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Merge branch 'PyPortfolio:main' into No_yfinance
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‎.github/actions/setup-project/action.yml‎

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# Action: Setup Project (composite action)
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#
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# Purpose: Bootstrap a Python project in GitHub Actions by:
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# - Installing Task, uv, and uvx into a local ./bin directory
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# - Detecting presence of pyproject.toml and exposing it as an output
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# - Creating a virtual environment with uv and syncing dependencies
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# Purpose: Bootstrap a Python project within GitHub Actions by:
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# - Installing uv and uvx into a local ./bin directory and adding it to PATH
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# - Detecting the presence of pyproject.toml and exposing that as an output
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# - Creating a virtual environment with uv and (optionally) syncing dependencies
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#
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# Inputs:
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# - python-version: Python version used for the virtual environment (default: 3.12)
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# - python-version: Python version for the uv-managed virtual environment (default: 3.12)
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#
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# Outputs:
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# - pyproject_exists: "true" if pyproject.toml exists, otherwise "false"
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#
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# Notes:
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# - Safe to run in repositories without pyproject.toml; dependency sync will be skipped.
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# - Used by workflows such as CI, Book, Marimo, and Release.
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# - Purely a CI helper — it does not modify repository files.
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name: 'Setup Project'
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description: 'Setup the project'
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runs:
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using: 'composite'
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steps:
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- name: Set up task, uv, uvx and the venv
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- name: Set up uv, uvx and the venv
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shell: bash
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run: |
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mkdir -p bin
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echo "Adding ./bin to PATH"
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echo "$(pwd)/bin" >> $GITHUB_PATH
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# Install Task
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curl -fsSL https://taskfile.dev/install.sh | sh -s -- -d -b ./bin
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# Install uv and uvx
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curl -fsSL https://astral.sh/uv/install.sh | UV_INSTALL_DIR="./bin" sh
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- name: Check version for task
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shell: bash
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run: |
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task --version
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- name: Check version for uv
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shell: bash
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run: |

‎.github/workflows/release.yml‎

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name: PyPI Release
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on:
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release:
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types: [published]
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jobs:
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check_tag:
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name: Check tag
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v5
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- uses: actions/setup-python@v6
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with:
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python-version: '3.11'
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- shell: bash
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run: |
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TAG="${{ github.event.release.tag_name }}"
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GH_TAG_NAME="${TAG#v}"
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PY_VERSION=$(python - <<'PY'
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import pathlib, tomllib
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data = tomllib.loads(pathlib.Path("pyproject.toml").read_text(encoding="utf-8"))
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print(data.get("project").get("version"))
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PY
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)
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if [ "${GH_TAG_NAME}" != "${PY_VERSION}" ]; then
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echo "::error::Tag (${GH_TAG_NAME}) does not match pyproject.toml version (${PY_VERSION})."
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exit 2
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fi
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build_wheels:
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name: Build wheels
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v5
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- uses: actions/setup-python@v6
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with:
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python-version: '3.11'
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- name: Build wheel
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run: |
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python -m pip install build
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python -m build --wheel --sdist --outdir wheelhouse
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- name: Store wheels
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uses: actions/upload-artifact@v4
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with:
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name: wheels
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path: wheelhouse/*
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pytest-nosoftdeps:
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name: no-softdeps
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runs-on: ${{ matrix.os }}
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needs: [build_wheels]
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strategy:
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fail-fast: false
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matrix:
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os: [ubuntu-latest, macos-latest, windows-latest]
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python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
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steps:
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- uses: actions/checkout@v5
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v6
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with:
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python-version: ${{ matrix.python-version }}
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- name: Setup macOS
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if: runner.os == 'macOS'
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run: |
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brew install libomp # https://github.com/pytorch/pytorch/issues/20030
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- name: Get full Python version
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id: full-python-version
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shell: bash
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run: echo version=$(python -c "import sys; print('-'.join(str(v) for v in sys.version_info))") >> $GITHUB_OUTPUT
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- name: Install dependencies
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shell: bash
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run: |
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pip install ".[dev]"
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- name: Show dependencies
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run: python -m pip list
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- name: Run pytest
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shell: bash
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run: python -m pytest tests
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upload_wheels:
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name: Upload wheels to PyPI
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runs-on: ubuntu-latest
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needs: [pytest-nosoftdeps]
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permissions:
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id-token: write
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steps:
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- uses: actions/download-artifact@v5
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with:
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name: wheels
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path: wheelhouse
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- name: Publish package to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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packages-dir: wheelhouse/

‎.gitignore‎

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artifacts
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bin

‎Makefile‎

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## Makefile — PyPortfolioOpt developer conveniences
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#
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# This Makefile exposes common local development tasks and a friendly
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# `make help` index.
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# Conventions used by the help generator:
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# - Lines with `##` after a target are turned into help text.
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# - Lines starting with `##@` create section headers in the help output.
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# This file does not affect the library itself; it only streamlines dev workflows.
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# Colors for pretty output in help messages
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BLUE := \033[36m
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BOLD := \033[1m
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GREEN := \033[32m
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RED := \033[31m
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RESET := \033[0m
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# Default goal when running `make` with no target
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.DEFAULT_GOAL := help
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# Declare phony targets (they don't produce files)
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.PHONY: install install-uv test fmt
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UV_INSTALL_DIR := ./bin
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##@ Bootstrap
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install-uv: ## ensure uv (and uvx) are installed locally
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@mkdir -p ${UV_INSTALL_DIR}
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@if [ -x "${UV_INSTALL_DIR}/uv" ] && [ -x "${UV_INSTALL_DIR}/uvx" ]; then \
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:; \
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else \
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printf "${BLUE}Installing uv${RESET}\n"; \
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curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=${UV_INSTALL_DIR} sh 2>/dev/null || { printf "${RED}[ERROR] Failed to install uv ${RESET}\n"; exit 1; }; \
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fi
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install: install-uv ## install
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@printf "${BLUE}[INFO] Creating virtual environment...${RESET}\n"
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# Create the virtual environment
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@./bin/uv venv --python 3.12 || { printf "${RED}[ERROR] Failed to create virtual environment${RESET}\n"; exit 1; }
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@printf "${BLUE}[INFO] Installing dependencies${RESET}\n"
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@./bin/uv sync --all-extras --frozen || { printf "${RED}[ERROR] Failed to install dependencies${RESET}\n"; exit 1; }
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##@ Development and Testing
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test: install ## run all tests
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@printf "${BLUE}[INFO] Running tests...${RESET}\n"
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@./bin/uv pip install pytest pytest-cov pytest-html
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@mkdir -p _tests/html-coverage _tests/html-report
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@./bin/uv run pytest tests --cov=pypfopt --cov-report=term --cov-report=html:_tests/html-coverage --html=_tests/html-report/report.html
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fmt: install-uv ## check the pre-commit hooks and the linting
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@./bin/uvx pre-commit run --all-files
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@./bin/uvx deptry .
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##@ Meta
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help: ## Display this help message
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+@printf "$(BOLD)Usage:$(RESET)\n"
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+@printf " make $(BLUE)<target>$(RESET)\n\n"
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+@printf "$(BOLD)Targets:$(RESET)\n"
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+@awk 'BEGIN {FS = ":.*##"; printf ""} /^[a-zA-Z_-]+:.*?##/ { printf " $(BLUE)%-15s$(RESET) %s\n", $$1, $$2 } /^##@/ { printf "\n$(BOLD)%s$(RESET)\n", substr($$0, 5) }' $(MAKEFILE_LIST)

‎README.md‎

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<p align="center">
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<img width=60% src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/logo_v1.png?raw=true">
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</p>
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## Welcome to PyPortfolioOpt
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<!-- buttons -->
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<p align="center">
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<a href="https://www.python.org">
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<img src="https://img.shields.io/pypi/pyversions/PyPortfolioOpt.svg"
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alt="python"></a> &nbsp;
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<a href="https://www.python.org">
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<img src="https://img.shields.io/badge/Platforms-linux--64,win--64,osx--64-orange.svg?style=flat-square"
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alt="platforms"></a> &nbsp;
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<a href="https://pypi.org/project/PyPortfolioOpt/">
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<img src="https://img.shields.io/badge/pypi-v1.5.6-brightgreen.svg"
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alt="pypi"></a> &nbsp;
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<a href="https://opensource.org/licenses/MIT">
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<img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
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alt="MIT license"></a> &nbsp;
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<a href="https://github.com/pyportfolio/pyportfolioopt/actions">
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<img src="https://github.com/pyportfolio/pyportfolioopt/actions/workflows/main.yml/badge.svg?branch=main"
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alt="build"></a> &nbsp;
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<a href="https://pepy.tech/project/pyportfolioopt">
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<img src="https://pepy.tech/badge/pyportfolioopt"
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alt="downloads"></a> &nbsp;
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<a href="https://mybinder.org/v2/gh/pyportfolio/pyportfolioopt/main/?filepath=cookbook">
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<img src="https://mybinder.org/badge_logo.svg"
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alt="binder"></a> &nbsp;
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</p>
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<a href="https://pyportfolioopt.readthedocs.io/en/latest/"><img src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/logo_v1.png?raw=true" width="275" align="right" /></a>
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<!-- content -->
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PyPortfolioOpt is a library implementing portfolio optimization methods, including
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classical mean-variance optimization, Black-Litterman allocation, or shrinkage and Hierarchical Risk Parity.
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PyPortfolioOpt is inspired by scikit-learn; it is **extensive** yet easily **extensible**, for casual investors, or professionals looking for an easy prototyping tool. Whether you are a fundamentals-oriented investor who has identified a
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handful of undervalued picks, or an algorithmic trader who has a basket of
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strategies, PyPortfolioOpt can help you combine your alpha sources in a risk-efficient way.
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PyPortfolioOpt is a library that implements portfolio optimization methods, including
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classical mean-variance optimization techniques and Black-Litterman allocation, as well as more
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recent developments in the field like shrinkage and Hierarchical Risk Parity.
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It is **extensive** yet easily **extensible**, and can be useful for either a casual investors, or a professional looking for an easy prototyping tool. Whether you are a fundamentals-oriented investor who has identified a
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handful of undervalued picks, or an algorithmic trader who has a basket of
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strategies, PyPortfolioOpt can help you combine your alpha sources
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in a risk-efficient way.
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<!-- buttons -->
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| | **[Documentation](https://pyportfolioopt.readthedocs.io/en/latest/)** · **[Tutorials](https://github.com/pyportfolio/pyportfolioopt/tree/main/cookbook)** · **[Release Notes](https://github.com/PyPortfolio/PyPortfolioOpt/releases)** |
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|---|---|
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| **Open&#160;Source** | [![MIT](https://img.shields.io/github/license/pyportfolio/pyportfolioopt)](https://github.com/pyportfolio/pyportfolioopt/blob/master/LICENSE) [![GC.OS Sponsored](https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=flat&colorA=0eac92&colorB=2077b4)](https://gc-os-ai.github.io/) | |
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| **Tutorials** | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pyportfolio/pyportfolioopt/main/?filepath=cookbook) |
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| **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord&label=discord&message=chat&color=lightgreen)](https://discord.gg/7uKdHfdcJG) [![!slack](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/pyportfolioopt/) |
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| **CI/CD** | [![github-actions](https://img.shields.io/github/actions/workflow/status/pyportfolio/pyportfolioopt/main.yml?logo=github)](https://github.com/pyportfolio/pyportfolioopt/actions/workflows/release.yml) [![readthedocs](https://img.shields.io/readthedocs/pyportfolioopt?logo=readthedocs)](https://pyportfolioopt.readthedocs.io/en/latest/?badge=latest) |
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| **Code** | [![!pypi](https://img.shields.io/pypi/v/pyportfolioopt?color=orange)](https://pypi.org/project/pyportfolioopt/) [![!python-versions](https://img.shields.io/pypi/pyversions/pyportfolioopt)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) |
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| **Downloads** | ![PyPI - Downloads](https://img.shields.io/pypi/dw/pyportfolioopt) ![PyPI - Downloads](https://img.shields.io/pypi/dm/pyportfolioopt) [![Downloads](https://static.pepy.tech/personalized-badge/pyportfolioopt?period=total&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/pyportfolioopt) |
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| **Citation** | [JOSS article](https://joss.theoj.org/papers/10.21105/joss.03066) |
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**PyPortfolioOpt has been [published](https://joss.theoj.org/papers/10.21105/joss.03066) in the Journal of Open Source Software 🎉**
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PyPortfolioOpt is now being maintained by [Tuan Tran](https://github.com/88d52bdba0366127fffca9dfa93895).
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<!-- content -->
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Head over to the **[documentation on ReadTheDocs](https://pyportfolioopt.readthedocs.io/en/latest/)** to get an in-depth look at the project, or check out the [cookbook](https://github.com/pyportfolio/pyportfolioopt/tree/main/cookbook) to see some examples showing the full process from downloading data to building a portfolio.
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ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
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```
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```result
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```
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- Market neutrality: for the `efficient_risk` and `efficient_return` methods, PyPortfolioOpt provides an option to form a market-neutral portfolio (i.e weights sum to zero). This is not possible for the max Sharpe portfolio and the min volatility portfolio because in those cases because they are not invariant with respect to leverage. Market neutrality requires negative weights:
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```python
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ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.1))
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```
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```result
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```
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One issue with mean-variance optimization is that it leads to many zero-weights. While these are
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"optimal" in-sample, there is a large body of research showing that this characteristic leads
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mean-variance portfolios to underperform out-of-sample. To that end, I have introduced an
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### Black-Litterman allocation
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As of v0.5.0, we now support Black-Litterman asset allocation, which allows you to combine
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Pyportfolioopt supports Black-Litterman asset allocation, which allows you to combine
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a prior estimate of returns (e.g the market-implied returns) with your own views to form a
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posterior estimate. This results in much better estimates of expected returns than just using
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the mean historical return. Check out the [docs](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html) for a discussion of the theory, as well as advice
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['GOOG', 'AAPL', 'FB', 'BABA', 'AMZN', 'GE', 'AMD', 'WMT', 'BAC', 'GM', 'T', 'UAA', 'SHLD', 'XOM', 'RRC', 'BBY', 'MA', 'PFE', 'JPM', 'SBUX']
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```
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```result
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```
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These tickers have been informally selected to meet several criteria:
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- reasonably liquid
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Contributions are _most welcome_. Have a look at the [Contribution Guide](https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/CONTRIBUTING.md) for more.
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I'd like to thank all of the people who have contributed to PyPortfolioOpt since its release in 2018.
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We'd like to thank all of the people who have contributed to PyPortfolioOpt since its release in 2018.
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Special shout-outs to:
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- Tuan Tran

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