Book Image

Machine Learning for Algorithmic Trading - Second Edition

By : Stefan Jansen
Book Image

Machine Learning for Algorithmic Trading - Second Edition

By: Stefan Jansen

Overview of this book

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.
Table of Contents (27 chapters)
24
References
25
Index

Generative Adversarial Networks for Synthetic Time-Series Data

Following the coverage of autoencoders in the previous chapter, this chapter introduces a second unsupervised deep learning technique: generative adversarial networks (GANs). As with autoencoders, GANs complement the methods for dimensionality reduction and clustering introduced in Chapter 13, Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning.

GANs were invented by Goodfellow et al. in 2014. Yann LeCun has called GANs the "most exciting idea in AI in the last ten years." A GAN trains two neural networks, called the generator and discriminator, in a competitive setting. The generator aims to produce samples that the discriminator is unable to distinguish from a given class of training data. The result is a generative model capable of producing synthetic samples representative of a certain target distribution but artificially and, thus, inexpensively...