Machine Learning for Algorithmic Trading – Second Edition
- Paperback: 820 pages
- Publisher: WOW! eBook (July 31, 2020)
- Language: English
- ISBN-10: 1839217715
- ISBN-13: 978-1839217715
Machine Learning for Algorithmic Trading, 2nd Edition: 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 thoroughly revised and expanded Machine Learning for Algorithmic Trading, Second Edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.
This edition 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 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.
- 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
- Adapt generative adversarial networks to create synthetic time series
- Design autoencoders to learn risk factors conditional on stock characteristics
By the end of the Machine Learning for Algorithmic Trading, 2nd Edition book, you will be proficient in translating machine learning model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.