

# Volara


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Volara is an open-source risk analysis and portfolio management system inspired by BlackRock's Aladdin platform. It aims to provide comprehensive risk assessment and management tools for stocks, securities, and other market instruments.

## Table of Contents

- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Features

- **Comprehensive Risk Analysis**: Assess risk for a wide range of financial instruments including stocks, bonds, derivatives, and more.
- **Real-Time Data Processing**: Continuously update risk assessments based on market changes.
- **Advanced Machine Learning Models**: Utilize state-of-the-art ML algorithms for predictive analytics and risk forecasting.
- **Customizable Risk Metrics**: Calculate and track various risk measures including VaR, Expected Shortfall, and custom metrics.
- **Portfolio Optimization**: Tools for constructing and rebalancing portfolios based on risk-return profiles.
- **Interactive Dashboards**: Visualize risk data and portfolio performance through customizable dashboards.
- **API Integration**: Easy integration with external data sources and other financial systems.

## Installation

To install Volara, run the following command:

```bash
pip install volara
```
## Usage

## Example

```python
from volara.main import fetch_stock_data, AdvancedRealTimeRiskAssessment
import time

if __name__ == "__main__":
    # Example usage
    # from data_integration import fetch_stock_data

    tickers = [
        "AAPL",
        "GOOGL",
        "MSFT",
        "AMZN",
        "^GSPC",
    ]  # Including S&P 500 for market returns
    historical_data = {
        ticker: fetch_stock_data(ticker) for ticker in tickers
    }

    risk_assessor = AdvancedRealTimeRiskAssessment(historical_data)
    risk_assessor.start_continuous_training()

    try:
        # Run for a while to allow some training iterations
        time.sleep(60)

        # Perform risk assessment
        risk_results = risk_assessor.run_risk_assessment(
            forecast_horizon=4
        )  # 1-year forecast

        # Output results
        risk_assessor.output_results(risk_results, "json")
        risk_assessor.output_results(risk_results, "csv")

        # Print some results
        for ticker, measures in risk_results.items():
            print(f"\nRisk Assessment for {ticker}:")
            for measure, value in measures.items():
                if isinstance(value, list):
                    print(
                        f"{measure}: [showing first 5 values] {value[:5]}"
                    )
                else:
                    print(f"{measure}: {value:.4f}")

    finally:
        # Ensure we stop the continuous training when done
        risk_assessor.stop_continuous_training()

```



For more detailed usage examples and API documentation, please visit our [User Guide](docs/user_guide.md).

## Contributing

We welcome contributions from the community! If you'd like to contribute to Volara, please follow these steps:

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

Please read our [Contributing Guidelines](CONTRIBUTING.md) for more details on our code of conduct, and the process for submitting pull requests.

## License
