About the Book
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.
Review :
"Algorithmic Trading is about timing the market using data and algorithms in order to improve your own trading performance, outcomes, and earnings. The wealth of techniques, algorithms, and models that are used for those purposes are presented comprehensively in this giant book and are also applicable to countless other predictive modeling applications and diverse use cases. That makes this an excellent machine learning book for all learners and users of predictive algorithms in data science and analytics."
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Dr Kirk Borne, Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton, and co-author of Ten Signs of Data Science Maturity
"Stock markets are one of the most uncertain sectors, where decision making is often more an art than a science. Machine Learning is one of the best resources to analyze a large amount of data and make the most reasonable predictions. In his book, Stefan Jansen describes all cutting-edge methods, starting from the basic concepts concerning the dynamics of a stock market and going deeper and deeper into the application of robust algorithms to implement predictive analytics. With a clear, concise, and effective style, the author guides the reader on a journey to discover time-series analysis, regression methods, Bayesian algorithms, NLP, and GANs. All algorithms are provided with financial explanations and practical examples to help the reader start making rational and intelligent investments!"
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Giuseppe Bonaccorso, Global Head of Innovative Data Science at Bayer Pharmaceuticals, and author of Mastering Machine Learning Algorithms Second Edition
"If you have done a finance module before, you will know that data and mathematics comes together very well in the world of trading. This idea is further reinforced in the book "The Man who Solved the Market" by Gregory Zuckerman. As the world of data grows in the 4 Vs dimension, namely Volume, Variety, Velocity, and Veracity, the circumstances present many opportunities for data to be used in algorithmic trading. Stefan covers the topic of algorithmic trading comprehensively, from selecting features and portfolio management to using text mining to spot trading opportunities. You will be able to find lots of possible use cases for Machine Learning in your trading! Together with the tools stated in the book which are open-source (no license fees!), your entry into the algorithmic trading world will be easier."
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Koo Ping Shung, Co-founder & Practicum Director at Data Science Rex, Co-founder of DataScience SG, and LinkedIn Top Voice 2020
