Machine Learning for Algorithmic Trading:Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
pages 页数：820 pages
Product Dimensions:19.05 x 4.7 x 23.5 cm
Publisher Finelybook 出版社：Packt Publishing; 2nd edition (31 July 2020)
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 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