Description
Machine Learning for Algorithmic Trading takes an in-depth and comprehensive look at how financial knowledge can be integrated with cutting-edge machine learning algorithms to help traders in financial markets create more profitable strategies. Taking a process-oriented approach, the author examines the entire algorithmic trading cycle, from market data collection and feature engineering to portfolio optimization.
With its extensive scope and detailed analysis, the book shows the reader how to extract alpha trading signals from text data, financial statements, and market behavior. It uses linear models, unsupervised algorithms for clustering, and even deep reinforcement learning to rigorously evaluate trading strategies on simulated platforms (backtesting).
Book Features
- A comprehensive review of the trends and main reasons for the dramatic growth of machine learning in the modern investment industry
- Step-by-step tutorial on extracting trading signals from various market data, news texts, and alternative data
- A complete guide to designing, adjusting, and evaluating alpha factors and optimizing portfolios based on risk
- Implementation and comparison of supervised, unsupervised (clustering) and reinforcement learning algorithms in the stock market
- Learn how to accurately backtest strategies on real historical data to measure performance before entering the market.
Book specifications
- Publisher: Packt
- Instructor/Author: Stefan Jansen
- Number of pages: 821
- Number of chapters: 23
- Format: PDF
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User Guide
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Download link
File(s) password: www.downloadly.ir
File size
16 MB
