OptiQuant 0.1.0
A comprehensive AI-based library for portfolio optimization
Requires Python: >=3.6

Sure! Below is the complete `README.md` file for your `OptiFolio` library, excluding the Machine Learning and Factor Models sections. This file provides an overview of the library, installation instructions, usage examples, and information on contributing to the project.

```markdown
# OptiFolio

OptiFolio is a comprehensive AI-based 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 OptiFolio, you can use pip:

```bash
pip install optifolio
```

## Usage

Here are some examples of how to use OptiFolio 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.

```python
from optifolio 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.

```python
from optifolio 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.

```python
from optifolio 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.

```python
from optifolio 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.

```python
from optifolio 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.

```python
from optifolio 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.

```python
from optifolio 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](LICENSE) file for details.

## Contact

For more information, please contact [Jotiraditya Banerjee](mailto:joti.ban.2710@gmail.com).
```

### Conclusion

This `README.md` file provides a comprehensive overview of the `OptiFolio` library, including installation instructions, usage examples, and information on contributing to the project. You can further customize it based on your specific needs and add more details as required.