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ML for Trade – 2nd Edition Join the ML4T Community! What’s new in the 2nd edition? Installation, Data Sources and Error Reports Outline & Chapter Summary Part 1: From Data to Strategy Development 01 Machine Learning for Trading: From Idea to Execution 02 Market & Fundamental Data: Sources and Techniques 03 Alternative Data for Finance: Categories and Use Cases 04 Financial Feature Engineering: How to Research Alpha Factors 05 Portfolio Optimization and Performance Evaluation Part 2: Machine Learning for Trading: Fundamentals 06 The Machine Learning Process 07 Linear Models: From Risk Factors to Return Predictions 08 The ML4T Workflow: From Model to Strategy Backtesting 09 Time Series Models for volatility forecasting and statistical arbitrage 10 Bayesian ML: Dynamic Sharpe Ratios and Pairs Trading 11 Random Forests: A Long-Short Strategy for Japanese Stocks 12 Strengthening Your Trading Strategy 13 Data-Driven Risk Factors and Asset Allocation 3 Shared with Unsupervised Natural Language Processing for Trading 14 Text Data for Trading : sentiment analysis 15 Topic Modeling: Summarizing Financial News 16 Word Embeddings for Earnings Calls and SEC Filings Part 4: Deep and Reinforcement Learning 17 Deep Learning for Trading 18 CNN for Financial Time Series and Satellite Images 19 RNN for Multivariate Time Series and Sentiment Analysis 20 Autoencoders for Contingent Risk Factors and asset prices 21 generative adversarial nets for synthetic time series data 22 Deep reinforcement learning: building a trading agent 23 conclusions and next steps 24 Appendix – Alpha factor library

This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a wide range of ML techniques from linear regression to deep reinforcement learning and demonstrates how to build, backtest and evaluate a trading strategy driven by model predictions.

This repo contains more than 150 notebooks that implement the concepts, algorithms, and use cases discussed in the book. They provide numerous examples that show:

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We strongly recommend that you review the notebooks while reading the book; they are usually in an exported state and often contain additional information not included due to space limitations.

To make it easy for readers to ask questions about the book’s content and code examples, as well as develop and implement their own strategies and industry developments, we offer an online platform.

Please join our community and connect with fellow traders interested in using ML for trading strategies, share your experience and learn from each other!

First, this book demonstrates how you can extract signals from a diverse set of data sources and design trading strategies for different asset classes using a wide range of supervised, unsupervised and amplification algorithms. It also provides relevant mathematical and statistical knowledge to facilitate the tuning of an algorithm or the interpretation of the results. Furthermore, it covers the financial background that will help you work with market and fundamental data, extract insightful features and manage the performance of a trading strategy.

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From a practical standpoint, the 2nd edition aims to equip you with the conceptual understanding and tools to develop your own ML-based trading strategies. To this end, it frames ML as a critical element in a process rather than a stand-alone exercise, introducing the end-to-end ML for trading workflows of data acquisition, function engineering, and model optimization strategy design and backtesting .

More specifically, the ML4T workflow starts with generating ideas for a well-defined investment universe, collecting relevant data, and extracting informative features. It also involves the design, tuning and evaluation of ML models suitable for the forecasting task. Finally, it requires developing trading strategies to act on the models’ predictive signals, as well as simulating and evaluating their performance on historical data using a backtesting engine. Once you decide to execute an algorithmic strategy in a real market, you will find yourself repeatedly iterating over this workflow to incorporate new information and a changing environment.

The second edition’s emphasis on the ML4t workflow translates into a new chapter on strategy backtesting, a new appendix describing over 100 different alpha factors, and many new practical applications. We have also rewritten most of the existing content for clarity and readability.

The trading applications now use a wider range of data sources beyond daily US stock prices, including international stocks and ETFs. It also demonstrates how to use ML for an intraday strategy with minute frequency equity data. Furthermore, it expands coverage of alternative data sources to include SEC filings for sentiment analysis and yield forecasts, as well as satellite imagery to classify land use.

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All applications now use the latest available (at the time of writing) software versions such as pandas 1.0 and TensorFlow 2.2. There is also a custom version of Zipline that makes it easy to incorporate machine learning model predictions when designing a trading strategy.

The code examples rely on a wide range of Python libraries from the data science and finance domains.

There is no need to try to install all libraries at once, as this increases the likelihood of encountering version conflicts. Instead, we recommend that you install the libraries required for a specific chapter as you go along.

Update March 2022: zipline reload, pyfolio reload, alphalens reload, and empirical reload are now available on the conda-forge channel. The channel ml4t only contains outdated versions and will be removed soon. Update April 2021: with the Zipline update, there is no need to use Docker anymore. The installation instructions now refer to OS-specific environment files which should simplify your management of the notebooks. Update February 2021: code sample release 2.0 updates the conda environments provided by the Docker image to Python 3.8, Pandas 1.2 and TensorFlow 1.2, among others; the Zipline backtesting environment now uses Python 3.6.

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If you have any problems installing the environments, downloading the data or running the code, please file an issue in the repo (here). Working with issues is described here. Update: You can download the algoseek data used in the book here. See instructions for preprocessing in Chapter 2 and an intraday example with a gradient amplification model in Chapter 12. Update: The figures guide contains color versions of the charts used in the book. Overview & Chapter Summary

The book has four parts that address different challenges that arise when acquiring and working with market, fundamental and alternative data acquisition, developing ML solutions for various predictive tasks in the trading context, and designing and evaluating a trading strategy which relies on predictive signals that through an ML model.

The guide for each chapter includes a READMY with additional information about content, code examples, and additional resources.

The first part provides a framework for developing trading strategies powered by machine learning (ML). It focuses on the data that drives the ML algorithms and strategies discussed in this book, outlines how to design and evaluate functions suitable for ML models, and how to evaluate a portfolio’s manage and measure performance while executing a trading strategy.

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This chapter examines industry trends that have led to the emergence of ML as a source of competitive advantage in the investment industry. We will also look at where ML fits into the investment process to enable algorithmic trading strategies.

This chapter shows how to work with market and fundamental data and describes critical aspects of the environment it reflects. For example, familiarity with different order types and the trading infrastructure is not only important for interpreting the data, but also for correctly designing backtest simulations. We also illustrate how to use Python to access and manipulate trading and financial statement data.

Practical examples demonstrate how to work with trading data from NASDAQ tick data and Algoseek minute bar data with a rich set of attributes that capture the supply-demand dynamics that we will later use for an ML-based intraday strategy. We also cover various data provider APIs and how to obtain financial statement information from the SEC.

This chapter outlines categories and use cases of alternative data, describes criteria for assessing the exploding number of sources and providers, and summarizes

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