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Machine Learning for Algorithmic Trading
Machine Learning for Algorithmic Trading

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

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Machine Learning for Algorithmic Trading

Market and Fundamental Data – Sources and Techniques

Data has always been an essential driver of trading, and traders have long made efforts to gain an advantage from access to superior information. These efforts date back at least to the rumors that the House of Rothschild benefited handsomely from bond purchases upon advance news about the British victory at Waterloo, which was carried by pigeons across the channel.

Today, investments in faster data access take the shape of the Go West consortium of leading high-frequency trading (HFT) firms that connects the Chicago Mercantile Exchange (CME) with Tokyo. The round-trip latency between the CME and the BATS (Better Alternative Trading System) exchanges in New York has dropped to close to the theoretical limit of eight milliseconds as traders compete to exploit arbitrage opportunities. At the same time, regulators and exchanges have started to introduce speed bumps that slow down trading to limit the adverse effects on competition of uneven access to information.

Traditionally, investors mostly relied on publicly available market and fundamental data. Efforts to create or acquire private datasets, for example, through proprietary surveys, were limited. Conventional strategies focus on equity fundamentals and build financial models on reported financials, possibly combined with industry or macro data to project earnings per share and stock prices. Alternatively, they leverage technical analysis to extract signals from market data using indicators computed from price and volume information.

Machine learning (ML) algorithms promise to exploit market and fundamental data more efficiently than human-defined rules and heuristics, particularly when combined with alternative data, which is the topic of the next chapter. We will illustrate how to apply ML algorithms ranging from linear models to recurrent neural networks (RNNs) to market and fundamental data and generate tradeable signals.

This chapter introduces market and fundamental data sources and explains how they reflect the environment in which they are created. The details of the trading environment matter not only for the proper interpretation of market data but also for the design and execution of your strategy and the implementation of realistic backtesting simulations.

We also illustrate how to access and work with trading and financial statement data from various sources using Python.

In particular, this chapter will cover the following topics:

  • How market data reflects the structure of the trading environment
  • Working with trade and quote data at minute frequency
  • Reconstructing an order book from tick data using Nasdaq ITCH
  • Summarizing tick data using various types of bars
  • Working with eXtensible Business Reporting Language (XBRL)-encoded electronic filings
  • Parsing and combining market and fundamental data to create a price-to-earnings (P/E) series
  • How to access various market and fundamental data sources using Python

You can find the code samples for this chapter and links to additional resources in the corresponding directory of the GitHub repository. The notebooks include color versions of the images.

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Key benefits

  • 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

Description

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.

Who is this book for?

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.

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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2020
Length: 820 pages
Edition : 2nd
Language : English
ISBN-13 : 9781839217715
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Table of Contents

25 Chapters
Machine Learning for Trading – From Idea to Execution Chevron down icon Chevron up icon
Market and Fundamental Data – Sources and Techniques Chevron down icon Chevron up icon
Alternative Data for Finance – Categories and Use Cases Chevron down icon Chevron up icon
Financial Feature Engineering – How to Research Alpha Factors Chevron down icon Chevron up icon
Portfolio Optimization and Performance Evaluation Chevron down icon Chevron up icon
The Machine Learning Process Chevron down icon Chevron up icon
Linear Models – From Risk Factors to Return Forecasts Chevron down icon Chevron up icon
The ML4T Workflow – From Model to Strategy Backtesting Chevron down icon Chevron up icon
Time-Series Models for Volatility Forecasts and Statistical Arbitrage Chevron down icon Chevron up icon
Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading Chevron down icon Chevron up icon
Random Forests – A Long-Short Strategy for Japanese Stocks Chevron down icon Chevron up icon
Boosting Your Trading Strategy Chevron down icon Chevron up icon
Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Chevron down icon Chevron up icon
Text Data for Trading – Sentiment Analysis Chevron down icon Chevron up icon
Topic Modeling – Summarizing Financial News Chevron down icon Chevron up icon
Word Embeddings for Earnings Calls and SEC Filings Chevron down icon Chevron up icon
Deep Learning for Trading Chevron down icon Chevron up icon
CNNs for Financial Time Series and Satellite Images Chevron down icon Chevron up icon
RNNs for Multivariate Time Series and Sentiment Analysis Chevron down icon Chevron up icon
Autoencoders for Conditional Risk Factors and Asset Pricing Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthetic Time-Series Data Chevron down icon Chevron up icon
Deep Reinforcement Learning – Building a Trading Agent Chevron down icon Chevron up icon
Conclusions and Next Steps Chevron down icon Chevron up icon
References Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(45 Ratings)
5 star 57.8%
4 star 8.9%
3 star 17.8%
2 star 11.1%
1 star 4.4%
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Alexandr Gonchar Oct 27, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The media could not be loaded. There is a good amount of literature on machine learning algorithms fundamentals and advances and the same I can tell about finance and econometrics books. The literature on the intersection of these fields is used to be very highly specialized and rather too complicated for the newcomers.This new book "Machine Learning for Algorithmic Trading" aims exactly to fill this gap and guides a reader through a clear roadmap:- getting and cleaning the data;- extracting predictive signals;- build trading strategies;- build portfolios of assets and strategies;- test their performance historically and in the simulations.Last but not least, this book is relying on popular and proven Python libraries and solutions alongside state-of-the-art techniques as generative adversarial networks for simulations and reinforcement learning for active trading. Highly recommend for new-coming practitioners and seasoned players in the field!
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Kutschera Apr 06, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good read
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Yuxing Yan Aug 18, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very good textbook for quantitative finance major students. If we have an MSF program, I will definitively adopt it as my textbook. Since all Python programs are available at Github, it will help me and my students to replicate many trading strategies explained in the book. It is my habit to replicate others’ results first, then try to modify their programs according to my own needs.
Amazon Verified review Amazon
Akshit shah Aug 15, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a machine learning practitioner.I always wanted to go into the trading space and apply my knowledge, I have been looking up the internet and had so many overwhelming knowledge. It was really difficult to find an ideal book. Then I came across this book and It helped me understand different ways and how to develop different models and understand the use of it in algorithmic trading. Thanks a lot for this book
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Frank S Dec 03, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For example, yes all of the photos are black and white. However in the preface there's very clear instruction on where you can find the color versions (PDFs in the github repo, for those who eschew prefaces) and if you intend to use any of the Python code that goes along with this tome, you'll see the color versions can often also be found in the Jupyter notebooks - a fact frequently referenced in the first two chapters.Second, getting python environments up and running smoothly is, unfortunately, rarely a very easy task. This is certainly not exclusive to this particular use case.If I had any complaint at all about the book, it's that it is overly thorough so you may find yourself slogging through some tedium as you begin. It's not broken up in a way that easily allows for skipping ahead (at least not for my prior knowledge set). In the first couple chapters I've found I've needed - on average - every other paragraph and that the subject matter is the source of the dryness, not the author's use of language; which so far has been smooth and flows far more gracefully than my own.I will update after more extensive use of the author's code.
Amazon Verified review Amazon
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