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Akshat111111 authored Jul 4, 2024
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# Real-Time Risk Management Module for Financial Derivatives

## Overview

This repository contains a real-time risk management module designed to enhance the "Hedging-of-Financial-Derivatives" project. The module integrates live market data feeds with risk assessment algorithms to optimize derivative hedging strategies dynamically.

## Features

- **Real-Time Calculations**: Calculates key risk metrics such as Value at Risk (VaR), Conditional Value at Risk (CVaR), and more using live market data.
- **Dynamic Strategy Optimization**: Automatically adjusts hedging strategies based on real-time risk assessments and predefined thresholds.
- **User Interface**: Provides visualization tools to monitor risk metrics and strategy performance.
- **Configurability**: Easily configurable through `config.py` for setting risk thresholds and other parameters.

## Installation

To use the real-time risk management module, follow these steps:

1. **Clone the Repository:**
```bash
git clone https://github.com/your_username/Hedging-of-Financial-Derivatives.git
cd Hedging-of-Financial-Derivatives
Install Dependencies:
Ensure you have Python 3 installed. Install required dependencies

pip install -r requirements.txt

Configuration:
Adjust configuration settings in config.py as per your requirements, including API keys and risk thresholds.


Usage
Running the Real-Time Risk Management Module
Run realtime_risk_management.py to start the module:
python realtime_risk_management.py

The module will continuously fetch live market data, calculate risk metrics, and optimize hedging strategies based on the latest information.

Monitoring and Visualization
Access the dashboard or UI (if implemented) to visualize risk metrics and strategy performance. Modify dashboard.py to customize visualization according to your needs.

Testing

Unit tests are available in the tests directory. Run tests using:


python -m unittest discover -s tests -p "*.py"

Contributing
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request. Ensure tests pass before submitting.

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

### Deployment Considerations

Deployment depends on your specific environment and requirements. Here’s an example using Docker for containerization:

#### Docker Deployment Steps:

1. **Dockerfile**: Create a `Dockerfile` in the project root to build your application environment and define how to run it.

2. **Build Docker Image**: Build a Docker image using the `Dockerfile`, which encapsulates your application and its dependencies.

3. **Run Docker Container**: Deploy the Docker container on your desired platform or infrastructure.

#### Example Dockerfile:

```dockerfile
# Dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "realtime_risk_management.py"]

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