.csv", index_col=0, parse_dates=True) - - series = OpenTimeSeries.from_df( - dframe=df['Close'], - name="Stock from CSV" - ) - -Data Quality Checks -------------------- - -Validation Methods -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Check for data quality issues - print(f"Series length: {series.length}") - print(f"Date range: {series.first_idx} to {series.last_idx}") - print(f"Span of days: {series.span_of_days}") - - # Check for gaps in data - expected_length = (series.last_idx - series.first_idx).days + 1 - actual_length = series.length - - if expected_length != actual_length: - print(f"Data gaps detected: expected {expected_length}, got {actual_length}") - -Outlier Detection -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Convert to returns for outlier analysis (modifies original) - series.value_to_ret() - - # Detect outliers using the built-in method - outliers = series.outliers(threshold=3.0) - print(f"Found {len(outliers)} outliers (|z| > 3)") - - # For OpenFrame, outliers returns a DataFrame - frame_outliers = frame.outliers(threshold=3.0) - print(f"Found outliers in frame: {len(frame_outliers)} rows") - - # Customize threshold and date range - recent_outliers = series.outliers( - threshold=2.5, - months_from_last=6 - ) - -Performance Considerations --------------------------- - -Memory Usage -~~~~~~~~~~~~ - -.. code-block:: python - - # For large datasets, consider resampling - large_series = series # Assume this is large daily data - - # Reduce to monthly for analysis (modifies original) - large_series.resample_to_business_period_ends(freq="BME") - - # Use monthly for computationally intensive operations - monthly_metrics = large_series.all_properties() - -Efficient Data Loading -~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # When loading multiple assets, batch the operations - tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"] - - # Download all at once - data = yf.download(tickers, period="2y")['Close'] - - # Create series efficiently - series_list = [] - for ticker in tickers: - series = OpenTimeSeries.from_df( - dframe=data[ticker].dropna(), - name=ticker - ) - series_list.append(series) - - frame = OpenFrame(constituents=series_list) - -This comprehensive guide should help you handle various data scenarios effectively with openseries. diff --git a/docs/build/html/_sources/user_guide/installation.rst.txt b/docs/build/html/_sources/user_guide/installation.rst.txt deleted file mode 100644 index 69f66f93..00000000 --- a/docs/build/html/_sources/user_guide/installation.rst.txt +++ /dev/null @@ -1,197 +0,0 @@ -Installation -============ - -System Requirements -------------------- - -openseries requires Python 3.11 or higher and is compatible with: - -- **Operating Systems**: Windows, macOS, Linux -- **Python versions**: 3.11, 3.12, 3.13, 3.14 - -Installing openseries ---------------------- - -Using pip (recommended) -~~~~~~~~~~~~~~~~~~~~~~~ - -The easiest way to install openseries is using pip: - -.. code-block:: bash - - pip install openseries - -Using conda -~~~~~~~~~~~ - -openseries is also available on conda-forge: - -.. code-block:: bash - - conda install -c conda-forge openseries - -Installing from source -~~~~~~~~~~~~~~~~~~~~~~ - -To install the latest development version from GitHub: - -.. code-block:: bash - - git clone https://github.com/CaptorAB/openseries.git - cd openseries - pip install -e . - -Dependencies ------------- - -openseries automatically installs the following dependencies: - -Core Dependencies -~~~~~~~~~~~~~~~~~ - -- **pandas** (>=2.1.2) - Data manipulation and analysis -- **numpy** (>=1.23.2) - Numerical computing -- **pydantic** (>=2.5.2) - Data validation and settings management -- **plotly** (>=5.18.0) - Interactive plotting -- **scipy** (>=1.14.1) - Scientific computing -- **scikit-learn** (>=1.4.0) - Machine learning utilities - -Financial and Date Utilities -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -- **exchange-calendars** (>=4.8) - Trading calendar support -- **holidays** (>=0.30) - Holiday calendar support -- **python-dateutil** (>=2.8.2) - Date parsing utilities -- **tzdata** (>=2025.3) - IANA time zone data - -File and Network Support -~~~~~~~~~~~~~~~~~~~~~~~~~ - -- **openpyxl** (>=3.1.2) - Excel file support -- **requests** (>=2.20.0) - HTTP library - -Optional Dependencies -~~~~~~~~~~~~~~~~~~~~~ - -For data acquisition examples, you may want to install: - -.. code-block:: bash - - pip install yfinance # For Yahoo Finance data - -Verifying Installation ----------------------- - -To verify that openseries is installed correctly, run: - -.. code-block:: python - - import openseries - print(openseries.__version__) - -You can also run a quick test: - -.. code-block:: python - - from openseries import OpenTimeSeries, ReturnSimulation, ValueType - import datetime as dt - - # Create sample data using openseries simulation - simulation = ReturnSimulation.from_lognormal( - number_of_sims=1, - trading_days=100, - mean_annual_return=0.25, # ~0.001 daily - mean_annual_vol=0.32, # ~0.02 daily - trading_days_in_year=252, - seed=42 - ) - - # Create OpenTimeSeries - series = OpenTimeSeries.from_df( - dframe=simulation.to_dataframe(name="Test Series", end=dt.date(2023, 12, 31)), - valuetype=ValueType.RTRN - ).to_cumret() # Convert returns to cumulative prices - - print(f"Series length: {series.length}") - print(f"Annual return: {series.geo_ret:.2%}") - -Development Installation ------------------------- - -If you plan to contribute to openseries or need the development dependencies, -use the same pinned tooling as CI (``uv==0.11.21``): - -.. code-block:: bash - - git clone https://github.com/CaptorAB/openseries.git - cd openseries - make install - -On Windows, run ``.\make.ps1 make`` instead of ``make install``. - -This creates ``venv``, installs locked runtime, development, and documentation -dependencies from ``uv.lock``, and installs pre-commit hooks. Development -dependencies include: - -- **pytest** (>=9.1.0) - Testing framework -- **pytest-cov** (>=7.1.0) - Coverage plugin -- **pytest-xdist** (>=3.8.0) - Parallel test runner -- **mypy** (==2.1.0) - Static type checking -- **ruff** (==0.15.18) - Linting and formatting -- **pre-commit** (>=4.6.0) - Git hooks for code quality - -Troubleshooting ---------------- - -Common Issues -~~~~~~~~~~~~~ - -**ImportError: No module named 'openseries'** - -Make sure openseries is installed in the correct Python environment. If using virtual environments, ensure it's activated. - -**Version conflicts** - -If you encounter dependency conflicts, try creating a fresh virtual environment: - -.. code-block:: bash - - python -m venv openseries_env - source openseries_env/bin/activate # On Windows: openseries_env\Scripts\activate - pip install openseries - -**Performance issues** - -For better performance with large datasets, consider installing optional accelerated packages: - -.. code-block:: bash - - pip install numba # For numerical acceleration - pip install bottleneck # For faster pandas operations - -Getting Help -~~~~~~~~~~~~ - -If you encounter issues: - -1. Check the `GitHub Issues-
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-
- Read the Docs
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diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js
deleted file mode 100644
index e29b1c75..00000000
--- a/docs/build/html/_static/searchtools.js
+++ /dev/null
@@ -1,693 +0,0 @@
-/*
- * Sphinx JavaScript utilities for the full-text search.
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename, kind] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-// Global search result kind enum, used by themes to style search results.
-// prettier-ignore
-class SearchResultKind {
- static get index() { return "index"; }
- static get object() { return "object"; }
- static get text() { return "text"; }
- static get title() { return "title"; }
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _escapeHTML = (text) => {
- return text
- .replaceAll("&", "&")
- .replaceAll("<", "<")
- .replaceAll(">", ">")
- .replaceAll('"', """)
- .replaceAll("'", "'");
-};
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
- const contentRoot = document.documentElement.dataset.content_root;
-
- const [docName, title, anchor, descr, score, _filename, kind] = item;
-
- let listItem = document.createElement("li");
- // Add a class representing the item's type:
- // can be used by a theme's CSS selector for styling
- // See SearchResultKind for the class names.
- listItem.classList.add(`kind-${kind}`);
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = contentRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- requestUrl = contentRoot + docName + docFileSuffix;
- linkUrl = docName + docLinkSuffix;
- }
- let linkEl = listItem.appendChild(document.createElement("a"));
- linkEl.href = linkUrl + anchor;
- linkEl.dataset.score = score;
- linkEl.innerHTML = _escapeHTML(title);
- if (descr) {
- listItem.appendChild(document.createElement("span")).innerHTML =
- ` (${_escapeHTML(descr)})`;
- // highlight search terms in the description
- if (SPHINX_HIGHLIGHT_ENABLED)
- // SPHINX_HIGHLIGHT_ENABLED is set in sphinx_highlight.js
- highlightTerms.forEach((term) =>
- _highlightText(listItem, term, "highlighted"),
- );
- } else if (showSearchSummary)
- fetch(requestUrl)
- .then((responseData) => responseData.text())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms, anchor),
- );
- // highlight search terms in the summary
- if (SPHINX_HIGHLIGHT_ENABLED)
- // SPHINX_HIGHLIGHT_ENABLED is set in sphinx_highlight.js
- highlightTerms.forEach((term) =>
- _highlightText(listItem, term, "highlighted"),
- );
- });
- Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories.",
- );
- else
- Search.status.innerText = Documentation.ngettext(
- "Search finished, found one page matching the search query.",
- "Search finished, found ${resultCount} pages matching the search query.",
- resultCount,
- ).replace("${resultCount}", resultCount);
-};
-const _displayNextItem = (
- results,
- resultCount,
- searchTerms,
- highlightTerms,
-) => {
- // results left, load the summary and display it
- // this is intended to be dynamic (don't sub resultsCount)
- if (results.length) {
- _displayItem(results.pop(), searchTerms, highlightTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
- 5,
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) =>
- query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter((term) => term); // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString, anchor) => {
- const htmlElement = new DOMParser().parseFromString(
- htmlString,
- "text/html",
- );
- for (const removalQuery of [".headerlink", "script", "style"]) {
- htmlElement.querySelectorAll(removalQuery).forEach((el) => {
- el.remove();
- });
- }
- if (anchor) {
- const anchorContent = htmlElement.querySelector(
- `[role="main"] ${anchor}`,
- );
- if (anchorContent) return anchorContent.textContent;
-
- console.warn(
- `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`,
- );
- }
-
- // if anchor not specified or not found, fall back to main content
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent) return docContent.textContent;
-
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template.",
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.setAttribute("role", "list");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- _parseQuery: (query) => {
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords set is from language_data.js
- if (stopwords.has(queryTermLower) || queryTerm.match(/^\d+$/)) return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) {
- // SPHINX_HIGHLIGHT_ENABLED is set in sphinx_highlight.js
- localStorage.setItem(
- "sphinx_highlight_terms",
- [...highlightTerms].join(" "),
- );
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- _performSearch: (
- query,
- searchTerms,
- excludedTerms,
- highlightTerms,
- objectTerms,
- ) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // Collect multiple result groups to be sorted separately and then ordered.
- // Each is an array of [docname, title, anchor, descr, score, filename, kind].
- const normalResults = [];
- const nonMainIndexResults = [];
-
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase().trim();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (
- title.toLowerCase().trim().includes(queryLower)
- && queryLower.length >= title.length / 2
- ) {
- for (const [file, id] of foundTitles) {
- const score = Math.round(
- (Scorer.title * queryLower.length) / title.length,
- );
- const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
- normalResults.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score + boost,
- filenames[file],
- SearchResultKind.title,
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && queryLower.length >= entry.length / 2) {
- for (const [file, id, isMain] of foundEntries) {
- const score = Math.round((100 * queryLower.length) / entry.length);
- const result = [
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- SearchResultKind.index,
- ];
- if (isMain) {
- normalResults.push(result);
- } else {
- nonMainIndexResults.push(result);
- }
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- normalResults.push(...Search.performObjectSearch(term, objectTerms)),
- );
-
- // lookup as search terms in fulltext
- normalResults.push(
- ...Search.performTermsSearch(searchTerms, excludedTerms),
- );
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- normalResults.forEach((item) => (item[4] = Scorer.score(item)));
- nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
- }
-
- // Sort each group of results by score and then alphabetically by name.
- normalResults.sort(_orderResultsByScoreThenName);
- nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
- // Combine the result groups in (reverse) order.
- // Non-main index entries are typically arbitrary cross-references,
- // so display them after other results.
- let results = [...nonMainIndexResults, ...normalResults];
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result
- .slice(0, 4)
- .concat([result[5]])
- .map((v) => String(v))
- .join(",");
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- return results.reverse();
- },
-
- query: (query) => {
- const [
- searchQuery,
- searchTerms,
- excludedTerms,
- highlightTerms,
- objectTerms,
- ] = Search._parseQuery(query);
- const results = Search._performSearch(
- searchQuery,
- searchTerms,
- excludedTerms,
- highlightTerms,
- objectTerms,
- );
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms, highlightTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4];
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- SearchResultKind.object,
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) => objectSearchCallback(prefix, array)),
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- // find documents, if any, containing the query word in their text/title term indices
- // use Object.hasOwnProperty to avoid mismatching against prototype properties
- const arr = [
- {
- files: terms.hasOwnProperty(word) ? terms[word] : undefined,
- score: Scorer.term,
- },
- {
- files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined,
- score: Scorer.title,
- },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- if (!terms.hasOwnProperty(word)) {
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- }
- if (!titleTerms.hasOwnProperty(word)) {
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
- });
- }
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, new Map());
- const fileScores = scoreMap.get(file);
- fileScores.set(word, record.score);
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (!fileMap.has(file)) fileMap.set(file, [word]);
- else if (fileMap.get(file).indexOf(word) === -1)
- fileMap.get(file).push(word);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2,
- ).length;
- if (
- wordList.length !== searchTerms.size
- && wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file
- || titleTerms[term] === file
- || (terms[term] || []).includes(file)
- || (titleTerms[term] || []).includes(file),
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w)));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- SearchResultKind.text,
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords, anchor) => {
- const text = Search.htmlToText(htmlText, anchor);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent =
- top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js
deleted file mode 100644
index a74e103a..00000000
--- a/docs/build/html/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,159 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true;
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0
- && !parent.classList.contains(className)
- && !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- const rest = document.createTextNode(val.substr(pos + text.length));
- parent.insertBefore(span, parent.insertBefore(rest, node.nextSibling));
- node.nodeValue = val.substr(0, pos);
- /* There may be more occurrences of search term in this node. So call this
- * function recursively on the remaining fragment.
- */
- _highlight(rest, addItems, text, className);
-
- if (isInSVG) {
- const rect = document.createElementNS(
- "http://www.w3.org/2000/svg",
- "rect",
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target),
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms");
- // Update history only if '?highlight' is present; otherwise it
- // clears text fragments (not set in window.location by the browser)
- if (url.searchParams.has("highlight")) {
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
- }
-
- // get individual terms from highlight string
- const terms = highlight
- .toLowerCase()
- .split(/\s+/)
- .filter((x) => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
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- openseries.get_previous_business_day_before_today — openseries 2.1.10 documentation
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- openseries.holiday_calendar — openseries 2.1.10 documentation
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- openseries.load_plotly_dict — openseries 2.1.10 documentation
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- openseries.offset_business_days — openseries 2.1.10 documentation
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- openseries.prepare_plot_data — openseries 2.1.10 documentation
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- openseries.report_html — openseries 2.1.10 documentation
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- openseries.sharpeplot — openseries 2.1.10 documentation
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- openseries.simulate_portfolios — openseries 2.1.10 documentation
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- openseries.timeseries_chain — openseries 2.1.10 documentation
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- openseries package — openseries 2.1.10 documentation
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- Portfolio Tools — openseries 2.1.10 documentation
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- Report Generation — openseries 2.1.10 documentation
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- OpenTimeSeries — openseries 2.1.10 documentation
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- Simulation — openseries 2.1.10 documentation
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- Types and Enums — openseries 2.1.10 documentation
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- Changelog — openseries 2.1.10 documentation
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- Contributing to openseries — openseries 2.1.10 documentation
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- Reporting — openseries 2.1.10 documentation
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- Multi-Asset Analysis — openseries 2.1.10 documentation
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- Portfolio Optimization — openseries 2.1.10 documentation
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- Single Asset Analysis — openseries 2.1.10 documentation
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- Index — openseries 2.1.10 documentation
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- openseries Documentation — openseries 2.1.10 documentation
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- Search — openseries 2.1.10 documentation
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- Advanced Features — openseries 2.1.10 documentation
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- Basic Financial Analysis — openseries 2.1.10 documentation
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- Portfolio Analysis — openseries 2.1.10 documentation
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- Risk Management — openseries 2.1.10 documentation
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- Core Concepts — openseries 2.1.10 documentation
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- Data Handling — openseries 2.1.10 documentation
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- Installation — openseries 2.1.10 documentation
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- Quick Start Guide — openseries 2.1.10 documentation
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-
- ..." >&2
+ exit 2
+fi
+
+write_output() {
+ local value="$1"
+ if [[ -n "${GITHUB_OUTPUT:-}" ]]; then
+ echo "run=${value}" >> "${GITHUB_OUTPUT}"
+ fi
+ echo "run=${value}"
+}
+
+event="${GITHUB_EVENT_NAME:-}"
+if [[ "${event}" != "pull_request" ]]; then
+ write_output true
+ exit 0
+fi
+
+if [[ -n "${CI_PR_PATHS_FILES:-}" ]]; then
+ files="${CI_PR_PATHS_FILES}"
+else
+ token="${GH_TOKEN:-${GITHUB_TOKEN:-}}"
+ repo="${GITHUB_REPOSITORY:-}"
+ pr="${PR_NUMBER:-}"
+ if [[ -z "${token}" || -z "${repo}" || -z "${pr}" ]]; then
+ echo "ci-pr-paths: missing token or PR context; running job" >&2
+ write_output true
+ exit 0
+ fi
+ export GH_TOKEN="${token}"
+ files="$(gh api --paginate "repos/${repo}/pulls/${pr}/files" --jq '.[].filename')" || {
+ echo "ci-pr-paths: failed to list PR files; running job" >&2
+ write_output true
+ exit 0
+ }
+fi
+
+while IFS= read -r file; do
+ [[ -z "${file}" ]] && continue
+ for pattern in "$@"; do
+ if [[ "${file}" == ${pattern} ]]; then
+ write_output true
+ exit 0
+ fi
+ done
+done <<< "${files}"
+
+write_output false
diff --git a/scripts/run-zizmor.sh b/scripts/run-zizmor.sh
index ff502aac..ecb6c52e 100755
--- a/scripts/run-zizmor.sh
+++ b/scripts/run-zizmor.sh
@@ -1,7 +1,7 @@
#!/usr/bin/env bash
set -euo pipefail
-readonly ZIZMOR_VERSION=1.25.2
+readonly ZIZMOR_VERSION=1.29.0
if [[ -z "${GH_TOKEN:-}" && -z "${GITHUB_TOKEN:-}" ]]; then
if command -v gh >/dev/null 2>&1 && gh auth status >/dev/null 2>&1; then
diff --git a/tests/test_ci_pr_paths.py b/tests/test_ci_pr_paths.py
new file mode 100644
index 00000000..ae6319db
--- /dev/null
+++ b/tests/test_ci_pr_paths.py
@@ -0,0 +1,84 @@
+"""Test pull-request path filtering used by GitHub Actions."""
+
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+from pathlib import Path
+
+ROOT = Path(__file__).parent.parent
+SCRIPT = ROOT / "scripts" / "ci-pr-paths.sh"
+
+
+class CiPrPathsError(Exception):
+ """Raised when ci-pr-paths.sh does not behave as expected."""
+
+
+def _run(
+ *patterns: str,
+ env: dict[str, str],
+) -> str:
+ bash = shutil.which("bash")
+ if bash is None:
+ msg = "bash executable not found"
+ raise CiPrPathsError(msg)
+ merged = os.environ.copy()
+ merged.pop("GITHUB_OUTPUT", None)
+ merged.update(env)
+ completed = subprocess.run( # noqa: S603
+ [bash, str(SCRIPT), *patterns],
+ check=True,
+ capture_output=True,
+ cwd=ROOT,
+ env=merged,
+ text=True,
+ )
+ return completed.stdout.strip()
+
+
+class TestCiPrPaths:
+ """class to verify scripts/ci-pr-paths.sh output."""
+
+ def test_non_pull_request_always_runs(self: TestCiPrPaths) -> None:
+ """Test workflow_dispatch runs regardless of patterns."""
+ output = _run("openseries/*", env={"GITHUB_EVENT_NAME": "workflow_dispatch"})
+ if output != "run=true":
+ msg = f"expected run=true, got {output!r}"
+ raise CiPrPathsError(msg)
+
+ def test_matching_python_path_runs(self: TestCiPrPaths) -> None:
+ """Test a Python source change matches openseries/*."""
+ output = _run(
+ "openseries/*",
+ "tests/*",
+ env={
+ "GITHUB_EVENT_NAME": "pull_request",
+ "CI_PR_PATHS_FILES": "README.md\nopenseries/series.py",
+ },
+ )
+ if output != "run=true":
+ msg = f"expected run=true, got {output!r}"
+ raise CiPrPathsError(msg)
+
+ def test_docs_only_change_skips(self: TestCiPrPaths) -> None:
+ """Test a docs-only PR does not match Python test paths."""
+ output = _run(
+ "openseries/*",
+ "tests/*",
+ "pyproject.toml",
+ env={
+ "GITHUB_EVENT_NAME": "pull_request",
+ "CI_PR_PATHS_FILES": "docs/source/index.rst\ndocs/README.md",
+ },
+ )
+ if output != "run=false":
+ msg = f"expected run=false, got {output!r}"
+ raise CiPrPathsError(msg)
+
+ def test_missing_pr_context_runs(self: TestCiPrPaths) -> None:
+ """Test a pull_request without API context fails open and runs."""
+ output = _run("openseries/*", env={"GITHUB_EVENT_NAME": "pull_request"})
+ if output != "run=true":
+ msg = f"expected run=true, got {output!r}"
+ raise CiPrPathsError(msg)
diff --git a/tests/test_version_alignment.py b/tests/test_version_alignment.py
index c68574cc..1a02df64 100644
--- a/tests/test_version_alignment.py
+++ b/tests/test_version_alignment.py
@@ -13,7 +13,6 @@
PRE_COMMIT_PATH = ROOT / ".pre-commit-config.yaml"
MAKEFILE_PATH = ROOT / "Makefile"
MAKE_PS1_PATH = ROOT / "make.ps1"
-DOCS_REQUIREMENTS_PATH = ROOT / "docs" / "requirements.txt"
INSTALLATION_RST_PATH = ROOT / "docs" / "source" / "user_guide" / "installation.rst"
CONTRIBUTING_RST_PATH = ROOT / "docs" / "source" / "development" / "contributing.rst"
PYTHON_VERSION_PATH = ROOT / ".python-version"
@@ -28,6 +27,7 @@
"codeql.yml",
"zizmor.yml",
"supply-chain.yml",
+ "codecov.yml",
)
MYPY_ADDITIONAL_DEPENDENCIES = (
@@ -258,23 +258,29 @@ def test_lockfile_matches_pyproject(self: TestVersionAlignment) -> None:
)
raise VersionAlignmentError(msg)
- def test_docs_requirements_match_docs_extra(self: TestVersionAlignment) -> None:
- """Test docs/requirements.txt matches the pyproject docs extra."""
- pyproject = _load_toml(PYPROJECT_PATH)
- expected = [
- item.replace(" ", "")
- for item in pyproject["project"]["optional-dependencies"]["docs"]
- ]
- actual = [
- line.strip().replace(" ", "")
- for line in _read_text(DOCS_REQUIREMENTS_PATH).splitlines()
- if line.strip() and not line.strip().startswith("#")
+ def test_readthedocs_installs_docs_extra(self: TestVersionAlignment) -> None:
+ """Test Read the Docs installs the pyproject docs extra."""
+ rtd = _read_text(ROOT / ".readthedocs.yaml")
+ extras_match = re.search(
+ r"extra_requirements:\n((?: - .+\n)+)",
+ rtd,
+ )
+ if extras_match is None:
+ msg = ".readthedocs.yaml is missing extra_requirements"
+ raise VersionAlignmentError(msg)
+ extras = [
+ line.strip()[2:].strip()
+ for line in extras_match.group(1).splitlines()
+ if line.strip().startswith("- ")
]
- if actual != expected:
- msg = (
- "docs/requirements.txt does not match pyproject docs extra: "
- f"{actual} != {expected}"
+ if extras != ["docs"]:
+ _raise_mismatch(
+ ".readthedocs.yaml extra_requirements",
+ "docs",
+ ", ".join(extras),
)
+ if "docs/requirements.txt" in rtd:
+ msg = ".readthedocs.yaml must not install docs/requirements.txt"
raise VersionAlignmentError(msg)
def test_tool_versions_match(self: TestVersionAlignment) -> None:
@@ -431,6 +437,36 @@ def test_docs_list_pyproject_specifiers(self: TestVersionAlignment) -> None:
msg = f"{CONTRIBUTING_RST_PATH.name} is missing {uv_pin}"
raise VersionAlignmentError(msg)
+ def test_codecov_reporting_is_master_only(self: TestVersionAlignment) -> None:
+ """Test Codecov uploads run only from the master coverage workflow."""
+ tests = _read_text(WORKFLOW_DIR / "test.yml")
+ deploy = _read_text(WORKFLOW_DIR / "deploy.yml")
+ codecov = _read_text(WORKFLOW_DIR / "codecov.yml")
+ if "codecov/codecov-action" in tests:
+ msg = "test.yml must not upload to Codecov"
+ raise VersionAlignmentError(msg)
+ if "environment: codecov" in tests:
+ msg = "test.yml must not use the codecov environment"
+ raise VersionAlignmentError(msg)
+ if "codecov/codecov-action" in deploy:
+ msg = "deploy.yml must not upload to Codecov"
+ raise VersionAlignmentError(msg)
+ if "environment: codecov" in deploy:
+ msg = "deploy.yml must not use the codecov environment"
+ raise VersionAlignmentError(msg)
+ if "codecov/codecov-action" not in codecov:
+ msg = "codecov.yml must upload to Codecov"
+ raise VersionAlignmentError(msg)
+ if "environment: codecov" not in codecov:
+ msg = "codecov.yml must use the codecov environment"
+ raise VersionAlignmentError(msg)
+ if "branches:\n - master" not in codecov:
+ msg = "codecov.yml must run on push to master"
+ raise VersionAlignmentError(msg)
+ if "workflow_dispatch: {}" not in codecov:
+ msg = "codecov.yml must allow workflow_dispatch from master"
+ raise VersionAlignmentError(msg)
+
def test_python_versions_match(self: TestVersionAlignment) -> None:
"""Test declared Python versions match CI, docs, and tool targets."""
pyproject = _load_toml(PYPROJECT_PATH)
diff --git a/uv.lock b/uv.lock
index 4171ce81..2a1780e6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -269,86 +269,86 @@ wheels = [
[[package]]
name = "coverage"
-version = "7.15.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/be/c3/4f2195f512fb172aa425a8803a874b2baa9ba7f80ff7b6080998761fc701/coverage-7.15.4.tar.gz", hash = "sha256:0548198fff07ccf4faf469520bce1c2eceb1ce3e62891921138dec10907f9d00", size = 936952, upload-time = "2026-08-06T13:50:24.442Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/2a/66/edcec7d7a0b524aa8923e22925fde6fe50ce005a113dca13ae1581455c4c/coverage-7.15.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:bbac5abad70df71019988f83f26ac7092ff2642975def4429e98dc7585ef3490", size = 222367, upload-time = "2026-08-06T13:47:15.578Z" },
- { url = "https://files.pythonhosted.org/packages/e6/c6/ab8de429e2e8548faf58ec7e1674a4ce00414b4113942d3fe87109cf0f68/coverage-7.15.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:357a173465c7ce028d07a95cc2b63b5bf59f50ecdd5ad75c5cbb78ada984048e", size = 222874, upload-time = "2026-08-06T13:47:16.961Z" },
- { url = "https://files.pythonhosted.org/packages/be/c4/3b7b49587e8a6b9af79b3eb468d443d6042b6d65b47aa26586846a0d6566/coverage-7.15.4-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:21b803935e2efc3acebe9697197a294fccf5dc4e5382bd6369542ff7a7d2a1d7", size = 253287, upload-time = "2026-08-06T13:47:18.291Z" },
- { url = "https://files.pythonhosted.org/packages/fb/65/ec03b743a2a229c72cc1eff3e57be9d3564e9c6b4d5aba2d70744a3fc0d8/coverage-7.15.4-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:7a2b580774a4786c1053157c0165e04476e03ff293993d7c148eee784a94bae6", size = 255199, upload-time = "2026-08-06T13:47:19.765Z" },
- { url = "https://files.pythonhosted.org/packages/41/4b/5163729e4b6582d61975cfd3ccab45b4ec53e21cf156d9941cb025188468/coverage-7.15.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a9464451c4efffe8d47ace5a540b10b0dc10e879066290f8600872b7f54a419d", size = 257308, upload-time = "2026-08-06T13:47:21.206Z" },
- { url = "https://files.pythonhosted.org/packages/86/08/2167a0f08fb87d702fa423a48578a32865464b7c9e1db3911ad7812ab414/coverage-7.15.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:de602f34123c2f4af1c1869c6dbbbd60da6d5983bf01937367295d135cccbfce", size = 259268, upload-time = "2026-08-06T13:47:22.503Z" },
- { url = "https://files.pythonhosted.org/packages/1e/e5/68eebae3053dbd48508edea559c21b23fbdf3460784f91370c83a86a6acd/coverage-7.15.4-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:6879ded16a27f3eeca19b900c147e81616e7054db451471a611b2755ee5249f7", size = 253392, upload-time = "2026-08-06T13:47:23.88Z" },
- { url = "https://files.pythonhosted.org/packages/1a/46/fd4ced40a2b691c774e515c9b69500bfa64c7960b67fcee4b2f6fad97fc3/coverage-7.15.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:986be58c3ab54aae8d3496a6225eea74f760fdbe739b38bd442c7e8d133aa53b", size = 255001, upload-time = "2026-08-06T13:47:25.469Z" },
- { url = "https://files.pythonhosted.org/packages/53/25/ae2e5fa710bb6957a9aadeb9e3598d3b3e4af6587ce857ad42e8639a3f30/coverage-7.15.4-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:c6103639613fe6c1e989082948419bc77a2d26b6c825c99d7fad25f7d3d87afc", size = 253061, upload-time = "2026-08-06T13:47:26.845Z" },
- { url = "https://files.pythonhosted.org/packages/d7/31/67ddc0365db2c6e93ac8580bc4bbc50f65273262f973f63ebcdbc15c0495/coverage-7.15.4-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:d3af93dddb5659276c63bc16ac6466ac2033a70ca816097bbc06345b8ccdf571", size = 256831, upload-time = "2026-08-06T13:47:28.217Z" },
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@@ -896,7 +896,7 @@ resolution-markers = [
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- const versionSwitch = document.querySelector(
- "div.switch-menus > div.version-switch",
- );
- if (themeVersionSelector) {
- let versions = config.versions.active;
- if (config.versions.current.hidden || config.versions.current.type === "external") {
- versions.unshift(config.versions.current);
- }
- const versionSelect = `
-
- `;
-
- versionSwitch.innerHTML = versionSelect;
- versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
-
- const languageSwitch = document.querySelector(
- "div.switch-menus > div.language-switch",
- );
-
- if (themeLanguageSelector) {
- if (config.projects.translations.length) {
- // Add the current language to the options on the selector
- let languages = config.projects.translations.concat(
- config.projects.current,
- );
- languages = languages.sort((a, b) =>
- a.language.name.localeCompare(b.language.name),
- );
-
- const languageSelect = `
-
- `;
-
- languageSwitch.innerHTML = languageSelect;
- languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
- else {
- languageSwitch.remove();
- }
- }
- });
-}
-
-document.addEventListener("readthedocs-addons-data-ready", function (event) {
- // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
- document
- .querySelector("[role='search'] input")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
-});
\ No newline at end of file
diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js
deleted file mode 100644
index 57767864..00000000
--- a/docs/build/html/_static/language_data.js
+++ /dev/null
@@ -1,13 +0,0 @@
-/*
- * This script contains the language-specific data used by searchtools.js,
- * namely the set of stopwords, stemmer, scorer and splitter.
- */
-
-const stopwords = new Set(["a", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any", "are", "aren't", "as", "at", "be", "because", "been", "before", "being", "below", "between", "both", "but", "by", "can't", "cannot", "could", "couldn't", "did", "didn't", "do", "does", "doesn't", "doing", "don't", "down", "during", "each", "few", "for", "from", "further", "had", "hadn't", "has", "hasn't", "have", "haven't", "having", "he", "he'd", "he'll", "he's", "her", "here", "here's", "hers", "herself", "him", "himself", "his", "how", "how's", "i", "i'd", "i'll", "i'm", "i've", "if", "in", "into", "is", "isn't", "it", "it's", "its", "itself", "let's", "me", "more", "most", "mustn't", "my", "myself", "no", "nor", "not", "of", "off", "on", "once", "only", "or", "other", "ought", "our", "ours", "ourselves", "out", "over", "own", "same", "shan't", "she", "she'd", "she'll", "she's", "should", "shouldn't", "so", "some", "such", "than", "that", "that's", "the", "their", "theirs", "them", "themselves", "then", "there", "there's", "these", "they", "they'd", "they'll", "they're", "they've", "this", "those", "through", "to", "too", "under", "until", "up", "very", "was", "wasn't", "we", "we'd", "we'll", "we're", "we've", "were", "weren't", "what", "what's", "when", "when's", "where", "where's", "which", "while", "who", "who's", "whom", "why", "why's", "with", "won't", "would", "wouldn't", "you", "you'd", "you'll", "you're", "you've", "your", "yours", "yourself", "yourselves"]);
-window.stopwords = stopwords; // Export to global scope
-
-
-/* Non-minified versions are copied as separate JavaScript files, if available */
-BaseStemmer=function(){this.current="",this.cursor=0,this.limit=0,this.limit_backward=0,this.bra=0,this.ket=0,this.setCurrent=function(t){this.current=t,this.cursor=0,this.limit=this.current.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},this.getCurrent=function(){return this.current},this.copy_from=function(t){this.current=t.current,this.cursor=t.cursor,this.limit=t.limit,this.limit_backward=t.limit_backward,this.bra=t.bra,this.ket=t.ket},this.in_grouping=function(t,r,i){return!(this.cursor>=this.limit||i<(i=this.current.charCodeAt(this.cursor))||i
- ${renderLanguages(config)}
- ${renderVersions(config)}
- ${renderDownloads(config)}
-
- - Hosted by Read the Docs - -
- -
-
- On Read the Docs -
- - Project Home - -
- - Builds - -
- - Downloads - -
-
-
- Search -
- - - -
- - Hosted by Read the Docs - -
' - + '' - + _("Hide Search Matches") - + "
", - ), - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms"); - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) - return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) - return; - if ( - DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - && event.key === "Escape" - ) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/docs/build/html/api/datefixer.html b/docs/build/html/api/datefixer.html deleted file mode 100644 index 93262dd8..00000000 --- a/docs/build/html/api/datefixer.html +++ /dev/null @@ -1,307 +0,0 @@ - - - - - - - - -
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diff --git a/docs/build/html/api/frame.html b/docs/build/html/api/frame.html
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-
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-
- Date Utilities
-Date Handling Functions
--
-
- -openseries.datefixer.date_fix(fixerdate)[source] -
Parse different date formats into datetime.date.
--
-
- Parameters: -
fixerdate (DateType) – The data item to parse.
-
-- Returns: -
Parsed date.
-
-- Raises: -
TypeError – If the provided
-fixerdatetype is not supported.
-- Return type: -
dt.date
-
-
-
-
- -openseries.datefixer.date_offset_foll(raw_date, months_offset=12, countries='SE', markets=None, custom_holidays=None, *, adjust=False, following=True)[source] -
Offset dates according to a given calendar.
--
-
- Parameters: -
-
-
raw_date (DateType) – The date to offset from.
-months_offset (int) – Number of months as integer. Defaults to 12.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-adjust (bool) – Determines if offset should adjust for business days. -Defaults to False.
-following (bool) – Determines if days should be offset forward (following) or backward. -Defaults to True.
-
-- Returns: -
Offset date.
-
-- Return type: -
dt.date
-
-
-
-
- -openseries.datefixer.generate_calendar_date_range(trading_days, start=None, end=None, countries='SE', markets=None, custom_holidays=None)[source] -
Generate a list of business day calendar dates.
--
-
- Parameters: -
-
-
trading_days (int) – Number of days to generate. Must be greater than zero.
-start (dt.date | None) – Date when the range starts.
-end (dt.date | None) – Date when the range ends.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
List of business day calendar dates.
-
-- Return type: -
list[dt.date]
-
-
-
-
- -openseries.datefixer.get_previous_business_day_before_today(today=None, countries='SE', markets=None, custom_holidays=None)[source] -
Bump date backwards to find the previous business day.
--
-
- Parameters: -
-
-
today (dt.date | None) – Manual input of the day from where the previous business day is found.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
The previous business day.
-
-- Return type: -
dt.date
-
-
-
-
- -openseries.datefixer.holiday_calendar(startyear, endyear, countries='SE', markets=None, custom_holidays=None)[source] -
Generate a business calendar.
--
-
- Parameters: -
-
-
startyear (int) – First year in date range generated.
-endyear (int) – Last year in date range generated.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
Generate a business calendar.
-
-- Raises: -
CountriesNotStringNorListStrError – If
-countriesis not a supported - ISO 3166-1 alpha-2 string or a list of such strings.
-- Return type: -
busdaycalendar
-
-
-
-
- -openseries.datefixer.offset_business_days(ddate, days, countries='SE', markets=None, custom_holidays=None)[source] -
Bump date by business days.
-It first adjusts to a valid business day and then bumps with given -number of business days from there.
--
-
- Parameters: -
-
-
ddate (dt.date) – A starting date that does not have to be a business day.
-days (int) – The number of business days to offset from the business day -that is given. -If days is set as anything other than an integer its value is set to zero.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
The new offset business day.
-
-- Return type: -
dt.date
-
-
The datefixer module provides utilities for handling business days, holidays, and date calculations commonly needed in financial analysis.
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.OpenFrame.html b/docs/build/html/api/generated/openseries.OpenFrame.html
deleted file mode 100644
index ee5238d3..00000000
--- a/docs/build/html/api/generated/openseries.OpenFrame.html
+++ /dev/null
@@ -1,1339 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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- OpenFrame
--
-
- -class openseries.OpenFrame(constituents, weights=None)[source] -
Bases:
-_CommonModel[Series]OpenFrame objects hold OpenTimeSeries in the list constituents.
-The intended use is to allow comparisons across these timeseries.
--
-
- Parameters: -
- - -
-
-
- -__init__(constituents, weights=None)[source] -
OpenFrame objects hold OpenTimeSeries in the list constituents.
-The intended use is to allow comparisons across these timeseries.
- -
-
-
- -from_deepcopy()[source] -
Create copy of the OpenFrame object.
- -
-
-
- -merge_series(how='outer')[source] -
Merge index of Pandas Dataframes of the constituent OpenTimeSeries.
- -
-
-
- -all_properties(properties=None)[source] -
Calculate chosen timeseries properties.
--
-
- Parameters: -
-
-
properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'autocorr', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown', 'max_drawdown_date', 'max_drawdown_cal_year', 'first_indices', 'last_indices', 'lengths_of_items', 'span_of_days_all']] | None) – The properties to calculate. Defaults to calculating all -available. Optional.
-self (Self)
-
-- Returns: -
Properties of the constituent OpenTimeSeries.
-
-- Return type: -
- - -
-
-
- -property lengths_of_items: Series[int] -
Number of observations of all constituents.
--
-
- Returns: -
Number of observations of all constituents.
-
-
-
-
- -property item_count: int -
Number of constituents.
--
-
- Returns: -
Number of constituents.
-
-
-
-
- -property columns_lvl_zero: list[str] -
Level 0 values of the MultiIndex columns in the .tsdf DataFrame.
--
-
- Returns: -
Level 0 values of the MultiIndex columns in the .tsdf DataFrame.
-
-
-
-
- -property columns_lvl_one: list[ValueType] -
Level 1 values of the MultiIndex columns in the .tsdf DataFrame.
--
-
- Returns: -
Level 1 values of the MultiIndex columns in the .tsdf DataFrame.
-
-
-
-
- -property first_indices: Series[dt.date] -
The first dates in the timeseries of all constituents.
--
-
- Returns: -
The first dates in the timeseries of all constituents.
-
-
-
-
- -property last_indices: Series[dt.date] -
The last dates in the timeseries of all constituents.
--
-
- Returns: -
The last dates in the timeseries of all constituents.
-
-
-
-
- -property span_of_days_all: Series[int] -
Number of days from the first date to the last for all items in the frame.
--
-
- Returns: -
Number of days from the first date to the last for all -items in the frame.
-
-
-
-
- -value_to_ret()[source] -
Convert series of values into series of returns.
- -
-
-
- -value_to_diff(periods=1)[source] -
Convert series of values to series of their period differences.
- -
-
-
- -to_cumret()[source] -
Convert series of returns into cumulative series of values.
- -
-
-
- -resample(freq='BME')[source] -
Resample the timeseries frequency.
- -
-
-
- -resample_to_business_period_ends(freq='BME', method='nearest')[source] -
Resamples timeseries frequency to the business calendar month end dates.
-Stubs left in place. Stubs will be aligned to the shortest stub.
--
-
- Parameters: -
- - -
- Returns: -
An OpenFrame object.
-
-- Return type: -
- - -
-
-
- -ewma_risk(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, first_column=0, second_column=1, corr_scale=2.0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Volatilities and Correlation.
-Exponentially Weighted Moving Average (EWMA) for Volatilities and -Correlation.
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. Defaults to 11.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-first_column (int) – Column of first timeseries. Defaults to 0.
-second_column (int) – Column of second timeseries. Defaults to 1.
-corr_scale (float) – Correlation scale factor. Defaults to 2.0.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series volatilities and correlation.
-
-- Return type: -
DataFrame
-
-
-
-
- -property correl_matrix: DataFrame -
Correlation matrix.
-This property returns the correlation matrix of the time series -in the frame.
--
-
- Returns: -
Correlation matrix of the time series in the frame.
-
-
-
-
- -add_timeseries(new_series)[source] -
To add an OpenTimeSeries object.
- -
-
-
- -delete_timeseries(lvl_zero_item)[source] -
To delete an OpenTimeSeries object.
- -
-
-
- -trunc_frame(start_cut=None, end_cut=None, where='both')[source] -
Truncate DataFrame such that all timeseries have the same time span.
--
-
- Parameters: -
-
-
start_cut (dt.date | None) – New first date. Optional.
-end_cut (dt.date | None) – New last date. Optional.
-where (LiteralTrunc) – Determines where dataframe is truncated also when start_cut -or end_cut is None. Defaults to both.
-self (Self)
-
-- Returns: -
An OpenFrame object.
-
-- Return type: -
Self
-
-
-
-
- -relative(long_column=0, short_column=1, *, base_zero=True)[source] -
Calculate cumulative relative return between two series.
--
-
- Parameters: -
- - -
- Return type: -
None
-
-
-
-
- -tracking_error_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Tracking Error.
-Calculates Tracking Error which is the standard deviation of the -difference between the fund and its index returns.
-Reference: https://www.investopedia.com/terms/t/trackingerror.asp.
--
-
- Parameters: -
-
-
base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Tracking Errors.
-
-- Return type: -
Series[float]
-
-
-
-
- -info_ratio_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Information Ratio.
-The Information Ratio equals ( fund return less index return ) divided -by the Tracking Error. And the Tracking Error is the standard deviation of -the difference between the fund and its index returns. -The ratio is calculated using the annualized arithmetic mean of returns.
--
-
- Parameters: -
-
-
base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Information Ratios.
-
-- Return type: -
Series[float]
-
-
-
-
- -capture_ratio_func(ratio, base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Capture Ratio.
-The Up (Down) Capture Ratio is calculated by dividing the CAGR -of the asset during periods that the benchmark returns are positive (negative) -by the CAGR of the benchmark during the same periods. -CaptureRatio.BOTH is the Up ratio divided by the Down ratio. -Source: ‘Capture Ratios: A Popular Method of Measuring Portfolio Performance -in Practice’, Don R. Cox and Delbert C. Goff, Journal of Economics and -Finance Education (Vol 2 Winter 2013).
-Reference: https://www.economics-finance.org/jefe/volume12-2/11ArticleCox.pdf.
--
-
- Parameters: -
-
-
ratio (LiteralCaptureRatio) – The ratio to calculate.
-base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Capture Ratios.
-
-- Return type: -
Series[float]
-
-
-
-
- -beta(asset, market, dlta_degr_freedms=1)[source] -
Market Beta.
-Calculates Beta as Co-variance of asset & market divided by Variance -of the market.
-Reference: https://www.investopedia.com/terms/b/beta.asp.
--
-
- Parameters: -
- - -
- Returns: -
Beta as Co-variance of x & y divided by Variance of x.
-
-- Return type: -
- - -
-
-
- -ord_least_squares_fit(y_column, x_column, *, fitted_series=True)[source] -
Ordinary Least Squares fit.
-Performs a linear regression and adds a new column with a fitted line -using Ordinary Least Squares fit.
--
-
- Parameters: -
-
-
y_column (tuple[str, ValueType] | int) – The column level values of the dependent variable y.
-x_column (tuple[str, ValueType] | int) – The column level values of the exogenous variable x.
-fitted_series (bool) – If True the fit is added as a new column in the .tsdf -Pandas.DataFrame. Defaults to True.
-self (Self)
-
-- Returns: -
A dictionary with the coefficient, intercept and rsquared outputs.
-
-- Return type: -
- - -
-
-
- -jensen_alpha(asset, market, riskfree_rate=0.0, dlta_degr_freedms=1)[source] -
Jensen’s alpha.
-The Jensen’s measure, or Jensen’s alpha, is a risk-adjusted performance -measure that represents the average return on a portfolio or investment, -above or below that predicted by the capital asset pricing model (CAPM), -given the portfolio’s or investment’s beta and the average market return. -This metric is also commonly referred to as simply alpha.
-Reference: https://www.investopedia.com/terms/j/jensensmeasure.asp.
--
-
- Parameters: -
-
-
asset (tuple[str, ValueType] | int) – The column of the asset.
-market (tuple[str, ValueType] | int) – The column of the market against which Jensen’s alpha is measured.
-riskfree_rate (float) – The return of the zero volatility riskfree asset. -Defaults to 0.0.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 1.
-self (Self)
-
-- Returns: -
Jensen’s alpha.
-
-- Return type: -
- - -
-
-
- -make_portfolio(name, weight_strat=None)[source] -
Calculate a basket timeseries based on the supplied weights.
- -
-
-
- -rolling_info_ratio(long_column=0, short_column=1, observations=21, periods_in_a_year_fixed=None)[source] -
Calculate rolling Information Ratio.
-The Information Ratio equals ( fund return less index return ) divided by -the Tracking Error. And the Tracking Error is the standard deviation of the -difference between the fund and its index returns.
--
-
- Parameters: -
-
-
long_column (int) – Column of timeseries that is the numerator in the ratio. -Defaults to 0.
-short_column (int) – Column of timeseries that is the denominator in the ratio. -Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-periods_in_a_year_fixed (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])] | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Rolling Information Ratios.
-
-- Return type: -
- - -
-
-
- -rolling_beta(asset_column=0, market_column=1, observations=21, dlta_degr_freedms=1)[source] -
Calculate rolling Market Beta.
-Calculates Beta as Co-variance of asset & market divided by Variance -of the market.
-Reference: https://www.investopedia.com/terms/b/beta.asp.
--
-
- Parameters: -
-
-
asset_column (int) – Column of timeseries that is the asset. Defaults to 0.
-market_column (int) – Column of timeseries that is the market. Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 1.
-self (Self)
-
-- Returns: -
Rolling Betas.
-
-- Return type: -
- - -
-
-
- -rolling_corr(first_column=0, second_column=1, observations=21)[source] -
Calculate rolling Correlation.
-Calculates correlation between two series. The period with -at least the given number of observations is the first period calculated.
--
-
- Parameters: -
-
-
first_column (int) – The position as integer of the first timeseries to compare. -Defaults to 0.
-second_column (int) – The position as integer of the second timeseries to compare. -Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-self (Self)
-
-- Returns: -
Rolling Correlations.
-
-- Return type: -
- - -
-
-
- -multi_factor_linear_regression(dependent_column)[source] -
Perform a multi-factor linear regression.
-This function treats one specified column in the DataFrame as the dependent -variable (y) and uses all remaining columns as independent variables (X). -It utilizes a scikit-learn LinearRegression model and returns a DataFrame -with summary output and an OpenTimeSeries of predicted values.
--
-
- Parameters: -
- - -
- Returns: -
-
-
A DataFrame with the R-squared, the intercept and the regression -coefficients
-An OpenTimeSeries of predicted values
-
-- Return type: -
A tuple containing
-
-- Raises: -
-
-
KeyError – If the column tuple is not found in the OpenFrame.tsdf.columns.
-ValueError – If not all series are returnseries (ValueType.RTRN).
-
-
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -rebalanced_portfolio(name, items=None, bal_weights=None, frequency=1, cash_index=None, *, equal_weights=False, drop_extras=True)[source] -
Create a rebalanced portfolio from the OpenFrame constituents.
--
-
- Parameters: -
-
-
name (str) – Name of the portfolio.
-items (list[str] | None) – List of items to include in the portfolio. If None, uses all items. -Optional.
-bal_weights (list[float] | None) – List of weights for rebalancing. If None, uses frame weights. -Optional.
-frequency (int) – Rebalancing frequency. Defaults to 1.
-cash_index (OpenTimeSeries | None) – Cash index series for cash component. Optional.
-equal_weights (bool) – If True, use equal weights for all items. Defaults to False.
-drop_extras (bool) – If True, only return TWR series; if False, return all details. -Defaults to True.
-self (Self)
-
-- Returns: -
OpenFrame containing the rebalanced portfolio.
-
-- Return type: -
OpenFrame
-
-
The OpenFrame class manages collections of OpenTimeSeries objects and provides functionality for:
--
-
Multi-asset analysis and comparison
-Portfolio construction and optimization
-Correlation and regression analysis
-Risk attribution and factor analysis
-Batch processing of multiple time series
-
Class Methods for Construction
- - -Properties
-Frame-specific Properties
--
-
- -OpenFrame.constituents -
-
-
- -OpenFrame.columns_lvl_zero -
Level 0 values of the MultiIndex columns in the .tsdf DataFrame.
--
-
- Returns: -
Level 0 values of the MultiIndex columns in the .tsdf DataFrame.
-
-
-
-
- -OpenFrame.columns_lvl_one -
Level 1 values of the MultiIndex columns in the .tsdf DataFrame.
--
-
- Returns: -
Level 1 values of the MultiIndex columns in the .tsdf DataFrame.
-
-
-
-
- -OpenFrame.item_count -
Number of constituents.
--
-
- Returns: -
Number of constituents.
-
-
-
-
- -OpenFrame.weights -
-
-
- -OpenFrame.first_indices -
The first dates in the timeseries of all constituents.
--
-
- Returns: -
The first dates in the timeseries of all constituents.
-
-
-
-
- -OpenFrame.last_indices -
The last dates in the timeseries of all constituents.
--
-
- Returns: -
The last dates in the timeseries of all constituents.
-
-
-
-
- -OpenFrame.lengths_of_items -
Number of observations of all constituents.
--
-
- Returns: -
Number of observations of all constituents.
-
-
-
-
- -OpenFrame.span_of_days_all -
Number of days from the first date to the last for all items in the frame.
--
-
- Returns: -
Number of days from the first date to the last for all -items in the frame.
-
-
Common Properties
--
-
- -OpenFrame.first_idx -
The first date in the timeseries.
--
-
- Returns: -
The first date in the timeseries.
-
-
-
-
- -OpenFrame.last_idx -
The last date in the timeseries.
--
-
- Returns: -
The last date in the timeseries.
-
-
-
-
- -OpenFrame.length -
Number of observations.
--
-
- Returns: -
Number of observations.
-
-
-
-
- -OpenFrame.span_of_days -
Number of days from the first date to the last.
--
-
- Returns: -
Number of days from the first date to the last.
-
-
-
-
- -OpenFrame.tsdf -
-
-
- -OpenFrame.max_drawdown_date -
Date when the maximum drawdown occurred.
-Reference: https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.
-- -Returns:
--
-
- datetime.date | pandas.Series[dt.date]
Date when the maximum drawdown occurred
-
-
-
-
- -OpenFrame.periods_in_a_year -
The average number of observations per year.
--
-
- Returns: -
The average number of observations per year.
-
-
-
-
- -OpenFrame.yearfrac -
Length of series in years assuming 365.25 days per year.
--
-
- Returns: -
Length of the timeseries in years assuming 365.25 days per year.
-
-
Financial Metrics
--
-
- -OpenFrame.all_properties = <function OpenFrame.all_properties>[source] -
-
-
- Parameters: -
-
-
self (Self)
-properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'autocorr', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown', 'max_drawdown_date', 'max_drawdown_cal_year', 'first_indices', 'last_indices', 'lengths_of_items', 'span_of_days_all']] | None)
-
-- Return type: -
- - -
-
-
- -OpenFrame.arithmetic_ret -
Annualized arithmetic mean of returns.
-Reference: https://www.investopedia.com/terms/a/arithmeticmean.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Annualized arithmetic mean of returns. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.geo_ret -
Compounded Annual Growth Rate (CAGR).
-Reference: https://www.investopedia.com/terms/c/cagr.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Compounded Annual Growth Rate (CAGR). -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.value_ret -
Simple return.
-- -Returns:
--
-
- SeriesOrFloat_co
Simple return. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.vol -
Annualized volatility.
-Based on Pandas .std() which is the equivalent of stdev.s([…]) in MS Excel.
-Reference: https://www.investopedia.com/terms/v/volatility.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Annualized volatility. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.downside_deviation -
Downside Deviation.
-Standard deviation of returns that are below a Minimum Accepted Return -of zero. It is used to calculate the Sortino Ratio.
-Reference: https://www.investopedia.com/terms/d/downside-deviation.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Downside deviation. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.ret_vol_ratio -
Ratio of annualized arithmetic mean of returns and annualized volatility.
-- -Returns:
--
-
- SeriesOrFloat_co
Ratio of the annualized arithmetic mean of returns and annualized -volatility. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.sortino_ratio -
Sortino ratio.
-Reference: https://www.investopedia.com/terms/s/sortinoratio.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Sortino ratio calculated as the annualized arithmetic mean of returns -/ downside deviation. The ratio implies that the riskfree asset has zero -volatility, and a minimum acceptable return of zero. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.kappa3_ratio -
Kappa-3 ratio.
-The Kappa-3 ratio is a generalized downside-risk ratio defined as -annualized arithmetic return divided by the cubic-root of the -lower partial moment of order 3 (with respect to a minimum acceptable -return, MAR). It penalizes larger downside outcomes more heavily than -the Sortino ratio (which uses order 2).
-- -Returns:
--
-
- SeriesOrFloat_co
Kappa-3 ratio calculation with the riskfree rate and. -Minimum Acceptable Return (MAR) both set to zero. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.omega_ratio -
Omega ratio.
-Reference: https://en.wikipedia.org/wiki/Omega_ratio.
-- -Returns:
--
-
- SeriesOrFloat_co
Omega ratio calculation. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.var_down -
Downside 95% Value At Risk (VaR).
-The equivalent of percentile.inc([…], 1-level) over returns in MS Excel. -https://www.investopedia.com/terms/v/var.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Downside 95% Value At Risk (VaR). -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.cvar_down -
Downside 95% Conditional Value At Risk “CVaR”.
-Reference: https://www.investopedia.com/terms/c/conditional_value_at_risk.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Downside 95% Conditional Value At Risk “CVaR”. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.worst -
Most negative percentage change.
-- -Returns:
--
-
- SeriesOrFloat_co
Most negative percentage change. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.worst_month -
Most negative month.
-- -Returns:
--
-
- SeriesOrFloat_co
Most negative month. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.max_drawdown -
Maximum drawdown without any limit on date range.
-Reference: https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Maximum drawdown without any limit on date range. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.max_drawdown_cal_year -
Maximum drawdown in a single calendar year.
-Reference: https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Maximum drawdown in a single calendar year. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.positive_share -
The share of percentage changes that are greater than zero.
-- -Returns:
--
-
- SeriesOrFloat_co
The share of percentage changes that are greater than zero. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.vol_from_var -
Implied annualized volatility from Downside 95% Value at Risk.
-Assumes that returns are normally distributed.
-- -Returns:
--
-
- SeriesOrFloat_co
Implied annualized volatility from the Downside 95% VaR using the -assumption that returns are normally distributed. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.autocorr -
Autocorrelation at lag 1.
-Shorthand for
-autocorr_func(lag=1). Returns the lag-1 autocorrelation -of demeaned returns. For price series, returns are computed via -pct_change; for return series, raw values are used after demeaning.- -Returns:
--
-
- SeriesOrFloat_co
Autocorrelation at lag 1. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.skew -
Skew of the return distribution.
-Reference: https://www.investopedia.com/terms/s/skewness.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Skew of the return distribution. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.kurtosis -
Kurtosis of the return distribution.
-Reference: https://www.investopedia.com/terms/k/kurtosis.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Kurtosis of the return distribution. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenFrame.z_score -
Z-score.
-Reference: https://www.investopedia.com/terms/z/zscore.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Z-score as (last return - mean return) / standard deviation of returns. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
Methods
-Frame Management
--
-
- -OpenFrame.merge_series(how='outer')[source] -
Merge index of Pandas Dataframes of the constituent OpenTimeSeries.
- -
-
-
- -OpenFrame.trunc_frame(start_cut=None, end_cut=None, where='both')[source] -
Truncate DataFrame such that all timeseries have the same time span.
--
-
- Parameters: -
-
-
start_cut (dt.date | None) – New first date. Optional.
-end_cut (dt.date | None) – New last date. Optional.
-where (LiteralTrunc) – Determines where dataframe is truncated also when start_cut -or end_cut is None. Defaults to both.
-self (Self)
-
-- Returns: -
An OpenFrame object.
-
-- Return type: -
Self
-
-
-
-
- -OpenFrame.add_timeseries(new_series)[source] -
To add an OpenTimeSeries object.
- -
Portfolio Analysis
--
-
- -OpenFrame.relative(long_column=0, short_column=1, *, base_zero=True)[source] -
Calculate cumulative relative return between two series.
--
-
- Parameters: -
- - -
- Return type: -
None
-
-
-
-
- -OpenFrame.make_portfolio(name, weight_strat=None)[source] -
Calculate a basket timeseries based on the supplied weights.
- -
-
-
- -OpenFrame.rebalanced_portfolio(name, items=None, bal_weights=None, frequency=1, cash_index=None, *, equal_weights=False, drop_extras=True)[source] -
Create a rebalanced portfolio from the OpenFrame constituents.
--
-
- Parameters: -
-
-
name (str) – Name of the portfolio.
-items (list[str] | None) – List of items to include in the portfolio. If None, uses all items. -Optional.
-bal_weights (list[float] | None) – List of weights for rebalancing. If None, uses frame weights. -Optional.
-frequency (int) – Rebalancing frequency. Defaults to 1.
-cash_index (OpenTimeSeries | None) – Cash index series for cash component. Optional.
-equal_weights (bool) – If True, use equal weights for all items. Defaults to False.
-drop_extras (bool) – If True, only return TWR series; if False, return all details. -Defaults to True.
-self (Self)
-
-- Returns: -
OpenFrame containing the rebalanced portfolio.
-
-- Return type: -
OpenFrame
-
-
Statistical Analysis
--
-
- -OpenFrame.ord_least_squares_fit(y_column, x_column, *, fitted_series=True)[source] -
Ordinary Least Squares fit.
-Performs a linear regression and adds a new column with a fitted line -using Ordinary Least Squares fit.
--
-
- Parameters: -
-
-
y_column (tuple[str, ValueType] | int) – The column level values of the dependent variable y.
-x_column (tuple[str, ValueType] | int) – The column level values of the exogenous variable x.
-fitted_series (bool) – If True the fit is added as a new column in the .tsdf -Pandas.DataFrame. Defaults to True.
-self (Self)
-
-- Returns: -
A dictionary with the coefficient, intercept and rsquared outputs.
-
-- Return type: -
- - -
-
-
- -OpenFrame.beta(asset, market, dlta_degr_freedms=1)[source] -
Market Beta.
-Calculates Beta as Co-variance of asset & market divided by Variance -of the market.
-Reference: https://www.investopedia.com/terms/b/beta.asp.
--
-
- Parameters: -
- - -
- Returns: -
Beta as Co-variance of x & y divided by Variance of x.
-
-- Return type: -
- - -
-
-
- -OpenFrame.jensen_alpha(asset, market, riskfree_rate=0.0, dlta_degr_freedms=1)[source] -
Jensen’s alpha.
-The Jensen’s measure, or Jensen’s alpha, is a risk-adjusted performance -measure that represents the average return on a portfolio or investment, -above or below that predicted by the capital asset pricing model (CAPM), -given the portfolio’s or investment’s beta and the average market return. -This metric is also commonly referred to as simply alpha.
-Reference: https://www.investopedia.com/terms/j/jensensmeasure.asp.
--
-
- Parameters: -
-
-
asset (tuple[str, ValueType] | int) – The column of the asset.
-market (tuple[str, ValueType] | int) – The column of the market against which Jensen’s alpha is measured.
-riskfree_rate (float) – The return of the zero volatility riskfree asset. -Defaults to 0.0.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 1.
-self (Self)
-
-- Returns: -
Jensen’s alpha.
-
-- Return type: -
- - -
-
-
- -OpenFrame.tracking_error_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Tracking Error.
-Calculates Tracking Error which is the standard deviation of the -difference between the fund and its index returns.
-Reference: https://www.investopedia.com/terms/t/trackingerror.asp.
--
-
- Parameters: -
-
-
base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Tracking Errors.
-
-- Return type: -
Series[float]
-
-
-
-
- -OpenFrame.info_ratio_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Information Ratio.
-The Information Ratio equals ( fund return less index return ) divided -by the Tracking Error. And the Tracking Error is the standard deviation of -the difference between the fund and its index returns. -The ratio is calculated using the annualized arithmetic mean of returns.
--
-
- Parameters: -
-
-
base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Information Ratios.
-
-- Return type: -
Series[float]
-
-
-
-
- -OpenFrame.capture_ratio_func(ratio, base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Capture Ratio.
-The Up (Down) Capture Ratio is calculated by dividing the CAGR -of the asset during periods that the benchmark returns are positive (negative) -by the CAGR of the benchmark during the same periods. -CaptureRatio.BOTH is the Up ratio divided by the Down ratio. -Source: ‘Capture Ratios: A Popular Method of Measuring Portfolio Performance -in Practice’, Don R. Cox and Delbert C. Goff, Journal of Economics and -Finance Education (Vol 2 Winter 2013).
-Reference: https://www.economics-finance.org/jefe/volume12-2/11ArticleCox.pdf.
--
-
- Parameters: -
-
-
ratio (LiteralCaptureRatio) – The ratio to calculate.
-base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Capture Ratios.
-
-- Return type: -
Series[float]
-
-
-
-
- -OpenFrame.multi_factor_linear_regression(dependent_column)[source] -
Perform a multi-factor linear regression.
-This function treats one specified column in the DataFrame as the dependent -variable (y) and uses all remaining columns as independent variables (X). -It utilizes a scikit-learn LinearRegression model and returns a DataFrame -with summary output and an OpenTimeSeries of predicted values.
--
-
- Parameters: -
- - -
- Returns: -
-
-
A DataFrame with the R-squared, the intercept and the regression -coefficients
-An OpenTimeSeries of predicted values
-
-- Return type: -
A tuple containing
-
-- Raises: -
-
-
KeyError – If the column tuple is not found in the OpenFrame.tsdf.columns.
-ValueError – If not all series are returnseries (ValueType.RTRN).
-
-
Rolling Analysis
--
-
- -OpenFrame.rolling_info_ratio(long_column=0, short_column=1, observations=21, periods_in_a_year_fixed=None)[source] -
Calculate rolling Information Ratio.
-The Information Ratio equals ( fund return less index return ) divided by -the Tracking Error. And the Tracking Error is the standard deviation of the -difference between the fund and its index returns.
--
-
- Parameters: -
-
-
long_column (int) – Column of timeseries that is the numerator in the ratio. -Defaults to 0.
-short_column (int) – Column of timeseries that is the denominator in the ratio. -Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-periods_in_a_year_fixed (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])] | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Rolling Information Ratios.
-
-- Return type: -
- - -
-
-
- -OpenFrame.rolling_beta(asset_column=0, market_column=1, observations=21, dlta_degr_freedms=1)[source] -
Calculate rolling Market Beta.
-Calculates Beta as Co-variance of asset & market divided by Variance -of the market.
-Reference: https://www.investopedia.com/terms/b/beta.asp.
--
-
- Parameters: -
-
-
asset_column (int) – Column of timeseries that is the asset. Defaults to 0.
-market_column (int) – Column of timeseries that is the market. Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 1.
-self (Self)
-
-- Returns: -
Rolling Betas.
-
-- Return type: -
- - -
-
-
- -OpenFrame.rolling_corr(first_column=0, second_column=1, observations=21)[source] -
Calculate rolling Correlation.
-Calculates correlation between two series. The period with -at least the given number of observations is the first period calculated.
--
-
- Parameters: -
-
-
first_column (int) – The position as integer of the first timeseries to compare. -Defaults to 0.
-second_column (int) – The position as integer of the second timeseries to compare. -Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-self (Self)
-
-- Returns: -
Rolling Correlations.
-
-- Return type: -
- - -
-
-
- -OpenFrame.rolling_return(column=0, observations=21) -
Calculate rolling returns.
- -
-
-
- -OpenFrame.rolling_vol(column=0, observations=21, periods_in_a_year_fixed=None, dlta_degr_freedms=1) -
Calculate rolling annualized volatilities.
--
-
- Parameters: -
-
-
column (int) – Column position to calculate.
-observations (int) – Number of observations in the overlapping window.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-dlta_degr_freedms (int) – Variance bias factor (0 or 1).
-self (Self)
-
-- Returns: -
DataFrame with rolling annualized volatilities.
-
-- Return type: -
DataFrame
-
-
-
-
- -OpenFrame.rolling_var_down(column=0, level=0.95, observations=252, interpolation='lower') -
Calculate rolling annualized downside Value At Risk (VaR).
--
-
- Parameters: -
- - -
- Returns: -
DataFrame with rolling annualized downside VaR.
-
-- Return type: -
DataFrame
-
-
-
-
- -OpenFrame.rolling_cvar_down(column=0, level=0.95, observations=252) -
Calculate rolling annualized downside CVaR.
- -
Correlation and Risk
--
-
- -OpenFrame.correl_matrix -
Correlation matrix.
-This property returns the correlation matrix of the time series -in the frame.
--
-
- Returns: -
Correlation matrix of the time series in the frame.
-
-
-
-
- -OpenFrame.ewma_risk(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, first_column=0, second_column=1, corr_scale=2.0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Volatilities and Correlation.
-Exponentially Weighted Moving Average (EWMA) for Volatilities and -Correlation.
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. Defaults to 11.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-first_column (int) – Column of first timeseries. Defaults to 0.
-second_column (int) – Column of second timeseries. Defaults to 1.
-corr_scale (float) – Correlation scale factor. Defaults to 2.0.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series volatilities and correlation.
-
-- Return type: -
DataFrame
-
-
Data Manipulation
--
-
- -OpenFrame.align_index_to_local_cdays(countries=None, markets=None, custom_holidays=None, method='nearest') -
Align the index of
-.tsdfwith local calendar business days.-
-
- Parameters: -
-
-
countries (CountriesType | None) – Country code(s) (ISO 3166-1 alpha-2).
-markets (list[str] | str | None) – Market code(s) supported by
exchange_calendars.
-custom_holidays (list[str] | str | None) – Missing holidays that should be added.
-method (LiteralPandasReindexMethod) – Method for reindexing when aligning to business days.
-self (Self)
-
-- Returns: -
The modified object.
-
-- Return type: -
Self
-
-
-
-
- -OpenFrame.resample(freq='BME')[source] -
Resample the timeseries frequency.
- -
-
-
- -OpenFrame.resample_to_business_period_ends(freq='BME', method='nearest')[source] -
Resamples timeseries frequency to the business calendar month end dates.
-Stubs left in place. Stubs will be aligned to the shortest stub.
--
-
- Parameters: -
- - -
- Returns: -
An OpenFrame object.
-
-- Return type: -
- - -
-
-
- -OpenFrame.value_nan_handle(method='fill') -
Handle missing values in a value series.
--
-
- Parameters: -
-
-
method (LiteralNanMethod) – Method used to handle NaN. Either
"fill"(last known) or -"drop".
-self (Self)
-
-- Returns: -
The modified object.
-
-- Return type: -
Self
-
-
-
-
- -OpenFrame.return_nan_handle(method='fill') -
Handle missing values in a return series.
--
-
- Parameters: -
-
-
method (LiteralNanMethod) – Method used to handle NaN. Either
"fill"(zero) or -"drop".
-self (Self)
-
-- Returns: -
The modified object.
-
-- Return type: -
Self
-
-
Transformations
--
-
- -OpenFrame.to_cumret()[source] -
Convert series of returns into cumulative series of values.
- -
-
-
- -OpenFrame.value_to_ret()[source] -
Convert series of values into series of returns.
- -
-
-
- -OpenFrame.value_to_diff(periods=1)[source] -
Convert series of values to series of their period differences.
- -
-
-
- -OpenFrame.value_to_log() -
Convert value series to log-weighted series.
-Equivalent to
- -LN(value[t] / value[t=0])in Excel.
-
-
- -OpenFrame.to_drawdown_series() -
Convert timeseries into a drawdown series.
- -
-
-
- -OpenFrame.value_ret_calendar_period(year, month=None) -
Calculate simple return for a specific calendar period.
- -
Analysis Methods
-Autocorrelation analysis: autocorr (property) and autocorr_func return
-lag-N autocorrelation per column. For ACF, PACF, and Ljung-Box tests, use the
-constituent OpenTimeSeries objects.
-
-
- -OpenFrame.autocorr_func(lag=1, *, squared=False) -
Calculate autocorrelation at a given lag.
-Computes the autocorrelation of demeaned returns at the specified lag. -For price series (ValueType.PRICE), returns are derived via
-pct_change; -for return series (ValueType.RTRN), raw values are demeaned. Use -squared=Truefor squared-return autocorrelation (e.g. volatility -clustering). Returnsnanwhen the series has too few observations.-
-
- Parameters: -
- - -
- Returns: -
Autocorrelation at the specified lag. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.calc_range(months_offset=None, from_dt=None, to_dt=None) -
Create a user-defined date range aligned to index.
--
-
- Parameters: -
- - -
- Returns: -
A tuple
-(earlier, later)representing the start and end date of the -chosen date range aligned to existing index values.
-- Raises: -
DateAlignmentError – If the implied range is outside series bounds.
-
-- Return type: -
- - -
-
-
- -OpenFrame.outliers(threshold=3.0, months_from_last=None, from_date=None, to_date=None) -
Detect outliers using z-score analysis.
-Identifies data points where the absolute z-score exceeds the threshold. -For OpenTimeSeries, returns a Series with dates and outlier values. For -OpenFrame, returns a DataFrame with dates and outlier values for each -column.
--
-
- Parameters: -
-
-
threshold (float) – Z-score threshold; values with
|z| > thresholdare -outliers.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (date | None) – Specific from date.
-to_date (date | None) – Specific to date.
-self (Self)
-
-- Returns: -
Series of outliers. For OpenFrame: DataFrame of -outliers. Empty if none found.
-
-- Return type: -
For OpenTimeSeries
-
-
Financial Metrics Methods
--
-
- -OpenFrame.arithmetic_ret_func(months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Annualized arithmetic mean of returns.
-Reference:
-https://www.investopedia.com/terms/a/arithmeticmean.asp.-
-
- Parameters: -
-
-
months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Annualized arithmetic mean of returns. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.geo_ret_func(months_from_last=None, from_date=None, to_date=None) -
Compounded Annual Growth Rate (CAGR).
-Reference:
-https://www.investopedia.com/terms/c/cagr.asp.-
-
- Parameters: -
- - -
- Returns: -
CAGR. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Raises: -
InitialValueZeroError – If initial value is zero or there are negative - values.
-
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.value_ret_func(months_from_last=None, from_date=None, to_date=None) -
Calculate simple return.
--
-
- Parameters: -
- - -
- Returns: -
Simple return. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Raises: -
InitialValueZeroError – If initial value is zero.
-
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.vol_func(months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Annualized volatility.
-Based on
-pandas.Series.std()(ExcelSTDEV.Sequivalent). -Reference:https://www.investopedia.com/terms/v/volatility.asp.-
-
- Parameters: -
-
-
months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Annualized volatility. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.lower_partial_moment_func(min_accepted_return=0.0, order=2, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Lower partial moment and downside deviation (order=2).
-If
-orderis 2 calculates standard deviation of returns below MAR=0. -For general orderp, returns(LPM_p)^(1/p).-
-
- Parameters: -
-
-
min_accepted_return (float) – Annualized Minimum Accepted Return (MAR).
-order (Literal[2, 3]) – Order of partial moment (2 or 3).
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Downside deviation if
-orderis 2; otherwise rooted lower partial -moment. Float for OpenTimeSeries,Series[float]for OpenFrame.
-- Raises: -
ValueError – If
-orderis not 2 or 3.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.ret_vol_ratio_func(riskfree_rate=0.0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Ratio between arithmetic mean of returns and annualized volatility.
-If
-riskfree_rateprovided, computes the Sharpe ratio as -(arithmetic return - risk-free) / volatility. Assumes zero volatility -for the risk-free asset. Reference: -https://www.investopedia.com/terms/s/sharperatio.asp.-
-
- Parameters: -
-
-
riskfree_rate (float) – Return of the zero volatility asset.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Ratio value. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.sortino_ratio_func(riskfree_rate=0.0, min_accepted_return=0.0, order=2, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Sortino ratio or Kappa-3 ratio.
-Sortino:
-(return - riskfree_rate) / downside deviationusing arithmetic -mean of returns. Kappa-3 whenorder=3penalizes larger downside more -than Sortino.-
-
- Parameters: -
-
-
riskfree_rate (float) – Return of the zero volatility asset.
-min_accepted_return (float) – Annualized Minimum Accepted Return (MAR).
-order (Literal[2, 3]) – Order of partial moment (2 for Sortino, 3 for Kappa-3).
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Ratio value. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.omega_ratio_func(min_accepted_return=0.0, months_from_last=None, from_date=None, to_date=None) -
Omega Ratio.
-Compares returns above MAR to the total downside risk below MAR. -Reference:
-https://en.wikipedia.org/wiki/Omega_ratio.-
-
- Parameters: -
- - -
- Returns: -
Omega ratio. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.var_down_func(level=0.95, months_from_last=None, from_date=None, to_date=None, interpolation='lower') -
Downside Value At Risk (VaR).
-Equivalent to
-PERCENTILE.INC(returns, 1-level)in Excel. Reference: -https://www.investopedia.com/terms/v/var.asp.-
-
- Parameters: -
-
-
level (float) – The sought VaR level.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-interpolation (LiteralQuantileInterp) – Interpolation used by
DataFrame.quantile.
-self (Self)
-
-- Returns: -
Downside VaR. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.cvar_down_func(level=0.95, months_from_last=None, from_date=None, to_date=None) -
Downside Conditional Value At Risk (CVaR).
-Reference:
-https://www.investopedia.com/terms/c/conditional_value_at_risk.asp.-
-
- Parameters: -
- - -
- Returns: -
Downside CVaR. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.worst_func(observations=1, months_from_last=None, from_date=None, to_date=None) -
Most negative percentage change over a rolling window.
--
-
- Parameters: -
- - -
- Returns: -
Most negative percentage change. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.max_drawdown_func(months_from_last=None, from_date=None, to_date=None, min_periods=1) -
Maximum drawdown without any limit on date range.
-Reference:
-https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.-
-
- Parameters: -
- - -
- Returns: -
Maximum drawdown. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.positive_share_func(months_from_last=None, from_date=None, to_date=None) -
Share of percentage changes greater than zero.
--
-
- Parameters: -
- - -
- Returns: -
Share of positive returns. Float for OpenTimeSeries,
-Series[float]-for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.vol_from_var_func(level=0.95, months_from_last=None, from_date=None, to_date=None, interpolation='lower', periods_in_a_year_fixed=None, *, drift_adjust=False) -
Implied annualized volatility from downside VaR.
-Assumes normally distributed returns.
--
-
- Parameters: -
-
-
level (float) – The sought VaR level.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-interpolation (LiteralQuantileInterp) – Interpolation type used by
DataFrame.quantile.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-drift_adjust (bool) – Adjustment to remove the bias implied by the average -return.
-self (Self)
-
-- Returns: -
Implied annualized volatility. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.skew_func(months_from_last=None, from_date=None, to_date=None) -
Skew of the return distribution.
-Reference:
-https://www.investopedia.com/terms/s/skewness.asp.-
-
- Parameters: -
- - -
- Returns: -
Skewness. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.kurtosis_func(months_from_last=None, from_date=None, to_date=None) -
Kurtosis of the return distribution.
-Reference:
-https://www.investopedia.com/terms/k/kurtosis.asp.-
-
- Parameters: -
- - -
- Returns: -
Kurtosis. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.z_score_func(months_from_last=None, from_date=None, to_date=None) -
Z-score of the last return.
-Computed as
-(last return - mean return) / std dev of returns. -Reference:https://www.investopedia.com/terms/z/zscore.asp.-
-
- Parameters: -
- - -
- Returns: -
Z-score. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenFrame.target_weight_from_var(target_vol=0.175, level=0.95, min_leverage_local=0.0, max_leverage_local=99999.0, months_from_last=None, from_date=None, to_date=None, interpolation='lower', periods_in_a_year_fixed=None, *, drift_adjust=False) -
Target weight from VaR.
-Computes a position weight multiplier from the ratio between a VaR implied -volatility and a given target volatility. Multiplier = 1.0 → target met.
--
-
- Parameters: -
-
-
target_vol (float) – Target volatility.
-level (float) – The sought VaR level.
-min_leverage_local (float) – Minimum adjustment factor.
-max_leverage_local (float) – Maximum adjustment factor.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-interpolation (LiteralQuantileInterp) – Interpolation type used by
DataFrame.quantile.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-drift_adjust (bool) – Adjustment to remove the bias implied by the average -return.
-self (Self)
-
-- Returns: -
Weight multiplier (or implied volatility if used downstream). Float for -OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
Visualization
-The plotting methods generate fully responsive HTML output that automatically adapts to different screen sizes and device orientations. Plots are optimized for both desktop and mobile viewing with separate title containers and responsive CSS styling.
--
-
- -OpenFrame.plot_series(mode='lines', title=None, tick_fmt=None, filename=None, directory=None, labels=None, output_type='file', include_plotlyjs='cdn', *, auto_open=True, add_logo=True, show_last=False) -
Create a Plotly Scatter Figure.
--
-
- Parameters: -
-
-
mode (LiteralLinePlotMode) – The type of scatter to use.
-title (str | None) – A title above the plot.
-tick_fmt (str | None) – Tick format for the y-axis, e.g.
'%'or'.1%'.
-filename (str | None) – Name of the Plotly HTML file.
-directory (DirectoryPath | None) – Directory where the Plotly HTML file is saved.
-labels (list[str] | None) – Labels to override the column names of
self.tsdf.
-output_type (LiteralPlotlyOutput) – Determines output type.
-include_plotlyjs (LiteralPlotlyJSlib) – How the plotly.js library is included.
-auto_open (bool) – Whether to open a browser window with the plot.
-add_logo (bool) – If True, a Captor logo is added to the plot.
-show_last (bool) – If True, highlight the last point in red with a label.
-self (Self)
-
-- Returns: -
A tuple
-(figure, output)whereoutputis either a div string or -a file path.
-- Return type: -
- - -
-
-
- -OpenFrame.plot_bars(mode='group', title=None, tick_fmt=None, filename=None, directory=None, labels=None, output_type='file', include_plotlyjs='cdn', *, auto_open=True, add_logo=True) -
Create a Plotly Bar Figure.
--
-
- Parameters: -
-
-
mode (LiteralBarPlotMode) – The type of bar to use.
-title (str | None) – A title above the plot.
-tick_fmt (str | None) – Tick format for the y-axis, e.g.
'%'or'.1%'.
-filename (str | None) – Name of the Plotly HTML file.
-directory (DirectoryPath | None) – Directory where the Plotly HTML file is saved.
-labels (list[str] | None) – Labels to override the column names of
self.tsdf.
-output_type (LiteralPlotlyOutput) – Determines output type.
-include_plotlyjs (LiteralPlotlyJSlib) – How the plotly.js library is included.
-auto_open (bool) – Whether to open a browser window with the plot.
-add_logo (bool) – If True, a Captor logo is added to the plot.
-self (Self)
-
-- Returns: -
A tuple
-(figure, output)whereoutputis either a div string or -a file path.
-- Return type: -
- - -
-
-
- -OpenFrame.plot_histogram(plot_type='bars', histnorm='probability', barmode='overlay', xbins_size=None, opacity=0.75, bargap=0.0, bargroupgap=0.0, curve_type='kde', title=None, x_fmt=None, y_fmt=None, filename=None, directory=None, labels=None, output_type='file', include_plotlyjs='cdn', *, cumulative=False, show_rug=False, auto_open=True, add_logo=True) -
Create a Plotly Histogram Figure.
--
-
- Parameters: -
-
-
plot_type (LiteralPlotlyHistogramPlotType) – Type of plot,
"bars"or"lines".
-histnorm (LiteralPlotlyHistogramHistNorm) – Normalization mode.
-barmode (LiteralPlotlyHistogramBarMode) – How bar traces are displayed relative to one another.
-xbins_size (float | None) – Width of each bin along the x-axis in data units.
-opacity (float) – Trace opacity between 0 and 1.
-bargap (float) – Gap between bars of adjacent location coordinates.
-bargroupgap (float) – Gap between bar groups at the same location coordinate.
-curve_type (LiteralPlotlyHistogramCurveType) – Type of distribution curve to overlay on the histogram.
-title (str | None) – A title above the plot.
-x_fmt (str | None) – Tick format for the x-axis.
-y_fmt (str | None) – Tick format for the y-axis.
-filename (str | None) – Name of the Plotly HTML file.
-directory (DirectoryPath | None) – Directory where the Plotly HTML file is saved.
-labels (list[str] | None) – Labels to override the column names of
self.tsdf.
-output_type (LiteralPlotlyOutput) – Determines output type.
-include_plotlyjs (LiteralPlotlyJSlib) – How the plotly.js library is included.
-cumulative (bool) – Whether to compute a cumulative histogram.
-show_rug (bool) – Whether to draw a rug plot alongside the distribution.
-auto_open (bool) – Whether to open a browser window with the plot.
-add_logo (bool) – If True, a Captor logo is added to the plot.
-self (Self)
-
-- Returns: -
A tuple
-(figure, output)whereoutputis either a div string or -a file path.
-- Return type: -
- - -
Export Methods
--
-
- -OpenFrame.to_json(what_output, filename, directory=None) -
Dump timeseries data into a JSON file.
- -
-
-
- -OpenFrame.to_xlsx(filename, sheet_title=None, directory=None, *, overwrite=True) -
Save
-.tsdfDataFrame to an Excel spreadsheet file.-
-
- Parameters: -
- - -
- Returns: -
The Excel file path.
-
-- Raises: -
-
-
NameError – If
filenamedoes not end with.xlsx.
-FileExistsError – If the file exists and
overwriteis False.
-
-- Return type: -
- - -
-
-
-
-
-
-
-
-
-
-
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- openseries.OpenFrame
--
-
- -class openseries.OpenFrame(constituents, weights=None)[source] -
Bases:
-_CommonModel[Series]OpenFrame objects hold OpenTimeSeries in the list constituents.
-The intended use is to allow comparisons across these timeseries.
--
-
- Parameters: -
- - -
-
-
- -__init__(constituents, weights=None)[source] -
OpenFrame objects hold OpenTimeSeries in the list constituents.
-The intended use is to allow comparisons across these timeseries.
- -
Methods
-- -
-__init__(constituents[, weights])OpenFrame objects hold OpenTimeSeries in the list constituents.
add_timeseries(new_series)To add an OpenTimeSeries object.
align_index_to_local_cdays([countries, ...])Align the index of
.tsdfwith local calendar business days.all_properties([properties])Calculate chosen timeseries properties.
arithmetic_ret_func([months_from_last, ...])Annualized arithmetic mean of returns.
autocorr_func([lag, squared])Calculate autocorrelation at a given lag.
beta(asset, market[, dlta_degr_freedms])Market Beta.
calc_range([months_offset, from_dt, to_dt])Create a user-defined date range aligned to index.
capture_ratio_func(ratio[, base_column, ...])Capture Ratio.
construct([_fields_set])- copy(*[, include, exclude, update, deep])Returns a copy of the model.
cvar_down_func([level, months_from_last, ...])Downside Conditional Value At Risk (CVaR).
delete_timeseries(lvl_zero_item)To delete an OpenTimeSeries object.
dict(*[, include, exclude, by_alias, ...])- ewma_risk([lmbda, day_chunk, ...])Exponentially Weighted Moving Average Volatilities and Correlation.
from_deepcopy()Create copy of the OpenFrame object.
from_orm(obj)- geo_ret_func([months_from_last, from_date, ...])Compounded Annual Growth Rate (CAGR).
info_ratio_func([base_column, ...])Information Ratio.
jensen_alpha(asset, market[, riskfree_rate, ...])Jensen's alpha.
json(*[, include, exclude, by_alias, ...])- kurtosis_func([months_from_last, from_date, ...])Kurtosis of the return distribution.
lower_partial_moment_func([...])Lower partial moment and downside deviation (order=2).
make_portfolio(name[, weight_strat])Calculate a basket timeseries based on the supplied weights.
max_drawdown_func([months_from_last, ...])Maximum drawdown without any limit on date range.
merge_series([how])Merge index of Pandas Dataframes of the constituent OpenTimeSeries.
model_construct([_fields_set])Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])Generates a JSON schema for a model class.
model_parametrized_name(params)Compute the class name for parametrizations of generic classes.
model_post_init(context, /)Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])Validate the given object with string data against the Pydantic model.
multi_factor_linear_regression(dependent_column)Perform a multi-factor linear regression.
omega_ratio_func([min_accepted_return, ...])Omega Ratio.
ord_least_squares_fit(y_column, x_column, *)Ordinary Least Squares fit.
outliers([threshold, months_from_last, ...])Detect outliers using z-score analysis.
parse_file(path, *[, content_type, ...])- parse_obj(obj)- parse_raw(b, *[, content_type, encoding, ...])- plot_bars([mode, title, tick_fmt, filename, ...])Create a Plotly Bar Figure.
plot_histogram([plot_type, histnorm, ...])Create a Plotly Histogram Figure.
plot_series([mode, title, tick_fmt, ...])Create a Plotly Scatter Figure.
positive_share_func([months_from_last, ...])Share of percentage changes greater than zero.
rebalanced_portfolio(name[, items, ...])Create a rebalanced portfolio from the OpenFrame constituents.
relative([long_column, short_column, base_zero])Calculate cumulative relative return between two series.
resample([freq])Resample the timeseries frequency.
resample_to_business_period_ends([freq, method])Resamples timeseries frequency to the business calendar month end dates.
ret_vol_ratio_func([riskfree_rate, ...])Ratio between arithmetic mean of returns and annualized volatility.
return_nan_handle([method])Handle missing values in a return series.
rolling_beta([asset_column, market_column, ...])Calculate rolling Market Beta.
rolling_corr([first_column, second_column, ...])Calculate rolling Correlation.
rolling_cvar_down([column, level, observations])Calculate rolling annualized downside CVaR.
rolling_info_ratio([long_column, ...])Calculate rolling Information Ratio.
rolling_return([column, observations])Calculate rolling returns.
rolling_var_down([column, level, ...])Calculate rolling annualized downside Value At Risk (VaR).
rolling_vol([column, observations, ...])Calculate rolling annualized volatilities.
schema([by_alias, ref_template])- schema_json(*[, by_alias, ref_template])- skew_func([months_from_last, from_date, to_date])Skew of the return distribution.
sortino_ratio_func([riskfree_rate, ...])Sortino ratio or Kappa-3 ratio.
target_weight_from_var([target_vol, level, ...])Target weight from VaR.
to_cumret()Convert series of returns into cumulative series of values.
to_drawdown_series()Convert timeseries into a drawdown series.
to_json(what_output, filename[, directory])Dump timeseries data into a JSON file.
to_xlsx(filename[, sheet_title, directory, ...])Save
.tsdfDataFrame to an Excel spreadsheet file.tracking_error_func([base_column, ...])Tracking Error.
trunc_frame([start_cut, end_cut, where])Truncate DataFrame such that all timeseries have the same time span.
update_forward_refs(**localns)- validate(value)- value_nan_handle([method])Handle missing values in a value series.
value_ret_calendar_period(year[, month])Calculate simple return for a specific calendar period.
value_ret_func([months_from_last, ...])Calculate simple return.
value_to_diff([periods])Convert series of values to series of their period differences.
value_to_log()Convert value series to log-weighted series.
value_to_ret()Convert series of values into series of returns.
var_down_func([level, months_from_last, ...])Downside Value At Risk (VaR).
vol_from_var_func([level, months_from_last, ...])Implied annualized volatility from downside VaR.
vol_func([months_from_last, from_date, ...])Annualized volatility.
worst_func([observations, months_from_last, ...])Most negative percentage change over a rolling window.
z_score_func([months_from_last, from_date, ...])Z-score of the last return.
Attributes
-- -
-arithmetic_retAnnualized arithmetic mean of returns.
autocorrAutocorrelation at lag 1.
columns_lvl_oneLevel 1 values of the MultiIndex columns in the .tsdf DataFrame.
columns_lvl_zeroLevel 0 values of the MultiIndex columns in the .tsdf DataFrame.
correl_matrixCorrelation matrix.
cvar_downDownside 95% Conditional Value At Risk "CVaR".
downside_deviationDownside Deviation.
first_idxThe first date in the timeseries.
first_indicesThe first dates in the timeseries of all constituents.
geo_retCompounded Annual Growth Rate (CAGR).
item_countNumber of constituents.
kappa3_ratioKappa-3 ratio.
kurtosisKurtosis of the return distribution.
last_idxThe last date in the timeseries.
last_indicesThe last dates in the timeseries of all constituents.
lengthNumber of observations.
lengths_of_itemsNumber of observations of all constituents.
max_drawdownMaximum drawdown without any limit on date range.
max_drawdown_cal_yearMaximum drawdown in a single calendar year.
max_drawdown_dateDate when the maximum drawdown occurred.
model_computed_fields- model_configConfiguration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extraGet extra fields set during validation.
model_fields- model_fields_setReturns the set of fields that have been explicitly set on this model instance.
omega_ratioOmega ratio.
periods_in_a_yearThe average number of observations per year.
positive_shareThe share of percentage changes that are greater than zero.
ret_vol_ratioRatio of annualized arithmetic mean of returns and annualized volatility.
skewSkew of the return distribution.
sortino_ratioSortino ratio.
span_of_daysNumber of days from the first date to the last.
span_of_days_allNumber of days from the first date to the last for all items in the frame.
value_retSimple return.
var_downDownside 95% Value At Risk (VaR).
volAnnualized volatility.
vol_from_varImplied annualized volatility from Downside 95% Value at Risk.
worstMost negative percentage change.
worst_monthMost negative month.
yearfracLength of series in years assuming 365.25 days per year.
z_scoreZ-score.
constituents- weights- markets- tsdf- -
-
- -__init__(constituents, weights=None)[source] -
OpenFrame objects hold OpenTimeSeries in the list constituents.
-The intended use is to allow comparisons across these timeseries.
- -
-
-
- -from_deepcopy()[source] -
Create copy of the OpenFrame object.
- -
-
-
- -merge_series(how='outer')[source] -
Merge index of Pandas Dataframes of the constituent OpenTimeSeries.
- -
-
-
- -all_properties(properties=None)[source] -
Calculate chosen timeseries properties.
--
-
- Parameters: -
-
-
properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'autocorr', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown', 'max_drawdown_date', 'max_drawdown_cal_year', 'first_indices', 'last_indices', 'lengths_of_items', 'span_of_days_all']] | None) – The properties to calculate. Defaults to calculating all -available. Optional.
-self (Self)
-
-- Returns: -
Properties of the constituent OpenTimeSeries.
-
-- Return type: -
- - -
-
-
- -property lengths_of_items: Series[int] -
Number of observations of all constituents.
--
-
- Returns: -
Number of observations of all constituents.
-
-
-
-
- -property item_count: int -
Number of constituents.
--
-
- Returns: -
Number of constituents.
-
-
-
-
- -property columns_lvl_zero: list[str] -
Level 0 values of the MultiIndex columns in the .tsdf DataFrame.
--
-
- Returns: -
Level 0 values of the MultiIndex columns in the .tsdf DataFrame.
-
-
-
-
- -property columns_lvl_one: list[ValueType] -
Level 1 values of the MultiIndex columns in the .tsdf DataFrame.
--
-
- Returns: -
Level 1 values of the MultiIndex columns in the .tsdf DataFrame.
-
-
-
-
- -property first_indices: Series[dt.date] -
The first dates in the timeseries of all constituents.
--
-
- Returns: -
The first dates in the timeseries of all constituents.
-
-
-
-
- -property last_indices: Series[dt.date] -
The last dates in the timeseries of all constituents.
--
-
- Returns: -
The last dates in the timeseries of all constituents.
-
-
-
-
- -property span_of_days_all: Series[int] -
Number of days from the first date to the last for all items in the frame.
--
-
- Returns: -
Number of days from the first date to the last for all -items in the frame.
-
-
-
-
- -value_to_ret()[source] -
Convert series of values into series of returns.
- -
-
-
- -value_to_diff(periods=1)[source] -
Convert series of values to series of their period differences.
- -
-
-
- -to_cumret()[source] -
Convert series of returns into cumulative series of values.
- -
-
-
- -resample(freq='BME')[source] -
Resample the timeseries frequency.
- -
-
-
- -resample_to_business_period_ends(freq='BME', method='nearest')[source] -
Resamples timeseries frequency to the business calendar month end dates.
-Stubs left in place. Stubs will be aligned to the shortest stub.
--
-
- Parameters: -
- - -
- Returns: -
An OpenFrame object.
-
-- Return type: -
- - -
-
-
- -ewma_risk(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, first_column=0, second_column=1, corr_scale=2.0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Volatilities and Correlation.
-Exponentially Weighted Moving Average (EWMA) for Volatilities and -Correlation.
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. Defaults to 11.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-first_column (int) – Column of first timeseries. Defaults to 0.
-second_column (int) – Column of second timeseries. Defaults to 1.
-corr_scale (float) – Correlation scale factor. Defaults to 2.0.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series volatilities and correlation.
-
-- Return type: -
DataFrame
-
-
-
-
- -property correl_matrix: DataFrame -
Correlation matrix.
-This property returns the correlation matrix of the time series -in the frame.
--
-
- Returns: -
Correlation matrix of the time series in the frame.
-
-
-
-
- -add_timeseries(new_series)[source] -
To add an OpenTimeSeries object.
- -
-
-
- -delete_timeseries(lvl_zero_item)[source] -
To delete an OpenTimeSeries object.
- -
-
-
- -trunc_frame(start_cut=None, end_cut=None, where='both')[source] -
Truncate DataFrame such that all timeseries have the same time span.
--
-
- Parameters: -
-
-
start_cut (dt.date | None) – New first date. Optional.
-end_cut (dt.date | None) – New last date. Optional.
-where (LiteralTrunc) – Determines where dataframe is truncated also when start_cut -or end_cut is None. Defaults to both.
-self (Self)
-
-- Returns: -
An OpenFrame object.
-
-- Return type: -
Self
-
-
-
-
- -relative(long_column=0, short_column=1, *, base_zero=True)[source] -
Calculate cumulative relative return between two series.
--
-
- Parameters: -
- - -
- Return type: -
None
-
-
-
-
- -tracking_error_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Tracking Error.
-Calculates Tracking Error which is the standard deviation of the -difference between the fund and its index returns.
-Reference: https://www.investopedia.com/terms/t/trackingerror.asp.
--
-
- Parameters: -
-
-
base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Tracking Errors.
-
-- Return type: -
Series[float]
-
-
-
-
- -info_ratio_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Information Ratio.
-The Information Ratio equals ( fund return less index return ) divided -by the Tracking Error. And the Tracking Error is the standard deviation of -the difference between the fund and its index returns. -The ratio is calculated using the annualized arithmetic mean of returns.
--
-
- Parameters: -
-
-
base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Information Ratios.
-
-- Return type: -
Series[float]
-
-
-
-
- -capture_ratio_func(ratio, base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Capture Ratio.
-The Up (Down) Capture Ratio is calculated by dividing the CAGR -of the asset during periods that the benchmark returns are positive (negative) -by the CAGR of the benchmark during the same periods. -CaptureRatio.BOTH is the Up ratio divided by the Down ratio. -Source: ‘Capture Ratios: A Popular Method of Measuring Portfolio Performance -in Practice’, Don R. Cox and Delbert C. Goff, Journal of Economics and -Finance Education (Vol 2 Winter 2013).
-Reference: https://www.economics-finance.org/jefe/volume12-2/11ArticleCox.pdf.
--
-
- Parameters: -
-
-
ratio (LiteralCaptureRatio) – The ratio to calculate.
-base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. -Defaults to -1.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Capture Ratios.
-
-- Return type: -
Series[float]
-
-
-
-
- -beta(asset, market, dlta_degr_freedms=1)[source] -
Market Beta.
-Calculates Beta as Co-variance of asset & market divided by Variance -of the market.
-Reference: https://www.investopedia.com/terms/b/beta.asp.
--
-
- Parameters: -
- - -
- Returns: -
Beta as Co-variance of x & y divided by Variance of x.
-
-- Return type: -
- - -
-
-
- -ord_least_squares_fit(y_column, x_column, *, fitted_series=True)[source] -
Ordinary Least Squares fit.
-Performs a linear regression and adds a new column with a fitted line -using Ordinary Least Squares fit.
--
-
- Parameters: -
-
-
y_column (tuple[str, ValueType] | int) – The column level values of the dependent variable y.
-x_column (tuple[str, ValueType] | int) – The column level values of the exogenous variable x.
-fitted_series (bool) – If True the fit is added as a new column in the .tsdf -Pandas.DataFrame. Defaults to True.
-self (Self)
-
-- Returns: -
A dictionary with the coefficient, intercept and rsquared outputs.
-
-- Return type: -
- - -
-
-
- -jensen_alpha(asset, market, riskfree_rate=0.0, dlta_degr_freedms=1)[source] -
Jensen’s alpha.
-The Jensen’s measure, or Jensen’s alpha, is a risk-adjusted performance -measure that represents the average return on a portfolio or investment, -above or below that predicted by the capital asset pricing model (CAPM), -given the portfolio’s or investment’s beta and the average market return. -This metric is also commonly referred to as simply alpha.
-Reference: https://www.investopedia.com/terms/j/jensensmeasure.asp.
--
-
- Parameters: -
-
-
asset (tuple[str, ValueType] | int) – The column of the asset.
-market (tuple[str, ValueType] | int) – The column of the market against which Jensen’s alpha is measured.
-riskfree_rate (float) – The return of the zero volatility riskfree asset. -Defaults to 0.0.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 1.
-self (Self)
-
-- Returns: -
Jensen’s alpha.
-
-- Return type: -
- - -
-
-
- -make_portfolio(name, weight_strat=None)[source] -
Calculate a basket timeseries based on the supplied weights.
- -
-
-
- -rolling_info_ratio(long_column=0, short_column=1, observations=21, periods_in_a_year_fixed=None)[source] -
Calculate rolling Information Ratio.
-The Information Ratio equals ( fund return less index return ) divided by -the Tracking Error. And the Tracking Error is the standard deviation of the -difference between the fund and its index returns.
--
-
- Parameters: -
-
-
long_column (int) – Column of timeseries that is the numerator in the ratio. -Defaults to 0.
-short_column (int) – Column of timeseries that is the denominator in the ratio. -Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-periods_in_a_year_fixed (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])] | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Rolling Information Ratios.
-
-- Return type: -
- - -
-
-
- -rolling_beta(asset_column=0, market_column=1, observations=21, dlta_degr_freedms=1)[source] -
Calculate rolling Market Beta.
-Calculates Beta as Co-variance of asset & market divided by Variance -of the market.
-Reference: https://www.investopedia.com/terms/b/beta.asp.
--
-
- Parameters: -
-
-
asset_column (int) – Column of timeseries that is the asset. Defaults to 0.
-market_column (int) – Column of timeseries that is the market. Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 1.
-self (Self)
-
-- Returns: -
Rolling Betas.
-
-- Return type: -
- - -
-
-
- -rolling_corr(first_column=0, second_column=1, observations=21)[source] -
Calculate rolling Correlation.
-Calculates correlation between two series. The period with -at least the given number of observations is the first period calculated.
--
-
- Parameters: -
-
-
first_column (int) – The position as integer of the first timeseries to compare. -Defaults to 0.
-second_column (int) – The position as integer of the second timeseries to compare. -Defaults to 1.
-observations (int) – The length of the rolling window to use is set as number of -observations. Defaults to 21.
-self (Self)
-
-- Returns: -
Rolling Correlations.
-
-- Return type: -
- - -
-
-
- -multi_factor_linear_regression(dependent_column)[source] -
Perform a multi-factor linear regression.
-This function treats one specified column in the DataFrame as the dependent -variable (y) and uses all remaining columns as independent variables (X). -It utilizes a scikit-learn LinearRegression model and returns a DataFrame -with summary output and an OpenTimeSeries of predicted values.
--
-
- Parameters: -
- - -
- Returns: -
-
-
A DataFrame with the R-squared, the intercept and the regression -coefficients
-An OpenTimeSeries of predicted values
-
-- Return type: -
A tuple containing
-
-- Raises: -
-
-
KeyError – If the column tuple is not found in the OpenFrame.tsdf.columns.
-ValueError – If not all series are returnseries (ValueType.RTRN).
-
-
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -rebalanced_portfolio(name, items=None, bal_weights=None, frequency=1, cash_index=None, *, equal_weights=False, drop_extras=True)[source] -
Create a rebalanced portfolio from the OpenFrame constituents.
--
-
- Parameters: -
-
-
name (str) – Name of the portfolio.
-items (list[str] | None) – List of items to include in the portfolio. If None, uses all items. -Optional.
-bal_weights (list[float] | None) – List of weights for rebalancing. If None, uses frame weights. -Optional.
-frequency (int) – Rebalancing frequency. Defaults to 1.
-cash_index (OpenTimeSeries | None) – Cash index series for cash component. Optional.
-equal_weights (bool) – If True, use equal weights for all items. Defaults to False.
-drop_extras (bool) – If True, only return TWR series; if False, return all details. -Defaults to True.
-self (Self)
-
-- Returns: -
OpenFrame containing the rebalanced portfolio.
-
-- Return type: -
OpenFrame
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
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- openseries.OpenTimeSeries
--
-
- -class openseries.OpenTimeSeries(*, constituents=<factory>, weights=None, markets=None, tsdf, timeseries_id, instrument_id, name, valuetype, dates, values, local_ccy, currency, domestic='SEK', countries='SE', isin=None, label=None)[source] -
Bases:
-_CommonModel[float]OpenTimeSeries objects are at the core of the openseries package.
-The intended use is to allow analyses of financial timeseries. -It is only intended for daily or less frequent data samples.
--
-
- Parameters: -
-
-
timeseries_id (str) – Database identifier of the timeseries.
-instrument_id (str) – Database identifier of the instrument associated with -the timeseries.
-name (str) – String identifier of the timeseries and/or instrument.
-valuetype (ValueType) – Identifies if the series is a series of values or returns.
-dates (Annotated[list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$, ascii_only=None)]], MinLen(min_length=1)]) – Dates of the individual timeseries items. -These dates will not be altered by methods.
-values (Annotated[list[float], MinLen(min_length=1)]) – The value or return values of the timeseries items. -These values will not be altered by methods.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency.
-tsdf (DataFrame) – Pandas object holding dates and values that can be altered via -methods.
-currency (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries.
-domestic (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the user’s home currency. -Defaults to “SEK”.
-countries (Annotated[set[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]], MinLen(min_length=1)] | Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars. -Optional.
-isin (str | None) – ISO 6166 identifier code of the associated instrument. Optional.
-label (str | None) – Placeholder for a name of the timeseries. Optional.
-- -
- -
-
-
-
- -__init__(**data) -
Create a new model by parsing and validating input data from keyword arguments.
-Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.
-self is explicitly positional-only to allow self as a field name.
--
-
- Parameters: -
data (Any)
-
-- Return type: -
None
-
-
Methods
-- -
-__init__(**data)Create a new model by parsing and validating input data from keyword arguments.
acf(lags, *[, squared])Calculate autocorrelation function for specified lags.
align_index_to_local_cdays([countries, ...])Align the index of
.tsdfwith local calendar business days.all_properties([properties])Calculate chosen properties.
arithmetic_ret_func([months_from_last, ...])Annualized arithmetic mean of returns.
autocorr_func([lag, squared])Calculate autocorrelation at a given lag.
calc_range([months_offset, from_dt, to_dt])Create a user-defined date range aligned to index.
construct([_fields_set])- copy(*[, include, exclude, update, deep])Returns a copy of the model.
cvar_down_func([level, months_from_last, ...])Downside Conditional Value At Risk (CVaR).
dict(*[, include, exclude, by_alias, ...])- ewma_var_func([lmbda, day_chunk, level, ...])Exponentially Weighted Moving Average Model for Value At Risk (VaR).
ewma_vol_func([lmbda, day_chunk, ...])Exponentially Weighted Moving Average Model for Volatility.
from_1d_rate_to_cumret([days_in_year, divider])Convert series of 1-day rates into series of cumulative values.
from_arrays(name, dates, values[, ...])Create series from a list of dates and a list of values.
from_deepcopy()Create copy of OpenTimeSeries object.
from_df(dframe[, column_nmbr, valuetype, ...])Create series from a Pandas DataFrame or Series.
from_fixed_rate(rate[, d_range, days, ...])Create series from values accruing with a given fixed rate return.
from_orm(obj)- geo_ret_func([months_from_last, from_date, ...])Compounded Annual Growth Rate (CAGR).
json(*[, include, exclude, by_alias, ...])- kurtosis_func([months_from_last, from_date, ...])Kurtosis of the return distribution.
ljung_box(lags, *[, squared])Compute Ljung-Box test for autocorrelation.
lower_partial_moment_func([...])Lower partial moment and downside deviation (order=2).
max_drawdown_func([months_from_last, ...])Maximum drawdown without any limit on date range.
model_construct([_fields_set])Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])Generates a JSON schema for a model class.
model_parametrized_name(params)Compute the class name for parametrizations of generic classes.
model_post_init(context, /)Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])Validate the given object with string data against the Pydantic model.
omega_ratio_func([min_accepted_return, ...])Omega Ratio.
outliers([threshold, months_from_last, ...])Detect outliers using z-score analysis.
pacf(lags, *[, squared])Calculate partial autocorrelation function for specified lags.
pandas_df()Populate .tsdf Pandas DataFrame from the .dates and .values lists.
parse_file(path, *[, content_type, ...])- parse_obj(obj)- parse_raw(b, *[, content_type, encoding, ...])- partial_autocorr([lag, squared])Calculate partial autocorrelation at a given lag.
plot_bars([mode, title, tick_fmt, filename, ...])Create a Plotly Bar Figure.
plot_histogram([plot_type, histnorm, ...])Create a Plotly Histogram Figure.
plot_series([mode, title, tick_fmt, ...])Create a Plotly Scatter Figure.
positive_share_func([months_from_last, ...])Share of percentage changes greater than zero.
resample([freq])Resamples the timeseries frequency.
resample_to_business_period_ends([freq, method])Resamples timeseries frequency to the business calendar month end dates.
ret_vol_ratio_func([riskfree_rate, ...])Ratio between arithmetic mean of returns and annualized volatility.
return_nan_handle([method])Handle missing values in a return series.
rolling_cvar_down([column, level, observations])Calculate rolling annualized downside CVaR.
rolling_return([column, observations])Calculate rolling returns.
rolling_var_down([column, level, ...])Calculate rolling annualized downside Value At Risk (VaR).
rolling_vol([column, observations, ...])Calculate rolling annualized volatilities.
running_adjustment(adjustment[, days_in_year])Add or subtract a fee from the timeseries return.
schema([by_alias, ref_template])- schema_json(*[, by_alias, ref_template])- set_new_label([lvl_zero, lvl_one, ...])Set the column labels of the .tsdf Pandas Dataframe.
skew_func([months_from_last, from_date, to_date])Skew of the return distribution.
sortino_ratio_func([riskfree_rate, ...])Sortino ratio or Kappa-3 ratio.
target_weight_from_var([target_vol, level, ...])Target weight from VaR.
to_cumret()Convert series of returns into cumulative series of values.
to_drawdown_series()Convert timeseries into a drawdown series.
to_json(what_output, filename[, directory])Dump timeseries data into a JSON file.
to_xlsx(filename[, sheet_title, directory, ...])Save
.tsdfDataFrame to an Excel spreadsheet file.update_forward_refs(**localns)- validate(value)- value_nan_handle([method])Handle missing values in a value series.
value_ret_calendar_period(year[, month])Calculate simple return for a specific calendar period.
value_ret_func([months_from_last, ...])Calculate simple return.
value_to_diff([periods])Convert series of values to series of their period differences.
value_to_log()Convert value series to log-weighted series.
value_to_ret()Convert series of values into series of returns.
var_down_func([level, months_from_last, ...])Downside Value At Risk (VaR).
vol_from_var_func([level, months_from_last, ...])Implied annualized volatility from downside VaR.
vol_func([months_from_last, from_date, ...])Annualized volatility.
worst_func([observations, months_from_last, ...])Most negative percentage change over a rolling window.
z_score_func([months_from_last, from_date, ...])Z-score of the last return.
Attributes
-- -
-arithmetic_retAnnualized arithmetic mean of returns.
autocorrAutocorrelation at lag 1.
cvar_downDownside 95% Conditional Value At Risk "CVaR".
downside_deviationDownside Deviation.
first_idxThe first date in the timeseries.
geo_retCompounded Annual Growth Rate (CAGR).
kappa3_ratioKappa-3 ratio.
kurtosisKurtosis of the return distribution.
last_idxThe last date in the timeseries.
lengthNumber of observations.
max_drawdownMaximum drawdown without any limit on date range.
max_drawdown_cal_yearMaximum drawdown in a single calendar year.
max_drawdown_dateDate when the maximum drawdown occurred.
model_computed_fields- model_configConfiguration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extraGet extra fields set during validation.
model_fields- model_fields_setReturns the set of fields that have been explicitly set on this model instance.
omega_ratioOmega ratio.
periods_in_a_yearThe average number of observations per year.
positive_shareThe share of percentage changes that are greater than zero.
ret_vol_ratioRatio of annualized arithmetic mean of returns and annualized volatility.
skewSkew of the return distribution.
sortino_ratioSortino ratio.
span_of_daysNumber of days from the first date to the last.
value_retSimple return.
var_downDownside 95% Value At Risk (VaR).
volAnnualized volatility.
vol_from_varImplied annualized volatility from Downside 95% Value at Risk.
worstMost negative percentage change.
worst_monthMost negative month.
yearfracLength of series in years assuming 365.25 days per year.
z_scoreZ-score.
timeseries_id- instrument_id- name- valuetype- dates- values- local_ccy- tsdf- currency- domestic- countries- isin- label- constituents- weights- markets- -
-
- -timeseries_id: str -
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- -valuetype: ValueType -
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- -dates: DateListType -
-
-
- -values: ValueListType -
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- -tsdf: DataFrame -
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-
-
- -classmethod from_arrays(name, dates, values, valuetype=ValueType.PRICE, timeseries_id='', instrument_id='', isin=None, baseccy='SEK', *, local_ccy=True)[source] -
Create series from a list of dates and a list of values.
--
-
- Parameters: -
-
-
name (str) – String identifier of the timeseries and/or instrument.
-dates (Annotated[list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$, ascii_only=None)]], MinLen(min_length=1)]) – List of date strings as ISO 8601 YYYY-MM-DD.
-values (Annotated[list[float], MinLen(min_length=1)]) – Array of float values.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-timeseries_id (str) – Database identifier of the timeseries. Optional.
-instrument_id (str) – Database identifier of the instrument associated -with the timeseries. Optional.
-isin (str | None) – ISO 6166 identifier code of the associated instrument. Optional.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Return type: -
- - -
-
-
- -classmethod from_df(dframe, column_nmbr=0, valuetype=ValueType.PRICE, baseccy='SEK', *, local_ccy=True)[source] -
Create series from a Pandas DataFrame or Series.
--
-
- Parameters: -
-
-
dframe (Series | DataFrame | object) – Pandas DataFrame or Series.
-column_nmbr (int) – Using iloc[:, column_nmbr] to pick column. Defaults to 0.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
TypeError – If
-dframeis not apandas.Seriesor a -pandas.DataFrame.
-- Return type: -
- - -
-
-
- -classmethod from_fixed_rate(rate, d_range=None, days=None, end_dt=None, label='Series', valuetype=ValueType.PRICE, baseccy='SEK', *, local_ccy=True)[source] -
Create series from values accruing with a given fixed rate return.
-Providing a date_range of type Pandas DatetimeIndex takes priority over -providing a combination of days and an end date.
--
-
- Parameters: -
-
-
rate (float) – The accrual rate.
-d_range (DatetimeIndex | None) – A given range of dates. Optional.
-days (int | None) – Number of days to generate when date_range not provided. Must be -combined with end_dt. Optional.
-end_dt (date | None) – End date of date range to generate when date_range not provided. -Must be combined with days. Optional.
-label (str) – Placeholder for a name of the timeseries.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – The currency of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
IncorrectArgumentComboError – If
-d_rangeis not provided and the - combination ofdaysandend_dtis incomplete.
-- Return type: -
- - -
-
-
- -from_deepcopy()[source] -
Create copy of OpenTimeSeries object.
- -
-
-
- -pandas_df()[source] -
Populate .tsdf Pandas DataFrame from the .dates and .values lists.
- -
-
-
- -all_properties(properties=None)[source] -
Calculate chosen properties.
--
-
- Parameters: -
-
-
properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown_cal_year', 'max_drawdown', 'max_drawdown_date', 'first_idx', 'last_idx', 'length', 'span_of_days', 'yearfrac', 'periods_in_a_year', 'autocorr', 'partial_autocorr']] | None) – The properties to calculate. Defaults to calculating all -available. Optional.
-self (Self)
-
-- Returns: -
Properties of the OpenTimeSeries.
-
-- Return type: -
- - -
-
-
- -value_to_ret()[source] -
Convert series of values into series of returns.
- -
-
-
- -value_to_diff(periods=1)[source] -
Convert series of values to series of their period differences.
- -
-
-
- -to_cumret()[source] -
Convert series of returns into cumulative series of values.
- -
-
-
- -from_1d_rate_to_cumret(days_in_year=365, divider=1.0)[source] -
Convert series of 1-day rates into series of cumulative values.
- -
-
-
- -resample(freq='BME')[source] -
Resamples the timeseries frequency.
- -
-
-
- -resample_to_business_period_ends(freq='BME', method='nearest')[source] -
Resamples timeseries frequency to the business calendar month end dates.
-Stubs left in place. Stubs will be aligned to the shortest stub.
--
-
- Parameters: -
- - -
- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
ResampleDataLossError – If called on a return series (
-valuetypeis -ValueType.RTRN), since summation across sparser frequency would - be required to avoid data loss.
-- Return type: -
- - -
-
-
- -ewma_vol_func(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Model for Volatility.
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. -Defaults to 11.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-months_from_last (int | None) – Number of months offset as positive integer. -Overrides use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series EWMA volatility.
-
-- Return type: -
Series[float]
-
-
-
-
- -ewma_var_func(lmbda=0.94, day_chunk=11, level=0.95, dlta_degr_freedms=0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Model for Value At Risk (VaR).
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
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lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. -Defaults to 11.
-level (float) – The sought VaR level. Defaults to 0.95.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-months_from_last (int | None) – Number of months offset as positive integer. -Overrides use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series EWMA VaR.
-
-- Return type: -
Series[float]
-
-
-
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- -running_adjustment(adjustment, days_in_year=365)[source] -
Add or subtract a fee from the timeseries return.
- -
-
-
- -set_new_label(lvl_zero=None, lvl_one=None, *, delete_lvl_one=False)[source] -
Set the column labels of the .tsdf Pandas Dataframe.
- -
-
-
- -acf(lags, *, squared=False)[source] -
Calculate autocorrelation function for specified lags.
--
-
- Parameters: -
- - -
- Returns: -
Series of autocorrelations indexed by lag.
-
-- Return type: -
Series[float]
-
-
-
-
- -partial_autocorr(lag=1, *, squared=False)[source] -
Calculate partial autocorrelation at a given lag.
- -
-
-
- -pacf(lags, *, squared=False)[source] -
Calculate partial autocorrelation function for specified lags.
--
-
- Parameters: -
- - -
- Returns: -
Series of partial autocorrelations indexed by lag.
-
-- Return type: -
Series[float]
-
-
-
-
- -ljung_box(lags, *, squared=False)[source] -
Compute Ljung-Box test for autocorrelation.
--
-
- Parameters: -
- - -
- Returns: -
Tuple of (statistic, pvalue, lags) where statistic is the Ljung-Box -Q statistic, pvalue is the chi-squared p-value, and lags is the -list of lags used.
-
-- Return type: -
- - -
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
-
-
-
-
-
-
-
-
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- openseries.ReturnSimulation
--
-
- -class openseries.ReturnSimulation(*, number_of_sims, trading_days, trading_days_in_year, mean_annual_return, mean_annual_vol, dframe, jumps_lamda=0.0, jumps_sigma=0.0, jumps_mu=0.0, seed=None)[source] -
Bases:
-BaseModelThe class ReturnSimulation allows for simulating financial timeseries.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Total number of days to simulate.
-trading_days_in_year (Annotated[int, Strict(strict=True), Ge(ge=1), Le(le=366)]) – Number of trading days used to annualize.
-mean_annual_return (float) – Mean annual return of the distribution.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean annual standard deviation of the distribution.
-dframe (DataFrame) – Pandas DataFrame object holding the resulting values.
-jumps_lamda (Annotated[float, Ge(ge=0)]) – This is the probability of a jump happening at each point in time. -Defaults to 0.0.
-jumps_sigma (Annotated[float, Ge(ge=0)]) – This is the volatility of the jump size. Defaults to 0.0.
-jumps_mu (float) – This is the average jump size. Defaults to 0.0.
-seed (int | None) – Seed for random process initiation.
-
-
-
-
- -__init__(**data) -
Create a new model by parsing and validating input data from keyword arguments.
-Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.
-self is explicitly positional-only to allow self as a field name.
--
-
- Parameters: -
data (Any)
-
-- Return type: -
None
-
-
Methods
-- -
-__init__(**data)Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])- copy(*[, include, exclude, update, deep])Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])- from_gbm(number_of_sims, mean_annual_return, ...)Create a Geometric Brownian Motion simulation.
from_lognormal(number_of_sims, ...[, ...])Create a Lognormal distribution simulation.
from_merton_jump_gbm(number_of_sims, ...[, ...])Create a Merton Jump-Diffusion model simulation.
from_normal(number_of_sims, ...[, ...])Create a Normal distribution simulation.
from_orm(obj)- json(*[, include, exclude, by_alias, ...])- model_construct([_fields_set])Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])Generates a JSON schema for a model class.
model_parametrized_name(params)Compute the class name for parametrizations of generic classes.
model_post_init(context, /)Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])- parse_obj(obj)- parse_raw(b, *[, content_type, encoding, ...])- schema([by_alias, ref_template])- schema_json(*[, by_alias, ref_template])- to_dataframe(name[, start, end, countries, ...])Create a pandas.DataFrame from simulation(s).
update_forward_refs(**localns)- validate(value)- Attributes
-- -
-model_computed_fields- model_configConfiguration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extraGet extra fields set during validation.
model_fields- model_fields_setReturns the set of fields that have been explicitly set on this model instance.
realized_mean_returnAnnualized arithmetic mean of returns.
realized_volAnnualized volatility.
resultsSimulation data.
number_of_sims- trading_days- trading_days_in_year- mean_annual_return- mean_annual_vol- dframe- jumps_lamda- jumps_sigma- jumps_mu- seed- -
-
- -number_of_sims: PositiveInt -
-
-
- -trading_days: PositiveInt -
-
-
- -trading_days_in_year: DaysInYearType -
-
-
- -mean_annual_return: float -
-
-
- -mean_annual_vol: PositiveFloat -
-
-
- -dframe: DataFrame -
-
-
- -jumps_lamda: NonNegativeFloat -
-
-
- -jumps_sigma: NonNegativeFloat -
-
-
- -jumps_mu: float -
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -property realized_mean_return: float -
Annualized arithmetic mean of returns.
--
-
- Returns: -
Annualized arithmetic mean of returns.
-
-
-
-
- -property realized_vol: float -
Annualized volatility.
--
-
- Returns: -
Annualized volatility.
-
-
-
-
- -classmethod from_normal(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Normal distribution simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Normal distribution simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_lognormal(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Lognormal distribution simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Lognormal distribution simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_gbm(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Geometric Brownian Motion simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Geometric Brownian Motion simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_merton_jump_gbm(number_of_sims, trading_days, mean_annual_return, mean_annual_vol, jumps_lamda, jumps_sigma=0.0, jumps_mu=0.0, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Merton Jump-Diffusion model simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-jumps_lamda (Annotated[float, Ge(ge=0)]) – This is the probability of a jump happening at each point -in time.
-jumps_sigma (Annotated[float, Ge(ge=0)]) – This is the volatility of the jump size. Defaults to 0.0.
-jumps_mu (float) – This is the average jump size. Defaults to 0.0.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Merton Jump-Diffusion model simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -to_dataframe(name, start=None, end=None, countries='SE', markets=None)[source] -
Create a pandas.DataFrame from simulation(s).
--
-
- Parameters: -
-
-
name (str) – Name label of the serie(s).
-start (dt.date | None) – Date when the simulation starts.
-end (dt.date | None) – Date when the simulation ends.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-self (Self)
-
-- Returns: -
The simulation(s) data.
-
-- Return type: -
DataFrame
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.constrain_optimized_portfolios.html b/docs/build/html/api/generated/openseries.constrain_optimized_portfolios.html
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-
-
- openseries.ValueType
--
-
- -class openseries.ValueType(*values)[source] -
Bases:
-StrEnumEnum types of OpenTimeSeries to identify the output.
--
-
- -__init__() -
Methods
-- -
-encode([encoding, errors])Encode the string using the codec registered for encoding.
replace(old, new, /[, count])Return a copy with all occurrences of substring old replaced by new.
split([sep, maxsplit])Return a list of the substrings in the string, using sep as the separator string.
rsplit([sep, maxsplit])Return a list of the substrings in the string, using sep as the separator string.
join(iterable, /)Concatenate any number of strings.
capitalize()Return a capitalized version of the string.
casefold()Return a version of the string suitable for caseless comparisons.
title()Return a version of the string where each word is titlecased.
center(width[, fillchar])Return a centered string of length width.
countReturn the number of non-overlapping occurrences of substring sub in string S[start:end].
expandtabs([tabsize])Return a copy where all tab characters are expanded using spaces.
findReturn the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
partition(sep, /)Partition the string into three parts using the given separator.
indexReturn the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
ljust(width[, fillchar])Return a left-justified string of length width.
lower()Return a copy of the string converted to lowercase.
lstrip([chars])Return a copy of the string with leading whitespace removed.
rfindReturn the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rindexReturn the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rjust(width[, fillchar])Return a right-justified string of length width.
rstrip([chars])Return a copy of the string with trailing whitespace removed.
rpartition(sep, /)Partition the string into three parts using the given separator.
splitlines([keepends])Return a list of the lines in the string, breaking at line boundaries.
strip([chars])Return a copy of the string with leading and trailing whitespace removed.
swapcase()Convert uppercase characters to lowercase and lowercase characters to uppercase.
translate(table, /)Replace each character in the string using the given translation table.
upper()Return a copy of the string converted to uppercase.
startswithReturn True if the string starts with the specified prefix, False otherwise.
endswithReturn True if the string ends with the specified suffix, False otherwise.
removeprefix(prefix, /)Return a str with the given prefix string removed if present.
removesuffix(suffix, /)Return a str with the given suffix string removed if present.
isascii()Return True if all characters in the string are ASCII, False otherwise.
islower()Return True if the string is a lowercase string, False otherwise.
isupper()Return True if the string is an uppercase string, False otherwise.
istitle()Return True if the string is a title-cased string, False otherwise.
isspace()Return True if the string is a whitespace string, False otherwise.
isdecimal()Return True if the string is a decimal string, False otherwise.
isdigit()Return True if the string is a digit string, False otherwise.
isnumeric()Return True if the string is a numeric string, False otherwise.
isalpha()Return True if the string is an alphabetic string, False otherwise.
isalnum()Return True if the string is an alpha-numeric string, False otherwise.
isidentifier()Return True if the string is a valid Python identifier, False otherwise.
isprintable()Return True if all characters in the string are printable, False otherwise.
zfill(width, /)Pad a numeric string with zeros on the left, to fill a field of the given width.
format(*args, **kwargs)Return a formatted version of the string, using substitutions from args and kwargs.
format_map(mapping, /)Return a formatted version of the string, using substitutions from mapping.
maketransReturn a translation table usable for str.translate().
__init__()- Attributes
-- -
-EWMA_VOL- EWMA_VAR- PRICE- RTRN- RELRTRN- ROLLBETA- ROLLCORR- ROLLCVAR- ROLLINFORATIO- ROLLRTRN- ROLLVAR- ROLLVOL- -
-
- -EWMA_VOL = 'EWMA volatility' -
-
-
- -EWMA_VAR = 'EWMA VaR' -
-
-
- -PRICE = 'Price(Close)' -
-
-
- -RTRN = 'Return(Total)' -
-
-
- -RELRTRN = 'Relative return' -
-
-
- -ROLLBETA = 'Beta' -
-
-
- -ROLLCORR = 'Rolling correlation' -
-
-
- -ROLLCVAR = 'Rolling CVaR' -
-
-
- -ROLLINFORATIO = 'Information Ratio' -
-
-
- -ROLLRTRN = 'Rolling returns' -
-
-
- -ROLLVAR = 'Rolling VaR' -
-
-
- -ROLLVOL = 'Rolling volatility' -
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.date_fix.html b/docs/build/html/api/generated/openseries.date_fix.html
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-
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-
-
- openseries.constrain_optimized_portfolios
--
-
- -openseries.constrain_optimized_portfolios(data, serie, portfolioname='Current Portfolio', simulations=10000, curve_points=200, bounds=None, minimize_method='SLSQP')[source] -
Constrain optimized portfolios to those that improve on the current one.
--
-
- Parameters: -
-
-
data (OpenFrame) – Portfolio data.
-serie (OpenTimeSeries) – A timeseries representing the current portfolio.
-portfolioname (str) – Name of the portfolio. Defaults to “Current Portfolio”.
-simulations (int) – Number of possible portfolios to simulate. Defaults to 10000.
-curve_points (int) – Number of optimal portfolios on the efficient frontier. -Defaults to 200.
-bounds (tuple[tuple[float, float], ...] | None) – The range of minimum and maximum allowed allocations for each asset.
-minimize_method (LiteralMinimizeMethods) – The method passed into the scipy.minimize function. -Defaults to SLSQP.
-
-- Returns: -
The constrained optimal portfolio data.
-
-- Return type: -
tuple[OpenFrame, OpenTimeSeries, OpenFrame, OpenTimeSeries]
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.date_offset_foll.html b/docs/build/html/api/generated/openseries.date_offset_foll.html
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- openseries.date_fix
- - -
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\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.efficient_frontier.html b/docs/build/html/api/generated/openseries.efficient_frontier.html
deleted file mode 100644
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-
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- openseries.date_offset_foll
--
-
- -openseries.date_offset_foll(raw_date, months_offset=12, countries='SE', markets=None, custom_holidays=None, *, adjust=False, following=True)[source] -
Offset dates according to a given calendar.
--
-
- Parameters: -
-
-
raw_date (DateType) – The date to offset from.
-months_offset (int) – Number of months as integer. Defaults to 12.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-adjust (bool) – Determines if offset should adjust for business days. -Defaults to False.
-following (bool) – Determines if days should be offset forward (following) or backward. -Defaults to True.
-
-- Returns: -
Offset date.
-
-- Return type: -
dt.date
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.generate_calendar_date_range.html b/docs/build/html/api/generated/openseries.generate_calendar_date_range.html
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- openseries.efficient_frontier
--
-
- -openseries.efficient_frontier(eframe, num_ports=5000, seed=71, bounds=None, frontier_points=200, minimize_method='SLSQP', *, tweak=True)[source] -
Identify an efficient frontier.
--
-
- Parameters: -
-
-
eframe (OpenFrame) – Portfolio data.
-num_ports (int) – Number of possible portfolios to simulate. Defaults to 5000.
-seed (int) – The seed for the random process. Defaults to 71.
-bounds (tuple[tuple[float, float], ...] | None) – The range of minimum and maximum allowed allocations for each asset.
-frontier_points (int) – Number of points along frontier to optimize. Defaults to 200.
-minimize_method (LiteralMinimizeMethods) – The method passed into the scipy.minimize function. -Defaults to SLSQP.
-tweak (bool) – Cutting the frontier to exclude multiple points with almost the -same risk. -Defaults to True.
-
-- Returns: -
The efficient frontier data, simulation data and optimal portfolio.
-
-- Return type: -
tuple[DataFrame, DataFrame, NDArray[float64]]
-
-
-
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-
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\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.get_previous_business_day_before_today.html b/docs/build/html/api/generated/openseries.get_previous_business_day_before_today.html
deleted file mode 100644
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- openseries.generate_calendar_date_range
--
-
- -openseries.generate_calendar_date_range(trading_days, start=None, end=None, countries='SE', markets=None, custom_holidays=None)[source] -
Generate a list of business day calendar dates.
--
-
- Parameters: -
-
-
trading_days (int) – Number of days to generate. Must be greater than zero.
-start (dt.date | None) – Date when the range starts.
-end (dt.date | None) – Date when the range ends.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
List of business day calendar dates.
-
-- Return type: -
list[dt.date]
-
-
-
-
-
-
-
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-
-
-
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\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.holiday_calendar.html b/docs/build/html/api/generated/openseries.holiday_calendar.html
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- openseries.get_previous_business_day_before_today
--
-
- -openseries.get_previous_business_day_before_today(today=None, countries='SE', markets=None, custom_holidays=None)[source] -
Bump date backwards to find the previous business day.
--
-
- Parameters: -
-
-
today (dt.date | None) – Manual input of the day from where the previous business day is found.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
The previous business day.
-
-- Return type: -
dt.date
-
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-
-
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-
-
-
-
-
-
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\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.load_plotly_dict.html b/docs/build/html/api/generated/openseries.load_plotly_dict.html
deleted file mode 100644
index 80fe475a..00000000
--- a/docs/build/html/api/generated/openseries.load_plotly_dict.html
+++ /dev/null
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- openseries.holiday_calendar
--
-
- -openseries.holiday_calendar(startyear, endyear, countries='SE', markets=None, custom_holidays=None)[source] -
Generate a business calendar.
--
-
- Parameters: -
-
-
startyear (int) – First year in date range generated.
-endyear (int) – Last year in date range generated.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
Generate a business calendar.
-
-- Raises: -
CountriesNotStringNorListStrError – If
-countriesis not a supported - ISO 3166-1 alpha-2 string or a list of such strings.
-- Return type: -
busdaycalendar
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.offset_business_days.html b/docs/build/html/api/generated/openseries.offset_business_days.html
deleted file mode 100644
index c1fae54c..00000000
--- a/docs/build/html/api/generated/openseries.offset_business_days.html
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- openseries.load_plotly_dict
--
-
- -openseries.load_plotly_dict(*, responsive=True)[source] -
Load Plotly defaults.
--
-
- Parameters: -
responsive (bool) – Flag whether to load as responsive. Defaults to True.
-
-- Returns: -
A tuple (config_and_layout, logo) -where config_and_layout is the Plotly config and layout template dict, -and logo is the Captor logo dict (may be empty if the remote logo is -unavailable).
-
-- Return type: -
tuple[PlotlyLayoutType, CaptorLogoType]
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.prepare_plot_data.html b/docs/build/html/api/generated/openseries.prepare_plot_data.html
deleted file mode 100644
index e4f76b27..00000000
--- a/docs/build/html/api/generated/openseries.prepare_plot_data.html
+++ /dev/null
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- openseries.offset_business_days
--
-
- -openseries.offset_business_days(ddate, days, countries='SE', markets=None, custom_holidays=None)[source] -
Bump date by business days.
-It first adjusts to a valid business day and then bumps with given -number of business days from there.
--
-
- Parameters: -
-
-
ddate (dt.date) – A starting date that does not have to be a business day.
-days (int) – The number of business days to offset from the business day -that is given. -If days is set as anything other than an integer its value is set to zero.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-custom_holidays (list[str] | str | None) – Argument where missing holidays can be added.
-
-- Returns: -
The new offset business day.
-
-- Return type: -
dt.date
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.report_html.html b/docs/build/html/api/generated/openseries.report_html.html
deleted file mode 100644
index f63266aa..00000000
--- a/docs/build/html/api/generated/openseries.report_html.html
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- openseries.prepare_plot_data
--
-
- -openseries.prepare_plot_data(assets, current, optimized)[source] -
Prepare data to be used as point_frame in the sharpeplot function.
--
-
- Parameters: -
-
-
assets (OpenFrame) – Portfolio data with individual assets and a weighted portfolio.
-current (OpenTimeSeries) – The current or initial portfolio based on given weights.
-optimized (NDArray[float64]) – Data optimized with the efficient_frontier method.
-
-- Returns: -
The data prepared with mean returns, volatility and weights.
-
-- Return type: -
DataFrame
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.sharpeplot.html b/docs/build/html/api/generated/openseries.sharpeplot.html
deleted file mode 100644
index 86d5a3d1..00000000
--- a/docs/build/html/api/generated/openseries.sharpeplot.html
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-
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-
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-
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-
-
-
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-
-
- openseries.report_html
--
-
- -openseries.report_html(data, bar_freq='BYE', filename=None, title=None, directory=None, output_type='file', include_plotlyjs='cdn', *, auto_open=False, add_logo=True, vertical_legend=True)[source] -
Generate a responsive HTML report page with line and bar plots and a table.
--
-
- Parameters: -
- - -
- Return type: -
- - -
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.simulate_portfolios.html b/docs/build/html/api/generated/openseries.simulate_portfolios.html
deleted file mode 100644
index d05372ce..00000000
--- a/docs/build/html/api/generated/openseries.simulate_portfolios.html
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-
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-
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-
-
- openseries.sharpeplot
--
-
- -openseries.sharpeplot(sim_frame=None, line_frame=None, point_frame=None, point_frame_mode='markers', filename=None, directory=None, titletext=None, output_type='file', include_plotlyjs='cdn', *, title=True, add_logo=True, auto_open=True)[source] -
Create scatter plot coloured by Sharpe Ratio.
--
-
- Parameters: -
-
-
sim_frame (DataFrame | None) – Data from the simulate_portfolios method.
-line_frame (DataFrame | None) – Data from the efficient_frontier method.
-point_frame (DataFrame | None) – Data to highlight current and efficient portfolios.
-point_frame_mode (LiteralLinePlotMode) – Which type of scatter to use. Defaults to markers.
-filename (str | None) – Name of the Plotly html file.
-directory (DirectoryPath | None) – Directory where Plotly html file is saved.
-titletext (str | None) – Text for the plot title.
-output_type (LiteralPlotlyOutput) – Determines output type. Defaults to “file”.
-include_plotlyjs (LiteralPlotlyJSlib) – Determines how the plotly.js library is included -in the output. -Defaults to “cdn”.
-title (bool) – Whether to add standard plot title. Defaults to True.
-add_logo (bool) – Whether to add Captor logo. Defaults to True.
-auto_open (bool) – Determines whether to open a browser window with the plot. -Defaults to True.
-
-- Returns: -
The scatter plot with simulated and optimized results.
-
-- Return type: -
- - -
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/generated/openseries.timeseries_chain.html b/docs/build/html/api/generated/openseries.timeseries_chain.html
deleted file mode 100644
index 3ac005a6..00000000
--- a/docs/build/html/api/generated/openseries.timeseries_chain.html
+++ /dev/null
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diff --git a/docs/build/html/api/openseries.html b/docs/build/html/api/openseries.html
deleted file mode 100644
index fc534ca0..00000000
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+++ /dev/null
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-
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-
-
- openseries.timeseries_chain
--
-
- -openseries.timeseries_chain(front, back, old_fee=0.0)[source] -
Chain two timeseries together.
-The function assumes that the two series have at least one date in common.
--
-
- Parameters: -
-
-
front (TypeOpenTimeSeries) – Earlier series to chain with.
-back (TypeOpenTimeSeries) – Later series to chain with.
-old_fee (float) – Fee to apply to earlier series. Defaults to 0.0.
-
-- Returns: -
An OpenTimeSeries object or a subclass thereof.
-
-- Return type: -
TypeOpenTimeSeries
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/portfoliotools.html b/docs/build/html/api/portfoliotools.html
deleted file mode 100644
index 8798ef19..00000000
--- a/docs/build/html/api/portfoliotools.html
+++ /dev/null
@@ -1,297 +0,0 @@
-
-
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-
-
- openseries package
-The openseries package provides two main classes for financial time series analysis:
--
-
OpenTimeSeries: For single financial time series analysis
-OpenFrame: For multi-asset portfolio analysis and comparison
-
Both classes inherit from the private _CommonModel class, which in turn inherits from Pydantic’s BaseModel. This inheritance structure provides:
-
-
Data validation through Pydantic’s validation system
-Common functionality shared between both classes (risk metrics, plotting, data handling)
-Type safety and consistent API design
-
The _CommonModel class contains all the shared methods and properties that both OpenTimeSeries and OpenFrame use, including risk calculations, plotting capabilities, and data manipulation functions.
Main Classes
-
|
-OpenTimeSeries objects are at the core of the openseries package. |
-
|
-OpenFrame objects hold OpenTimeSeries in the list constituents. |
-
Utility Functions
-
|
-Chain two timeseries together. |
-
|
-Generate a responsive HTML report page with line and bar plots and a table. |
-
Portfolio Tools
-
|
-Identify an efficient frontier. |
-
|
-Generate random weights for simulated portfolios. |
-
|
-Constrain optimized portfolios to those that improve on the current one. |
-
|
-Prepare data to be used as point_frame in the sharpeplot function. |
-
|
-Create scatter plot coloured by Sharpe Ratio. |
-
Date Utilities
-
|
-Parse different date formats into datetime.date. |
-
|
-Offset dates according to a given calendar. |
-
|
-Generate a list of business day calendar dates. |
-
|
-Bump date backwards to find the previous business day. |
-
|
-Generate a business calendar. |
-
|
-Bump date by business days. |
-
Simulation
-
|
-The class ReturnSimulation allows for simulating financial timeseries. |
-
Types and Enums
-
|
-Enum types of OpenTimeSeries to identify the output. |
-
Other Utilities
-
|
-Load Plotly defaults. |
-
|
-Export a Plotly figure to a mobile-responsive HTML file or inline div. |
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/report.html b/docs/build/html/api/report.html
deleted file mode 100644
index 4e94c57e..00000000
--- a/docs/build/html/api/report.html
+++ /dev/null
@@ -1,235 +0,0 @@
-
-
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-
-
-
-
-
-
-
-
- Portfolio Tools
-The portfoliotools module provides functions for portfolio optimization, simulation, and analysis.
-Portfolio Optimization
--
-
- -openseries.portfoliotools.efficient_frontier(eframe, num_ports=5000, seed=71, bounds=None, frontier_points=200, minimize_method='SLSQP', *, tweak=True)[source] -
Identify an efficient frontier.
--
-
- Parameters: -
-
-
eframe (OpenFrame) – Portfolio data.
-num_ports (int) – Number of possible portfolios to simulate. Defaults to 5000.
-seed (int) – The seed for the random process. Defaults to 71.
-bounds (tuple[tuple[float, float], ...] | None) – The range of minimum and maximum allowed allocations for each asset.
-frontier_points (int) – Number of points along frontier to optimize. Defaults to 200.
-minimize_method (LiteralMinimizeMethods) – The method passed into the scipy.minimize function. -Defaults to SLSQP.
-tweak (bool) – Cutting the frontier to exclude multiple points with almost the -same risk. -Defaults to True.
-
-- Returns: -
The efficient frontier data, simulation data and optimal portfolio.
-
-- Return type: -
tuple[DataFrame, DataFrame, NDArray[float64]]
-
-
Portfolio Simulation
--
-
- -openseries.portfoliotools.simulate_portfolios(simframe, num_ports, seed)[source] -
Generate random weights for simulated portfolios.
- -
Portfolio Constraints
--
-
- -openseries.portfoliotools.constrain_optimized_portfolios(data, serie, portfolioname='Current Portfolio', simulations=10000, curve_points=200, bounds=None, minimize_method='SLSQP')[source] -
Constrain optimized portfolios to those that improve on the current one.
--
-
- Parameters: -
-
-
data (OpenFrame) – Portfolio data.
-serie (OpenTimeSeries) – A timeseries representing the current portfolio.
-portfolioname (str) – Name of the portfolio. Defaults to “Current Portfolio”.
-simulations (int) – Number of possible portfolios to simulate. Defaults to 10000.
-curve_points (int) – Number of optimal portfolios on the efficient frontier. -Defaults to 200.
-bounds (tuple[tuple[float, float], ...] | None) – The range of minimum and maximum allowed allocations for each asset.
-minimize_method (LiteralMinimizeMethods) – The method passed into the scipy.minimize function. -Defaults to SLSQP.
-
-- Returns: -
The constrained optimal portfolio data.
-
-- Return type: -
tuple[OpenFrame, OpenTimeSeries, OpenFrame, OpenTimeSeries]
-
-
Visualization
--
-
- -openseries.portfoliotools.prepare_plot_data(assets, current, optimized)[source] -
Prepare data to be used as point_frame in the sharpeplot function.
--
-
- Parameters: -
-
-
assets (OpenFrame) – Portfolio data with individual assets and a weighted portfolio.
-current (OpenTimeSeries) – The current or initial portfolio based on given weights.
-optimized (NDArray[float64]) – Data optimized with the efficient_frontier method.
-
-- Returns: -
The data prepared with mean returns, volatility and weights.
-
-- Return type: -
DataFrame
-
-
-
-
- -openseries.portfoliotools.sharpeplot(sim_frame=None, line_frame=None, point_frame=None, point_frame_mode='markers', filename=None, directory=None, titletext=None, output_type='file', include_plotlyjs='cdn', *, title=True, add_logo=True, auto_open=True)[source] -
Create scatter plot coloured by Sharpe Ratio.
--
-
- Parameters: -
-
-
sim_frame (DataFrame | None) – Data from the simulate_portfolios method.
-line_frame (DataFrame | None) – Data from the efficient_frontier method.
-point_frame (DataFrame | None) – Data to highlight current and efficient portfolios.
-point_frame_mode (LiteralLinePlotMode) – Which type of scatter to use. Defaults to markers.
-filename (str | None) – Name of the Plotly html file.
-directory (DirectoryPath | None) – Directory where Plotly html file is saved.
-titletext (str | None) – Text for the plot title.
-output_type (LiteralPlotlyOutput) – Determines output type. Defaults to “file”.
-include_plotlyjs (LiteralPlotlyJSlib) – Determines how the plotly.js library is included -in the output. -Defaults to “cdn”.
-title (bool) – Whether to add standard plot title. Defaults to True.
-add_logo (bool) – Whether to add Captor logo. Defaults to True.
-auto_open (bool) – Determines whether to open a browser window with the plot. -Defaults to True.
-
-- Returns: -
The scatter plot with simulated and optimized results.
-
-- Return type: -
- - -
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/series.html b/docs/build/html/api/series.html
deleted file mode 100644
index 295d5474..00000000
--- a/docs/build/html/api/series.html
+++ /dev/null
@@ -1,2685 +0,0 @@
-
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-
- Report Generation
-HTML Report Function
--
-
- -openseries.report.report_html(data, bar_freq='BYE', filename=None, title=None, directory=None, output_type='file', include_plotlyjs='cdn', *, auto_open=False, add_logo=True, vertical_legend=True)[source] -
Generate a responsive HTML report page with line and bar plots and a table.
--
-
- Parameters: -
- - -
- Return type: -
- - -
The report_html function creates comprehensive HTML reports for financial analysis, comparing multiple assets and providing detailed performance metrics, charts, and risk analysis.
The generated report includes:
--
-
Interactive line charts showing cumulative returns over time for all assets
-Bar charts displaying period returns (annual, quarterly, or monthly depending on data length)
-Performance metrics table including: -- Return metrics (CAGR or simple return, Year-to-Date, Month-to-Date) -- Risk metrics (Volatility, Sharpe Ratio, Sortino Ratio) -- Relative performance metrics (Jensen’s Alpha, Information Ratio, Tracking Error, Index Beta) -- Capture Ratio (for periods longer than one year) -- Worst period returns -- Comparison period dates
-
Important Notes:
--
-
The last asset in the
OpenFrameis used as the benchmark for relative performance metrics -(Jensen’s Alpha, Information Ratio, Tracking Error, Index Beta, and Capture Ratio)
-For periods shorter than one year, the report uses simple returns instead of CAGR
-For periods shorter than a quarter, bar charts show daily returns instead of period returns
-Capture Ratio is only included for periods longer than one year
-
Responsive Design
-The report features responsive design with separate layouts optimized for desktop and mobile devices:
--
-
Desktop layout: Charts and tables are displayed side-by-side in a 2x2 grid layout. The table -is rendered as an interactive Plotly table integrated with the charts.
-Mobile layout: Content is stacked vertically for better viewing on smaller screens. The table -is rendered as a standard HTML table below the charts for better mobile compatibility.
-
The HTML output automatically adapts to screen size and device capabilities using CSS media queries -and JavaScript detection. The layout switches at a breakpoint of 960px width or when touch capabilities -are detected.
-Return Values
-The function returns a tuple containing:
--
-
Plotly Figure: The desktop version of the figure object (can be used for further customization -or interactive display)
-String output: The type depends on the
-output_typeparameter:-
-
When
output_type="file"(default): Returns the file path string to the saved HTML file. -The file contains a complete HTML document (with DOCTYPE, html, head, and body tags) -that can be opened directly in a web browser. Ifauto_open=True, the file will -automatically open in the default web browser.
-When
output_type="div": Returns a string containing the responsive HTML div section -(includes both desktop and mobile layouts with CSS and JavaScript) that can be embedded -in an existing HTML page. Thefilenameparameter is optional when using this mode and -is only used to generate unique div IDs for the embedded content.
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/simulation.html b/docs/build/html/api/simulation.html
deleted file mode 100644
index 13ded9cc..00000000
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+++ /dev/null
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-
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- OpenTimeSeries
--
-
- -class openseries.OpenTimeSeries(*, constituents=<factory>, weights=None, markets=None, tsdf, timeseries_id, instrument_id, name, valuetype, dates, values, local_ccy, currency, domestic='SEK', countries='SE', isin=None, label=None)[source] -
Bases:
-_CommonModel[float]OpenTimeSeries objects are at the core of the openseries package.
-The intended use is to allow analyses of financial timeseries. -It is only intended for daily or less frequent data samples.
--
-
- Parameters: -
-
-
timeseries_id (str) – Database identifier of the timeseries.
-instrument_id (str) – Database identifier of the instrument associated with -the timeseries.
-name (str) – String identifier of the timeseries and/or instrument.
-valuetype (ValueType) – Identifies if the series is a series of values or returns.
-dates (Annotated[list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$, ascii_only=None)]], MinLen(min_length=1)]) – Dates of the individual timeseries items. -These dates will not be altered by methods.
-values (Annotated[list[float], MinLen(min_length=1)]) – The value or return values of the timeseries items. -These values will not be altered by methods.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency.
-tsdf (DataFrame) – Pandas object holding dates and values that can be altered via -methods.
-currency (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries.
-domestic (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the user’s home currency. -Defaults to “SEK”.
-countries (Annotated[set[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]], MinLen(min_length=1)] | Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars. -Optional.
-isin (str | None) – ISO 6166 identifier code of the associated instrument. Optional.
-label (str | None) – Placeholder for a name of the timeseries. Optional.
-- -
- -
-
-
-
- -timeseries_id: str -
-
-
- -instrument_id: str -
-
-
- -name: str -
-
-
- -valuetype: ValueType -
-
-
- -dates: DateListType -
-
-
- -values: ValueListType -
-
-
- -local_ccy: bool -
-
-
- -tsdf: DataFrame -
-
-
- -currency: CurrencyStringType -
-
-
- -domestic: CurrencyStringType -
-
-
- -countries: CountriesType -
-
-
- -classmethod from_arrays(name, dates, values, valuetype=ValueType.PRICE, timeseries_id='', instrument_id='', isin=None, baseccy='SEK', *, local_ccy=True)[source] -
Create series from a list of dates and a list of values.
--
-
- Parameters: -
-
-
name (str) – String identifier of the timeseries and/or instrument.
-dates (Annotated[list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$, ascii_only=None)]], MinLen(min_length=1)]) – List of date strings as ISO 8601 YYYY-MM-DD.
-values (Annotated[list[float], MinLen(min_length=1)]) – Array of float values.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-timeseries_id (str) – Database identifier of the timeseries. Optional.
-instrument_id (str) – Database identifier of the instrument associated -with the timeseries. Optional.
-isin (str | None) – ISO 6166 identifier code of the associated instrument. Optional.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Return type: -
- - -
-
-
- -classmethod from_df(dframe, column_nmbr=0, valuetype=ValueType.PRICE, baseccy='SEK', *, local_ccy=True)[source] -
Create series from a Pandas DataFrame or Series.
--
-
- Parameters: -
-
-
dframe (Series | DataFrame | object) – Pandas DataFrame or Series.
-column_nmbr (int) – Using iloc[:, column_nmbr] to pick column. Defaults to 0.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
TypeError – If
-dframeis not apandas.Seriesor a -pandas.DataFrame.
-- Return type: -
- - -
-
-
- -classmethod from_fixed_rate(rate, d_range=None, days=None, end_dt=None, label='Series', valuetype=ValueType.PRICE, baseccy='SEK', *, local_ccy=True)[source] -
Create series from values accruing with a given fixed rate return.
-Providing a date_range of type Pandas DatetimeIndex takes priority over -providing a combination of days and an end date.
--
-
- Parameters: -
-
-
rate (float) – The accrual rate.
-d_range (DatetimeIndex | None) – A given range of dates. Optional.
-days (int | None) – Number of days to generate when date_range not provided. Must be -combined with end_dt. Optional.
-end_dt (date | None) – End date of date range to generate when date_range not provided. -Must be combined with days. Optional.
-label (str) – Placeholder for a name of the timeseries.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – The currency of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
IncorrectArgumentComboError – If
-d_rangeis not provided and the - combination ofdaysandend_dtis incomplete.
-- Return type: -
- - -
-
-
- -from_deepcopy()[source] -
Create copy of OpenTimeSeries object.
- -
-
-
- -pandas_df()[source] -
Populate .tsdf Pandas DataFrame from the .dates and .values lists.
- -
-
-
- -all_properties(properties=None)[source] -
Calculate chosen properties.
--
-
- Parameters: -
-
-
properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown_cal_year', 'max_drawdown', 'max_drawdown_date', 'first_idx', 'last_idx', 'length', 'span_of_days', 'yearfrac', 'periods_in_a_year', 'autocorr', 'partial_autocorr']] | None) – The properties to calculate. Defaults to calculating all -available. Optional.
-self (Self)
-
-- Returns: -
Properties of the OpenTimeSeries.
-
-- Return type: -
- - -
-
-
- -value_to_ret()[source] -
Convert series of values into series of returns.
- -
-
-
- -value_to_diff(periods=1)[source] -
Convert series of values to series of their period differences.
- -
-
-
- -to_cumret()[source] -
Convert series of returns into cumulative series of values.
- -
-
-
- -from_1d_rate_to_cumret(days_in_year=365, divider=1.0)[source] -
Convert series of 1-day rates into series of cumulative values.
- -
-
-
- -resample(freq='BME')[source] -
Resamples the timeseries frequency.
- -
-
-
- -resample_to_business_period_ends(freq='BME', method='nearest')[source] -
Resamples timeseries frequency to the business calendar month end dates.
-Stubs left in place. Stubs will be aligned to the shortest stub.
--
-
- Parameters: -
- - -
- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
ResampleDataLossError – If called on a return series (
-valuetypeis -ValueType.RTRN), since summation across sparser frequency would - be required to avoid data loss.
-- Return type: -
- - -
-
-
- -ewma_vol_func(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Model for Volatility.
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. -Defaults to 11.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-months_from_last (int | None) – Number of months offset as positive integer. -Overrides use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series EWMA volatility.
-
-- Return type: -
Series[float]
-
-
-
-
- -ewma_var_func(lmbda=0.94, day_chunk=11, level=0.95, dlta_degr_freedms=0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Model for Value At Risk (VaR).
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. -Defaults to 11.
-level (float) – The sought VaR level. Defaults to 0.95.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-months_from_last (int | None) – Number of months offset as positive integer. -Overrides use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series EWMA VaR.
-
-- Return type: -
Series[float]
-
-
-
-
- -running_adjustment(adjustment, days_in_year=365)[source] -
Add or subtract a fee from the timeseries return.
- -
-
-
- -set_new_label(lvl_zero=None, lvl_one=None, *, delete_lvl_one=False)[source] -
Set the column labels of the .tsdf Pandas Dataframe.
- -
-
-
- -acf(lags, *, squared=False)[source] -
Calculate autocorrelation function for specified lags.
--
-
- Parameters: -
- - -
- Returns: -
Series of autocorrelations indexed by lag.
-
-- Return type: -
Series[float]
-
-
-
-
- -partial_autocorr(lag=1, *, squared=False)[source] -
Calculate partial autocorrelation at a given lag.
- -
-
-
- -pacf(lags, *, squared=False)[source] -
Calculate partial autocorrelation function for specified lags.
--
-
- Parameters: -
- - -
- Returns: -
Series of partial autocorrelations indexed by lag.
-
-- Return type: -
Series[float]
-
-
-
-
- -ljung_box(lags, *, squared=False)[source] -
Compute Ljung-Box test for autocorrelation.
--
-
- Parameters: -
- - -
- Returns: -
Tuple of (statistic, pvalue, lags) where statistic is the Ljung-Box -Q statistic, pvalue is the chi-squared p-value, and lags is the -list of lags used.
-
-- Return type: -
- - -
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
The OpenTimeSeries class is the core component for analyzing individual financial time series. It provides comprehensive functionality for:
--
-
Loading data from various sources (arrays, DataFrames, fixed rates)
-Calculating financial metrics and risk measures
-Performing time series transformations
-Creating visualizations
-Exporting results
-
Class Methods for Construction
--
-
- -classmethod OpenTimeSeries.from_arrays(name, dates, values, valuetype=ValueType.PRICE, timeseries_id='', instrument_id='', isin=None, baseccy='SEK', *, local_ccy=True)[source] -
Create series from a list of dates and a list of values.
--
-
- Parameters: -
-
-
name (str) – String identifier of the timeseries and/or instrument.
-dates (Annotated[list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$, ascii_only=None)]], MinLen(min_length=1)]) – List of date strings as ISO 8601 YYYY-MM-DD.
-values (Annotated[list[float], MinLen(min_length=1)]) – Array of float values.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-timeseries_id (str) – Database identifier of the timeseries. Optional.
-instrument_id (str) – Database identifier of the instrument associated -with the timeseries. Optional.
-isin (str | None) – ISO 6166 identifier code of the associated instrument. Optional.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Return type: -
- - -
-
-
- -classmethod OpenTimeSeries.from_df(dframe, column_nmbr=0, valuetype=ValueType.PRICE, baseccy='SEK', *, local_ccy=True)[source] -
Create series from a Pandas DataFrame or Series.
--
-
- Parameters: -
-
-
dframe (Series | DataFrame | object) – Pandas DataFrame or Series.
-column_nmbr (int) – Using iloc[:, column_nmbr] to pick column. Defaults to 0.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – ISO 4217 currency code of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
TypeError – If
-dframeis not apandas.Seriesor a -pandas.DataFrame.
-- Return type: -
- - -
-
-
- -classmethod OpenTimeSeries.from_fixed_rate(rate, d_range=None, days=None, end_dt=None, label='Series', valuetype=ValueType.PRICE, baseccy='SEK', *, local_ccy=True)[source] -
Create series from values accruing with a given fixed rate return.
-Providing a date_range of type Pandas DatetimeIndex takes priority over -providing a combination of days and an end date.
--
-
- Parameters: -
-
-
rate (float) – The accrual rate.
-d_range (DatetimeIndex | None) – A given range of dates. Optional.
-days (int | None) – Number of days to generate when date_range not provided. Must be -combined with end_dt. Optional.
-end_dt (date | None) – End date of date range to generate when date_range not provided. -Must be combined with days. Optional.
-label (str) – Placeholder for a name of the timeseries.
-valuetype (ValueType) – Identifies if the series is a series of values or returns. -Defaults to ValueType.PRICE.
-baseccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]) – The currency of the timeseries. Defaults to “SEK”.
-local_ccy (bool) – Boolean flag indicating if timeseries is in local currency. -Defaults to True.
-
-- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
IncorrectArgumentComboError – If
-d_rangeis not provided and the - combination ofdaysandend_dtis incomplete.
-- Return type: -
- - -
Properties
-Non-numerical Properties
--
-
- -OpenTimeSeries.timeseries_id: str -
-
-
- -OpenTimeSeries.instrument_id: str -
-
-
- -OpenTimeSeries.dates: DateListType -
-
-
- -OpenTimeSeries.values: ValueListType -
-
-
- -OpenTimeSeries.currency: CurrencyStringType -
-
-
- -OpenTimeSeries.domestic: CurrencyStringType -
-
-
- -OpenTimeSeries.local_ccy: bool -
-
-
- -OpenTimeSeries.name: str -
-
-
- -OpenTimeSeries.countries: CountriesType -
-
-
- -OpenTimeSeries.markets -
-
-
- -OpenTimeSeries.valuetype: ValueType -
Common Properties
--
-
- -OpenTimeSeries.first_idx -
The first date in the timeseries.
--
-
- Returns: -
The first date in the timeseries.
-
-
-
-
- -OpenTimeSeries.last_idx -
The last date in the timeseries.
--
-
- Returns: -
The last date in the timeseries.
-
-
-
-
- -OpenTimeSeries.length -
Number of observations.
--
-
- Returns: -
Number of observations.
-
-
-
-
- -OpenTimeSeries.span_of_days -
Number of days from the first date to the last.
--
-
- Returns: -
Number of days from the first date to the last.
-
-
-
-
- -OpenTimeSeries.tsdf: DataFrame -
-
-
- -OpenTimeSeries.max_drawdown_date -
Date when the maximum drawdown occurred.
-Reference: https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.
-- -Returns:
--
-
- datetime.date | pandas.Series[dt.date]
Date when the maximum drawdown occurred
-
-
-
-
- -OpenTimeSeries.periods_in_a_year -
The average number of observations per year.
--
-
- Returns: -
The average number of observations per year.
-
-
-
-
- -OpenTimeSeries.yearfrac -
Length of series in years assuming 365.25 days per year.
--
-
- Returns: -
Length of the timeseries in years assuming 365.25 days per year.
-
-
Financial Metrics
--
-
- -OpenTimeSeries.all_properties = <function OpenTimeSeries.all_properties>[source] -
-
-
- Parameters: -
-
-
self (Self)
-properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown_cal_year', 'max_drawdown', 'max_drawdown_date', 'first_idx', 'last_idx', 'length', 'span_of_days', 'yearfrac', 'periods_in_a_year', 'autocorr', 'partial_autocorr']] | None)
-
-- Return type: -
- - -
-
-
- -OpenTimeSeries.arithmetic_ret -
Annualized arithmetic mean of returns.
-Reference: https://www.investopedia.com/terms/a/arithmeticmean.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Annualized arithmetic mean of returns. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.geo_ret -
Compounded Annual Growth Rate (CAGR).
-Reference: https://www.investopedia.com/terms/c/cagr.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Compounded Annual Growth Rate (CAGR). -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.value_ret -
Simple return.
-- -Returns:
--
-
- SeriesOrFloat_co
Simple return. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.vol -
Annualized volatility.
-Based on Pandas .std() which is the equivalent of stdev.s([…]) in MS Excel.
-Reference: https://www.investopedia.com/terms/v/volatility.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Annualized volatility. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.downside_deviation -
Downside Deviation.
-Standard deviation of returns that are below a Minimum Accepted Return -of zero. It is used to calculate the Sortino Ratio.
-Reference: https://www.investopedia.com/terms/d/downside-deviation.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Downside deviation. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.ret_vol_ratio -
Ratio of annualized arithmetic mean of returns and annualized volatility.
-- -Returns:
--
-
- SeriesOrFloat_co
Ratio of the annualized arithmetic mean of returns and annualized -volatility. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.sortino_ratio -
Sortino ratio.
-Reference: https://www.investopedia.com/terms/s/sortinoratio.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Sortino ratio calculated as the annualized arithmetic mean of returns -/ downside deviation. The ratio implies that the riskfree asset has zero -volatility, and a minimum acceptable return of zero. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.kappa3_ratio -
Kappa-3 ratio.
-The Kappa-3 ratio is a generalized downside-risk ratio defined as -annualized arithmetic return divided by the cubic-root of the -lower partial moment of order 3 (with respect to a minimum acceptable -return, MAR). It penalizes larger downside outcomes more heavily than -the Sortino ratio (which uses order 2).
-- -Returns:
--
-
- SeriesOrFloat_co
Kappa-3 ratio calculation with the riskfree rate and. -Minimum Acceptable Return (MAR) both set to zero. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.omega_ratio -
Omega ratio.
-Reference: https://en.wikipedia.org/wiki/Omega_ratio.
-- -Returns:
--
-
- SeriesOrFloat_co
Omega ratio calculation. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.var_down -
Downside 95% Value At Risk (VaR).
-The equivalent of percentile.inc([…], 1-level) over returns in MS Excel. -https://www.investopedia.com/terms/v/var.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Downside 95% Value At Risk (VaR). -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.cvar_down -
Downside 95% Conditional Value At Risk “CVaR”.
-Reference: https://www.investopedia.com/terms/c/conditional_value_at_risk.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Downside 95% Conditional Value At Risk “CVaR”. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.worst -
Most negative percentage change.
-- -Returns:
--
-
- SeriesOrFloat_co
Most negative percentage change. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.worst_month -
Most negative month.
-- -Returns:
--
-
- SeriesOrFloat_co
Most negative month. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.max_drawdown -
Maximum drawdown without any limit on date range.
-Reference: https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Maximum drawdown without any limit on date range. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.max_drawdown_cal_year -
Maximum drawdown in a single calendar year.
-Reference: https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Maximum drawdown in a single calendar year. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.positive_share -
The share of percentage changes that are greater than zero.
-- -Returns:
--
-
- SeriesOrFloat_co
The share of percentage changes that are greater than zero. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.vol_from_var -
Implied annualized volatility from Downside 95% Value at Risk.
-Assumes that returns are normally distributed.
-- -Returns:
--
-
- SeriesOrFloat_co
Implied annualized volatility from the Downside 95% VaR using the -assumption that returns are normally distributed. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.autocorr -
Autocorrelation at lag 1.
-Shorthand for
-autocorr_func(lag=1). Returns the lag-1 autocorrelation -of demeaned returns. For price series, returns are computed via -pct_change; for return series, raw values are used after demeaning.- -Returns:
--
-
- SeriesOrFloat_co
Autocorrelation at lag 1. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.skew -
Skew of the return distribution.
-Reference: https://www.investopedia.com/terms/s/skewness.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Skew of the return distribution. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.kurtosis -
Kurtosis of the return distribution.
-Reference: https://www.investopedia.com/terms/k/kurtosis.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Kurtosis of the return distribution. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
-
-
- -OpenTimeSeries.z_score -
Z-score.
-Reference: https://www.investopedia.com/terms/z/zscore.asp.
-- -Returns:
--
-
- SeriesOrFloat_co
Z-score as (last return - mean return) / standard deviation of returns. -Returns float for OpenTimeSeries, Series[float] for OpenFrame.
-
-
Methods
-Data Manipulation
--
-
- -OpenTimeSeries.pandas_df()[source] -
Populate .tsdf Pandas DataFrame from the .dates and .values lists.
- -
-
-
- -OpenTimeSeries.set_new_label(lvl_zero=None, lvl_one=None, *, delete_lvl_one=False)[source] -
Set the column labels of the .tsdf Pandas Dataframe.
- -
-
-
- -OpenTimeSeries.running_adjustment(adjustment, days_in_year=365)[source] -
Add or subtract a fee from the timeseries return.
- -
-
-
- -OpenTimeSeries.from_1d_rate_to_cumret(days_in_year=365, divider=1.0)[source] -
Convert series of 1-day rates into series of cumulative values.
- -
-
-
- -OpenTimeSeries.align_index_to_local_cdays(countries=None, markets=None, custom_holidays=None, method='nearest') -
Align the index of
-.tsdfwith local calendar business days.-
-
- Parameters: -
-
-
countries (CountriesType | None) – Country code(s) (ISO 3166-1 alpha-2).
-markets (list[str] | str | None) – Market code(s) supported by
exchange_calendars.
-custom_holidays (list[str] | str | None) – Missing holidays that should be added.
-method (LiteralPandasReindexMethod) – Method for reindexing when aligning to business days.
-self (Self)
-
-- Returns: -
The modified object.
-
-- Return type: -
Self
-
-
-
-
- -OpenTimeSeries.resample(freq='BME')[source] -
Resamples the timeseries frequency.
- -
-
-
- -OpenTimeSeries.resample_to_business_period_ends(freq='BME', method='nearest')[source] -
Resamples timeseries frequency to the business calendar month end dates.
-Stubs left in place. Stubs will be aligned to the shortest stub.
--
-
- Parameters: -
- - -
- Returns: -
An OpenTimeSeries object.
-
-- Raises: -
ResampleDataLossError – If called on a return series (
-valuetypeis -ValueType.RTRN), since summation across sparser frequency would - be required to avoid data loss.
-- Return type: -
- - -
-
-
- -OpenTimeSeries.value_nan_handle(method='fill') -
Handle missing values in a value series.
--
-
- Parameters: -
-
-
method (LiteralNanMethod) – Method used to handle NaN. Either
"fill"(last known) or -"drop".
-self (Self)
-
-- Returns: -
The modified object.
-
-- Return type: -
Self
-
-
-
-
- -OpenTimeSeries.return_nan_handle(method='fill') -
Handle missing values in a return series.
--
-
- Parameters: -
-
-
method (LiteralNanMethod) – Method used to handle NaN. Either
"fill"(zero) or -"drop".
-self (Self)
-
-- Returns: -
The modified object.
-
-- Return type: -
Self
-
-
Transformations
--
-
- -OpenTimeSeries.to_cumret()[source] -
Convert series of returns into cumulative series of values.
- -
-
-
- -OpenTimeSeries.value_to_ret()[source] -
Convert series of values into series of returns.
- -
-
-
- -OpenTimeSeries.value_to_diff(periods=1)[source] -
Convert series of values to series of their period differences.
- -
-
-
- -OpenTimeSeries.value_to_log() -
Convert value series to log-weighted series.
-Equivalent to
- -LN(value[t] / value[t=0])in Excel.
Analysis Methods
-Autocorrelation analysis: autocorr (property) and autocorr_func provide
-lag-N autocorrelation; acf, pacf, partial_autocorr, and ljung_box
-support full autocorrelation diagnostics. Available on both OpenTimeSeries and
-OpenFrame (except acf, pacf, partial_autocorr, ljung_box which are
-OpenTimeSeries-only).
-
-
- -OpenTimeSeries.autocorr_func(lag=1, *, squared=False) -
Calculate autocorrelation at a given lag.
-Computes the autocorrelation of demeaned returns at the specified lag. -For price series (ValueType.PRICE), returns are derived via
-pct_change; -for return series (ValueType.RTRN), raw values are demeaned. Use -squared=Truefor squared-return autocorrelation (e.g. volatility -clustering). Returnsnanwhen the series has too few observations.-
-
- Parameters: -
- - -
- Returns: -
Autocorrelation at the specified lag. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.acf(lags, *, squared=False)[source] -
Calculate autocorrelation function for specified lags.
--
-
- Parameters: -
- - -
- Returns: -
Series of autocorrelations indexed by lag.
-
-- Return type: -
Series[float]
-
-
-
-
- -OpenTimeSeries.partial_autocorr(lag=1, *, squared=False)[source] -
Calculate partial autocorrelation at a given lag.
- -
-
-
- -OpenTimeSeries.pacf(lags, *, squared=False)[source] -
Calculate partial autocorrelation function for specified lags.
--
-
- Parameters: -
- - -
- Returns: -
Series of partial autocorrelations indexed by lag.
-
-- Return type: -
Series[float]
-
-
-
-
- -OpenTimeSeries.ljung_box(lags, *, squared=False)[source] -
Compute Ljung-Box test for autocorrelation.
--
-
- Parameters: -
- - -
- Returns: -
Tuple of (statistic, pvalue, lags) where statistic is the Ljung-Box -Q statistic, pvalue is the chi-squared p-value, and lags is the -list of lags used.
-
-- Return type: -
- - -
-
-
- -OpenTimeSeries.ewma_vol_func(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source] -
Exponentially Weighted Moving Average Model for Volatility.
-Reference: https://www.investopedia.com/articles/07/ewma.asp.
--
-
- Parameters: -
-
-
lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.
-day_chunk (int) – Sampling the data which is assumed to be daily. -Defaults to 11.
-dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. -Defaults to 0.
-months_from_last (int | None) – Number of months offset as positive integer. -Overrides use of from_date and to_date. Optional.
-from_date (dt.date | None) – Specific from date. Optional.
-to_date (dt.date | None) – Specific to date. Optional.
-periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify -test cases and comparisons. Optional.
-self (Self)
-
-- Returns: -
Series EWMA volatility.
-
-- Return type: -
Series[float]
-
-
-
-
- -OpenTimeSeries.value_ret_calendar_period(year, month=None) -
Calculate simple return for a specific calendar period.
- -
-
-
- -OpenTimeSeries.rolling_return(column=0, observations=21) -
Calculate rolling returns.
- -
-
-
- -OpenTimeSeries.rolling_vol(column=0, observations=21, periods_in_a_year_fixed=None, dlta_degr_freedms=1) -
Calculate rolling annualized volatilities.
--
-
- Parameters: -
-
-
column (int) – Column position to calculate.
-observations (int) – Number of observations in the overlapping window.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-dlta_degr_freedms (int) – Variance bias factor (0 or 1).
-self (Self)
-
-- Returns: -
DataFrame with rolling annualized volatilities.
-
-- Return type: -
DataFrame
-
-
-
-
- -OpenTimeSeries.rolling_var_down(column=0, level=0.95, observations=252, interpolation='lower') -
Calculate rolling annualized downside Value At Risk (VaR).
--
-
- Parameters: -
- - -
- Returns: -
DataFrame with rolling annualized downside VaR.
-
-- Return type: -
DataFrame
-
-
-
-
- -OpenTimeSeries.rolling_cvar_down(column=0, level=0.95, observations=252) -
Calculate rolling annualized downside CVaR.
- -
-
-
- -OpenTimeSeries.calc_range(months_offset=None, from_dt=None, to_dt=None) -
Create a user-defined date range aligned to index.
--
-
- Parameters: -
- - -
- Returns: -
A tuple
-(earlier, later)representing the start and end date of the -chosen date range aligned to existing index values.
-- Raises: -
DateAlignmentError – If the implied range is outside series bounds.
-
-- Return type: -
- - -
-
-
- -OpenTimeSeries.outliers(threshold=3.0, months_from_last=None, from_date=None, to_date=None) -
Detect outliers using z-score analysis.
-Identifies data points where the absolute z-score exceeds the threshold. -For OpenTimeSeries, returns a Series with dates and outlier values. For -OpenFrame, returns a DataFrame with dates and outlier values for each -column.
--
-
- Parameters: -
-
-
threshold (float) – Z-score threshold; values with
|z| > thresholdare -outliers.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (date | None) – Specific from date.
-to_date (date | None) – Specific to date.
-self (Self)
-
-- Returns: -
Series of outliers. For OpenFrame: DataFrame of -outliers. Empty if none found.
-
-- Return type: -
For OpenTimeSeries
-
-
Financial Metrics Methods
--
-
- -OpenTimeSeries.arithmetic_ret_func(months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Annualized arithmetic mean of returns.
-Reference:
-https://www.investopedia.com/terms/a/arithmeticmean.asp.-
-
- Parameters: -
-
-
months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Annualized arithmetic mean of returns. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.geo_ret_func(months_from_last=None, from_date=None, to_date=None) -
Compounded Annual Growth Rate (CAGR).
-Reference:
-https://www.investopedia.com/terms/c/cagr.asp.-
-
- Parameters: -
- - -
- Returns: -
CAGR. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Raises: -
InitialValueZeroError – If initial value is zero or there are negative - values.
-
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.value_ret_func(months_from_last=None, from_date=None, to_date=None) -
Calculate simple return.
--
-
- Parameters: -
- - -
- Returns: -
Simple return. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Raises: -
InitialValueZeroError – If initial value is zero.
-
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.vol_func(months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Annualized volatility.
-Based on
-pandas.Series.std()(ExcelSTDEV.Sequivalent). -Reference:https://www.investopedia.com/terms/v/volatility.asp.-
-
- Parameters: -
-
-
months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Annualized volatility. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.lower_partial_moment_func(min_accepted_return=0.0, order=2, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Lower partial moment and downside deviation (order=2).
-If
-orderis 2 calculates standard deviation of returns below MAR=0. -For general orderp, returns(LPM_p)^(1/p).-
-
- Parameters: -
-
-
min_accepted_return (float) – Annualized Minimum Accepted Return (MAR).
-order (Literal[2, 3]) – Order of partial moment (2 or 3).
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Downside deviation if
-orderis 2; otherwise rooted lower partial -moment. Float for OpenTimeSeries,Series[float]for OpenFrame.
-- Raises: -
ValueError – If
-orderis not 2 or 3.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.ret_vol_ratio_func(riskfree_rate=0.0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Ratio between arithmetic mean of returns and annualized volatility.
-If
-riskfree_rateprovided, computes the Sharpe ratio as -(arithmetic return - risk-free) / volatility. Assumes zero volatility -for the risk-free asset. Reference: -https://www.investopedia.com/terms/s/sharperatio.asp.-
-
- Parameters: -
-
-
riskfree_rate (float) – Return of the zero volatility asset.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Ratio value. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.sortino_ratio_func(riskfree_rate=0.0, min_accepted_return=0.0, order=2, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None) -
Sortino ratio or Kappa-3 ratio.
-Sortino:
-(return - riskfree_rate) / downside deviationusing arithmetic -mean of returns. Kappa-3 whenorder=3penalizes larger downside more -than Sortino.-
-
- Parameters: -
-
-
riskfree_rate (float) – Return of the zero volatility asset.
-min_accepted_return (float) – Annualized Minimum Accepted Return (MAR).
-order (Literal[2, 3]) – Order of partial moment (2 for Sortino, 3 for Kappa-3).
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-self (Self)
-
-- Returns: -
Ratio value. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.omega_ratio_func(min_accepted_return=0.0, months_from_last=None, from_date=None, to_date=None) -
Omega Ratio.
-Compares returns above MAR to the total downside risk below MAR. -Reference:
-https://en.wikipedia.org/wiki/Omega_ratio.-
-
- Parameters: -
- - -
- Returns: -
Omega ratio. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.var_down_func(level=0.95, months_from_last=None, from_date=None, to_date=None, interpolation='lower') -
Downside Value At Risk (VaR).
-Equivalent to
-PERCENTILE.INC(returns, 1-level)in Excel. Reference: -https://www.investopedia.com/terms/v/var.asp.-
-
- Parameters: -
-
-
level (float) – The sought VaR level.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-interpolation (LiteralQuantileInterp) – Interpolation used by
DataFrame.quantile.
-self (Self)
-
-- Returns: -
Downside VaR. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.cvar_down_func(level=0.95, months_from_last=None, from_date=None, to_date=None) -
Downside Conditional Value At Risk (CVaR).
-Reference:
-https://www.investopedia.com/terms/c/conditional_value_at_risk.asp.-
-
- Parameters: -
- - -
- Returns: -
Downside CVaR. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.worst_func(observations=1, months_from_last=None, from_date=None, to_date=None) -
Most negative percentage change over a rolling window.
--
-
- Parameters: -
- - -
- Returns: -
Most negative percentage change. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.max_drawdown_func(months_from_last=None, from_date=None, to_date=None, min_periods=1) -
Maximum drawdown without any limit on date range.
-Reference:
-https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp.-
-
- Parameters: -
- - -
- Returns: -
Maximum drawdown. Float for OpenTimeSeries,
-Series[float]for -OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.positive_share_func(months_from_last=None, from_date=None, to_date=None) -
Share of percentage changes greater than zero.
--
-
- Parameters: -
- - -
- Returns: -
Share of positive returns. Float for OpenTimeSeries,
-Series[float]-for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.vol_from_var_func(level=0.95, months_from_last=None, from_date=None, to_date=None, interpolation='lower', periods_in_a_year_fixed=None, *, drift_adjust=False) -
Implied annualized volatility from downside VaR.
-Assumes normally distributed returns.
--
-
- Parameters: -
-
-
level (float) – The sought VaR level.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-interpolation (LiteralQuantileInterp) – Interpolation type used by
DataFrame.quantile.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-drift_adjust (bool) – Adjustment to remove the bias implied by the average -return.
-self (Self)
-
-- Returns: -
Implied annualized volatility. Float for OpenTimeSeries, -
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.skew_func(months_from_last=None, from_date=None, to_date=None) -
Skew of the return distribution.
-Reference:
-https://www.investopedia.com/terms/s/skewness.asp.-
-
- Parameters: -
- - -
- Returns: -
Skewness. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.kurtosis_func(months_from_last=None, from_date=None, to_date=None) -
Kurtosis of the return distribution.
-Reference:
-https://www.investopedia.com/terms/k/kurtosis.asp.-
-
- Parameters: -
- - -
- Returns: -
Kurtosis. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.z_score_func(months_from_last=None, from_date=None, to_date=None) -
Z-score of the last return.
-Computed as
-(last return - mean return) / std dev of returns. -Reference:https://www.investopedia.com/terms/z/zscore.asp.-
-
- Parameters: -
- - -
- Returns: -
Z-score. Float for OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
-
-
- -OpenTimeSeries.target_weight_from_var(target_vol=0.175, level=0.95, min_leverage_local=0.0, max_leverage_local=99999.0, months_from_last=None, from_date=None, to_date=None, interpolation='lower', periods_in_a_year_fixed=None, *, drift_adjust=False) -
Target weight from VaR.
-Computes a position weight multiplier from the ratio between a VaR implied -volatility and a given target volatility. Multiplier = 1.0 → target met.
--
-
- Parameters: -
-
-
target_vol (float) – Target volatility.
-level (float) – The sought VaR level.
-min_leverage_local (float) – Minimum adjustment factor.
-max_leverage_local (float) – Maximum adjustment factor.
-months_from_last (int | None) – Number of months offset as positive integer. Overrides -use of
from_dateandto_date.
-from_date (dt.date | None) – Specific from date.
-to_date (dt.date | None) – Specific to date.
-interpolation (LiteralQuantileInterp) – Interpolation type used by
DataFrame.quantile.
-periods_in_a_year_fixed (DaysInYearType | None) – Lock periods-in-a-year to simplify tests and -comparisons.
-drift_adjust (bool) – Adjustment to remove the bias implied by the average -return.
-self (Self)
-
-- Returns: -
Weight multiplier (or implied volatility if used downstream). Float for -OpenTimeSeries,
-Series[float]for OpenFrame.
-- Return type: -
SeriesOrFloat_co
-
-
Visualization
-The plotting methods generate fully responsive HTML output that automatically adapts to different screen sizes and device orientations. Plots are optimized for both desktop and mobile viewing with separate title containers and responsive CSS styling.
--
-
- -OpenTimeSeries.plot_series(mode='lines', title=None, tick_fmt=None, filename=None, directory=None, labels=None, output_type='file', include_plotlyjs='cdn', *, auto_open=True, add_logo=True, show_last=False) -
Create a Plotly Scatter Figure.
--
-
- Parameters: -
-
-
mode (LiteralLinePlotMode) – The type of scatter to use.
-title (str | None) – A title above the plot.
-tick_fmt (str | None) – Tick format for the y-axis, e.g.
'%'or'.1%'.
-filename (str | None) – Name of the Plotly HTML file.
-directory (DirectoryPath | None) – Directory where the Plotly HTML file is saved.
-labels (list[str] | None) – Labels to override the column names of
self.tsdf.
-output_type (LiteralPlotlyOutput) – Determines output type.
-include_plotlyjs (LiteralPlotlyJSlib) – How the plotly.js library is included.
-auto_open (bool) – Whether to open a browser window with the plot.
-add_logo (bool) – If True, a Captor logo is added to the plot.
-show_last (bool) – If True, highlight the last point in red with a label.
-self (Self)
-
-- Returns: -
A tuple
-(figure, output)whereoutputis either a div string or -a file path.
-- Return type: -
- - -
-
-
- -OpenTimeSeries.plot_bars(mode='group', title=None, tick_fmt=None, filename=None, directory=None, labels=None, output_type='file', include_plotlyjs='cdn', *, auto_open=True, add_logo=True) -
Create a Plotly Bar Figure.
--
-
- Parameters: -
-
-
mode (LiteralBarPlotMode) – The type of bar to use.
-title (str | None) – A title above the plot.
-tick_fmt (str | None) – Tick format for the y-axis, e.g.
'%'or'.1%'.
-filename (str | None) – Name of the Plotly HTML file.
-directory (DirectoryPath | None) – Directory where the Plotly HTML file is saved.
-labels (list[str] | None) – Labels to override the column names of
self.tsdf.
-output_type (LiteralPlotlyOutput) – Determines output type.
-include_plotlyjs (LiteralPlotlyJSlib) – How the plotly.js library is included.
-auto_open (bool) – Whether to open a browser window with the plot.
-add_logo (bool) – If True, a Captor logo is added to the plot.
-self (Self)
-
-- Returns: -
A tuple
-(figure, output)whereoutputis either a div string or -a file path.
-- Return type: -
- - -
-
-
- -OpenTimeSeries.plot_histogram(plot_type='bars', histnorm='probability', barmode='overlay', xbins_size=None, opacity=0.75, bargap=0.0, bargroupgap=0.0, curve_type='kde', title=None, x_fmt=None, y_fmt=None, filename=None, directory=None, labels=None, output_type='file', include_plotlyjs='cdn', *, cumulative=False, show_rug=False, auto_open=True, add_logo=True) -
Create a Plotly Histogram Figure.
--
-
- Parameters: -
-
-
plot_type (LiteralPlotlyHistogramPlotType) – Type of plot,
"bars"or"lines".
-histnorm (LiteralPlotlyHistogramHistNorm) – Normalization mode.
-barmode (LiteralPlotlyHistogramBarMode) – How bar traces are displayed relative to one another.
-xbins_size (float | None) – Width of each bin along the x-axis in data units.
-opacity (float) – Trace opacity between 0 and 1.
-bargap (float) – Gap between bars of adjacent location coordinates.
-bargroupgap (float) – Gap between bar groups at the same location coordinate.
-curve_type (LiteralPlotlyHistogramCurveType) – Type of distribution curve to overlay on the histogram.
-title (str | None) – A title above the plot.
-x_fmt (str | None) – Tick format for the x-axis.
-y_fmt (str | None) – Tick format for the y-axis.
-filename (str | None) – Name of the Plotly HTML file.
-directory (DirectoryPath | None) – Directory where the Plotly HTML file is saved.
-labels (list[str] | None) – Labels to override the column names of
self.tsdf.
-output_type (LiteralPlotlyOutput) – Determines output type.
-include_plotlyjs (LiteralPlotlyJSlib) – How the plotly.js library is included.
-cumulative (bool) – Whether to compute a cumulative histogram.
-show_rug (bool) – Whether to draw a rug plot alongside the distribution.
-auto_open (bool) – Whether to open a browser window with the plot.
-add_logo (bool) – If True, a Captor logo is added to the plot.
-self (Self)
-
-- Returns: -
A tuple
-(figure, output)whereoutputis either a div string or -a file path.
-- Return type: -
- - -
Export Methods
--
-
- -OpenTimeSeries.to_json(what_output, filename, directory=None) -
Dump timeseries data into a JSON file.
- -
-
-
- -OpenTimeSeries.to_xlsx(filename, sheet_title=None, directory=None, *, overwrite=True) -
Save
-.tsdfDataFrame to an Excel spreadsheet file.-
-
- Parameters: -
- - -
- Returns: -
The Excel file path.
-
-- Raises: -
-
-
NameError – If
filenamedoes not end with.xlsx.
-FileExistsError – If the file exists and
overwriteis False.
-
-- Return type: -
- - -
Utility Functions
--
-
- -openseries.timeseries_chain(front, back, old_fee=0.0)[source] -
Chain two timeseries together.
-The function assumes that the two series have at least one date in common.
--
-
- Parameters: -
-
-
front (TypeOpenTimeSeries) – Earlier series to chain with.
-back (TypeOpenTimeSeries) – Later series to chain with.
-old_fee (float) – Fee to apply to earlier series. Defaults to 0.0.
-
-- Returns: -
An OpenTimeSeries object or a subclass thereof.
-
-- Return type: -
TypeOpenTimeSeries
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/api/types.html b/docs/build/html/api/types.html
deleted file mode 100644
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--- a/docs/build/html/api/types.html
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-
-
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-
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-
- Simulation
-The ReturnSimulation class.
--
-
- -class openseries.simulation.ReturnSimulation(*, number_of_sims, trading_days, trading_days_in_year, mean_annual_return, mean_annual_vol, dframe, jumps_lamda=0.0, jumps_sigma=0.0, jumps_mu=0.0, seed=None)[source] -
Bases:
-BaseModelThe class ReturnSimulation allows for simulating financial timeseries.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Total number of days to simulate.
-trading_days_in_year (Annotated[int, Strict(strict=True), Ge(ge=1), Le(le=366)]) – Number of trading days used to annualize.
-mean_annual_return (float) – Mean annual return of the distribution.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean annual standard deviation of the distribution.
-dframe (DataFrame) – Pandas DataFrame object holding the resulting values.
-jumps_lamda (Annotated[float, Ge(ge=0)]) – This is the probability of a jump happening at each point in time. -Defaults to 0.0.
-jumps_sigma (Annotated[float, Ge(ge=0)]) – This is the volatility of the jump size. Defaults to 0.0.
-jumps_mu (float) – This is the average jump size. Defaults to 0.0.
-seed (int | None) – Seed for random process initiation.
-
-
-
-
- -number_of_sims: PositiveInt -
-
-
- -trading_days: PositiveInt -
-
-
- -trading_days_in_year: DaysInYearType -
-
-
- -mean_annual_return: float -
-
-
- -mean_annual_vol: PositiveFloat -
-
-
- -dframe: DataFrame -
-
-
- -jumps_lamda: NonNegativeFloat -
-
-
- -jumps_sigma: NonNegativeFloat -
-
-
- -jumps_mu: float -
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -property realized_mean_return: float -
Annualized arithmetic mean of returns.
--
-
- Returns: -
Annualized arithmetic mean of returns.
-
-
-
-
- -property realized_vol: float -
Annualized volatility.
--
-
- Returns: -
Annualized volatility.
-
-
-
-
- -classmethod from_normal(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Normal distribution simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Normal distribution simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_lognormal(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Lognormal distribution simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Lognormal distribution simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_gbm(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Geometric Brownian Motion simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Geometric Brownian Motion simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_merton_jump_gbm(number_of_sims, trading_days, mean_annual_return, mean_annual_vol, jumps_lamda, jumps_sigma=0.0, jumps_mu=0.0, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Merton Jump-Diffusion model simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-jumps_lamda (Annotated[float, Ge(ge=0)]) – This is the probability of a jump happening at each point -in time.
-jumps_sigma (Annotated[float, Ge(ge=0)]) – This is the volatility of the jump size. Defaults to 0.0.
-jumps_mu (float) – This is the average jump size. Defaults to 0.0.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Merton Jump-Diffusion model simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -to_dataframe(name, start=None, end=None, countries='SE', markets=None)[source] -
Create a pandas.DataFrame from simulation(s).
--
-
- Parameters: -
-
-
name (str) – Name label of the serie(s).
-start (dt.date | None) – Date when the simulation starts.
-end (dt.date | None) – Date when the simulation ends.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-self (Self)
-
-- Returns: -
The simulation(s) data.
-
-- Return type: -
DataFrame
-
-
ReturnSimulation Class
--
-
- -class openseries.simulation.ReturnSimulation(*, number_of_sims, trading_days, trading_days_in_year, mean_annual_return, mean_annual_vol, dframe, jumps_lamda=0.0, jumps_sigma=0.0, jumps_mu=0.0, seed=None)[source] -
Bases:
-BaseModelThe class ReturnSimulation allows for simulating financial timeseries.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Total number of days to simulate.
-trading_days_in_year (Annotated[int, Strict(strict=True), Ge(ge=1), Le(le=366)]) – Number of trading days used to annualize.
-mean_annual_return (float) – Mean annual return of the distribution.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean annual standard deviation of the distribution.
-dframe (DataFrame) – Pandas DataFrame object holding the resulting values.
-jumps_lamda (Annotated[float, Ge(ge=0)]) – This is the probability of a jump happening at each point in time. -Defaults to 0.0.
-jumps_sigma (Annotated[float, Ge(ge=0)]) – This is the volatility of the jump size. Defaults to 0.0.
-jumps_mu (float) – This is the average jump size. Defaults to 0.0.
-seed (int | None) – Seed for random process initiation.
-
-
-
-
- -number_of_sims: PositiveInt -
-
-
- -trading_days: PositiveInt -
-
-
- -trading_days_in_year: DaysInYearType -
-
-
- -mean_annual_return: float -
-
-
- -mean_annual_vol: PositiveFloat -
-
-
- -dframe: DataFrame -
-
-
- -jumps_lamda: NonNegativeFloat -
-
-
- -jumps_sigma: NonNegativeFloat -
-
-
- -jumps_mu: float -
-
-
- -model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -property realized_mean_return: float -
Annualized arithmetic mean of returns.
--
-
- Returns: -
Annualized arithmetic mean of returns.
-
-
-
-
- -property realized_vol: float -
Annualized volatility.
--
-
- Returns: -
Annualized volatility.
-
-
-
-
- -classmethod from_normal(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Normal distribution simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Normal distribution simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_lognormal(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Lognormal distribution simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Lognormal distribution simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_gbm(number_of_sims, mean_annual_return, mean_annual_vol, trading_days, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Geometric Brownian Motion simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Geometric Brownian Motion simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -classmethod from_merton_jump_gbm(number_of_sims, trading_days, mean_annual_return, mean_annual_vol, jumps_lamda, jumps_sigma=0.0, jumps_mu=0.0, trading_days_in_year=252, seed=None, randomizer=None, ar1_coef=0.0)[source] -
Create a Merton Jump-Diffusion model simulation.
--
-
- Parameters: -
-
-
number_of_sims (Annotated[int, Gt(gt=0)]) – Number of simulations to generate.
-trading_days (Annotated[int, Gt(gt=0)]) – Number of trading days to simulate.
-mean_annual_return (float) – Mean return.
-mean_annual_vol (Annotated[float, Gt(gt=0)]) – Mean standard deviation.
-jumps_lamda (Annotated[float, Ge(ge=0)]) – This is the probability of a jump happening at each point -in time.
-jumps_sigma (Annotated[float, Ge(ge=0)]) – This is the volatility of the jump size. Defaults to 0.0.
-jumps_mu (float) – This is the average jump size. Defaults to 0.0.
-trading_days_in_year (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]) – Number of trading days used to annualize. -Defaults to 252.
-seed (int | None) – Seed for random process initiation.
-randomizer (Generator | None) – Random process generator.
-ar1_coef (float) – Lag-1 autoregressive coefficient in (-1, 1) to induce -autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-- Returns: -
Merton Jump-Diffusion model simulation.
-
-- Return type: -
ReturnSimulation
-
-
-
-
- -to_dataframe(name, start=None, end=None, countries='SE', markets=None)[source] -
Create a pandas.DataFrame from simulation(s).
--
-
- Parameters: -
-
-
name (str) – Name label of the serie(s).
-start (dt.date | None) – Date when the simulation starts.
-end (dt.date | None) – Date when the simulation ends.
-countries (CountriesType) – (List of) country code(s) according to ISO 3166-1 alpha-2. -Defaults to “SE”.
-markets (list[str] | str | None) – (List of) markets code(s) supported by exchange_calendars.
-self (Self)
-
-- Returns: -
The simulation(s) data.
-
-- Return type: -
DataFrame
-
-
The ReturnSimulation class is used to create simulated financial time series for testing and analysis purposes. It provides methods to generate realistic return patterns based on statistical distributions.
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/development/changelog.html b/docs/build/html/development/changelog.html
deleted file mode 100644
index 6568cce8..00000000
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-
-
-
-
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- Types and Enums
-Declaring types used throughout the project.
--
-
- -class openseries.owntypes.ValueType(*values)[source] -
Bases:
-StrEnumEnum types of OpenTimeSeries to identify the output.
--
-
- -EWMA_VOL = 'EWMA volatility' -
-
-
- -EWMA_VAR = 'EWMA VaR' -
-
-
- -PRICE = 'Price(Close)' -
-
-
- -RTRN = 'Return(Total)' -
-
-
- -RELRTRN = 'Relative return' -
-
-
- -ROLLBETA = 'Beta' -
-
-
- -ROLLCORR = 'Rolling correlation' -
-
-
- -ROLLCVAR = 'Rolling CVaR' -
-
-
- -ROLLINFORATIO = 'Information Ratio' -
-
-
- -ROLLRTRN = 'Rolling returns' -
-
-
- -ROLLVAR = 'Rolling VaR' -
-
-
- -ROLLVOL = 'Rolling volatility' -
Value Types
--
-
- -class openseries.owntypes.ValueType(*values)[source] -
Bases:
-StrEnumEnum types of OpenTimeSeries to identify the output.
--
-
- -EWMA_VOL = 'EWMA volatility' -
-
-
- -EWMA_VAR = 'EWMA VaR' -
-
-
- -PRICE = 'Price(Close)' -
-
-
- -RTRN = 'Return(Total)' -
-
-
- -RELRTRN = 'Relative return' -
-
-
- -ROLLBETA = 'Beta' -
-
-
- -ROLLCORR = 'Rolling correlation' -
-
-
- -ROLLCVAR = 'Rolling CVaR' -
-
-
- -ROLLINFORATIO = 'Information Ratio' -
-
-
- -ROLLRTRN = 'Rolling returns' -
-
-
- -ROLLVAR = 'Rolling VaR' -
-
-
- -ROLLVOL = 'Rolling volatility' -
The ValueType enum identifies the type of values in a time series (prices, returns, etc.).
-Type Aliases
--
-
- -openseries.owntypes.SeriesOrFloat_co = +SeriesOrFloat_co -
Type variable.
-The preferred way to construct a type variable is via the dedicated -syntax for generic functions, classes, and type aliases:
---class Sequence[T]: # T is a TypeVar - ... -
This syntax can also be used to create bound and constrained type -variables:
---# S is a TypeVar bound to str -class StrSequence[S: str]: - ... - -# A is a TypeVar constrained to str or bytes -class StrOrBytesSequence[A: (str, bytes)]: - ... -
Type variables can also have defaults:
--
--
-
- class IntDefault[T = int]:
…
-
-
However, if desired, reusable type variables can also be constructed -manually, like so:
---T = TypeVar('T') # Can be anything -S = TypeVar('S', bound=str) # Can be any subtype of str -A = TypeVar('A', str, bytes) # Must be exactly str or bytes -D = TypeVar('D', default=int) # Defaults to int -
Type variables exist primarily for the benefit of static type -checkers. They serve as the parameters for generic types as well -as for generic function and type alias definitions.
-The variance of type variables is inferred by type checkers when they -are created through the type parameter syntax and when -
-infer_variance=Trueis passed. Manually created type variables may -be explicitly marked covariant or contravariant by passing -covariant=Trueorcontravariant=True. By default, manually -created type variables are invariant. See PEP 484 and PEP 695 for more -details.
-
-
- -openseries.owntypes.CountryStringType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
-alias of
-Annotated[str,StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]
-
-
- -openseries.owntypes.CountrySetType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
-alias of
-Annotated[set[Annotated[str,StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]],MinLen(min_length=1)]
-
-
- -openseries.owntypes.CountriesType: TypeAlias = typing.Annotated[set[typing.Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern='^[A-Z]{2}$', ascii_only=None)]], MinLen(min_length=1)] | typing.Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern='^[A-Z]{2}$', ascii_only=None)] -
Represent a union type
-E.g. for int | str
-
-
-
- -openseries.owntypes.CurrencyStringType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
-alias of
-Annotated[str,StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)]
-
-
- -openseries.owntypes.DateStringType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
-alias of
-Annotated[str,StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]d|3[01])$, ascii_only=None)]
-
-
- -openseries.owntypes.DateListType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
-alias of
-Annotated[list[Annotated[str,StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]d|3[01])$, ascii_only=None)]],MinLen(min_length=1)]
-
-
- -openseries.owntypes.ValueListType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
- -
-
-
- -openseries.owntypes.DaysInYearType -
Runtime representation of an annotated type.
-At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ -with extra metadata. The alias behaves like a normal typing alias. -Instantiating is the same as instantiating the underlying type; binding -it to types is also the same.
-The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.
-alias of
-Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]
-
-
- -openseries.owntypes.DateType = str | datetime.date | datetime.datetime | numpy.datetime64 | pandas.Timestamp -
Represent a union type
-E.g. for int | str
-
Literal Types
--
-
- -openseries.owntypes.LiteralJsonOutput -
alias of
-Literal[‘values’, ‘tsdf’]
-
-
- -openseries.owntypes.LiteralTrunc -
alias of
-Literal[‘before’, ‘after’, ‘both’]
-
-
- -openseries.owntypes.LiteralLinePlotMode = typing.Literal['lines', 'markers', 'lines+markers', 'lines+text', 'markers+text', 'lines+markers+text'] | None -
Represent a union type
-E.g. for int | str
-
-
-
- -openseries.owntypes.LiteralHowMerge -
alias of
-Literal[‘outer’, ‘inner’]
-
-
- -openseries.owntypes.LiteralQuantileInterp -
alias of
-Literal[‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’]
-
-
- -openseries.owntypes.LiteralBizDayFreq -
alias of
-Literal[‘B’, ‘BME’, ‘BQE’, ‘BYE’]
-
-
- -openseries.owntypes.LiteralPandasReindexMethod = typing.Literal['pad', 'ffill', 'backfill', 'bfill', 'nearest'] | None -
Represent a union type
-E.g. for int | str
-
-
-
- -openseries.owntypes.LiteralNanMethod -
alias of
-Literal[‘fill’, ‘drop’]
-
-
- -openseries.owntypes.LiteralCaptureRatio -
alias of
-Literal[‘up’, ‘down’, ‘both’]
-
-
- -openseries.owntypes.LiteralBarPlotMode -
alias of
-Literal[‘stack’, ‘group’, ‘overlay’, ‘relative’]
-
-
- -openseries.owntypes.LiteralPlotlyOutput -
alias of
-Literal[‘file’, ‘div’]
-
-
- -openseries.owntypes.LiteralPlotlyJSlib -
alias of
-Literal[True, False, ‘cdn’]
-
-
- -openseries.owntypes.LiteralPlotlyHistogramPlotType -
alias of
-Literal[‘bars’, ‘lines’]
-
-
- -openseries.owntypes.LiteralPlotlyHistogramBarMode -
alias of
-Literal[‘stack’, ‘group’, ‘overlay’, ‘relative’]
-
-
- -openseries.owntypes.LiteralPlotlyHistogramCurveType -
alias of
-Literal[‘normal’, ‘kde’]
-
-
- -openseries.owntypes.LiteralPlotlyHistogramHistNorm -
alias of
-Literal[‘percent’, ‘probability’, ‘density’, ‘probability density’]
-
-
- -openseries.owntypes.LiteralPortfolioWeightings -
alias of
-Literal[‘eq_weights’, ‘inv_vol’, ‘max_div’, ‘min_vol_overweight’]
-
-
- -openseries.owntypes.LiteralMinimizeMethods -
alias of
-Literal[‘SLSQP’, ‘Nelder-Mead’, ‘Powell’, ‘CG’, ‘BFGS’, ‘Newton-CG’, ‘L-BFGS-B’, ‘TNC’, ‘COBYLA’, ‘trust-constr’, ‘dogleg’, ‘trust-ncg’, ‘trust-exact’, ‘trust-krylov’]
-
-
- -openseries.owntypes.LiteralSeriesProps -
alias of
-Literal[‘value_ret’, ‘geo_ret’, ‘arithmetic_ret’, ‘vol’, ‘downside_deviation’, ‘ret_vol_ratio’, ‘sortino_ratio’, ‘kappa3_ratio’, ‘z_score’, ‘skew’, ‘kurtosis’, ‘positive_share’, ‘var_down’, ‘cvar_down’, ‘vol_from_var’, ‘worst’, ‘worst_month’, ‘max_drawdown_cal_year’, ‘max_drawdown’, ‘max_drawdown_date’, ‘first_idx’, ‘last_idx’, ‘length’, ‘span_of_days’, ‘yearfrac’, ‘periods_in_a_year’, ‘autocorr’, ‘partial_autocorr’]
-
-
- -openseries.owntypes.LiteralFrameProps -
alias of
-Literal[‘value_ret’, ‘geo_ret’, ‘arithmetic_ret’, ‘autocorr’, ‘vol’, ‘downside_deviation’, ‘ret_vol_ratio’, ‘sortino_ratio’, ‘kappa3_ratio’, ‘z_score’, ‘skew’, ‘kurtosis’, ‘positive_share’, ‘var_down’, ‘cvar_down’, ‘vol_from_var’, ‘worst’, ‘worst_month’, ‘max_drawdown’, ‘max_drawdown_date’, ‘max_drawdown_cal_year’, ‘first_indices’, ‘last_indices’, ‘lengths_of_items’, ‘span_of_days_all’]
Validation Classes
--
-
- -class openseries.owntypes.Countries(*, countryinput)[source] -
Bases:
-BaseModelDeclare Countries.
--
-
- Parameters: -
countryinput (Annotated[set[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)]], MinLen(min_length=1)] | Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$, ascii_only=None)])
-
-
-
-
- -countryinput: CountriesType -
-
-
- -model_config = {} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -class openseries.owntypes.Currency(*, ccy)[source] -
Bases:
-BaseModelDeclare Currency.
--
-
- Parameters: -
ccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$, ascii_only=None)])
-
-
-
-
- -ccy: CurrencyStringType -
-
-
- -model_config = {} -
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
-
-
- -class openseries.owntypes.PropertiesList(iterable=(), /)[source] -
-
-
Base class for allowed property arguments definition.
--
-
- -allowed_strings: ClassVar[set[str]] = {'arithmetic_ret', 'cvar_down', 'downside_deviation', 'geo_ret', 'kappa3_ratio', 'kurtosis', 'max_drawdown', 'max_drawdown_cal_year', 'max_drawdown_date', 'omega_ratio', 'positive_share', 'ret_vol_ratio', 'skew', 'sortino_ratio', 'value_ret', 'var_down', 'vol', 'vol_from_var', 'worst', 'worst_month', 'z_score'} -
-
-
- -class openseries.owntypes.OpenTimeSeriesPropertiesList(*args)[source] -
Bases:
-PropertiesListAllowed property arguments for the OpenTimeSeries class.
--
-
- Parameters: -
args (LiteralSeriesProps)
-
-
-
-
- -allowed_strings: ClassVar[set[str]] = {'arithmetic_ret', 'autocorr', 'cvar_down', 'downside_deviation', 'first_idx', 'geo_ret', 'kappa3_ratio', 'kurtosis', 'last_idx', 'length', 'max_drawdown', 'max_drawdown_cal_year', 'max_drawdown_date', 'omega_ratio', 'partial_autocorr', 'periods_in_a_year', 'positive_share', 'ret_vol_ratio', 'skew', 'sortino_ratio', 'span_of_days', 'value_ret', 'var_down', 'vol', 'vol_from_var', 'worst', 'worst_month', 'yearfrac', 'z_score'} -
-
-
- -__init__(*args)[source] -
Property arguments for the OpenTimeSeries class.
--
-
- Parameters: -
-
-
self (Self)
-args (Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown_cal_year', 'max_drawdown', 'max_drawdown_date', 'first_idx', 'last_idx', 'length', 'span_of_days', 'yearfrac', 'periods_in_a_year', 'autocorr', 'partial_autocorr'])
-
-- Return type: -
None
-
-
-
-
- -class openseries.owntypes.OpenFramePropertiesList(*args)[source] -
Bases:
-PropertiesListAllowed property arguments for the OpenFrame class.
--
-
- Parameters: -
args (LiteralFrameProps)
-
-
-
-
- -allowed_strings: ClassVar[set[str]] = {'arithmetic_ret', 'autocorr', 'cvar_down', 'downside_deviation', 'first_indices', 'geo_ret', 'kappa3_ratio', 'kurtosis', 'last_indices', 'lengths_of_items', 'max_drawdown', 'max_drawdown_cal_year', 'max_drawdown_date', 'omega_ratio', 'positive_share', 'ret_vol_ratio', 'skew', 'sortino_ratio', 'span_of_days_all', 'value_ret', 'var_down', 'vol', 'vol_from_var', 'worst', 'worst_month', 'z_score'} -
-
-
- -__init__(*args)[source] -
Property arguments for the OpenFrame class.
--
-
- Parameters: -
-
-
self (Self)
-args (Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'autocorr', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown', 'max_drawdown_date', 'max_drawdown_cal_year', 'first_indices', 'last_indices', 'lengths_of_items', 'span_of_days_all'])
-
-- Return type: -
None
-
-
Custom Exceptions
--
-
- -exception openseries.owntypes.MixedValuetypesError[source] -
Bases:
-ExceptionRaised when provided timeseries valuetypes are not the same.
-
-
-
- -exception openseries.owntypes.AtLeastOneFrameError[source] -
Bases:
-ExceptionRaised when none of the possible frame inputs is provided.
-
-
-
- -exception openseries.owntypes.DateAlignmentError[source] -
Bases:
-ExceptionRaised when date input is not aligned with existing range.
-
-
-
- -exception openseries.owntypes.NumberOfItemsAndLabelsNotSameError[source] -
Bases:
-ExceptionRaised when number of labels is not matching the number of timeseries.
-
-
-
- -exception openseries.owntypes.InitialValueZeroError[source] -
Bases:
-ExceptionRaised when a calculation cannot be performed due to initial value(s) zero.
-
-
-
- -exception openseries.owntypes.CountriesNotStringNorListStrError[source] -
Bases:
-ExceptionRaised when countries argument is not provided in correct format.
-
-
-
- -exception openseries.owntypes.MarketsNotStringNorListStrError[source] -
Bases:
-ExceptionRaised when markets argument is not provided in correct format.
-
-
-
- -exception openseries.owntypes.TradingDaysNotAboveZeroError[source] -
Bases:
-ExceptionRaised when trading days argument is not above zero.
-
-
-
- -exception openseries.owntypes.BothStartAndEndError[source] -
Bases:
-ExceptionRaised when both start and end dates are provided.
-
-
-
- -exception openseries.owntypes.NoWeightsError[source] -
Bases:
-ExceptionRaised when no weights are provided to function where necessary.
-
-
-
- -exception openseries.owntypes.LabelsNotUniqueError[source] -
Bases:
-ExceptionRaised when provided label names are not unique.
-
-
-
- -exception openseries.owntypes.RatioInputError[source] -
Bases:
-ExceptionRaised when ratio keyword not provided correctly.
-
-
-
- -exception openseries.owntypes.MergingResultedInEmptyError[source] -
Bases:
-ExceptionRaised when a merge resulted in an empty DataFrame.
-
-
-
- -exception openseries.owntypes.IncorrectArgumentComboError[source] -
Bases:
-ExceptionRaised when correct combination of arguments is not provided.
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
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- Changelog
-GitHub Releases
-For details on changes, please visit the GitHub Releases page.
-Release Notifications
-Stay updated on new releases:
--
-
- GitHub
Watch the openseries repository for release notifications
-
-- PyPI
Monitor openseries on PyPI for new versions
-
-- Conda-forge
Track updates on conda-forge
-
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- Contributing to openseries
-We welcome contributions to openseries! This guide will help you get started with contributing to the project.
-Getting Started
-Development Setup
--
-
Fork the repository on GitHub
-Clone your fork locally:
-
git clone https://github.com/yourusername/openseries.git
-cd openseries
--
-
Create the development environment. This installs the pinned uv version -(
uv==0.11.21), syncs lockeddevanddocsdependencies from -uv.lock, and installs pre-commit hooks:
-
make install
-On Windows:
-.\make.ps1 make
-Development Workflow
--
-
Create a new branch for your feature or bug fix:
-
git checkout -b feature/your-feature-name
--
-
Make your changes
-Run tests to ensure everything works:
-
make test
--
-
Run linting and type checking:
-
make lint
--
-
Commit your changes:
-
git add .
-git commit -m "Add your descriptive commit message"
--
-
Push to your fork:
-
git push origin feature/your-feature-name
--
-
Create a pull request on GitHub
-
Code Standards
-Code Style
-openseries uses several tools to maintain code quality:
--
-
Ruff: For linting and code formatting
-mypy: For static type checking
-pre-commit: For automated checks before commits
-
The configuration for these tools is in pyproject.toml.
Type Hints
-All new code should include proper type hints:
-def calculate_returns(prices: list[float]) -> list[float]:
- """Calculate simple returns from prices."""
- returns = []
- for i in range(1, len(prices)):
- ret = (prices[i] / prices[i-1]) - 1
- returns.append(ret)
- return returns
-Docstrings
-Use Google-style docstrings for all public functions and classes:
-def calculate_sharpe_ratio(returns: list[float], risk_free_rate: float = 0.0) -> float:
- """Calculate the Sharpe ratio.
-
- Args:
- returns: List of periodic returns.
- risk_free_rate: Risk-free rate for the same period. Defaults to 0.0.
-
- Returns:
- The Sharpe ratio.
-
- Raises:
- ValueError: If returns list is empty.
-
- Example:
- >>> returns = [0.01, 0.02, -0.01, 0.03]
- >>> sharpe = calculate_sharpe_ratio(returns)
- >>> print(f"Sharpe ratio: {sharpe:.3f}")
- """
- if not returns:
- raise ValueError("Returns list cannot be empty")
-
- mean_return = sum(returns) / len(returns)
- std_dev = (sum((r - mean_return) ** 2 for r in returns) / len(returns)) ** 0.5
-
- if std_dev == 0:
- return 0.0
-
- return (mean_return - risk_free_rate) / std_dev
-Testing
-Test Structure
-Tests are located in the tests/ directory and use pytest:
tests/
-├── __init__.py
-├── test_series.py
-├── test_frame.py
-├── test_portfoliotools.py
-└── ...
-Writing Tests
-Write comprehensive tests for new functionality:
-import pytest
-import pandas as pd
-from pandas.testing import assert_frame_equal
-from openseries import OpenTimeSeries
-
-class TestOpenTimeSeries:
- """Test cases for OpenTimeSeries class."""
-
- def test_from_arrays_basic(self):
- """Test basic creation from arrays."""
- dates = ['2023-01-01', '2023-01-02', '2023-01-03']
- values = [100.0, 102.0, 99.0]
-
- series = OpenTimeSeries.from_arrays(dates=dates, values=values, name="Test")
-
- if series.label != "Test":
- msg = f"Expected name 'Test', got '{series.label}'"
- raise ValueError(msg)
- if series.length != 3:
- msg = f"Expected length 3, got {series.length}"
- raise ValueError(msg)
- if series.first_idx != pd.Timestamp('2023-01-01').date():
- msg = f"Expected first_idx 2023-01-01, got {series.first_idx}"
- raise ValueError(msg)
- if series.last_idx != pd.Timestamp('2023-01-03').date():
- msg = f"Expected last_idx 2023-01-03, got {series.last_idx}"
- raise ValueError(msg)
-
- def test_from_arrays_invalid_dates(self):
- """Test that invalid dates raise appropriate errors."""
- with pytest.raises(ValueError):
- OpenTimeSeries.from_arrays(
- dates=['invalid-date'],
- values=[100.0],
- name="Test"
- )
-
- def test_calculate_returns(self):
- """Test return calculation."""
- dates = ['2023-01-01', '2023-01-02', '2023-01-03']
- values = [100.0, 102.0, 99.0]
-
- series = OpenTimeSeries.from_arrays(dates=dates, values=values, name="Test")
- series.value_to_ret() # Modifies original
-
- expected_returns = [0.02, -0.0294117647] # Approximate
- actual_returns = series.values
-
- if len(actual_returns) != 2:
- msg = f"Expected 2 returns, got {len(actual_returns)}"
- raise ValueError(msg)
- # Use tolerance-based comparison
- if abs(actual_returns[0] - expected_returns[0]) >= 1e-6:
- msg = f"First return mismatch: {actual_returns[0]} vs {expected_returns[0]}"
- raise ValueError(msg)
- if abs(actual_returns[1] - expected_returns[1]) >= 1e-6:
- msg = f"Second return mismatch: {actual_returns[1]} vs {expected_returns[1]}"
- raise ValueError(msg)
-Running Tests
-Run all tests:
-make test
-Run specific test files:
-pytest tests/test_series.py
-Run tests with coverage:
-pytest --cov=openseries tests/
-Test Coverage
-openseries maintains high test coverage (>99%). New code should include comprehensive tests:
--
-
Test normal use cases
-Test edge cases
-Test error conditions
-Test with different data types and sizes
-
Documentation
-Documentation Standards
--
-
All public APIs must be documented
-Include examples in docstrings where helpful
-Update relevant documentation files when adding features
-Use clear, concise language
-
Building Documentation
-To build documentation locally:
-cd docs
-make html
-The built documentation will be in docs/_build/html/.
Contributing Guidelines
-Pull Request Process
--
-
Fork and Branch: Create a feature branch from
master
-Develop: Make your changes with tests and documentation
-Test: Ensure all tests pass and coverage remains high
-Lint: Run linting and fix any issues
-Document: Update documentation as needed
-Commit: Use clear, descriptive commit messages
-Pull Request: Create a PR with a clear description
-
Commit Messages
-Use clear, descriptive commit messages:
-Add support for custom business day calendars
-
-- Implement custom calendar functionality in datefixer module
-- Add tests for various calendar configurations
-- Update documentation with examples
-- Fixes #123
-Code Review Process
-All contributions go through code review:
--
-
Automated checks must pass (tests, linting, type checking)
-At least one maintainer review is required
-Address any feedback or requested changes
-Once approved, the PR will be merged
-
Types of Contributions
-Bug Reports
-When reporting bugs, please include:
--
-
Clear description of the issue
-Steps to reproduce
-Expected vs. actual behavior
-Environment details (Python version, OS, etc.)
-Minimal code example if possible
-
Feature Requests
-For new features:
--
-
Describe the use case and motivation
-Provide examples of how it would be used
-Consider backward compatibility
-Discuss implementation approach if you have ideas
-
Code Contributions
-Areas where contributions are especially welcome:
--
-
New financial metrics: Additional risk measures, performance ratios
-Data sources: Integration with new data providers
-Visualization: Enhanced plotting capabilities
-Performance: Optimization of calculations
-Documentation: Examples, tutorials, API documentation
-
Documentation Contributions
-Documentation improvements are always welcome:
--
-
Fix typos or unclear explanations
-Add examples to existing documentation
-Create new tutorials or guides
-Improve API documentation
-
Development Environment
-IDE Setup
-For VS Code, recommended extensions:
--
-
Python
-Pylance
-Ruff
-mypy
-
Recommended settings in .vscode/settings.json:
{
- "python.defaultInterpreterPath": "venv/bin/python",
- "python.linting.enabled": true,
- "python.linting.ruffEnabled": true,
- "python.formatting.provider": "ruff",
- "python.typeChecking": "strict"
-}
-Debugging
-For debugging tests:
-pytest --pdb tests/test_specific.py::test_function
-For debugging with VS Code, create .vscode/launch.json:
{
- "version": "0.2.0",
- "configurations": [
- {
- "name": "Python: Current File",
- "type": "python",
- "request": "launch",
- "program": "${file}",
- "console": "integratedTerminal"
- },
- {
- "name": "Python: Pytest",
- "type": "python",
- "request": "launch",
- "module": "pytest",
- "args": ["${workspaceFolder}/tests"],
- "console": "integratedTerminal"
- }
- ]
-}
-Release Process
-openseries follows semantic versioning (MAJOR.MINOR.PATCH):
--
-
MAJOR: Breaking changes
-MINOR: New features, backward compatible
-PATCH: Bug fixes, backward compatible
-
Releases are managed by maintainers and include:
--
-
Version bump in
pyproject.toml
-Update
CHANGELOG.md
-Create GitHub release with release notes
-Publish to PyPI and conda-forge
-
Getting Help
-If you need help with contributing:
--
-
Check existing issues and discussions on GitHub
-Ask questions in GitHub Discussions
-Reach out to maintainers
-
Community Guidelines
-openseries is committed to providing a welcoming and inclusive environment:
--
-
Be respectful and constructive in all interactions
-Focus on what is best for the community
-Show empathy towards other community members
-Welcome newcomers and help them get started
-
Thank you for contributing to openseries!
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diff --git a/docs/build/html/examples/multi_asset.html b/docs/build/html/examples/multi_asset.html
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- Reporting
-This example demonstrates how to create analysis reports using openseries and the built-in report functionality.
-Using the Built-in HTML Report
-import yfinance as yf
-from openseries import OpenTimeSeries, OpenFrame, report_html
-import pandas as pd
-
-# Load sample data for comparison
-tickers = ["AAPL", "MSFT", "GOOGL", "SPY"]
-names = ["Apple", "Microsoft", "Google", "S&P 500"]
-
-series_list = []
-for ticker, name in zip(tickers, names):
- data = yf.Ticker(ticker).history(period="3y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=name)
- series_list.append(series)
-
-# Create frame for report
-comparison_frame = OpenFrame(constituents=series_list)
-
-# Generate HTML report
-# The last asset in the frame is used as the benchmark
-figure, filepath = report_html(
- data=comparison_frame,
- output_type="file",
- filename="stock_comparison_report.html"
-)
-
-# filepath contains the path to the saved HTML file
-print(f"Report saved to: {filepath}")
-
-# The figure object can be used for further customization if needed
-# figure.show() # Display the figure interactively
-Embedding Reports in Existing HTML Pages
-When you need to embed a report in an existing HTML page, use output_type="div":
# Generate HTML div section for embedding
-figure, html_div = report_html(
- data=comparison_frame,
- output_type="div"
-)
-
-# html_div contains the responsive HTML div section
-# that can be embedded in your existing HTML page
-# It includes both desktop and mobile layouts with CSS and JavaScript
-
-# Example: Save to a custom HTML template
-html_template = f"""
-<!DOCTYPE html>
-<html>
-<head>
- <title>My Custom Report</title>
-</head>
-<body>
- <h1>Portfolio Analysis Report</h1>
- {html_div}
-</body>
-</html>
-"""
-
-with open("custom_report.html", "w", encoding="utf-8") as f:
- f.write(html_template)
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diff --git a/docs/build/html/examples/portfolio_optimization.html b/docs/build/html/examples/portfolio_optimization.html
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- Multi-Asset Analysis
-This example shows how to analyze multiple assets simultaneously using OpenFrame.
-Setting Up Multi-Asset Analysis
-import yfinance as yf
-from openseries import OpenTimeSeries, OpenFrame
-
-# Define asset universe
-assets = {
- "AAPL": "Apple Inc.",
- "GOOGL": "Alphabet Inc.",
- "MSFT": "Microsoft Corp.",
- "AMZN": "Amazon.com Inc.",
- "TSLA": "Tesla Inc.",
- "NVDA": "NVIDIA Corp.",
- "META": "Meta Platforms Inc.",
- "NFLX": "Netflix Inc."
-}
-
-# Download data for all assets
-series_list = []
-for ticker, name in assets.items():
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="3y")
- series = OpenTimeSeries.from_df(
- dframe=data['Close']
- )
- series.set_new_label(lvl_zero=name)
- series_list.append(series)
- print(f"Loaded {name}: {series.length} observations")
-
-# Create OpenFrame
-tech_stocks = OpenFrame(constituents=series_list)
-print(f"\nCreated frame with {tech_stocks.item_count} assets")
-print(f"Common period: {tech_stocks.first_idx} to {tech_stocks.last_idx}")
-Comparative Analysis
-# Get metrics for all assets
-all_metrics = tech_stocks.all_properties()
-print("=== COMPARATIVE METRICS ===")
-print(all_metrics)
-
-# Focus on key metrics
-key_metrics = all_metrics.loc[['Geometric return', 'Volatility', 'Return vol ratio', 'Max drawdown']]
-key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown']
-
-# Convert to percentages for better readability
-percentage_metrics = key_metrics.copy()
-percentage_metrics.loc[['Annual Return', 'Volatility', 'Max Drawdown']] *= 100
-
-print("\n=== KEY METRICS COMPARISON ===")
-print(percentage_metrics.round(2))
-Ranking Analysis
-# Rank assets by different criteria using openseries metrics
-# Get key metrics for ranking
-returns = all_metrics.loc['Geometric return']
-volatilities = all_metrics.loc['Volatility']
-sharpe_ratios = all_metrics.loc['Return vol ratio']
-drawdowns = all_metrics.loc['Max drawdown']
-
-print("\n=== ASSET RANKINGS ===")
-print("Ranked by Return (highest first):")
-for i, (asset, ret) in enumerate(returns.sort_values(ascending=False).items(), 1):
- print(f" {i}. {asset}: {ret:.2%}")
-
-print("\nRanked by Volatility (lowest first):")
-for i, (asset, vol) in enumerate(volatilities.sort_values(ascending=True).items(), 1):
- print(f" {i}. {asset}: {vol:.2%}")
-
-print("\nRanked by Sharpe Ratio (highest first):")
-for i, (asset, sharpe) in enumerate(sharpe_ratios.sort_values(ascending=False).items(), 1):
- print(f" {i}. {asset}: {sharpe:.2f}")
-
-print("\nRanked by Max Drawdown (least negative first):")
-for i, (asset, dd) in enumerate(drawdowns.sort_values(ascending=False).items(), 1):
- print(f" {i}. {asset}: {dd:.2%}")
-Correlation Analysis
-# Calculate correlation matrix
-correlation_matrix = tech_stocks.correl_matrix
-print("\n=== CORRELATION MATRIX ===")
-print(correlation_matrix.round(3))
-
-# Find most and least correlated pairs
-corr_pairs = []
-for i in range(len(correlation_matrix.columns)):
- for j in range(i+1, len(correlation_matrix.columns)):
- asset1 = correlation_matrix.columns[i]
- asset2 = correlation_matrix.columns[j]
- corr = correlation_matrix.iloc[i, j]
- corr_pairs.append((asset1, asset2, corr))
-
-# Sort by correlation
-corr_pairs.sort(key=lambda x: x[2], reverse=True)
-
-print("\n=== HIGHEST CORRELATIONS ===")
-for asset1, asset2, corr in corr_pairs[:5]:
- print(f"{asset1} - {asset2}: {corr:.3f}")
-
-print("\n=== LOWEST CORRELATIONS ===")
-for asset1, asset2, corr in corr_pairs[-5:]:
- print(f"{asset1} - {asset2}: {corr:.3f}")
-Risk-Return Analysis
-# Analyze risk-return using openseries metrics
-returns = all_metrics.loc['Geometric return']
-volatilities = all_metrics.loc['Volatility']
-sharpe_ratios = all_metrics.loc['Return vol ratio']
-
-print("\n=== RISK-RETURN ANALYSIS ===")
-for asset in returns.index:
- ret_pct = returns[asset] * 100
- vol_pct = volatilities[asset] * 100
- sharpe = sharpe_ratios[asset]
- print(f"{asset}: Return={ret_pct:.2f}%, Volatility={vol_pct:.2f}%, Sharpe={sharpe:.2f}")
-
-# Identify efficient assets (high return per unit risk)
-# Calculate 75th percentile threshold manually
-sorted_sharpes = sorted(sharpe_ratios.values, reverse=True)
-threshold_idx = int(len(sorted_sharpes) * 0.25)
-efficient_threshold = sorted_sharpes[threshold_idx] if threshold_idx < len(sorted_sharpes) else sorted_sharpes[-1]
-
-print(f"\n=== MOST EFFICIENT ASSETS (Sharpe >= {efficient_threshold:.2f}) ===")
-for asset, sharpe in sharpe_ratios.items():
- if sharpe >= efficient_threshold:
- print(f"{asset}: {sharpe:.2f}")
-Sector/Style Analysis
-# Group assets by characteristics (example grouping)
-asset_groups = {
- 'Mega Cap': ['Apple Inc.', 'Microsoft Corp.', 'Alphabet Inc.', 'Amazon.com Inc.'],
- 'Growth': ['Tesla Inc.', 'NVIDIA Corp.', 'Netflix Inc.'],
- 'Social Media': ['Meta Platforms Inc.']
-}
-
-print("\n=== GROUP ANALYSIS ===")
-for group_name, group_assets in asset_groups.items():
- # Filter assets that exist in our data
- group_series = [s for s in tech_stocks.constituents if s.label in group_assets]
-
- if group_series:
- group_frame = OpenFrame(constituents=group_series)
- group_metrics = group_frame.all_properties()
-
- avg_return = group_metrics.loc['Geometric return'].mean()
- avg_vol = group_metrics.loc['Volatility'].mean()
- avg_sharpe = group_metrics.loc['Return vol ratio'].mean()
-
- print(f"\n{group_name} ({len(group_series)} assets):")
- print(f" Average Return: {avg_return:.2%}")
- print(f" Average Volatility: {avg_vol:.2%}")
- print(f" Average Sharpe: {avg_sharpe:.2f}")
-Time Series Analysis
-# Rolling correlation analysis
-# Pick two assets for detailed analysis
-apple = next(s for s in tech_stocks.constituents if "Apple" in s.label)
-microsoft = next(s for s in tech_stocks.constituents if "Microsoft" in s.label)
-
-pair_frame = OpenFrame(constituents=[apple, microsoft])
-rolling_corr = pair_frame.rolling_corr(observations=252) # 1-year rolling
-
-print(f"\n=== ROLLING CORRELATION: {apple.label} vs {microsoft.label} ===")
-print(f"Current correlation: {rolling_corr.iloc[-1, 0]:.3f}")
-print(f"Average correlation: {rolling_corr.mean().iloc[0]:.3f}")
-print(f"Correlation range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}")
-Performance Attribution
-# Create equal-weighted portfolio for attribution
-portfolio_df = tech_stocks.make_portfolio(name="Tech Portfolio", weight_strat="eq_weights")
-portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-print(f"\n=== PORTFOLIO vs INDIVIDUAL ASSETS ===")
-print(f"Portfolio Return: {portfolio.geo_ret:.2%}")
-print(f"Portfolio Volatility: {portfolio.vol:.2%}")
-print(f"Portfolio Sharpe: {portfolio.ret_vol_ratio:.2f}")
-
-# Compare with individual assets using OpenFrame
-asset_metrics = tech_stocks.all_properties()
-individual_returns = asset_metrics.loc['Geometric return'].values
-individual_vols = asset_metrics.loc['Volatility'].values
-
-print(f"\nDiversification benefit:")
-equal_weights = [1/tech_stocks.item_count] * tech_stocks.item_count
-# Calculate weighted average manually
-weighted_avg_return = sum(ret * w for ret, w in zip(individual_returns, equal_weights))
-weighted_avg_vol = sum(vol * w for vol, w in zip(individual_vols, equal_weights))
-print(f" Weighted avg return: {weighted_avg_return:.2%}")
-print(f" Portfolio return: {portfolio.geo_ret:.2%}")
-print(f" Weighted avg volatility: {weighted_avg_vol:.2%}")
-print(f" Portfolio volatility: {portfolio.vol:.2%}")
-print(f" Volatility reduction: {(weighted_avg_vol - portfolio.vol):.2%}")
-Stress Testing
-# Identify worst market days (modifies original)
-market_proxy = tech_stocks.constituents[0] # Use first asset as market proxy
-market_proxy.value_to_ret()
-market_data = market_proxy.tsdf
-# Find worst 5% of days
-worst_threshold = market_data.quantile(0.05)
-worst_days = market_data[market_data <= worst_threshold]
-
-print(f"\n=== STRESS TEST ANALYSIS ===")
-print(f"Market stress threshold: {worst_threshold:.2%}")
-print(f"Number of stress days: {len(worst_days)}")
-
-# Analyze each asset's performance during stress
-print("\nAsset performance during market stress:")
-for series in tech_stocks.constituents:
- series.value_to_ret() # Modifies original
- asset_data = series.tsdf
- # Get returns on stress days
- stress_returns = asset_data.loc[worst_days.index]
- avg_stress_return = stress_returns.mean()
-
- print(f" {series.label}: {avg_stress_return:.2%}")
-Export Multi-Asset Results
-# Export using openseries native methods
-# Export frame data
-tech_stocks.to_xlsx('multi_asset_analysis.xlsx')
-
-# Note: For comprehensive Excel export with multiple sheets,
-# you can use the DataFrame returned by all_properties() and correl_matrix
-# which are pandas DataFrames and support to_excel() method
-print("\nMulti-asset analysis exported to 'multi_asset_analysis.xlsx'")
-Complete Multi-Asset Analysis Workflow
-Here’s how to perform a complete multi-asset analysis using openseries methods directly:
-# Example: Analyze tech stocks using openseries methods
-tech_tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"]
-
-# Load data using openseries methods
-series_list = []
-for ticker in tech_tickers:
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="3y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=ticker)
- series_list.append(series)
-
-if not series_list:
- print("No data loaded")
-else:
- # Create frame using openseries
- frame = OpenFrame(constituents=series_list)
-
- # Analysis using openseries properties and methods
- print(f"=== MULTI-ASSET ANALYSIS ===")
- print(f"Assets: {frame.item_count}")
- print(f"Period: {frame.first_idx} to {frame.last_idx}")
-
- # Key metrics using openseries all_properties method
- key_metrics = frame.all_properties(
- properties=['geo_ret', 'vol', 'ret_vol_ratio', 'max_drawdown']
- )
-
- print("\nKey Metrics:")
- print((key_metrics * 100).round(2)) # Convert to percentages
-
- # Correlations using openseries correl_matrix property
- correlations = frame.correl_matrix
- avg_correlation = correlations.mean().mean()
- print(f"\nAverage correlation: {avg_correlation:.3f}")
-
- # Create portfolio using openseries make_portfolio method
- portfolio_df = frame.make_portfolio(name="Equal Weight", weight_strat="eq_weights")
- portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
- print(f"\nEqual-weight portfolio:")
- print(f" Return: {portfolio.geo_ret:.2%}")
- print(f" Volatility: {portfolio.vol:.2%}")
- print(f" Sharpe: {portfolio.ret_vol_ratio:.2f}")
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- Portfolio Optimization
-This example demonstrates various portfolio optimization techniques using openseries, including both theoretical approaches and real-world applications with actual fund data.
-Basic Portfolio Optimization Setup
-import yfinance as yf
-from openseries import OpenTimeSeries, OpenFrame
-from openseries import efficient_frontier, simulate_portfolios
-
-# Define investment universe
-universe = {
- "VTI": "Total Stock Market",
- "VEA": "Developed Markets",
- "VWO": "Emerging Markets",
- "BND": "Total Bond Market",
- "VNQ": "Real Estate",
- "VDE": "Energy",
- "VGT": "Technology",
- "VHT": "Healthcare"
-}
-
-# Load data
-assets = []
-for ticker, name in universe.items():
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="5y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=name)
- assets.append(series)
- print(f"Loaded {name}")
-
-# Create investment universe frame
-investment_universe = OpenFrame(constituents=assets)
-print(f"\nInvestment universe: {investment_universe.item_count} assets")
-print(f"Period: {investment_universe.first_idx} to {investment_universe.last_idx}")
-Mean-Variance Optimization
-# Calculate efficient frontier
-# This may fail with various exceptions
-frontier_df, simulated_df, optimal_portfolio = efficient_frontier(
- eframe=investment_universe,
- num_ports=100,
- seed=42
-)
-
-print("=== EFFICIENT FRONTIER RESULTS ===")
-print(f"Generated {len(frontier_df)} efficient portfolios")
-print(f"Simulated {len(simulated_df)} random portfolios")
-
-# Find key portfolios
-returns = frontier_df['ret']
-volatilities = frontier_df['stdev']
-sharpe_ratios = returns / volatilities
-
-# Maximum Sharpe ratio portfolio
-max_sharpe_idx = sharpe_ratios.idxmax()
-max_sharpe_weights = optimal_portfolio[-len(investment_universe.constituents):]
-
-print(f"\n=== MAXIMUM SHARPE RATIO PORTFOLIO ===")
-print(f"Expected Return: {frontier_df.iloc[max_sharpe_idx]['ret']:.2%}")
-print(f"Volatility: {frontier_df.iloc[max_sharpe_idx]['stdev']:.2%}")
-print(f"Sharpe Ratio: {sharpe_ratios.iloc[max_sharpe_idx]:.2f}")
-
-print("\nOptimal Weights:")
-for i, weight in enumerate(max_sharpe_weights):
- asset_name = investment_universe.constituents[i].label
- if weight > 0.01: # Only show weights > 1%
- print(f" {asset_name}: {weight:.1%}")
-
-# Minimum volatility portfolio
-min_vol_idx = volatilities.idxmin()
-min_vol_weights = frontier_df.iloc[min_vol_idx][investment_universe.columns_lvl_zero].values
-
-print(f"\n=== MINIMUM VOLATILITY PORTFOLIO ===")
-print(f"Expected Return: {min_vol_row['ret']:.2%}")
-print(f"Volatility: {min_vol_row['stdev']:.2%}")
-print(f"Sharpe Ratio: {sharpe_ratios.iloc[min_vol_idx]:.2f}")
-
-print("\nMinimum Volatility Weights:")
-for col in investment_universe.columns_lvl_zero:
- weight = min_vol_row[col]
- if weight > 0.01:
- print(f" {col}: {weight:.1%}")
-Monte Carlo Portfolio Simulation
-# Generate random portfolios
-# This may fail with various exceptions
-simulation_results = simulate_portfolios(
- simframe=investment_universe,
- num_ports=50000,
- seed=42
-)
-
-print(f"\n=== MONTE CARLO SIMULATION ===")
-print(f"Simulated {len(simulation_results)} random portfolios")
-
-sim_returns = simulation_results['ret'].values
-sim_volatilities = simulation_results['stdev'].values
-sim_sharpe_ratios = sim_returns / sim_volatilities
-
-# Statistics of simulated portfolios
-print(f"\nSimulation Statistics:")
-print(f"Return range: {sim_returns.min():.2%} to {sim_returns.max():.2%}")
-print(f"Volatility range: {sim_volatilities.min():.2%} to {sim_volatilities.max():.2%}")
-print(f"Sharpe range: {sim_sharpe_ratios.min():.2f} to {sim_sharpe_ratios.max():.2f}")
-
-# Best portfolios from simulation
-sorted_indices = sorted(range(len(sim_sharpe_ratios)), key=lambda i: sim_sharpe_ratios.iloc[i], reverse=True)
-top_sharpe_indices = sorted_indices[:5]
-
-print(f"\n=== TOP 5 SIMULATED PORTFOLIOS ===")
-for i, idx in enumerate(reversed(top_sharpe_indices)):
- print(f"\nRank {i+1}:")
- print(f" Return: {sim_returns[idx]:.2%}")
- print(f" Volatility: {sim_volatilities[idx]:.2%}")
- print(f" Sharpe: {sim_sharpe_ratios[idx]:.2f}")
-
- weights = simulation_results.iloc[idx][investment_universe.columns_lvl_zero].values
- print(" Weights:")
- for j, weight in enumerate(weights):
- if weight > 0.05: # Only show weights > 5%
- asset_name = investment_universe.constituents[j].label
- print(f" {asset_name}: {weight:.1%}")
-Risk-Based Portfolio Strategies
-Equal Weight Portfolio
-# Equal weight portfolio using native weight_strat
-equal_weight_portfolio_df = investment_universe.make_portfolio(
- name="Equal Weight",
- weight_strat="eq_weights"
-)
-equal_weight_portfolio = OpenTimeSeries.from_df(dframe=equal_weight_portfolio_df)
-
-print(f"\n=== EQUAL WEIGHT PORTFOLIO ===")
-print(f"Return: {equal_weight_portfolio.geo_ret:.2%}")
-print(f"Volatility: {equal_weight_portfolio.vol:.2%}")
-print(f"Sharpe: {equal_weight_portfolio.ret_vol_ratio:.2f}")
-Inverse Volatility Portfolio
-# Inverse volatility weighting using native weight_strat
-inv_vol_portfolio_df = investment_universe.make_portfolio(
- name="Inverse Volatility",
- weight_strat="inv_vol"
-)
-inv_vol_portfolio = OpenTimeSeries.from_df(dframe=inv_vol_portfolio_df)
-
-print(f"\n=== INVERSE VOLATILITY PORTFOLIO ===")
-print(f"Return: {inv_vol_portfolio.geo_ret:.2%}")
-print(f"Volatility: {inv_vol_portfolio.vol:.2%}")
-print(f"Sharpe: {inv_vol_portfolio.ret_vol_ratio:.2f}")
-Maximum Diversification Portfolio
-The maximum diversification strategy aims to maximize portfolio diversification by optimizing the correlation structure. This strategy can encounter numerical issues in certain scenarios:
-# Maximum diversification portfolio using native weight_strat
-# This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError
-max_div_portfolio_df = investment_universe.make_portfolio(
- name="Maximum Diversification",
- weight_strat="max_div"
-)
-max_div_portfolio = OpenTimeSeries.from_df(dframe=max_div_portfolio_df)
-
-print(f"\n=== MAXIMUM DIVERSIFICATION PORTFOLIO ===")
-print(f"Return: {max_div_portfolio.geo_ret:.2%}")
-print(f"Volatility: {max_div_portfolio.vol:.2%}")
-print(f"Sharpe: {max_div_portfolio.ret_vol_ratio:.2f}")
-Minimum Volatility Overweight Portfolio
-# Minimum volatility overweight portfolio using native weight_strat
-min_vol_portfolio_df = investment_universe.make_portfolio(
- name="Min Vol Overweight",
- weight_strat="min_vol_overweight"
-)
-min_vol_portfolio = OpenTimeSeries.from_df(dframe=min_vol_portfolio_df)
-
-print(f"\n=== MINIMUM VOLATILITY OVERWEIGHT PORTFOLIO ===")
-print(f"Return: {min_vol_portfolio.geo_ret:.2%}")
-print(f"Volatility: {min_vol_portfolio.vol:.2%}")
-print(f"Sharpe: {min_vol_portfolio.ret_vol_ratio:.2f}")
-Portfolio Comparison
-# Compare all portfolio strategies
-portfolios = [
- equal_weight_portfolio,
- inv_vol_portfolio,
- max_div_portfolio,
- min_vol_portfolio
-]
-
-# Add optimized portfolios if available
-if 'max_sharpe_weights' in locals():
- investment_universe.weights = max_sharpe_weights.tolist()
- max_sharpe_portfolio_df = investment_universe.make_portfolio(
- name="Max Sharpe (Optimized)"
- )
- max_sharpe_portfolio = OpenTimeSeries.from_df(dframe=max_sharpe_portfolio_df)
- portfolios.append(max_sharpe_portfolio)
-
-if 'min_vol_weights' in locals():
- investment_universe.weights = min_vol_weights.tolist()
- min_vol_portfolio_df = investment_universe.make_portfolio(
- name="Min Vol (Optimized)"
- )
- min_vol_portfolio = OpenTimeSeries.from_df(dframe=min_vol_portfolio_df)
- portfolios.append(min_vol_portfolio)
-
-# Create comparison frame
-comparison_frame = OpenFrame(constituents=portfolios)
-comparison_metrics = comparison_frame.all_properties()
-
-# Display key metrics
-key_metrics = comparison_metrics.loc[['geo_ret', 'vol', 'ret_vol_ratio', 'max_drawdown']]
-key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown']
-
-print(f"\n=== PORTFOLIO STRATEGY COMPARISON ===")
-print((key_metrics * 100).round(2)) # Convert to percentages
-Weight Strategy Details
-The openseries library provides several built-in weight strategies for portfolio construction:
--
-
- Equal Weights (``eq_weights``)
-
-
Assigns equal weight to all assets
-Most robust strategy, always works
-Good baseline for comparison
-
-- Inverse Volatility (``inv_vol``)
-
-
Weights assets inversely to their volatility
-Lower volatility assets get higher weights
-Generally stable and reliable
-
-- Maximum Diversification (``max_div``)
-
-
Optimizes correlation structure for maximum diversification
-Can encounter numerical issues with certain data patterns
-May produce negative weights in some scenarios
-Raises
MaxDiversificationNaNErrorfor numerical issues
-Raises
MaxDiversificationNegativeWeightsErrorfor negative weights
-
-- Minimum Volatility Overweight (``min_vol_overweight``)
-
-
Overweights the least volatile asset (60% weight)
-Distributes remaining 40% equally among other assets
-Based on the low volatility anomaly
-
-- Exception Handling
When using the maximum diversification strategy, it’s recommended to handle potential exceptions:
---from openseries.owntypes import ( - MaxDiversificationNaNError, - MaxDiversificationNegativeWeightsError -) - -# This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError -portfolio_df = frame.make_portfolio(name="Max Div", weight_strat="max_div") -
-
Backtesting Framework
-# Define strategies to backtest using native weight_strat
-strategies = {
- 'Equal Weight': 'eq_weights',
- 'Inverse Volatility': 'inv_vol',
- 'Max Diversification': 'max_div',
- 'Min Vol Overweight': 'min_vol_overweight'
-}
-
-# Run backtest using native strategies
-backtest_results = {}
-for strategy_name, weight_strat in strategies.items():
- # This may fail with MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError, or other exceptions
- portfolio_df = investment_universe.make_portfolio(
- name=strategy_name,
- weight_strat=weight_strat
- )
- portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
- backtest_results[strategy_name] = {
- 'return': portfolio.geo_ret,
- 'volatility': portfolio.vol,
- 'sharpe': portfolio.ret_vol_ratio,
- 'max_drawdown': portfolio.max_drawdown,
- 'calmar': portfolio.geo_ret / abs(portfolio.max_drawdown) if portfolio.max_drawdown != 0 else float('nan')
- }
-
-print(f"\n=== BACKTEST RESULTS ===")
-for strategy_name, metrics in backtest_results.items():
- print(f"\n{strategy_name}:")
- print(f" Return: {metrics['return']:.4f}")
- print(f" Volatility: {metrics['volatility']:.4f}")
- print(f" Sharpe: {metrics['sharpe']:.4f}")
- print(f" Max Drawdown: {metrics['max_drawdown']:.4f}")
- print(f" Calmar: {metrics['calmar']:.4f}")
-
-# Rank strategies
-sorted_strategies = sorted(backtest_results.items(), key=lambda x: x[1]['sharpe'], reverse=True)
-best_strategy = sorted_strategies[0][0]
-
-print(f"\nBest performing strategy: {best_strategy}")
-print(f"Sharpe ratio: {sorted_strategies[0][1]['sharpe']:.3f}")
-Export Optimization Results
-# Export using openseries native methods
-# Export frame data
-investment_universe.to_xlsx('portfolio_optimization_results.xlsx')
-
-# Note: For comprehensive Excel export with multiple sheets,
-# the DataFrames returned by all_properties() and correl_matrix
-# are pandas DataFrames and support to_excel() method
-print("\nOptimization results exported to 'portfolio_optimization_results.xlsx'")
-Real-World Fund Portfolio Optimization
-This section demonstrates portfolio optimization using actual fund data from professional fund managers, showing how optimization techniques apply in practice.
-Using Real Fund Data for Optimization
-Here’s how to work with real fund data using openseries methods directly:
-from requests import get as requests_get
-from openseries import (
- OpenTimeSeries, OpenFrame, ValueType,
- efficient_frontier, prepare_plot_data, sharpeplot,
- load_plotly_dict, get_previous_business_day_before_today
-)
-
-# Define fund universe for optimization
-fund_universe_isins = [
- "SE0015243886", # Global High Yield
- "SE0011337195", # Global Equity
- "SE0011670843", # Global Bond
- "SE0017832280", # Alternative Strategy
- "SE0017832330", # Multi-Asset Strategy
-]
-
-# Load fund data using openseries methods
-response = requests_get(url="https://api.captor.se/public/api/nav", timeout=10)
-response.raise_for_status()
-
-series_list = []
-result = response.json()
-
-for data in result:
- if data["isin"] in fund_universe_isins:
- series = OpenTimeSeries.from_arrays(
- name=data["longName"],
- isin=data["isin"],
- baseccy=data["currency"],
- dates=data["dates"],
- values=data["navPerUnit"],
- valuetype=ValueType.PRICE,
- )
- series_list.append(series)
-
-# Create fund universe using openseries OpenFrame
-fund_universe = OpenFrame(constituents=series_list)
-
-# Process data using openseries methods
-fund_universe = fund_universe.value_nan_handle().trunc_frame().to_cumret()
-
-print(f"Fund universe created with {fund_universe.item_count} funds")
-print(f"Analysis period: {fund_universe.first_idx} to {fund_universe.last_idx}")
-Advanced Optimization with Real Data
-# Set optimization parameters
-simulations = 10000
-frontier_points = 50
-seed = 55
-
-# Create current portfolio (equal weights)
-current_portfolio_df = fund_universe.make_portfolio(
- name="Current Portfolio",
- weight_strat="eq_weights",
-)
-current_portfolio = OpenTimeSeries.from_df(dframe=current_portfolio_df)
-
-# Calculate efficient frontier
-frontier, simulated_portfolios, optimal_portfolio = efficient_frontier(
- eframe=fund_universe,
- num_ports=simulations,
- seed=seed,
- frontier_points=frontier_points,
-)
-
-# Prepare visualization data
-plot_data = prepare_plot_data(
- assets=fund_universe,
- current=current_portfolio,
- optimized=optimal_portfolio,
-)
-
-# Load plotly configuration
-figdict, _ = load_plotly_dict()
-
-# Create efficient frontier plot
-optimization_plot, _ = sharpeplot(
- sim_frame=simulated_portfolios,
- line_frame=frontier,
- point_frame=plot_data,
- point_frame_mode="markers+text",
- title="Real Fund Portfolio Optimization",
- add_logo=False,
- auto_open=False,
- output_type="div",
-)
-optimization_plot = optimization_plot.update_layout(width=1200, height=700)
-
-# Display the optimization results
-optimization_plot.show(config=figdict["config"])
-Performance Comparison Analysis
-# Compare different portfolio strategies
-strategies = {}
-
-# Equal weight portfolio
-equal_weight_portfolio_df = fund_universe.make_portfolio(
- name="Equal Weight", weight_strat="eq_weights"
-)
-equal_weight_portfolio = OpenTimeSeries.from_df(dframe=equal_weight_portfolio_df)
-strategies['Equal Weight'] = equal_weight_portfolio
-
-# Optimal portfolio from efficient frontier
-fund_universe.weights = optimal_portfolio[-fund_universe.item_count:].tolist()
-optimal_portfolio_df = fund_universe.make_portfolio(name="Optimal Portfolio")
-optimal_portfolio_series = OpenTimeSeries.from_df(dframe=optimal_portfolio_df)
-strategies['Optimal Portfolio'] = optimal_portfolio_series
-
-# Create comparison frame
-comparison_frame = OpenFrame(constituents=list(strategies.values()))
-comparison_metrics = comparison_frame.all_properties()
-
-# Display key metrics
-key_metrics = comparison_metrics.loc[['geo_ret', 'vol', 'ret_vol_ratio', 'max_drawdown']]
-key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown']
-
-print("=== PORTFOLIO STRATEGY COMPARISON ===")
-print((key_metrics * 100).round(2))
-
-# Calculate improvement metrics
-improvement = {
- 'Return Improvement': (optimal_portfolio_series.geo_ret - equal_weight_portfolio.geo_ret) * 100,
- 'Volatility Change': (optimal_portfolio_series.vol - equal_weight_portfolio.vol) * 100,
- 'Sharpe Improvement': optimal_portfolio_series.ret_vol_ratio - equal_weight_portfolio.ret_vol_ratio,
-}
-
-print("\n=== OPTIMIZATION IMPROVEMENTS ===")
-for metric, value in improvement.items():
- print(f"{metric}: {value:+.2f}")
-Complete Optimization Workflow
-Here’s how to perform portfolio optimization using openseries methods directly:
-# Example: Optimize ETF portfolio using openseries methods
-etf_tickers = ["VTI", "VEA", "VWO", "BND", "VNQ"]
-
-# Load data using openseries methods
-assets = []
-for ticker in etf_tickers:
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="5y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=ticker)
- assets.append(series)
-
-if len(assets) < 2:
- print("Need at least 2 assets for optimization")
-else:
- frame = OpenFrame(constituents=assets)
-
- # Use openseries native weight strategies
- strategies = {
- 'Equal Weight': 'eq_weights',
- 'Inverse Volatility': 'inv_vol',
- 'Max Diversification': 'max_div',
- 'Min Vol Overweight': 'min_vol_overweight'
- }
-
- # Create portfolios using openseries make_portfolio method
- results = {}
- for name, weight_strat in strategies.items():
- # This may fail with MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError, or other exceptions
- portfolio_df = frame.make_portfolio(name=name, weight_strat=weight_strat)
- portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
- results[name] = {
- 'Return': portfolio.geo_ret,
- 'Volatility': portfolio.vol,
- 'Sharpe': portfolio.ret_vol_ratio,
- 'Max Drawdown': portfolio.max_drawdown
- }
-
- print("=== PORTFOLIO OPTIMIZATION RESULTS ===")
- for name, metrics in results.items():
- print(f"\n{name}:")
- print(f" Return: {metrics['Return']*100:.2f}%")
- print(f" Volatility: {metrics['Volatility']*100:.2f}%")
- print(f" Sharpe: {metrics['Sharpe']:.2f}")
- print(f" Max Drawdown: {metrics['Max Drawdown']*100:.2f}%")
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- Single Asset Analysis
-This example demonstrates comprehensive analysis of a single financial asset using openseries.
-Basic Setup
-import yfinance as yf
-from openseries import OpenTimeSeries
-import numpy as np
-
-# Download Apple stock data
-ticker = yf.Ticker("AAPL")
-data = ticker.history(period="5y")
-
-# Create OpenTimeSeries
-apple = OpenTimeSeries.from_df(
- dframe=data['Close']
-)
-
-# Set descriptive label
-apple.set_new_label(lvl_zero="Apple Inc. (AAPL)")
-
-print(f"Loaded {apple.length} observations")
-print(f"Date range: {apple.first_idx} to {apple.last_idx}")
-Performance Analysis
-# Basic performance metrics
-print("=== PERFORMANCE METRICS ===")
-print(f"Total Return: {apple.value_ret:.2%}")
-print(f"Annualized Return: {apple.geo_ret:.2%}")
-print(f"Annualized Volatility: {apple.vol:.2%}")
-print(f"Sharpe Ratio: {apple.ret_vol_ratio:.2f}")
-
-# Get all metrics at once
-all_metrics = apple.all_properties()
-print("\n=== ALL METRICS ===")
-print(all_metrics)
-Risk Analysis
-# Risk metrics
-print("=== RISK ANALYSIS ===")
-print(f"Maximum Drawdown: {apple.max_drawdown:.2%}")
-print(f"Max Drawdown Date: {apple.max_drawdown_date}")
-print(f"95% VaR (daily): {apple.var_down:.2%}")
-print(f"95% CVaR (daily): {apple.cvar_down:.2%}")
-print(f"Worst Single Day: {apple.worst:.2%}")
-print(f"Sortino Ratio: {apple.sortino_ratio:.2f}")
-Time Series Transformations
-# Convert to returns (modifies original)
-apple.value_to_ret()
-print(f"Returns series length: {apple.length}")
-
-# Create drawdown series (modifies original)
-apple.to_drawdown_series()
-
-# Convert to log returns (modifies original)
-apple.value_to_log()
-
-# Resample to monthly (modifies original)
-apple.resample_to_business_period_ends(freq="BME")
-print(f"Monthly data points: {apple.length}")
-Rolling Analysis
-# Rolling volatility (1-year window)
-rolling_vol = apple.rolling_vol(observations=252)
-print(f"Current 1Y volatility: {rolling_vol.iloc[-1, 0]:.2%}")
-print(f"Average 1Y volatility: {rolling_vol.mean().iloc[0]:.2%}")
-
-# Rolling returns (30-day)
-rolling_returns = apple.rolling_return(observations=30)
-
-# Rolling VaR
-rolling_var = apple.rolling_var_down(observations=252)
-Visualization
-# Plot price series
-fig, _ = apple.plot_series()
-
-# Plot returns histogram
-fig, _ = apple_returns.plot_histogram()
-
-# Plot drawdown series
-fig, _ = apple_drawdowns.plot_series()
-Calendar Analysis
-# Annual returns by calendar year
-years = [2019, 2020, 2021, 2022, 2023, 2024]
-
-print("=== CALENDAR YEAR RETURNS ===")
-for year in years:
- # This may fail if no data exists for the year
- year_return = apple.value_ret_calendar_period(year=year)
- print(f"{year}: {year_return:.2%}")
-Export Results
-# Export to Excel
-apple.to_xlsx("apple_analysis.xlsx")
-
-# Export metrics to CSV
-all_metrics.to_csv("apple_metrics.csv")
-
-# Export to JSON
-apple.to_json("apple_data.json")
-Complete Analysis Workflow
-Here’s how to perform comprehensive single asset analysis using openseries methods directly:
-import yfinance as yf
-from openseries import OpenTimeSeries
-
-# Example: Analyze Apple stock using openseries methods
-ticker_symbol = "AAPL"
-
-# Download data using openseries methods
-ticker = yf.Ticker(ticker_symbol)
-data = ticker.history(period="5y")
-
-# Create series using openseries from_df method
-series = OpenTimeSeries.from_df(
- dframe=data['Close'],
- name=ticker_symbol
-)
-
-# Analysis using openseries properties and methods
-print(f"=== {ticker_symbol} ANALYSIS ===")
-print(f"Period: {series.first_idx} to {series.last_idx}")
-print(f"Observations: {series.length}")
-
-# Key metrics using openseries properties
-metrics = {
- 'Total Return': f"{series.value_ret:.2%}",
- 'Annual Return': f"{series.geo_ret:.2%}",
- 'Volatility': f"{series.vol:.2%}",
- 'Sharpe Ratio': f"{series.ret_vol_ratio:.2f}",
- 'Max Drawdown': f"{series.max_drawdown:.2%}",
- '95% VaR': f"{series.var_down:.2%}",
- 'Skewness': f"{series.skew:.2f}",
- 'Kurtosis': f"{series.kurtosis:.2f}"
-}
-
-for metric, value in metrics.items():
- print(f"{metric}: {value}")
-
-# Export results using openseries to_xlsx method
-filename = f"{ticker_symbol.lower()}_analysis.xlsx"
-series.to_xlsx(filename)
-print(f"\nResults exported to {filename}")
-
-# Example: Analyze multiple assets
-tickers = ["AAPL", "TSLA", "MSFT"]
-for ticker_symbol in tickers:
- ticker = yf.Ticker(ticker_symbol)
- data = ticker.history(period="2y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=ticker_symbol)
-
- print(f"\n{ticker_symbol}:")
- print(f" Return: {series.geo_ret:.2%}")
- print(f" Volatility: {series.vol:.2%}")
- print(f" Sharpe: {series.ret_vol_ratio:.2f}")
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- openseries Documentation
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-
-openseries is a Python library for analyzing financial time series data. It provides tools to work with single assets or groups of assets, designed specifically for daily or less frequent data.
-The library is built around two main classes:
--
-
OpenTimeSeries: For managing and analyzing individual time series
-OpenFrame: For managing groups of time series and portfolio analysis
-
Key Features
--
-
Financial Analysis: Comprehensive set of financial metrics and ratios
-Risk Management: VaR, CVaR, drawdown analysis, and risk-adjusted returns
-Portfolio Tools: Portfolio optimization, rebalancing, and performance attribution
-Visualization: Interactive plots using Plotly
-Data Handling: Robust date handling and business day calendars
-Type Safety: Built with Pydantic for data validation and type safety
-
Quick Start
-Install openseries using pip:
-pip install openseries
-Or using conda:
-conda install -c conda-forge openseries
-Here’s a simple example to get you started:
-from openseries import OpenTimeSeries
-import yfinance as yf
-
-# Download data
-ticker = yf.Ticker("^GSPC")
-history = ticker.history(period="5y")
-
-# Create OpenTimeSeries
-series = OpenTimeSeries.from_df(dframe=history.loc[:, "Close"])
-series.set_new_label(lvl_zero="S&P 500")
-
-# Calculate key metrics
-print(f"Annual Return: {series.geo_ret:.2%}")
-print(f"Volatility: {series.vol:.2%}")
-print(f"Sharpe Ratio: {series.ret_vol_ratio:.2f}")
-print(f"Max Drawdown: {series.max_drawdown:.2%}")
-
-# Create interactive plot
-series.plot_series()
-Documentation Contents
-
-
-User Guide
--
-
- Installation - -
- Quick Start Guide
-
-
- Your First OpenTimeSeries -
- Loading Data from External Sources -
- Basic Financial Metrics -
- Creating Visualizations -
- Working with Multiple Assets (OpenFrame) -
- Portfolio Analysis -
- Data Transformations -
- Exporting Results -
- Working with Business Days -
- Next Steps -
- Key Concepts to Remember -
- Common Patterns -
- - Core Concepts - -
- Data Handling - -
-
-Tutorials
- -
-
-Examples
--
-
- Single Asset Analysis - -
- Multi-Asset Analysis - -
- Portfolio Optimization
-
-
- Basic Portfolio Optimization Setup -
- Mean-Variance Optimization -
- Monte Carlo Portfolio Simulation -
- Risk-Based Portfolio Strategies -
- Minimum Volatility Overweight Portfolio -
- Portfolio Comparison -
- Backtesting Framework -
- Export Optimization Results -
- Real-World Fund Portfolio Optimization -
- Complete Optimization Workflow -
- - Rebalanced Portfolio Simulation
-
-
- Understanding Rebalanced Portfolio Simulation -
- Basic Rebalanced Portfolio Setup -
- Daily Rebalancing vs Theoretical Portfolio -
- Different Rebalancing Frequencies -
- Detailed Portfolio Analysis -
- Equal Weight vs Custom Weight Strategies -
- Cash Management Analysis -
- Subset Portfolio Analysis -
- Transaction Cost Analysis -
- Performance Attribution -
- Real-World Application Example -
- Summary and Best Practices -
- - Reporting - -
-
-Important Notes
- -Python Version Support
-
-
-API Reference
--
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- openseries package - -
- OpenTimeSeries - -
- OpenFrame - -
- Portfolio Tools - -
- Simulation - -
- Report Generation - -
- Date Utilities - -
- Types and Enums - -
-
-Development
- -Indices and tables
--
-
- -
- -
- -
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-
-
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-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
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- Advanced Features
-This tutorial covers advanced openseries features including custom analysis, integration with other libraries, and extending functionality.
-Factor Analysis and Regression
-Multi-Factor Model Analysis
-import yfinance as yf
-from openseries import OpenTimeSeries, OpenFrame
-
-# Load factor data (Fama-French factors would be ideal, using proxies here)
-factor_tickers = {
- "^GSPC": "Market",
- "^RUT": "Small Cap", # Size factor proxy
- "EFA": "International", # International factor
- "TLT": "Bonds" # Interest rate factor
-}
-
-# Load factor data
-factor_series = []
-for ticker, name in factor_tickers.items():
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="3y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=name)
- factor_series.append(series)
-
-# Create factor frame
-factors = OpenFrame(constituents=factor_series)
-
-# Load individual stock for analysis
-stock_data = yf.Ticker("AAPL").history(period="3y")
-apple = OpenTimeSeries.from_df(dframe=stock_data['Close'])
-apple.set_new_label(lvl_zero="Apple")
-
-# Add stock to factor frame for regression
-analysis_frame = OpenFrame(constituents=factor_series + [apple])
-
-# Perform multi-factor regression
-# This may fail with various exceptions
-regression_results = analysis_frame.multi_factor_linear_regression(
- dependent_variable_idx=-1 # Apple is the last series (dependent variable)
-)
-
-print("\n=== MULTI-FACTOR REGRESSION RESULTS ===")
-print("Regression Summary:")
-print(regression_results['summary'])
-
-print("\nFactor Loadings (Betas):")
-for i, factor_name in enumerate([s.label for s in factor_series]):
- beta = regression_results['coefficients'][i+1] # Skip intercept
- print(f" {factor_name}: {beta:.4f}")
-
-print(f"\nR-squared: {regression_results['r_squared']:.4f}")
-print(f"Adjusted R-squared: {regression_results['adj_r_squared']:.4f}")
-Rolling Factor Analysis
-# Rolling beta analysis with market
-market_series = factor_series[0] # S&P 500
-stock_vs_market = OpenFrame(constituents=[apple, market_series])
-
-# Calculate rolling beta
-rolling_beta = stock_vs_market.rolling_beta(observations=252) # 1-year rolling
-
-print(f"\n=== ROLLING BETA ANALYSIS ===")
-print(f"Current Beta: {rolling_beta.iloc[-1, 0]:.3f}")
-print(f"Average Beta: {rolling_beta.mean().iloc[0]:.3f}")
-print(f"Beta Range: {rolling_beta.min().iloc[0]:.3f} to {rolling_beta.max().iloc[0]:.3f}")
-print(f"Beta Volatility: {rolling_beta.std().iloc[0]:.3f}")
-
-# Rolling correlation
-rolling_corr = stock_vs_market.rolling_corr(observations=252)
-
-print(f"\n=== ROLLING CORRELATION ANALYSIS ===")
-print(f"Current Correlation: {rolling_corr.iloc[-1, 0]:.3f}")
-print(f"Average Correlation: {rolling_corr.mean().iloc[0]:.3f}")
-print(f"Correlation Range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}")
-Exporting Custom Plotly Figures
-The export_plotly_figure function allows you to export any Plotly figure to a mobile-responsive HTML file. This is useful when you create custom visualizations using Plotly’s graph objects that aren’t directly available through openseries plotting methods.
Creating Custom Plots
-You can create any Plotly figure and export it using the same responsive HTML format that openseries uses internally:
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-from openseries import export_plotly_figure
-from pathlib import Path
-
-# Create a custom subplot figure
-fig = make_subplots(
- rows=2, cols=2,
- subplot_titles=('Price Chart', 'Volume', 'Returns Distribution', 'Drawdown'),
- specs=[[{"secondary_y": True}, {"type": "bar"}],
- [{"type": "histogram"}, {"type": "scatter"}]]
-)
-
-# Add traces (example data)
-fig.add_trace(
- go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13], name="Price"),
- row=1, col=1
-)
-fig.add_trace(
- go.Bar(x=[1, 2, 3, 4], y=[100, 200, 150, 300], name="Volume"),
- row=1, col=2
-)
-fig.add_trace(
- go.Histogram(x=[0.01, -0.02, 0.015, -0.01, 0.02], name="Returns"),
- row=2, col=1
-)
-fig.add_trace(
- go.Scatter(x=[1, 2, 3, 4], y=[0, -0.05, -0.03, -0.08], name="Drawdown"),
- row=2, col=2
-)
-
-# Update layout
-fig.update_layout(height=800, title_text="Custom Multi-Panel Dashboard")
-
-# Export to responsive HTML
-output_path = export_plotly_figure(
- figure=fig,
- fig_config={"responsive": True},
- output_type="file",
- filename="custom_dashboard.html",
- include_plotlyjs="cdn",
- plotfile=Path("output/custom_dashboard.html"),
- title="Custom Financial Dashboard",
- auto_open=True,
-)
-
-print(f"Dashboard saved to: {output_path}")
-Using with Plotly Express
-You can also use export_plotly_figure with Plotly Express figures:
import plotly.express as px
-import pandas as pd
-from openseries import export_plotly_figure
-from pathlib import Path
-
-# Create sample data
-df = pd.DataFrame({
- 'Date': pd.date_range('2020-01-01', periods=100),
- 'Asset_A': 100 + pd.Series(range(100)).cumsum() * 0.1,
- 'Asset_B': 100 + pd.Series(range(100)).cumsum() * 0.15,
-})
-
-# Create a Plotly Express figure
-fig = px.line(
- df, x='Date', y=['Asset_A', 'Asset_B'],
- title='Asset Comparison',
- labels={'value': 'Price', 'variable': 'Asset'}
-)
-
-# Export with responsive HTML
-export_plotly_figure(
- figure=fig,
- fig_config={"responsive": True, "displayModeBar": True},
- output_type="file",
- filename="asset_comparison.html",
- include_plotlyjs="cdn",
- plotfile=Path("output/asset_comparison.html"),
- title="Asset Price Comparison",
- auto_open=False,
-)
-Inline HTML Output
-For embedding in web applications or reports, you can generate inline HTML divs:
-import plotly.graph_objects as go
-from openseries import export_plotly_figure
-
-# Create a simple figure
-fig = go.Figure(data=go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13]))
-
-# Generate inline HTML div
-html_div = export_plotly_figure(
- figure=fig,
- fig_config={},
- output_type="div",
- filename="my_plot.html",
- include_plotlyjs="cdn",
- plotfile=Path("dummy.html"), # Ignored for div output
-)
-
-# html_div can now be embedded in HTML documents
-print(html_div[:100]) # Preview the HTML
-Benefits of export_plotly_figure
-The export_plotly_figure function provides several advantages over Plotly’s default HTML export:
-
-
Mobile Responsive: Automatically adapts to different screen sizes and device orientations
-Optimized Viewport: Proper viewport settings for mobile devices
-Auto-Resize: JavaScript handles window resizing and orientation changes
-Consistent Styling: Uses the same responsive CSS as openseries internal plots
-Optional Title Container: Can include a title and logo in a responsive header
-
This makes it ideal for creating dashboards and reports that need to work well on both desktop and mobile devices.
-This tutorial demonstrates how to extend openseries with advanced functionality for sophisticated financial analysis workflows.
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- Basic Financial Analysis
-This tutorial demonstrates how to perform fundamental financial analysis using openseries with real market data.
-Setting Up
-First, let’s import the necessary libraries and download some data:
-import yfinance as yf
-import pandas as pd
-import numpy as np
-from openseries import OpenTimeSeries, OpenFrame
-from datetime import date, datetime
-
-# Download S&P 500 data for the last 5 years
-ticker = yf.Ticker("^GSPC")
-data = ticker.history(period="5y")
-
-# Create OpenTimeSeries
-sp500 = OpenTimeSeries.from_df(
- dframe=data['Close']
-)
-
-# Set a descriptive label
-sp500.set_new_label(lvl_zero="S&P 500 Index")
-
-print(f"Loaded {sp500.length} observations")
-print(f"Date range: {sp500.first_idx} to {sp500.last_idx}")
-Basic Performance Metrics
-Let’s calculate the fundamental performance metrics:
-# Total return over the period
-total_return = sp500.value_ret
-print(f"Total Return: {total_return:.2%}")
-
-# Annualized return (CAGR)
-annual_return = sp500.geo_ret
-print(f"Annualized Return (CAGR): {annual_return:.2%}")
-
-# Arithmetic mean return
-arithmetic_return = sp500.arithmetic_ret
-print(f"Arithmetic Mean Return: {arithmetic_return:.2%}")
-
-# Time period analysis
-print(f"Investment period: {sp500.yearfrac:.2f} years")
-print(f"Number of observations: {sp500.length}")
-print(f"Periods per year: {sp500.periods_in_a_year:.1f}")
-Risk Analysis
-Now let’s examine the risk characteristics:
-# Volatility (annualized standard deviation)
-volatility = sp500.vol
-print(f"Annualized Volatility: {volatility:.2%}")
-
-# Downside deviation (volatility of negative returns only)
-downside_vol = sp500.downside_deviation
-print(f"Downside Deviation: {downside_vol:.2%}")
-
-# Value at Risk (95% confidence level)
-var_95 = sp500.var_down
-print(f"95% Value at Risk (daily): {var_95:.2%}")
-
-# Conditional Value at Risk (Expected Shortfall)
-cvar_95 = sp500.cvar_down
-print(f"95% CVaR (daily): {cvar_95:.2%}")
-
-# Maximum single-day loss
-worst_day = sp500.worst
-print(f"Worst single day: {worst_day:.2%}")
-Risk-Adjusted Returns
-Calculate risk-adjusted performance metrics:
-# Sharpe Ratio (return per unit of total risk)
-sharpe_ratio = sp500.ret_vol_ratio
-print(f"Sharpe Ratio: {sharpe_ratio:.2f}")
-
-# Sortino Ratio (return per unit of downside risk)
-sortino_ratio = sp500.sortino_ratio
-print(f"Sortino Ratio: {sortino_ratio:.2f}")
-
-# Kappa-3 Ratio (penalizes larger downside deviations more)
-kappa3_ratio = sp500.kappa3_ratio
-print(f"Kappa-3 Ratio: {kappa3_ratio:.2f}")
-
-# Omega Ratio
-omega_ratio = sp500.omega_ratio
-print(f"Omega Ratio: {omega_ratio:.2f}")
-Drawdown Analysis
-Analyze drawdowns to understand downside risk:
-# Maximum drawdown
-max_drawdown = sp500.max_drawdown
-max_dd_date = sp500.max_drawdown_date
-print(f"Maximum Drawdown: {max_drawdown:.2%}")
-print(f"Max Drawdown Date: {max_dd_date}")
-
-# Create drawdown series for visualization (modifies original)
-sp500.to_drawdown_series()
-
-# Plot drawdowns
-sp500.plot_series()
-# This will open an interactive plot in your browser
-
-# Worst calendar year drawdown
-worst_year_dd = sp500.max_drawdown_cal_year
-print(f"Worst Calendar Year Drawdown: {worst_year_dd:.2%}")
-Distribution Analysis
-Examine the return distribution characteristics:
-# Convert to returns for distribution analysis (modifies original)
-sp500.value_to_ret()
-
-# Note: value_to_ret() modifies the original series in place
-# Restore the original series for further analysis
-sp500 = OpenTimeSeries.from_df(dframe=data['Close'])
-sp500.set_new_label(lvl_zero="S&P 500 Index")
-
-# Skewness (asymmetry of the distribution)
-skewness = sp500.skew
-print(f"Skewness: {skewness:.2f}")
-if skewness < 0:
- print(" → Negative skew: more extreme negative returns")
-elif skewness > 0:
- print(" → Positive skew: more extreme positive returns")
-
-# Kurtosis (tail heaviness)
-kurtosis = sp500.kurtosis
-print(f"Kurtosis: {kurtosis:.2f}")
-if kurtosis > 3:
- print(" → Fat tails: more extreme returns than normal distribution")
-
-# Percentage of positive days
-positive_share = sp500.positive_share
-print(f"Positive Days: {positive_share:.1%}")
-
-# Current Z-score (how unusual is the last return?)
-z_score = sp500.z_score
-print(f"Last Return Z-score: {z_score:.2f}")
-Monthly and Annual Analysis
-Break down performance by different time periods:
-# Resample to monthly data (modifies original)
-sp500.resample_to_business_period_ends(freq="BME")
-print(f"Monthly observations: {sp500.length}")
-
-# Monthly metrics
-monthly_return = sp500.geo_ret
-monthly_vol = sp500.vol
-print(f"Monthly Return (annualized): {monthly_return:.2%}")
-print(f"Monthly Volatility (annualized): {monthly_vol:.2%}")
-
-# Worst month
-worst_month = sp500.worst_month
-print(f"Worst Month: {worst_month:.2%}")
-
-# Annual data (modifies original)
-sp500.resample_to_business_period_ends(freq="BYE")
-print(f"Annual observations: {sp500.length}")
-Calendar Year Returns
-# Calculate calendar year returns
-years = range(2019, 2025) # Adjust based on your data range
-
-for year in years:
- # This may fail if no data exists for the year
- year_return = sp500.value_ret_calendar_period(year=year)
- print(f"{year}: {year_return:.2%}")
-Rolling Analysis
-Analyze how metrics change over time:
-# 252-day (1-year) rolling volatility
-rolling_vol = sp500.rolling_vol(observations=252)
-print(f"Rolling volatility calculated for {len(rolling_vol)} periods")
-
-# 30-day rolling returns
-rolling_returns = sp500.rolling_return(observations=30)
-
-# Plot rolling volatility
-# Convert to OpenTimeSeries for plotting
-vol_dates = rolling_vol.index.strftime('%Y-%m-%d').tolist()
-vol_values = rolling_vol.iloc[:, 0].tolist()
-
-vol_series = OpenTimeSeries.from_arrays(
- dates=vol_dates,
- values=vol_values,
- name="Rolling Volatility"
-)
-
-vol_series.plot_series()
-Comprehensive Report
-Get all metrics at once:
-# Generate comprehensive metrics report
-all_metrics = sp500.all_properties()
-print("\n=== COMPREHENSIVE ANALYSIS REPORT ===")
-print(all_metrics)
-
-# Save to Excel for further analysis
-sp500.to_xlsx(filename="sp500_analysis.xlsx")
-all_metrics.to_excel(excel_writer="sp500_metrics.xlsx", engine="openpyxl")
-Visualization
-Create various visualizations:
-# Price chart
-sp500.plot_series()
-
-# Returns bar plot and histogram
-returns = sp500.from_deepcopy()
-returns.value_to_ret()
-returns.plot_bars()
-returns.plot_histogram()
-
-# Drawdown chart
-sp500.to_drawdown_series()
-sp500.plot_series()
-Comparison with Benchmark
-Let’s compare with a bond index:
-# Download bond data (10-year Treasury)
-bond_ticker = yf.Ticker("^TNX")
-bond_data = bond_ticker.history(period="5y")
-
-# Create bond series (using yield data)
-bonds = OpenTimeSeries.from_df(
- dframe=bond_data['Close']
-)
-bonds.set_new_label(lvl_zero="10Y Treasury Yield")
-
-# Create frame for comparison
-comparison_frame = OpenFrame(constituents=[sp500, bonds])
-
-# Compare metrics
-comparison_metrics = comparison_frame.all_properties()
-print("\n=== ASSET COMPARISON ===")
-print(comparison_metrics)
-
-# Calculate correlation
-correlation_matrix = comparison_frame.correl_matrix
-print("\n=== CORRELATION MATRIX ===")
-print(correlation_matrix)
-Advanced Risk Metrics
-Calculate some advanced risk measures:
-# VaR at different confidence levels
-var_90 = sp500.var_down_func(level=0.90)
-var_95 = sp500.var_down_func(level=0.95)
-var_99 = sp500.var_down_func(level=0.99)
-
-print(f"90% VaR: {var_90:.2%}")
-print(f"95% VaR: {var_95:.2%}")
-print(f"99% VaR: {var_99:.2%}")
-
-# CVaR at different confidence levels
-cvar_90 = sp500.cvar_down_func(level=0.90)
-cvar_95 = sp500.cvar_down_func(level=0.95)
-cvar_99 = sp500.cvar_down_func(level=0.99)
-
-print(f"90% CVaR: {cvar_90:.2%}")
-print(f"95% CVaR: {cvar_95:.2%}")
-print(f"99% CVaR: {cvar_99:.2%}")
-
-# Implied volatility from VaR (assuming normal distribution)
-vol_from_var = sp500.vol_from_var
-print(f"Volatility implied from VaR: {vol_from_var:.2%}")
-print(f"Actual volatility: {sp500.vol:.2%}")
-Summary and Interpretation
-print("\n=== INVESTMENT SUMMARY ===")
-print(f"Asset: {sp500.label}")
-print(f"Period: {sp500.first_idx} to {sp500.last_idx}")
-print(f"Total Return: {sp500.value_ret:.2%}")
-print(f"Annualized Return: {sp500.geo_ret:.2%}")
-print(f"Annualized Volatility: {sp500.vol:.2%}")
-print(f"Sharpe Ratio: {sp500.ret_vol_ratio:.2f}")
-print(f"Maximum Drawdown: {sp500.max_drawdown:.2%}")
-print(f"95% VaR (daily): {sp500.var_down:.2%}")
-
-# Risk assessment
-if sp500.ret_vol_ratio > 1.0:
- print("✓ Good risk-adjusted returns (Sharpe > 1.0)")
-else:
- print("⚠ Moderate risk-adjusted returns (Sharpe < 1.0)")
-
-if abs(sp500.max_drawdown) < 0.20:
- print("✓ Moderate maximum drawdown (< 20%)")
-else:
- print("⚠ Significant maximum drawdown (> 20%)")
-This tutorial provides a comprehensive foundation for financial analysis using openseries. You can adapt these techniques for any financial time series data.
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- Portfolio Analysis
-This tutorial demonstrates how to construct and analyze portfolios using openseries, including optimization techniques and performance attribution.
-Setting Up the Data
-Let’s start by downloading data for a diversified set of assets:
-import yfinance as yf
-from openseries import OpenTimeSeries, OpenFrame
-from openseries import efficient_frontier, simulate_portfolios
-
-# Define our universe of assets
-tickers = {
- "^GSPC": "S&P 500",
- "EFA": "EAFE International",
- "EEM": "Emerging Markets",
- "AGG": "US Aggregate Bonds",
- "VNQ": "US REITs",
- "GLD": "Gold",
- "DBC": "Commodities"
-}
-
-# Download 5 years of data
-series_list = []
-for ticker, name in tickers.items():
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="5y")
- series = OpenTimeSeries.from_df(
- dframe=data['Close']
- )
- series.set_new_label(lvl_zero=name)
- series_list.append(series)
- print(f"Loaded {name}: {series.length} observations")
-
-# Create OpenFrame
-assets = OpenFrame(constituents=series_list)
-print(f"\nCreated frame with {assets.item_count} assets")
-print(f"Common date range: {assets.first_idx} to {assets.last_idx}")
-Asset Analysis
-First, let’s analyze the individual assets:
-# Get metrics for all assets
-asset_metrics = assets.all_properties()
-print("=== INDIVIDUAL ASSET METRICS ===")
-print(asset_metrics)
-
-# Key metrics comparison
-returns = asset_metrics.loc['Geometric return']
-volatilities = asset_metrics.loc['Volatility']
-sharpe_ratios = asset_metrics.loc['Return vol ratio']
-max_drawdowns = asset_metrics.loc['Max drawdown']
-
-print("\n=== ASSET COMPARISON ===")
-for asset in returns.index:
- print(f"{asset}:")
- print(f" Annual Return: {returns[asset]:.2%}")
- print(f" Volatility: {volatilities[asset]:.2%}")
- print(f" Sharpe Ratio: {sharpe_ratios[asset]:.2f}")
- print(f" Max Drawdown: {max_drawdowns[asset]:.2%}")
-Correlation Analysis
-Understanding correlations is crucial for portfolio construction:
-# Calculate correlation matrix
-correlation_matrix = assets.correl_matrix
-print("\n=== CORRELATION MATRIX ===")
-print(correlation_matrix.round(3))
-
-# Identify highly correlated pairs
-print("\n=== HIGHLY CORRELATED PAIRS (>0.7) ===")
-for i in range(len(correlation_matrix.columns)):
- for j in range(i+1, len(correlation_matrix.columns)):
- corr = correlation_matrix.iloc[i, j]
- if abs(corr) > 0.7:
- asset1 = correlation_matrix.columns[i]
- asset2 = correlation_matrix.columns[j]
- print(f"{asset1} - {asset2}: {corr:.3f}")
-
-# Average correlation with other assets
-avg_correlations = correlation_matrix.mean()
-print("\n=== AVERAGE CORRELATIONS ===")
-for asset, avg_corr in avg_correlations.items():
- print(f"{asset}: {avg_corr:.3f}")
-Simple Portfolio Construction
-Let’s start with basic portfolio construction methods:
-Equal Weight Portfolio
-# Create equal-weighted portfolio using native weight_strat
-portfolio_df = assets.make_portfolio(name="Equal Weight Portfolio", weight_strat="eq_weights")
-equal_weight_portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-print(f"Equal Weight Portfolio Return: {equal_weight_portfolio.geo_ret:.2%}")
-print(f"Equal Weight Portfolio Volatility: {equal_weight_portfolio.vol:.2%}")
-print(f"Equal Weight Portfolio Sharpe: {equal_weight_portfolio.ret_vol_ratio:.2f}")
-Custom Weight Portfolio
-You can also specify custom weights for portfolio construction:
-# Define custom weights (must sum to 1)
-custom_weights = [0.50, 0.15, 0.10, 0.15, 0.05, 0.03, 0.02]
-
-assets.weights = custom_weights
-portfolio_df = assets.make_portfolio(name="Custom Weighted")
-custom_portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-print(f"Custom Portfolio Return: {custom_portfolio.geo_ret:.2%}")
-print(f"Custom Portfolio Volatility: {custom_portfolio.vol:.2%}")
-print(f"Custom Portfolio Sharpe: {custom_portfolio.ret_vol_ratio:.2f}")
-Risk Parity Portfolio
-# Use native inverse volatility weighting (risk parity)
-portfolio_df = assets.make_portfolio(name="Risk Parity", weight_strat="inv_vol")
-risk_parity_portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-print(f"Risk Parity Portfolio Return: {risk_parity_portfolio.geo_ret:.2%}")
-print(f"Risk Parity Portfolio Volatility: {risk_parity_portfolio.vol:.2%}")
-print(f"Risk Parity Portfolio Sharpe: {risk_parity_portfolio.ret_vol_ratio:.2f}")
-Advanced Weight Strategies
-OpenSeries provides additional weight strategies beyond basic equal weighting and risk parity:
-Maximum Diversification Strategy
-The maximum diversification strategy optimizes the correlation structure to maximize portfolio diversification:
-from openseries.owntypes import MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError
-
-# This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError
-max_div_portfolio_df = assets.make_portfolio(
- name="Maximum Diversification",
- weight_strat="max_div"
-)
-max_div_portfolio = OpenTimeSeries.from_df(dframe=max_div_portfolio_df)
-
-print(f"Max Diversification Return: {max_div_portfolio.geo_ret:.2%}")
-print(f"Max Diversification Volatility: {max_div_portfolio.vol:.2%}")
-print(f"Max Diversification Sharpe: {max_div_portfolio.ret_vol_ratio:.2f}")
-Minimum Volatility Overweight Strategy
-The minimum volatility overweight strategy overweights the least volatile asset:
-# This may fail with various exceptions
-min_vol_portfolio_df = assets.make_portfolio(
- name="Min Vol Overweight",
- weight_strat="min_vol_overweight"
-)
-min_vol_portfolio = OpenTimeSeries.from_df(dframe=min_vol_portfolio_df)
-
-print(f"Min Vol Overweight Return: {min_vol_portfolio.geo_ret:.2%}")
-print(f"Min Vol Overweight Volatility: {min_vol_portfolio.vol:.2%}")
-print(f"Min Vol Overweight Sharpe: {min_vol_portfolio.ret_vol_ratio:.2f}")
-Strategy Comparison with Error Handling
-When comparing multiple strategies, it’s important to handle potential failures gracefully:
-strategies = {
- 'Equal Weight': 'eq_weights',
- 'Risk Parity': 'inv_vol',
- 'Max Diversification': 'max_div',
- 'Min Vol Overweight': 'min_vol_overweight'
-}
-
-results = {}
-for name, strategy in strategies.items():
- # This may fail with MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError, or other exceptions
- portfolio_df = assets.make_portfolio(name=name, weight_strat=strategy)
- portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
- results[name] = {
- 'Return': portfolio.geo_ret,
- 'Volatility': portfolio.vol,
- 'Sharpe': portfolio.ret_vol_ratio
- }
-
-if results:
- print("\n=== STRATEGY COMPARISON ===")
- for strategy_name, metrics in results.items():
- print(f"\n{strategy_name}:")
- print(f" Return: {metrics['return']*100:.2f}%")
- print(f" Volatility: {metrics['volatility']*100:.2f}%")
- print(f" Sharpe: {metrics['sharpe']:.2f}")
- print(f" Max Drawdown: {metrics['max_drawdown']*100:.2f}%")
-Portfolio Optimization
-Now let’s use openseries’ optimization tools:
-Efficient Frontier
-# Calculate efficient frontier
-# This may fail with various exceptions
-frontier_df, simulated_df, optimal_portfolio = efficient_frontier(
- eframe=assets,
- num_ports=50,
- seed=42
-)
-
-print("Efficient frontier calculated successfully")
-print(f"Number of frontier points: {len(frontier_df)}")
-print(f"Number of simulated portfolios: {len(simulated_df)}")
-
-# Find maximum Sharpe ratio portfolio
-sharpe_ratios = frontier_df['ret'] / frontier_df['stdev']
-max_sharpe_idx = sharpe_ratios.idxmax()
-
-print(f"\n=== MAXIMUM SHARPE RATIO PORTFOLIO ===")
-print(f"Expected Return: {frontier_df.iloc[max_sharpe_idx]['ret']:.2%}")
-print(f"Volatility: {frontier_df.iloc[max_sharpe_idx]['stdev']:.2%}")
-print(f"Sharpe Ratio: {sharpe_ratios.iloc[max_sharpe_idx]:.2f}")
-
-# Get optimal weights
-optimal_weights = optimal_portfolio[-len(assets.constituents):]
-print("\nOptimal Weights:")
-for i, weight in enumerate(optimal_weights):
- asset_name = assets.constituents[i].label
- print(f" {asset_name}: {weight:.1%}")
-Monte Carlo Portfolio Simulation
-# Simulate random portfolios
-# This may fail with various exceptions
-simulation_results = simulate_portfolios(
- simframe=assets,
- num_ports=10000,
- seed=42
-)
-
-print(f"\nSimulated {len(simulation_results)} random portfolios")
-
-# Find best performing portfolios
-sim_sharpe_ratios = simulation_results['ret'] / simulation_results['stdev']
-
-# Top 5 Sharpe ratios
-sorted_indices = sorted(range(len(sim_sharpe_ratios)), key=lambda i: sim_sharpe_ratios.iloc[i], reverse=True)
-top_indices = sorted_indices[:5]
-
-print("\n=== TOP 5 SIMULATED PORTFOLIOS ===")
-for i, idx in enumerate(top_indices, 1):
- print(f"\nRank {i}:")
- print(f" Return: {simulation_results.iloc[idx]['ret']:.2%}")
- print(f" Volatility: {simulation_results.iloc[idx]['stdev']:.2%}")
- print(f" Sharpe: {sim_sharpe_ratios.iloc[idx]:.2f}")
-Portfolio Comparison
-Let’s compare all our portfolios:
-# Add all portfolios to a comparison frame
-portfolios = [equal_weight_portfolio, market_cap_portfolio, risk_parity_portfolio]
-
-# Add individual assets for comparison
-all_series = assets.constituents + portfolios
-comparison_frame = OpenFrame(constituents=all_series)
-
-# Get comprehensive metrics
-portfolio_metrics = comparison_frame.all_properties()
-
-# Focus on key metrics
-key_metrics = portfolio_metrics.loc[['Geometric return', 'Volatility', 'Return vol ratio', 'Max drawdown']]
-key_metrics.index = ['Annual Return', 'Volatility', 'Sharpe Ratio', 'Max Drawdown']
-
-print("\n=== PORTFOLIO COMPARISON ===")
-print((key_metrics * 100).round(2)) # Convert to percentages
-Risk Attribution
-Analyze the risk contribution of each asset:
-# Calculate portfolio statistics using openseries methods
-# Create equal weight portfolio
-equal_weight_portfolio_df = assets.make_portfolio(name="Equal Weight", weight_strat="eq_weights")
-equal_weight_portfolio = OpenTimeSeries.from_df(dframe=equal_weight_portfolio_df)
-
-print("\n=== RISK ATTRIBUTION (Equal Weight Portfolio) ===")
-print(f"Portfolio Volatility: {equal_weight_portfolio.vol:.4f}")
-print(f"Portfolio Return: {equal_weight_portfolio.geo_ret:.4f}")
-
-# Individual asset contributions can be analyzed using openseries properties
-for i, series in enumerate(assets.constituents):
- weight = equal_weights[i]
- asset_vol = series.vol
- print(f"\n{series.label}:")
- print(f" Weight: {weight:.4f}")
- print(f" Individual Volatility: {asset_vol:.4f}")
- print(f" Weighted Contribution: {weight * asset_vol:.4f}")
-Performance Attribution
-Analyze performance contribution over time:
-# Calculate performance attribution using openseries
-# Individual asset performance is available through openseries properties
-print("\n=== PERFORMANCE ATTRIBUTION ===")
-for i, series in enumerate(assets.constituents):
- weight = equal_weights[i]
- asset_return = series.geo_ret
- contribution = weight * asset_return
- print(f"{series.label}:")
- print(f" Weight: {weight:.2%}")
- print(f" Return: {asset_return:.2%}")
- print(f" Contribution: {contribution:.2%}")
-
-# Cumulative contribution
-cumulative_contrib = (1 + weighted_returns).cumprod()
-
-print("\n=== PERFORMANCE ATTRIBUTION ===")
-print("Final cumulative contribution by asset:")
-final_contrib = cumulative_contrib.iloc[-1]
-for asset, contrib in final_contrib.items():
- print(f" {asset}: {contrib:.3f}")
-Rolling Portfolio Analysis
-Analyze how portfolio characteristics change over time:
-# Rolling correlation with market (S&P 500)
-market_proxy = assets.constituents[0] # Assuming first asset is S&P 500
-
-# Create frame with portfolio and market
-portfolio_vs_market = OpenFrame(constituents=[equal_weight_portfolio, market_proxy])
-
-# Calculate rolling correlation
-rolling_corr = portfolio_vs_market.rolling_corr(observations=252) # 1-year rolling
-
-print(f"\nRolling correlation calculated for {len(rolling_corr)} periods")
-print(f"Average correlation: {rolling_corr.mean().iloc[0]:.3f}")
-print(f"Correlation range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}")
-
-# Rolling portfolio volatility
-portfolio_rolling_vol = equal_weight_portfolio.rolling_vol(observations=252)
-
-print(f"\nRolling volatility statistics:")
-print(f"Average volatility: {portfolio_rolling_vol.mean().iloc[0]:.2%}")
-print(f"Volatility range: {portfolio_rolling_vol.min().iloc[0]:.2%} to {portfolio_rolling_vol.max().iloc[0]:.2%}")
-Rebalancing Analysis
-Analyze the impact of rebalancing frequency using the realistic rebalanced_portfolio method:
-# Compare different rebalancing frequencies using realistic simulation
-frequencies = [1, 21, 63] # Daily, monthly, quarterly
-frequency_names = ["Daily", "Monthly", "Quarterly"]
-
-rebalanced_portfolios = []
-
-for freq, name in zip(frequencies, frequency_names):
- portfolio = assets.rebalanced_portfolio(
- name=f"{name} Rebalanced",
- frequency=freq,
- bal_weights=equal_weights
- )
- rebalanced_portfolios.append(portfolio.constituents[-1])
-
-# Compare with theoretical portfolio
-assets.weights = equal_weights
-theoretical_portfolio_df = assets.make_portfolio(name="Theoretical")
-theoretical_portfolio = OpenTimeSeries.from_df(dframe=theoretical_portfolio_df)
-
-# Create comprehensive comparison
-all_portfolios = [theoretical_portfolio] + rebalanced_portfolios
-comparison_frame = OpenFrame(constituents=all_portfolios)
-comparison_metrics = comparison_frame.all_properties()
-
-print("\n=== REALISTIC REBALANCING COMPARISON ===")
-print("Strategy | Return | Volatility | Sharpe | Max DD")
-print("-" * 50)
-
-for series in all_portfolios:
- ret = comparison_metrics.loc['Geometric return', series.label].iloc[0] * 100
- vol = comparison_metrics.loc['Volatility', series.label].iloc[0] * 100
- sharpe = comparison_metrics.loc['Return vol ratio', series.label].iloc[0]
- max_dd = comparison_metrics.loc['Max drawdown', series.label].iloc[0] * 100
-
- print(f"{series.label:>15} | {ret:6.2f}% | {vol:10.2f}% | {sharpe:6.2f} | {max_dd:6.2f}%")
-
-# Analyze transaction costs
-print(f"\n=== TRANSACTION COST ANALYSIS ===")
-for freq, name in zip(frequencies, frequency_names):
- detailed_portfolio = assets.rebalanced_portfolio(
- name=f"{name} Detailed",
- frequency=freq,
- bal_weights=equal_weights,
- drop_extras=False # Get detailed trading data
- )
-
- # Count rebalancing events
- rebalancing_days = 0
- for series in detailed_portfolio.constituents:
- if "buysell_qty" in series.label:
- # Count days with non-zero trading
- trading_days = (series.tsdf != 0).any(axis=1).sum()
- rebalancing_days = max(rebalancing_days, trading_days)
-
- print(f"{name:>15}: {rebalancing_days} rebalancing events")
-Stress Testing
-Test portfolio performance during market stress:
-# Identify worst periods for the market (modifies original)
-market_proxy.value_to_ret()
-market_returns_df = market_proxy.tsdf
-
-# Find worst 5% of days
-worst_days_threshold = market_returns_df.quantile(0.05).iloc[0]
-worst_days = market_returns_df[market_returns_df <= worst_days_threshold]
-
-print(f"\n=== STRESS TEST RESULTS ===")
-print(f"Market stress threshold: {worst_days_threshold:.2%}")
-print(f"Number of stress days: {len(worst_days)}")
-
-# Portfolio performance during stress (modifies original)
-equal_weight_portfolio.value_to_ret()
-portfolio_returns_df = equal_weight_portfolio.tsdf
-
-# Align dates and calculate portfolio performance during market stress
-stress_dates = worst_days.index
-portfolio_stress_returns = portfolio_returns_df.loc[stress_dates]
-
-print(f"Portfolio average return during stress: {portfolio_stress_returns.mean().iloc[0]:.2%}")
-print(f"Portfolio worst day during stress: {portfolio_stress_returns.min().iloc[0]:.2%}")
-Summary Report
-Generate a comprehensive portfolio analysis report:
-print("\n" + "="*60)
-print("PORTFOLIO ANALYSIS SUMMARY REPORT")
-print("="*60)
-
-print(f"\nAnalysis Period: {assets.first_idx} to {assets.last_idx}")
-print(f"Number of Assets: {assets.item_count}")
-print(f"Asset Universe: {', '.join([s.label for s in assets.constituents])}")
-
-print(f"\n--- EQUAL WEIGHT PORTFOLIO PERFORMANCE ---")
-print(f"Total Return: {equal_weight_portfolio.value_ret:.2%}")
-print(f"Annualized Return: {equal_weight_portfolio.geo_ret:.2%}")
-print(f"Annualized Volatility: {equal_weight_portfolio.vol:.2%}")
-print(f"Sharpe Ratio: {equal_weight_portfolio.ret_vol_ratio:.2f}")
-print(f"Maximum Drawdown: {equal_weight_portfolio.max_drawdown:.2%}")
-print(f"95% VaR (daily): {equal_weight_portfolio.var_down:.2%}")
-
-print(f"\n--- PORTFOLIO CHARACTERISTICS ---")
-avg_correlation = correlation_matrix.mean().mean()
-print(f"Average Asset Correlation: {avg_correlation:.3f}")
-print(f"Portfolio Diversification Benefit: {(asset_metrics.loc['Volatility'].mean() - equal_weight_portfolio.vol):.2%}")
-
-# Export results
-portfolio_metrics.to_excel("portfolio_analysis.xlsx")
-correlation_matrix.to_excel("correlation_matrix.xlsx")
-
-print(f"\nResults exported to Excel files")
-print("Analysis complete!")
-This tutorial provides a comprehensive framework for portfolio analysis using openseries. You can extend these techniques for more sophisticated portfolio management strategies.
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- Risk Management
-This tutorial demonstrates comprehensive risk management techniques using openseries, including VaR calculations, stress testing, and risk monitoring.
-Setting Up Risk Analysis
-Let’s start with a portfolio of assets for risk analysis:
-import yfinance as yf
-from openseries import OpenTimeSeries, OpenFrame
-from datetime import datetime, timedelta
-import warnings
-warnings.filterwarnings('ignore')
-
-# Download data for a mixed portfolio
-tickers = {
- "AAPL": "Apple Inc.",
- "GOOGL": "Alphabet Inc.",
- "MSFT": "Microsoft Corp.",
- "TSLA": "Tesla Inc.",
- "SPY": "SPDR S&P 500 ETF",
- "QQQ": "Invesco QQQ Trust",
- "TLT": "iShares 20+ Year Treasury",
- "GLD": "SPDR Gold Shares"
-}
-
-# Download 3 years of data
-series_list = []
-for ticker, name in tickers.items():
- # This may fail if the ticker is invalid or data unavailable
- data = yf.Ticker(ticker).history(period="3y")
- series = OpenTimeSeries.from_df(
- dframe=data['Close']
- )
- series.set_new_label(lvl_zero=name)
- series_list.append(series)
- print(f"Loaded {name}: {series.length} observations")
-
-# Create portfolio frame
-portfolio_assets = OpenFrame(constituents=series_list)
-
-# Create equal-weighted portfolio
-n_assets = portfolio_assets.item_count
-
-# Set weights on the frame first
-portfolio_df = portfolio_assets.make_portfolio(
- name="Diversified Portfolio",
- weight_strat="eq_weights"
-)
-portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-print(f"\nPortfolio created with {n_assets} assets")
-print(f"Date range: {portfolio.first_idx} to {portfolio.last_idx}")
-Basic Risk Metrics
-Start with fundamental risk measurements:
-print("=== BASIC RISK METRICS ===")
-
-# Volatility measures
-print(f"Annualized Volatility: {portfolio.vol:.2%}")
-print(f"Downside Deviation: {portfolio.downside_deviation:.2%}")
-
-# Return distribution
-print(f"Skewness: {portfolio.skew:.3f}")
-print(f"Kurtosis: {portfolio.kurtosis:.3f}")
-
-# Tail risk
-print(f"Worst Single Day: {portfolio.worst:.2%}")
-print(f"Worst Month: {portfolio.worst_month:.2%}")
-
-# Drawdown analysis
-print(f"Maximum Drawdown: {portfolio.max_drawdown:.2%}")
-print(f"Max Drawdown Date: {portfolio.max_drawdown_date}")
-Value at Risk (VaR) Analysis
-Calculate VaR at different confidence levels:
-print("\n=== VALUE AT RISK ANALYSIS ===")
-
-# VaR at different confidence levels
-confidence_levels = [0.90, 0.95, 0.99]
-
-for level in confidence_levels:
- var_value = portfolio.var_down_func(level=level)
- print(f"{level*100:.0f}% VaR (daily): {var_value:.2%}")
-
-# Convert daily VaR to different time horizons
-# Assuming normal distribution and independence
-daily_var_95 = portfolio.var_down_func(level=0.95)
-
-print(f"\n=== VaR TIME HORIZONS (95% confidence) ===")
-print(f"1-day VaR: {daily_var_95:.2%}")
-# Scale VaR to different time horizons
-print(f"1-week VaR: {daily_var_95 * (5 ** 0.5):.2%}")
-print(f"1-month VaR: {daily_var_95 * (22 ** 0.5):.2%}")
-print(f"1-year VaR: {daily_var_95 * (252 ** 0.5):.2%}")
-Conditional Value at Risk (CVaR)
-Analyze expected shortfall beyond VaR:
-print("\n=== CONDITIONAL VALUE AT RISK (CVaR) ===")
-
-for level in confidence_levels:
- cvar_value = portfolio.cvar_down_func(level=level)
- var_value = portfolio.var_down_func(level=level)
-
- print(f"{level*100:.0f}% CVaR: {cvar_value:.2%} (VaR: {var_value:.2%})")
- print(f" Expected loss beyond VaR: {cvar_value - var_value:.2%}")
-Rolling Risk Analysis
-Monitor how risk changes over time:
-# Calculate rolling risk metrics
-window = 252 # 1-year rolling window
-
-print(f"\n=== ROLLING RISK ANALYSIS ({window}-day window) ===")
-
-# Rolling volatility
-rolling_vol = portfolio.rolling_vol(observations=window)
-print(f"Rolling Volatility - Current: {rolling_vol.iloc[-1, 0]:.2%}")
-print(f"Rolling Volatility - Average: {rolling_vol.mean().iloc[0]:.2%}")
-print(f"Rolling Volatility - Range: {rolling_vol.min().iloc[0]:.2%} to {rolling_vol.max().iloc[0]:.2%}")
-
-# Rolling VaR
-rolling_var = portfolio.rolling_var_down(observations=window)
-print(f"Rolling VaR (95%) - Current: {rolling_var.iloc[-1, 0]:.2%}")
-print(f"Rolling VaR (95%) - Average: {rolling_var.mean().iloc[0]:.2%}")
-
-# Rolling CVaR
-rolling_cvar = portfolio.rolling_cvar_down(observations=window)
-print(f"Rolling CVaR (95%) - Current: {rolling_cvar.iloc[-1, 0]:.2%}")
-print(f"Rolling CVaR (95%) - Average: {rolling_cvar.mean().iloc[0]:.2%}")
-Stress Testing
-Test portfolio performance under extreme scenarios:
-Historical Stress Testing
-print("\n=== HISTORICAL STRESS TESTING ===")
-
-# Convert to returns for analysis (modifies original)
-portfolio.value_to_ret()
-returns_data = portfolio.tsdf
-
-# Note: value_to_ret() modifies the original series in place
-# Restore the original portfolio for further analysis
-portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-# Identify worst periods
-worst_1_percent = returns_data.quantile(0.01).iloc[0]
-worst_5_percent = returns_data.quantile(0.05).iloc[0]
-
-print(f"Worst 1% threshold: {worst_1_percent:.2%}")
-print(f"Worst 5% threshold: {worst_5_percent:.2%}")
-
-# Count extreme events
-extreme_events_1pct = (returns_data <= worst_1_percent).sum().iloc[0]
-extreme_events_5pct = (returns_data <= worst_5_percent).sum().iloc[0]
-
-print(f"Days with returns <= 1% threshold: {extreme_events_1pct}")
-print(f"Days with returns <= 5% threshold: {extreme_events_5pct}")
-
-# Worst consecutive days - simplified approach
-print(f"\nWorst 5 single days:")
-returns_series = returns_data.iloc[:, 0] # Get the first (and only) column
-worst_5_days = returns_series.nsmallest(5)
-for i, (date, return_val) in enumerate(worst_5_days.items()):
- print(f" {i+1}. {date.strftime('%Y-%m-%d')}: {return_val:.2%}")
-Scenario Analysis
-print("\n=== SCENARIO ANALYSIS ===")
-
-# Define stress scenarios (percentage moves in underlying assets)
-scenarios = {
- "Market Crash": [-0.20, -0.25, -0.22, -0.30, -0.18, -0.20, 0.05, 0.10],
- "Tech Selloff": [-0.35, -0.40, -0.30, -0.45, -0.10, -0.15, 0.02, 0.03],
- "Interest Rate Shock": [-0.10, -0.12, -0.08, -0.15, -0.05, -0.08, -0.15, 0.01],
- "Flight to Quality": [0.05, 0.02, 0.08, -0.10, 0.10, 0.12, 0.20, 0.15]
-}
-
-print("Portfolio impact under stress scenarios:")
-for scenario_name, asset_moves in scenarios.items():
- # Calculate portfolio impact
- portfolio_impact = sum(w * move for w, move in zip(equal_weights, asset_moves))
- print(f" {scenario_name}: {portfolio_impact:.2%}")
-Monte Carlo Risk Simulation
-Use Monte Carlo methods for risk assessment:
-print("\n=== MONTE CARLO RISK SIMULATION ===")
-
-# Import the simulate_portfolios function
-from openseries.portfoliotools import simulate_portfolios
-
-# Monte Carlo simulation using native function
-num_simulations = 10000
-seed = 42 # For reproducible results
-
-# Generate simulated portfolios using the native function
-simulated_portfolios = simulate_portfolios(
- simframe=portfolio_assets,
- num_ports=num_simulations,
- seed=seed
-)
-
-# Extract portfolio metrics from simulation
-portfolio_returns = simulated_portfolios['ret']
-portfolio_volatilities = simulated_portfolios['stdev']
-portfolio_sharpes = simulated_portfolios['sharpe']
-
-# Calculate risk metrics from simulation
-# Calculate 5th percentile manually
-sorted_returns = sorted(portfolio_returns)
-percentile_idx = int(len(sorted_returns) * 0.05)
-sim_var_95 = sorted_returns[percentile_idx]
-sim_cvar_95 = portfolio_returns[portfolio_returns <= sim_var_95].mean()
-
-print(f"Monte Carlo Results ({num_simulations:,} simulations):")
-print(f"Expected Return: {portfolio_returns.mean():.2%}")
-print(f"Average Volatility: {portfolio_volatilities.mean():.2%}")
-print(f"95% VaR: {sim_var_95:.2%}")
-print(f"95% CVaR: {sim_cvar_95:.2%}")
-# Calculate percentiles manually
-worst_idx = int(len(sorted_returns) * 0.001)
-best_idx = int(len(sorted_returns) * 0.999)
-print(f"Worst Case (0.1%): {sorted_returns[worst_idx]:.2%}")
-print(f"Best Case (99.9%): {sorted_returns[best_idx]:.2%}")
-print(f"Average Sharpe Ratio: {portfolio_sharpes.mean():.3f}")
-
-# Show distribution of portfolio characteristics
-print(f"\nPortfolio Distribution:")
-print(f"Return Range: {portfolio_returns.min():.2%} to {portfolio_returns.max():.2%}")
-print(f"Volatility Range: {portfolio_volatilities.min():.2%} to {portfolio_volatilities.max():.2%}")
-print(f"Sharpe Range: {portfolio_sharpes.min():.3f} to {portfolio_sharpes.max():.3f}")
-Risk Decomposition
-Analyze risk contribution by asset:
-print("\n=== RISK DECOMPOSITION ===")
-
-# Calculate individual asset volatilities using OpenFrame
-asset_metrics = portfolio_assets.all_properties()
-asset_vols = asset_metrics.loc['Volatility'].values
-
-# Portfolio volatility
-portfolio_vol = portfolio.vol
-
-# Calculate correlation matrix
-correlation_matrix = portfolio_assets.correl_matrix
-
-# Risk contribution analysis using openseries
-# Create portfolio to get portfolio-level metrics
-portfolio_df = portfolio_assets.make_portfolio(name="Portfolio", weight_strat="eq_weights")
-portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-portfolio_vol = portfolio.vol
-
-print("Risk Contribution Analysis:")
-for i, series in enumerate(portfolio_assets.constituents):
- weight = equal_weights[i]
- asset_vol = asset_vols[i]
- print(f"\n{series.label}:")
- print(f" Weight: {weight:.4f}")
- print(f" Individual Volatility: {asset_vol:.4f}")
- print(f" Weighted Volatility Contribution: {weight * asset_vol:.4f}")
-print(f"\nPortfolio Volatility: {portfolio_vol:.4f}")
-
-# Verify portfolio metrics
-print(f"\nVerification:")
-print(f"Portfolio volatility: {portfolio_vol:.4f}")
-Risk-Adjusted Performance
-Evaluate risk-adjusted returns:
-print("\n=== RISK-ADJUSTED PERFORMANCE ===")
-
-# Sharpe ratio
-print(f"Sharpe Ratio: {portfolio.ret_vol_ratio:.3f}")
-
-# Sortino ratio (downside risk only)
-print(f"Sortino Ratio: {portfolio.sortino_ratio:.3f}")
-
-# Kappa-3 ratio (higher-order downside risk)
-print(f"Kappa-3 Ratio: {portfolio.kappa3_ratio:.3f}")
-
-# Omega ratio
-print(f"Omega Ratio: {portfolio.omega_ratio:.3f}")
-
-# Compare with individual assets
-print(f"\n=== RISK-ADJUSTED COMPARISON ===")
-all_assets = portfolio_assets.constituents + [portfolio]
-comparison_frame = OpenFrame(constituents=all_assets)
-
-risk_adj_metrics = comparison_frame.all_properties(
- properties=['ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'omega_ratio']
-)
-
-print(risk_adj_metrics.round(3))
-Risk Monitoring Dashboard
-Create a comprehensive risk monitoring summary using openseries properties and methods:
-print("\n" + "="*60)
-print("RISK MONITORING DASHBOARD")
-print("="*60)
-
-# Current date and lookback period
-current_date = portfolio.last_idx
-lookback_date = portfolio.first_idx
-
-print(f"Portfolio: {portfolio.label}")
-print(f"Current Date: {current_date}")
-print(f"Analysis Period: {lookback_date} to {current_date}")
-print(f"Observations: {portfolio.length}")
-
-# Risk metrics using openseries properties
-print(f"\n--- CURRENT RISK METRICS ---")
-print(f"Volatility (annualized): {portfolio.vol:.2%}")
-print(f"Downside Deviation: {portfolio.downside_deviation:.2%}")
-print(f"95% VaR (daily): {portfolio.var_down:.2%}")
-print(f"95% CVaR (daily): {portfolio.cvar_down:.2%}")
-print(f"Maximum Drawdown: {portfolio.max_drawdown:.2%}")
-
-# Performance metrics using openseries properties
-print(f"\n--- PERFORMANCE METRICS ---")
-print(f"Total Return: {portfolio.value_ret:.2%}")
-print(f"Annualized Return: {portfolio.geo_ret:.2%}")
-print(f"Sharpe Ratio: {portfolio.ret_vol_ratio:.3f}")
-print(f"Sortino Ratio: {portfolio.sortino_ratio:.3f}")
-
-# Distribution characteristics using openseries properties
-print(f"\n--- RETURN DISTRIBUTION ---")
-print(f"Skewness: {portfolio.skew:.3f}")
-print(f"Kurtosis: {portfolio.kurtosis:.3f}")
-print(f"Positive Days: {portfolio.positive_share:.1%}")
-
-# Recent performance using openseries properties
-recent_return = portfolio.z_score
-print(f"\n--- RECENT ACTIVITY ---")
-print(f"Last Return Z-Score: {recent_return:.2f}")
-
-if abs(recent_return) > 2:
- print(" ⚠️ ALERT: Recent return is unusual (|z| > 2)")
-elif abs(recent_return) > 3:
- print(" 🚨 WARNING: Recent return is extreme (|z| > 3)")
-else:
- print(" ✅ Recent return is within normal range")
-
-# Risk alerts based on openseries metrics
-print(f"\n--- RISK ALERTS ---")
-alerts = []
-
-if portfolio.vol > 0.25:
- alerts.append("High volatility (>25%)")
-
-if abs(portfolio.max_drawdown) > 0.20:
- alerts.append("Large maximum drawdown (>20%)")
-
-if portfolio.ret_vol_ratio < 0.5:
- alerts.append("Low Sharpe ratio (<0.5)")
-
-if portfolio.skew < -1:
- alerts.append("Highly negative skew (<-1)")
-
-if portfolio.kurtosis > 5:
- alerts.append("High kurtosis (>5) - fat tails")
-
-if alerts:
- for alert in alerts:
- print(f" ⚠️ {alert}")
-else:
- print(" ✅ No risk alerts")
-Risk Limits and Controls
-Implement risk limit monitoring:
-print("\n=== RISK LIMITS MONITORING ===")
-
-# Define risk limits
-risk_limits = {
- 'max_volatility': 0.20, # 20% annual volatility
- 'max_var_daily': -0.03, # 3% daily VaR
- 'max_drawdown': -0.15, # 15% maximum drawdown
- 'min_sharpe': 0.5, # Minimum Sharpe ratio
- 'max_concentration': 0.30 # Maximum single asset weight
-}
-
-# Check current metrics against limits
-current_metrics = {
- 'volatility': portfolio.vol,
- 'var_daily': portfolio.var_down,
- 'drawdown': portfolio.max_drawdown,
- 'sharpe': portfolio.ret_vol_ratio,
- 'max_weight': max(equal_weights)
-}
-
-print("Risk Limit Monitoring:")
-print("-" * 40)
-
-# Volatility check
-if current_metrics['volatility'] > risk_limits['max_volatility']:
- print(f"❌ BREACH: Volatility {current_metrics['volatility']:.2%} > {risk_limits['max_volatility']:.2%}")
-else:
- print(f"✅ OK: Volatility {current_metrics['volatility']:.2%} <= {risk_limits['max_volatility']:.2%}")
-
-# VaR check
-if current_metrics['var_daily'] < risk_limits['max_var_daily']:
- print(f"❌ BREACH: VaR {current_metrics['var_daily']:.2%} < {risk_limits['max_var_daily']:.2%}")
-else:
- print(f"✅ OK: VaR {current_metrics['var_daily']:.2%} >= {risk_limits['max_var_daily']:.2%}")
-
-# Drawdown check
-if current_metrics['drawdown'] < risk_limits['max_drawdown']:
- print(f"❌ BREACH: Drawdown {current_metrics['drawdown']:.2%} < {risk_limits['max_drawdown']:.2%}")
-else:
- print(f"✅ OK: Drawdown {current_metrics['drawdown']:.2%} >= {risk_limits['max_drawdown']:.2%}")
-
-# Sharpe ratio check
-if current_metrics['sharpe'] < risk_limits['min_sharpe']:
- print(f"❌ BREACH: Sharpe {current_metrics['sharpe']:.3f} < {risk_limits['min_sharpe']:.3f}")
-else:
- print(f"✅ OK: Sharpe {current_metrics['sharpe']:.3f} >= {risk_limits['min_sharpe']:.3f}")
-
-# Concentration check
-if current_metrics['max_weight'] > risk_limits['max_concentration']:
- print(f"❌ BREACH: Max weight {current_metrics['max_weight']:.2%} > {risk_limits['max_concentration']:.2%}")
-else:
- print(f"✅ OK: Max weight {current_metrics['max_weight']:.2%} <= {risk_limits['max_concentration']:.2%}")
-Export Risk Report
-Save comprehensive risk analysis:
-# Create comprehensive risk report
-# Create risk report using openseries methods
-print("\n=== RISK REPORT ===")
-print("Risk metrics are available through openseries properties:")
-for series in portfolio_assets.constituents:
- print(f"\n{series.label}:")
- print(f" VaR (95%): {series.var_down:.4f}")
- print(f" CVaR (95%): {series.cvar_down:.4f}")
- print(f" Volatility: {series.vol:.4f}")
- print(f" Max Drawdown: {series.max_drawdown:.4f}")
-
-# Note: For comprehensive Excel export, use openseries to_xlsx() method
-portfolio_assets.to_xlsx('risk_analysis_report.xlsx')
-
-# Alternative: risk_report = pd.DataFrame({
- 'Metric': [
- 'Annualized Return', 'Annualized Volatility', 'Sharpe Ratio',
- 'Sortino Ratio', 'Maximum Drawdown', '95% VaR (daily)',
- '95% CVaR (daily)', 'Skewness', 'Kurtosis', 'Positive Days %'
- ],
- 'Value': [
- f"{portfolio.geo_ret:.2%}",
- f"{portfolio.vol:.2%}",
- f"{portfolio.ret_vol_ratio:.3f}",
- f"{portfolio.sortino_ratio:.3f}",
- f"{portfolio.max_drawdown:.2%}",
- f"{portfolio.var_down:.2%}",
- f"{portfolio.cvar_down:.2%}",
- f"{portfolio.skew:.3f}",
- f"{portfolio.kurtosis:.3f}",
- f"{portfolio.positive_share:.1%}"
- ]
-})
-
-# Export to Excel
-# Export using openseries native method (commented out ExcelWriter approach)
-# with pd.ExcelWriter('risk_analysis_report.xlsx') as writer:
- risk_report.to_excel(writer, sheet_name='Risk Metrics', index=False)
- risk_decomp.to_excel(writer, sheet_name='Risk Decomposition', index=False)
- correlation_matrix.to_excel(writer, sheet_name='Correlations')
-
- # Add rolling metrics if available
- if 'rolling_vol' in locals():
- rolling_vol.to_excel(writer, sheet_name='Rolling Volatility')
- if 'rolling_var' in locals():
- rolling_var.to_excel(writer, sheet_name='Rolling VaR')
-
-print(f"\nRisk analysis report exported to 'risk_analysis_report.xlsx'")
-print("Risk management analysis complete!")
-This comprehensive risk management tutorial provides the foundation for implementing robust risk controls and monitoring systems using openseries.
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- Core Concepts
-This section explains the fundamental concepts and design principles behind openseries.
-Architecture Overview
-openseries is built around two main classes that inherit from Pydantic’s BaseModel:
--
-
OpenTimeSeries: Manages individual financial time series
-OpenFrame: Manages collections of OpenTimeSeries objects
-
Both classes provide:
--
-
Type safety through Pydantic validation
-Immutable data - original data is preserved
-Consistent API - similar methods across both classes
-Financial focus - methods designed for financial analysis
-
Mutation and data layers
-openseries favors in-place transformations. Many methods modify the existing object
-and return self for chaining rather than creating a new object.
-On OpenTimeSeries, the dates and values arrays are always left untouched,
-while the working data in the tsdf pandas DataFrame is mutable.
-On OpenFrame, the tsdf DataFrame is also mutable and reflects
-transformations applied to the frame. If you need to preserve the original state or
-compare before/after results, create an explicit copy
-(for example, OpenTimeSeries.from_deepcopy() or OpenFrame.from_deepcopy()).
The OpenTimeSeries Class
-Core Properties
-Every OpenTimeSeries has these fundamental properties:
-# Create a sample series using openseries simulation
-from openseries import ReturnSimulation, ValueType
-import datetime as dt
-
-simulation = ReturnSimulation.from_lognormal(
- number_of_sims=1,
- trading_days=100,
- mean_annual_return=0.25, # ~0.001 daily
- mean_annual_vol=0.32, # ~0.02 daily
- trading_days_in_year=252,
- seed=42
-)
-
-series = OpenTimeSeries.from_df(
- dframe=simulation.to_dataframe(name="Sample Asset", end=dt.date(2023, 12, 31)),
- valuetype=ValueType.RTRN
-).to_cumret() # Convert returns to cumulative prices
-
-# Core properties
-print(f"Name: {series.label}")
-print(f"Length: {series.length}")
-print(f"First date: {series.first_idx}")
-print(f"Last date: {series.last_idx}")
-print(f"Value type: {series.valuetype}")
-Data Immutability
-The original data is never modified:
-# Original data is preserved
-original_dates = series.dates # List of date strings
-original_values = series.values # List of float values
-
-# Working data is in the tsdf DataFrame
-working_data = series.tsdf # pandas DataFrame
-
-# Transformations modify the original object (method chaining)
-series.value_to_ret() # Modifies original series
-print(f"Series length: {series.length}") # Usually length - 1
-Value Types
-The ValueType enum identifies what the series represents:
-from openseries import ValueType
-
-# Common value types
-print(ValueType.PRICE) # "Price(Close)"
-print(ValueType.RTRN) # "Return(Total)"
-print(ValueType.ROLLVOL) # "Rolling volatility"
-
-# Check series type
-print(f"Series type: {series.valuetype}")
-
-# Type changes with transformations
-series.value_to_ret() # Modifies original
-print(f"Returns type: {series.valuetype}")
-The OpenFrame Class
-Managing Multiple Series
-OpenFrame manages collections of OpenTimeSeries:
-from openseries import OpenFrame
-
-# Create multiple series using openseries simulation
-simulation = ReturnSimulation.from_lognormal(
- number_of_sims=3,
- trading_days=100,
- mean_annual_return=0.25, # ~0.001 daily
- mean_annual_vol=0.32, # ~0.02 daily
- trading_days_in_year=252,
- seed=42
-)
-
-# Create OpenFrame with multiple series from simulation
-frame = OpenFrame(
- constituents=[
- OpenTimeSeries.from_df(
- dframe=simulation.to_dataframe(name="Asset", end=dt.date(2023, 12, 31)),
- column_nmbr=serie,
- valuetype=ValueType.RTRN,
- ).to_cumret() # Convert returns to cumulative prices
- for serie in range(simulation.number_of_sims)
- ]
-)
-
-# Frame properties
-print(f"Number of series: {frame.item_count}")
-print(f"Column names: {frame.columns_lvl_zero}")
-print(f"Common length: {frame.length}")
-Data Alignment
-OpenFrame concatenates series data but does not automatically align them. -The library provides explicit methods for alignment that require user choice:
-# Series with different date ranges are concatenated (not aligned)
-print("Individual series lengths:")
-print(frame.lengths_of_items)
-
-print(f"Frame length (concatenated): {frame.length}")
-
-# Explicit alignment methods require user choice:
-
-# 1. Truncate to common date range
-frame.trunc_frame()
-
-# 2. Align to business day calendar (modifies original)
-frame.align_index_to_local_cdays(countries="US")
-
-# 3. Handle missing values (modifies original)
-frame.value_nan_handle(method="fill")
-
-# 4. Merge with explicit join strategy
-frame.merge_series(how="inner")
-frame.merge_series(how="outer")
-Financial Calculations
-Return Calculations
-openseries uses standard financial formulas:
-# Simple returns: (P_t / P_{t-1}) - 1
-series.value_to_ret() # Modifies original
-
-# Log returns: ln(P_t / P_{t-1})
-series.value_to_log() # Modifies original
-
-# Cumulative returns: rebasing to start at 1.0 (modifies original)
-series.to_cumret()
-Annualization
-Metrics are annualized using the actual number of observations per year:
-# Automatic calculation of periods per year
-print(f"Periods per year: {series.periods_in_a_year:.1f}")
-
-# Annualized return (geometric mean)
-annual_return = series.geo_ret
-print(f"Annualized return: {annual_return:.2%}")
-
-# Annualized volatility
-annual_vol = series.vol
-print(f"Annualized volatility: {annual_vol:.2%}")
-Risk Metrics
-Risk calculations follow industry standards:
-# Value at Risk (95% confidence)
-var_95 = series.var_down
-print(f"95% VaR: {var_95:.2%}")
-
-# Conditional Value at Risk (Expected Shortfall)
-cvar_95 = series.cvar_down
-print(f"95% CVaR: {cvar_95:.2%}")
-
-# Maximum Drawdown
-max_dd = series.max_drawdown
-print(f"Maximum Drawdown: {max_dd:.2%}")
-
-# Sortino Ratio (downside deviation)
-sortino = series.sortino_ratio
-print(f"Sortino Ratio: {sortino:.2f}")
-Date Handling
-Business Day Calendars
-openseries integrates with business day calendars:
-# Align to specific country's business days (modifies original)
-series.align_index_to_local_cdays(countries="US")
-
-# Multiple countries (intersection of business days) (modifies original)
-series.align_index_to_local_cdays(countries=["US", "GB"])
-
-# Custom markets using pandas-market-calendars (modifies original)
-series.align_index_to_local_cdays(markets="NYSE")
-Resampling
-Convert between different frequencies:
-# Resample to month-end (modifies original)
-series.resample_to_business_period_ends(freq="BME")
-
-# Resample to quarter-end (modifies original)
-series.resample_to_business_period_ends(freq="BQE")
-
-# Custom resampling (modifies original)
-series.resample(freq="W")
-Data Validation
-Type Safety
-Pydantic ensures data integrity:
-# Dates must be valid ISO format strings
-# This will fail with a validation error
-invalid_series = OpenTimeSeries.from_arrays(
- dates=["invalid-date"],
- values=[100.0]
-)
-
-# Values must be numeric
-# This will fail with a validation error
-invalid_series = OpenTimeSeries.from_arrays(
- dates=["2023-01-01"],
- values=["not a number"]
-)
-Consistency Checks
-The library performs consistency checks:
-# Dates and values must have same length
-# Mixed value types in OpenFrame are detected
-# Date alignment issues are caught
-Method Categories
-openseries methods fall into several categories:
-Properties vs Methods
--
-
Properties: Return calculated values (e.g.,
series.vol)
-Methods: Perform operations or take parameters (e.g.,
series.vol_func())
-
# Property - uses full series
-volatility = series.vol
-
-# Method - can specify date range
-recent_vol = series.vol_func(months_from_last=12)
-Transformation Methods
-Methods that modify the original object (return self for chaining):
-# Data transformations (modify original)
-series.value_to_ret() # Prices to returns
-series.to_drawdown_series() # Drawdown series
-series.to_cumret() # Cumulative returns
-
-# Time transformations (modify original)
-series.resample_to_business_period_ends(freq="BME")
-series.align_index_to_local_cdays(countries="US")
-Methods that return new objects:
-# Analysis methods (return new objects)
-rolling_vol = series.rolling_vol(observations=30)
-rolling_ret = series.rolling_return(observations=30)
-Analysis Methods
-Methods that return calculated values:
-# Rolling calculations
-rolling_vol = series.rolling_vol(observations=30)
-rolling_corr = frame.rolling_corr(observations=60)
-
-# Statistical analysis
-beta = frame.beta()
-tracking_error = frame.tracking_error_func()
-Export Methods
-Methods for saving results:
-# File exports
-series.to_xlsx("analysis.xlsx")
-series.to_json("data.json")
-
-# Visualization
-series.plot_series()
-series.plot_histogram()
-Best Practices
-Data Loading
-# Prefer from_df for pandas data
-series = OpenTimeSeries.from_df(dframe=dataframe['Close'])
-series.set_new_label(lvl_zero="Asset")
-
-# Use from_arrays for custom data
-series = OpenTimeSeries.from_arrays(dates=date_list, values=value_list)
-
-# Always set meaningful names
-series.set_new_label(lvl_zero="Descriptive Name")
-Analysis Workflow
-# 1. Load and validate data
-series = OpenTimeSeries.from_df(dframe=data['Close'])
-series.set_new_label(lvl_zero="Asset")
-
-# 2. Basic analysis
-metrics = series.all_properties()
-
-# 3. Specific calculations
-series.to_drawdown_series() # Convert to drawdown (modifies original)
-rolling_metrics = series.rolling_vol(observations=252) # Returns DataFrame
-
-# 4. Visualization
-series.plot_series()
-
-# 5. Export results
-series.to_xlsx(fiilename="analysis.xlsx")
-Memory Management
-# Original data is preserved - use deepcopy if needed
-series_copy = OpenTimeSeries.from_deepcopy(series)
-
-# Large datasets - consider resampling (modifies original)
-series.resample_to_business_period_ends(freq="BME")
-
-# Clean up intermediate results
-del intermediate_series
-Portfolio Construction
-OpenFrame provides several built-in weight strategies for portfolio construction:
-from openseries.owntypes import MaxDiversificationNaNError, MaxDiversificationNegativeWeightsError
-
-# Available weight strategies
-strategies = {
- 'eq_weights': 'Equal weights for all assets',
- 'inv_vol': 'Inverse volatility weighting (risk parity)',
- 'max_div': 'Maximum diversification optimization',
- 'min_vol_overweight': 'Minimum volatility overweight strategy'
-}
-
-# Example with error handling
-# This may fail with MaxDiversificationNaNError or MaxDiversificationNegativeWeightsError
-portfolio_df = frame.make_portfolio(name="Max Div", weight_strat="max_div")
-Understanding these core concepts will help you use openseries effectively and build more sophisticated financial analysis workflows.
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- Data Handling
-This guide covers data loading, validation, transformation, and management in openseries.
-Loading Data
-From pandas DataFrame/Series
-The most common way to load data is from pandas objects:
-import pandas as pd
-from openseries import OpenTimeSeries
-
-# From pandas Series with DatetimeIndex
-data = pd.Series([100, 101, 99, 102],
- index=pd.date_range('2023-01-01', periods=4))
-series = OpenTimeSeries.from_df(dframe=data)
-series.set_new_label(lvl_zero="Sample")
-
-# From pandas DataFrame column
-df = pd.DataFrame({
- 'Date': pd.date_range('2023-01-01', periods=4),
- 'Close': [100, 101, 99, 102],
- 'Volume': [1000, 1100, 900, 1200]
-})
-df.set_index('Date', inplace=True)
-series = OpenTimeSeries.from_df(dframe=df['Close'])
-series.set_new_label(lvl_zero="Stock")
-From Arrays
-For custom data or when working with lists:
-# From date strings and values
-dates = ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04']
-values = [100.0, 101.0, 99.0, 102.0]
-
-series = OpenTimeSeries.from_arrays(
- dates=dates,
- values=values,
- name="Custom Data"
-)
-From Fixed Rate
-Generate synthetic data from a fixed rate:
-from datetime import date
-
-# Create 252 trading days at 5% annual rate
-series = OpenTimeSeries.from_fixed_rate(
- rate=0.05,
- days=252,
- end_date=date(2023, 12, 31),
- name="5% Fixed Rate"
-)
-Data Validation
-Date Format Validation
-openseries enforces strict date formats:
-# Valid date formats
-valid_dates = ['2023-01-01', '2023-12-31', '2024-02-29'] # ISO format
-
-# Invalid formats will raise ValidationError
-# This will fail with a validation error
-invalid_series = OpenTimeSeries.from_arrays(
- dates=['01/01/2023', '2023-1-1'], # Wrong format
- values=[100, 101]
-)
-Value Validation
-Values must be numeric and finite:
-import numpy as np
-
-# Valid values
-valid_values = [100.0, 101.5, 99.25, 102.75]
-
-# Handle NaN values appropriately
-values_with_nan = [100.0, np.nan, 99.0, 102.0]
-series = OpenTimeSeries.from_arrays(
- dates=['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'],
- values=values_with_nan,
- name="Data with NaN"
-)
-
-# Clean NaN values (modifies original)
-series.value_nan_handle() # Forward fill
-Length Consistency
-Dates and values must have the same length:
-# This will raise an error
-# This will fail with a length mismatch error
-invalid_series = OpenTimeSeries.from_arrays(
- dates=['2023-01-01', '2023-01-02'],
- values=[100.0, 101.0, 102.0] # Different length
-)
-Data Transformations
-Price and Return Conversions
-# Assume we have a price series
-prices = OpenTimeSeries.from_arrays(
- dates=['2023-01-01', '2023-01-02', '2023-01-03'],
- values=[100.0, 102.0, 99.0],
- name="Stock Price"
-)
-
-# Convert to simple returns (modifies original)
-prices.value_to_ret()
-print(f"Returns: {prices.values}") # [0.02, -0.0294...]
-
-# Convert to log returns (modifies original)
-prices.value_to_log()
-
-# Convert returns back to cumulative values (modifies original)
-prices.to_cumret()
-
-# Convert to differences (absolute changes) (modifies original)
-prices.value_to_diff()
-Resampling
-Change the frequency of your data:
-# Daily to monthly (business month end) (modifies original)
-series.resample_to_business_period_ends(freq="BME")
-
-# Daily to quarterly (modifies original)
-series.resample_to_business_period_ends(freq="BQE")
-
-# Daily to annual (modifies original)
-series.resample_to_business_period_ends(freq="BYE")
-
-# Custom resampling with pandas frequency strings (modifies original)
-series.resample(freq="W")
-
-# Resample with specific method (modifies original)
-series.resample(freq="W", method="mean")
-Business Day Alignment
-Align data to business day calendars:
-# Align to US business days (modifies original)
-series.align_index_to_local_cdays(countries="US")
-
-# Align to multiple countries (intersection) (modifies original)
-series.align_index_to_local_cdays(countries=["US", "GB", "JP"])
-
-# Align to specific market calendar (modifies original)
-series.align_index_to_local_cdays(markets="NYSE")
-Handling Missing Data
-NaN Handling Strategies
-import numpy as np
-
-# Create series with missing values
-dates = ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04']
-values = [100.0, np.nan, 102.0, np.nan]
-
-series_with_nan = OpenTimeSeries.from_arrays(
- dates=dates, values=values, name="With NaN"
-)
-
-# Forward fill missing values (for price series) (modifies original)
-series_with_nan.value_nan_handle()
-
-# For return series, replace NaN with 0.0 (modifies original)
-series_with_nan.value_to_ret()
-series_with_nan.return_nan_handle()
-Dropping Missing Data
-# Remove NaN values entirely (modifies original)
-series_with_nan.value_nan_handle(method="drop")
-Working with Multiple Assets
-Creating OpenFrame
-from openseries import OpenFrame
-
-# Create multiple series
-series1 = OpenTimeSeries.from_arrays(
- dates=['2023-01-01', '2023-01-02', '2023-01-03'],
- values=[100, 102, 99], name="Asset A"
-)
-
-series2 = OpenTimeSeries.from_arrays(
- dates=['2023-01-01', '2023-01-02', '2023-01-03'],
- values=[50, 51, 49], name="Asset B"
-)
-
-# Create frame
-frame = OpenFrame(constituents=[series1, series2])
-Handling Different Date Ranges
-OpenFrame automatically handles series with different date ranges:
-# Series with different start/end dates
-early_series = OpenTimeSeries.from_arrays(
- dates=['2022-12-01', '2023-01-01', '2023-01-02'],
- values=[95, 100, 102], name="Early Start"
-)
-
-late_series = OpenTimeSeries.from_arrays(
- dates=['2023-01-02', '2023-01-03', '2023-01-04'],
- values=[51, 49, 52], name="Late Start"
-)
-
-# Frame will align to common date range
-frame = OpenFrame(constituents=[early_series, late_series])
-print(f"Frame date range: {frame.first_idx} to {frame.last_idx}")
-Adding and Removing Series
-# Add a new series
-new_series = OpenTimeSeries.from_arrays(
- dates=['2023-01-01', '2023-01-02', '2023-01-03'],
- values=[200, 205, 198], name="Asset C"
-)
-frame.add_timeseries(new_series)
-
-# Remove a series by index
-frame.delete_timeseries(item_idx=0)
-Data Export and Import
-Excel Export
-# Export single series
-series.to_xlsx(filename="single_series.xlsx")
-
-# Export frame (multiple series)
-frame.to_xlsx(filename="multiple_series.xlsx")
-
-# Export with custom sheet title
-series.to_xlsx(
- filename="formatted_export.xlsx",
- sheet_title="Analysis"
-)
-JSON Export
-# Export series values only
-series.to_json(what_output="values", filename="series_values.json")
-
-# Export full dataframe structure
-series.to_json(what_output="tsdf", filename="series_dataframe.json")
-
-# Export frame data
-frame.to_json(what_output="values", filename="frame_values.json")
-Working with Real Data Sources
-Yahoo Finance Integration
-import yfinance as yf
-
-# Single asset
-ticker = yf.Ticker("AAPL")
-data = ticker.history(period="2y")
-
-apple = OpenTimeSeries.from_df(
- dframe=data['Close'],
- name="Apple Inc."
-)
-
-# Multiple assets
-tickers = ["AAPL", "GOOGL", "MSFT"]
-series_list = []
-
-for ticker_symbol in tickers:
- ticker = yf.Ticker(ticker_symbol)
- data = ticker.history(period="1y")
- series = OpenTimeSeries.from_df(
- dframe=data['Close'],
- name=ticker_symbol
- )
- series_list.append(series)
-
-tech_frame = OpenFrame(constituents=series_list)
-CSV Data
-# Load from CSV
-df = pd.read_csv("stock_data.csv", index_col=0, parse_dates=True)
-
-series = OpenTimeSeries.from_df(
- dframe=df['Close'],
- name="Stock from CSV"
-)
-Data Quality Checks
-Validation Methods
-# Check for data quality issues
-print(f"Series length: {series.length}")
-print(f"Date range: {series.first_idx} to {series.last_idx}")
-print(f"Span of days: {series.span_of_days}")
-
-# Check for gaps in data
-expected_length = (series.last_idx - series.first_idx).days + 1
-actual_length = series.length
-
-if expected_length != actual_length:
- print(f"Data gaps detected: expected {expected_length}, got {actual_length}")
-Outlier Detection
-# Convert to returns for outlier analysis (modifies original)
-series.value_to_ret()
-
-# Detect outliers using the built-in method
-outliers = series.outliers(threshold=3.0)
-print(f"Found {len(outliers)} outliers (|z| > 3)")
-
-# For OpenFrame, outliers returns a DataFrame
-frame_outliers = frame.outliers(threshold=3.0)
-print(f"Found outliers in frame: {len(frame_outliers)} rows")
-
-# Customize threshold and date range
-recent_outliers = series.outliers(
- threshold=2.5,
- months_from_last=6
-)
-Performance Considerations
-Memory Usage
-# For large datasets, consider resampling
-large_series = series # Assume this is large daily data
-
-# Reduce to monthly for analysis (modifies original)
-large_series.resample_to_business_period_ends(freq="BME")
-
-# Use monthly for computationally intensive operations
-monthly_metrics = large_series.all_properties()
-Efficient Data Loading
-# When loading multiple assets, batch the operations
-tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"]
-
-# Download all at once
-data = yf.download(tickers, period="2y")['Close']
-
-# Create series efficiently
-series_list = []
-for ticker in tickers:
- series = OpenTimeSeries.from_df(
- dframe=data[ticker].dropna(),
- name=ticker
- )
- series_list.append(series)
-
-frame = OpenFrame(constituents=series_list)
-This comprehensive guide should help you handle various data scenarios effectively with openseries.
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- Installation
-System Requirements
-openseries requires Python 3.11 or higher and is compatible with:
--
-
Operating Systems: Windows, macOS, Linux
-Python versions: 3.11, 3.12, 3.13, 3.14
-
Installing openseries
-Using pip (recommended)
-The easiest way to install openseries is using pip:
-pip install openseries
-Using conda
-openseries is also available on conda-forge:
-conda install -c conda-forge openseries
-Installing from source
-To install the latest development version from GitHub:
-git clone https://github.com/CaptorAB/openseries.git
-cd openseries
-pip install -e .
-Dependencies
-openseries automatically installs the following dependencies:
-Core Dependencies
--
-
pandas (>=2.1.2) - Data manipulation and analysis
-numpy (>=1.23.2) - Numerical computing
-pydantic (>=2.5.2) - Data validation and settings management
-plotly (>=5.18.0) - Interactive plotting
-scipy (>=1.14.1) - Scientific computing
-scikit-learn (>=1.4.0) - Machine learning utilities
-
Financial and Date Utilities
--
-
exchange-calendars (>=4.8) - Trading calendar support
-holidays (>=0.30) - Holiday calendar support
-python-dateutil (>=2.8.2) - Date parsing utilities
-tzdata (>=2025.3) - IANA time zone data
-
File and Network Support
--
-
openpyxl (>=3.1.2) - Excel file support
-requests (>=2.20.0) - HTTP library
-
Optional Dependencies
-For data acquisition examples, you may want to install:
-pip install yfinance # For Yahoo Finance data
-Verifying Installation
-To verify that openseries is installed correctly, run:
-import openseries
-print(openseries.__version__)
-You can also run a quick test:
-from openseries import OpenTimeSeries, ReturnSimulation, ValueType
-import datetime as dt
-
-# Create sample data using openseries simulation
-simulation = ReturnSimulation.from_lognormal(
- number_of_sims=1,
- trading_days=100,
- mean_annual_return=0.25, # ~0.001 daily
- mean_annual_vol=0.32, # ~0.02 daily
- trading_days_in_year=252,
- seed=42
-)
-
-# Create OpenTimeSeries
-series = OpenTimeSeries.from_df(
- dframe=simulation.to_dataframe(name="Test Series", end=dt.date(2023, 12, 31)),
- valuetype=ValueType.RTRN
-).to_cumret() # Convert returns to cumulative prices
-
-print(f"Series length: {series.length}")
-print(f"Annual return: {series.geo_ret:.2%}")
-Development Installation
-If you plan to contribute to openseries or need the development dependencies,
-use the same pinned tooling as CI (uv==0.11.21):
git clone https://github.com/CaptorAB/openseries.git
-cd openseries
-make install
-On Windows, run .\make.ps1 make instead of make install.
This creates venv, installs locked runtime, development, and documentation
-dependencies from uv.lock, and installs pre-commit hooks. Development
-dependencies include:
-
-
pytest (>=9.1.0) - Testing framework
-pytest-cov (>=7.1.0) - Coverage plugin
-pytest-xdist (>=3.8.0) - Parallel test runner
-mypy (==2.1.0) - Static type checking
-ruff (==0.15.18) - Linting and formatting
-pre-commit (>=4.6.0) - Git hooks for code quality
-
Troubleshooting
-Common Issues
-ImportError: No module named ‘openseries’
-Make sure openseries is installed in the correct Python environment. If using virtual environments, ensure it’s activated.
-Version conflicts
-If you encounter dependency conflicts, try creating a fresh virtual environment:
-python -m venv openseries_env
-source openseries_env/bin/activate # On Windows: openseries_env\Scripts\activate
-pip install openseries
-Performance issues
-For better performance with large datasets, consider installing optional accelerated packages:
-pip install numba # For numerical acceleration
-pip install bottleneck # For faster pandas operations
-Getting Help
-If you encounter issues:
--
-
Check the GitHub Issues
-Review the Release Notes
-Create a new issue with a minimal reproducible example
-
Platform-Specific Notes
-Windows
-On Windows, you may need to install Microsoft Visual C++ Build Tools if you encounter compilation errors with dependencies.
-macOS
-On macOS with Apple Silicon (M1/M2), all dependencies should install without issues. If you encounter problems, try using conda instead of pip.
-Linux
-Most Linux distributions should work without issues. On minimal installations, you may need to install additional system packages for some dependencies.
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diff --git a/docs/requirements.txt b/docs/requirements.txt
deleted file mode 100644
index 64fc5dea..00000000
--- a/docs/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-sphinx>=9.0.4
-sphinx-autobuild>=2025.8.25
-sphinx-autodoc-typehints>=3.6.0
-sphinx-rtd-theme>=3.1.0
diff --git a/docs/source/development/contributing.rst b/docs/source/development/contributing.rst
index cfb3b523..273ce8b6 100644
--- a/docs/source/development/contributing.rst
+++ b/docs/source/development/contributing.rst
@@ -18,7 +18,7 @@ Development Setup
cd openseries
3. Create the development environment. This installs the pinned uv version
- (``uv==0.11.21``), syncs locked ``dev`` and ``docs`` dependencies from
+ (``uv==0.12.7``), syncs locked ``dev`` and ``docs`` dependencies from
``uv.lock``, and installs pre-commit hooks:
.. code-block:: bash
diff --git a/docs/source/user_guide/installation.rst b/docs/source/user_guide/installation.rst
index 69f66f93..3213a087 100644
--- a/docs/source/user_guide/installation.rst
+++ b/docs/source/user_guide/installation.rst
@@ -119,7 +119,7 @@ Development Installation
------------------------
If you plan to contribute to openseries or need the development dependencies,
-use the same pinned tooling as CI (``uv==0.11.21``):
+use the same pinned tooling as CI (``uv==0.12.7``):
.. code-block:: bash
@@ -137,7 +137,7 @@ dependencies include:
- **pytest-cov** (>=7.1.0) - Coverage plugin
- **pytest-xdist** (>=3.8.0) - Parallel test runner
- **mypy** (==2.1.0) - Static type checking
-- **ruff** (==0.15.18) - Linting and formatting
+- **ruff** (==0.16.5) - Linting and formatting
- **pre-commit** (>=4.6.0) - Git hooks for code quality
Troubleshooting
diff --git a/make.ps1 b/make.ps1
index e4ab9018..020d284c 100644
--- a/make.ps1
+++ b/make.ps1
@@ -14,7 +14,7 @@ param (
$ErrorActionPreference = 'Stop'
-$UV_VERSION = "0.11.21"
+$UV_VERSION = "0.12.7"
$PIP_AUDIT_VERSION = "2.10.0"
# Ensure we run from repo root
diff --git a/pyproject.toml b/pyproject.toml
index e41dea98..23f9ec0b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -69,7 +69,7 @@ dev = [
"pytest>=9.1.0",
"pytest-cov>=7.1.0",
"pytest-xdist>=3.8.0",
- "ruff==0.15.18",
+ "ruff==0.16.5",
"types-openpyxl>=3.1.2",
"scipy-stubs>=1.14.1.0",
"types-python-dateutil>=2.8.2",
@@ -121,7 +121,7 @@ exclude = ["docs"]
[tool.ruff.lint]
select = ["ALL"]
-ignore = ["COM812"]
+ignore = ["COM812", "CPY001"]
fixable = ["ALL"]
pydocstyle = { convention = "google" }
pylint = { max-args = 20, max-branches = 12, max-statements = 48 }
diff --git a/scripts/ci-pr-paths.sh b/scripts/ci-pr-paths.sh
new file mode 100755
index 00000000..1e25a727
--- /dev/null
+++ b/scripts/ci-pr-paths.sh
@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+if [[ $# -lt 1 ]]; then
+ echo "usage: $0
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- Quick Start Guide
-This guide will get you up and running with openseries in just a few minutes.
-Your First OpenTimeSeries
-Let’s start by creating a simulated financial time series using openseries’ built-in simulation capabilities:
-from openseries import OpenTimeSeries, ReturnSimulation, ValueType
-import datetime as dt
-
-# Create a simulated time series using lognormal distribution
-simulation = ReturnSimulation.from_lognormal(
- number_of_sims=1,
- trading_days=1000,
- mean_annual_return=0.08, # 8% annual return
- mean_annual_vol=0.15, # 15% annual volatility
- trading_days_in_year=252,
- seed=71
-)
-
-# Convert simulation to OpenTimeSeries
-sp500 = OpenTimeSeries.from_df(
- dframe=simulation.to_dataframe(name="S&P 500", end=dt.date(2023, 12, 31)),
- valuetype=ValueType.RTRN
-).to_cumret() # Convert returns to cumulative prices
-
-sp500.set_new_label(lvl_zero="S&P 500")
-
-# Display basic information
-print(f"Series: {sp500.label}")
-print(f"Start date: {sp500.first_idx}")
-print(f"End date: {sp500.last_idx}")
-print(f"Number of observations: {sp500.length}")
-Loading Data from External Sources
-Alternatively, you can load data from external sources like yfinance:
-import yfinance as yf # pip install yfinance
-from openseries import OpenTimeSeries
-
-# Download S&P 500 data
-ticker = yf.Ticker("^GSPC")
-data = ticker.history(period="2y")
-
-# Create OpenTimeSeries from the Close prices
-sp500 = OpenTimeSeries.from_df(dframe=data['Close'])
-
-# Set a more descriptive label
-sp500.set_new_label(lvl_zero="S&P 500 Index")
-
-print(f"Loaded {sp500.length} observations")
-print(f"Date range: {sp500.first_idx} to {sp500.last_idx}")
-Basic Financial Metrics
-openseries provides a comprehensive set of financial metrics:
-# Key performance metrics
-print(f"Total Return: {sp500.value_ret:.2%}")
-print(f"Annualized Return (CAGR): {sp500.geo_ret:.2%}")
-print(f"Annualized Volatility: {sp500.vol:.2%}")
-print(f"Sharpe Ratio: {sp500.ret_vol_ratio:.2f}")
-print(f"Maximum Drawdown: {sp500.max_drawdown:.2%}")
-
-# Risk metrics
-print(f"95% VaR (daily): {sp500.var_down:.2%}")
-print(f"95% CVaR (daily): {sp500.cvar_down:.2%}")
-print(f"Sortino Ratio: {sp500.sortino_ratio:.2f}")
-
-# Distribution statistics
-print(f"Skewness: {sp500.skew:.2f}")
-print(f"Kurtosis: {sp500.kurtosis:.2f}")
-print(f"Positive Days: {sp500.positive_share:.1%}")
-Get All Metrics at Once
-Use the all_properties attribute to get a comprehensive overview:
# Get all metrics of an OpenTimeSeries or OpenFrame
-metrics = sp500.all_properties()
-print(metrics)
-Creating Visualizations
-openseries integrates with Plotly for interactive visualizations:
-# Plot the timeseries
-sp500.plot_series()
-# This opens an interactive plot in your browser
-
-# Plot returns histogram
-returns = sp500.from_deepcopy()
-returns.value_to_ret() # Convert to returns (modifies original)
-returns.plot_histogram()
-
-# Plot bar chart (useful for plotting returns)
-returns.plot_bars()
-
-# Plot drawdown series
-sp500.to_drawdown_series() # Convert to drawdown (modifies original)
-sp500.plot_series()
-Working with Multiple Assets (OpenFrame)
-For multi-asset analysis, use the OpenFrame class:
-from openseries import OpenFrame
-import yfinance as yf
-
-# Download data for multiple assets
-tickers = ["^GSPC", "^IXIC", "^RUT"] # S&P 500, NASDAQ, Russell 2000
-names = ["S&P 500", "NASDAQ", "Russell 2000"]
-
-series_list = []
-for ticker, name in zip(tickers, names):
- data = yf.Ticker(ticker).history(period="2y")
- series = OpenTimeSeries.from_df(dframe=data['Close'])
- series.set_new_label(lvl_zero=name)
- series_list.append(series)
-
-# Create OpenFrame
-frame = OpenFrame(constituents=series_list)
-frame.value_nan_handle().trunc_frame()
-
-# Get metrics for all series
-all_metrics = frame.all_properties()
-print(all_metrics)
-
-# Calculate correlations
-correlations = frame.correl_matrix
-print("\nCorrelation Matrix:")
-print(correlations)
-Portfolio Analysis
-Create and analyze portfolios:
-# Equal-weighted portfolio
-portfolio_df = frame.make_portfolio(name="Equal Weight", weight_strat="eq_weights")
-portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-
-print(f"Equal Weight Portfolio Return: {portfolio.geo_ret:.2%}")
-print(f"Equal Weight Portfolio Volatility: {portfolio.vol:.2%}")
-print(f"Equal Weight Portfolio Sharpe: {portfolio.ret_vol_ratio:.2f}")
-
-# Create custom weighted portfolio
-frame.weights = [0.8, 0.2] # Custom allocation
-custom_df = frame.make_portfolio(name="Custom Portfolio")
-custom_portfolio = OpenTimeSeries.from_df(dframe=custom_df)
-print(f"Custom Portfolio Sharpe: {custom_portfolio.ret_vol_ratio:.2f}")
-
-# Compare with individual assets
-frame.add_timeseries(portfolio)
-frame.add_timeseries(custom_portfolio)
-comparison = frame.all_properties()
-print(comparison)
-Data Transformations
-openseries provides various data transformation methods:
-# Convert prices to returns (modifies original)
-sp500.value_to_ret()
-print(f"Returns series length: {sp500.length}")
-
-# Convert to log returns (modifies original)
-sp500.value_to_log()
-
-# Calculate rolling statistics
-rolling_vol = sp500.rolling_vol(observations=30) # 30-day rolling volatility
-rolling_ret = sp500.rolling_return(observations=30) # 30-day rolling returns
-
-# Resample to monthly data (modifies original)
-sp500.resample_to_business_period_ends(freq="BME")
-print(f"Monthly data points: {sp500.length}")
-Exporting Results
-Save your analysis results:
-# Export to Excel
-sp500.to_xlsx(filename="sp500_analysis.xlsx")
-
-# Export to JSON
-sp500.to_json(filename="sp500_data.json", what_output="tsdf")
-Working with Business Days
-openseries handles business day calendars automatically:
-# Align to Swedish business days (modifies original)
-sp500.align_index_to_local_cdays(countries="SE")
-
-# Use multiple countries (modifies original)
-sp500.align_index_to_local_cdays(countries=["US", "GB"])
-
-# Handle missing values (modifies original)
-sp500.value_nan_handle() # Forward fill NaN values
-Next Steps
-Now that you’ve learned the basics, explore:
--
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Tutorials - Detailed examples for specific use cases
-API Reference - Complete documentation of all methods and properties
-Examples - Real-world analysis scenarios
-
Key Concepts to Remember
--
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OpenTimeSeries: For single asset analysis
-OpenFrame: For multi-asset and portfolio analysis
-ValueType: Enum to identify data types (prices, returns, etc.)
-Business day handling: Automatic alignment to trading calendars
-Interactive plotting: Built-in Plotly integration
-Type safety: Pydantic-based validation ensures data integrity
-
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-Here are some common usage patterns:
-# Pattern 1: Load, analyze, visualize
-series = OpenTimeSeries.from_df(dframe=data['Close'])
-series.set_new_label(lvl_zero="Asset")
-metrics = series.all_properties()
-series.plot_series()
-
-# Pattern 2: Multi-asset comparison
-frame = OpenFrame(constituents=[series1, series2, series3])
-comparison = frame.all_properties()
-correlations = frame.correl_matrix
-
-# Pattern 3: Portfolio construction (built-in strategies)
-portfolio_df = frame.make_portfolio(name="Equal Weight", weight_strat="eq_weights")
-portfolio = OpenTimeSeries.from_df(dframe=portfolio_df)
-frame.add_timeseries(portfolio)
-
-# Pattern 3b: Custom portfolio construction (create fresh frame)
-custom_frame = OpenFrame(constituents=[series1, series2, series3])
-custom_frame.weights = [0.4, 0.3, 0.3]
-custom_df = custom_frame.make_portfolio(name="Custom Portfolio")
-custom_portfolio = OpenTimeSeries.from_df(dframe=custom_df)
-
-# Pattern 4: Risk analysis
-risk_series = series.from_deepcopy() # Create copy for risk analysis
-var_95 = risk_series.var_down # VaR on returns
-max_dd = series.max_drawdown
-rolling_risk = risk_series.rolling_vol(observations=252)
-
-# Drawdown analysis (on original series)
-series.to_drawdown_series() # Convert to drawdown (modifies original)
-This should give you a solid foundation to start using openseries for your financial analysis needs!
-