Hands-On Machine Learning for Algorithmic Trading

Gradient descent

Gradient descent is a general-purpose optimization algorithm that will find stationary points of smooth functions. The solution will be a global optimum if the objective function is convex. Variations of gradient descent are widely used in the training of complex neural networks, but also to compute solutions for MLE problems.

The algorithm uses the gradient of the objective function that contains its partial derivatives with respect to the parameters. These derivatives indicate how much the objective changes for infinitesimal steps in the direction of the corresponding parameters. It turns out that the maximal change of the function value results from a step in the direction of the gradient itself.

Hence, when minimizing ...