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Machine Learning for Algorithmic Trading - Second Edition
Machine Learning for Algorithmic Trading - Second Edition

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

By Stefan Jansen
$46.99 $31.99
Book Jul 2020 822 pages 2nd Edition
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Machine Learning for Algorithmic Trading - Second Edition

Machine Learning for Trading – From Idea to Execution

Algorithmic trading relies on computer programs that execute algorithms to automate some or all elements of a trading strategy. Algorithms are a sequence of steps or rules designed to achieve a goal. They can take many forms and facilitate optimization throughout the investment process, from idea generation to asset allocation, trade execution, and risk management.

Machine learning (ML) involves algorithms that learn rules or patterns from data to achieve a goal such as minimizing a prediction error. The examples in this book will illustrate how ML algorithms can extract information from data to support or automate key investment activities. These activities include observing the market and analyzing data to form expectations about the future and decide on placing buy or sell orders, as well as managing the resulting portfolio to produce attractive returns relative to the risk.

Ultimately, the goal...

The rise of ML in the investment industry

The investment industry has evolved dramatically over the last several decades and continues to do so amid increased competition, technological advances, and a challenging economic environment. This section reviews key trends that have shaped the overall investment environment and the context for algorithmic trading and the use of ML more specifically.

The trends that have propelled algorithmic trading and ML to their current prominence include:

  • Changes in the market microstructure, such as the spread of electronic trading and the integration of markets across asset classes and geographies
  • The development of investment strategies framed in terms of risk-factor exposure, as opposed to asset classes
  • The revolutions in computing power, data generation and management, and statistical methods, including breakthroughs in deep learning
  • The outperformance of the pioneers in algorithmic trading relative to human...

Designing and executing an ML-driven strategy

In this book, we demonstrate how ML fits into the overall process of designing, executing, and evaluating a trading strategy. To this end, we'll assume that an ML-based strategy is driven by data sources that contain predictive signals for the target universe and strategy, which, after suitable preprocessing and feature engineering, permit an ML model to predict asset returns or other strategy inputs. The model predictions, in turn, translate into buy or sell orders based on human discretion or automated rules, which in turn may be manually encoded or learned by another ML algorithm in an end-to-end approach.

Figure 1.1 depicts the key steps in this workflow, which also shapes the organization of this book:

Figure 1.1: The ML4T workflow

Part 1 introduces important skills and techniques that apply across different strategies and ML use cases. These include the following:

  • How to source and manage important...

ML for trading – strategies and use cases

In practice, we apply ML to trading in the context of a specific strategy to meet a certain business goal. In this section, we briefly describe how trading strategies have evolved and diversified, and outline real-world examples of ML applications, highlighting how they relate to the content covered in this book.

The evolution of algorithmic strategies

Quantitative strategies have evolved and become more sophisticated in three waves:

  1. In the 1980s and 1990s, signals often emerged from academic research and used a single or very few inputs derived from market and fundamental data. AQR, one of the largest quantitative hedge funds today, was founded in 1998 to implement such strategies at scale. These signals are now largely commoditized and available as ETF, such as basic mean-reversion strategies.
  2. In the 2000s, factor-based investing proliferated based on the pioneering work by Eugene Fama and Kenneth French and...

Summary

In this chapter, we reviewed key industry trends around algorithmic trading strategies, the emergence of alternative data, and the use of ML to exploit these new sources of informational advantage. Furthermore, we introduced key elements of the ML4T workflow and outlined important use cases of ML for trading in the context of different strategies.

In the next two chapters, we will take a closer look at the oil that fuels any algorithmic trading strategy—the market, fundamental, and alternative data sources—using ML.

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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.

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

Product Details

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Publication date : Jul 31, 2020
Length 822 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781839217715
Category :

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Product Details


Publication date : Jul 31, 2020
Length 822 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781839217715
Category :

Table of Contents

27 Chapters
Preface Chevron down icon Chevron up icon
1. Machine Learning for Trading – From Idea to Execution Chevron down icon Chevron up icon
2. Market and Fundamental Data – Sources and Techniques Chevron down icon Chevron up icon
3. Alternative Data for Finance – Categories and Use Cases Chevron down icon Chevron up icon
4. Financial Feature Engineering – How to Research Alpha Factors Chevron down icon Chevron up icon
5. Portfolio Optimization and Performance Evaluation Chevron down icon Chevron up icon
6. The Machine Learning Process Chevron down icon Chevron up icon
7. Linear Models – From Risk Factors to Return Forecasts Chevron down icon Chevron up icon
8. The ML4T Workflow – From Model to Strategy Backtesting Chevron down icon Chevron up icon
9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage Chevron down icon Chevron up icon
10. Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading Chevron down icon Chevron up icon
11. Random Forests – A Long-Short Strategy for Japanese Stocks Chevron down icon Chevron up icon
12. Boosting Your Trading Strategy Chevron down icon Chevron up icon
13. Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Chevron down icon Chevron up icon
14. Text Data for Trading – Sentiment Analysis Chevron down icon Chevron up icon
15. Topic Modeling – Summarizing Financial News Chevron down icon Chevron up icon
16. Word Embeddings for Earnings Calls and SEC Filings Chevron down icon Chevron up icon
17. Deep Learning for Trading Chevron down icon Chevron up icon
18. CNNs for Financial Time Series and Satellite Images Chevron down icon Chevron up icon
19. RNNs for Multivariate Time Series and Sentiment Analysis Chevron down icon Chevron up icon
20. Autoencoders for Conditional Risk Factors and Asset Pricing Chevron down icon Chevron up icon
21. Generative Adversarial Networks for Synthetic Time-Series Data Chevron down icon Chevron up icon
22. Deep Reinforcement Learning – Building a Trading Agent Chevron down icon Chevron up icon
23. Conclusions and Next Steps Chevron down icon Chevron up icon
24. References Chevron down icon Chevron up icon
25. Index Chevron down icon Chevron up icon
Appendix: Alpha Factor Library Chevron down icon Chevron up icon

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