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

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. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe 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.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 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 Who this book is forIf 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.

Table of Contents:
Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Financial Feature Engineering Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models The ML4T Workflow Time-Series Models for Volatility Forecasts and Statistical Arbitrage Bayesian ML Random Forests Boosting Your Trading Strategy Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Text Data for Trading Topic Modeling Word Embeddings for Earnings Calls and SEC Filings Deep Learning for Trading CNNs for Financial Time Series and Satellite Images RNNs for Multivariate Time Series and Sentiment Analysis Autoencoders for Conditional Risk Factors and Asset Pricing Generative Adversarial Networks for Synthetic Time-Series Data Deep Reinforcement Learning Conclusions and Next Steps Appendix

About the Author :
Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems. Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank. He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.