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LSTM CodeTrading

Overview

LSTM CodeTrading is a deep learning-based framework for building and testing trading strategies using Long Short-Term Memory (LSTM) neural networks. This project aims to predict financial market trends by leveraging sequential data and time-series analysis, providing insights for informed trading decisions.

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Features

• LSTM Model Architecture: Leverages LSTM networks for capturing temporal dependencies in financial data.
• Customizable Trading Strategies: Enables backtesting and evaluation of user-defined strategies.
• Data Preprocessing: Includes modules for handling and cleaning time-series data.
• Visualization Tools: Provides charts and metrics to evaluate model performance and trading results.

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Requirements

To run this project, ensure you have the following dependencies installed:

• Python 3.8 or higher
• TensorFlow/Keras
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Any other dependencies specific to your project

Install the required dependencies using:

pip install -r requirements.txt

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Usage

1. Clone the Repository:

   git clone https://github.com/ENKI0311/LSTM_CodeTrading.git
   cd LSTM_CodeTrading

2. Prepare the Dataset:

   • Place your historical financial data in the data/ directory.
   • Ensure the data is in a CSV format with appropriate columns (e.g., Date, Open, High, Low, Close, Volume).

3. Train the Model: Run the training script:

   python train.py

4. Backtest the Strategy: Evaluate the strategy's performance:

   python backtest.py

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Project Structure

LSTM_CodeTrading/
├── data/               # Historical financial datasets
├── models/             # Saved LSTM models
├── scripts/            # Core scripts (train, predict, backtest)
├── utils/              # Utility functions for data processing and evaluation
├── results/            # Backtesting results and visualizations
└── README.md           # Project documentation

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure your code adheres to the existing style and includes tests where appropriate.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Contact

For questions or suggestions, feel free to reach out:

• Author: John Chilton (replace with your name if needed)
• Email: [[email protected]]
• GitHub: ENKI0311

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