Machine Learning for Algorithmic Trading
This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading challenges, and manage the strategy development process from idea to backtesting and evaluation.
Offered by
Difficulty Level
Intermediate
Completion Time
27h24m
Language
English
About Book
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.
Machine Learning for Algorithmic Trading
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 27h24m total length
Machine Learning for Trading – From Idea to Execution
Market and Fundamental Data – Sources and Techniques
Alternative Data for Finance – Categories and Use Cases
Financial Feature Engineering – How to Research Alpha Factors
Portfolio Optimization and Performance Evaluation
The Machine Learning Process
Linear Models – From Risk Factors to Return Forecasts
The ML4T Workflow – From Model to Strategy Backtesting
Time-Series Models for Volatility Forecasts and Statistical Arbitrage
Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading
Random Forests – A Long-Short Strategy for Japanese Stocks
Boosting Your Trading Strategy
Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning
Text Data for Trading – Sentiment Analysis
Topic Modeling – Summarizing Financial News
Word Embeddings for Earnings Calls and SEC Filings
Deep Learning for Trading
CNNs for Financial Time Series and Satellite Images
RNNs for Multivariate Time Series and Sentiment Analysis
Autoencoders for Conditional Risk Factors and Asset Pricing
Generative Adversarial Networks for Synthetic Time-Series Data
Deep Reinforcement Learning – Building a Trading Agent
Conclusions and Next Steps
Appendix – Alpha Factor Library
Related Resources
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