Machine Learning Applications
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 and Supervised Learning Workflow and Algorithms.
- 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.