Algorithmic Trading with Python and Machine Learning

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.