This repository features a comprehensive algorithmic trading system designed for multi-asset strategies. It leverages various machine learning techniques, including deep neural networks (DNN) and linear regression, to optimize trading strategies. The system incorporates backtesting capabilities using vectorized methods, ensuring efficient evaluation of strategies.
For detailed releases and updates, visit the Releases section. You can download the necessary files and execute them for your trading analysis.
- Features
- Technologies Used
- Installation
- Usage
- Strategies Implemented
- Machine Learning Integration
- Backtesting
- Contributing
- License
- Multi-asset trading strategies
- Implementation of Simple Moving Average (SMA)
- Machine Learning techniques for strategy optimization
- Deep Neural Networks for predictive modeling
- K-means clustering for market segmentation
- Linear regression for trend analysis
- Random walk simulation for risk assessment
- Vectorized backtesting for efficiency
- Python
- TensorFlow
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebooks
- Google Colab
To get started with the trading system, follow these steps:
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Clone the repository:
git clone https://github.com/arnolddelaguila/Advanced-Multi-Asset-Algorithmic-Trading-System-with-Machine-Learning-Integration.git
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Navigate to the directory:
cd Advanced-Multi-Asset-Algorithmic-Trading-System-with-Machine-Learning-Integration -
Install required packages:
pip install -r requirements.txt
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Open in Google Colab: You can also run the notebooks directly in Google Colab for an interactive experience.
After installation, you can start using the trading system by following these steps:
- Open the Jupyter Notebook or Google Colab file.
- Load your data for the assets you want to trade.
- Select the trading strategy you want to implement.
- Run the cells to execute the strategy and view results.
For a comprehensive guide on usage, refer to the documentation in the repository.
The SMA strategy calculates the average price over a specified period. This method helps identify trends and potential buy/sell signals.
This technique segments the market into clusters based on asset behavior. It allows traders to identify similar asset movements and optimize their trading strategies accordingly.
Linear regression analyzes the relationship between asset prices and other variables. This method helps in predicting future price movements based on historical data.
The random walk model simulates price movements. It provides insights into the unpredictability of markets and assists in risk management.
The trading system integrates several machine learning techniques to enhance decision-making:
DNNs are used for complex pattern recognition in financial data. They help in predicting asset prices based on historical trends.
Machine learning algorithms are employed to optimize trading strategies. This process involves fine-tuning parameters to maximize returns while minimizing risks.
Backtesting is crucial for evaluating trading strategies. The system uses vectorized methods to simulate trades based on historical data. This approach ensures efficiency and accuracy in performance evaluation.
- Load historical data for the assets.
- Choose the trading strategy.
- Run the backtesting function to evaluate performance metrics.
The backtesting module provides various performance metrics, including:
- Total return
- Sharpe ratio
- Maximum drawdown
- Win rate
Contributions are welcome. If you have suggestions or improvements, please fork the repository and submit a pull request. Ensure your code adheres to the existing style and includes tests where applicable.
This project is licensed under the MIT License. See the LICENSE file for details.
For more information and updates, check the Releases section. Download the necessary files and execute them to explore the trading strategies in depth.