A tool that helps you better invest in the stock market.
- Portfolio Management: Create and manage your investment portfolio.
- Portfolio Optimization: Optimize your portfolio to achieve desired returns with minimal risk.
- Monte Carlo Simulation: Run simulations to predict future performance of your portfolio.
- Dashboard: Visualize your portfolio's performance with interactive charts.
The package is not yet available on PyPI. To install the package, clone the repository and generate a wheel file:
git clone https://github.com/mdeboute/pystock.git
cd pystock
uv sync
uv buildThen, install the wheel file:
pip install dist/pystock-0.1.0-py3-none-any.whl-
Run the Streamlit app:
streamlit run app/app.py
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Navigate through the sidebar to access different features:
- Dashboard: Upload your portfolio file to view its performance.
- Monte Carlo Simulation: Run simulations to predict future performance.
- Portfolio Optimization: Optimize your portfolio to achieve desired returns.
- app Contains the Streamlit app and views.
- pystock: Contains the core logic for portfolio management and optimization.
- tests: Contains unit tests for the project.
- pyproject.toml: Project configuration and dependencies.
Please follow the CONTRIBUTING.md guidelines.
uv is used to manage Python version, library dependencies, packaging and publishing.
To install the needed dependencies:
uv syncIf you add or update some dependencies, you must push the uv.lock.
Ruff is used to ensure isort and flake8 standards with some linting functionnalities.
You can format using:
ruff format pystock/For linting, you can check for errors with (adding option --fix will update your files when ruff is able to do so):
ruff check pystock/Configuration can be found in pyproject.toml.
Code coverage is done with
pytest --cov=.Documentation can be built using sphinx and by installing required dependencies.
python -m sphinx docs/source docs/build/This project is licensed under the MIT License.