This project is designed to backtest trading strategies on historical market data, with a specific focus on equity markets. It provides a tiny-framework for analyzing and refining strategies before deploying them in live environments.
I have personally used this repository to backtest my own trading strategies on equities, to evaluate their performance and optimize them.
- Data Integration: Supports integration of historical market data for realistic backtesting scenarios.
- Interactive Jupyter Notebook: The
main.ipynbnotebook offers a user-friendly interface to run backtests and visualize results. - Dependency Management: Utilizes
pyproject.tomlandpoetry.lockfor a consistent and reproducible development environment.
To get started with this project, follow these steps:
-
Clone the Repository:
git clone https://github.com/surya-x/strategy-backtesting.git cd strategy-backtesting -
Install Dependencies:
Ensure you have Poetry installed. Then, run:
poetry install
-
Access the Jupyter Notebook:
Start the Jupyter Notebook server:
poetry run jupyter-lab
Open
strategy1.ipynb,strategy2.ipynbto explore and execute the backtesting framework.
.github/workflows/: GitHub Actions workflows for CI/CD.algorithmic_trading/: Scripts and modules related to trading algorithms.data/: Directory designated for historical market data files.strategy1.ipynb,strategy2.ipynb: Jupyter Notebook for interactive backtesting.poetry.lock&pyproject.toml: Files for dependency management using Poetry.
This repository has been specifically utilized to backtest trading strategies on equities. By using historical equity data, I have been able to assess the viability and optimize the performance of my strategies.
Contributions are welcome! Please fork this repository, create a new branch for your feature or bug fix, and submit a pull request for review.
This project is licensed under the MIT License.
Happy backtesting!