Process, analyze, and engineer features from large financial time series data sets, and create predictive financial time series models by training and validating machine learning algorithms. For general information on machine learning, see Machine Learning in MATLAB (Statistics and Machine Learning Toolbox) and Supervised Learning Workflow and Algorithms (Statistics and Machine Learning Toolbox).
This topic introduces a series of examples that provide a general workflow for illustrating how capabilities in MATLAB® apply to statistical arbitrage.
Apply techniques for managing, processing, and visualizing large amounts of financial data in MATLAB®.
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.
Use Bayesian optimization to tune hyperparameters in the algorithmic trading model, supervised by the end-of-day return.