Modern Portfolio Theory Optimizer

This project is a Python implementation of Modern Portfolio Theory (MPT) to optimize a portfolio of stocks using historical data. The goal is to maximize the Sharpe ratio, which represents the risk-adjusted return of the portfolio.

Methodologies

Two separate methodologies are presented:

• using Scipy optimizer
• using a MonteCarlo simulation across 100,000 samples

Features

• Fetch stock price data using yfinance library
• Calculate daily returns, mean returns, and covariance matrix
• Optimize the portfolio by maximizing the Sharpe ratio
• Display individual stock performance, optimized portfolio weights, and overall portfolio performance

Dependencies

• numpy
• pandas
• yfinance
• datetime
• scipy

To install the dependencies, run:

pip install numpy pandas yfinance datetime scipy

Usage

1. Define the stocks in your portfolio by modifying the stocks list:

stocks = ['BHP.AX', 'BXB.AX', 'CSL.AX', 'MQG.AX', 'NAB.AX', 'RIO.AX', 'RMD.AX', 'SHL.AX', 'TCL.AX', 'WDS.AX', 'WTC.AX', 'TNE.AX', 'TLX.AX', 'APA.AX', 'TLS.AX']

2. Set the time period for the analysis by modifying the start_date and end_date variables:

end_date = datetime.datetime.now() start_date = end_date - datetime.timedelta(days=180)

3. Run the script to fetch stock price data, calculate returns and optimize the portfolio:

python mpt_optimizer.py

4. The script will display the individual stock performance, optimized portfolio weights, and overall portfolio performance, including expected return, volatility, and Sharpe ratio.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Please make sure to update tests as appropriate.

License

MIT