https://github.com/l-gre/tradeai
A Python-based project exploring algorithmic trading strategies, including backtesting, real-time data integration, and predictive modeling with TensorFlow and Keras. Key topics include technical indicators, risk management, and leveraging AWS and broker APIs for automated trading.
https://github.com/l-gre/tradeai
algorithmic-trading aws backtesting broker-api keras machine-learning matplotlib numpy pandas portfolio-optimisation python real-time-data-integration risk-management technical-indicators tensorflow
Last synced: 4 months ago
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A Python-based project exploring algorithmic trading strategies, including backtesting, real-time data integration, and predictive modeling with TensorFlow and Keras. Key topics include technical indicators, risk management, and leveraging AWS and broker APIs for automated trading.
- Host: GitHub
- URL: https://github.com/l-gre/tradeai
- Owner: L-Gre
- Created: 2025-01-14T16:13:49.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-14T17:03:07.000Z (4 months ago)
- Last Synced: 2025-01-14T18:56:34.393Z (4 months ago)
- Topics: algorithmic-trading, aws, backtesting, broker-api, keras, machine-learning, matplotlib, numpy, pandas, portfolio-optimisation, python, real-time-data-integration, risk-management, technical-indicators, tensorflow
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Algorithmic Trading (Python/ML)
## Overview
This project showcases the creation of algorithmic trading strategies using Python, with a focus on integrating machine learning techniques to enhance decision-making. Key features include backtesting engines, real-time data integration and predictive models powered by TensorFlow and Keras.
## Key Features
- **Backtesting Engine**: Evaluate trading strategies using historical data with Python, Pandas, and NumPy.
- **Real-Time Data Integration**: Connect with broker APIs (OANDA) for live trading.
- **Machine Learning Models**: Utilise TensorFlow and Keras for predictive modelling to forecast market trends.
- **Technical Indicators**: Implement and analyse popular trading indicators like moving averages and RSI.
- **Risk Management**: Design strategies with an emphasis on managing trading risks effectively.
- **AWS Integration**: Use AWS for scalable data storage and computation.
## Technologies Used
- **Programming Language**: Python
- **Libraries**: Pandas, NumPy, Matplotlib, TensorFlow, Keras, Scikit-learn
- **Tools**: AWS, Broker APIs, Git
- **Concepts**: Backtesting, Technical Indicators, Risk Management, Real-Time Trading
## Disclaimer
THIS PROJECT IS FOR EDUCATIONAL PURPOSES ONLY AND DOES NOT CONSTITUTE INVESTMENT ADVICE. THE STRATEGIES AND METHODS PRESENTED HERE SHOULD NOT BE USED FOR ACTUAL TRADING WITHOUT PROFESSIONAL GUIDANCE. USE THIS PROJECT AT YOUR OWN RISK.