机器学习应用

将机器学习技术应用于金融应用

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 MATLAB 和Supervised Learning Workflow and Algorithms。

主题

使用机器学习进行统计套利：简介
简要了解统计套利的工作流，然后通过一系列示例来了解如何应用 MATLAB^® 中的功能。
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.
Backtest Deep Learning Model for Algorithmic Trading of Limit Order Book Data
Apply a backtest strategy to measure the performance of a long short-term memory (LSTM) neural network, which is trained and validated on limit order book (LOB) data of a security.