Summary
This video explores how machine learning and AI are revolutionizing trading and investing decisions through neural networks. It delves into feed forward neural networks' structure and process, and how recurrent neural networks with memory enhance tasks like stock data prediction. The discussion includes training models with backward and forward propagation, evaluating different strategies, and highlighting the potential of applying AI in trading for better portfolio management.
Introduction to Neural Networks in Trading
Machine learning and AI have influenced trading and investing decisions. Neural networks are introduced as a way to create efficient strategies that can outperform the S&P 500 Buy and Hold returns by incorporating machine learning and AI.
Feed Forward Neural Network
Explanation of feed forward neural networks, their structure, and how they process information through input layers, hidden layers, and output layers with weights and adjustments.
Recurrent Neural Network
Description of recurrent neural networks that use memory to retain past information for tasks like stock data prediction and sentence generation.
Back Propagation Through Time
Explanation of back propagation through time in recurrent neural networks and how it helps to remember important details over multiple time steps.
Code Implementation and Training
Discussion on importing libraries, data preprocessing, setting parameters, training the model with backward and forward propagation, and testing the model on historical data.
Performance Evaluation
Evaluation of different trading strategies using neural networks compared to linear regression, support vector machine, and decision tree models, highlighting the potential of applying AI in trading.
Conclusion and Future Prospects
Overview of the significance of neural networks in trading, potential advancements, and prospects for applying newer strategies in portfolio management and trading using AI.
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