(as of Apr 29, 2024 07:34:47 UTC – Details)
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 FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook 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 learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes dataWho 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 ContentsMachine Learning for Trading – From Idea to ExecutionMarket and Fundamental Data – Sources and TechniquesAlternative Data for Finance – Categories and Use CasesFinancial Feature Engineering – How to Research Alpha FactorsPortfolio Optimization and Performance EvaluationThe Machine Learning ProcessLinear Models – From Risk Factors to Return ForecastsThe ML4T Workflow – From Model to Strategy Backtesting(N.B. Please use the Look Inside option to see further chapters)
From the Publisher
What’s new in this second edition of Machine Learning for Algorithmic Trading?
This second edition adds a ton of examples that illustrate the ML4T workflow from universe selection, feature engineering and ML model development to strategy design and evaluation. A new chapter on strategy backtesting shows how to work with backtrader and Zipline, and a new appendix describes and tests over 100 different alpha factors.
The book also replicates research recently published in top journals on topics such as extracting risk factors conditioned on stock characteristics with an autoencoder, creating synthetic training data using GANs, and applying a CNN to time series converted to image format to predict returns.
The strategies now target asset classes and trading scenarios beyond US equities at a daily frequency, like international stocks and ETFs or minute-frequency data for an intraday strategy. It also expands coverage of alternative data such as SEC filings to predict earnings surprises, satellite images to classify land use, or financial news to extract topics.
What are the key takeaways from your book?
Using machine learning for trading poses several unique challenges: first, fierce competition due to potentially high rewards in highly efficient market limits the predictive signal in historical market data. Therefore, data becomes the single most important ingredient for a predictive model and requires careful sourcing and handling. In addition, domain expertise is key to realizing the value contained in data through smart feature engineering while avoiding some of the pitfalls of using ML.
Furthermore, ML for trading requires a workflow that integrates predictive modeling with decision making. Many books on ML show how to make good predictions, but to succeed in trading we need to translate predictions into a profitable strategy of buying and selling assets. While we should always keep the ultimate use case of an ML application in mind during development, the opportunities and methodological challenges of backtesting are fairly unique to the trading domain.
How does Machine Learning for Algorithmic Trading differ from other algo trading books?
Compared to more generic ML books, not that many recent alternatives focus on both ML and trading. This book is perhaps the most comprehensive introduction because it covers both financial and ML fundamentals but also replicates recent research applications published by top hedge funds like AQR or at leading ML conferences like NeurIPS.
It is not only quite long with more than 800 pages, but also includes many resources for further study. There are over 150 notebooks that illustrate ML techniques from data sourcing and model development to strategy backtesting and evaluation. In addition, the book lists numerous references and resources so that readers can build on this material to build their own ML for trading practice.
Finally, it covers alternative data sources beyond market and fundamental data. There are three chapters on text data that show for example how to use SEC filings to predict earnings surprises with deep learning, and the book also covers working with image data.
Publisher : Packt Publishing; 2nd ed. edition (July 31, 2020)
Language : English
Paperback : 822 pages
ISBN-10 : 1839217715
ISBN-13 : 978-1839217715
Item Weight : 3.1 pounds
Dimensions : 9.25 x 7.52 x 1.69 inches