num_pages: 0
description: 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. Purchase of the print or Kindle book includes a free eBook in the PDF format. 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. 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. (N.B. Please use the Look Inside option to see further chapters)
language: English
title: Machine Learning for Algorithmic Trading: Predictive Models to Extract Signals from Market and Alternative Data for Systemic Trading Strategies with Python
publisher: Packt Publishing