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

Key takeaways and lessons learned

A central goal of the book was to demonstrate the workflow of extracting signals from data using ML to inform a trading strategy. Figure 23.1 outlines this ML-for-trading workflow. The key takeaways summarized in this section relate to specific challenges we encounter when building sophisticated predictive models for large datasets in the context of financial markets:

Figure 23.1: Key elements of using ML for trading

Important insights to keep in mind as you proceed to the practice of ML for trading include the following:

  • Data is the single most important ingredient that requires careful sourcing and handling.
  • Domain expertise is key to realizing the value contained in data and avoiding some of the pitfalls of using ML.
  • ML offers tools that you can adapt and combine to create solutions for your use case.
  • The choices of model objectives and performance diagnostics are key to productive iterations toward...