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Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Jul 31, 2020

Length
822 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781839217715

Category :

Languages :

Concepts :

Tools :

- 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

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.

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

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Jul 31, 2020

Length
822 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781839217715

Category :

Languages :

Concepts :

Tools :

Preface

1. Machine Learning for Trading – From Idea to Execution

2. Market and Fundamental Data – Sources and Techniques

3. Alternative Data for Finance – Categories and Use Cases

4. Financial Feature Engineering – How to Research Alpha Factors

5. Portfolio Optimization and Performance Evaluation

6. The Machine Learning Process

7. Linear Models – From Risk Factors to Return Forecasts

8. The ML4T Workflow – From Model to Strategy Backtesting

9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage

10. Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading

11. Random Forests – A Long-Short Strategy for Japanese Stocks

12. Boosting Your Trading Strategy

13. Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning

14. Text Data for Trading – Sentiment Analysis

15. Topic Modeling – Summarizing Financial News

16. Word Embeddings for Earnings Calls and SEC Filings

17. Deep Learning for Trading

18. CNNs for Financial Time Series and Satellite Images

19. RNNs for Multivariate Time Series and Sentiment Analysis

20. Autoencoders for Conditional Risk Factors and Asset Pricing

21. Generative Adversarial Networks for Synthetic Time-Series Data

22. Deep Reinforcement Learning – Building a Trading Agent

23. Conclusions and Next Steps

24. References

25. Index

Appendix: Alpha Factor Library

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