4.3 KiB
Mean Reversion Trading System for EUR/USD
Welcome to the Mean Reversion Trading System project, specifically tailored for the EUR/USD forex pair. This project combines the precision of machine learning (ML), the robustness of Backtrader for strategy evaluation, and the real-world applicability of trading via the Oanda platform. It’s structured for scalability, leveraging containerization for development consistency and serverless architecture for operational efficiency.
Project Overview
This system is designed for traders and developers interested in exploring and deploying automated trading strategies. It focuses on the mean reversion principle, a well-regarded concept in finance that suggests asset prices and returns eventually move back towards the mean or average. This project aims to capitalize on this phenomenon using historical EUR/USD data, predictive modeling, and live execution.
Project Structure
/mean-reversion-trading
|-- /data
| |-- /raw # Store raw historical data from Oanda
| `-- /processed # Processed data, ready for ML
|
|-- /models
| |-- /trained # Location for storing trained models
| `-- model_training.py # Script for ML model training
|
|-- /strategies
| |-- mean_reversion_strategy.py # Strategy implementation for Backtrader
|
|-- /backtesting
| |-- backtest.py # Backtesting script using Backtrader
|
|-- /trading
| |-- live_trade.py # Live trading execution script
|
|-- /utils
| |-- data_fetcher.py # Utility script for data retrieval
| |-- feature_engineering.py # Data processing and feature engineering
| `-- indicators.py # Custom technical indicators
|
|-- Dockerfile # For containerization
|-- requirements.txt # Python package dependencies
`-- README.md # Project documentation
Detailed Component Overview
- /data: Contains both raw and processed datasets. Raw data is directly fetched from Oanda, while processed data includes features engineered to enhance model training.
- /models: This directory is pivotal for the ML lifecycle, encompassing scripts for training, validation, and serialization of models.
- /strategies: Implements trading strategies within the Backtrader framework, enabling a seamless transition from theory to practical testing.
- /backtesting & /trading: These separate concerns between historical strategy evaluation and real-world application, ensuring a clear pathway from concept to execution.
- /utils: A collection of utility scripts support the data pipeline, from fetching and cleaning to feature engineering, alongside custom indicator development for strategic analysis.
Getting Started
Installation
Ensure Python 3.8+ is installed on your system. Clone this repository and navigate into the project directory. Set up a virtual environment and install the required dependencies:
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
pip install -r requirements.txt
Environment Configuration
Create a .env file in the project root. Add your Oanda API credentials here:
OANDA_ACCOUNT_ID=<your_account_id>
OANDA_API_KEY=<your_api_key>
Running the Project
Data Preparation
Fetch and prepare your data for analysis and model training:
python utils/data_fetcher.py
Model Training
Train your model using historical data:
python models/model_training.py
Backtesting
Evaluate your strategy with Backtrader:
python backtesting/backtest.py
Live Trading
Execute your strategy in the live market:
python trading/live_trade.py
Deployment
For containerization, build the Docker image with:
docker build -t mean-reversion-trading .
To run the containerized application:
docker run -it mean-reversion-trading
For serverless deployment, refer to your cloud provider's documentation for deploying Docker containers and setting up serverless functions for tasks like scheduled data fetching and model retraining.
Contributing
We welcome contributions! Whether it's feature requests, bug reports, or code contributions, please feel free to reach out or submit a pull request.
License
Distributed under the MIT License. See LICENSE for more information.