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Machine Learning Applications

Apply machine learning techniques to financial 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.

Topics

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.