Options Volatility Tracker & Backtesting Framework

Status: Private research documentation shell Privacy Note: The private research/backtesting code, Charles Schwab API OAuth handlers, database reset scripts, and experimental strategy research and local model artifacts are stored in a local, private Git repository to protect credential security and keep runtime state private. This repository serves as a public documentation shell to highlight the architecture and workflow.

Project Overview

A Python-based backtesting and options analytics research project designed to process historical market data, evaluate options pricing metrics, and study quantitative analytics and backtesting concepts.

Related Public Proof Shells

This repository is the research and validation lane inside Michael Panico's market-data / quant-adjacent analytics map. The private production quant runtime is intentionally not public and is not linked from this docs shell.

• Portfolio market-data project page
• Market & Quant Analytics Lab Docs
• Institutional Market Data Engine Docs
• Market Data Dashboard Docs
• Portfolio Website Docs

Key Capabilities

• Backtesting Engine: Built a custom Python backtesting framework supporting transaction costs, walk-forward validation, and strict no-look-ahead controls to prevent data leakage during model training.
• Volatility Analytics: Developed an options analytics module to compare implied vs. realized volatility, calculate Black-Scholes Greeks, visualize volatility skew, and estimate expected market moves using synthetic/public data inputs.
• Risk-Adjusted Performance: Automated calculation of key portfolio metrics including Sharpe/Sortino ratios, maximum drawdowns, and VaR/CVaR against benchmark comparisons.
• Database Architecture: Manages historical tick data and pre-computed features using a local SQL data warehouse.

Tech Stack

• Languages: Python
• Data Engineering: SQL, pandas, NumPy
• Machine Learning: scikit-learn (Gradient Boosting, regression modeling)
• Analytics: Statistical Data Analysis, Cross-Validation, Backtesting, Risk Analytics

Sanitized Code Excerpts

Select non-sensitive files and architectural components (such as DataFrame schemas and feature engineering definitions) are provided in the examples/sanitized-code-excerpts/ directory to demonstrate data structures without exposing proprietary strategy implementations.