This project performs a comprehensive analysis of the Dow Jones Industrial Average (DJAI) stocks, implements a mean reversion trading strategy, and compares its performance to the DJAI benchmark.
- Fetches up-to-date DJAI constituent data from Wikipedia
- Downloads 10 years of historical stock data for DJAI components using yfinance
- Implements a mean reversion strategy based on daily biggest losers
- Calculates key performance metrics: annualized return, volatility, Sharpe ratio, and max drawdown
- Compares the strategy's performance against the DJAI benchmark (DIA ETF)
- Python 3.7+
- pandas
- yfinance
- numpy
- matplotlib (for visualization, if needed)
Run the Jupyter notebook DJAI Stock Analysis.ipynb to execute the analysis:
jupyter notebook "DJAI Stock Analysis.ipynb"
The notebook will guide you through the following steps:
- Fetching DJAI constituent data
- Downloading historical stock data
- Calculating daily returns
- Implementing the mean reversion strategy
- Calculating performance metrics
- Comparing results with the DJAI benchmark
The notebook will output performance metrics for both the mean reversion strategy and the DJAI benchmark, including:
- Annualized Return
- Annualized Volatility
- Sharpe Ratio
It will also provide a comparison of the strategy's performance against the benchmark.
Contributions to improve the analysis or add new features are welcome. Please feel free to submit a pull request or open an issue to discuss potential changes/additions.
This project is open source and available under the MIT License.
This project is for educational purposes only. It is not financial advice and should not be used to make investment decisions. Always do your own research and consult with a qualified financial advisor before making any investment.