Neural Networks in Trading

Overview

• Machine Learning (ML) and Artificial Intelligence (AI) are influencing various fields, including trading and investing.
• Discussing neural networks in trading to develop strategies surpassing S&P 500 Buy and Hold returns.

Introduction to Neural Networks

• Neural networks function similarly to the human brain with neurons.
• Can recognize images (e.g., identifying a cat in a picture) through training.

Types of Neural Networks

Feed Forward Neural Network

• Basic neural network for image classification.
• Example: Identifying a cat from an image through input, hidden, and output layers.
• Adjusts weights based on errors using backpropagation.

Recurrent Neural Network (RNN)

• Suitable for sequential data, like stock prices.
• Has memory, allowing it to remember previous information.
• Backpropagation through time enhances its memory capabilities.

Long Short-Term Memory (LSTM)

• A type of RNN designed to avoid the vanishing gradient problem.
• Uses 'forget gates' to retain important past information.

Neural Networks in Stock Trading

• RNNs can predict stock trends using past price data, RSI, volume, etc.
• Computationally intensive; requires significant power for complex models.
• Models like QuantConnect use CPUs; GPUs may enhance future capabilities.

Strategy Development

• Hyperparameter tuning is crucial (input features, epochs, learning rate).
• Neural networks can improve existing trading strategies and portfolio rebalancing.

Challenges and Solutions

• RNNs face computational challenges and the vanishing gradient problem.
• LSTM mitigates memory loss through forget gates.

Practical Application

• Example strategy using RNN with QuantConnect.
• Neural networks could enhance trading strategies by improving metrics like CAGR and drawdown ratio.

Performance and Optimization

• Comparison with S&P 500: Neural network strategies offer better CAGR to drawdown ratios.
• Potential for leverage to further improve returns.

Conclusion

• Growing potential for ML and AI in trading.
• Future possibilities include applying neural networks across various trading strategies and portfolio management.

Additional Resources

• Suggested to have foundational knowledge in Python and algorithmic trading.
• For more detailed strategies, refer to the complete course covering neural networks and ML strategies.

• This lecture series includes discussions on regression, decision trees, and more advanced ML models.