Hedges | AI-Powered Trading Platform

Hedges is an autonomous investment engine that uses NLP and Reinforcement Learning to generate trading signals and execute trades.

It is designed to be modular and extensible, allowing for easy experimentation with different models and strategies.

Project Structure

hedges/
├── README.md
├── pyproject.toml          # manage Python dependencies
├── config/
│   └── settings.yaml       # central config for API keys, model params, etc.
├── data/
│   ├── raw/                # unprocessed data dumps (news, market data, etc.)
│   ├── interim/            # any partial cleaning or intermediate files
│   └── processed/          # final, cleaned data ready for modeling
├── src/
│   ├── nlp_signals/        # FinGPT (or other NLP) logic
│   │   ├── __init__.py
│   │   ├── pipeline.py     # text ingestion, cleaning, feature extraction
│   │   └── sentiment.py    # e.g. sentiment classification
│   ├── rl_trading/         # FinRL logic
│   │   ├── __init__.py
│   │   ├── train.py        # RL agent training
│   │   └── evaluate.py     # backtesting & performance metrics
│   └── execution/
│       ├── __init__.py
│       ├── live_trader.py  # broker connections, order execution
│       └── portfolio.py    # basic portfolio tracking
├── tests/
|   ├── conftest.py         # pytest fixtures
│   ├── test_nlp.py         # test NLP pipeline
│   │── test_rl.py          # test RL training
│   ├── test_backtesting.py # test RL backtesting
│   └── test_execution.py   # test live trading
└── scripts/
    ├── run_nlp_pipeline.sh
    ├── run_rl_training.sh
    ├── run_backtest.sh
    ├── run_live_trading.sh
    ├── run_tests.sh
    └── run_all.sh

Installation

This project uses Poetry for dependency management. Install it first if you don't have it already.

pip install poetry

Clone the repository and install dependencies:

git clone git@github.com:dnstock/hedges.git
cd hedges
poetry install

Configuration

Create Settings File

API keys, model hyperparameters, and other settings are stored in config/settings.yaml.

Create it by copying config/settings_template.yaml and modifying in the necessary values.

cp config/settings_template.yaml config/settings.yaml

Create Environment File (optional)

Sensitive information like API keys can optionally be stored in an .env file.

Any variables defined here will override those in config/settings.yaml.

cp .env_template .env

Usage

From the project root directory:

./scripts/run_nlp_pipeline.sh   # run NLP pipeline
./scripts/run_rl_training.sh    # train RL agent
./scripts/run_backtest.sh       # evaluate agent performance
./scripts/run_live_trading.sh   # execute or simulate live trades
./scripts/run_all.sh            # runs all modules (except live trading)

Testing

Run tests with pytest via the run_tests.sh script:

./scripts/run_tests.sh          # run all tests (default)
./scripts/run_tests.sh nlp      # run NLP pipeline tests
./scripts/run_tests.sh rl       # run RL training tests
./scripts/run_tests.sh bt       # run backtesting tests
./scripts/run_tests.sh lt       # run live trading tests

Contributing

Contributions are welcome!

To contribute, please follow these steps:

1. Fork the repository

gh repo fork git@github.com:dnstock/hedges.git

2. Create a new branch

git checkout -b feature/my-feature

3. Make your changes

git add .
git commit -m "Add my feature"

4. Commit your changes to your fork

git push origin feature/my-feature

5. Create a pull request

gh pr create

6. Wait for review and approval

gh pr status

7. Make any requested changes

git add .
git commit -m "Address review comments"
git push origin feature/my-feature

8. Merge your changes

gh pr merge

9. Celebrate your contribution!

🎉

Support

For support, please contact the author.

Authors

• Dan Harcsztark (dnstock)

License

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