This project is aimed at preparing and analyzing a low-risk, moderate return portfolio. The system supports the sourcing, analysis, and visualization of stock data along with an optimization workflow.
- src/: Contains the analysis script (
analyze.py) that:- Sources data from the Yahoo yfinance API.
- Generates analysis plots including:
- Trend analysis and seasonal decomposition.
- Differencing of prices (i.e., returns) and corresponding partial autocorrelation.
- Auto correlation and partial autocorrelation for stock prices.
- src/data/: Stores the sourced data for each ticker.
- src/plots/: Holds the generated analysis plots.
- src/requirements.txt: Lists all the Python package dependencies.
- src/tickers.txt: Contains user-provided tickers to parse, source, and create data.
- optimization/optim.ipynb: Contains the notebook for portfolio optimization:
- Implements Markowitz optimization.
- Performs Monte Carlo simulations for benchmarking equity performance against the S&P500.
- Includes methodologies for dollar cost averaging, projecting returns, and estimating the probability of outperformance.
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Clone the Repository
git clone <repository-url> -
Install Dependencies
Run the following command to install required packages:pip install -r requirements.txt -
Setup Ticker Symbols
Edittickers.txtto include your target ticker symbols (e.g., AAPL, GOOG4 MSFT). -
Running the Analysis Execute the analysis script from the project root:
python src/analyze.py -
Portfolio Optimization
Explore the notebook in the
optimization/directory to:- Test different portfolio allocation strategies.
- Compare portfolio performance against benchmark indices.
- Utilize Monte Carlo methods for risk simulation.
For an overview of the project and detailed explanations, refer to: