You're reading from Machine Learning for Algorithmic Trading Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781839217715

Length 820 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Stefan Jansen

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Table of Contents (27) Chapters

Preface

1. Machine Learning for Trading – From Idea to Execution

2. Market and Fundamental Data – Sources and Techniques FREE CHAPTER

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

Index

Symbols

1/N portfolio 132

(first), high, low, and closing (last) price and volume (OHLCV) 35

LSTM architecture 599, 600

Accession Number (adsh) 55

AdaBoost algorithm 367

advantages 369

disadvantages 369

used, for predicting monthly price moves 369, 371

AdaGrad 528

adaptive boosting 366, 367

adaptive learning rates

about 527

AdaGrad 528

adaptive moment derivation (Adam) 528

RMSProp 528

adaptive moment derivation (Adam) 528

agglomerative clustering 435

aggressive strategies 4

Akaike information criterion (AIC) 183

AlexNet 564

AlexNet performance

comparing 566, 567

algorithm

finding, for task 149

Algorithm API 243

algorithmic trading libraries

Alpha Trading Labs

Interactive Brokers

pybacktest

Python Algorithmic Trading Library (PyAlgoTrade)

QuantConnect

Trading with Python

ultrafinance

WorldQuant

AlgoSeek 41

AlgoSeek intraday...

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You're reading from Machine Learning for Algorithmic Trading Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Table of Contents (27) Chapters

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You're reading from Machine Learning for Algorithmic Trading Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Table of Contents (27) Chapters

Index

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