Machine Learning for Algorithmic Trading, 2nd Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python Front Cover

Machine Learning for Algorithmic Trading, 2nd Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

  • Length: 828 pages
  • Edition: 2
  • Publisher:
  • Publication Date: 2020-08-11
  • ISBN-10: 1839217715
  • ISBN-13: 9781839217715
  • Sales Rank: #310131 (See Top 100 Books)

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.

Key Features

  • Design, train, and evaluate machine learning algorithms that underpin automated trading strategies
  • Create your own research and strategy development process to apply predictive modeling to trading decisions
  • Leverage natural language processing and deep learning to extract tradeable signals from market and alternative data

Book Description

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This thoroughly revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.

This edition introduces the end-to-end machine learning for trading workflow from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this workflow using examples that range from linear models and tree-based ensembles to deep-learning techniques from the cutting edge of the research frontier.

This revised version 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 a machine learning model to predict returns from price data for US and international stocks and ETFs. It also demonstrates 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 of the book, you will be proficient in translating machine learning model predictions into a trading strategy that operates at daily or intraday horizons and evaluate its performance.

What you will learn

  • Leverage market, fundamental, and alternative text and image data
  • Research and evaluate alpha factors using statistics, Alphalens, and SHAP values
  • Implement machine learning techniques to solve investment and trading problems
  • Design and fine-tune supervised, unsupervised, and reinforcement learning models
  • Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio
  • Create a pairs trading strategy based on cointegration for US equities and ETFs
  • Train a gradient boosting model to predict intraday returns using Algoseek’s high-quality trades and quotes data

Who This Book Is For

If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.

Some understanding of Python and machine learning techniques is required.

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