Machine Learning for Statistical Arbitrage: Introduction

This topic introduces a series of examples that provide a general workflow for illustrating how capabilities in MATLAB^® apply to statistical arbitrage.

Machine Learning for Statistical Arbitrage I: Data Management and Visualization

Apply techniques for managing, processing, and visualizing large amounts of financial data in MATLAB®.

Machine Learning for Statistical Arbitrage II: Feature Engineering and Model Development

Create a continuous-time Markov model of limit order book (LOB) dynamics, and develop a strategy for algorithmic trading based on patterns observed in the data.

Machine Learning for Statistical Arbitrage III: Training, Tuning, and Prediction

Use Bayesian optimization to tune hyperparameters in the algorithmic trading model, supervised by the end-of-day return.