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处理、分析和设计大型金融时间序列数据集的特征,并通过训练和验证机器学习算法来创建具有预测性的金融时间序列模型。有关机器学习的一般信息,请参阅 Machine Learning in MATLABSupervised 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.