https://github.com/deepbiolab/drl-trading
Research on Deep Reinforcement Learning (DRL) for stock trading
https://github.com/deepbiolab/drl-trading
ai-trading ddpg dqn reinforcement-learning
Last synced: 2 months ago
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Research on Deep Reinforcement Learning (DRL) for stock trading
- Host: GitHub
- URL: https://github.com/deepbiolab/drl-trading
- Owner: deepbiolab
- License: mit
- Created: 2025-02-18T00:37:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-25T16:55:29.000Z (over 1 year ago)
- Last Synced: 2025-05-23T22:38:08.350Z (about 1 year ago)
- Topics: ai-trading, ddpg, dqn, reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 3.95 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep Reinforcement Learning Trading Agent
This project implements a Deep Reinforcement Learning (DRL) agent for trading financial assets using historical market data. The agent is trained using the Deep Q-Learning (DQN) algorithm and supports both training and testing modes.
## Features
- **Deep Q-Learning Implementation**:
- Supports epsilon-greedy exploration.
- Uses experience replay and target networks for stable learning.
- **Customizable Environment**:
- Simulates trading scenarios with historical market data.
- Supports multiple technical indicators like Bollinger Bands and Moving Averages.
- **Data Normalization**:
- Static and rolling normalization methods.
- Automatic method selection based on data characteristics.
- **Technical Indicators**:
- Automatic parameter selection for Moving Averages, Bollinger Bands, RSI, and more.
- **Visualization**:
- Plots trading behavior and model losses.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-repo/drl-trading.git
cd drl-trading
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Ensure you have the dataset in the `datasets/` directory. The default dataset is `datasets/AAPL_2009-2010_6m_raw_1d.csv`.
## Usage
### Command-Line Arguments
The main script (`main.py`) uses command-line arguments to configure the training and testing process. Below is a list of supported arguments:
| Argument | Default Value | Description |
| -------- | ------------- | ----------- |
| `--data_path` | `datasets/AAPL_2009-2010_6m_raw_1d.csv`| Path to the dataset. |
| `--backtest_data` | `datasets/GOOG_2009-2010_6m_raw_1d.csv`| Path to the backtestdataset. |
| `--cv` | `True` | Whether to use cross validation. |
| `--n_folds` | `5` | Number of folds for cross validation. |
| `--train_split` | `0.8` | Train/test split ratio. |
| `--n_episodes` | `3` | Number of training episodes. |
| `--window` | `1` | Window size for averaging scores. |
| `--window_size` | `1` | Observation window size. |
| `--epsilon` | `1.0` | Initial epsilon for exploration. |
| `--eps_min` | `0.01` | Minimum epsilon value. |
| `--eps_decay` | `0.995` | Epsilon decay rate. |
| `--buffer_size` | `1000` | Replay buffer size. |
| `--batch_size` | `32` | Training batch size. |
| `--gamma` | `0.95` | Discount factor. |
| `--alpha` | `1e-3` | Soft update parameter. |
| `--lr` | `0.001` | Learning rate. |
| `--update_step` | `4` | Steps between network updates. |
| `--seed` | `42` | Random seed. |
| `--verbose` | `False` | Enable verbose output. |
| `--model_path` | `checkpoint.pth` | Path to save/load the model. |
| `--mode` | `both` | Run mode: `train`, `backtest`. |
### Example Commands
#### Train and Test
```bash
python main.py
```
#### Cross Validation
```bash
python main.py --cv --n_folds 5
```
#### Backtest Only
```bash
python main.py --mode backtest \
--model_path checkpoints/checkpoint.pth \
--backtest_data datasets/GOOG_2009-2010_6m_raw_1d.csv
```
## Project Structure
```
drl-trading/
├── datasets/
│ ├── GOOG_2009-2010_6m_raw_1d.csv # Directory for backtesting
│ └── AAPL_2009-2010_6m_raw_1d.csv # Directory for training and testing datasets
├── src/ # Source code directory
│ ├── agent.py # DQN agent implementation
│ ├── environment.py # Trading environment
│ ├── features.py # Technical indicators computation
│ ├── model.py # Neural network model for DQN
│ ├── preprocess.py # Data preprocessing utilities
│ ├── normalize.py # Data normalization utilities
│ ├── replay_buffer.py # Experience replay buffer
│ └── plots.py # Plotting utilities
├── checkpoints/ # Directory for saving model checkpoints
│ └── checkpoint.pth # Checkpoint file
├── results/ # Directory for backtesting results
│ └── results.csv # Results file
├── main.py # Main script for training and testing
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
## Development
### Add New Features
1. Extend the `src/` directory with new modules.
2. Update `main.py` to integrate new features or arguments.
3. Add test cases to ensure functionality.
### Testing
Run the scripts in `src/` individually to test specific components. For example:
```bash
python -m src.environment
```
## Contributions
Contributions are welcome! Please fork the repository and submit a pull request with detailed explanations of your changes.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.