Backpropagation in Trading

Backpropagation is the fundamental algorithm used to train neural networks by calculating the gradient of the loss function with respect to the network's weights. It works by propagating the error backwards through the layers of the network, allowing it to adjust its internal parameters to minimize the prediction error.

In the context of building trading models, backpropagation is what allows the network to learn from its past mistakes. As the model makes predictions, it compares them to actual market outcomes and uses backpropagation to update its weights accordingly.

This iterative process is what enables the model to improve its performance over time. It is the engine that powers the learning process in deep learning models used for volatility forecasting.

Mastering backpropagation is essential for anyone building custom neural network models for financial applications.