Optiquantfolio 0.1.1

A comprehensive AI-based library for portfolio optimization

# Optiquantfolio

Optiquantfolio is a comprehensive library for portfolio optimization. It includes various models such as CAPM, Treynor Ratio, Minimum Variance Portfolio, Robust Optimization, Hierarchical Risk Parity, Genetic Algorithm, and Stochastic Control Models.

## Features

- **CAPM**: Calculate expected returns using the Capital Asset Pricing Model.
- **Treynor Ratio**: Measure the risk-adjusted return of a portfolio.
- **Minimum Variance Portfolio**: Optimize a portfolio to minimize variance.
- **Hierarchical Risk Parity**: Allocate assets using hierarchical clustering.
- **Genetic Algorithm**: Optimize portfolios using genetic algorithms.
- **Stochastic Control**: Optimize portfolios using stochastic control techniques.
- **Robust Optimization**: Perform robust portfolio optimization with uncertainty handling.

## Installation

To install Optiquantfolio, you can use pip:

```bash
pip install optiquantfolio

Usage

Here are some examples of how to use Optiquantfolio for different optimization methods:

CAPM

Input:

• tickers: List of stock tickers (e.g., ['AAPL', 'MSFT']).
• risk_free_rate: Risk-free rate (e.g., 0.01).
• market_ticker: Market index ticker (e.g., '^GSPC').
• start_date: Start date for data fetching (e.g., '2020-01-01').
• end_date: End date for data fetching (e.g., '2023-01-01').

Output:

• Expected return for the portfolio.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='CAPM', tickers=['AAPL', 'MSFT'], risk_free_rate=0.01, market_ticker='^GSPC', start_date='2020-01-01', end_date='2023-01-01')
print(result)

Treynor Ratio

Input:

• portfolio_return: Return of the portfolio (e.g., 0.05).
• risk_free_rate: Risk-free rate (e.g., 0.01).
• portfolio_beta: Beta of the portfolio (e.g., 1.2).

Output:

• Treynor ratio for the portfolio.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='Treynor', portfolio_return=0.05, risk_free_rate=0.01, portfolio_beta=1.2)
print(result)

Minimum Variance Portfolio

Input:

• tickers: List of stock tickers (e.g., ['AAPL', 'MSFT', 'GOOG', 'AMZN']).
• start_date: Start date for data fetching (e.g., '2020-01-01').
• end_date: End date for data fetching (e.g., '2023-01-01').

Output:

• Weights for the minimum variance portfolio.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='MVP', tickers=['AAPL', 'MSFT', 'GOOG', 'AMZN'], start_date='2020-01-01', end_date='2023-01-01')
print(result)

Hierarchical Risk Parity

Input:

• tickers: List of stock tickers (e.g., ['AAPL', 'MSFT', 'GOOG', 'AMZN']).
• start_date: Start date for data fetching (e.g., '2020-01-01').
• end_date: End date for data fetching (e.g., '2023-01-01').

Output:

• Cluster order for hierarchical risk parity.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='HRP', tickers=['AAPL', 'MSFT', 'GOOG', 'AMZN'], start_date='2020-01-01', end_date='2023-01-01')
print(result)

Genetic Algorithm

Input:

• tickers: List of stock tickers (e.g., ['AAPL', 'MSFT', 'GOOG', 'AMZN']).
• start_date: Start date for data fetching (e.g., '2020-01-01').
• end_date: End date for data fetching (e.g., '2023-01-01').

Output:

• Optimized weights for the portfolio.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='GA', tickers=['AAPL', 'MSFT', 'GOOG', 'AMZN'], start_date='2020-01-01', end_date='2023-01-01')
print(result)

Stochastic Control

Input:

• tickers: List of stock tickers (e.g., ['AAPL', 'MSFT', 'GOOG', 'AMZN']).
• start_date: Start date for data fetching (e.g., '2020-01-01').
• end_date: End date for data fetching (e.g., '2023-01-01').

Output:

• Optimized portfolio based on stochastic control techniques.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='Stochastic', tickers=['AAPL', 'MSFT', 'GOOG', 'AMZN'], start_date='2020-01-01', end_date='2023-01-01')
print(result)

Robust Optimization

Input:

• tickers: List of stock tickers (e.g., ['AAPL', 'MSFT', 'GOOG', 'AMZN']).
• start_date: Start date for data fetching (e.g., '2020-01-01').
• end_date: End date for data fetching (e.g., '2023-01-01').

Output:

• Optimized weights for the robust portfolio.

from optiquantfolio import optimize_portfolio

# Example usage
result = optimize_portfolio(method='Robust', tickers=['AAPL', 'MSFT', 'GOOG', 'AMZN'], start_date='2020-01-01', end_date='2023-01-01')
print(result)

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For more information, please contact Jotiraditya Banerjee.