MACHINE LEARNING FOR ALGORITHMIC TRADING

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.

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.

You will learn to:

• 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
• Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader
• 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