https://github.com/arec1b0/hft_trading_system
High-frequency trading system with backtesting and risk management.
https://github.com/arec1b0/hft_trading_system
algorithms-trading backtesting cpp hft-trading risk-management
Last synced: 6 months ago
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High-frequency trading system with backtesting and risk management.
- Host: GitHub
- URL: https://github.com/arec1b0/hft_trading_system
- Owner: arec1b0
- License: mit
- Created: 2024-09-04T06:00:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-21T02:33:35.000Z (about 1 year ago)
- Last Synced: 2025-03-20T23:33:59.143Z (7 months ago)
- Topics: algorithms-trading, backtesting, cpp, hft-trading, risk-management
- Language: C++
- Homepage:
- Size: 455 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# HFT Trading System
## Project Overview
This is a High-Frequency Trading (HFT) system designed to test various trading strategies through backtesting, as well as to manage risk with a set of defined risk strategies. The system includes a modular design for strategies, risk management, logging, and monitoring, allowing for easy expansion and customization.
## Features
- **Backtesting**: Simulates trades on historical data to assess strategy performance.
- **Trading Strategies**:
- Scalping
- Mean Reversion
- Custom strategies can be easily added.
- **Risk Management**:
- Max Drawdown Strategy
- Exposure Limit Strategy
- Support for additional risk strategies.
- **Logging and Monitoring**: Captures key metrics and logs during system execution.
- **Docker and Kubernetes Support**: The system can be containerized using Docker and orchestrated with Kubernetes.
## Installation
### Prerequisites
- **CMake**: Make sure CMake is installed.
- **GCC**: C++ compiler for building the project.
- **Google Test**: For running the unit tests.
- **Docker** (optional): To run the system in a containerized environment.
### Building the Project
1. Clone the repository:
```bash
git clone https://github.com/dkrizhanovskyi/hft_trading_system.git
cd hft_trading_system
```
2. Build the project:
```bash
mkdir build && cd build
cmake ..
make
```
### Running the Application
Once the project is built, you can run the system using the following command:
```bash
./HFTTradingSystem
```
### Running with Docker
1. Build the Docker image:
```bash
docker build -t hft_trading_system .
```
2. Run the container:
```bash
docker run -p 8080:8080 hft_trading_system
```
### Running the Tests
Unit tests are located in the `tests/` directory. To run the tests, use the provided script or run them manually:
```bash
./scripts/run_tests.sh
```
Alternatively, if you are using CMake:
```bash
cd build
ctest --output-on-failure
```
## Usage
- **Backtesting**: The system allows you to run backtests using historical data. Strategies are configurable through the `strategy_manager` module.
- **Risk Management**: Implement risk strategies to avoid significant losses during trading. Custom risk strategies can be added.
- **Logging**: All trades and system metrics are logged, making it easier to track system performance.
## Configuration
Configurations are stored in `include/config/settings.h`. Key parameters such as file paths for historical data, strategy thresholds, and risk limits can be adjusted here.
Example:
```cpp
// include/config/settings.h
const std::string HISTORICAL_DATA_FILE = "data/historical_data.csv";
const double MAX_DRAWDOWN_LIMIT = 0.2;
const int SCALPING_STRATEGY_THRESHOLD = 5;
```
## Deployment
### Using Kubernetes
1. Deploy the application using Kubernetes:
```bash
kubectl apply -f kubernetes/deployment.yaml
kubectl apply -f kubernetes/service.yaml
```
2. Check the status of pods and services:
```bash
kubectl get pods
kubectl get services
```
### Using Terraform
1. Initialize Terraform:
```bash
cd terraform
terraform init
```
2. Apply the Terraform configuration:
```bash
terraform apply
```
## Contributing
Feel free to fork this repository and contribute by submitting pull requests. Please ensure that your changes are well tested and documented.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.