Portfolio Analysis and Optimization Project

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.

Project Structure

• 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.

Getting Started

1. Clone the Repository

   git clone <repository-url>
   

2. Install Dependencies
   Run the following command to install required packages:

   pip install -r requirements.txt
   

3. Setup Ticker Symbols
   Edit tickers.txt to include your target ticker symbols (e.g., AAPL, GOOG4 MSFT).

4. Running the Analysis Execute the analysis script from the project root:

   python src/analyze.py
   

5. 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.

Presentation Materials

For an overview of the project and detailed explanations, refer to:

• Project Presentation (PDF)
• Project Video Explanation