From 122e293769f2dece6ca3255001ecff67a1e7ac81 Mon Sep 17 00:00:00 2001 From: Martin H Karrin Date: Sat, 29 Aug 2026 10:47:30 +0200 Subject: [PATCH 1/7] Stop re-running validation workflows after merge to cut Actions minutes. PR jobs are path-filtered so required checks still pass, docs build without deploying until master, and generated HTML plus Read the Docs are dropped in favor of GitHub Pages. Co-authored-by: Cursor --- .github/workflows/codeql.yml | 27 +- .github/workflows/docs.yml | 65 +- .github/workflows/supply-chain.yml | 52 +- .github/workflows/test.yml | 39 +- .github/workflows/zizmor.yml | 24 +- .readthedocs.yaml | 20 - README.md | 2 +- SECURITY.md | 19 +- docs/README.md | 14 +- docs/build/html/.buildinfo | 4 - .../html/.doctrees/api/datefixer.doctree | Bin 63560 -> 0 bytes docs/build/html/.doctrees/api/frame.doctree | Bin 902955 -> 0 bytes .../generated/openseries.OpenFrame.doctree | Bin 395598 -> 0 bytes .../openseries.OpenTimeSeries.doctree | Bin 403507 -> 0 bytes .../openseries.ReturnSimulation.doctree | Bin 199224 -> 0 bytes .../generated/openseries.ValueType.doctree | Bin 85950 -> 0 bytes ...ies.constrain_optimized_portfolios.doctree | Bin 16414 -> 0 bytes .../api/generated/openseries.date_fix.doctree | Bin 6933 -> 0 bytes .../openseries.date_offset_foll.doctree | Bin 16054 -> 0 bytes .../openseries.efficient_frontier.doctree | Bin 16640 -> 0 bytes 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docs/build/html/user_guide/data_handling.html delete mode 100644 docs/build/html/user_guide/installation.html delete mode 100644 docs/build/html/user_guide/quickstart.html delete mode 100644 docs/requirements.txt create mode 100755 scripts/ci-pr-paths.sh create mode 100644 tests/test_ci_pr_paths.py diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 3a5ff871..a53207ac 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -3,6 +3,7 @@ name: codeql.yml permissions: contents: read actions: read # Required for setup-python and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering env: FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true" @@ -10,13 +11,11 @@ env: UV_PROJECT_ENVIRONMENT: venv concurrency: - group: codeql-${{ github.ref }} + group: codeql-${{ github.event_name == 'pull_request' && github.head_ref || github.ref }} cancel-in-progress: true on: workflow_dispatch: {} - push: - branches: [master] pull_request: branches: [master] schedule: @@ -28,6 +27,8 @@ jobs: runs-on: ubuntu-latest permissions: contents: read + actions: read # Required for setup-python and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering security-events: write # Required for github/codeql-action/analyze to upload results steps: - name: Check out repository @@ -35,25 +36,45 @@ jobs: with: persist-credentials: false + - name: Filter paths + id: paths + env: + GH_TOKEN: ${{ github.token }} + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_REPOSITORY: ${{ github.repository }} + PR_NUMBER: ${{ github.event.pull_request.number }} + run: bash scripts/ci-pr-paths.sh + 'openseries/*' + 'tests/*' + 'pyproject.toml' + 'uv.lock' + '.github/workflows/codeql.yml' + 'scripts/ci-pr-paths.sh' + - name: Initialize CodeQL + if: ${{ steps.paths.outputs.run == 'true' }} uses: github/codeql-action/init@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4.37.7 with: languages: python - name: Set up Python + if: ${{ steps.paths.outputs.run == 'true' }} uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version-file: .python-version - name: Set up uv + if: ${{ steps.paths.outputs.run == 'true' }} uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: version: ${{ env.UV_VERSION }} - name: Sync dependencies (locked) + if: ${{ steps.paths.outputs.run == 'true' }} run: uv sync --locked --extra dev - name: Perform CodeQL analysis + if: ${{ steps.paths.outputs.run == 'true' }} uses: github/codeql-action/analyze@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4.37.7 with: category: /language:python diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 60072f34..56eb2a46 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -13,32 +13,37 @@ concurrency: cancel-in-progress: true on: - # Only run on pushes to master for documentation-related changes - # This prevents running on every commit, only when docs actually change push: branches: - master paths: - 'docs/**' - - '.readthedocs.yaml' - # Manual trigger for immediate updates (e.g., after releases) + - 'openseries/**' + - 'pyproject.toml' + - 'uv.lock' + - '.python-version' + - '.github/workflows/docs.yml' + pull_request: + branches: + - master + paths: + - 'docs/**' + - 'openseries/**' + - 'pyproject.toml' + - 'uv.lock' + - '.python-version' + - '.github/workflows/docs.yml' workflow_dispatch: {} jobs: - build-and-deploy: - name: Build and deploy documentation + build: + name: Build documentation defaults: run: shell: bash runs-on: ubuntu-latest - if: github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' permissions: contents: read - pages: write # Required for actions/deploy-pages to deploy to GitHub Pages - id-token: write # Required for OIDC token for GitHub Pages deployment - environment: - name: github-pages - steps: - name: Check out GitHub repository uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 @@ -65,12 +70,36 @@ jobs: run: uv run --directory docs make builddocs - name: Check for documentation warnings - run: | - uv run --directory docs make strict || { - echo "Documentation build failed with warnings/errors:" - echo "This indicates potential issues that should be fixed." - exit 1 - } + run: uv run --directory docs make strict + + - name: Upload HTML artifact + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: docs-html + path: docs/build/html + if-no-files-found: error + retention-days: 1 + + deploy: + name: Deploy documentation + needs: build + if: ${{ github.event_name == 'workflow_dispatch' || github.event_name == 'push' }} + defaults: + run: + shell: bash + runs-on: ubuntu-latest + permissions: + contents: read + pages: write # Required for actions/deploy-pages to deploy to GitHub Pages + id-token: write # Required for OIDC token for GitHub Pages deployment + environment: + name: github-pages + steps: + - name: Download HTML artifact + uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1 + with: + name: docs-html + path: docs/build/html - name: Setup Pages uses: actions/configure-pages@45bfe0192ca1faeb007ade9deae92b16b8254a0d # v6.0.0 diff --git a/.github/workflows/supply-chain.yml b/.github/workflows/supply-chain.yml index cd1843e7..1d46ac21 100644 --- a/.github/workflows/supply-chain.yml +++ b/.github/workflows/supply-chain.yml @@ -3,6 +3,7 @@ name: supply-chain.yml permissions: contents: read actions: read # Required for setup-uv and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering env: FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true" @@ -16,10 +17,10 @@ concurrency: on: workflow_dispatch: {} - push: - branches: [master] pull_request: branches: [master] + schedule: + - cron: "15 6 * * 1" jobs: lockfile: @@ -31,12 +32,27 @@ jobs: with: persist-credentials: false + - name: Filter paths + id: paths + env: + GH_TOKEN: ${{ github.token }} + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_REPOSITORY: ${{ github.repository }} + PR_NUMBER: ${{ github.event.pull_request.number }} + run: bash scripts/ci-pr-paths.sh + 'uv.lock' + 'pyproject.toml' + '.github/workflows/supply-chain.yml' + 'scripts/ci-pr-paths.sh' + - name: Set up uv + if: ${{ steps.paths.outputs.run == 'true' }} uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: version: ${{ env.UV_VERSION }} - name: Check uv.lock is up to date + if: ${{ steps.paths.outputs.run == 'true' }} run: uv lock --check osv-scanner: @@ -48,7 +64,21 @@ jobs: with: persist-credentials: false + - name: Filter paths + id: paths + env: + GH_TOKEN: ${{ github.token }} + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_REPOSITORY: ${{ github.repository }} + PR_NUMBER: ${{ github.event.pull_request.number }} + run: bash scripts/ci-pr-paths.sh + 'uv.lock' + 'pyproject.toml' + '.github/workflows/supply-chain.yml' + 'scripts/ci-pr-paths.sh' + - name: Run OSV-Scanner + if: ${{ steps.paths.outputs.run == 'true' }} uses: google/osv-scanner-action/osv-scanner-action@8deb546fdb875b9996d27d4950be7312dac076a1 # v2.5.0 with: scan-args: --lockfile=uv.lock @@ -63,23 +93,41 @@ jobs: with: persist-credentials: false + - name: Filter paths + id: paths + env: + GH_TOKEN: ${{ github.token }} + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_REPOSITORY: ${{ github.repository }} + PR_NUMBER: ${{ github.event.pull_request.number }} + run: bash scripts/ci-pr-paths.sh + 'uv.lock' + 'pyproject.toml' + '.github/workflows/supply-chain.yml' + 'scripts/ci-pr-paths.sh' + - name: Set up Python + if: ${{ steps.paths.outputs.run == 'true' }} uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version-file: .python-version - name: Set up uv + if: ${{ steps.paths.outputs.run == 'true' }} uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: version: ${{ env.UV_VERSION }} - name: Sync dependencies (locked) + if: ${{ steps.paths.outputs.run == 'true' }} run: uv sync --locked --extra dev - name: Export requirements for pip-audit + if: ${{ steps.paths.outputs.run == 'true' }} run: uv export --locked --extra dev --no-emit-project -o requirements-audit.txt - name: Run pip-audit + if: ${{ steps.paths.outputs.run == 'true' }} env: PIP_AUDIT_VERSION: ${{ env.PIP_AUDIT_VERSION }} run: uvx pip-audit==${PIP_AUDIT_VERSION} -r requirements-audit.txt diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index f4c77b76..fd4b0351 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -3,6 +3,7 @@ name: tests.yml permissions: contents: read actions: read # Required for setup-python and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering env: FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true" @@ -15,9 +16,6 @@ concurrency: on: workflow_dispatch: {} - push: - branches: - - master pull_request: branches: - master @@ -31,6 +29,9 @@ jobs: run: shell: bash permissions: + contents: read + actions: read # Required for setup-python and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering issues: write # Required for actions/github-script to create issues on test failure steps: @@ -41,27 +42,51 @@ jobs: ref: ${{ github.event_name == 'workflow_dispatch' && github.ref || github.sha }} persist-credentials: false + - name: Filter paths + id: paths + env: + GH_TOKEN: ${{ github.token }} + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_REPOSITORY: ${{ github.repository }} + PR_NUMBER: ${{ github.event.pull_request.number }} + run: bash scripts/ci-pr-paths.sh + 'openseries/*' + 'tests/*' + 'pyproject.toml' + 'uv.lock' + '.python-version' + '.github/workflows/test.yml' + 'Makefile' + 'make.ps1' + '.pre-commit-config.yaml' + 'scripts/ci-pr-paths.sh' + - name: Set up Python + if: ${{ steps.paths.outputs.run == 'true' }} uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version-file: '.python-version' - name: Set up uv + if: ${{ steps.paths.outputs.run == 'true' }} uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: version: ${{ env.UV_VERSION }} - name: Sync dependencies (locked) + if: ${{ steps.paths.outputs.run == 'true' }} run: uv sync --locked --extra dev - name: Check and fix with Ruff + if: ${{ steps.paths.outputs.run == 'true' }} run: uv run ruff check ./tests/*.py ./openseries/*.py --fix --exit-non-zero-on-fix - name: Format with Ruff + if: ${{ steps.paths.outputs.run == 'true' }} run: uv run ruff format - name: Manage Mypy cache - if: ${{ github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository }} + if: ${{ steps.paths.outputs.run == 'true' && (github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository) }} uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0 id: mypy-cache with: @@ -70,9 +95,11 @@ jobs: restore-keys: mypy-cache- - name: Type check with Mypy + if: ${{ steps.paths.outputs.run == 'true' }} run: uv run mypy --cache-dir .mypy_cache . - name: Tests with Pytest + if: ${{ steps.paths.outputs.run == 'true' }} id: pytest continue-on-error: true run: uv run pytest @@ -104,7 +131,7 @@ jobs: run: exit 1 - name: Upload test results to Codecov - if: ${{ github.ref_name == 'master' && (github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository) }} + if: ${{ steps.paths.outputs.run == 'true' && (github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository) }} uses: codecov/codecov-action@fb8b3582c8e4def4969c97caa2f19720cb33a72f # v7.0.0 with: token: ${{ secrets.CODECOV_TOKEN }} @@ -113,7 +140,7 @@ jobs: verbose: true - name: Upload coverage to Codecov - if: ${{ github.ref_name == 'master' && (github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository) }} + if: ${{ steps.paths.outputs.run == 'true' && (github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository) }} uses: codecov/codecov-action@fb8b3582c8e4def4969c97caa2f19720cb33a72f # v7.0.0 with: token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/zizmor.yml b/.github/workflows/zizmor.yml index 57621d47..2a1feb97 100644 --- a/.github/workflows/zizmor.yml +++ b/.github/workflows/zizmor.yml @@ -3,6 +3,7 @@ name: zizmor.yml permissions: contents: read actions: read # Required for setup-uv and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering env: FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true" @@ -15,8 +16,6 @@ concurrency: on: workflow_dispatch: {} - push: - branches: [master] pull_request: branches: [master] @@ -25,6 +24,9 @@ jobs: name: Run zizmor audit runs-on: ubuntu-latest permissions: + contents: read + actions: read # Required for setup-uv and other actions to read action metadata + pull-requests: read # Required to list PR files for path filtering security-events: write # Required for github/codeql-action/upload-sarif to upload SARIF results steps: - name: Checkout repository @@ -34,26 +36,40 @@ jobs: ref: ${{ github.event_name == 'workflow_dispatch' && github.ref || github.sha }} persist-credentials: false + - name: Filter paths + id: paths + env: + GH_TOKEN: ${{ github.token }} + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_REPOSITORY: ${{ github.repository }} + PR_NUMBER: ${{ github.event.pull_request.number }} + run: bash scripts/ci-pr-paths.sh + '.github/*' + 'scripts/run-zizmor.sh' + 'scripts/ci-pr-paths.sh' + - name: Set up uv + if: ${{ steps.paths.outputs.run == 'true' }} uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: version: ${{ env.UV_VERSION }} - name: Run zizmor audit + if: ${{ steps.paths.outputs.run == 'true' }} env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} ZIZMOR_VERSION: ${{ env.ZIZMOR_VERSION }} run: uvx zizmor==${ZIZMOR_VERSION} --strict-collection --pedantic . - name: Generate zizmor SARIF report - if: always() + if: ${{ always() && steps.paths.outputs.run == 'true' }} env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} ZIZMOR_VERSION: ${{ env.ZIZMOR_VERSION }} run: uvx zizmor==${ZIZMOR_VERSION} --strict-collection --pedantic --format=sarif . > results.sarif - name: Upload SARIF file - if: always() + if: ${{ always() && steps.paths.outputs.run == 'true' }} uses: github/codeql-action/upload-sarif@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4.37.7 with: sarif_file: results.sarif diff --git a/.readthedocs.yaml b/.readthedocs.yaml deleted file mode 100644 index e411019b..00000000 --- a/.readthedocs.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# Read the Docs configuration file for openseries -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -version: 2 - -build: - os: ubuntu-24.04 - tools: - python: "3.14" - -sphinx: - builder: html - configuration: docs/source/conf.py - fail_on_warning: true - -python: - install: - - requirements: docs/requirements.txt - - method: pip - path: . diff --git a/README.md b/README.md index 01c891b0..6cbed45c 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ [![Python version](https://img.shields.io/pypi/pyversions/openseries.svg)](https://www.python.org/) [![GitHub Action Test Suite](https://github.com/CaptorAB/openseries/actions/workflows/test.yml/badge.svg)](https://github.com/CaptorAB/openseries/actions/workflows/test.yml) [![codecov](https://img.shields.io/codecov/c/gh/CaptorAB/openseries?logo=codecov)](https://codecov.io/gh/CaptorAB/openseries/branch/master) -[![Documentation Status](https://readthedocs.org/projects/openseries/badge/?version=latest)](https://captorab.github.io/openseries/) +[![Documentation](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://captorab.github.io/openseries/) [![uv](https://img.shields.io/badge/package%20manager-uv-blueviolet)](https://github.com/astral-sh/uv) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://beta.ruff.rs/docs/) [![GitHub License](https://img.shields.io/github/license/CaptorAB/openseries)](https://github.com/CaptorAB/openseries/blob/master/LICENSE.md) diff --git a/SECURITY.md b/SECURITY.md index 2cb5d5c7..1cdc2a88 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -19,11 +19,20 @@ vulnerabilities. - **CI workflows** use hash-pinned actions, default read-only `contents`, and [zizmor](https://github.com/woodruffw/zizmor) audits. - **Dependencies** are locked in `uv.lock`; CI runs `uv sync --locked` and - supply-chain scans (`supply-chain.yml`). + supply-chain scans (`supply-chain.yml`) on pull requests that change lockfiles + and on a weekly schedule. - **Releases** build from the signed git tag, record `SHA256SUMS` for artifacts, and verify checksums before publish. - **`pull_request_target` is not used**; PR CI runs on `pull_request` with read-only defaults and fork guards on cache restore and issue creation. +- **Validation workflows** (`tests.yml`, `supply-chain.yml`, `zizmor.yml`, + `codeql.yml`) run on pull requests, not again on merge to `master`. Path + filters skip heavy steps when the PR does not touch relevant files, while the + jobs still report success so required checks are not left pending. +- **CodeQL** full analysis runs on pull requests that change Python sources and + on a weekly schedule; it is not repeated on every push to `master`. +- **Documentation** is built and deployed to GitHub Pages only + (`docs.yml`). Pull requests build without deploying. - **Release tagging** is isolated in a reusable workflow (`release-tag.yml`); build and PyPI publish run in `deploy.yml` because PyPI Trusted Publishing does not support reusable workflows. `deploy.yml` is the only manual entry @@ -48,9 +57,13 @@ YAML alone: - Restrict deployment branches to `master` - Do not expose secrets to fork PR workflows 4. **Branch protection on `master`**: - - Require status checks from `tests.yml`, `supply-chain.yml`, `zizmor.yml`, - and CodeQL before merge + - Require status checks from `tests.yml`, `supply-chain.yml`, and + `zizmor.yml` before merge. Do not require Read the Docs. CodeQL may remain + required; the `codeql.yml` job no-ops on PRs that do not change Python + sources. - Require review for changes under `.github/workflows/` + - Disconnect the Read the Docs GitHub integration (docs are GitHub Pages + only) so PR checks are not duplicated there. 5. **Dependabot**: keep weekly updates with cooldown enabled (see `.github/dependabot.yml`). 6. **Audit log**: periodically review GitHub audit log for workflow or secret diff --git a/docs/README.md b/docs/README.md index 1a3bfb20..b0ac13b2 100644 --- a/docs/README.md +++ b/docs/README.md @@ -83,13 +83,16 @@ docs/ └── make.bat # Build commands (Windows) ``` -## ReadTheDocs Integration +## GitHub Pages -This documentation is configured for [ReadTheDocs](https://readthedocs.org/) hosting: +Documentation is built with Sphinx and published to +[GitHub Pages](https://captorab.github.io/openseries/) by `.github/workflows/docs.yml`: -- Configuration: `.readthedocs.yaml` in the project root -- Dependencies: Managed through `pyproject.toml` optional docs dependencies -- Build process: Automated on ReadTheDocs +- Pull requests that touch documentation sources or library code build docs + (warnings as errors) without deploying +- Pushes to `master` and manual `workflow_dispatch` (including after a PyPI + release) build and deploy +- Dependencies come from the `docs` extra in `pyproject.toml` / `uv.lock` ## Writing Documentation @@ -196,7 +199,6 @@ When contributing to documentation: **Missing modules:** - Install missing dependencies: `uv sync --locked --extra docs` -- For ReadTheDocs builds, `docs/requirements.txt` must match the `docs` extra in `pyproject.toml` **Broken links:** diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo deleted file mode 100644 index c57888bc..00000000 --- a/docs/build/html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file records the configuration used when building these files. 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    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/datefixer.html b/docs/build/html/_modules/openseries/datefixer.html deleted file mode 100644 index d17fca60..00000000 --- a/docs/build/html/_modules/openseries/datefixer.html +++ /dev/null @@ -1,651 +0,0 @@ - - - - - - - - openseries.datefixer — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.datefixer

    -"""Date related utilities."""
-
-from __future__ import annotations
-
-import datetime as dt
-from typing import TYPE_CHECKING, cast
-
-import exchange_calendars as exchcal  # type: ignore[import-untyped]
-from dateutil.relativedelta import relativedelta
-from holidays import (
-    country_holidays,
-    list_supported_countries,
-)
-from numpy import array, busdaycalendar, datetime64, is_busday, where
-from pandas import (
-    DataFrame,
-    DatetimeIndex,
-    Index,
-    Timestamp,
-    concat,
-    date_range,
-)
-from pandas.tseries.offsets import CustomBusinessDay
-
-from .owntypes import (
-    BothStartAndEndError,
-    CountriesNotStringNorListStrError,
-    MarketsNotStringNorListStrError,
-    TradingDaysNotAboveZeroError,
-)
-
-if TYPE_CHECKING:
-    from .owntypes import (  # pragma: no cover
-        CountriesType,
-        DateType,
-        LiteralBizDayFreq,
-    )
-
-__all__ = [
-    "date_fix",
-    "date_offset_foll",
-    "generate_calendar_date_range",
-    "get_previous_business_day_before_today",
-    "holiday_calendar",
-    "offset_business_days",
-]
-
-
-def market_holidays(
-    startyear: int,
-    endyear: int,
-    markets: str | list[str],
-) -> list[str]:
-    """Return a list of holiday dates mapping to list of markets closed.
-
-    Args:
-        startyear: First year (inclusive) to consider.
-        endyear: Last year (inclusive) to consider.
-        markets: String or list of market codes supported by exchange_calendars.
-
-    Returns:
-        List of holiday dates.
-
-    Raises:
-        MarketsNotStringNorListStrError: If any market code is not supported by
-            ``exchange_calendars`` or the input is not a string or list of strings.
-    """
-    market_list = [markets] if isinstance(markets, str) else list(markets)
-
-    supported = exchcal.get_calendar_names()
-
-    if not all(m in supported for m in market_list):
-        msg = (
-            "Argument markets must be a string market code or a list of market "
-            "codes supported by exchange_calendars."
-        )
-        raise MarketsNotStringNorListStrError(msg)
-
-    holidays: list[str] = []
-    for m in market_list:
-        cal = exchcal.get_calendar(m)
-        cal_hols = cal.regular_holidays.holidays()
-        my_hols: list[str] = [
-            date.date().strftime("%Y-%m-%d")
-            for date in cal_hols
-            if (startyear <= date.date().year <= endyear)
-        ]
-        holidays.extend(my_hols)
-
-    return list(set(holidays))
-
-
-
    
-[docs]
-def holiday_calendar(
-    startyear: int,
-    endyear: int,
-    countries: CountriesType = "SE",
-    markets: list[str] | str | None = None,
-    custom_holidays: list[str] | str | None = None,
-) -> busdaycalendar:
-    """Generate a business calendar.
-
-    Args:
-        startyear: First year in date range generated.
-        endyear: Last year in date range generated.
-        countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-            Defaults to "SE".
-        markets: (List of) markets code(s) supported by exchange_calendars.
-        custom_holidays: Argument where missing holidays can be added.
-
-    Returns:
-        Generate a business calendar.
-
-    Raises:
-        CountriesNotStringNorListStrError: If ``countries`` is not a supported
-            ISO 3166-1 alpha-2 string or a list of such strings.
-    """
-    startyear -= 1
-    endyear += 1
-    if startyear == endyear:
-        endyear += 1
-    years = list(range(startyear, endyear))
-
-    if isinstance(countries, str) and countries in list_supported_countries():
-        staging = country_holidays(country=countries, years=years)
-        hols = list(staging.keys())
-    elif isinstance(countries, (list, set)) and all(
-        country in list_supported_countries() for country in countries
-    ):
-        country: str
-        countryholidays: list[dt.date | str] = []
-        for country in countries:
-            staging = country_holidays(country=country, years=years)
-            countryholidays += list(staging)
-        hols = cast("list[dt.date]", list(countryholidays))
-    else:
-        msg = (
-            "Argument countries must be a string country code or "
-            "a list of string country codes according to ISO 3166-1 alpha-2."
-        )
-        raise CountriesNotStringNorListStrError(msg)
-
-    if markets:
-        market_hols = market_holidays(
-            startyear=startyear,
-            endyear=endyear,
-            markets=markets,
-        )
-        dt_mkt_hols = [date_fix(fixerdate=ddate) for ddate in market_hols]
-        hols.extend(dt_mkt_hols)
-
-    if custom_holidays:
-        custom_list = (
-            [custom_holidays]
-            if isinstance(custom_holidays, str)
-            else list(custom_holidays)
-        )
-        hols.extend([date_fix(fixerdate=ddate) for ddate in custom_list])
-
-    return busdaycalendar(holidays=array(sorted(set(hols)), dtype="datetime64[D]"))
    
-
-
-
-
    
-[docs]
-def date_fix(
-    fixerdate: DateType,
-) -> dt.date:
-    """Parse different date formats into datetime.date.
-
-    Args:
-        fixerdate: The data item to parse.
-
-    Returns:
-        Parsed date.
-
-    Raises:
-        TypeError: If the provided ``fixerdate`` type is not supported.
-    """
-    msg = f"Unknown date format {fixerdate!s} of type {type(fixerdate)!s} encountered"
-    if isinstance(fixerdate, Timestamp | dt.datetime):
-        return fixerdate.date()
-    if isinstance(fixerdate, dt.date):
-        return fixerdate
-    if isinstance(fixerdate, datetime64):
-        return (
-            dt.datetime.strptime(str(fixerdate)[:10], "%Y-%m-%d").astimezone().date()
-        )
-    if isinstance(fixerdate, str):
-        return dt.datetime.strptime(fixerdate, "%Y-%m-%d").astimezone().date()
-    raise TypeError(msg)
    
-
-
-
-
    
-[docs]
-def date_offset_foll(
-    raw_date: DateType,
-    months_offset: int = 12,
-    countries: CountriesType = "SE",
-    markets: list[str] | str | None = None,
-    custom_holidays: list[str] | str | None = None,
-    *,
-    adjust: bool = False,
-    following: bool = True,
-) -> dt.date:
-    """Offset dates according to a given calendar.
-
-    Args:
-        raw_date: The date to offset from.
-        months_offset: Number of months as integer. Defaults to 12.
-        countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-            Defaults to "SE".
-        markets: (List of) markets code(s) supported by exchange_calendars.
-        custom_holidays: Argument where missing holidays can be added.
-        adjust: Determines if offset should adjust for business days.
-            Defaults to False.
-        following: Determines if days should be offset forward (following) or backward.
-            Defaults to True.
-
-    Returns:
-        Offset date.
-    """
-    raw_date = date_fix(raw_date)
-    month_delta = relativedelta(months=months_offset)
-
-    day_delta = relativedelta(days=1) if following else relativedelta(days=-1)
-
-    new_date = raw_date + month_delta
-
-    if adjust:
-        startyear = min([raw_date.year, new_date.year])
-        endyear = max([raw_date.year, new_date.year])
-        calendar = holiday_calendar(
-            startyear=startyear,
-            endyear=endyear,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-        )
-        while not is_busday(dates=new_date, busdaycal=calendar):
-            new_date += day_delta
-
-    return new_date
    
-
-
-
-
    
-[docs]
-def get_previous_business_day_before_today(
-    today: dt.date | None = None,
-    countries: CountriesType = "SE",
-    markets: list[str] | str | None = None,
-    custom_holidays: list[str] | str | None = None,
-) -> dt.date:
-    """Bump date backwards to find the previous business day.
-
-    Args:
-        today: Manual input of the day from where the previous business day is found.
-        countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-            Defaults to "SE".
-        markets: (List of) markets code(s) supported by exchange_calendars.
-        custom_holidays: Argument where missing holidays can be added.
-
-    Returns:
-        The previous business day.
-    """
-    if today is None:
-        today = dt.datetime.now().astimezone().date()
-
-    return date_offset_foll(
-        raw_date=today - dt.timedelta(days=1),
-        months_offset=0,
-        countries=countries,
-        markets=markets,
-        custom_holidays=custom_holidays,
-        adjust=True,
-        following=False,
-    )
    
-
-
-
-
    
-[docs]
-def offset_business_days(
-    ddate: dt.date,
-    days: int,
-    countries: CountriesType = "SE",
-    markets: list[str] | str | None = None,
-    custom_holidays: list[str] | str | None = None,
-) -> dt.date:
-    """Bump date by business days.
-
-    It first adjusts to a valid business day and then bumps with given
-    number of business days from there.
-
-    Args:
-        ddate: A starting date that does not have to be a business day.
-        days: 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: (List of) country code(s) according to ISO 3166-1 alpha-2.
-            Defaults to "SE".
-        markets: (List of) markets code(s) supported by exchange_calendars.
-        custom_holidays: Argument where missing holidays can be added.
-
-    Returns:
-        The new offset business day.
-    """
-    try:
-        days = int(days)
-    except TypeError:
-        days = 0
-
-    if days <= 0:
-        scaledtoyeardays = int((days * 372 / 250) // 1) - 365
-        ndate = ddate + dt.timedelta(days=scaledtoyeardays)
-        calendar = holiday_calendar(
-            startyear=ndate.year,
-            endyear=ddate.year,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-        )
-        local_bdays: list[dt.date] = [
-            bday.date()
-            for bday in date_range(
-                periods=abs(scaledtoyeardays),
-                end=ddate,
-                freq=CustomBusinessDay(calendar=calendar),
-            )
-        ]
-    else:
-        scaledtoyeardays = int((days * 372 / 250) // 1) + 365
-        ndate = ddate + dt.timedelta(days=scaledtoyeardays)
-        calendar = holiday_calendar(
-            startyear=ddate.year,
-            endyear=ndate.year,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-        )
-        local_bdays = [
-            bday.date()
-            for bday in date_range(
-                start=ddate,
-                periods=scaledtoyeardays,
-                freq=CustomBusinessDay(calendar=calendar),
-            )
-        ]
-
-    while ddate not in local_bdays:
-        if days <= 0:
-            ddate -= dt.timedelta(days=1)
-        else:
-            ddate += dt.timedelta(days=1)
-
-    idx = where(array(local_bdays) == ddate)[0]
-
-    return cast("dt.date", local_bdays[idx[0] + days])
    
-
-
-
-
    
-[docs]
-def generate_calendar_date_range(
-    trading_days: int,
-    start: dt.date | None = None,
-    end: dt.date | None = None,
-    countries: CountriesType = "SE",
-    markets: list[str] | str | None = None,
-    custom_holidays: list[str] | str | None = None,
-) -> list[dt.date]:
-    """Generate a list of business day calendar dates.
-
-    Args:
-        trading_days: Number of days to generate. Must be greater than zero.
-        start: Date when the range starts.
-        end: Date when the range ends.
-        countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-            Defaults to "SE".
-        markets: (List of) markets code(s) supported by exchange_calendars.
-        custom_holidays: Argument where missing holidays can be added.
-
-    Returns:
-        List of business day calendar dates.
-    """
-    if trading_days < 1:
-        msg = "Argument trading_days must be greater than zero."
-        raise TradingDaysNotAboveZeroError(msg)
-
-    if start and not end:
-        adjusted_start = date_offset_foll(
-            raw_date=start,
-            months_offset=0,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-            adjust=True,
-            following=True,
-        )
-        tmp_range = date_range(
-            start=adjusted_start,
-            periods=trading_days * 365 // 252,
-            freq="D",
-        )
-        calendar = holiday_calendar(
-            startyear=adjusted_start.year,
-            endyear=date_fix(tmp_range.tolist()[-1]).year,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-        )
-        return [
-            d.date()
-            for d in date_range(
-                start=adjusted_start,
-                periods=trading_days,
-                freq=CustomBusinessDay(calendar=calendar),
-            )
-        ]
-
-    if end and not start:
-        adjusted_end = date_offset_foll(
-            raw_date=end,
-            months_offset=0,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-            adjust=True,
-            following=False,
-        )
-        tmp_range = date_range(
-            end=adjusted_end,
-            periods=trading_days * 365 // 252,
-            freq="D",
-        )
-        calendar = holiday_calendar(
-            startyear=date_fix(tmp_range.tolist()[0]).year,
-            endyear=adjusted_end.year,
-            countries=countries,
-            markets=markets,
-            custom_holidays=custom_holidays,
-        )
-        return [
-            d.date()
-            for d in date_range(
-                end=adjusted_end,
-                periods=trading_days,
-                freq=CustomBusinessDay(calendar=calendar),
-            )
-        ]
-
-    msg = (
-        "Provide exactly one of start or end date. "
-        "Date range is inferred from number of trading days."
-    )
-    raise BothStartAndEndError(msg)
    
-
-
-
-def _do_resample_to_business_period_ends(
-    data: DataFrame,
-    freq: LiteralBizDayFreq,
-    countries: CountriesType,
-    markets: list[str] | str | None = None,
-    custom_holidays: list[str] | str | None = None,
-) -> DatetimeIndex:
-    """Resample timeseries frequency to business calendar month end dates.
-
-    Stubs left in place. Stubs will be aligned to the shortest stub.
-
-    Args:
-        data: The timeseries data.
-        freq: The date offset string that sets the resampled frequency.
-        countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-        markets: (List of) markets code(s) supported by exchange_calendars.
-        custom_holidays: Argument where missing holidays can be added.
-
-    Returns:
-        A date range aligned to business period ends.
-
-    """
-    copydata = data.copy()
-    copydata.index = DatetimeIndex(copydata.index)
-    copydata = copydata.resample(rule=freq).last()
-    copydata = copydata.drop(index=copydata.index[-1])
-    copydata.index = Index(d.date() for d in DatetimeIndex(copydata.index))
-
-    copydata = concat([data.head(n=1), copydata, data.tail(n=1)]).sort_index()
-
-    dates = DatetimeIndex(
-        [copydata.index[0]]
-        + [
-            date_offset_foll(
-                raw_date=dt.date(d.year, d.month, 1)
-                + relativedelta(months=1)
-                - dt.timedelta(days=1),
-                months_offset=0,
-                countries=countries,
-                markets=markets,
-                custom_holidays=custom_holidays,
-                adjust=True,
-                following=False,
-            )
-            for d in copydata.index[1:-1]
-        ]
-        + [copydata.index[-1]],
-    )
-    return DatetimeIndex(dates.drop_duplicates())
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/frame.html b/docs/build/html/_modules/openseries/frame.html deleted file mode 100644 index 82e91883..00000000 --- a/docs/build/html/_modules/openseries/frame.html +++ /dev/null @@ -1,2273 +0,0 @@ - - - - - - - - openseries.frame — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.frame

    -"""The OpenFrame class."""
-
-from __future__ import annotations
-
-from copy import deepcopy
-from functools import reduce
-from logging import getLogger
-from typing import TYPE_CHECKING, Any, Self, cast
-
-from numpy import (
-    array,
-    asarray,
-    bool_,
-    concatenate,
-    corrcoef,
-    cov,
-    diff,
-    divide,
-    float64,
-    isinf,
-    isnan,
-    linalg,
-    log,
-    nan,
-    sqrt,
-    std,
-)
-from pandas import (
-    DataFrame,
-    DatetimeIndex,
-    Index,
-    MultiIndex,
-    Series,
-    concat,
-    merge,
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    import datetime as dt
-
-    from numpy.typing import NDArray
-    from pandas import Series as _Series
-    from pandas import Timestamp
-
-    SeriesFloat = _Series[float]
-else:
-    SeriesFloat = Series
-
-from pydantic import field_validator
-from sklearn.linear_model import LinearRegression  # type: ignore[import-untyped]
-
-from ._common_model import _calculate_time_factor, _CommonModel, _get_base_column_data
-from .datefixer import _do_resample_to_business_period_ends
-from .owntypes import (
-    DaysInYearType,
-    LabelsNotUniqueError,
-    LiteralBizDayFreq,
-    LiteralCaptureRatio,
-    LiteralFrameProps,
-    LiteralHowMerge,
-    LiteralPandasReindexMethod,
-    LiteralPortfolioWeightings,
-    LiteralTrunc,
-    MaxDiversificationNaNError,
-    MaxDiversificationNegativeWeightsError,
-    MergingResultedInEmptyError,
-    MixedValuetypesError,
-    MultipleCurrenciesError,
-    NoWeightsError,
-    OpenFramePropertiesList,
-    PortfolioItemsNotWithinFrameError,
-    RatioInputError,
-    ResampleDataLossError,
-    ValueType,
-    WeightsNotProvidedError,
-)
-from .series import OpenTimeSeries
-
-logger = getLogger(__name__)
-
-__all__ = ["OpenFrame"]
-
-
-
    
-[docs]
-class OpenFrame(_CommonModel[SeriesFloat]):
-    """OpenFrame objects hold OpenTimeSeries in the list constituents.
-
-    The intended use is to allow comparisons across these timeseries.
-
-    Args:
-        constituents: List of objects of Class OpenTimeSeries.
-        weights: List of weights in float format. Optional.
-    """
-
-    @field_validator("constituents")
-    @classmethod
-    def _check_labels_unique(
-        cls: type[OpenFrame],
-        tseries: list[OpenTimeSeries],
-    ) -> list[OpenTimeSeries]:
-        """Pydantic validator ensuring that OpenFrame labels are unique."""
-        labls = [x.label for x in tseries]
-        if len(set(labls)) != len(labls):
-            msg = "TimeSeries names/labels must be unique"
-            raise LabelsNotUniqueError(msg)
-        return tseries
-
-
    
-[docs]
-    def __init__(
-        self: Self,
-        constituents: list[OpenTimeSeries],
-        weights: list[float] | None = None,
-    ) -> None:
-        """OpenFrame objects hold OpenTimeSeries in the list constituents.
-
-        The intended use is to allow comparisons across these timeseries.
-
-        Args:
-            constituents: List of objects of Class OpenTimeSeries.
-            weights: List of weights in float format. Optional.
-        """
-        copied_constituents = [ts.from_deepcopy() for ts in constituents]
-
-        super().__init__(
-            constituents=copied_constituents,
-            weights=weights,
-        )
-        self._set_tsdf()
    
-
-
-    def _set_tsdf(self: Self) -> None:
-        """Set the tsdf DataFrame."""
-        if self.constituents is not None and len(self.constituents) != 0:
-            if len(self.constituents) == 1:
-                self.tsdf = self.constituents[0].tsdf.copy()
-            else:
-                self.tsdf = concat(
-                    [x.tsdf for x in self.constituents], axis="columns", sort=True
-                )
-        else:
-            logger.warning("OpenFrame() was passed an empty list.")
-
-    def _coerce_result(
-        self: Self,
-        result: Series[float],
-        name: str,
-    ) -> SeriesFloat:
-        return Series(
-            data=result,
-            index=self.tsdf.columns,
-            name=name,
-            dtype="float64",
-        )
-
-
    
-[docs]
-    def from_deepcopy(self: Self) -> Self:
-        """Create copy of the OpenFrame object.
-
-        Returns:
-            An OpenFrame object.
-        """
-        return deepcopy(self)
    
-
-
-
    
-[docs]
-    def merge_series(
-        self: Self,
-        how: LiteralHowMerge = "outer",
-    ) -> Self:
-        """Merge index of Pandas Dataframes of the constituent OpenTimeSeries.
-
-        Args:
-            how: The Pandas merge method. Defaults to "outer".
-
-        Returns:
-            An OpenFrame object.
-        """
-        lvl_zero = list(self.columns_lvl_zero)
-        self.tsdf = reduce(
-            lambda left, right: merge(
-                left=left,
-                right=right,
-                how=how,
-                left_index=True,
-                right_index=True,
-            ),
-            [x.tsdf for x in self.constituents],
-        )
-
-        mapper = dict(zip(self.columns_lvl_zero, lvl_zero, strict=True))
-        self.tsdf = self.tsdf.rename(columns=mapper, level=0)
-
-        if self.tsdf.empty:
-            msg = (
-                "Merging OpenTimeSeries DataFrames with "
-                f"argument how={how} produced an empty DataFrame."
-            )
-            raise MergingResultedInEmptyError(msg)
-
-        if how == "inner":
-            for xerie in self.constituents:
-                xerie.tsdf = xerie.tsdf.loc[self.tsdf.index]
-        return self
    
-
-
-
    
-[docs]
-    def all_properties(
-        self: Self,
-        properties: list[LiteralFrameProps] | None = None,
-    ) -> DataFrame:
-        """Calculate chosen timeseries properties.
-
-        Args:
-            properties: The properties to calculate. Defaults to calculating all
-                available. Optional.
-
-        Returns:
-            Properties of the constituent OpenTimeSeries.
-        """
-        if properties:
-            props = OpenFramePropertiesList(*properties)
-            prop_list = [getattr(self, x) for x in props]
-        else:
-            prop_list = [
-                getattr(self, x) for x in OpenFramePropertiesList.allowed_strings
-            ]
-        return cast("DataFrame", concat(prop_list, axis="columns").T)
    
-
-
-    @property
-    def lengths_of_items(self: Self) -> Series[int]:
-        """Number of observations of all constituents.
-
-        Returns:
-            Number of observations of all constituents.
-        """
-        return Series(
-            data=[self.tsdf[col].count() for col in self.tsdf.columns],
-            index=self.tsdf.columns,
-            name="observations",
-        ).astype(int)
-
-    @property
-    def item_count(self: Self) -> int:
-        """Number of constituents.
-
-        Returns:
-            Number of constituents.
-        """
-        return len(self.constituents)
-
-    @property
-    def columns_lvl_zero(self: Self) -> 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.
-        """
-        return list(self.tsdf.columns.get_level_values(0))
-
-    @property
-    def columns_lvl_one(self: Self) -> 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.
-        """
-        return list(self.tsdf.columns.get_level_values(1))
-
-    @property
-    def _value_types(self: Self) -> list[bool]:
-        """Cached value type checks for efficiency.
-
-        Returns:
-            List of booleans indicating if each column is ValueType.RTRN.
-        """
-        return [x == ValueType.RTRN for x in self.tsdf.columns.get_level_values(1)]
-
-    @property
-    def first_indices(self: Self) -> Series[dt.date]:
-        """The first dates in the timeseries of all constituents.
-
-        Returns:
-            The first dates in the timeseries of all constituents.
-        """
-        return Series(
-            data=[i.first_idx for i in self.constituents],
-            index=self.tsdf.columns,
-            name="first indices",
-            dtype="datetime64[ns]",
-        ).dt.date
-
-    @property
-    def last_indices(self: Self) -> Series[dt.date]:
-        """The last dates in the timeseries of all constituents.
-
-        Returns:
-            The last dates in the timeseries of all constituents.
-        """
-        return Series(
-            data=[i.last_idx for i in self.constituents],
-            index=self.tsdf.columns,
-            name="last indices",
-            dtype="datetime64[ns]",
-        ).dt.date
-
-    @property
-    def span_of_days_all(self: Self) -> 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.
-        """
-        return Series(
-            data=[c.span_of_days for c in self.constituents],
-            index=self.tsdf.columns,
-            name="span of days",
-        ).astype(int)
-
-
    
-[docs]
-    def value_to_ret(self: Self) -> Self:
-        """Convert series of values into series of returns.
-
-        Returns:
-            The returns of the values in the series.
-        """
-        returns = self.tsdf.ffill().pct_change()
-        returns.iloc[0] = 0
-        new_labels: list[ValueType] = [ValueType.RTRN] * self.item_count
-        arrays = cast(
-            "Any",
-            [
-                self.tsdf.columns.get_level_values(0),
-                new_labels,
-            ],
-        )
-        returns.columns = MultiIndex.from_arrays(arrays)
-        self.tsdf = returns.copy()
-        return self
    
-
-
-
    
-[docs]
-    def value_to_diff(self: Self, periods: int = 1) -> Self:
-        """Convert series of values to series of their period differences.
-
-        Args:
-            periods: The number of periods between observations over which
-                difference is calculated. Defaults to 1.
-
-        Returns:
-            An OpenFrame object.
-        """
-        self.tsdf = self.tsdf.diff(periods=periods)
-        self.tsdf.iloc[0] = 0
-        new_labels: list[ValueType] = [ValueType.RTRN] * self.item_count
-        arrays = cast(
-            "Any",
-            [
-                self.tsdf.columns.get_level_values(0),
-                new_labels,
-            ],
-        )
-        self.tsdf.columns = MultiIndex.from_arrays(arrays)
-        return self
    
-
-
-
    
-[docs]
-    def to_cumret(self: Self) -> Self:
-        """Convert series of returns into cumulative series of values.
-
-        Returns:
-            An OpenFrame object.
-        """
-        vtypes = self._value_types
-        if not any(vtypes):
-            returns = self.tsdf.ffill().pct_change()
-            returns.iloc[0] = 0
-        elif all(vtypes):
-            returns = self.tsdf.copy()
-            returns.iloc[0] = 0
-        else:
-            msg = "Mix of series types will give inconsistent results"
-            raise MixedValuetypesError(msg)
-
-        returns = returns.add(1.0)
-        self.tsdf = returns.cumprod(axis=0) / returns.iloc[0]
-
-        new_labels: list[ValueType] = [ValueType.PRICE] * self.item_count
-        arrays = cast(
-            "Any",
-            [
-                self.tsdf.columns.get_level_values(0),
-                new_labels,
-            ],
-        )
-        self.tsdf.columns = MultiIndex.from_arrays(arrays)
-        return self
    
-
-
-
    
-[docs]
-    def resample(
-        self: Self,
-        freq: LiteralBizDayFreq | str = "BME",
-    ) -> Self:
-        """Resample the timeseries frequency.
-
-        Args:
-            freq: The date offset string that sets the resampled frequency.
-                Defaults to "BME".
-
-        Returns:
-            An OpenFrame object.
-        """
-        vtypes = self._value_types
-        if not any(vtypes):
-            value_type = ValueType.PRICE
-        elif all(vtypes):
-            value_type = ValueType.RTRN
-        else:
-            msg = "Mix of series types will give inconsistent results"
-            raise MixedValuetypesError(msg)
-
-        self.tsdf.index = DatetimeIndex(self.tsdf.index)
-        if value_type == ValueType.PRICE:
-            self.tsdf = self.tsdf.resample(freq).last()
-        else:
-            self.tsdf = self.tsdf.resample(freq).sum()
-        self.tsdf.index = Index(DatetimeIndex(self.tsdf.index).date)
-        for xerie in self.constituents:
-            xerie.tsdf.index = DatetimeIndex(xerie.tsdf.index)
-            if value_type == ValueType.PRICE:
-                xerie.tsdf = xerie.tsdf.resample(freq).last()
-            else:
-                xerie.tsdf = xerie.tsdf.resample(freq).sum()
-            xerie.tsdf.index = Index(DatetimeIndex(xerie.tsdf.index).date)
-
-        return self
    
-
-
-
    
-[docs]
-    def resample_to_business_period_ends(
-        self: Self,
-        freq: LiteralBizDayFreq = "BME",
-        method: LiteralPandasReindexMethod = "nearest",
-    ) -> Self:
-        """Resamples timeseries frequency to the business calendar month end dates.
-
-        Stubs left in place. Stubs will be aligned to the shortest stub.
-
-        Args:
-            freq: The date offset string that sets the resampled frequency.
-                Defaults to "BME".
-            method: Controls the method used to align values across columns.
-                Defaults to nearest.
-
-        Returns:
-            An OpenFrame object.
-        """
-        vtypes = self._value_types
-        if any(vtypes):
-            msg = (
-                "Do not run resample_to_business_period_ends on return series. "
-                "The operation will pick the last data point in the sparser series. "
-                "It will not sum returns and therefore data will be lost."
-            )
-            raise ResampleDataLossError(msg)
-
-        for xerie in self.constituents:
-            dates = _do_resample_to_business_period_ends(
-                data=xerie.tsdf,
-                freq=freq,
-                countries=xerie.countries,
-                markets=xerie.markets,
-            )
-            xerie.tsdf = xerie.tsdf.reindex(
-                [deyt.date() for deyt in dates],
-                method=method,
-            )
-
-        arrays = [
-            self.tsdf.columns.get_level_values(0),
-            self.tsdf.columns.get_level_values(1),
-        ]
-
-        self._set_tsdf()
-
-        self.tsdf.columns = MultiIndex.from_arrays(arrays)
-
-        return self
    
-
-
-
    
-[docs]
-    def ewma_risk(
-        self: Self,
-        lmbda: float = 0.94,
-        day_chunk: int = 11,
-        dlta_degr_freedms: int = 0,
-        first_column: int = 0,
-        second_column: int = 1,
-        corr_scale: float = 2.0,
-        months_from_last: int | None = None,
-        from_date: dt.date | None = None,
-        to_date: dt.date | None = None,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> DataFrame:
-        """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.
-
-        Args:
-            lmbda: Scaling factor to determine weighting. Defaults to 0.94.
-            day_chunk: Sampling the data which is assumed to be daily. Defaults to 11.
-            dlta_degr_freedms: Variance bias factor taking the value 0 or 1.
-                Defaults to 0.
-            first_column: Column of first timeseries. Defaults to 0.
-            second_column: Column of second timeseries. Defaults to 1.
-            corr_scale: Correlation scale factor. Defaults to 2.0.
-            months_from_last: Number of months offset as positive integer. Overrides
-                use of from_date and to_date. Optional.
-            from_date: Specific from date. Optional.
-            to_date: Specific to date. Optional.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Series volatilities and correlation.
-        """
-        earlier, later = self.calc_range(
-            months_offset=months_from_last,
-            from_dt=from_date,
-            to_dt=to_date,
-        )
-        if periods_in_a_year_fixed is None:
-            fraction = (later - earlier).days / 365.25
-            how_many = (
-                self.tsdf.loc[cast("Timestamp", earlier) : cast("Timestamp", later)]
-                .count()
-                .iloc[0]
-            )
-            time_factor = how_many / fraction
-        else:
-            time_factor = periods_in_a_year_fixed
-
-        corr_label = (
-            cast("tuple[str, str]", self.tsdf.iloc[:, first_column].name)[0]
-            + "_VS_"
-            + cast("tuple[str, str]", self.tsdf.iloc[:, second_column].name)[0]
-        )
-        cols = [
-            cast("tuple[str, str]", self.tsdf.iloc[:, first_column].name)[0],
-            cast("tuple[str, str]", self.tsdf.iloc[:, second_column].name)[0],
-        ]
-
-        data = self.tsdf.loc[
-            cast("Timestamp", earlier) : cast("Timestamp", later)
-        ].copy()
-
-        for rtn in cols:
-            arr = concatenate([array([nan]), diff(log(data[(rtn, ValueType.PRICE)]))])
-            data[rtn, ValueType.RTRN] = arr
-
-        raw_one = [
-            data[(cols[0], ValueType.RTRN)]
-            .iloc[1:day_chunk]
-            .std(ddof=dlta_degr_freedms)
-            * sqrt(time_factor),
-        ]
-        raw_two = [
-            data[(cols[1], ValueType.RTRN)]
-            .iloc[1:day_chunk]
-            .std(ddof=dlta_degr_freedms)
-            * sqrt(time_factor),
-        ]
-        rm = data[(cols[0], ValueType.RTRN)].iloc[1:day_chunk]
-        m: NDArray[float64] = asarray(rm, dtype=float64)
-        ry = data[(cols[1], ValueType.RTRN)].iloc[1:day_chunk]
-        y: NDArray[float64] = asarray(ry, dtype=float64)
-
-        raw_cov = [cov(m=m, y=y, ddof=dlta_degr_freedms)[0][1]]
-
-        r1 = data[(cols[0], ValueType.RTRN)]
-        r2 = data[(cols[1], ValueType.RTRN)]
-
-        alpha = 1.0 - lmbda
-
-        s1 = r1.pow(2) * time_factor
-        s2 = r2.pow(2) * time_factor
-        sc = r1 * r2 * time_factor
-
-        s1.iloc[0] = float(raw_one[0] ** 2)
-        s2.iloc[0] = float(raw_two[0] ** 2)
-        sc.iloc[0] = float(raw_cov[0])
-
-        m1 = s1.ewm(alpha=alpha, adjust=False).mean()
-        m2 = s2.ewm(alpha=alpha, adjust=False).mean()
-        mc = sc.ewm(alpha=alpha, adjust=False).mean()
-
-        m1v = m1.to_numpy(copy=False)
-        m2v = m2.to_numpy(copy=False)
-        mcv = mc.to_numpy(copy=False)
-
-        vol1 = sqrt(m1v)
-        vol2 = sqrt(m2v)
-        denom = corr_scale * vol1 * vol2
-
-        corr = mcv / denom
-        corr[denom == 0.0] = nan
-
-        return DataFrame(
-            index=[*cols, corr_label],
-            columns=data.index,
-            data=[vol1, vol2, corr],
-        ).T
    
-
-
-    @property
-    def correl_matrix(self: Self) -> 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.
-        """
-        corr_matrix = (
-            self.tsdf.ffill()
-            .pct_change()
-            .corr(
-                method="pearson",
-                min_periods=1,
-            )
-        )
-        corr_matrix.columns = corr_matrix.columns.get_level_values(0)
-        corr_matrix.index = corr_matrix.index.get_level_values(0)
-        corr_matrix.index.name = "Correlation"
-        return corr_matrix
-
-
    
-[docs]
-    def add_timeseries(
-        self: Self,
-        new_series: OpenTimeSeries,
-    ) -> Self:
-        """To add an OpenTimeSeries object.
-
-        Args:
-            new_series: The timeseries to add.
-
-        Returns:
-            An OpenFrame object.
-        """
-        self.constituents += [new_series]
-        self.tsdf = concat([self.tsdf, new_series.tsdf], axis="columns", sort=True)
-        return self
    
-
-
-
    
-[docs]
-    def delete_timeseries(self: Self, lvl_zero_item: str) -> Self:
-        """To delete an OpenTimeSeries object.
-
-        Args:
-            lvl_zero_item: The .tsdf column level 0 value of the timeseries to delete.
-
-        Returns:
-            An OpenFrame object.
-        """
-        if self.weights:
-            new_c, new_w = [], []
-            for serie, weight in zip(self.constituents, self.weights, strict=True):
-                if serie.label != lvl_zero_item:
-                    new_c.append(serie)
-                    new_w.append(weight)
-            self.constituents = new_c
-            self.weights = new_w
-        else:
-            self.constituents = [
-                item for item in self.constituents if item.label != lvl_zero_item
-            ]
-        self.tsdf = self.tsdf.drop(lvl_zero_item, axis="columns", level=0)
-        return self
    
-
-
-
    
-[docs]
-    def trunc_frame(
-        self: Self,
-        start_cut: dt.date | None = None,
-        end_cut: dt.date | None = None,
-        where: LiteralTrunc = "both",
-    ) -> Self:
-        """Truncate DataFrame such that all timeseries have the same time span.
-
-        Args:
-            start_cut: New first date. Optional.
-            end_cut: New last date. Optional.
-            where: Determines where dataframe is truncated also when start_cut
-                or end_cut is None. Defaults to both.
-
-        Returns:
-            An OpenFrame object.
-        """
-        if not start_cut and where in ["before", "both"]:
-            start_cut = self.first_indices.max()
-        if not end_cut and where in ["after", "both"]:
-            end_cut = self.last_indices.min()
-        self.tsdf = self.tsdf.sort_index()
-        self.tsdf = self.tsdf.truncate(before=start_cut, after=end_cut)
-
-        for xerie in self.constituents:
-            xerie.tsdf = xerie.tsdf.truncate(
-                before=start_cut,
-                after=end_cut,
-            )
-        if len(set(self.first_indices)) != 1:
-            msg = (
-                f"One or more constituents still "
-                f"not truncated to same start dates.\n"
-                f"{self.tsdf.head()}"
-            )
-            logger.warning(msg)
-        if len(set(self.last_indices)) != 1:
-            msg = (
-                f"One or more constituents still "
-                f"not truncated to same end dates.\n"
-                f"{self.tsdf.tail()}"
-            )
-            logger.warning(msg)
-        return self
    
-
-
-
    
-[docs]
-    def relative(
-        self: Self,
-        long_column: int = 0,
-        short_column: int = 1,
-        *,
-        base_zero: bool = True,
-    ) -> None:
-        """Calculate cumulative relative return between two series.
-
-        Args:
-            long_column: Column number of timeseries bought. Defaults to 0.
-            short_column: Column number of timeseries sold. Defaults to 1.
-            base_zero: If set to False 1.0 is added to allow for a capital base and
-                to allow a volatility calculation. Defaults to True.
-        """
-        rel_label = (
-            cast("tuple[str, str]", self.tsdf.iloc[:, long_column].name)[0]
-            + "_over_"
-            + cast("tuple[str, str]", self.tsdf.iloc[:, short_column].name)[0]
-        )
-        if base_zero:
-            self.tsdf[rel_label, ValueType.RELRTRN] = (
-                self.tsdf.iloc[:, long_column] - self.tsdf.iloc[:, short_column]
-            )
-        else:
-            self.tsdf[rel_label, ValueType.RELRTRN] = (
-                1.0 + self.tsdf.iloc[:, long_column] - self.tsdf.iloc[:, short_column]
-            )
-        self.constituents += [
-            OpenTimeSeries.from_df(self.tsdf.iloc[:, -1]),
-        ]
    
-
-
-
    
-[docs]
-    def tracking_error_func(
-        self: Self,
-        base_column: tuple[str, ValueType] | int = -1,
-        months_from_last: int | None = None,
-        from_date: dt.date | None = None,
-        to_date: dt.date | None = None,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> Series[float]:
-        """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.
-
-        Args:
-            base_column: Column of timeseries that is the denominator in the ratio.
-                Defaults to -1.
-            months_from_last: Number of months offset as positive integer. Overrides
-                use of from_date and to_date. Optional.
-            from_date: Specific from date. Optional.
-            to_date: Specific to date. Optional.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Tracking Errors.
-        """
-        earlier, later = self.calc_range(
-            months_offset=months_from_last,
-            from_dt=from_date,
-            to_dt=to_date,
-        )
-
-        shortdf, short_item, short_label = _get_base_column_data(
-            self=self,
-            base_column=base_column,
-            earlier=earlier,
-            later=later,
-        )
-
-        time_factor = _calculate_time_factor(
-            data=shortdf,
-            earlier=earlier,
-            later=later,
-            periods_in_a_year_fixed=periods_in_a_year_fixed,
-        )
-
-        shortdf_returns = shortdf.ffill().pct_change()
-
-        terrors = []
-        for item in self.tsdf:
-            if item == short_item:
-                terrors.append(0.0)
-            else:
-                longdf = self.tsdf.loc[
-                    cast("Timestamp", earlier) : cast("Timestamp", later)
-                ][item]
-                relative = longdf.ffill().pct_change() - shortdf_returns
-                vol = float(relative.std() * sqrt(time_factor))
-                terrors.append(vol)
-
-        return Series(
-            data=terrors,
-            index=self.tsdf.columns,
-            name=f"Tracking Errors vs {short_label}",
-            dtype="float64",
-        )
    
-
-
-
    
-[docs]
-    def info_ratio_func(
-        self: Self,
-        base_column: tuple[str, ValueType] | int = -1,
-        months_from_last: int | None = None,
-        from_date: dt.date | None = None,
-        to_date: dt.date | None = None,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> Series[float]:
-        """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.
-
-        Args:
-            base_column: Column of timeseries that is the denominator in the ratio.
-                Defaults to -1.
-            months_from_last: Number of months offset as positive integer. Overrides
-                use of from_date and to_date. Optional.
-            from_date: Specific from date. Optional.
-            to_date: Specific to date. Optional.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Information Ratios.
-        """
-        earlier, later = self.calc_range(
-            months_offset=months_from_last,
-            from_dt=from_date,
-            to_dt=to_date,
-        )
-
-        shortdf, short_item, short_label = _get_base_column_data(
-            self=self,
-            base_column=base_column,
-            earlier=earlier,
-            later=later,
-        )
-
-        time_factor = _calculate_time_factor(
-            data=shortdf,
-            earlier=earlier,
-            later=later,
-            periods_in_a_year_fixed=periods_in_a_year_fixed,
-        )
-
-        shortdf_returns = shortdf.ffill().pct_change()
-
-        ratios = []
-        for item in self.tsdf:
-            if item == short_item:
-                ratios.append(0.0)
-            else:
-                longdf = self.tsdf.loc[
-                    cast("Timestamp", earlier) : cast("Timestamp", later)
-                ][item]
-                relative = longdf.ffill().pct_change() - shortdf_returns
-                ret = float(relative.mean() * time_factor)
-                vol = float(relative.std() * sqrt(time_factor))
-                ratios.append(ret / vol)
-
-        return Series(
-            data=ratios,
-            index=self.tsdf.columns,
-            name=f"Info Ratios vs {short_label}",
-            dtype="float64",
-        )
    
-
-
-    def _calculate_cagr_from_returns(
-        self: Self,
-        returns_array: NDArray[float64],
-        mask: NDArray[bool_],
-        time_factor: float,
-    ) -> float:
-        """Calculate CAGR from returns array with mask.
-
-        Args:
-            returns_array: Returns array.
-            mask: Boolean mask.
-            time_factor: Time factor for annualization.
-
-        Returns:
-            CAGR value.
-        """
-        masked_array = returns_array[mask] + 1.0
-        if len(masked_array) == 0:
-            return 0.0
-        exponent = 1 / (len(masked_array) / time_factor)
-        return float(masked_array.prod() ** exponent - 1)
-
-    def _calculate_capture_ratio_for_item(
-        self: Self,
-        ratio: LiteralCaptureRatio,
-        longdf_returns_np: NDArray[float64],
-        shortdf_returns_np: NDArray[float64],
-        up_mask: NDArray[bool_],
-        down_mask: NDArray[bool_],
-        time_factor: float,
-    ) -> float:
-        """Calculate capture ratio for a single item.
-
-        Args:
-            ratio: Ratio type to calculate.
-            longdf_returns_np: Long returns array.
-            shortdf_returns_np: Short returns array.
-            up_mask: Up mask.
-            down_mask: Down mask.
-            time_factor: Time factor.
-
-        Returns:
-            Capture ratio value.
-
-        Raises:
-            RatioInputError: If ratio is invalid.
-        """
-        if ratio == "up":
-            up_rtrn = self._calculate_cagr_from_returns(
-                longdf_returns_np, up_mask, time_factor
-            )
-            up_idx_return = self._calculate_cagr_from_returns(
-                shortdf_returns_np, up_mask, time_factor
-            )
-            if up_idx_return == 0.0:
-                return 0.0
-            return up_rtrn / up_idx_return
-
-        if ratio == "down":
-            down_return = self._calculate_cagr_from_returns(
-                longdf_returns_np, down_mask, time_factor
-            )
-            down_idx_return = self._calculate_cagr_from_returns(
-                shortdf_returns_np, down_mask, time_factor
-            )
-            if down_idx_return == 0.0:
-                return 0.0
-            return down_return / down_idx_return
-
-        if ratio == "both":
-            up_rtrn = self._calculate_cagr_from_returns(
-                longdf_returns_np, up_mask, time_factor
-            )
-            up_idx_return = self._calculate_cagr_from_returns(
-                shortdf_returns_np, up_mask, time_factor
-            )
-            down_return = self._calculate_cagr_from_returns(
-                longdf_returns_np, down_mask, time_factor
-            )
-            down_idx_return = self._calculate_cagr_from_returns(
-                shortdf_returns_np, down_mask, time_factor
-            )
-            if up_idx_return == 0.0 or down_idx_return == 0.0:
-                return 0.0
-            return (up_rtrn / up_idx_return) / (down_return / down_idx_return)
-
-        msg = "ratio must be one of 'up', 'down' or 'both'."
-        raise RatioInputError(msg)
-
-
    
-[docs]
-    def capture_ratio_func(
-        self: Self,
-        ratio: LiteralCaptureRatio,
-        base_column: tuple[str, ValueType] | int = -1,
-        months_from_last: int | None = None,
-        from_date: dt.date | None = None,
-        to_date: dt.date | None = None,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> Series[float]:
-        """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.
-
-        Args:
-            ratio: The ratio to calculate.
-            base_column: Column of timeseries that is the denominator in the ratio.
-                Defaults to -1.
-            months_from_last: Number of months offset as positive integer. Overrides
-                use of from_date and to_date. Optional.
-            from_date: Specific from date. Optional.
-            to_date: Specific to date. Optional.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Capture Ratios.
-        """
-        loss_limit: float = 0.0
-        earlier, later = self.calc_range(
-            months_offset=months_from_last,
-            from_dt=from_date,
-            to_dt=to_date,
-        )
-        fraction: float = (later - earlier).days / 365.25
-
-        shortdf, short_item, short_label = _get_base_column_data(
-            self=self,
-            base_column=base_column,
-            earlier=earlier,
-            later=later,
-        )
-
-        if periods_in_a_year_fixed:
-            time_factor = float(periods_in_a_year_fixed)
-        else:
-            time_factor = shortdf.count() / fraction
-
-        shortdf_returns = shortdf.ffill().pct_change()
-        shortdf_returns_np = cast("NDArray[float64]", shortdf_returns.to_numpy())
-        up_mask = shortdf_returns_np > loss_limit
-        down_mask = shortdf_returns_np < loss_limit
-
-        ratios = []
-        for item in self.tsdf:
-            if item == short_item:
-                ratios.append(0.0)
-            else:
-                longdf = self.tsdf.loc[
-                    cast("Timestamp", earlier) : cast("Timestamp", later)
-                ][item]
-                longdf_returns = longdf.ffill().pct_change()
-                longdf_returns_np = cast("NDArray[float64]", longdf_returns.to_numpy())
-                ratio_value = self._calculate_capture_ratio_for_item(
-                    ratio=ratio,
-                    longdf_returns_np=longdf_returns_np,
-                    shortdf_returns_np=shortdf_returns_np,
-                    up_mask=up_mask,
-                    down_mask=down_mask,
-                    time_factor=time_factor,
-                )
-                ratios.append(ratio_value)
-
-        ratio_names = {
-            "up": f"Up Capture Ratios vs {short_label}",
-            "down": f"Down Capture Ratios vs {short_label}",
-            "both": f"Up-Down Capture Ratios vs {short_label}",
-        }
-        resultname = ratio_names[ratio]
-
-        return Series(
-            data=ratios,
-            index=self.tsdf.columns,
-            name=resultname,
-            dtype="float64",
-        )
    
-
-
-    def _extract_column_value(
-        self: Self,
-        column: tuple[str, ValueType] | int,
-        vtypes: list[bool],
-        param_name: str = "column",
-    ) -> Series[float]:
-        """Extract column value based on value types.
-
-        Args:
-            column: Column reference.
-            vtypes: Value types list.
-            param_name: Parameter name for error messages.
-
-        Returns:
-            Series value.
-
-        Raises:
-            TypeError: If column type is invalid.
-        """
-        msg = f"{param_name} should be a tuple[str, ValueType] or an integer."
-        if isinstance(column, tuple):
-            if all(vtypes):
-                return self.tsdf[column]
-            return self.tsdf[column].ffill().pct_change().iloc[1:]
-        if isinstance(column, int):
-            if all(vtypes):
-                return self.tsdf.iloc[:, column]
-            return self.tsdf.iloc[:, column].ffill().pct_change().iloc[1:]
-        raise TypeError(msg)
-
-
    
-[docs]
-    def beta(
-        self: Self,
-        asset: tuple[str, ValueType] | int,
-        market: tuple[str, ValueType] | int,
-        dlta_degr_freedms: int = 1,
-    ) -> float:
-        """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.
-
-        Args:
-            asset: The column of the asset.
-            market: The column of the market against which Beta is measured.
-            dlta_degr_freedms: Variance bias factor taking the value 0 or 1.
-                Defaults to 1.
-
-        Returns:
-            Beta as Co-variance of x & y divided by Variance of x.
-        """
-        vtypes = self._value_types
-        if not (all(vtypes) or not any(vtypes)):
-            msg = "Mix of series types will give inconsistent results"
-            raise MixedValuetypesError(msg)
-
-        y_value = self._extract_column_value(asset, vtypes, param_name="asset")
-        x_value = self._extract_column_value(market, vtypes, param_name="market")
-
-        covariance = cov(m=y_value, y=x_value, ddof=dlta_degr_freedms)
-        beta = covariance[0, 1] / covariance[1, 1]
-
-        return float(beta)
    
-
-
-
    
-[docs]
-    def ord_least_squares_fit(
-        self: Self,
-        y_column: tuple[str, ValueType] | int,
-        x_column: tuple[str, ValueType] | int,
-        *,
-        fitted_series: bool = True,
-    ) -> dict[str, float]:
-        """Ordinary Least Squares fit.
-
-        Performs a linear regression and adds a new column with a fitted line
-        using Ordinary Least Squares fit.
-
-        Args:
-            y_column: The column level values of the dependent variable y.
-            x_column: The column level values of the exogenous variable x.
-            fitted_series: If True the fit is added as a new column in the .tsdf
-                Pandas.DataFrame. Defaults to True.
-
-        Returns:
-            A dictionary with the coefficient, intercept and rsquared outputs.
-        """
-        msg = "y_column should be a tuple[str, ValueType] or an integer."
-        if isinstance(y_column, tuple):
-            y_value = self.tsdf[y_column].to_numpy()
-            y_label = cast(
-                "tuple[str, str]",
-                self.tsdf[y_column].name,
-            )[0]
-        elif isinstance(y_column, int):
-            y_value = self.tsdf.iloc[:, y_column].to_numpy()
-            y_label = cast("tuple[str, str]", self.tsdf.iloc[:, y_column].name)[0]
-        else:
-            raise TypeError(msg)
-
-        msg = "x_column should be a tuple[str, ValueType] or an integer."
-        if isinstance(x_column, tuple):
-            x_value = self.tsdf[x_column].to_numpy().reshape(-1, 1)
-            x_label = cast(
-                "tuple[str, str]",
-                self.tsdf[x_column].name,
-            )[0]
-        elif isinstance(x_column, int):
-            x_value = self.tsdf.iloc[:, x_column].to_numpy().reshape(-1, 1)
-            x_label = cast("tuple[str, str]", self.tsdf.iloc[:, x_column].name)[0]
-        else:
-            raise TypeError(msg)
-
-        model = LinearRegression(fit_intercept=True)
-        model.fit(x_value, y_value)
-        if fitted_series:
-            self.tsdf[y_label, x_label] = model.predict(x_value)
-        return {
-            "coefficient": float(model.coef_[0]),
-            "intercept": float(model.intercept_),
-            "rsquared": model.score(x_value, y_value),
-        }
    
-
-
-
    
-[docs]
-    def jensen_alpha(
-        self: Self,
-        asset: tuple[str, ValueType] | int,
-        market: tuple[str, ValueType] | int,
-        riskfree_rate: float = 0.0,
-        dlta_degr_freedms: int = 1,
-    ) -> float:
-        """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.
-
-        Args:
-            asset: The column of the asset.
-            market: The column of the market against which Jensen's alpha is measured.
-            riskfree_rate: The return of the zero volatility riskfree asset.
-                Defaults to 0.0.
-            dlta_degr_freedms: Variance bias factor taking the value 0 or 1.
-                Defaults to 1.
-
-        Returns:
-            Jensen's alpha.
-        """
-        vtypes = self._value_types
-        if not (all(vtypes) or not any(vtypes)):
-            msg = "Mix of series types will give inconsistent results"
-            raise MixedValuetypesError(msg)
-
-        asset_rtn = self._extract_column_value(asset, vtypes, param_name="asset")
-        market_rtn = self._extract_column_value(market, vtypes, param_name="market")
-
-        asset_rtn_mean = float(asset_rtn.mean() * self.periods_in_a_year)
-        market_rtn_mean = float(market_rtn.mean() * self.periods_in_a_year)
-
-        covariance = cov(m=asset_rtn, y=market_rtn, ddof=dlta_degr_freedms)
-        beta = covariance[0, 1] / covariance[1, 1]
-
-        return float(
-            asset_rtn_mean - riskfree_rate - beta * (market_rtn_mean - riskfree_rate),
-        )
    
-
-
-    def _prepare_returns_for_portfolio(self: Self) -> DataFrame:
-        """Prepare returns DataFrame for portfolio calculation.
-
-        Returns:
-            Returns DataFrame.
-
-        Raises:
-            MixedValuetypesError: If series types are mixed.
-        """
-        vtypes = self._value_types
-        if not any(vtypes):
-            returns = self.tsdf.ffill().pct_change()
-            returns.iloc[0] = 0
-            return returns
-        if all(vtypes):
-            return self.tsdf
-        msg = "Mix of series types will give inconsistent results"
-        raise MixedValuetypesError(msg)
-
-    def _calculate_eq_weights(self: Self) -> list[float]:
-        """Calculate equal weights.
-
-        Returns:
-            List of equal weights.
-        """
-        return [1.0 / self.item_count] * self.item_count
-
-    def _calculate_inv_vol_weights(self: Self, returns: DataFrame) -> list[float]:
-        """Calculate inverse volatility weights.
-
-        Args:
-            returns: Returns DataFrame.
-
-        Returns:
-            List of inverse volatility weights.
-        """
-        vol = divide(1.0, std(returns, axis=0, ddof=1))
-        vol[isinf(vol)] = nan
-        return list(divide(vol, vol.sum()))
-
-    def _calculate_max_div_weights(self: Self, returns: DataFrame) -> list[float]:
-        """Calculate maximum diversification weights.
-
-        Args:
-            returns: Returns DataFrame.
-
-        Returns:
-            List of maximum diversification weights.
-
-        Raises:
-            MaxDiversificationNaNError: If correlation matrix has NaN values.
-            MaxDiversificationNegativeWeightsError: If weights are negative.
-        """
-        corr_matrix = corrcoef(returns.T)
-        corr_matrix[isinf(corr_matrix)] = nan
-        corr_matrix[isnan(corr_matrix)] = nan
-
-        msga = "max_div weight strategy failed: correlation matrix contains NaN values"
-        if isnan(corr_matrix).any():
-            raise MaxDiversificationNaNError(msga)
-
-        try:
-            inv_corr_sum = linalg.inv(corr_matrix).sum(axis=1)
-
-            msgb = (
-                "max_div weight strategy failed: "
-                "inverse correlation matrix sum contains NaN values"
-            )
-            if isnan(inv_corr_sum).any():
-                raise MaxDiversificationNaNError(msgb)
-
-            weights = list(divide(inv_corr_sum, inv_corr_sum.sum()))
-
-            msgc = "max_div weight strategy failed: final weights contain NaN values"
-            if any(isnan(weight) for weight in weights):  # pragma: no cover
-                raise MaxDiversificationNaNError(msgc)
-
-            msgd = (
-                "max_div weight strategy failed: negative weights detected"
-                f" - weights: {[round(w, 6) for w in weights]}"
-            )
-            if any(weight < 0 for weight in weights):
-                raise MaxDiversificationNegativeWeightsError(msgd)
-
-        except linalg.LinAlgError as e:
-            msge = (
-                "max_div weight strategy failed: "
-                f"correlation matrix is singular - {e!s}"
-            )
-            raise MaxDiversificationNaNError(msge) from e
-        else:
-            return weights
-
-    def _calculate_min_vol_overweight_weights(
-        self: Self,
-        returns: DataFrame,
-    ) -> list[float]:
-        """Calculate minimum volatility overweight weights.
-
-        Args:
-            returns: Returns DataFrame.
-
-        Returns:
-            List of minimum volatility overweight weights.
-        """
-        vols = std(returns, axis=0, ddof=1)
-        min_vol_idx = vols.argmin()
-        min_vol_weight = 0.6
-        remaining_weight = 0.4
-        weights = [remaining_weight / (self.item_count - 1)] * self.item_count
-        weights[min_vol_idx] = min_vol_weight
-        return weights
-
-    def _calculate_weights_from_strategy(
-        self: Self,
-        weight_strat: LiteralPortfolioWeightings,
-        returns: DataFrame,
-    ) -> list[float]:
-        """Calculate weights based on strategy.
-
-        Args:
-            weight_strat: Weight calculation strategy.
-            returns: Returns DataFrame.
-
-        Returns:
-            List of weights.
-
-        Raises:
-            NotImplementedError: If strategy is not implemented.
-        """
-        if weight_strat == "eq_weights":
-            return self._calculate_eq_weights()
-        if weight_strat == "inv_vol":
-            return self._calculate_inv_vol_weights(returns)
-        if weight_strat == "max_div":
-            return self._calculate_max_div_weights(returns)
-        if weight_strat == "min_vol_overweight":
-            return self._calculate_min_vol_overweight_weights(returns)
-
-        msg = "Weight strategy not implemented"
-        raise NotImplementedError(msg)
-
-
    
-[docs]
-    def make_portfolio(
-        self: Self,
-        name: str,
-        weight_strat: LiteralPortfolioWeightings | None = None,
-    ) -> DataFrame:
-        """Calculate a basket timeseries based on the supplied weights.
-
-        Args:
-            name: Name of the basket timeseries.
-            weight_strat: Weight calculation strategies. Optional.
-
-        Returns:
-            A basket timeseries.
-        """
-        if self.weights is None and weight_strat is None:
-            msg = (
-                "OpenFrame weights property must be provided "
-                "to run the make_portfolio method."
-            )
-            raise NoWeightsError(msg)
-
-        returns = self._prepare_returns_for_portfolio()
-
-        if weight_strat:
-            self.weights = self._calculate_weights_from_strategy(
-                weight_strat=weight_strat,
-                returns=returns,
-            )
-
-        return DataFrame(
-            data=(returns @ array(self.weights)).add(1.0).cumprod(),
-            index=self.tsdf.index,
-            columns=[[name], [ValueType.PRICE]],
-            dtype="float64",
-        )
    
-
-
-
    
-[docs]
-    def rolling_info_ratio(
-        self: Self,
-        long_column: int = 0,
-        short_column: int = 1,
-        observations: int = 21,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> DataFrame:
-        """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.
-
-        Args:
-            long_column: Column of timeseries that is the numerator in the ratio.
-                Defaults to 0.
-            short_column: Column of timeseries that is the denominator in the ratio.
-                Defaults to 1.
-            observations: The length of the rolling window to use is set as number of
-                observations. Defaults to 21.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Rolling Information Ratios.
-        """
-        long_label = cast(
-            "tuple[str, str]",
-            self.tsdf.iloc[:, long_column].name,
-        )[0]
-        short_label = cast(
-            "tuple[str, str]",
-            self.tsdf.iloc[:, short_column].name,
-        )[0]
-        ratio_label = f"{long_label} / {short_label}"
-        if periods_in_a_year_fixed:
-            time_factor = float(periods_in_a_year_fixed)
-        else:
-            time_factor = self.periods_in_a_year
-
-        relative = (
-            1.0 + self.tsdf.iloc[:, long_column] - self.tsdf.iloc[:, short_column]
-        )
-
-        retseries = (
-            relative.ffill()
-            .pct_change()
-            .rolling(observations, min_periods=observations)
-            .sum()
-        )
-        retdf = retseries.dropna().to_frame()
-
-        voldf = relative.ffill().pct_change().rolling(
-            observations,
-            min_periods=observations,
-        ).std() * sqrt(time_factor)
-        voldf = voldf.dropna().to_frame()
-
-        ratiodf = (retdf.iloc[:, 0] / voldf.iloc[:, 0]).to_frame()
-        ratiodf.columns = [[ratio_label], ["Information Ratio"]]
-
-        return DataFrame(ratiodf)
    
-
-
-
    
-[docs]
-    def rolling_beta(
-        self: Self,
-        asset_column: int = 0,
-        market_column: int = 1,
-        observations: int = 21,
-        dlta_degr_freedms: int = 1,
-    ) -> DataFrame:
-        """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.
-
-        Args:
-            asset_column: Column of timeseries that is the asset. Defaults to 0.
-            market_column: Column of timeseries that is the market. Defaults to 1.
-            observations: The length of the rolling window to use is set as number of
-                observations. Defaults to 21.
-            dlta_degr_freedms: Variance bias factor taking the value 0 or 1.
-                Defaults to 1.
-
-        Returns:
-            Rolling Betas.
-        """
-        market_label = cast("tuple[str, str]", self.tsdf.iloc[:, market_column].name)[
-            0
-        ]
-        asset_label = cast("tuple[str, str]", self.tsdf.iloc[:, asset_column].name)[0]
-        beta_label = f"{asset_label} / {market_label}"
-
-        rolling = (
-            self.tsdf.ffill()
-            .pct_change()
-            .rolling(
-                observations,
-                min_periods=observations,
-            )
-        )
-
-        rcov = rolling.cov(ddof=dlta_degr_freedms)
-        rcov = rcov.dropna()
-
-        rollbetaseries = rcov.iloc[:, asset_column].xs(
-            market_label,
-            level=1,
-        ) / rcov.iloc[
-            :,
-            market_column,
-        ].xs(
-            market_label,
-            level=1,
-        )
-        rollbeta = rollbetaseries.to_frame()
-        rollbeta.index = rollbeta.index.get_level_values(0)
-        rollbeta.columns = MultiIndex.from_arrays([[beta_label], ["Beta"]])
-
-        return rollbeta
    
-
-
-
    
-[docs]
-    def rolling_corr(
-        self: Self,
-        first_column: int = 0,
-        second_column: int = 1,
-        observations: int = 21,
-    ) -> DataFrame:
-        """Calculate rolling Correlation.
-
-        Calculates correlation between two series. The period with
-        at least the given number of observations is the first period calculated.
-
-        Args:
-            first_column: The position as integer of the first timeseries to compare.
-                Defaults to 0.
-            second_column: The position as integer of the second timeseries to compare.
-                Defaults to 1.
-            observations: The length of the rolling window to use is set as number of
-                observations. Defaults to 21.
-
-        Returns:
-            Rolling Correlations.
-        """
-        corr_label = (
-            cast("tuple[str, str]", self.tsdf.iloc[:, first_column].name)[0]
-            + "_VS_"
-            + cast("tuple[str, str]", self.tsdf.iloc[:, second_column].name)[0]
-        )
-        first_series = (
-            self.tsdf.iloc[:, first_column]
-            .ffill()
-            .pct_change()[1:]
-            .rolling(observations, min_periods=observations)
-        )
-        second_series = self.tsdf.iloc[:, second_column].ffill().pct_change()[1:]
-        corrdf = first_series.corr(other=second_series).dropna().to_frame()
-        corrdf.columns = MultiIndex.from_arrays(
-            [
-                [corr_label],
-                ["Rolling correlation"],
-            ],
-        )
-
-        return DataFrame(corrdf)
    
-
-
-
    
-[docs]
-    def multi_factor_linear_regression(
-        self: Self,
-        dependent_column: tuple[str, ValueType],
-    ) -> tuple[DataFrame, OpenTimeSeries]:
-        """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.
-
-        Args:
-            dependent_column: A tuple key to select the column in the
-                OpenFrame.tsdf.columns to use as the dependent variable.
-
-        Returns:
-            A tuple containing:
-            - A DataFrame with the R-squared, the intercept and the regression
-              coefficients
-            - An OpenTimeSeries of predicted values
-
-        Raises:
-            KeyError: If the column tuple is not found in the OpenFrame.tsdf.columns.
-            ValueError: If not all series are returnseries (ValueType.RTRN).
-        """
-        key_msg = (
-            f"Tuple ({dependent_column[0]}, "
-            f"{dependent_column[1].value}) not found in data."
-        )
-        if dependent_column not in self.tsdf.columns:
-            raise KeyError(key_msg)
-
-        vtype_msg = "All series should be of ValueType.RTRN."
-        if not all(x == ValueType.RTRN for x in self.tsdf.columns.get_level_values(1)):
-            raise MixedValuetypesError(vtype_msg)
-
-        dependent = self.tsdf[dependent_column]
-        factors = self.tsdf.drop(columns=[dependent_column])
-        indx = ["R-square", "Intercept", *factors.columns.get_level_values(0)]
-
-        model = LinearRegression()
-        model.fit(factors, dependent)
-
-        predictions = OpenTimeSeries.from_arrays(
-            name=f"Predicted {dependent_column[0]}",
-            dates=[date.strftime("%Y-%m-%d") for date in self.tsdf.index],
-            values=list(model.predict(factors)),
-            valuetype=ValueType.RTRN,
-        )
-
-        output = [model.score(factors, dependent), model.intercept_, *model.coef_]
-
-        result = DataFrame(data=output, index=indx, columns=[dependent_column[0]])
-
-        return result, predictions.to_cumret()
    
-
-
-    def _validate_and_prepare_rebalance_inputs(
-        self: Self,
-        items: list[str] | None,
-        bal_weights: list[float] | None,
-        *,
-        equal_weights: bool,
-    ) -> tuple[list[str], list[float]]:
-        """Validate and prepare inputs for rebalanced portfolio.
-
-        Args:
-            items: List of items to include. If None, uses all items.
-            bal_weights: List of weights. If None, uses frame weights.
-            equal_weights: If True, use equal weights.
-
-        Returns:
-            Tuple of (validated items, validated weights).
-
-        Raises:
-            WeightsNotProvidedError: If weights are required but not provided.
-            TypeError: If items is not a list.
-            PortfolioItemsNotWithinFrameError: If items are invalid.
-        """
-        if bal_weights is None and not equal_weights:
-            if self.weights is None:
-                msg = "Weights must be provided."
-                raise WeightsNotProvidedError(msg)
-            bal_weights = list(self.weights)
-
-        if items is None:
-            items = list(self.columns_lvl_zero)
-        else:
-            msg = "Items must be passed as list."
-            if not isinstance(items, list):
-                raise TypeError(msg)
-            if not items:
-                msg = "Items for portfolio must be within SeriesFrame items."
-                raise PortfolioItemsNotWithinFrameError(msg)
-            if not set(items) <= set(self.columns_lvl_zero):
-                msg = "Items for portfolio must be within SeriesFrame items."
-                raise PortfolioItemsNotWithinFrameError(msg)
-
-        if equal_weights:
-            bal_weights = [1 / len(items)] * len(items)
-
-        return items, cast("list[float]", bal_weights)
-
-    def _initialize_rebalance_output(
-        self: Self,
-        items: list[str],
-        name: str,
-        cash_values: list[float],
-    ) -> dict[str, dict[str, list[float]]]:
-        """Initialize output structure for rebalanced portfolio.
-
-        Args:
-            items: List of items in portfolio.
-            name: Name of the portfolio.
-            cash_values: Cash index values.
-
-        Returns:
-            Initialized output dictionary.
-        """
-        output = {
-            item: {
-                ValueType.PRICE: [],
-                "buysell_qty": [0.0] * self.length,
-                "position": [0.0] * self.length,
-                "value": [0.0] * self.length,
-                "twr": [0.0] * self.length,
-                "settle": [0.0] * self.length,
-            }
-            for item in items
-        }
-        output.update(
-            {
-                "cash": {
-                    ValueType.PRICE: cash_values,
-                    "buysell_qty": [0.0] * self.length,
-                    "position": [0.0] * self.length,
-                    "value": [0.0] * self.length,
-                    "twr": [0.0] * self.length,
-                    "settle": [0.0] * self.length,
-                },
-                name: {
-                    ValueType.PRICE: [1.0] + [0.0] * (self.length - 1),
-                    "buysell_qty": [-1.0] + [0.0] * (self.length - 1),
-                    "position": [-1.0] + [0.0] * (self.length - 1),
-                    "value": [-1.0] + [0.0] * (self.length - 1),
-                    "twr": [1.0] + [0.0] * (self.length - 1),
-                    "settle": [1.0] + [0.0] * (self.length - 1),
-                },
-            },
-        )
-        return output
-
-    def _initialize_first_day_positions(
-        self: Self,
-        items: list[str],
-        bal_weights: list[float],
-        output: dict[str, dict[str, list[float]]],
-        name: str,
-    ) -> None:
-        """Initialize positions for the first day.
-
-        Args:
-            items: List of items in portfolio.
-            bal_weights: Weights for each item.
-            output: Output dictionary to update.
-            name: Name of the portfolio.
-        """
-        for item, weight in zip(items, bal_weights, strict=False):
-            output[item][ValueType.PRICE] = cast(
-                "list[float]",
-                self.tsdf[(item, ValueType.PRICE)].to_numpy().tolist(),
-            )
-            output[item]["buysell_qty"][0] = (
-                weight / self.tsdf[(item, ValueType.PRICE)].iloc[0]
-            )
-            output[item]["position"][0] = output[item]["buysell_qty"][0]
-            output[item]["value"][0] = (
-                output[item]["position"][0] * output[item][ValueType.PRICE][0]
-            )
-            output[item]["settle"][0] = (
-                -output[item]["buysell_qty"][0] * output[item][ValueType.PRICE][0]
-            )
-            output["cash"]["buysell_qty"][0] += output[item]["settle"][0]
-            output[item]["twr"][0] = (
-                output[item]["value"][0] / -output[item]["settle"][0]
-            )
-
-        output["cash"]["position"][0] = (
-            output["cash"]["buysell_qty"][0] + output[name]["settle"][0]
-        )
-        output["cash"]["settle"][0] = -output["cash"]["position"][0]
-
-    def _process_rebalancing_day(
-        self: Self,
-        day: int,
-        items: list[str],
-        bal_weights: list[float],
-        output: dict[str, dict[str, list[float]]],
-        name: str,
-    ) -> tuple[float, float]:
-        """Process a rebalancing day.
-
-        Args:
-            day: Current day index.
-            items: List of items in portfolio.
-            bal_weights: Target weights for rebalancing.
-            output: Output dictionary to update.
-            name: Name of the portfolio.
-
-        Returns:
-            Tuple of (portfolio_value, settle_value).
-        """
-        portfolio_value = 0.0
-        settle_value = 0.0
-
-        for item, weight in zip(items, bal_weights, strict=False):
-            output[item]["buysell_qty"][day] = (
-                weight
-                - output[item]["value"][day - 1] / -output[name]["value"][day - 1]
-            ) / output[item][ValueType.PRICE][day]
-            output[item]["position"][day] = (
-                output[item]["position"][day - 1] + output[item]["buysell_qty"][day]
-            )
-            output[item]["value"][day] = (
-                output[item]["position"][day] * output[item][ValueType.PRICE][day]
-            )
-            portfolio_value += output[item]["value"][day]
-            output[item]["twr"][day] = (
-                output[item]["value"][day]
-                / (output[item]["value"][day - 1] - output[item]["settle"][day])
-                * output[item]["twr"][day - 1]
-            )
-            output[item]["settle"][day] = (
-                -output[item]["buysell_qty"][day] * output[item][ValueType.PRICE][day]
-            )
-            settle_value += output[item]["settle"][day]
-
-        return portfolio_value, settle_value
-
-    def _process_non_rebalancing_day(
-        self: Self,
-        day: int,
-        items: list[str],
-        output: dict[str, dict[str, list[float]]],
-    ) -> float:
-        """Process a non-rebalancing day.
-
-        Args:
-            day: Current day index.
-            items: List of items in portfolio.
-            output: Output dictionary to update.
-
-        Returns:
-            Portfolio value.
-        """
-        portfolio_value = 0.0
-
-        for item in items:
-            output[item]["position"][day] = output[item]["position"][day - 1]
-            output[item]["value"][day] = (
-                output[item]["position"][day] * output[item][ValueType.PRICE][day]
-            )
-            portfolio_value += output[item]["value"][day]
-            output[item]["twr"][day] = (
-                output[item]["value"][day]
-                / (output[item]["value"][day - 1] - output[item]["settle"][day])
-                * output[item]["twr"][day - 1]
-            )
-
-        return portfolio_value
-
-    def _update_cash_and_portfolio(
-        self: Self,
-        day: int,
-        portfolio_value: float,
-        settle_value: float,
-        output: dict[str, dict[str, list[float]]],
-        name: str,
-    ) -> None:
-        """Update cash and portfolio values for a day.
-
-        Args:
-            day: Current day index.
-            portfolio_value: Total portfolio value (before cash).
-            settle_value: Total settle value.
-            output: Output dictionary to update.
-            name: Name of the portfolio.
-        """
-        output["cash"]["buysell_qty"][day] = settle_value
-        output["cash"]["position"][day] = (
-            output["cash"]["position"][day - 1]
-            * output["cash"][ValueType.PRICE][day]
-            / output["cash"][ValueType.PRICE][day - 1]
-            + output["cash"]["buysell_qty"][day]
-        )
-        output["cash"]["value"][day] = output["cash"]["position"][day]
-        total_portfolio_value = portfolio_value + output["cash"]["value"][day]
-        output[name]["position"][day] = output[name]["position"][day - 1]
-        output[name]["value"][day] = -total_portfolio_value
-        output[name]["twr"][day] = (
-            output[name]["value"][day] / output[name]["position"][day]
-        )
-        output[name][ValueType.PRICE][day] = output[name]["twr"][day]
-
-    def _build_rebalance_result(
-        self: Self,
-        output: dict[str, dict[str, list[float]]],
-        instruments: list[str],
-        subheaders: list[str | ValueType],
-    ) -> DataFrame:
-        """Build result DataFrame from output dictionary.
-
-        Args:
-            output: Output dictionary with all calculated values.
-            instruments: List of instrument names.
-            subheaders: List of subheader names.
-
-        Returns:
-            DataFrame with MultiIndex columns.
-        """
-        result = DataFrame()
-        for outvalue in output.values():
-            result = concat(
-                [
-                    result,
-                    DataFrame(data=outvalue, index=self.tsdf.index),
-                ],
-                axis="columns",
-            )
-        lvlone, lvltwo = [], []
-        for instr in instruments:
-            lvlone.extend([instr] * 6)
-            lvltwo.extend(subheaders)
-        result.columns = MultiIndex.from_arrays([lvlone, lvltwo])
-        return result
-
-
    
-[docs]
-    def rebalanced_portfolio(
-        self: Self,
-        name: str,
-        items: list[str] | None = None,
-        bal_weights: list[float] | None = None,
-        frequency: int = 1,
-        cash_index: OpenTimeSeries | None = None,
-        *,
-        equal_weights: bool = False,
-        drop_extras: bool = True,
-    ) -> OpenFrame:
-        """Create a rebalanced portfolio from the OpenFrame constituents.
-
-        Args:
-            name: Name of the portfolio.
-            items: List of items to include in the portfolio. If None, uses all items.
-                Optional.
-            bal_weights: List of weights for rebalancing. If None, uses frame weights.
-                Optional.
-            frequency: Rebalancing frequency. Defaults to 1.
-            cash_index: Cash index series for cash component. Optional.
-            equal_weights: If True, use equal weights for all items. Defaults to False.
-            drop_extras: If True, only return TWR series; if False, return all details.
-                Defaults to True.
-
-        Returns:
-            OpenFrame containing the rebalanced portfolio.
-        """
-        items, bal_weights = self._validate_and_prepare_rebalance_inputs(
-            items,
-            bal_weights,
-            equal_weights=equal_weights,
-        )
-
-        if cash_index:
-            cash_index.tsdf = cash_index.tsdf.reindex(self.tsdf.index)
-            cash_values: list[float] = cast(
-                "list[float]", cash_index.tsdf.iloc[:, 0].to_numpy().tolist()
-            )
-        else:
-            cash_values = [1.0] * self.length
-
-        if self.tsdf.isna().to_numpy().any():
-            self.value_nan_handle()
-
-        ccies = list({serie.currency for serie in self.constituents})
-        if len(ccies) != 1:
-            msg = "Items for portfolio must be denominated in same currency."
-            raise MultipleCurrenciesError(msg)
-        currency = ccies[0]
-
-        instruments = [*items, "cash", name]
-        subheaders = [
-            ValueType.PRICE,
-            "buysell_qty",
-            "position",
-            "value",
-            "twr",
-            "settle",
-        ]
-
-        output = self._initialize_rebalance_output(
-            items=items,
-            name=name,
-            cash_values=cash_values,
-        )
-
-        self._initialize_first_day_positions(
-            items=items,
-            bal_weights=bal_weights,
-            output=output,
-            name=name,
-        )
-
-        counter = 1
-        for day in range(1, self.length):
-            if day == frequency * counter:
-                portfolio_value, settle_value = self._process_rebalancing_day(
-                    day=day,
-                    items=items,
-                    bal_weights=bal_weights,
-                    output=output,
-                    name=name,
-                )
-                counter += 1
-            else:
-                portfolio_value = self._process_non_rebalancing_day(
-                    day=day,
-                    items=items,
-                    output=output,
-                )
-                settle_value = 0.0
-
-            self._update_cash_and_portfolio(
-                day=day,
-                portfolio_value=portfolio_value,
-                settle_value=settle_value,
-                output=output,
-                name=name,
-            )
-
-        result = self._build_rebalance_result(
-            output=output,
-            instruments=instruments,
-            subheaders=subheaders,
-        )
-
-        series = []
-        if drop_extras:
-            used_constituents = [
-                item for item in self.constituents if item.label in items
-            ]
-            series.extend(
-                [
-                    OpenTimeSeries.from_df(
-                        dframe=result[(item.label, "twr")],
-                        valuetype=ValueType.PRICE,
-                        baseccy=item.currency,
-                        local_ccy=item.local_ccy,
-                    )
-                    for item in used_constituents
-                ]
-            )
-            series.append(
-                OpenTimeSeries.from_df(
-                    dframe=result[(name, "twr")],
-                    valuetype=ValueType.PRICE,
-                    baseccy=currency,
-                    local_ccy=True,
-                ),
-            )
-        else:
-            series.extend(
-                [
-                    OpenTimeSeries.from_df(
-                        dframe=result.loc[:, col],
-                        valuetype=ValueType.PRICE,
-                        baseccy=currency,
-                        local_ccy=True,
-                    ).set_new_label(f"{col[0]}, {col[1]!s}")
-                    for col in result.columns
-                ]
-            )
-
-        return OpenFrame(series)
    
-
    
-
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/load_plotly.html b/docs/build/html/_modules/openseries/load_plotly.html deleted file mode 100644 index ebab9690..00000000 --- a/docs/build/html/_modules/openseries/load_plotly.html +++ /dev/null @@ -1,215 +0,0 @@ - - - - - - - - openseries.load_plotly — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.load_plotly

    -"""Function to load plotly layout and configuration from local json file."""
-
-from __future__ import annotations
-
-from json import load
-from logging import getLogger
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import requests
-from requests.exceptions import ConnectionError as RequestsConnectionError
-
-if TYPE_CHECKING:
-    from .owntypes import CaptorLogoType, PlotlyLayoutType  # pragma: no cover
-
-logger = getLogger(__name__)
-
-__all__ = ["load_plotly_dict"]
-
-
-def _check_remote_file_existence(url: str) -> bool:
-    """Check if remote file exists.
-
-    Args:
-        url: Path to remote file.
-
-    Returns:
-        True if url is valid and False otherwise.
-    """
-    ok_code = 200
-
-    try:
-        response = requests.head(url, timeout=30)
-        if response.status_code != ok_code:
-            return False
-    except RequestsConnectionError:
-        return False
-    return True
-
-
-
    
-[docs]
-def load_plotly_dict(
-    *,
-    responsive: bool = True,
-) -> tuple[PlotlyLayoutType, CaptorLogoType]:
-    """Load Plotly defaults.
-
-    Args:
-        responsive: Flag whether to load as responsive. Defaults to True.
-
-    Returns:
-        tuple[PlotlyLayoutType, CaptorLogoType]: 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).
-    """
-    package_dir = Path(__file__).parent
-    layoutfile = package_dir / "plotly_layouts.json"
-    logofile = package_dir / "plotly_captor_logo.json"
-
-    with layoutfile.open(mode="r", encoding="utf-8") as layout_file:
-        fig = load(layout_file)
-    with logofile.open(mode="r", encoding="utf-8") as logo_file:
-        logo = load(logo_file)
-
-    if not _check_remote_file_existence(url=logo["source"]):
-        msg = f"Failed to add logo image from URL {logo['source']}"
-        logger.warning(msg)
-        logo = {}
-
-    fig["config"].update({"responsive": responsive})
-
-    return fig, logo
    
-
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/owntypes.html b/docs/build/html/_modules/openseries/owntypes.html deleted file mode 100644 index 592963dc..00000000 --- a/docs/build/html/_modules/openseries/owntypes.html +++ /dev/null @@ -1,619 +0,0 @@ - - - - - - - - openseries.owntypes — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.owntypes

    -"""Declaring types used throughout the project."""
-
-from __future__ import annotations
-
-import datetime as dt
-from enum import StrEnum
-from pprint import pformat
-from typing import (
-    TYPE_CHECKING,
-    Annotated,
-    ClassVar,
-    Literal,
-    Self,
-    TypeAlias,
-    TypeVar,
-)
-
-from annotated_types import MinLen
-from numpy import datetime64
-from pandas import Series, Timestamp
-from pydantic import BaseModel, Field, StringConstraints
-
-if TYPE_CHECKING:
-    from pandas import Series as _Series
-
-    SeriesFloat = _Series[float]
-else:
-    SeriesFloat = Series
-
-__all__ = ["ValueType"]
-
-
-SeriesOrFloat_co = TypeVar("SeriesOrFloat_co", float, SeriesFloat, covariant=True)
-
-
-CountryStringType = Annotated[
-    str,
-    StringConstraints(
-        strip_whitespace=True,
-        pattern=r"^[A-Z]{2}$",
-        to_upper=True,
-        min_length=2,
-        max_length=2,
-        strict=True,
-    ),
-]
-CountrySetType: TypeAlias = Annotated[set[CountryStringType], MinLen(1)]
-CountriesType: TypeAlias = CountrySetType | CountryStringType
-
-
-
    
-[docs]
-class Countries(BaseModel):
-    """Declare Countries."""
-
-    countryinput: CountriesType
    
-
-
-
-CurrencyStringType = Annotated[
-    str,
-    StringConstraints(
-        pattern=r"^[A-Z]{3}$",
-        to_upper=True,
-        min_length=3,
-        max_length=3,
-        strict=True,
-        strip_whitespace=True,
-    ),
-]
-
-
-
    
-[docs]
-class Currency(BaseModel):
-    """Declare Currency."""
-
-    ccy: CurrencyStringType
    
-
-
-
-DateStringType = Annotated[
-    str,
-    StringConstraints(
-        pattern=r"^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$",
-        strip_whitespace=True,
-        strict=True,
-        min_length=10,
-        max_length=10,
-    ),
-]
-DateListType: TypeAlias = Annotated[list[DateStringType], MinLen(1)]
-
-ValueListType: TypeAlias = Annotated[list[float], MinLen(1)]
-
-DaysInYearType = Annotated[int, Field(strict=True, ge=1, le=366)]
-
-DateType = str | dt.date | dt.datetime | datetime64 | Timestamp
-
-PlotlyConfigType = (
-    str
-    | int
-    | float
-    | bool
-    | list[str]
-    | dict[str, str | int | float | bool | list[str]]
-)
-
-PlotlyLayoutType = dict[str, PlotlyConfigType]
-
-CaptorLogoType = dict[str, str | float]
-
-LiteralJsonOutput = Literal["values", "tsdf"]
-LiteralTrunc = Literal["before", "after", "both"]
-LiteralLinePlotMode = (
-    Literal[
-        "lines",
-        "markers",
-        "lines+markers",
-        "lines+text",
-        "markers+text",
-        "lines+markers+text",
-    ]
-    | None
-)
-LiteralHowMerge = Literal["outer", "inner"]
-LiteralQuantileInterp = Literal["linear", "lower", "higher", "midpoint", "nearest"]
-LiteralBizDayFreq = Literal["B", "BME", "BQE", "BYE"]
-LiteralPandasReindexMethod = (
-    Literal["pad", "ffill", "backfill", "bfill", "nearest"] | None
-)
-LiteralNanMethod = Literal["fill", "drop"]
-LiteralCaptureRatio = Literal["up", "down", "both"]
-LiteralBarPlotMode = Literal["stack", "group", "overlay", "relative"]
-LiteralPlotlyOutput = Literal["file", "div"]
-LiteralPlotlyJSlib = Literal[True, False, "cdn"]
-LiteralPlotlyHistogramPlotType = Literal["bars", "lines"]
-LiteralPlotlyHistogramBarMode = Literal["stack", "group", "overlay", "relative"]
-LiteralPlotlyHistogramCurveType = Literal["normal", "kde"]
-LiteralPlotlyHistogramHistNorm = Literal[
-    "percent",
-    "probability",
-    "density",
-    "probability density",
-]
-LiteralPortfolioWeightings = Literal[
-    "eq_weights", "inv_vol", "max_div", "min_vol_overweight"
-]
-LiteralMinimizeMethods = Literal[
-    "SLSQP",
-    "Nelder-Mead",
-    "Powell",
-    "CG",
-    "BFGS",
-    "Newton-CG",
-    "L-BFGS-B",
-    "TNC",
-    "COBYLA",
-    "trust-constr",
-    "dogleg",
-    "trust-ncg",
-    "trust-exact",
-    "trust-krylov",
-]
-
-LiteralSeriesProps = 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",
-]
-LiteralFrameProps = 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",
-]
-
-
-
    
-[docs]
-class PropertiesList(list[str]):
-    """Base class for allowed property arguments definition."""
-
-    allowed_strings: ClassVar[set[str]] = {
-        "value_ret",
-        "geo_ret",
-        "arithmetic_ret",
-        "vol",
-        "downside_deviation",
-        "ret_vol_ratio",
-        "sortino_ratio",
-        "kappa3_ratio",
-        "omega_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",
-    }
-
-    def _validate(self: Self) -> None:
-        """Validate the string input of the all_properties method."""
-        seen = set()
-        invalids = set()
-        duplicates = set()
-        msg = ""
-        for item in self:
-            if item not in self.allowed_strings:
-                invalids.add(item)
-            if item in seen:
-                duplicates.add(item)
-            seen.add(item)
-        if len(invalids) != 0:
-            msg += (
-                f"Invalid string(s): {list(invalids)}.\nAllowed strings are:"
-                f"\n{pformat(self.allowed_strings)}\n"
-            )
-        if len(duplicates) != 0:
-            msg += f"Duplicate string(s): {list(duplicates)}."
-        if len(msg) != 0:
-            raise PropertiesInputValidationError(msg)
    
-
-
-
-
    
-[docs]
-class OpenTimeSeriesPropertiesList(PropertiesList):
-    """Allowed property arguments for the OpenTimeSeries class."""
-
-    allowed_strings: ClassVar[set[str]] = PropertiesList.allowed_strings | {
-        "first_idx",
-        "last_idx",
-        "length",
-        "span_of_days",
-        "yearfrac",
-        "periods_in_a_year",
-        "autocorr",
-        "partial_autocorr",
-    }
-
-
    
-[docs]
-    def __init__(
-        self: Self,
-        *args: LiteralSeriesProps,
-    ) -> None:
-        """Property arguments for the OpenTimeSeries class."""
-        super().__init__(args)
-        self._validate()
    
-
    
-
-
-
-
    
-[docs]
-class OpenFramePropertiesList(PropertiesList):
-    """Allowed property arguments for the OpenFrame class."""
-
-    allowed_strings: ClassVar[set[str]] = PropertiesList.allowed_strings | {
-        "autocorr",
-        "first_indices",
-        "last_indices",
-        "lengths_of_items",
-        "span_of_days_all",
-    }
-
-
    
-[docs]
-    def __init__(self: Self, *args: LiteralFrameProps) -> None:
-        """Property arguments for the OpenFrame class."""
-        super().__init__(args)
-        self._validate()
    
-
    
-
-
-
-
    
-[docs]
-class ValueType(StrEnum):
-    """Enum 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"
    
-
-
-
-
    
-[docs]
-class MixedValuetypesError(Exception):
-    """Raised when provided timeseries valuetypes are not the same."""
    
-
-
-
-
    
-[docs]
-class AtLeastOneFrameError(Exception):
-    """Raised when none of the possible frame inputs is provided."""
    
-
-
-
-
    
-[docs]
-class DateAlignmentError(Exception):
-    """Raised when date input is not aligned with existing range."""
    
-
-
-
-
    
-[docs]
-class NumberOfItemsAndLabelsNotSameError(Exception):
-    """Raised when number of labels is not matching the number of timeseries."""
    
-
-
-
-
    
-[docs]
-class InitialValueZeroError(Exception):
-    """Raised when a calculation cannot be performed due to initial value(s) zero."""
    
-
-
-
-
    
-[docs]
-class CountriesNotStringNorListStrError(Exception):
-    """Raised when countries argument is not provided in correct format."""
    
-
-
-
-
    
-[docs]
-class MarketsNotStringNorListStrError(Exception):
-    """Raised when markets argument is not provided in correct format."""
    
-
-
-
-
    
-[docs]
-class TradingDaysNotAboveZeroError(Exception):
-    """Raised when trading days argument is not above zero."""
    
-
-
-
-
    
-[docs]
-class BothStartAndEndError(Exception):
-    """Raised when both start and end dates are provided."""
    
-
-
-
-
    
-[docs]
-class NoWeightsError(Exception):
-    """Raised when no weights are provided to function where necessary."""
    
-
-
-
-
    
-[docs]
-class LabelsNotUniqueError(Exception):
-    """Raised when provided label names are not unique."""
    
-
-
-
-
    
-[docs]
-class RatioInputError(Exception):
-    """Raised when ratio keyword not provided correctly."""
    
-
-
-
-
    
-[docs]
-class MergingResultedInEmptyError(Exception):
-    """Raised when a merge resulted in an empty DataFrame."""
    
-
-
-
-
    
-[docs]
-class IncorrectArgumentComboError(Exception):
-    """Raised when correct combination of arguments is not provided."""
    
-
-
-
-
    
-[docs]
-class PropertiesInputValidationError(Exception):
-    """Raised when duplicate strings are provided."""
    
-
-
-
-
    
-[docs]
-class ResampleDataLossError(Exception):
-    """Raised when user attempts to run resample_to_business_period_ends on returns."""
    
-
-
-
-class WeightsNotProvidedError(Exception):
-    """Raised when weights are not provided."""
-
-
-class MultipleCurrenciesError(Exception):
-    """Raised when multiple currencies are provided."""
-
-
-class PortfolioItemsNotWithinFrameError(Exception):
-    """Raised when portfolio items are not within frame."""
-
-
-class MaxDiversificationNaNError(Exception):
-    """Raised when max_div weight strategy produces NaN values."""
-
-
-class MaxDiversificationNegativeWeightsError(Exception):
-    """Raised when max_div weight strategy produces negative weights."""
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/portfoliotools.html b/docs/build/html/_modules/openseries/portfoliotools.html deleted file mode 100644 index 7ef4f58a..00000000 --- a/docs/build/html/_modules/openseries/portfoliotools.html +++ /dev/null @@ -1,1043 +0,0 @@ - - - - - - - - openseries.portfoliotools — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.portfoliotools

    -"""Defining the portfolio tools for the OpenFrame class."""
-
-from __future__ import annotations
-
-from inspect import stack
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, cast
-
-from numpy import (
-    append,
-    array,
-    einsum,
-    float64,
-    inf,
-    isnan,
-    linspace,
-    nan,
-    sqrt,
-)
-from numpy import (
-    sum as npsum,
-)
-from pandas import (
-    DataFrame,
-    Series,
-    concat,
-)
-from plotly.graph_objs import Figure  # type: ignore[import-untyped]
-from plotly.io import to_html  # type: ignore[import-untyped]
-from plotly.offline import plot  # type: ignore[import-untyped]
-from scipy.optimize import minimize
-
-from .load_plotly import load_plotly_dict
-from .owntypes import (
-    AtLeastOneFrameError,
-    LiteralLinePlotMode,
-    LiteralMinimizeMethods,
-    LiteralPlotlyJSlib,
-    LiteralPlotlyOutput,
-    MixedValuetypesError,
-    ValueType,
-)
-from .series import OpenTimeSeries
-from .simulation import _random_generator
-
-if TYPE_CHECKING:  # pragma: no cover
-    from collections.abc import Callable
-
-    from numpy.typing import NDArray
-    from pydantic import DirectoryPath
-
-    from .frame import OpenFrame
-
-__all__ = [
-    "constrain_optimized_portfolios",
-    "efficient_frontier",
-    "prepare_plot_data",
-    "sharpeplot",
-    "simulate_portfolios",
-]
-
-
-
    
-[docs]
-def simulate_portfolios(
-    simframe: OpenFrame,
-    num_ports: int,
-    seed: int,
-) -> DataFrame:
-    """Generate random weights for simulated portfolios.
-
-    Args:
-        simframe: Return data for portfolio constituents.
-        num_ports: Number of possible portfolios to simulate.
-        seed: The seed for the random process.
-
-    Returns:
-        The resulting data.
-    """
-    copi = simframe.from_deepcopy()
-
-    vtypes = [x == ValueType.RTRN for x in copi.tsdf.columns.get_level_values(1)]
-    if not any(vtypes):
-        copi.value_to_ret()
-        log_ret = copi.tsdf.copy()[1:]
-    elif all(vtypes):
-        log_ret = copi.tsdf.copy()
-    else:
-        msg = "Mix of series types will give inconsistent results"
-        raise MixedValuetypesError(msg)
-
-    log_ret.columns = log_ret.columns.get_level_values(0)
-
-    cov_matrix = log_ret.cov() * simframe.periods_in_a_year
-    mean_returns = log_ret.mean() * simframe.periods_in_a_year
-
-    randomizer = _random_generator(seed=seed)
-    all_weights = randomizer.random((num_ports, simframe.item_count))
-    all_weights = all_weights / all_weights.sum(axis=1, keepdims=True)
-
-    ret_arr = all_weights @ mean_returns
-    vol_arr = sqrt(einsum("ij,jk,ik->i", all_weights, cov_matrix, all_weights))
-    sharpe_arr = ret_arr / vol_arr
-
-    simdf = concat(
-        [
-            DataFrame({"stdev": vol_arr, "ret": ret_arr, "sharpe": sharpe_arr}),
-            DataFrame(all_weights, columns=simframe.columns_lvl_zero),
-        ],
-        axis="columns",
-    )
-    simdf = simdf.replace([inf, -inf], nan)
-    return simdf.dropna()
    
-
-
-
-def _prepare_returns_for_frontier(eframe: OpenFrame) -> tuple[DataFrame, OpenFrame]:
-    """Prepare returns DataFrame for frontier calculation.
-
-    Args:
-        eframe: Portfolio data.
-
-    Returns:
-        Tuple of (log_ret DataFrame, copied frame).
-
-    Raises:
-        MixedValuetypesError: If series types are mixed.
-    """
-    if eframe.weights is None:
-        eframe.weights = [1.0 / eframe.item_count] * eframe.item_count
-
-    copi = eframe.from_deepcopy()
-
-    vtypes = [x == ValueType.RTRN for x in copi.tsdf.columns.get_level_values(1)]
-    if not any(vtypes):
-        copi.value_to_ret()
-        log_ret = copi.tsdf.copy()[1:]
-    elif all(vtypes):
-        log_ret = copi.tsdf.copy()
-    else:
-        msg = "Mix of series types will give inconsistent results"
-        raise MixedValuetypesError(msg)
-
-    log_ret.columns = log_ret.columns.get_level_values(0)
-    return log_ret, copi
-
-
-def _calculate_frontier_bounds(
-    simulated: DataFrame,
-    log_ret: DataFrame,
-    periods_in_a_year: float,
-) -> tuple[float, float]:
-    """Calculate frontier return bounds.
-
-    Args:
-        simulated: Simulated portfolios DataFrame.
-        log_ret: Returns DataFrame.
-        periods_in_a_year: Periods in a year.
-
-    Returns:
-        Tuple of (min_return, max_return).
-    """
-    min_stdev_idx = simulated["stdev"].idxmin()
-    frontier_min = cast("float", simulated.loc[min_stdev_idx, "ret"])
-
-    arithmetic_means = array(log_ret.mean() * periods_in_a_year)
-    cleaned_arithmetic_means = arithmetic_means[~isnan(arithmetic_means)]
-
-    frontier_max = float(cleaned_arithmetic_means.max())
-    return frontier_min, frontier_max
-
-
-def _build_frontier_line(
-    log_ret: DataFrame,
-    frontier_min: float,
-    frontier_max: float,
-    frontier_points: int,
-    periods_in_a_year: float,
-    init_guess: NDArray[float64],
-    bounds: tuple[tuple[float, float], ...],
-    minimize_method: LiteralMinimizeMethods,
-) -> tuple[list[float], list[NDArray[float64]]]:
-    """Build frontier line points.
-
-    Args:
-        log_ret: Returns DataFrame.
-        frontier_min: Minimum return.
-        frontier_max: Maximum return.
-        frontier_points: Number of points.
-        periods_in_a_year: Periods in a year.
-        init_guess: Initial guess for optimization.
-        bounds: Optimization bounds.
-        minimize_method: Minimization method.
-
-    Returns:
-        Tuple of (frontier_x, frontier_weights).
-    """
-
-    def _check_sum(weights: NDArray[float64]) -> float:
-        return cast("float", npsum(weights) - 1)
-
-    def _get_ret_vol_sr(
-        lg_ret: DataFrame,
-        weights: NDArray[float64],
-        per_in_yr: float,
-    ) -> NDArray[float64]:
-        ret = npsum(lg_ret.mean() * weights) * per_in_yr
-        volatility = sqrt(weights.T @ (lg_ret.cov() * per_in_yr @ weights))
-        sr = ret / volatility
-        return cast("NDArray[float64]", array([ret, volatility, sr]))
-
-    def _diff_return(
-        lg_ret: DataFrame,
-        weights: NDArray[float64],
-        per_in_yr: float,
-        poss_return: float,
-    ) -> float64:
-        return cast(
-            "float64",
-            _get_ret_vol_sr(lg_ret=lg_ret, weights=weights, per_in_yr=per_in_yr)[0]
-            - poss_return,
-        )
-
-    def _minimize_volatility(
-        weights: NDArray[float64],
-    ) -> float64:
-        return cast(
-            "float64",
-            _get_ret_vol_sr(
-                lg_ret=log_ret,
-                weights=weights,
-                per_in_yr=periods_in_a_year,
-            )[1],
-        )
-
-    frontier_y = linspace(start=frontier_min, stop=frontier_max, num=frontier_points)
-    frontier_x = []
-    frontier_weights = []
-
-    for possible_return in frontier_y:
-        cons = cast(
-            "Any",
-            [
-                {"type": "eq", "fun": _check_sum},
-                {
-                    "type": "eq",
-                    "fun": lambda w, poss_return=possible_return: _diff_return(
-                        lg_ret=log_ret,
-                        weights=w,
-                        per_in_yr=periods_in_a_year,
-                        poss_return=poss_return,
-                    ),
-                },
-            ],
-        )
-
-        result = minimize(
-            fun=_minimize_volatility,
-            x0=init_guess,
-            method=minimize_method,
-            bounds=bounds,
-            constraints=cons,
-        )
-
-        frontier_x.append(result["fun"])
-        frontier_weights.append(result["x"])
-
-    return frontier_x, frontier_weights
-
-
-def _build_frontier_dataframe(
-    frontier_x: list[float],
-    frontier_y: NDArray[float64],
-    frontier_weights: list[NDArray[float64]],
-    columns_lvl_zero: list[str],
-) -> DataFrame:
-    """Build frontier DataFrame.
-
-    Args:
-        frontier_x: Frontier volatility values.
-        frontier_y: Frontier return values.
-        frontier_weights: Frontier weight arrays.
-        columns_lvl_zero: Column names.
-
-    Returns:
-        Frontier DataFrame.
-    """
-    line_df = concat(
-        [
-            DataFrame(data=frontier_weights, columns=columns_lvl_zero),
-            DataFrame({"stdev": frontier_x, "ret": frontier_y}),
-        ],
-        axis="columns",
-    )
-    line_df["sharpe"] = line_df.ret / line_df.stdev
-
-    limit_small = 0.0001
-    line_df = line_df.mask(line_df.abs() < limit_small, 0.0)
-
-    weight_cols = columns_lvl_zero
-    weight_header = "<br><br>Weights:<br>"
-    line_df["text"] = line_df[weight_cols].apply(
-        lambda row: (
-            weight_header
-            + "<br>".join([f"{row[col]:.1%}  {col}" for col in weight_cols])
-        ),
-        axis=1,
-    )
-
-    return line_df
-
-
-def _apply_tweak(line_df: DataFrame) -> DataFrame:
-    """Apply tweak to frontier DataFrame.
-
-    Args:
-        line_df: Frontier DataFrame.
-
-    Returns:
-        Tweaked DataFrame.
-    """
-    limit_tweak = 0.001
-    line_df["stdev_diff"] = line_df.stdev.ffill().pct_change()
-    line_df = line_df.loc[line_df.stdev_diff.abs() > limit_tweak]
-    return line_df.drop(columns="stdev_diff")
-
-
-def _create_optimization_functions(
-    log_ret: DataFrame,
-    periods_in_a_year: float,
-) -> tuple[
-    Callable[[NDArray[float64]], float],
-    Callable[[NDArray[float64]], NDArray[float64]],
-    Callable[[NDArray[float64]], float64],
-]:
-    """Create optimization helper functions.
-
-    Args:
-        log_ret: Returns DataFrame.
-        periods_in_a_year: Periods in a year.
-
-    Returns:
-        Tuple of (_check_sum, _get_ret_vol_sr, _neg_sharpe) functions.
-    """
-
-    def _check_sum(weights: NDArray[float64]) -> float:
-        return cast("float", npsum(weights) - 1)
-
-    def _get_ret_vol_sr(weights: NDArray[float64]) -> NDArray[float64]:
-        ret = npsum(log_ret.mean() * weights) * periods_in_a_year
-        volatility = sqrt(weights.T @ (log_ret.cov() * periods_in_a_year @ weights))
-        sr = ret / volatility
-        return cast("NDArray[float64]", array([ret, volatility, sr]))
-
-    def _neg_sharpe(weights: NDArray[float64]) -> float64:
-        return cast("float64", _get_ret_vol_sr(weights)[2] * -1)
-
-    return _check_sum, _get_ret_vol_sr, _neg_sharpe
-
-
-def _optimize_max_sharpe_portfolio(
-    init_guess: NDArray[float64],
-    bounds: tuple[tuple[float, float], ...],
-    minimize_method: LiteralMinimizeMethods,
-    _check_sum: Callable[[NDArray[float64]], float],
-    _get_ret_vol_sr: Callable[[NDArray[float64]], NDArray[float64]],
-    _neg_sharpe: Callable[[NDArray[float64]], float64],
-) -> tuple[NDArray[float64], NDArray[float64]]:
-    """Optimize maximum Sharpe ratio portfolio.
-
-    Args:
-        init_guess: Initial guess for optimization.
-        bounds: Optimization bounds.
-        minimize_method: Minimization method.
-        _check_sum: Check sum constraint function.
-        _get_ret_vol_sr: Get return, volatility, Sharpe ratio function.
-        _neg_sharpe: Negative Sharpe ratio function.
-
-    Returns:
-        Tuple of (optimal metrics, optimal weights).
-    """
-    constraints = cast("Any", [{"type": "eq", "fun": _check_sum}])
-    opt_results = minimize(
-        fun=_neg_sharpe,
-        x0=init_guess,
-        method=minimize_method,
-        bounds=bounds,
-        constraints=constraints,
-    )
-
-    optimal = _get_ret_vol_sr(opt_results.x)
-
-    return optimal, opt_results.x
-
-
-
    
-[docs]
-def efficient_frontier(
-    eframe: OpenFrame,
-    num_ports: int = 5000,
-    seed: int = 71,
-    bounds: tuple[tuple[float, float], ...] | None = None,
-    frontier_points: int = 200,
-    minimize_method: LiteralMinimizeMethods = "SLSQP",
-    *,
-    tweak: bool = True,
-) -> tuple[DataFrame, DataFrame, NDArray[float64]]:
-    """Identify an efficient frontier.
-
-    Args:
-        eframe: Portfolio data.
-        num_ports: Number of possible portfolios to simulate. Defaults to 5000.
-        seed: The seed for the random process. Defaults to 71.
-        bounds: The range of minimum and maximum allowed allocations for each asset.
-        frontier_points: Number of points along frontier to optimize. Defaults to 200.
-        minimize_method: The method passed into the scipy.minimize function.
-            Defaults to SLSQP.
-        tweak: 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.
-    """
-    log_ret, copi = _prepare_returns_for_frontier(eframe)
-
-    simulated = simulate_portfolios(simframe=copi, num_ports=num_ports, seed=seed)
-
-    frontier_min, frontier_max = _calculate_frontier_bounds(
-        simulated=simulated,
-        log_ret=log_ret,
-        periods_in_a_year=copi.periods_in_a_year,
-    )
-
-    if not bounds:
-        bounds = tuple((0.0, 1.0) for _ in range(eframe.item_count))
-    init_guess = array(eframe.weights)
-
-    _check_sum, _get_ret_vol_sr, _neg_sharpe = _create_optimization_functions(
-        log_ret=log_ret,
-        periods_in_a_year=copi.periods_in_a_year,
-    )
-
-    optimal, opt_weights = _optimize_max_sharpe_portfolio(
-        init_guess=init_guess,
-        bounds=bounds,
-        minimize_method=minimize_method,
-        _check_sum=_check_sum,
-        _get_ret_vol_sr=_get_ret_vol_sr,
-        _neg_sharpe=_neg_sharpe,
-    )
-
-    frontier_y = linspace(start=frontier_min, stop=frontier_max, num=frontier_points)
-    frontier_x, frontier_weights = _build_frontier_line(
-        log_ret=log_ret,
-        frontier_min=frontier_min,
-        frontier_max=frontier_max,
-        frontier_points=frontier_points,
-        periods_in_a_year=copi.periods_in_a_year,
-        init_guess=init_guess,
-        bounds=bounds,
-        minimize_method=minimize_method,
-    )
-
-    line_df = _build_frontier_dataframe(
-        frontier_x=frontier_x,
-        frontier_y=frontier_y,
-        frontier_weights=frontier_weights,
-        columns_lvl_zero=eframe.columns_lvl_zero,
-    )
-
-    if tweak:
-        line_df = _apply_tweak(line_df)
-
-    return line_df, simulated, append(optimal, opt_weights)
    
-
-
-
-
    
-[docs]
-def constrain_optimized_portfolios(
-    data: OpenFrame,
-    serie: OpenTimeSeries,
-    portfolioname: str = "Current Portfolio",
-    simulations: int = 10000,
-    curve_points: int = 200,
-    bounds: tuple[tuple[float, float], ...] | None = None,
-    minimize_method: LiteralMinimizeMethods = "SLSQP",
-) -> tuple[OpenFrame, OpenTimeSeries, OpenFrame, OpenTimeSeries]:
-    """Constrain optimized portfolios to those that improve on the current one.
-
-    Args:
-        data: Portfolio data.
-        serie: A timeseries representing the current portfolio.
-        portfolioname: Name of the portfolio. Defaults to "Current Portfolio".
-        simulations: Number of possible portfolios to simulate. Defaults to 10000.
-        curve_points: Number of optimal portfolios on the efficient frontier.
-            Defaults to 200.
-        bounds: The range of minimum and maximum allowed allocations for each asset.
-        minimize_method: The method passed into the scipy.minimize function.
-            Defaults to SLSQP.
-
-    Returns:
-        The constrained optimal portfolio data.
-
-    """
-    lr_frame = data.from_deepcopy()
-    mv_frame = data.from_deepcopy()
-
-    if not bounds:
-        bounds = tuple((0.0, 1.0) for _ in range(data.item_count))
-
-    front_frame, _, _ = efficient_frontier(
-        eframe=data,
-        num_ports=simulations,
-        frontier_points=curve_points,
-        bounds=bounds,
-        minimize_method=minimize_method,
-    )
-
-    condition_least_ret = front_frame.ret > serie.arithmetic_ret
-    least_ret_frame = front_frame[condition_least_ret].sort_values(by="stdev")
-    least_ret_port: Series[float] = least_ret_frame.iloc[0]
-    least_ret_port_name = f"Minimize vol & target return of {portfolioname}"
-    least_ret_weights: list[float] = [
-        least_ret_port.loc[c] for c in lr_frame.columns_lvl_zero
-    ]
-    lr_frame.weights = least_ret_weights
-    resleast = OpenTimeSeries.from_df(lr_frame.make_portfolio(least_ret_port_name))
-
-    condition_most_vol = front_frame.stdev < serie.vol
-    most_vol_frame = front_frame[condition_most_vol].sort_values(
-        by="ret",
-        ascending=False,
-    )
-    most_vol_port: Series[float] = most_vol_frame.iloc[0]
-    most_vol_port_name = f"Maximize return & target risk of {portfolioname}"
-    most_vol_weights: list[float] = [
-        most_vol_port.loc[c] for c in mv_frame.columns_lvl_zero
-    ]
-    mv_frame.weights = most_vol_weights
-    resmost = OpenTimeSeries.from_df(mv_frame.make_portfolio(most_vol_port_name))
-
-    return lr_frame, resleast, mv_frame, resmost
    
-
-
-
-
    
-[docs]
-def prepare_plot_data(
-    assets: OpenFrame,
-    current: OpenTimeSeries,
-    optimized: NDArray[float64],
-) -> DataFrame:
-    """Prepare data to be used as point_frame in the sharpeplot function.
-
-    Args:
-        assets: Portfolio data with individual assets and a weighted portfolio.
-        current: The current or initial portfolio based on given weights.
-        optimized: Data optimized with the efficient_frontier method.
-
-    Returns:
-        The data prepared with mean returns, volatility and weights.
-    """
-    txt = "<br><br>Weights:<br>" + "<br>".join(
-        [
-            f"{wgt:.1%}  {nm}"
-            for wgt, nm in zip(
-                cast("list[float]", assets.weights),
-                assets.columns_lvl_zero,
-                strict=True,
-            )
-        ],
-    )
-
-    opt_text_list = [
-        f"{wgt:.1%}  {nm}"
-        for wgt, nm in zip(optimized[3:], assets.columns_lvl_zero, strict=True)
-    ]
-    opt_text = "<br><br>Weights:<br>" + "<br>".join(opt_text_list)
-    plotframe = DataFrame(
-        data=[
-            assets.arithmetic_ret,
-            assets.vol,
-            Series(
-                data=[""] * assets.item_count,
-                index=assets.vol.index,
-            ),
-        ],
-        index=["ret", "stdev", "text"],
-    )
-    plotframe.columns = plotframe.columns.get_level_values(0)
-    plotframe["Max Sharpe Portfolio"] = Series(
-        data=[optimized[0], optimized[1], opt_text],
-        index=plotframe.index,
-        dtype=object,
-    )
-    if current.label is not None:
-        label = current.label
-        plotframe[label] = Series(
-            data=[current.arithmetic_ret, current.vol, txt],
-            index=plotframe.index,
-            dtype=object,
-        )
-
-    return plotframe
    
-
-
-
-def _determine_output_directory(directory: DirectoryPath | None) -> Path:
-    """Determine output directory for plot file.
-
-    Args:
-        directory: Optional directory path.
-
-    Returns:
-        Path to output directory.
-    """
-    if directory:
-        return Path(directory).resolve()
-    if Path.home().joinpath("Documents").exists():
-        return Path.home().joinpath("Documents")
-    return Path(stack()[2].filename).parent
-
-
-def _add_simulated_portfolios_trace(
-    figure: Figure,
-    sim_frame: DataFrame,
-    returns: list[float],
-    risk: list[float],
-) -> None:
-    """Add simulated portfolios trace to figure.
-
-    Args:
-        figure: Plotly figure.
-        sim_frame: Simulated portfolios DataFrame.
-        returns: List to extend with returns.
-        risk: List to extend with risk values.
-    """
-    returns.extend(list(sim_frame.loc[:, "ret"]))
-    risk.extend(list(sim_frame.loc[:, "stdev"]))
-    figure.add_scatter(
-        x=sim_frame.loc[:, "stdev"],
-        y=sim_frame.loc[:, "ret"],
-        hoverinfo="skip",
-        marker={
-            "size": 10,
-            "opacity": 0.5,
-            "color": sim_frame.loc[:, "sharpe"],
-            "colorscale": "Jet",
-            "reversescale": True,
-            "colorbar": {"thickness": 20, "title": "Ratio<br>ret / vol"},
-        },
-        mode="markers",
-        name="simulated portfolios",
-    )
-
-
-def _add_efficient_frontier_trace(
-    figure: Figure,
-    line_frame: DataFrame,
-    returns: list[float],
-    risk: list[float],
-) -> None:
-    """Add efficient frontier trace to figure.
-
-    Args:
-        figure: Plotly figure.
-        line_frame: Efficient frontier DataFrame.
-        returns: List to extend with returns.
-        risk: List to extend with risk values.
-    """
-    returns.extend(list(line_frame.loc[:, "ret"]))
-    risk.extend(list(line_frame.loc[:, "stdev"]))
-    figure.add_scatter(
-        x=line_frame.loc[:, "stdev"],
-        y=line_frame.loc[:, "ret"],
-        text=line_frame.loc[:, "text"],
-        xhoverformat=".2%",
-        yhoverformat=".2%",
-        hovertemplate="Return %{y}<br>Vol %{x}%{text}",
-        hoverlabel_align="right",
-        line={"width": 2.5, "dash": "solid"},
-        mode="lines",
-        name="Efficient frontier",
-    )
-
-
-def _add_point_frame_traces(
-    figure: Figure,
-    point_frame: DataFrame,
-    point_frame_mode: LiteralLinePlotMode,
-    fig: dict[str, Any],
-    returns: list[float],
-    risk: list[float],
-) -> None:
-    """Add point frame traces to figure.
-
-    Args:
-        figure: Plotly figure.
-        point_frame: Point frame DataFrame.
-        point_frame_mode: Mode for point frame traces.
-        fig: Plotly figure dictionary.
-        returns: List to extend with returns.
-        risk: List to extend with risk values.
-    """
-    layout_dict = cast(
-        "dict[str, str | int | float | bool | list[str]]",
-        fig["layout"],
-    )
-    base_colorway = cast("list[str]", layout_dict.get("colorway", []))
-    if len(base_colorway) < len(point_frame.columns) and base_colorway:
-        repeats = (len(point_frame.columns) + len(base_colorway) - 1) // len(
-            base_colorway
-        )
-        colorway = (base_colorway * repeats)[: len(point_frame.columns)]
-    else:
-        colorway = base_colorway[: len(point_frame.columns)]
-    for col, clr in zip(point_frame.columns, colorway, strict=True):
-        returns.extend([cast("float", point_frame.loc["ret", col])])
-        risk.extend([cast("float", point_frame.loc["stdev", col])])
-        figure.add_scatter(
-            x=[point_frame.loc["stdev", col]],
-            y=[point_frame.loc["ret", col]],
-            xhoverformat=".2%",
-            yhoverformat=".2%",
-            hovertext=[point_frame.loc["text", col]],
-            hovertemplate="Return %{y}<br>Vol %{x}%{hovertext}",
-            hoverlabel_align="right",
-            marker={"size": 20, "color": clr},
-            mode=point_frame_mode,
-            name=col,
-            text=col,
-            textfont={"size": 14},
-            textposition="bottom center",
-        )
-
-
-def _configure_figure_layout(
-    figure: Figure,
-    titletext: str | None,
-    logo: dict[str, Any],
-    *,
-    title: bool = True,
-    add_logo: bool = True,
-) -> None:
-    """Configure figure layout.
-
-    Args:
-        figure: Plotly figure.
-        title: Whether to add title.
-        titletext: Optional title text.
-        add_logo: Whether to add logo.
-        logo: Logo dictionary.
-    """
-    figure.update_layout(
-        xaxis={"tickformat": ".1%"},
-        xaxis_title="volatility",
-        yaxis={
-            "tickformat": ".1%",
-            "scaleanchor": "x",
-            "scaleratio": 1,
-        },
-        yaxis_title="annual return",
-        showlegend=False,
-    )
-    if title:
-        if titletext is None:
-            titletext = "<b>Risk and Return</b><br>"
-        figure.update_layout(title={"text": titletext, "font": {"size": 36}})
-
-    if add_logo:
-        figure.add_layout_image(logo)
-
-
-def _generate_sharpeplot_output(
-    figure: Figure,
-    plotfile: Path,
-    filename: str,
-    output_type: LiteralPlotlyOutput,
-    include_plotlyjs: LiteralPlotlyJSlib,
-    fig: dict[str, Any],
-    *,
-    auto_open: bool = True,
-) -> str:
-    """Generate output for sharpeplot.
-
-    Args:
-        figure: Plotly figure.
-        plotfile: Path to plot file.
-        filename: Filename.
-        output_type: Output type.
-        include_plotlyjs: How to include plotly.js.
-        fig: Plotly figure dictionary.
-        auto_open: Whether to auto-open.
-
-    Returns:
-        Output string.
-    """
-    if output_type == "file":
-        plot(
-            figure_or_data=figure,
-            filename=str(plotfile),
-            auto_open=auto_open,
-            auto_play=False,
-            link_text="",
-            include_plotlyjs=include_plotlyjs,
-            config=fig["config"],
-            output_type=output_type,
-        )
-        return str(plotfile)
-
-    div_id = filename.split(maxsplit=1, sep=".")[0]
-    return cast(
-        "str",
-        to_html(
-            fig=figure,
-            config=fig["config"],
-            auto_play=False,
-            include_plotlyjs=include_plotlyjs,
-            full_html=False,
-            div_id=div_id,
-        ),
-    )
-
-
-
    
-[docs]
-def sharpeplot(
-    sim_frame: DataFrame | None = None,
-    line_frame: DataFrame | None = None,
-    point_frame: DataFrame | None = None,
-    point_frame_mode: LiteralLinePlotMode = "markers",
-    filename: str | None = None,
-    directory: DirectoryPath | None = None,
-    titletext: str | None = None,
-    output_type: LiteralPlotlyOutput = "file",
-    include_plotlyjs: LiteralPlotlyJSlib = "cdn",
-    *,
-    title: bool = True,
-    add_logo: bool = True,
-    auto_open: bool = True,
-) -> tuple[Figure, str]:
-    """Create scatter plot coloured by Sharpe Ratio.
-
-    Args:
-        sim_frame: Data from the simulate_portfolios method.
-        line_frame: Data from the efficient_frontier method.
-        point_frame: Data to highlight current and efficient portfolios.
-        point_frame_mode: Which type of scatter to use. Defaults to markers.
-        filename: Name of the Plotly html file.
-        directory: Directory where Plotly html file is saved.
-        titletext: Text for the plot title.
-        output_type: Determines output type. Defaults to "file".
-        include_plotlyjs: Determines how the plotly.js library is included
-            in the output.
-            Defaults to "cdn".
-        title: Whether to add standard plot title. Defaults to True.
-        add_logo: Whether to add Captor logo. Defaults to True.
-        auto_open: Determines whether to open a browser window with the plot.
-            Defaults to True.
-
-    Returns:
-        The scatter plot with simulated and optimized results.
-    """
-    if sim_frame is None and line_frame is None and point_frame is None:
-        msg = "One of sim_frame, line_frame or point_frame must be provided."
-        raise AtLeastOneFrameError(msg)
-
-    returns: list[float] = []
-    risk: list[float] = []
-
-    dirpath = _determine_output_directory(directory)
-    if not filename:
-        filename = "sharpeplot.html"
-    plotfile = dirpath.joinpath(filename)
-
-    fig, logo = load_plotly_dict()
-    figure = Figure(fig)
-
-    if sim_frame is not None:
-        _add_simulated_portfolios_trace(figure, sim_frame, returns, risk)
-    if line_frame is not None:
-        _add_efficient_frontier_trace(figure, line_frame, returns, risk)
-    if point_frame is not None:
-        _add_point_frame_traces(
-            figure, point_frame, point_frame_mode, fig, returns, risk
-        )
-
-    _configure_figure_layout(figure, titletext, logo, title=title, add_logo=add_logo)
-
-    string_output = _generate_sharpeplot_output(
-        figure,
-        plotfile,
-        filename,
-        output_type,
-        include_plotlyjs,
-        fig,
-        auto_open=auto_open,
-    )
-
-    return figure, string_output
    
-
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/report.html b/docs/build/html/_modules/openseries/report.html deleted file mode 100644 index 44cd0985..00000000 --- a/docs/build/html/_modules/openseries/report.html +++ /dev/null @@ -1,816 +0,0 @@ - - - - - - - - openseries.report — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.report

    -"""Functions related to HTML reports."""
-
-from __future__ import annotations
-
-from inspect import stack
-from itertools import cycle
-from json import dumps as json_dumps
-from logging import getLogger
-from pathlib import Path
-from secrets import choice
-from string import ascii_letters
-from typing import TYPE_CHECKING, Any, cast
-from warnings import catch_warnings, simplefilter
-from webbrowser import open as webbrowser_open
-
-from pandas import DataFrame, Index, Series, Timestamp, concat, isna
-from plotly.graph_objs import Bar, Figure, Scatter  # type: ignore[import-untyped]
-from plotly.utils import PlotlyJSONEncoder  # type: ignore[import-untyped]
-
-from .html_utils import _get_base_css, _get_plotly_script
-from .load_plotly import load_plotly_dict
-from .owntypes import (
-    CaptorLogoType,
-    LiteralBizDayFreq,
-    LiteralFrameProps,
-    LiteralPlotlyJSlib,
-    LiteralPlotlyOutput,
-    ValueType,
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .frame import OpenFrame
-
-logger = getLogger(__name__)
-
-__all__ = ["report_html"]
-
-
-def calendar_period_returns(
-    data: OpenFrame,
-    freq: LiteralBizDayFreq = "BYE",
-    *,
-    relabel: bool = True,
-) -> DataFrame:
-    """Generate a table of returns with appropriate table labels."""
-    copied = data.from_deepcopy()
-    copied.resample_to_business_period_ends(freq=freq)
-    copied.value_to_ret()
-    cldr = copied.tsdf.iloc[1:].copy()
-    if relabel:
-        if freq.upper() == "BYE":
-            cldr.index = Index([d.year for d in cldr.index])
-        elif freq.upper() == "BQE":
-            cldr.index = Index(
-                [Timestamp(d).to_period("Q").strftime("Q%q %Y") for d in cldr.index],
-            )
-        else:
-            cldr.index = Index([d.strftime("%b %y") for d in cldr.index])
-    return cldr
-
-
-def _dumps_plotly(obj: object) -> str:
-    return json_dumps(obj, cls=PlotlyJSONEncoder)
-
-
-def _fmt_dates(idx: Index) -> list[str]:
-    return [Timestamp(d).strftime("%Y-%m-%d") for d in idx]
-
-
-def _metrics_table_html(df: DataFrame) -> str:
-    return df.to_html(index=False, escape=False, classes=["metrics"], border=0)
-
-
-def _get_report_properties_and_labels(
-    yearfrac: float,
-) -> tuple[list[str], list[str], list[str]]:
-    """Get properties and labels based on yearfrac."""
-    if yearfrac > 1.0:
-        properties = [
-            "geo_ret",
-            "vol",
-            "ret_vol_ratio",
-            "sortino_ratio",
-            "worst_month",
-            "first_indices",
-            "last_indices",
-        ]
-        labels_init = [
-            "Return (CAGR)",
-            "Volatility",
-            "Sharpe Ratio",
-            "Sortino Ratio",
-            "Worst Month",
-            "Comparison Start",
-            "Comparison End",
-            "Jensen's Alpha",
-            "Information Ratio",
-            "Tracking Error (weekly)",
-            "Capture Ratio (monthly)",
-            "Index Beta (weekly)",
-        ]
-        labels_final = [
-            "Return (CAGR)",
-            "Year-to-Date",
-            "Month-to-Date",
-            "Volatility",
-            "Sharpe Ratio",
-            "Sortino Ratio",
-            "Jensen's Alpha",
-            "Information Ratio",
-            "Tracking Error (weekly)",
-            "Index Beta (weekly)",
-            "Capture Ratio (monthly)",
-            "Worst Month",
-            "Comparison Start",
-            "Comparison End",
-        ]
-    else:
-        properties = [
-            "value_ret",
-            "vol",
-            "ret_vol_ratio",
-            "sortino_ratio",
-            "worst",
-            "first_indices",
-            "last_indices",
-        ]
-        labels_init = [
-            "Return (simple)",
-            "Volatility",
-            "Sharpe Ratio",
-            "Sortino Ratio",
-            "Worst Day",
-            "Comparison Start",
-            "Comparison End",
-            "Jensen's Alpha",
-            "Information Ratio",
-            "Tracking Error (weekly)",
-            "Index Beta (weekly)",
-        ]
-        labels_final = [
-            "Return (simple)",
-            "Year-to-Date",
-            "Month-to-Date",
-            "Volatility",
-            "Sharpe Ratio",
-            "Sortino Ratio",
-            "Jensen's Alpha",
-            "Information Ratio",
-            "Tracking Error (weekly)",
-            "Index Beta (weekly)",
-            "Worst Day",
-            "Comparison Start",
-            "Comparison End",
-        ]
-    return properties, labels_init, labels_final
-
-
-def _create_line_traces(data: OpenFrame) -> list[Scatter]:
-    """Create line traces for the plot."""
-    x_line = _fmt_dates(data.tsdf.index)
-    line_traces: list[Scatter] = []
-    for item, lbl in enumerate(data.columns_lvl_zero):
-        line_traces.append(
-            Scatter(
-                x=x_line,
-                y=data.tsdf.iloc[:, item].tolist(),
-                hovertemplate=f"{lbl}<br>%{{y:.2%}}<br>%{{x}}<extra></extra>",
-                line={"width": 2.5, "dash": "solid"},
-                mode="lines",
-                name=lbl,
-                showlegend=True,
-            ),
-        )
-    return line_traces
-
-
-def _create_bar_traces(
-    data: OpenFrame,
-    bar_freq: LiteralBizDayFreq,
-) -> list[Bar]:
-    """Create bar traces for the plot."""
-    quarter_of_year = 0.25
-    if data.yearfrac < quarter_of_year:
-        tmp = data.from_deepcopy()
-        bdf = tmp.value_to_ret().tsdf.iloc[1:]
-    else:
-        bdf = calendar_period_returns(data=data, freq=bar_freq)
-
-    x_bar = [str(x) for x in bdf.index]
-    bar_traces: list[Bar] = []
-    for item in range(data.item_count):
-        col_name = cast("tuple[str, ValueType]", bdf.iloc[:, item].name)
-        bar_traces.append(
-            Bar(
-                x=x_bar,
-                y=bdf.iloc[:, item].tolist(),
-                hovertemplate=f"{col_name[0]}<br>%{{y:.2%}}<br>%{{x}}<extra></extra>",
-                name=col_name[0],
-                showlegend=False,
-            ),
-        )
-    return bar_traces
-
-
-def _add_jensen_alpha(
-    rpt_df: DataFrame,
-    data: OpenFrame,
-) -> DataFrame:
-    """Add Jensen's Alpha to the report dataframe."""
-    alpha_frame = data.from_deepcopy()
-    alpha_frame.to_cumret()
-    with catch_warnings():
-        simplefilter("ignore")
-        alphas: list[str | float] = [
-            alpha_frame.jensen_alpha(
-                asset=(aname, ValueType.PRICE),
-                market=(alpha_frame.columns_lvl_zero[-1], ValueType.PRICE),
-                riskfree_rate=0.0,
-            )
-            for aname in alpha_frame.columns_lvl_zero[:-1]
-        ]
-    alphas.append("")
-    ar = DataFrame(
-        data=alphas,
-        index=data.tsdf.columns,
-        columns=["Jensen's Alpha"],
-    ).T
-    return concat([rpt_df, ar])
-
-
-def _add_information_ratio(
-    rpt_df: DataFrame,
-    data: OpenFrame,
-) -> DataFrame:
-    """Add Information Ratio to the report dataframe."""
-    ir = data.info_ratio_func()
-    ir.name = "Information Ratio"
-    ir.iloc[-1] = None
-    ir_df = ir.to_frame().T
-    return concat([rpt_df, ir_df])
-
-
-def _add_tracking_error(
-    rpt_df: DataFrame,
-    data: OpenFrame,
-) -> DataFrame:
-    """Add Tracking Error to the report dataframe."""
-    te_frame = data.from_deepcopy()
-    te_frame.resample("7D")
-    with catch_warnings():
-        simplefilter("ignore")
-        te: Series[float] | Series[str] = te_frame.tracking_error_func()
-    if te.hasnans:
-        te = Series(
-            data=[""] * te_frame.item_count,
-            index=te_frame.tsdf.columns,
-            name="Tracking Error (weekly)",
-        )
-    else:
-        te.iloc[-1] = None
-        te.name = "Tracking Error (weekly)"
-    te_df = te.to_frame().T
-    return concat([rpt_df, te_df])
-
-
-def _add_capture_ratio(
-    rpt_df: DataFrame,
-    data: OpenFrame,
-    formats: list[str],
-) -> tuple[DataFrame, list[str]]:
-    """Add Capture Ratio to the report dataframe."""
-    crm = data.from_deepcopy()
-    crm.resample("ME")
-    cru_save = Series(
-        data=[""] * crm.item_count,
-        index=crm.tsdf.columns,
-        name="Capture Ratio (monthly)",
-    )
-    with catch_warnings():
-        simplefilter("ignore")
-        try:
-            cru: Series[float] | Series[str] = crm.capture_ratio_func(ratio="both")
-        except ZeroDivisionError as exc:  # pragma: no cover
-            msg = f"Capture ratio calculation error: {exc!s}"  # pragma: no cover
-            logger.warning(msg)  # pragma: no cover
-            cru = cru_save  # pragma: no cover
-    if cru.hasnans:
-        cru = cru_save
-    else:
-        cru.iloc[-1] = None
-        cru.name = "Capture Ratio (monthly)"
-    cru_df = cru.to_frame().T
-    return concat([rpt_df, cru_df]), formats
-
-
-def _add_beta(
-    rpt_df: DataFrame,
-    data: OpenFrame,
-) -> DataFrame:
-    """Add Index Beta to the report dataframe."""
-    beta_frame = data.from_deepcopy()
-    beta_frame.resample("7D").value_nan_handle("drop")
-    beta_frame.to_cumret()
-    betas: list[str | float] = [
-        beta_frame.beta(
-            asset=(bname, ValueType.PRICE),
-            market=(beta_frame.columns_lvl_zero[-1], ValueType.PRICE),
-        )
-        for bname in beta_frame.columns_lvl_zero[:-1]
-    ]
-    betas.append("")
-    br = DataFrame(
-        data=betas,
-        index=data.tsdf.columns,
-        columns=["Index Beta (weekly)"],
-    ).T
-    return concat([rpt_df, br])
-
-
-def _add_ytd_mtd(
-    rpt_df: DataFrame,
-    data: OpenFrame,
-) -> DataFrame:
-    """Add Year-to-Date and Month-to-Date to the report dataframe."""
-    this_year = data.last_idx.year
-    this_month = data.last_idx.month
-    ytd = data.value_ret_calendar_period(year=this_year).map("{:.2%}".format)
-    ytd.name = "Year-to-Date"
-    mtd = data.value_ret_calendar_period(year=this_year, month=this_month).map(
-        "{:.2%}".format,
-    )
-    mtd.name = "Month-to-Date"
-    ytd_df = ytd.to_frame().T
-    mtd_df = mtd.to_frame().T
-    return concat([rpt_df, ytd_df, mtd_df])
-
-
-def _get_output_directory(directory: Path | None) -> Path:
-    """Determine the output directory."""
-    if directory:
-        return Path(directory).resolve()
-    if Path.home().joinpath("Documents").exists():
-        return Path.home() / "Documents"
-    return Path(stack()[1].filename).parent
-
-
-def _get_plotly_layouts(
-    layout_theme: dict[str, Any],
-    colorway: list[str],
-    item_count: int,
-) -> tuple[dict[str, Any], dict[str, Any]]:
-    """Get line and bar layouts for plotly."""
-    line_layout = dict(layout_theme)
-    line_layout.update(
-        {
-            "colorway": colorway[:item_count] if colorway else None,
-            "margin": {"l": 50, "r": 20, "t": 20, "b": 40},
-            "xaxis": {"gridcolor": "#EEEEEE", "automargin": True, "tickangle": -45},
-            "yaxis": {"tickformat": ".2%", "gridcolor": "#EEEEEE", "automargin": True},
-            "showlegend": False,
-        },
-    )
-
-    bar_layout = dict(layout_theme)
-    bar_layout.update(
-        {
-            "barmode": "group",
-            "margin": {"l": 50, "r": 20, "t": 10, "b": 80},
-            "xaxis": {"gridcolor": "#EEEEEE", "automargin": True, "tickangle": -45},
-            "yaxis": {"tickformat": ".2%", "gridcolor": "#EEEEEE", "automargin": True},
-            "showlegend": False,
-        },
-    )
-
-    return line_layout, bar_layout
-
-
-def _get_logo_html(logo: CaptorLogoType, *, add_logo: bool) -> str:
-    """Get logo HTML."""
-    if not add_logo:
-        return ""
-    try:
-        src = cast("dict[str, Any]", logo).get("source", "")
-    except (KeyError, AttributeError, TypeError):
-        src = ""
-    if src:
-        return f'<img src="{src}" alt="Captor" style="height:68px;" />'
-    return "CAPTOR"
-
-
-def _get_legend_html(line_traces: list[Scatter], colorway: list[str]) -> str:
-    """Generate HTML for the legend at the bottom of the page."""
-    legend_items = []
-    color_cycle = cycle(colorway or ["#66725B"])
-    for trace in line_traces:
-        name = trace.name or ""
-        color = next(color_cycle)
-        legend_items.append(
-            f'<div class="legend-item">'
-            f'<div class="legend-color" style="background-color:{color};"></div>'
-            f"<span>{name}</span>"
-            f"</div>",
-        )
-    if legend_items:
-        return f'<div class="legend-container">{"".join(legend_items)}</div>'
-    return ""
-
-
-def _get_css() -> str:
-    """Get CSS styles for the HTML report."""
-    base_css = _get_base_css()
-    return (
-        base_css
-        + """
-    .header{display:grid;grid-template-columns:140px 1fr 140px;gap:12px;
-    align-items:start;}
-    h1{margin:0;text-align:center;font-size:45px;font-weight:800;}
-    .layout{display:grid;grid-template-columns:1.2fr .9fr;
-    grid-template-areas:"charts table";gap:22px;align-items:start;margin-top:12px;}
-    .charts{grid-area:charts;display:grid;grid-template-rows:auto auto;gap:18px;}
-    .table{grid-area:table;}
-    .plot{width:100%;height:380px;}
-    .plot.bar{height:300px;}
-    @media (max-width:980px){
-      .page{padding:24px;padding-bottom:24px;}
-      .header{grid-template-columns:120px 1fr;}
-      h1{font-size:36px;}
-      .layout{grid-template-columns:1fr;grid-template-areas:"table" "charts";gap:16px;}
-      .plot{height:380px;}
-      .plot.bar{height:300px;}
-      table.metrics{table-layout:fixed;width:auto;}
-      table.metrics thead th{min-width:120px;width:120px;white-space:nowrap;}
-      table.metrics thead th:first-child{width:180px;}
-      table.metrics tbody td{min-width:120px;width:120px;}
-      table.metrics tbody td:first-child{width:180px;}
-    }
-    table.metrics{width:100%;border-collapse:separate;border-spacing:0;font-size:12px;
-    border-radius:4px;overflow:hidden;table-layout:fixed;}
-    table.metrics thead th{background:var(--header);color:white;padding:8px 10px;
-    font-weight:700;text-align:center;word-wrap:break-word;word-break:break-word;}
-    table.metrics thead th:first-child{background:var(--header2);text-align:left;
-    width:180px;}
-    table.metrics tbody td{padding:7px 10px;border-bottom:1px solid white;
-    border-right:1px solid white;text-align:center;background:var(--paper);}
-    table.metrics tbody td:first-child{text-align:left;font-weight:600;color:white;
-    background:var(--header);width:180px;}
-    table.metrics tbody td:last-child{background:var(--cell2);}
-    .legend-container{margin-top:24px;padding-top:20px;padding-bottom:16px;
-    display:flex;justify-content:center;flex-wrap:wrap;gap:24px;flex-shrink:0;}
-    .legend-item{display:flex;align-items:center;gap:8px;font-size:14px;}
-    .legend-color{width:20px;height:3px;border-radius:2px;}
-    @media (min-width:981px){
-      html,body{overflow-y:auto;}
-    }
-    """
-    )
-
-
-def _write_html_file(
-    plotfile: Path,
-    html: str,
-    *,
-    auto_open: bool,
-) -> str:
-    """Write HTML file and optionally open it."""
-    plotfile.parent.mkdir(parents=True, exist_ok=True)
-    plotfile.write_text(html, encoding="utf-8")
-    if auto_open:
-        try:
-            webbrowser_open(plotfile.as_uri())
-        except OSError as exc:
-            logger.warning("Failed to open browser: %s", exc)
-    return str(plotfile)
-
-
-def _generate_html(
-    title: str | None,
-    css: str,
-    plotly_script: str,
-    logo_html: str,
-    table_html: str,
-    line_payload: dict[str, Any],
-    bar_payload: dict[str, Any],
-    legend_html: str,
-) -> str:
-    """Generate the HTML string."""
-    return f"""<!doctype html>
-<html lang="sv">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width,initial-scale=1" />
-<title>{title or ""}</title>
-<style>{css}</style>
-{plotly_script}
-</head>
-<body>
-<div class="page">
-  <div class="header">
-    <div>{logo_html}</div>
-    <div><h1>{title or ""}</h1></div>
-    <div></div>
-  </div>
-  <div class="layout">
-    <div class="charts">
-      <div id="lineplot" class="plot"></div>
-      <div id="barplot" class="plot bar"></div>
-    </div>
-    <div class="table">{table_html}</div>
-  </div>
-  {legend_html}
-</div>
-<script>
-const line = {_dumps_plotly(line_payload)};
-const bar = {_dumps_plotly(bar_payload)};
-Plotly.newPlot("lineplot", line.data, line.layout, line.config);
-Plotly.newPlot("barplot", bar.data, bar.layout, bar.config);
-window.addEventListener("resize", () => {{
-  Plotly.Plots.resize("lineplot");
-  Plotly.Plots.resize("barplot");
-}});
-</script>
-</body>
-</html>
-"""
-
-
-
    
-[docs]
-def report_html(
-    data: OpenFrame,
-    bar_freq: LiteralBizDayFreq = "BYE",
-    filename: str | None = None,
-    title: str | None = None,
-    directory: Path | None = None,
-    output_type: LiteralPlotlyOutput = "file",
-    include_plotlyjs: LiteralPlotlyJSlib = "cdn",
-    *,
-    auto_open: bool = False,
-    add_logo: bool = True,
-    vertical_legend: bool = True,
-) -> tuple[Figure, str]:
-    """Generate a responsive HTML report page with line and bar plots and a table."""
-    copied = data.from_deepcopy()
-    copied.trunc_frame().value_nan_handle().to_cumret()
-
-    properties, labels_init, labels_final = _get_report_properties_and_labels(
-        copied.yearfrac,
-    )
-
-    line_traces = _create_line_traces(copied)
-    bar_traces = _create_bar_traces(copied, bar_freq)
-
-    rpt_df = copied.all_properties(
-        properties=cast("list[LiteralFrameProps]", properties),
-    )
-    rpt_df = _add_jensen_alpha(rpt_df, copied)
-    rpt_df = _add_information_ratio(rpt_df, copied)
-    rpt_df = _add_tracking_error(rpt_df, copied)
-
-    if copied.yearfrac > 1.0:
-        rpt_df, _ = _add_capture_ratio(rpt_df, copied, [])
-
-    rpt_df = _add_beta(rpt_df, copied)
-    rpt_df.index = Index(labels_init)
-    rpt_df = _add_ytd_mtd(rpt_df, copied)
-    rpt_df = rpt_df.reindex(labels_final)
-
-    format_map = {
-        "Return (CAGR)": "{:.2%}",
-        "Return (simple)": "{:.2%}",
-        "Year-to-Date": "{:.2%}",
-        "Month-to-Date": "{:.2%}",
-        "Volatility": "{:.2%}",
-        "Sharpe Ratio": "{:.2f}",
-        "Sortino Ratio": "{:.2f}",
-        "Jensen's Alpha": "{:.2%}",
-        "Information Ratio": "{:.2f}",
-        "Tracking Error (weekly)": "{:.2%}",
-        "Index Beta (weekly)": "{:.2f}",
-        "Capture Ratio (monthly)": "{:.2f}",
-        "Worst Month": "{:.2%}",
-        "Worst Day": "{:.2%}",
-        "Comparison Start": "{:%Y-%m-%d}",
-        "Comparison End": "{:%Y-%m-%d}",
-    }
-    formats = [format_map.get(label, "{:.2f}") for label in labels_final]
-
-    for item, f in zip(rpt_df.index, formats, strict=False):
-        rpt_df.loc[item] = rpt_df.loc[item].apply(
-            lambda x, fmt=f: (
-                ""
-                if (
-                    x is None
-                    or (not isinstance(x, str) and isna(x))
-                    or (isinstance(x, str) and x.lower() in ("nan", "nan%", ""))
-                )
-                else (
-                    str(x)
-                    if isinstance(x, str)
-                    else (
-                        Timestamp(x).strftime("%Y-%m-%d")
-                        if "%Y-%m-%d" in fmt and not isinstance(x, str)
-                        else fmt.format(x)
-                    )
-                )
-            ),
-        )
-
-    rpt_df.index = Index([f"<b>{x}</b>" for x in rpt_df.index])
-    rpt_df = rpt_df.reset_index()
-
-    colmns = ["", *copied.columns_lvl_zero]
-    rpt_df.columns = colmns
-    table_html = _metrics_table_html(rpt_df)
-
-    dirpath = _get_output_directory(directory=directory)
-
-    if not filename:
-        filename = "".join(choice(ascii_letters) for _ in range(6)) + ".html"
-
-    plotfile = dirpath / filename
-
-    fig_theme, logo = load_plotly_dict()
-    layout_theme = cast("dict[str, Any]", fig_theme.get("layout", {}))
-    colorway: list[str] = cast("dict[str, list[str]]", layout_theme).get(
-        "colorway", []
-    )
-
-    line_layout, bar_layout = _get_plotly_layouts(
-        layout_theme=layout_theme,
-        colorway=colorway,
-        item_count=copied.item_count,
-    )
-
-    config = cast("dict[str, Any]", fig_theme.get("config", {})) or {}
-    config = {**config, "responsive": True, "displayModeBar": False}
-
-    plotly_script = _get_plotly_script(include_plotlyjs=include_plotlyjs)
-    logo_html = _get_logo_html(logo=logo, add_logo=add_logo)
-    css = _get_css()
-
-    line_payload = {
-        "data": [t.to_plotly_json() for t in line_traces],
-        "layout": line_layout,
-        "config": config,
-    }
-    bar_payload = {
-        "data": [t.to_plotly_json() for t in bar_traces],
-        "layout": bar_layout,
-        "config": config,
-    }
-
-    if not vertical_legend:
-        logger.debug("Horizontal legend layout requested.")
-    legend_html = _get_legend_html(line_traces=line_traces, colorway=colorway)
-
-    html = _generate_html(
-        title=title,
-        css=css,
-        plotly_script=plotly_script,
-        logo_html=logo_html,
-        table_html=table_html,
-        line_payload=line_payload,
-        bar_payload=bar_payload,
-        legend_html=legend_html,
-    )
-
-    if output_type == "file":
-        output = _write_html_file(plotfile=plotfile, html=html, auto_open=auto_open)
-    else:
-        output = html
-
-    fig_return = Figure(data=line_traces)
-    return fig_return, output
    
-
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/series.html b/docs/build/html/_modules/openseries/series.html deleted file mode 100644 index 6b070b91..00000000 --- a/docs/build/html/_modules/openseries/series.html +++ /dev/null @@ -1,1278 +0,0 @@ - - - - - - - - openseries.series — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.series

    -"""The OpenTimeSeries class."""
-
-from __future__ import annotations
-
-import datetime as dt
-from copy import deepcopy
-from logging import getLogger
-from typing import TYPE_CHECKING, Any, Self, TypeVar, cast
-
-if TYPE_CHECKING:  # pragma: no cover
-    from numpy.typing import NDArray
-    from pandas import Timestamp
-
-from numpy import (
-    append,
-    array,
-    asarray,
-    cumprod,
-    diff,
-    float64,
-    insert,
-    isnan,
-    log,
-    sqrt,
-    square,
-)
-from pandas import (
-    DataFrame,
-    DatetimeIndex,
-    Index,
-    MultiIndex,
-    Series,
-    date_range,
-)
-from pydantic import field_validator, model_validator
-from scipy.stats import chi2, norm
-
-from ._common_model import (
-    _calculate_time_factor,
-    _CommonModel,
-    _demeaned_returns_for_autocorr,
-)
-from .datefixer import _do_resample_to_business_period_ends, date_fix
-from .owntypes import (
-    Countries,
-    CountriesType,
-    Currency,
-    CurrencyStringType,
-    DateAlignmentError,
-    DateListType,
-    DaysInYearType,
-    IncorrectArgumentComboError,
-    LiteralBizDayFreq,
-    LiteralPandasReindexMethod,
-    LiteralSeriesProps,
-    MarketsNotStringNorListStrError,
-    OpenTimeSeriesPropertiesList,
-    ResampleDataLossError,
-    ValueListType,
-    ValueType,
-)
-
-logger = getLogger(__name__)
-
-__all__ = ["OpenTimeSeries", "timeseries_chain"]
-
-TypeOpenTimeSeries = TypeVar("TypeOpenTimeSeries", bound="OpenTimeSeries")
-
-
-
    
-[docs]
-class OpenTimeSeries(_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.
-
-    Args:
-        timeseries_id: Database identifier of the timeseries.
-        instrument_id: Database identifier of the instrument associated with
-            the timeseries.
-        name: String identifier of the timeseries and/or instrument.
-        valuetype: Identifies if the series is a series of values or returns.
-        dates: Dates of the individual timeseries items.
-            These dates will not be altered by methods.
-        values: The value or return values of the timeseries items.
-            These values will not be altered by methods.
-        local_ccy: Boolean flag indicating if timeseries is in local currency.
-        tsdf: Pandas object holding dates and values that can be altered via
-            methods.
-        currency: ISO 4217 currency code of the timeseries.
-        domestic: ISO 4217 currency code of the user's home currency.
-            Defaults to "SEK".
-        countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-            Defaults to "SE".
-        markets: (List of) markets code(s) supported by exchange_calendars.
-            Optional.
-        isin: ISO 6166 identifier code of the associated instrument. Optional.
-        label: 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 = "SEK"
-    countries: CountriesType = "SE"
-    isin: str | None = None
-    label: str | None = None
-
-    @field_validator("domestic", mode="before")
-    @classmethod
-    def _validate_domestic(cls, value: CurrencyStringType) -> CurrencyStringType:
-        """Pydantic validator to ensure domestic field is validated."""
-        Currency(ccy=value)
-        return value
-
-    @field_validator("countries", mode="before")
-    @classmethod
-    def _validate_countries(cls, value: CountriesType) -> CountriesType:
-        """Pydantic validator to ensure countries field is validated."""
-        Countries(countryinput=value)
-        return value
-
-    @field_validator("markets", mode="before")
-    @classmethod
-    def _validate_markets(
-        cls,
-        value: list[str] | str | None,
-    ) -> list[str] | str | None:
-        """Pydantic validator to ensure markets field is validated.
-
-        Raises:
-            MarketsNotStringNorListStrError: If ``markets`` is neither a string
-                nor a non-empty list of strings.
-        """
-        msg = (
-            "'markets' must be a string or list of strings, "
-            f"got {type(value).__name__!r}"
-        )
-        if value is None or isinstance(value, str):
-            return value
-        if isinstance(value, list):
-            if all(isinstance(item, str) for item in value) and len(value) != 0:
-                return value
-            item_msg = "All items in 'markets' must be strings."
-            raise MarketsNotStringNorListStrError(item_msg)
-        raise MarketsNotStringNorListStrError(msg)
-
-    @model_validator(mode="after")
-    def _dates_and_values_validate(self: Self) -> Self:
-        """Pydantic validator to ensure dates and values are validated.
-
-        Raises:
-            ValueError: If dates are not unique or if numbers of dates and values
-                do not match the shape of ``tsdf``.
-        """
-        values_list_length = len(self.values)
-        dates_list_length = len(self.dates)
-        dates_set_length = len(set(self.dates))
-        if dates_list_length != dates_set_length:
-            msg = "Dates are not unique"
-            raise ValueError(msg)
-        if (
-            (dates_list_length != values_list_length)
-            or (len(self.tsdf.index) != self.tsdf.shape[0])
-            or (self.tsdf.shape[1] != 1)
-        ):
-            msg = "Number of dates and values passed do not match"
-            raise ValueError(msg)
-        return self
-
-    def _coerce_result(
-        self: Self,
-        result: Series[float],
-        name: str,
-    ) -> float:
-        _ = name
-        return float(asarray(a=result, dtype=float64).squeeze())
-
-
    
-[docs]
-    @classmethod
-    def from_arrays(
-        cls,
-        name: str,
-        dates: DateListType,
-        values: ValueListType,
-        valuetype: ValueType = ValueType.PRICE,
-        timeseries_id: str = "",
-        instrument_id: str = "",
-        isin: str | None = None,
-        baseccy: CurrencyStringType = "SEK",
-        *,
-        local_ccy: bool = True,
-    ) -> Self:
-        """Create series from a list of dates and a list of values.
-
-        Args:
-            name: String identifier of the timeseries and/or instrument.
-            dates: List of date strings as ISO 8601 YYYY-MM-DD.
-            values: Array of float values.
-            valuetype: Identifies if the series is a series of values or returns.
-                Defaults to ValueType.PRICE.
-            timeseries_id: Database identifier of the timeseries. Optional.
-            instrument_id: Database identifier of the instrument associated
-                with the timeseries. Optional.
-            isin: ISO 6166 identifier code of the associated instrument. Optional.
-            baseccy: ISO 4217 currency code of the timeseries. Defaults to "SEK".
-            local_ccy: Boolean flag indicating if timeseries is in local currency.
-                Defaults to True.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        return cls(
-            name=name,
-            label=name,
-            dates=dates,
-            values=values,
-            valuetype=valuetype,
-            timeseries_id=timeseries_id,
-            instrument_id=instrument_id,
-            isin=isin,
-            currency=baseccy,
-            local_ccy=local_ccy,
-            tsdf=DataFrame(
-                data=values,
-                index=[deyt.date() for deyt in DatetimeIndex(dates)],
-                columns=[[name], [valuetype]],
-                dtype="float64",
-            ),
-        )
    
-
-
-
    
-[docs]
-    @classmethod
-    def from_df(
-        cls,
-        dframe: Series | DataFrame | object,
-        column_nmbr: int = 0,
-        valuetype: ValueType = ValueType.PRICE,
-        baseccy: CurrencyStringType = "SEK",
-        *,
-        local_ccy: bool = True,
-    ) -> Self:
-        """Create series from a Pandas DataFrame or Series.
-
-        Args:
-            dframe: Pandas DataFrame or Series.
-            column_nmbr: Using iloc[:, column_nmbr] to pick column. Defaults to 0.
-            valuetype: Identifies if the series is a series of values or returns.
-                Defaults to ValueType.PRICE.
-            baseccy: ISO 4217 currency code of the timeseries. Defaults to "SEK".
-            local_ccy: Boolean flag indicating if timeseries is in local currency.
-                Defaults to True.
-
-        Returns:
-            An OpenTimeSeries object.
-
-        Raises:
-            TypeError: If ``dframe`` is not a ``pandas.Series`` or a
-                ``pandas.DataFrame``.
-        """
-        msg = "Argument dframe must be pandas Series or DataFrame."
-        values: list[float]
-        pandas_obj: Series | DataFrame
-        if isinstance(dframe, Series):
-            pandas_obj = dframe
-            if isinstance(dframe.name, tuple):
-                label, _ = dframe.name
-            else:
-                label = dframe.name
-            values = dframe.to_numpy().tolist()
-        elif isinstance(dframe, DataFrame):
-            pandas_obj = dframe
-            values = dframe.iloc[:, column_nmbr].to_list()
-            if isinstance(dframe.columns, MultiIndex):
-                if _check_if_none(
-                    dframe.columns.get_level_values(0).to_numpy()[column_nmbr],
-                ):
-                    label = "Series"
-                    msg = f"Label missing. Adding: {label}"
-                    logger.warning(msg)
-                else:
-                    label = dframe.columns.get_level_values(0).to_numpy()[column_nmbr]
-                if _check_if_none(
-                    dframe.columns.get_level_values(1).to_numpy()[column_nmbr],
-                ):
-                    valuetype = ValueType.PRICE
-                    msg = f"valuetype missing. Adding: {valuetype.value}"
-                    logger.warning(msg)
-                else:
-                    valuetype = dframe.columns.get_level_values(1).to_numpy()[
-                        column_nmbr
-                    ]
-            else:
-                label = dframe.columns.to_numpy()[column_nmbr]
-        else:
-            raise TypeError(msg)
-
-        dates = [date_fix(d).strftime("%Y-%m-%d") for d in pandas_obj.index]
-
-        return cls(
-            timeseries_id="",
-            instrument_id="",
-            currency=baseccy,
-            dates=dates,
-            name=label,
-            label=label,
-            valuetype=valuetype,
-            values=values,
-            local_ccy=local_ccy,
-            tsdf=DataFrame(
-                data=values,
-                index=[deyt.date() for deyt in DatetimeIndex(dates)],
-                columns=[[label], [valuetype]],
-                dtype="float64",
-            ),
-        )
    
-
-
-
    
-[docs]
-    @classmethod
-    def from_fixed_rate(
-        cls,
-        rate: float,
-        d_range: DatetimeIndex | None = None,
-        days: int | None = None,
-        end_dt: dt.date | None = None,
-        label: str = "Series",
-        valuetype: ValueType = ValueType.PRICE,
-        baseccy: CurrencyStringType = "SEK",
-        *,
-        local_ccy: bool = True,
-    ) -> Self:
-        """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.
-
-        Args:
-            rate: The accrual rate.
-            d_range: A given range of dates. Optional.
-            days: Number of days to generate when date_range not provided. Must be
-                combined with end_dt. Optional.
-            end_dt: End date of date range to generate when date_range not provided.
-                Must be combined with days. Optional.
-            label: Placeholder for a name of the timeseries.
-            valuetype: Identifies if the series is a series of values or returns.
-                Defaults to ValueType.PRICE.
-            baseccy: The currency of the timeseries. Defaults to "SEK".
-            local_ccy: Boolean flag indicating if timeseries is in local currency.
-                Defaults to True.
-
-        Returns:
-            An OpenTimeSeries object.
-
-        Raises:
-            IncorrectArgumentComboError: If ``d_range`` is not provided and the
-                combination of ``days`` and ``end_dt`` is incomplete.
-        """
-        if d_range is None:
-            if days is not None and end_dt is not None:
-                d_range = DatetimeIndex(
-                    [d.date() for d in date_range(periods=days, end=end_dt, freq="D")],
-                )
-            else:
-                msg = "If d_range is not provided both days and end_dt must be."
-                raise IncorrectArgumentComboError(msg)
-        deltas = array([i.days for i in d_range[1:] - d_range[:-1]])
-        arr: list[float] = list(cumprod(insert(1 + deltas * rate / 365, 0, 1.0)))
-        dates = [d.strftime("%Y-%m-%d") for d in d_range]
-
-        return cls(
-            timeseries_id="",
-            instrument_id="",
-            currency=baseccy,
-            dates=dates,
-            name=label,
-            label=label,
-            valuetype=valuetype,
-            values=arr,
-            local_ccy=local_ccy,
-            tsdf=DataFrame(
-                data=arr,
-                index=[d.date() for d in DatetimeIndex(dates)],
-                columns=[[label], [valuetype]],
-                dtype="float64",
-            ),
-        )
    
-
-
-
    
-[docs]
-    def from_deepcopy(self: Self) -> Self:
-        """Create copy of OpenTimeSeries object.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        return deepcopy(self)
    
-
-
-
    
-[docs]
-    def pandas_df(self: Self) -> Self:
-        """Populate .tsdf Pandas DataFrame from the .dates and .values lists.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        dframe = DataFrame(
-            data=self.values,
-            index=[d.date() for d in DatetimeIndex(self.dates)],
-            columns=[[self.label], [self.valuetype]],
-            dtype="float64",
-        )
-        self.tsdf = dframe
-
-        return self
    
-
-
-
    
-[docs]
-    def all_properties(
-        self: Self,
-        properties: list[LiteralSeriesProps] | None = None,
-    ) -> DataFrame:
-        """Calculate chosen properties.
-
-        Args:
-            properties: The properties to calculate. Defaults to calculating all
-                available. Optional.
-
-        Returns:
-            Properties of the OpenTimeSeries.
-        """
-        if not properties:
-            properties = cast(
-                "list[LiteralSeriesProps]",
-                OpenTimeSeriesPropertiesList.allowed_strings,
-            )
-
-        props = OpenTimeSeriesPropertiesList(*properties)
-
-        def _prop_value(name: str) -> float | int | dt.date | Series[float]:
-            attr = getattr(self, name)
-            return cast(
-                "float | int | dt.date | Series[float]",
-                attr() if callable(attr) else attr,
-            )
-
-        pdf = DataFrame.from_dict(
-            {x: _prop_value(x) for x in props},
-            orient="index",
-        )
-        pdf.columns = self.tsdf.columns
-        return pdf
    
-
-
-
    
-[docs]
-    def value_to_ret(self: Self) -> Self:
-        """Convert series of values into series of returns.
-
-        Returns:
-            The returns of the values in the series.
-        """
-        returns = self.tsdf.ffill().pct_change()
-        returns.iloc[0] = 0
-        self.valuetype = ValueType.RTRN
-        arrays = cast("Any", [[self.label], [self.valuetype]])
-        returns.columns = MultiIndex.from_arrays(arrays)
-        self.tsdf = returns.copy()
-        return self
    
-
-
-
    
-[docs]
-    def value_to_diff(self: Self, periods: int = 1) -> Self:
-        """Convert series of values to series of their period differences.
-
-        Args:
-            periods: The number of periods between observations over which difference
-                is calculated. Defaults to 1.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        self.tsdf = self.tsdf.diff(periods=periods)
-        self.tsdf.iloc[0] = 0
-        self.valuetype = ValueType.RTRN
-        self.tsdf.columns = MultiIndex.from_arrays(
-            [
-                [self.label],
-                [self.valuetype],
-            ],
-        )
-        return self
    
-
-
-
    
-[docs]
-    def to_cumret(self: Self) -> Self:
-        """Convert series of returns into cumulative series of values.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        if self.valuetype == ValueType.PRICE:
-            self.value_to_ret()
-
-        self.tsdf = self.tsdf.add(1.0)
-        self.tsdf = self.tsdf.cumprod(axis=0) / self.tsdf.iloc[0]
-
-        self.valuetype = ValueType.PRICE
-        self.tsdf.columns = MultiIndex.from_arrays(
-            [
-                [self.label],
-                [self.valuetype],
-            ],
-        )
-        return self
    
-
-
-
    
-[docs]
-    def from_1d_rate_to_cumret(
-        self: Self,
-        days_in_year: int = 365,
-        divider: float = 1.0,
-    ) -> Self:
-        """Convert series of 1-day rates into series of cumulative values.
-
-        Args:
-            days_in_year: Calendar days per year used as divisor. Defaults to 365.
-            divider: Convenience divider for when the 1-day rate is not scaled
-                correctly. Defaults to 1.0.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        arr: NDArray[float64] = array(self.values) / divider
-
-        deltas = array([i.days for i in self.tsdf.index[1:] - self.tsdf.index[:-1]])
-        arr = cast(
-            "NDArray[float64]",
-            cumprod(
-                a=insert(
-                    arr=1.0 + deltas * arr[:-1] / days_in_year, obj=0, values=1.0
-                ),
-            ),
-        )
-
-        self.dates = [d.strftime("%Y-%m-%d") for d in self.tsdf.index]
-        self.values = list(arr)
-        self.valuetype = ValueType.PRICE
-        self.tsdf = DataFrame(
-            data=self.values,
-            index=[d.date() for d in DatetimeIndex(self.dates)],
-            columns=[[self.label], [self.valuetype]],
-            dtype="float64",
-        )
-
-        return self
    
-
-
-
    
-[docs]
-    def resample(
-        self: Self,
-        freq: LiteralBizDayFreq | str = "BME",
-    ) -> Self:
-        """Resamples the timeseries frequency.
-
-        Args:
-            freq: The date offset string that sets the resampled frequency.
-                Defaults to "BME".
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        self.tsdf.index = DatetimeIndex(self.tsdf.index)
-        if self.valuetype == ValueType.RTRN:
-            self.tsdf = self.tsdf.resample(freq).sum()
-        else:
-            self.tsdf = self.tsdf.resample(freq).last()
-        self.tsdf.index = Index(DatetimeIndex(self.tsdf.index).date)
-        return self
    
-
-
-
    
-[docs]
-    def resample_to_business_period_ends(
-        self: Self,
-        freq: LiteralBizDayFreq = "BME",
-        method: LiteralPandasReindexMethod = "nearest",
-    ) -> Self:
-        """Resamples timeseries frequency to the business calendar month end dates.
-
-        Stubs left in place. Stubs will be aligned to the shortest stub.
-
-        Args:
-            freq: The date offset string that sets the resampled frequency.
-                Defaults to BME.
-            method: Controls the method used to align values across columns.
-                Defaults to nearest.
-
-        Returns:
-            An OpenTimeSeries object.
-
-        Raises:
-            ResampleDataLossError: If called on a return series (``valuetype`` is
-                ``ValueType.RTRN``), since summation across sparser frequency would
-                be required to avoid data loss.
-        """
-        if self.valuetype == ValueType.RTRN:
-            msg = (
-                "Do not run resample_to_business_period_ends on return series. "
-                "The operation will pick the last data point in the sparser series. "
-                "It will not sum returns and therefore data will be lost."
-            )
-            raise ResampleDataLossError(msg)
-
-        dates = _do_resample_to_business_period_ends(
-            data=self.tsdf,
-            freq=freq,
-            countries=self.countries,
-            markets=self.markets,
-        )
-        self.tsdf = self.tsdf.reindex([deyt.date() for deyt in dates], method=method)
-        return self
    
-
-
-
    
-[docs]
-    def ewma_vol_func(
-        self: Self,
-        lmbda: float = 0.94,
-        day_chunk: int = 11,
-        dlta_degr_freedms: int = 0,
-        months_from_last: int | None = None,
-        from_date: dt.date | None = None,
-        to_date: dt.date | None = None,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> Series[float]:
-        """Exponentially Weighted Moving Average Model for Volatility.
-
-        Reference: https://www.investopedia.com/articles/07/ewma.asp.
-
-        Args:
-            lmbda: Scaling factor to determine weighting. Defaults to 0.94.
-            day_chunk: Sampling the data which is assumed to be daily.
-                Defaults to 11.
-            dlta_degr_freedms: Variance bias factor taking the value 0 or 1.
-                Defaults to 0.
-            months_from_last: Number of months offset as positive integer.
-                Overrides use of from_date and to_date. Optional.
-            from_date: Specific from date. Optional.
-            to_date: Specific to date. Optional.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Series EWMA volatility.
-        """
-        earlier, later = self.calc_range(
-            months_offset=months_from_last,
-            from_dt=from_date,
-            to_dt=to_date,
-        )
-        time_factor = _calculate_time_factor(
-            data=self.tsdf.loc[
-                cast("Timestamp", earlier) : cast("Timestamp", later)
-            ].iloc[:, 0],
-            earlier=earlier,
-            later=later,
-            periods_in_a_year_fixed=periods_in_a_year_fixed,
-        )
-
-        data = self.tsdf.loc[
-            cast("Timestamp", earlier) : cast("Timestamp", later)
-        ].copy()
-
-        data.loc[:, (self.label, ValueType.RTRN)] = log(
-            data.loc[:, self.tsdf.columns.to_numpy()[0]],
-        ).diff()
-
-        rawdata = [
-            data[(self.label, ValueType.RTRN)]
-            .iloc[1:day_chunk]
-            .std(ddof=dlta_degr_freedms)
-            * sqrt(time_factor),
-        ]
-
-        for item in data[(self.label, ValueType.RTRN)].iloc[1:]:
-            prev = rawdata[-1]
-            rawdata.append(
-                sqrt(
-                    square(item) * time_factor * (1 - lmbda) + square(prev) * lmbda,
-                ),
-            )
-
-        return Series(
-            data=rawdata,
-            index=data.index,
-            name=(self.label, ValueType.EWMA_VOL),
-            dtype="float64",
-        )
    
-
-
-
    
-[docs]
-    def ewma_var_func(
-        self: Self,
-        lmbda: float = 0.94,
-        day_chunk: int = 11,
-        level: float = 0.95,
-        dlta_degr_freedms: int = 0,
-        months_from_last: int | None = None,
-        from_date: dt.date | None = None,
-        to_date: dt.date | None = None,
-        periods_in_a_year_fixed: DaysInYearType | None = None,
-    ) -> Series[float]:
-        """Exponentially Weighted Moving Average Model for Value At Risk (VaR).
-
-        Reference: https://www.investopedia.com/articles/07/ewma.asp.
-
-        Args:
-            lmbda: Scaling factor to determine weighting. Defaults to 0.94.
-            day_chunk: Sampling the data which is assumed to be daily.
-                Defaults to 11.
-            level: The sought VaR level. Defaults to 0.95.
-            dlta_degr_freedms: Variance bias factor taking the value 0 or 1.
-                Defaults to 0.
-            months_from_last: Number of months offset as positive integer.
-                Overrides use of from_date and to_date. Optional.
-            from_date: Specific from date. Optional.
-            to_date: Specific to date. Optional.
-            periods_in_a_year_fixed: Allows locking the periods-in-a-year to simplify
-                test cases and comparisons. Optional.
-
-        Returns:
-            Series EWMA VaR.
-        """
-        earlier, later = self.calc_range(
-            months_offset=months_from_last,
-            from_dt=from_date,
-            to_dt=to_date,
-        )
-        time_factor = _calculate_time_factor(
-            data=self.tsdf.loc[
-                cast("Timestamp", earlier) : cast("Timestamp", later)
-            ].iloc[:, 0],
-            earlier=earlier,
-            later=later,
-            periods_in_a_year_fixed=periods_in_a_year_fixed,
-        )
-
-        data = self.tsdf.loc[
-            cast("Timestamp", earlier) : cast("Timestamp", later)
-        ].copy()
-
-        data.loc[:, (self.label, ValueType.RTRN)] = log(
-            data.loc[:, self.tsdf.columns.to_numpy()[0]],
-        ).diff()
-
-        rawdata = [
-            data[(self.label, ValueType.RTRN)]
-            .iloc[1:day_chunk]
-            .std(ddof=dlta_degr_freedms)
-            * sqrt(time_factor),
-        ]
-
-        for item in data[(self.label, ValueType.RTRN)].iloc[1:]:
-            prev = rawdata[-1]
-            rawdata.append(
-                sqrt(
-                    square(item) * time_factor * (1 - lmbda) + square(prev) * lmbda,
-                ),
-            )
-
-        return Series(
-            data=array(rawdata) * norm.ppf(1 - level),
-            index=data.index,
-            name=(self.label, ValueType.EWMA_VAR),
-            dtype="float64",
-        )
    
-
-
-
    
-[docs]
-    def running_adjustment(
-        self: Self,
-        adjustment: float,
-        days_in_year: int = 365,
-    ) -> Self:
-        """Add or subtract a fee from the timeseries return.
-
-        Args:
-            adjustment: Fee to add or subtract.
-            days_in_year: The calculation divisor and assumed number of days in a
-                calendar year. Defaults to 365.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        if self.valuetype == ValueType.RTRN:
-            ra_df = self.tsdf.copy()
-            initial_value = 1.0
-            returns_input = True
-        else:
-            initial_value = cast("float", self.tsdf.iloc[0, 0])
-            ra_df = self.tsdf.ffill().pct_change()
-            returns_input = False
-        ra_df = ra_df.dropna()
-
-        dates_index = DatetimeIndex(ra_df.index)
-        dates_list = [self.first_idx] + [d.date() for d in dates_index]
-
-        dates_np = array(
-            [dt.datetime.combine(d, dt.time()) for d in dates_list],
-            dtype="datetime64[D]",
-        )
-        date_diffs = cast(
-            "NDArray[float64]",
-            diff(dates_np).astype("timedelta64[D]").astype(float64),
-        )
-
-        returns_array = cast(
-            "NDArray[float64]",
-            ra_df.iloc[:, 0].to_numpy(),
-        )
-
-        adjustment_factors = (
-            1.0 + returns_array + adjustment * date_diffs / days_in_year
-        )
-
-        values_array = cumprod(insert(adjustment_factors, 0, initial_value))
-        values = list(values_array)
-
-        self.tsdf = DataFrame(data=values, index=dates_list)
-        self.valuetype = ValueType.PRICE
-        self.tsdf.columns = MultiIndex.from_arrays(
-            [
-                [self.label],
-                [self.valuetype],
-            ],
-        )
-        self.tsdf.index = Index(DatetimeIndex(self.tsdf.index).date)
-        if returns_input:
-            self.value_to_ret()
-        return self
    
-
-
-
    
-[docs]
-    def set_new_label(
-        self: Self,
-        lvl_zero: str | None = None,
-        lvl_one: ValueType | None = None,
-        *,
-        delete_lvl_one: bool = False,
-    ) -> Self:
-        """Set the column labels of the .tsdf Pandas Dataframe.
-
-        Args:
-            lvl_zero: New level zero label. Optional.
-            lvl_one: New level one label. Optional.
-            delete_lvl_one: If True the level one label is deleted. Defaults to False.
-
-        Returns:
-            An OpenTimeSeries object.
-        """
-        if lvl_zero is None and lvl_one is None:
-            self.tsdf.columns = MultiIndex.from_arrays(
-                [[self.label], [self.valuetype]],
-            )
-        elif lvl_zero is not None and lvl_one is None:
-            self.tsdf.columns = MultiIndex.from_arrays([[lvl_zero], [self.valuetype]])
-            self.label = lvl_zero
-        elif lvl_zero is None and lvl_one is not None:
-            self.tsdf.columns = MultiIndex.from_arrays([[self.label], [lvl_one]])
-            self.valuetype = lvl_one
-        else:
-            self.tsdf.columns = MultiIndex.from_arrays([[lvl_zero], [lvl_one]])
-            self.label, self.valuetype = lvl_zero, cast("ValueType", lvl_one)
-        if delete_lvl_one:
-            self.tsdf.columns = self.tsdf.columns.get_level_values(0)
-        return self
    
-
-
-    def _returns_series(self: Self, *, squared: bool = False) -> Series[float]:
-        """Return demeaned return series for autocorrelation analysis."""
-        data: Series[float] = self.tsdf.iloc[:, 0]
-        return _demeaned_returns_for_autocorr(
-            series=data, valuetype=self.valuetype, squared=squared
-        )
-
-
    
-[docs]
-    def acf(
-        self: Self,
-        lags: int | list[int],
-        *,
-        squared: bool = False,
-    ) -> Series[float]:
-        """Calculate autocorrelation function for specified lags.
-
-        Args:
-            lags: If int, compute ACF from lag 0 to this value (inclusive).
-                If list, compute ACF at lag 0 plus each lag in the list.
-            squared: If True, compute ACF of squared returns. Defaults to False.
-
-        Returns:
-            Series of autocorrelations indexed by lag.
-        """
-        rets = self._returns_series(squared=squared)
-        if isinstance(lags, int):
-            lag_list = list(range(lags + 1))
-        else:
-            lag_list = sorted({0} | set(lags))
-        values: list[float] = []
-        for lag in lag_list:
-            if lag == 0:
-                values.append(1.0)
-            else:
-                values.append(float(rets.autocorr(lag=lag)))
-        return Series(
-            data=values,
-            index=lag_list,
-            name="ACF",
-            dtype="float64",
-        )
    
-
-
-
    
-[docs]
-    def partial_autocorr(self: Self, lag: int = 1, *, squared: bool = False) -> float:
-        """Calculate partial autocorrelation at a given lag.
-
-        Args:
-            lag: The lag at which to compute partial autocorrelation. Defaults to 1.
-            squared: If True, compute partial autocorrelation of squared returns.
-                Defaults to False.
-
-        Returns:
-            Partial autocorrelation at the specified lag.
-        """
-        pacf_series = self.pacf(lags=lag, squared=squared)
-        return float(pacf_series.loc[lag])
    
-
-
-
    
-[docs]
-    def pacf(
-        self: Self,
-        lags: int | list[int],
-        *,
-        squared: bool = False,
-    ) -> Series[float]:
-        """Calculate partial autocorrelation function for specified lags.
-
-        Args:
-            lags: If int, compute PACF from lag 0 to this value (inclusive).
-                If list, compute PACF at lag 0 plus each lag in the list.
-            squared: If True, compute PACF of squared returns. Defaults to False.
-
-        Returns:
-            Series of partial autocorrelations indexed by lag.
-        """
-        if isinstance(lags, int):
-            lag_list = list(range(lags + 1))
-        else:
-            lag_list = sorted({0} | set(lags))
-        max_lag = max(lag_list) if lag_list else 0
-        acf_vals = self.acf(lags=max_lag, squared=squared)
-        acf_arr = array([acf_vals.loc[k] for k in range(max_lag + 1)])
-        pacf_values: list[float] = [1.0]
-        phi: list[list[float]] = []
-        for k in range(1, max_lag + 1):
-            if k == 1:
-                phi_kk = acf_arr[1]
-            else:
-                numer = acf_arr[k]
-                denom = 1.0
-                for j in range(k - 1):
-                    numer -= phi[k - 2][j] * acf_arr[k - 1 - j]
-                    denom -= phi[k - 2][j] * acf_arr[j + 1]
-                phi_kk = numer / denom
-            phi_row = [0.0] * k
-            for j in range(k - 1):
-                phi_row[j] = phi[k - 2][j] - phi_kk * phi[k - 2][k - 2 - j]
-            phi_row[k - 1] = phi_kk
-            phi.append(phi_row)
-            pacf_values.append(phi_kk)
-        result = {lag: pacf_values[lag] for lag in lag_list}
-        return Series(
-            data=[result[lag] for lag in lag_list],
-            index=lag_list,
-            name="PACF",
-            dtype="float64",
-        )
    
-
-
-
    
-[docs]
-    def ljung_box(
-        self: Self,
-        lags: int | list[int],
-        *,
-        squared: bool = False,
-    ) -> tuple[float, float, list[int]]:
-        """Compute Ljung-Box test for autocorrelation.
-
-        Args:
-            lags: If int, use lags 1 through this value. If list, use the given
-                lags (lag 0 excluded from test).
-            squared: If True, test autocorrelation of squared returns.
-                Defaults to False.
-
-        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.
-        """
-        rets = self._returns_series(squared=squared)
-        n = len(rets)
-        if isinstance(lags, int):
-            lag_list = list(range(1, lags + 1))
-        else:
-            lag_list = sorted({k for k in lags if k > 0})
-        if not lag_list:
-            return 0.0, 1.0, []
-        r_k_sq_sum = 0.0
-        for k in lag_list:
-            if k < n:
-                r_k = float(rets.autocorr(lag=k))
-                r_k_sq_sum += r_k**2 / (n - k)
-        q_stat = n * (n + 2) * r_k_sq_sum
-        df = len(lag_list)
-        pval = float(1.0 - chi2.cdf(q_stat, df))
-        return q_stat, pval, lag_list
    
-
    
-
-
-
-
    
-[docs]
-def timeseries_chain(
-    front: TypeOpenTimeSeries,
-    back: TypeOpenTimeSeries,
-    old_fee: float = 0.0,
-) -> TypeOpenTimeSeries:
-    """Chain two timeseries together.
-
-    The function assumes that the two series have at least one date in common.
-
-    Args:
-        front: Earlier series to chain with.
-        back: Later series to chain with.
-        old_fee: Fee to apply to earlier series. Defaults to 0.0.
-
-    Returns:
-        An OpenTimeSeries object or a subclass thereof.
-    """
-    old = front.from_deepcopy()
-    old.running_adjustment(old_fee)
-    new = back.from_deepcopy()
-    idx = 0
-    first = new.tsdf.index[idx]
-
-    if old.last_idx < first:
-        msg = "Timeseries dates must overlap to allow them to be chained."
-        raise DateAlignmentError(msg)
-
-    while first not in old.tsdf.index:
-        idx += 1
-        first = new.tsdf.index[idx]
-        if first > old.tsdf.index[-1]:
-            msg = "Failed to find a matching date between series"
-            raise DateAlignmentError(msg)
-
-    dates: list[str] = [x.strftime("%Y-%m-%d") for x in old.tsdf.index if x < first]
-
-    old_values = Series(old.tsdf.iloc[: len(dates), 0])
-    old_values = old_values.mul(
-        Series(new.tsdf.iloc[:, 0]).loc[first]
-        / Series(old.tsdf.iloc[:, 0]).loc[first],
-    )
-    values = append(old_values, new.tsdf.iloc[:, 0])
-
-    dates.extend([x.strftime("%Y-%m-%d") for x in new.tsdf.index])
-
-    return back.__class__(
-        timeseries_id=new.timeseries_id,
-        instrument_id=new.instrument_id,
-        currency=new.currency,
-        dates=dates,
-        name=new.name,
-        label=new.name,
-        valuetype=new.valuetype,
-        values=list(values),
-        local_ccy=new.local_ccy,
-        tsdf=DataFrame(
-            data=values,
-            index=[d.date() for d in DatetimeIndex(dates)],
-            columns=[[new.label], [new.valuetype]],
-            dtype="float64",
-        ),
-    )
    
-
-
-
-def _check_if_none(item: object) -> bool:
-    """Check if a variable is None or equivalent.
-
-    Args:
-        item: Variable to be checked.
-
-    Returns:
-        Answer to whether the variable is None or equivalent.
-    """
-    if item is None:
-        return True
-
-    try:
-        return cast("bool", isnan(cast("float", item)))
-    except (TypeError, ValueError):
-        return len(str(item)) == 0
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/openseries/simulation.html b/docs/build/html/_modules/openseries/simulation.html deleted file mode 100644 index 6fb00d83..00000000 --- a/docs/build/html/_modules/openseries/simulation.html +++ /dev/null @@ -1,673 +0,0 @@ - - - - - - - - openseries.simulation — openseries 2.1.9 documentation - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -

    Source code for openseries.simulation

    -"""The ReturnSimulation class."""
-
-from __future__ import annotations
-
-from functools import cached_property
-from typing import TYPE_CHECKING, Self, TypedDict, cast
-
-try:
-    from typing import Unpack
-except ImportError:  # pragma: no cover
-    from typing_extensions import Unpack
-
-if TYPE_CHECKING:
-    import datetime as dt  # pragma: no cover
-
-from numpy import multiply, sqrt
-from numpy.random import PCG64, Generator, SeedSequence
-from pandas import (
-    DataFrame,
-    Index,
-    MultiIndex,
-    concat,
-)
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    NonNegativeFloat,
-    PositiveFloat,
-    PositiveInt,
-)
-
-from .datefixer import generate_calendar_date_range
-from .owntypes import (
-    CountriesType,
-    DaysInYearType,
-    ValueType,
-)
-
-__all__ = ["ReturnSimulation"]
-
-
-class _JumpParams(TypedDict, total=False):
-    """TypedDict for jump diffusion parameters."""
-
-    jumps_lamda: NonNegativeFloat
-    jumps_sigma: NonNegativeFloat
-    jumps_mu: float
-
-
-def _validate_ar1_coef(ar1_coef: float) -> None:
-    """Validate ar1_coef is in (-1, 1) for stationarity."""
-    if not -1.0 < ar1_coef < 1.0:
-        msg = f"ar1_coef must be in (-1, 1) for stationarity, got {ar1_coef}"
-        raise ValueError(msg)
-
-
-def _apply_ar1_filter(returns: DataFrame, ar1_coef: float) -> DataFrame:
-    """Apply AR(1) filter to returns to introduce lag-1 autocorrelation.
-
-    r_t = ar1_coef * r_{t-1} + sqrt(1 - ar1_coef**2) * innovation_t
-    Preserves mean and variance of the base process.
-
-    Args:
-        returns: DataFrame of shape (number_of_sims, trading_days).
-        ar1_coef: Lag-1 autocorrelation coefficient in (-1, 1).
-
-    Returns:
-        Filtered returns.
-    """
-    if ar1_coef == 0.0:
-        return returns
-    arr = returns.to_numpy(copy=True)
-    scale = sqrt(1.0 - ar1_coef * ar1_coef)
-    for t in range(1, arr.shape[1]):
-        arr[:, t] = ar1_coef * arr[:, t - 1] + scale * arr[:, t]
-    return DataFrame(data=arr, dtype="float64")
-
-
-def _random_generator(seed: int | None) -> Generator:
-    """Make a Numpy Random Generator object.
-
-    Args:
-        seed: Random seed.
-
-    Returns:
-        Numpy random process generator.
-    """
-    ss = SeedSequence(entropy=seed)
-    bg = PCG64(seed=cast("int | None", ss))
-    return Generator(bit_generator=bg)
-
-
-def _create_base_simulation(
-    cls: type[ReturnSimulation],
-    returns: DataFrame,
-    number_of_sims: PositiveInt,
-    trading_days: PositiveInt,
-    trading_days_in_year: DaysInYearType,
-    mean_annual_return: float,
-    mean_annual_vol: PositiveFloat,
-    seed: int | None = None,
-    **kwargs: Unpack[_JumpParams],
-) -> ReturnSimulation:
-    """Common logic for creating simulations.
-
-    Args:
-        cls: The ReturnSimulation class.
-        returns: The calculated returns data.
-        number_of_sims: Number of simulations to generate.
-        trading_days: Number of trading days to simulate.
-        trading_days_in_year: Number of trading days used to annualize.
-        mean_annual_return: Mean annual return.
-        mean_annual_vol: Mean annual volatility.
-        seed: Seed for random process initiation.
-        **kwargs: Additional keyword arguments for jump parameters.
-
-    Returns:
-        A ReturnSimulation instance.
-    """
-    return cls(
-        number_of_sims=number_of_sims,
-        trading_days=trading_days,
-        trading_days_in_year=trading_days_in_year,
-        mean_annual_return=mean_annual_return,
-        mean_annual_vol=mean_annual_vol,
-        dframe=returns,
-        seed=seed,
-        **kwargs,
-    )
-
-
-
    
-[docs]
-class ReturnSimulation(BaseModel):
-    """The class ReturnSimulation allows for simulating financial timeseries.
-
-    Args:
-        number_of_sims: Number of simulations to generate.
-        trading_days: Total number of days to simulate.
-        trading_days_in_year: Number of trading days used to annualize.
-        mean_annual_return: Mean annual return of the distribution.
-        mean_annual_vol: Mean annual standard deviation of the distribution.
-        dframe: Pandas DataFrame object holding the resulting values.
-        jumps_lamda: This is the probability of a jump happening at each point in time.
-            Defaults to 0.0.
-        jumps_sigma: This is the volatility of the jump size. Defaults to 0.0.
-        jumps_mu: This is the average jump size. Defaults to 0.0.
-        seed: 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 = 0.0
-    jumps_sigma: NonNegativeFloat = 0.0
-    jumps_mu: float = 0.0
-    seed: int | None = None
-
-    model_config = ConfigDict(
-        arbitrary_types_allowed=True,
-        validate_assignment=True,
-        revalidate_instances="always",
-    )
-
-
    
-[docs]
-    @cached_property
-    def results(self: Self) -> DataFrame:
-        """Simulation data.
-
-        Returns:
-            Simulation data.
-        """
-        return self.dframe.add(1.0).cumprod(axis="columns").T
    
-
-
-    @property
-    def realized_mean_return(self: Self) -> float:
-        """Annualized arithmetic mean of returns.
-
-        Returns:
-            Annualized arithmetic mean of returns.
-        """
-        return cast(
-            "float",
-            (
-                self.results.ffill().pct_change().mean() * self.trading_days_in_year
-            ).iloc[0],
-        )
-
-    @property
-    def realized_vol(self: Self) -> float:
-        """Annualized volatility.
-
-        Returns:
-            Annualized volatility.
-        """
-        return cast(
-            "float",
-            (
-                self.results.ffill().pct_change().std()
-                * sqrt(self.trading_days_in_year)
-            ).iloc[0],
-        )
-
-
    
-[docs]
-    @classmethod
-    def from_normal(
-        cls: type[ReturnSimulation],
-        number_of_sims: PositiveInt,
-        mean_annual_return: float,
-        mean_annual_vol: PositiveFloat,
-        trading_days: PositiveInt,
-        trading_days_in_year: DaysInYearType = 252,
-        seed: int | None = None,
-        randomizer: Generator | None = None,
-        ar1_coef: float = 0.0,
-    ) -> ReturnSimulation:
-        """Create a Normal distribution simulation.
-
-        Args:
-            number_of_sims: Number of simulations to generate.
-            trading_days: Number of trading days to simulate.
-            mean_annual_return: Mean return.
-            mean_annual_vol: Mean standard deviation.
-            trading_days_in_year: Number of trading days used to annualize.
-                Defaults to 252.
-            seed: Seed for random process initiation.
-            randomizer: Random process generator.
-            ar1_coef: Lag-1 autoregressive coefficient in (-1, 1) to induce
-                autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-        Returns:
-            Normal distribution simulation.
-        """
-        _validate_ar1_coef(ar1_coef)
-        if not randomizer:
-            randomizer = _random_generator(seed=seed)
-
-        returns_df = DataFrame(
-            data=randomizer.normal(
-                loc=mean_annual_return / trading_days_in_year,
-                scale=mean_annual_vol / sqrt(trading_days_in_year),
-                size=(number_of_sims, trading_days),
-            ),
-            dtype="float64",
-        )
-        returns = _apply_ar1_filter(returns_df, ar1_coef)
-
-        return _create_base_simulation(
-            cls=cls,
-            returns=returns,
-            number_of_sims=number_of_sims,
-            trading_days=trading_days,
-            trading_days_in_year=trading_days_in_year,
-            mean_annual_return=mean_annual_return,
-            mean_annual_vol=mean_annual_vol,
-            seed=seed,
-        )
    
-
-
-
    
-[docs]
-    @classmethod
-    def from_lognormal(
-        cls: type[ReturnSimulation],
-        number_of_sims: PositiveInt,
-        mean_annual_return: float,
-        mean_annual_vol: PositiveFloat,
-        trading_days: PositiveInt,
-        trading_days_in_year: DaysInYearType = 252,
-        seed: int | None = None,
-        randomizer: Generator | None = None,
-        ar1_coef: float = 0.0,
-    ) -> ReturnSimulation:
-        """Create a Lognormal distribution simulation.
-
-        Args:
-            number_of_sims: Number of simulations to generate.
-            trading_days: Number of trading days to simulate.
-            mean_annual_return: Mean return.
-            mean_annual_vol: Mean standard deviation.
-            trading_days_in_year: Number of trading days used to annualize.
-                Defaults to 252.
-            seed: Seed for random process initiation.
-            randomizer: Random process generator.
-            ar1_coef: Lag-1 autoregressive coefficient in (-1, 1) to induce
-                autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-        Returns:
-            Lognormal distribution simulation.
-        """
-        _validate_ar1_coef(ar1_coef)
-        if not randomizer:
-            randomizer = _random_generator(seed=seed)
-
-        returns_df = DataFrame(
-            data=(
-                randomizer.lognormal(
-                    mean=mean_annual_return / trading_days_in_year,
-                    sigma=mean_annual_vol / sqrt(trading_days_in_year),
-                    size=(number_of_sims, trading_days),
-                )
-                - 1
-            ),
-            dtype="float64",
-        )
-        returns = _apply_ar1_filter(returns_df, ar1_coef)
-
-        return _create_base_simulation(
-            cls=cls,
-            returns=returns,
-            number_of_sims=number_of_sims,
-            trading_days=trading_days,
-            trading_days_in_year=trading_days_in_year,
-            mean_annual_return=mean_annual_return,
-            mean_annual_vol=mean_annual_vol,
-            seed=seed,
-        )
    
-
-
-
    
-[docs]
-    @classmethod
-    def from_gbm(
-        cls: type[ReturnSimulation],
-        number_of_sims: PositiveInt,
-        mean_annual_return: float,
-        mean_annual_vol: PositiveFloat,
-        trading_days: PositiveInt,
-        trading_days_in_year: DaysInYearType = 252,
-        seed: int | None = None,
-        randomizer: Generator | None = None,
-        ar1_coef: float = 0.0,
-    ) -> ReturnSimulation:
-        """Create a Geometric Brownian Motion simulation.
-
-        Args:
-            number_of_sims: Number of simulations to generate.
-            trading_days: Number of trading days to simulate.
-            mean_annual_return: Mean return.
-            mean_annual_vol: Mean standard deviation.
-            trading_days_in_year: Number of trading days used to annualize.
-                Defaults to 252.
-            seed: Seed for random process initiation.
-            randomizer: Random process generator.
-            ar1_coef: Lag-1 autoregressive coefficient in (-1, 1) to induce
-                autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-        Returns:
-            Geometric Brownian Motion simulation.
-        """
-        _validate_ar1_coef(ar1_coef)
-        if not randomizer:
-            randomizer = _random_generator(seed=seed)
-
-        drift = (mean_annual_return - 0.5 * mean_annual_vol**2.0) * (
-            1.0 / trading_days_in_year
-        )
-
-        normal_mean = 0.0
-        wiener = randomizer.normal(
-            loc=normal_mean,
-            scale=sqrt(1.0 / trading_days_in_year) * mean_annual_vol,
-            size=(number_of_sims, trading_days),
-        )
-
-        returns_df = DataFrame(data=drift + wiener, dtype="float64")
-        returns = _apply_ar1_filter(returns_df, ar1_coef)
-
-        return _create_base_simulation(
-            cls=cls,
-            returns=returns,
-            number_of_sims=number_of_sims,
-            trading_days=trading_days,
-            trading_days_in_year=trading_days_in_year,
-            mean_annual_return=mean_annual_return,
-            mean_annual_vol=mean_annual_vol,
-            seed=seed,
-        )
    
-
-
-
    
-[docs]
-    @classmethod
-    def from_merton_jump_gbm(
-        cls: type[ReturnSimulation],
-        number_of_sims: PositiveInt,
-        trading_days: PositiveInt,
-        mean_annual_return: float,
-        mean_annual_vol: PositiveFloat,
-        jumps_lamda: NonNegativeFloat,
-        jumps_sigma: NonNegativeFloat = 0.0,
-        jumps_mu: float = 0.0,
-        trading_days_in_year: DaysInYearType = 252,
-        seed: int | None = None,
-        randomizer: Generator | None = None,
-        ar1_coef: float = 0.0,
-    ) -> ReturnSimulation:
-        """Create a Merton Jump-Diffusion model simulation.
-
-        Args:
-            number_of_sims: Number of simulations to generate.
-            trading_days: Number of trading days to simulate.
-            mean_annual_return: Mean return.
-            mean_annual_vol: Mean standard deviation.
-            jumps_lamda: This is the probability of a jump happening at each point
-                in time.
-            jumps_sigma: This is the volatility of the jump size. Defaults to 0.0.
-            jumps_mu: This is the average jump size. Defaults to 0.0.
-            trading_days_in_year: Number of trading days used to annualize.
-                Defaults to 252.
-            seed: Seed for random process initiation.
-            randomizer: Random process generator.
-            ar1_coef: Lag-1 autoregressive coefficient in (-1, 1) to induce
-                autocorrelation. Defaults to 0.0 (i.i.d. returns).
-
-        Returns:
-            Merton Jump-Diffusion model simulation.
-        """
-        _validate_ar1_coef(ar1_coef)
-        if not randomizer:
-            randomizer = _random_generator(seed=seed)
-
-        normal_mean = 0.0
-        wiener = randomizer.normal(
-            loc=normal_mean,
-            scale=sqrt(1.0 / trading_days_in_year) * mean_annual_vol,
-            size=(number_of_sims, trading_days),
-        )
-
-        poisson_jumps = multiply(
-            randomizer.poisson(
-                lam=jumps_lamda * (1.0 / trading_days_in_year),
-                size=(number_of_sims, trading_days),
-            ),
-            randomizer.normal(
-                loc=jumps_mu,
-                scale=jumps_sigma,
-                size=(number_of_sims, trading_days),
-            ),
-        )
-
-        drift = (
-            mean_annual_return
-            - 0.5 * mean_annual_vol**2.0
-            - jumps_lamda * (jumps_mu + jumps_sigma**2.0)
-        ) * (1.0 / trading_days_in_year)
-
-        raw_returns = poisson_jumps + drift + wiener
-        raw_returns[:, 0] = 0.0
-
-        returns_df = DataFrame(data=raw_returns, dtype="float64")
-        returns = _apply_ar1_filter(returns_df, ar1_coef)
-
-        return _create_base_simulation(
-            cls=cls,
-            returns=returns,
-            number_of_sims=number_of_sims,
-            trading_days=trading_days,
-            trading_days_in_year=trading_days_in_year,
-            mean_annual_return=mean_annual_return,
-            mean_annual_vol=mean_annual_vol,
-            seed=seed,
-            jumps_lamda=jumps_lamda,
-            jumps_sigma=jumps_sigma,
-            jumps_mu=jumps_mu,
-        )
    
-
-
-
    
-[docs]
-    def to_dataframe(
-        self: Self,
-        name: str,
-        start: dt.date | None = None,
-        end: dt.date | None = None,
-        countries: CountriesType = "SE",
-        markets: list[str] | str | None = None,
-    ) -> DataFrame:
-        """Create a pandas.DataFrame from simulation(s).
-
-        Args:
-            name: Name label of the serie(s).
-            start: Date when the simulation starts.
-            end: Date when the simulation ends.
-            countries: (List of) country code(s) according to ISO 3166-1 alpha-2.
-                Defaults to "SE".
-            markets: (List of) markets code(s) supported by exchange_calendars.
-
-        Returns:
-            The simulation(s) data.
-        """
-        d_range = generate_calendar_date_range(
-            trading_days=self.trading_days,
-            start=start,
-            end=end,
-            countries=countries,
-            markets=markets,
-        )
-
-        if self.number_of_sims == 1:
-            sdf = self.dframe.iloc[0].T.to_frame()
-            sdf.index = Index(d_range)
-            sdf.columns = MultiIndex.from_arrays(
-                [
-                    [name],
-                    [ValueType.RTRN],
-                ],
-            )
-            return sdf
-
-        df_list = [
-            DataFrame(
-                data=self.dframe.iloc[item].values,
-                index=Index(d_range),
-                columns=MultiIndex.from_arrays(
-                    [
-                        [f"{name}_{item}"],
-                        [ValueType.RTRN],
-                    ],
-                ),
-            )
-            for item in range(self.number_of_sims)
-        ]
-        return concat(df_list, axis="columns", sort=True)
    
-
    
-
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright Captor Fund Management AB.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/docs/build/html/_sources/api/datefixer.rst.txt b/docs/build/html/_sources/api/datefixer.rst.txt deleted file mode 100644 index 53e8ae02..00000000 --- a/docs/build/html/_sources/api/datefixer.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -Date Utilities -============== - -.. currentmodule:: openseries.datefixer - -Date Handling Functions ------------------------ - -.. autofunction:: date_fix - :no-index: -.. autofunction:: date_offset_foll - :no-index: -.. autofunction:: generate_calendar_date_range - :no-index: -.. autofunction:: get_previous_business_day_before_today - :no-index: -.. autofunction:: holiday_calendar - :no-index: -.. autofunction:: offset_business_days - :no-index: - -The datefixer module provides utilities for handling business days, holidays, and date calculations commonly needed in financial analysis. diff --git a/docs/build/html/_sources/api/frame.rst.txt b/docs/build/html/_sources/api/frame.rst.txt deleted file mode 100644 index a644139c..00000000 --- a/docs/build/html/_sources/api/frame.rst.txt +++ /dev/null @@ -1,289 +0,0 @@ -OpenFrame -========= - -.. currentmodule:: openseries - -.. autoclass:: OpenFrame - :undoc-members: - :show-inheritance: - :no-index: - -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 ------------------------------- - -.. automethod:: OpenFrame.from_deepcopy - :no-index: - -Properties ----------- - -Frame-specific Properties -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenFrame.constituents - :no-index: -.. autoattribute:: OpenFrame.columns_lvl_zero - :no-index: -.. autoattribute:: OpenFrame.columns_lvl_one - :no-index: -.. autoattribute:: OpenFrame.item_count - :no-index: -.. autoattribute:: OpenFrame.weights - :no-index: -.. autoattribute:: OpenFrame.first_indices - :no-index: -.. autoattribute:: OpenFrame.last_indices - :no-index: -.. autoattribute:: OpenFrame.lengths_of_items - :no-index: -.. autoattribute:: OpenFrame.span_of_days_all - :no-index: - -Common Properties -~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenFrame.first_idx - :no-index: -.. autoattribute:: OpenFrame.last_idx - :no-index: -.. autoattribute:: OpenFrame.length - :no-index: -.. autoattribute:: OpenFrame.span_of_days - :no-index: -.. autoattribute:: OpenFrame.tsdf - :no-index: -.. autoattribute:: OpenFrame.max_drawdown_date - :no-index: -.. autoattribute:: OpenFrame.periods_in_a_year - :no-index: -.. autoattribute:: OpenFrame.yearfrac - :no-index: - -Financial Metrics -~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenFrame.all_properties - :no-index: -.. autoattribute:: OpenFrame.arithmetic_ret - :no-index: -.. autoattribute:: OpenFrame.geo_ret - :no-index: -.. autoattribute:: OpenFrame.value_ret - :no-index: -.. autoattribute:: OpenFrame.vol - :no-index: -.. autoattribute:: OpenFrame.downside_deviation - :no-index: -.. autoattribute:: OpenFrame.ret_vol_ratio - :no-index: -.. autoattribute:: OpenFrame.sortino_ratio - :no-index: -.. autoattribute:: OpenFrame.kappa3_ratio - :no-index: -.. autoattribute:: OpenFrame.omega_ratio - :no-index: -.. autoattribute:: OpenFrame.var_down - :no-index: -.. autoattribute:: OpenFrame.cvar_down - :no-index: -.. autoattribute:: OpenFrame.worst - :no-index: -.. autoattribute:: OpenFrame.worst_month - :no-index: -.. autoattribute:: OpenFrame.max_drawdown - :no-index: -.. autoattribute:: OpenFrame.max_drawdown_cal_year - :no-index: -.. autoattribute:: OpenFrame.positive_share - :no-index: -.. autoattribute:: OpenFrame.vol_from_var - :no-index: -.. autoattribute:: OpenFrame.autocorr - :no-index: -.. autoattribute:: OpenFrame.skew - :no-index: -.. autoattribute:: OpenFrame.kurtosis - :no-index: -.. autoattribute:: OpenFrame.z_score - :no-index: - -Methods -------- - -Frame Management -~~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.merge_series - :no-index: -.. automethod:: OpenFrame.trunc_frame - :no-index: -.. automethod:: OpenFrame.add_timeseries - :no-index: -.. automethod:: OpenFrame.delete_timeseries - :no-index: - -Portfolio Analysis -~~~~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.relative - :no-index: -.. automethod:: OpenFrame.make_portfolio - :no-index: -.. automethod:: OpenFrame.rebalanced_portfolio - :no-index: - -Statistical Analysis -~~~~~~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.ord_least_squares_fit - :no-index: -.. automethod:: OpenFrame.beta - :no-index: -.. automethod:: OpenFrame.jensen_alpha - :no-index: -.. automethod:: OpenFrame.tracking_error_func - :no-index: -.. automethod:: OpenFrame.info_ratio_func - :no-index: -.. automethod:: OpenFrame.capture_ratio_func - :no-index: -.. automethod:: OpenFrame.multi_factor_linear_regression - :no-index: - -Rolling Analysis -~~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.rolling_info_ratio - :no-index: -.. automethod:: OpenFrame.rolling_beta - :no-index: -.. automethod:: OpenFrame.rolling_corr - :no-index: -.. automethod:: OpenFrame.rolling_return - :no-index: -.. automethod:: OpenFrame.rolling_vol - :no-index: -.. automethod:: OpenFrame.rolling_var_down - :no-index: -.. automethod:: OpenFrame.rolling_cvar_down - :no-index: - -Correlation and Risk -~~~~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenFrame.correl_matrix - :no-index: -.. automethod:: OpenFrame.ewma_risk - :no-index: - -Data Manipulation -~~~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.align_index_to_local_cdays - :no-index: -.. automethod:: OpenFrame.resample - :no-index: -.. automethod:: OpenFrame.resample_to_business_period_ends - :no-index: -.. automethod:: OpenFrame.value_nan_handle - :no-index: -.. automethod:: OpenFrame.return_nan_handle - :no-index: - -Transformations -~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.to_cumret - :no-index: -.. automethod:: OpenFrame.value_to_ret - :no-index: -.. automethod:: OpenFrame.value_to_diff - :no-index: -.. automethod:: OpenFrame.value_to_log - :no-index: -.. automethod:: OpenFrame.to_drawdown_series - :no-index: -.. automethod:: OpenFrame.value_ret_calendar_period - :no-index: - -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. - -.. automethod:: OpenFrame.autocorr_func - :no-index: -.. automethod:: OpenFrame.calc_range - :no-index: -.. automethod:: OpenFrame.outliers - :no-index: - -Financial Metrics Methods -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.arithmetic_ret_func - :no-index: -.. automethod:: OpenFrame.geo_ret_func - :no-index: -.. automethod:: OpenFrame.value_ret_func - :no-index: -.. automethod:: OpenFrame.vol_func - :no-index: -.. automethod:: OpenFrame.lower_partial_moment_func - :no-index: -.. automethod:: OpenFrame.ret_vol_ratio_func - :no-index: -.. automethod:: OpenFrame.sortino_ratio_func - :no-index: -.. automethod:: OpenFrame.omega_ratio_func - :no-index: -.. automethod:: OpenFrame.var_down_func - :no-index: -.. automethod:: OpenFrame.cvar_down_func - :no-index: -.. automethod:: OpenFrame.worst_func - :no-index: -.. automethod:: OpenFrame.max_drawdown_func - :no-index: -.. automethod:: OpenFrame.positive_share_func - :no-index: -.. automethod:: OpenFrame.vol_from_var_func - :no-index: -.. automethod:: OpenFrame.skew_func - :no-index: -.. automethod:: OpenFrame.kurtosis_func - :no-index: -.. automethod:: OpenFrame.z_score_func - :no-index: -.. automethod:: OpenFrame.target_weight_from_var - :no-index: - -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. - -.. automethod:: OpenFrame.plot_series - :no-index: -.. automethod:: OpenFrame.plot_bars - :no-index: -.. automethod:: OpenFrame.plot_histogram - :no-index: - -Export Methods -~~~~~~~~~~~~~~ - -.. automethod:: OpenFrame.to_json - :no-index: -.. automethod:: OpenFrame.to_xlsx - :no-index: diff --git a/docs/build/html/_sources/api/generated/openseries.OpenFrame.rst.txt b/docs/build/html/_sources/api/generated/openseries.OpenFrame.rst.txt deleted file mode 100644 index 2639c005..00000000 --- a/docs/build/html/_sources/api/generated/openseries.OpenFrame.rst.txt +++ /dev/null @@ -1,156 +0,0 @@ -openseries.OpenFrame -==================== - -.. currentmodule:: openseries - -.. autoclass:: OpenFrame - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~OpenFrame.__init__ - ~OpenFrame.add_timeseries - ~OpenFrame.align_index_to_local_cdays - ~OpenFrame.all_properties - ~OpenFrame.arithmetic_ret_func - ~OpenFrame.autocorr_func - ~OpenFrame.beta - ~OpenFrame.calc_range - ~OpenFrame.capture_ratio_func - ~OpenFrame.construct - ~OpenFrame.copy - ~OpenFrame.cvar_down_func - ~OpenFrame.delete_timeseries - ~OpenFrame.dict - ~OpenFrame.ewma_risk - ~OpenFrame.from_deepcopy - ~OpenFrame.from_orm - ~OpenFrame.geo_ret_func - ~OpenFrame.info_ratio_func - ~OpenFrame.jensen_alpha - ~OpenFrame.json - ~OpenFrame.kurtosis_func - ~OpenFrame.lower_partial_moment_func - ~OpenFrame.make_portfolio - ~OpenFrame.max_drawdown_func - ~OpenFrame.merge_series - ~OpenFrame.model_construct - ~OpenFrame.model_copy - ~OpenFrame.model_dump - ~OpenFrame.model_dump_json - ~OpenFrame.model_json_schema - ~OpenFrame.model_parametrized_name - ~OpenFrame.model_post_init - ~OpenFrame.model_rebuild - ~OpenFrame.model_validate - ~OpenFrame.model_validate_json - ~OpenFrame.model_validate_strings - ~OpenFrame.multi_factor_linear_regression - ~OpenFrame.omega_ratio_func - ~OpenFrame.ord_least_squares_fit - ~OpenFrame.outliers - ~OpenFrame.parse_file - ~OpenFrame.parse_obj - ~OpenFrame.parse_raw - ~OpenFrame.plot_bars - ~OpenFrame.plot_histogram - ~OpenFrame.plot_series - ~OpenFrame.positive_share_func - ~OpenFrame.rebalanced_portfolio - ~OpenFrame.relative - ~OpenFrame.resample - ~OpenFrame.resample_to_business_period_ends - ~OpenFrame.ret_vol_ratio_func - ~OpenFrame.return_nan_handle - ~OpenFrame.rolling_beta - ~OpenFrame.rolling_corr - ~OpenFrame.rolling_cvar_down - ~OpenFrame.rolling_info_ratio - ~OpenFrame.rolling_return - ~OpenFrame.rolling_var_down - ~OpenFrame.rolling_vol - ~OpenFrame.schema - ~OpenFrame.schema_json - ~OpenFrame.skew_func - ~OpenFrame.sortino_ratio_func - ~OpenFrame.target_weight_from_var - ~OpenFrame.to_cumret - ~OpenFrame.to_drawdown_series - ~OpenFrame.to_json - ~OpenFrame.to_xlsx - ~OpenFrame.tracking_error_func - ~OpenFrame.trunc_frame - ~OpenFrame.update_forward_refs - ~OpenFrame.validate - ~OpenFrame.value_nan_handle - ~OpenFrame.value_ret_calendar_period - ~OpenFrame.value_ret_func - ~OpenFrame.value_to_diff - ~OpenFrame.value_to_log - ~OpenFrame.value_to_ret - ~OpenFrame.var_down_func - ~OpenFrame.vol_from_var_func - ~OpenFrame.vol_func - ~OpenFrame.worst_func - ~OpenFrame.z_score_func - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~OpenFrame.arithmetic_ret - ~OpenFrame.autocorr - ~OpenFrame.columns_lvl_one - ~OpenFrame.columns_lvl_zero - ~OpenFrame.correl_matrix - ~OpenFrame.cvar_down - ~OpenFrame.downside_deviation - ~OpenFrame.first_idx - ~OpenFrame.first_indices - ~OpenFrame.geo_ret - ~OpenFrame.item_count - ~OpenFrame.kappa3_ratio - ~OpenFrame.kurtosis - ~OpenFrame.last_idx - ~OpenFrame.last_indices - ~OpenFrame.length - ~OpenFrame.lengths_of_items - ~OpenFrame.max_drawdown - ~OpenFrame.max_drawdown_cal_year - ~OpenFrame.max_drawdown_date - ~OpenFrame.model_computed_fields - ~OpenFrame.model_config - ~OpenFrame.model_extra - ~OpenFrame.model_fields - ~OpenFrame.model_fields_set - ~OpenFrame.omega_ratio - ~OpenFrame.periods_in_a_year - ~OpenFrame.positive_share - ~OpenFrame.ret_vol_ratio - ~OpenFrame.skew - ~OpenFrame.sortino_ratio - ~OpenFrame.span_of_days - ~OpenFrame.span_of_days_all - ~OpenFrame.value_ret - ~OpenFrame.var_down - ~OpenFrame.vol - ~OpenFrame.vol_from_var - ~OpenFrame.worst - ~OpenFrame.worst_month - ~OpenFrame.yearfrac - ~OpenFrame.z_score - ~OpenFrame.constituents - ~OpenFrame.weights - ~OpenFrame.markets - ~OpenFrame.tsdf - - \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.OpenTimeSeries.rst.txt b/docs/build/html/_sources/api/generated/openseries.OpenTimeSeries.rst.txt deleted file mode 100644 index 1c3dbc26..00000000 --- a/docs/build/html/_sources/api/generated/openseries.OpenTimeSeries.rst.txt +++ /dev/null @@ -1,155 +0,0 @@ -openseries.OpenTimeSeries -========================= - -.. currentmodule:: openseries - -.. autoclass:: OpenTimeSeries - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~OpenTimeSeries.__init__ - ~OpenTimeSeries.acf - ~OpenTimeSeries.align_index_to_local_cdays - ~OpenTimeSeries.all_properties - ~OpenTimeSeries.arithmetic_ret_func - ~OpenTimeSeries.autocorr_func - ~OpenTimeSeries.calc_range - ~OpenTimeSeries.construct - ~OpenTimeSeries.copy - ~OpenTimeSeries.cvar_down_func - ~OpenTimeSeries.dict - ~OpenTimeSeries.ewma_var_func - ~OpenTimeSeries.ewma_vol_func - ~OpenTimeSeries.from_1d_rate_to_cumret - ~OpenTimeSeries.from_arrays - ~OpenTimeSeries.from_deepcopy - ~OpenTimeSeries.from_df - ~OpenTimeSeries.from_fixed_rate - ~OpenTimeSeries.from_orm - ~OpenTimeSeries.geo_ret_func - ~OpenTimeSeries.json - ~OpenTimeSeries.kurtosis_func - ~OpenTimeSeries.ljung_box - ~OpenTimeSeries.lower_partial_moment_func - ~OpenTimeSeries.max_drawdown_func - ~OpenTimeSeries.model_construct - ~OpenTimeSeries.model_copy - ~OpenTimeSeries.model_dump - ~OpenTimeSeries.model_dump_json - ~OpenTimeSeries.model_json_schema - ~OpenTimeSeries.model_parametrized_name - ~OpenTimeSeries.model_post_init - ~OpenTimeSeries.model_rebuild - ~OpenTimeSeries.model_validate - ~OpenTimeSeries.model_validate_json - ~OpenTimeSeries.model_validate_strings - ~OpenTimeSeries.omega_ratio_func - ~OpenTimeSeries.outliers - ~OpenTimeSeries.pacf - ~OpenTimeSeries.pandas_df - ~OpenTimeSeries.parse_file - ~OpenTimeSeries.parse_obj - ~OpenTimeSeries.parse_raw - ~OpenTimeSeries.partial_autocorr - ~OpenTimeSeries.plot_bars - ~OpenTimeSeries.plot_histogram - ~OpenTimeSeries.plot_series - ~OpenTimeSeries.positive_share_func - ~OpenTimeSeries.resample - ~OpenTimeSeries.resample_to_business_period_ends - ~OpenTimeSeries.ret_vol_ratio_func - ~OpenTimeSeries.return_nan_handle - ~OpenTimeSeries.rolling_cvar_down - ~OpenTimeSeries.rolling_return - ~OpenTimeSeries.rolling_var_down - ~OpenTimeSeries.rolling_vol - ~OpenTimeSeries.running_adjustment - ~OpenTimeSeries.schema - ~OpenTimeSeries.schema_json - ~OpenTimeSeries.set_new_label - ~OpenTimeSeries.skew_func - ~OpenTimeSeries.sortino_ratio_func - ~OpenTimeSeries.target_weight_from_var - ~OpenTimeSeries.to_cumret - ~OpenTimeSeries.to_drawdown_series - ~OpenTimeSeries.to_json - ~OpenTimeSeries.to_xlsx - ~OpenTimeSeries.update_forward_refs - ~OpenTimeSeries.validate - ~OpenTimeSeries.value_nan_handle - ~OpenTimeSeries.value_ret_calendar_period - ~OpenTimeSeries.value_ret_func - ~OpenTimeSeries.value_to_diff - ~OpenTimeSeries.value_to_log - ~OpenTimeSeries.value_to_ret - ~OpenTimeSeries.var_down_func - ~OpenTimeSeries.vol_from_var_func - ~OpenTimeSeries.vol_func - ~OpenTimeSeries.worst_func - ~OpenTimeSeries.z_score_func - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~OpenTimeSeries.arithmetic_ret - ~OpenTimeSeries.autocorr - ~OpenTimeSeries.cvar_down - ~OpenTimeSeries.downside_deviation - ~OpenTimeSeries.first_idx - ~OpenTimeSeries.geo_ret - ~OpenTimeSeries.kappa3_ratio - ~OpenTimeSeries.kurtosis - ~OpenTimeSeries.last_idx - ~OpenTimeSeries.length - ~OpenTimeSeries.max_drawdown - ~OpenTimeSeries.max_drawdown_cal_year - ~OpenTimeSeries.max_drawdown_date - ~OpenTimeSeries.model_computed_fields - ~OpenTimeSeries.model_config - ~OpenTimeSeries.model_extra - ~OpenTimeSeries.model_fields - ~OpenTimeSeries.model_fields_set - ~OpenTimeSeries.omega_ratio - ~OpenTimeSeries.periods_in_a_year - ~OpenTimeSeries.positive_share - ~OpenTimeSeries.ret_vol_ratio - ~OpenTimeSeries.skew - ~OpenTimeSeries.sortino_ratio - ~OpenTimeSeries.span_of_days - ~OpenTimeSeries.value_ret - ~OpenTimeSeries.var_down - ~OpenTimeSeries.vol - ~OpenTimeSeries.vol_from_var - ~OpenTimeSeries.worst - ~OpenTimeSeries.worst_month - ~OpenTimeSeries.yearfrac - ~OpenTimeSeries.z_score - ~OpenTimeSeries.timeseries_id - ~OpenTimeSeries.instrument_id - ~OpenTimeSeries.name - ~OpenTimeSeries.valuetype - ~OpenTimeSeries.dates - ~OpenTimeSeries.values - ~OpenTimeSeries.local_ccy - ~OpenTimeSeries.tsdf - ~OpenTimeSeries.currency - ~OpenTimeSeries.domestic - ~OpenTimeSeries.countries - ~OpenTimeSeries.isin - ~OpenTimeSeries.label - ~OpenTimeSeries.constituents - ~OpenTimeSeries.weights - ~OpenTimeSeries.markets - - \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.ReturnSimulation.rst.txt b/docs/build/html/_sources/api/generated/openseries.ReturnSimulation.rst.txt deleted file mode 100644 index e616fb7e..00000000 --- a/docs/build/html/_sources/api/generated/openseries.ReturnSimulation.rst.txt +++ /dev/null @@ -1,73 +0,0 @@ -openseries.ReturnSimulation -=========================== - -.. currentmodule:: openseries - -.. autoclass:: ReturnSimulation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ReturnSimulation.__init__ - ~ReturnSimulation.construct - ~ReturnSimulation.copy - ~ReturnSimulation.dict - ~ReturnSimulation.from_gbm - ~ReturnSimulation.from_lognormal - ~ReturnSimulation.from_merton_jump_gbm - ~ReturnSimulation.from_normal - ~ReturnSimulation.from_orm - ~ReturnSimulation.json - ~ReturnSimulation.model_construct - ~ReturnSimulation.model_copy - ~ReturnSimulation.model_dump - ~ReturnSimulation.model_dump_json - ~ReturnSimulation.model_json_schema - ~ReturnSimulation.model_parametrized_name - ~ReturnSimulation.model_post_init - ~ReturnSimulation.model_rebuild - ~ReturnSimulation.model_validate - ~ReturnSimulation.model_validate_json - ~ReturnSimulation.model_validate_strings - ~ReturnSimulation.parse_file - ~ReturnSimulation.parse_obj - ~ReturnSimulation.parse_raw - ~ReturnSimulation.schema - ~ReturnSimulation.schema_json - ~ReturnSimulation.to_dataframe - ~ReturnSimulation.update_forward_refs - ~ReturnSimulation.validate - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~ReturnSimulation.model_computed_fields - ~ReturnSimulation.model_config - ~ReturnSimulation.model_extra - ~ReturnSimulation.model_fields - ~ReturnSimulation.model_fields_set - ~ReturnSimulation.realized_mean_return - ~ReturnSimulation.realized_vol - ~ReturnSimulation.results - ~ReturnSimulation.number_of_sims - ~ReturnSimulation.trading_days - ~ReturnSimulation.trading_days_in_year - ~ReturnSimulation.mean_annual_return - ~ReturnSimulation.mean_annual_vol - ~ReturnSimulation.dframe - ~ReturnSimulation.jumps_lamda - ~ReturnSimulation.jumps_sigma - ~ReturnSimulation.jumps_mu - ~ReturnSimulation.seed - - \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.ValueType.rst.txt b/docs/build/html/_sources/api/generated/openseries.ValueType.rst.txt deleted file mode 100644 index fd1cfe87..00000000 --- a/docs/build/html/_sources/api/generated/openseries.ValueType.rst.txt +++ /dev/null @@ -1,86 +0,0 @@ -openseries.ValueType -==================== - -.. currentmodule:: openseries - -.. autoclass:: ValueType - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ValueType.encode - ~ValueType.replace - ~ValueType.split - ~ValueType.rsplit - ~ValueType.join - ~ValueType.capitalize - ~ValueType.casefold - ~ValueType.title - ~ValueType.center - ~ValueType.count - ~ValueType.expandtabs - ~ValueType.find - ~ValueType.partition - ~ValueType.index - ~ValueType.ljust - ~ValueType.lower - ~ValueType.lstrip - ~ValueType.rfind - ~ValueType.rindex - ~ValueType.rjust - ~ValueType.rstrip - ~ValueType.rpartition - ~ValueType.splitlines - ~ValueType.strip - ~ValueType.swapcase - ~ValueType.translate - ~ValueType.upper - ~ValueType.startswith - ~ValueType.endswith - ~ValueType.removeprefix - ~ValueType.removesuffix - ~ValueType.isascii - ~ValueType.islower - ~ValueType.isupper - ~ValueType.istitle - ~ValueType.isspace - ~ValueType.isdecimal - ~ValueType.isdigit - ~ValueType.isnumeric - ~ValueType.isalpha - ~ValueType.isalnum - ~ValueType.isidentifier - ~ValueType.isprintable - ~ValueType.zfill - ~ValueType.format - ~ValueType.format_map - ~ValueType.maketrans - ~ValueType.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~ValueType.EWMA_VOL - ~ValueType.EWMA_VAR - ~ValueType.PRICE - ~ValueType.RTRN - ~ValueType.RELRTRN - ~ValueType.ROLLBETA - ~ValueType.ROLLCORR - ~ValueType.ROLLCVAR - ~ValueType.ROLLINFORATIO - ~ValueType.ROLLRTRN - ~ValueType.ROLLVAR - ~ValueType.ROLLVOL - - \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.constrain_optimized_portfolios.rst.txt b/docs/build/html/_sources/api/generated/openseries.constrain_optimized_portfolios.rst.txt deleted file mode 100644 index 8ac01906..00000000 --- a/docs/build/html/_sources/api/generated/openseries.constrain_optimized_portfolios.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.constrain\_optimized\_portfolios -=========================================== - -.. currentmodule:: openseries - -.. autofunction:: constrain_optimized_portfolios \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.date_fix.rst.txt b/docs/build/html/_sources/api/generated/openseries.date_fix.rst.txt deleted file mode 100644 index 997c00c9..00000000 --- a/docs/build/html/_sources/api/generated/openseries.date_fix.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.date\_fix -==================== - -.. currentmodule:: openseries - -.. autofunction:: date_fix \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.date_offset_foll.rst.txt b/docs/build/html/_sources/api/generated/openseries.date_offset_foll.rst.txt deleted file mode 100644 index e69616f9..00000000 --- a/docs/build/html/_sources/api/generated/openseries.date_offset_foll.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.date\_offset\_foll -============================= - -.. currentmodule:: openseries - -.. autofunction:: date_offset_foll \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.efficient_frontier.rst.txt b/docs/build/html/_sources/api/generated/openseries.efficient_frontier.rst.txt deleted file mode 100644 index 8983301e..00000000 --- a/docs/build/html/_sources/api/generated/openseries.efficient_frontier.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.efficient\_frontier -============================== - -.. currentmodule:: openseries - -.. autofunction:: efficient_frontier \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.generate_calendar_date_range.rst.txt b/docs/build/html/_sources/api/generated/openseries.generate_calendar_date_range.rst.txt deleted file mode 100644 index cba4c6a9..00000000 --- a/docs/build/html/_sources/api/generated/openseries.generate_calendar_date_range.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.generate\_calendar\_date\_range -========================================== - -.. currentmodule:: openseries - -.. autofunction:: generate_calendar_date_range \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.get_previous_business_day_before_today.rst.txt b/docs/build/html/_sources/api/generated/openseries.get_previous_business_day_before_today.rst.txt deleted file mode 100644 index 38e2e8a6..00000000 --- a/docs/build/html/_sources/api/generated/openseries.get_previous_business_day_before_today.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.get\_previous\_business\_day\_before\_today -====================================================== - -.. currentmodule:: openseries - -.. autofunction:: get_previous_business_day_before_today \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.holiday_calendar.rst.txt b/docs/build/html/_sources/api/generated/openseries.holiday_calendar.rst.txt deleted file mode 100644 index 49edd9e7..00000000 --- a/docs/build/html/_sources/api/generated/openseries.holiday_calendar.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.holiday\_calendar -============================ - -.. currentmodule:: openseries - -.. autofunction:: holiday_calendar \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.load_plotly_dict.rst.txt b/docs/build/html/_sources/api/generated/openseries.load_plotly_dict.rst.txt deleted file mode 100644 index e52afff4..00000000 --- a/docs/build/html/_sources/api/generated/openseries.load_plotly_dict.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.load\_plotly\_dict -============================= - -.. currentmodule:: openseries - -.. autofunction:: load_plotly_dict \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.offset_business_days.rst.txt b/docs/build/html/_sources/api/generated/openseries.offset_business_days.rst.txt deleted file mode 100644 index 46c7f971..00000000 --- a/docs/build/html/_sources/api/generated/openseries.offset_business_days.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.offset\_business\_days -================================= - -.. currentmodule:: openseries - -.. autofunction:: offset_business_days \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.prepare_plot_data.rst.txt b/docs/build/html/_sources/api/generated/openseries.prepare_plot_data.rst.txt deleted file mode 100644 index ecfbb4c2..00000000 --- a/docs/build/html/_sources/api/generated/openseries.prepare_plot_data.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.prepare\_plot\_data -============================== - -.. currentmodule:: openseries - -.. autofunction:: prepare_plot_data \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.report_html.rst.txt b/docs/build/html/_sources/api/generated/openseries.report_html.rst.txt deleted file mode 100644 index a9d7292a..00000000 --- a/docs/build/html/_sources/api/generated/openseries.report_html.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.report\_html -======================= - -.. currentmodule:: openseries - -.. autofunction:: report_html \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.sharpeplot.rst.txt b/docs/build/html/_sources/api/generated/openseries.sharpeplot.rst.txt deleted file mode 100644 index 7e75303f..00000000 --- a/docs/build/html/_sources/api/generated/openseries.sharpeplot.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.sharpeplot -===================== - -.. currentmodule:: openseries - -.. autofunction:: sharpeplot \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.simulate_portfolios.rst.txt b/docs/build/html/_sources/api/generated/openseries.simulate_portfolios.rst.txt deleted file mode 100644 index 915fef82..00000000 --- a/docs/build/html/_sources/api/generated/openseries.simulate_portfolios.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.simulate\_portfolios -=============================== - -.. currentmodule:: openseries - -.. autofunction:: simulate_portfolios \ No newline at end of file diff --git a/docs/build/html/_sources/api/generated/openseries.timeseries_chain.rst.txt b/docs/build/html/_sources/api/generated/openseries.timeseries_chain.rst.txt deleted file mode 100644 index 1a4902c1..00000000 --- a/docs/build/html/_sources/api/generated/openseries.timeseries_chain.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -openseries.timeseries\_chain -============================ - -.. currentmodule:: openseries - -.. autofunction:: timeseries_chain \ No newline at end of file diff --git a/docs/build/html/_sources/api/openseries.rst.txt b/docs/build/html/_sources/api/openseries.rst.txt deleted file mode 100644 index 2761c330..00000000 --- a/docs/build/html/_sources/api/openseries.rst.txt +++ /dev/null @@ -1,90 +0,0 @@ -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 ------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.OpenTimeSeries - openseries.OpenFrame - -Utility Functions ------------------ - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.timeseries_chain - openseries.report_html - -Portfolio Tools ---------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.efficient_frontier - openseries.simulate_portfolios - openseries.constrain_optimized_portfolios - openseries.prepare_plot_data - openseries.sharpeplot - -Date Utilities --------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.date_fix - openseries.date_offset_foll - openseries.generate_calendar_date_range - openseries.get_previous_business_day_before_today - openseries.holiday_calendar - openseries.offset_business_days - -Simulation ----------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.ReturnSimulation - -Types and Enums ---------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.ValueType - -Other Utilities ---------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - openseries.load_plotly_dict - openseries.export_plotly_figure diff --git a/docs/build/html/_sources/api/portfoliotools.rst.txt b/docs/build/html/_sources/api/portfoliotools.rst.txt deleted file mode 100644 index d41b4812..00000000 --- a/docs/build/html/_sources/api/portfoliotools.rst.txt +++ /dev/null @@ -1,32 +0,0 @@ -Portfolio Tools -=============== - -.. currentmodule:: openseries.portfoliotools - -The portfoliotools module provides functions for portfolio optimization, simulation, and analysis. - -Portfolio Optimization ------------------------ - -.. autofunction:: efficient_frontier - :no-index: - -Portfolio Simulation ---------------------- - -.. autofunction:: simulate_portfolios - :no-index: - -Portfolio Constraints ---------------------- - -.. autofunction:: constrain_optimized_portfolios - :no-index: - -Visualization -------------- - -.. autofunction:: prepare_plot_data - :no-index: -.. autofunction:: sharpeplot - :no-index: diff --git a/docs/build/html/_sources/api/report.rst.txt b/docs/build/html/_sources/api/report.rst.txt deleted file mode 100644 index b80ac6e1..00000000 --- a/docs/build/html/_sources/api/report.rst.txt +++ /dev/null @@ -1,67 +0,0 @@ -Report Generation -================= - -.. currentmodule:: openseries.report - -HTML Report Function --------------------- - -.. autofunction:: report_html - :no-index: - -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 ``OpenFrame`` is 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_type`` parameter: - - - 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. If ``auto_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. The ``filename`` parameter is optional when using this mode and - is only used to generate unique div IDs for the embedded content. diff --git a/docs/build/html/_sources/api/series.rst.txt b/docs/build/html/_sources/api/series.rst.txt deleted file mode 100644 index b899915a..00000000 --- a/docs/build/html/_sources/api/series.rst.txt +++ /dev/null @@ -1,272 +0,0 @@ -OpenTimeSeries -============== - -.. currentmodule:: openseries - -.. autoclass:: OpenTimeSeries - :undoc-members: - :show-inheritance: - :special-members: __init__ - :no-index: - -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 ------------------------------- - -.. automethod:: OpenTimeSeries.from_arrays - :no-index: -.. automethod:: OpenTimeSeries.from_df - :no-index: -.. automethod:: OpenTimeSeries.from_fixed_rate - :no-index: -.. automethod:: OpenTimeSeries.from_deepcopy - :no-index: - -Properties ----------- - -Non-numerical Properties -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenTimeSeries.timeseries_id - :no-index: -.. autoattribute:: OpenTimeSeries.instrument_id - :no-index: -.. autoattribute:: OpenTimeSeries.dates - :no-index: -.. autoattribute:: OpenTimeSeries.values - :no-index: -.. autoattribute:: OpenTimeSeries.currency - :no-index: -.. autoattribute:: OpenTimeSeries.domestic - :no-index: -.. autoattribute:: OpenTimeSeries.local_ccy - :no-index: -.. autoattribute:: OpenTimeSeries.name - :no-index: -.. autoattribute:: OpenTimeSeries.isin - :no-index: -.. autoattribute:: OpenTimeSeries.label - :no-index: -.. autoattribute:: OpenTimeSeries.countries - :no-index: -.. autoattribute:: OpenTimeSeries.markets - :no-index: -.. autoattribute:: OpenTimeSeries.valuetype - :no-index: - -Common Properties -~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenTimeSeries.first_idx - :no-index: -.. autoattribute:: OpenTimeSeries.last_idx - :no-index: -.. autoattribute:: OpenTimeSeries.length - :no-index: -.. autoattribute:: OpenTimeSeries.span_of_days - :no-index: -.. autoattribute:: OpenTimeSeries.tsdf - :no-index: -.. autoattribute:: OpenTimeSeries.max_drawdown_date - :no-index: -.. autoattribute:: OpenTimeSeries.periods_in_a_year - :no-index: -.. autoattribute:: OpenTimeSeries.yearfrac - :no-index: - -Financial Metrics -~~~~~~~~~~~~~~~~~ - -.. autoattribute:: OpenTimeSeries.all_properties - :no-index: -.. autoattribute:: OpenTimeSeries.arithmetic_ret - :no-index: -.. autoattribute:: OpenTimeSeries.geo_ret - :no-index: -.. autoattribute:: OpenTimeSeries.value_ret - :no-index: -.. autoattribute:: OpenTimeSeries.vol - :no-index: -.. autoattribute:: OpenTimeSeries.downside_deviation - :no-index: -.. autoattribute:: OpenTimeSeries.ret_vol_ratio - :no-index: -.. autoattribute:: OpenTimeSeries.sortino_ratio - :no-index: -.. autoattribute:: OpenTimeSeries.kappa3_ratio - :no-index: -.. autoattribute:: OpenTimeSeries.omega_ratio - :no-index: -.. autoattribute:: OpenTimeSeries.var_down - :no-index: -.. autoattribute:: OpenTimeSeries.cvar_down - :no-index: -.. autoattribute:: OpenTimeSeries.worst - :no-index: -.. autoattribute:: OpenTimeSeries.worst_month - :no-index: -.. autoattribute:: OpenTimeSeries.max_drawdown - :no-index: -.. autoattribute:: OpenTimeSeries.max_drawdown_cal_year - :no-index: -.. autoattribute:: OpenTimeSeries.positive_share - :no-index: -.. autoattribute:: OpenTimeSeries.vol_from_var - :no-index: -.. autoattribute:: OpenTimeSeries.autocorr - :no-index: -.. autoattribute:: OpenTimeSeries.skew - :no-index: -.. autoattribute:: OpenTimeSeries.kurtosis - :no-index: -.. autoattribute:: OpenTimeSeries.z_score - :no-index: - -Methods -------- - -Data Manipulation -~~~~~~~~~~~~~~~~~ - -.. automethod:: OpenTimeSeries.pandas_df - :no-index: -.. automethod:: OpenTimeSeries.set_new_label - :no-index: -.. automethod:: OpenTimeSeries.running_adjustment - :no-index: -.. automethod:: OpenTimeSeries.from_1d_rate_to_cumret - :no-index: -.. automethod:: OpenTimeSeries.align_index_to_local_cdays - :no-index: -.. automethod:: OpenTimeSeries.resample - :no-index: -.. automethod:: OpenTimeSeries.resample_to_business_period_ends - :no-index: -.. automethod:: OpenTimeSeries.value_nan_handle - :no-index: -.. automethod:: OpenTimeSeries.return_nan_handle - :no-index: - -Transformations -~~~~~~~~~~~~~~~ - -.. automethod:: OpenTimeSeries.to_cumret - :no-index: -.. automethod:: OpenTimeSeries.value_to_ret - :no-index: -.. automethod:: OpenTimeSeries.value_to_diff - :no-index: -.. automethod:: OpenTimeSeries.value_to_log - :no-index: -.. automethod:: OpenTimeSeries.to_drawdown_series - :no-index: - -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). - -.. automethod:: OpenTimeSeries.autocorr_func - :no-index: -.. automethod:: OpenTimeSeries.acf - :no-index: -.. automethod:: OpenTimeSeries.partial_autocorr - :no-index: -.. automethod:: OpenTimeSeries.pacf - :no-index: -.. automethod:: OpenTimeSeries.ljung_box - :no-index: -.. automethod:: OpenTimeSeries.ewma_vol_func - :no-index: -.. automethod:: OpenTimeSeries.value_ret_calendar_period - :no-index: -.. automethod:: OpenTimeSeries.rolling_return - :no-index: -.. automethod:: OpenTimeSeries.rolling_vol - :no-index: -.. automethod:: OpenTimeSeries.rolling_var_down - :no-index: -.. automethod:: OpenTimeSeries.rolling_cvar_down - :no-index: -.. automethod:: OpenTimeSeries.calc_range - :no-index: -.. automethod:: OpenTimeSeries.outliers - :no-index: - -Financial Metrics Methods -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. automethod:: OpenTimeSeries.arithmetic_ret_func - :no-index: -.. automethod:: OpenTimeSeries.geo_ret_func - :no-index: -.. automethod:: OpenTimeSeries.value_ret_func - :no-index: -.. automethod:: OpenTimeSeries.vol_func - :no-index: -.. automethod:: OpenTimeSeries.lower_partial_moment_func - :no-index: -.. automethod:: OpenTimeSeries.ret_vol_ratio_func - :no-index: -.. automethod:: OpenTimeSeries.sortino_ratio_func - :no-index: -.. automethod:: OpenTimeSeries.omega_ratio_func - :no-index: -.. automethod:: OpenTimeSeries.var_down_func - :no-index: -.. automethod:: OpenTimeSeries.cvar_down_func - :no-index: -.. automethod:: OpenTimeSeries.worst_func - :no-index: -.. automethod:: OpenTimeSeries.max_drawdown_func - :no-index: -.. automethod:: OpenTimeSeries.positive_share_func - :no-index: -.. automethod:: OpenTimeSeries.vol_from_var_func - :no-index: -.. automethod:: OpenTimeSeries.skew_func - :no-index: -.. automethod:: OpenTimeSeries.kurtosis_func - :no-index: -.. automethod:: OpenTimeSeries.z_score_func - :no-index: -.. automethod:: OpenTimeSeries.target_weight_from_var - :no-index: - -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. - -.. automethod:: OpenTimeSeries.plot_series - :no-index: -.. automethod:: OpenTimeSeries.plot_bars - :no-index: -.. automethod:: OpenTimeSeries.plot_histogram - :no-index: - -Export Methods -~~~~~~~~~~~~~~ - -.. automethod:: OpenTimeSeries.to_json - :no-index: -.. automethod:: OpenTimeSeries.to_xlsx - :no-index: - -Utility Functions ------------------ - -.. autofunction:: timeseries_chain - :no-index: diff --git a/docs/build/html/_sources/api/simulation.rst.txt b/docs/build/html/_sources/api/simulation.rst.txt deleted file mode 100644 index 59ee70fc..00000000 --- a/docs/build/html/_sources/api/simulation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -Simulation -========== - -.. currentmodule:: openseries.simulation - -.. automodule:: openseries.simulation - :members: - :undoc-members: - :show-inheritance: - :no-index: - -ReturnSimulation Class ----------------------- - -.. autoclass:: ReturnSimulation - :members: - :undoc-members: - :show-inheritance: - :no-index: - :special-members: __init__ - -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. diff --git a/docs/build/html/_sources/api/types.rst.txt b/docs/build/html/_sources/api/types.rst.txt deleted file mode 100644 index f0d22e58..00000000 --- a/docs/build/html/_sources/api/types.rst.txt +++ /dev/null @@ -1,107 +0,0 @@ -Types and Enums -=============== - -.. currentmodule:: openseries.owntypes - -.. automodule:: openseries.owntypes - :members: - :undoc-members: - :show-inheritance: - :no-index: - -Value Types ------------ - -.. autoclass:: ValueType - :members: - :undoc-members: - :show-inheritance: - :no-index: - -The ValueType enum identifies the type of values in a time series (prices, returns, etc.). - -Type Aliases ------------- - -.. autodata:: SeriesOrFloat_co -.. autodata:: CountryStringType -.. autodata:: CountrySetType -.. autodata:: CountriesType -.. autodata:: CurrencyStringType -.. autodata:: DateStringType -.. autodata:: DateListType -.. autodata:: ValueListType -.. autodata:: DaysInYearType -.. autodata:: DateType - -Literal Types -------------- - -.. autodata:: LiteralJsonOutput -.. autodata:: LiteralTrunc -.. autodata:: LiteralLinePlotMode -.. autodata:: LiteralHowMerge -.. autodata:: LiteralQuantileInterp -.. autodata:: LiteralBizDayFreq -.. autodata:: LiteralPandasReindexMethod -.. autodata:: LiteralNanMethod -.. autodata:: LiteralCaptureRatio -.. autodata:: LiteralBarPlotMode -.. autodata:: LiteralPlotlyOutput -.. autodata:: LiteralPlotlyJSlib -.. autodata:: LiteralPlotlyHistogramPlotType -.. autodata:: LiteralPlotlyHistogramBarMode -.. autodata:: LiteralPlotlyHistogramCurveType -.. autodata:: LiteralPlotlyHistogramHistNorm -.. autodata:: LiteralPortfolioWeightings -.. autodata:: LiteralMinimizeMethods -.. autodata:: LiteralSeriesProps -.. autodata:: LiteralFrameProps - -Validation Classes ------------------- - -.. autoclass:: Countries - :members: - :undoc-members: - :show-inheritance: - -.. autoclass:: Currency - :members: - :undoc-members: - :show-inheritance: - -.. autoclass:: PropertiesList - :members: - :undoc-members: - :show-inheritance: - -.. autoclass:: OpenTimeSeriesPropertiesList - :members: - :undoc-members: - :show-inheritance: - -.. autoclass:: OpenFramePropertiesList - :members: - :undoc-members: - :show-inheritance: - -Custom Exceptions ------------------ - -.. autoexception:: MixedValuetypesError -.. autoexception:: AtLeastOneFrameError -.. autoexception:: DateAlignmentError -.. autoexception:: NumberOfItemsAndLabelsNotSameError -.. autoexception:: InitialValueZeroError -.. autoexception:: CountriesNotStringNorListStrError -.. autoexception:: MarketsNotStringNorListStrError -.. autoexception:: TradingDaysNotAboveZeroError -.. autoexception:: BothStartAndEndError -.. autoexception:: NoWeightsError -.. autoexception:: LabelsNotUniqueError -.. autoexception:: RatioInputError -.. autoexception:: MergingResultedInEmptyError -.. autoexception:: IncorrectArgumentComboError -.. autoexception:: PropertiesInputValidationError -.. autoexception:: ResampleDataLossError diff --git a/docs/build/html/_sources/development/changelog.rst.txt b/docs/build/html/_sources/development/changelog.rst.txt deleted file mode 100644 index e962d128..00000000 --- a/docs/build/html/_sources/development/changelog.rst.txt +++ /dev/null @@ -1,21 +0,0 @@ -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 `_ diff --git a/docs/build/html/_sources/development/contributing.rst.txt b/docs/build/html/_sources/development/contributing.rst.txt deleted file mode 100644 index b5931a2b..00000000 --- a/docs/build/html/_sources/development/contributing.rst.txt +++ /dev/null @@ -1,456 +0,0 @@ -Contributing to openseries -========================== - -We welcome contributions to openseries! This guide will help you get started with contributing to the project. - -Getting Started ---------------- - -Development Setup -~~~~~~~~~~~~~~~~~ - -1. Fork the repository on GitHub -2. Clone your fork locally: - -.. code-block:: bash - - git clone https://github.com/yourusername/openseries.git - cd openseries - -3. Install uv (if not already installed): - -.. code-block:: bash - - pip install uv - -4. Create a virtual environment and install dependencies: - -.. code-block:: bash - - uv venv venv - source venv/bin/activate - uv pip install -e ".[dev,docs]" - -5. Install pre-commit hooks: - -.. code-block:: bash - - pre-commit install - -Development Workflow -~~~~~~~~~~~~~~~~~~~~ - -1. Create a new branch for your feature or bug fix: - -.. code-block:: bash - - git checkout -b feature/your-feature-name - -2. Make your changes -3. Run tests to ensure everything works: - -.. code-block:: bash - - make test - -4. Run linting and type checking: - -.. code-block:: bash - - make lint - -5. Commit your changes: - -.. code-block:: bash - - git add . - git commit -m "Add your descriptive commit message" - -6. Push to your fork: - -.. code-block:: bash - - git push origin feature/your-feature-name - -7. 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: - -.. code-block:: python - - 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: - -.. code-block:: python - - 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: - -.. code-block:: bash - - tests/ - ├── __init__.py - ├── test_series.py - ├── test_frame.py - ├── test_portfoliotools.py - └── ... - -Writing Tests -~~~~~~~~~~~~~ - -Write comprehensive tests for new functionality: - -.. code-block:: python - - 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: - -.. code-block:: bash - - make test - -Run specific test files: - -.. code-block:: bash - - pytest tests/test_series.py - -Run tests with coverage: - -.. code-block:: bash - - 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: - -.. code-block:: bash - - cd docs - make html - -The built documentation will be in ``docs/_build/html/``. - -Contributing Guidelines ------------------------ - -Pull Request Process -~~~~~~~~~~~~~~~~~~~~ - -1. **Fork and Branch**: Create a feature branch from ``master`` -2. **Develop**: Make your changes with tests and documentation -3. **Test**: Ensure all tests pass and coverage remains high -4. **Lint**: Run linting and fix any issues -5. **Document**: Update documentation as needed -6. **Commit**: Use clear, descriptive commit messages -7. **Pull Request**: Create a PR with a clear description - -Commit Messages -~~~~~~~~~~~~~~~ - -Use clear, descriptive commit messages: - -.. code-block:: text - - 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: - -1. Automated checks must pass (tests, linting, type checking) -2. At least one maintainer review is required -3. Address any feedback or requested changes -4. 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``: - -.. code-block:: 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: - -.. code-block:: bash - - pytest --pdb tests/test_specific.py::test_function - -For debugging with VS Code, create ``.vscode/launch.json``: - -.. code-block:: 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: - -1. Version bump in ``pyproject.toml`` -2. Update ``CHANGELOG.md`` -3. Create GitHub release with release notes -4. 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! diff --git a/docs/build/html/_sources/examples/custom_reports.rst.txt b/docs/build/html/_sources/examples/custom_reports.rst.txt deleted file mode 100644 index 0c71c645..00000000 --- a/docs/build/html/_sources/examples/custom_reports.rst.txt +++ /dev/null @@ -1,75 +0,0 @@ -Reporting -========= - -This example demonstrates how to create analysis reports using openseries and the built-in report functionality. - -Using the Built-in HTML Report -------------------------------- - -.. code-block:: python - - 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"``: - -.. code-block:: python - - # 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""" - - - - My Custom Report - - -

    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 3ae5fc99..00000000 --- a/docs/build/html/_sources/user_guide/installation.rst.txt +++ /dev/null @@ -1,196 +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,<3.0.0) - Data manipulation and analysis -- **numpy** (>=1.23.2,!=2.3.0,<3.0.0) - Numerical computing -- **pydantic** (>=2.5.2,<3.0.0) - Data validation and settings management -- **plotly** (>=5.18.0) - Interactive plotting -- **scipy** (>=1.11.4,<2.0.0) - Scientific computing -- **scikit-learn** (>=1.4.0,<2.0.0) - Machine learning utilities - -Financial and Date Utilities -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -- **exchange-calendars** (>=4.8,<6.0) - Trading calendar support -- **holidays** (>=0.30,<1.0) - Holiday calendar support -- **python-dateutil** (>=2.8.2,<4.0.0) - Date parsing utilities - -File and Network Support -~~~~~~~~~~~~~~~~~~~~~~~~~ - -- **openpyxl** (>=3.1.2,<5.0.0) - Excel file support -- **requests** (>=2.20.0,<3.0.0) - 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: - -.. code-block:: bash - - git clone https://github.com/CaptorAB/openseries.git - cd openseries - - # Install uv (if not already installed) - pip install uv - - # Create a virtual environment and install dependencies - uv venv venv - source venv/bin/activate - uv pip install -e ".[dev]" - -This will install additional development dependencies including: - -- **pytest** - Testing framework -- **mypy** - Static type checking -- **ruff** - Linting and formatting -- **pre-commit** - 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 `_ -2. Review the `Release Notes `_ -3. 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. diff --git a/docs/build/html/_sources/user_guide/quickstart.rst.txt b/docs/build/html/_sources/user_guide/quickstart.rst.txt deleted file mode 100644 index 2f781d3a..00000000 --- a/docs/build/html/_sources/user_guide/quickstart.rst.txt +++ /dev/null @@ -1,291 +0,0 @@ -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: - -.. code-block:: python - - 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: - -.. code-block:: python - - 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: - -.. code-block:: python - - # 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: - -.. code-block:: python - - # Get all metrics of an OpenTimeSeries or OpenFrame - metrics = sp500.all_properties() - print(metrics) - -Creating Visualizations ------------------------ - -openseries integrates with Plotly for interactive visualizations: - -.. code-block:: python - - # 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: - -.. code-block:: python - - 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: - -.. code-block:: python - - # 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: - -.. code-block:: python - - # 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: - -.. code-block:: python - - # 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: - -.. code-block:: python - - # 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: - -1. **Tutorials** - Detailed examples for specific use cases -2. **API Reference** - Complete documentation of all methods and properties -3. **Examples** - Real-world analysis scenarios - -Key Concepts to Remember ------------------------- - -- **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 - -Common Patterns ---------------- - -Here are some common usage patterns: - -.. code-block:: python - - # 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! diff --git a/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 81415803..00000000 --- a/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,123 +0,0 @@ -/* Compatability shim for jQuery and underscores.js. - 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