Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

I. THÔNG TIN SẢN PHẨM
Mã sản phẩm : STT605
Nhà xuất bản:  Packt Publishing; 2nd edition (July 31, 2020)
Tác giả :  Stefan Jansen
Ngôn Ngữ : Tiếng Anh
ISBN      : 1839217715
Số trang    : 820 pages
Hình thức : Bìa Mềm, IN ĐEN TRẮNG
Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

II. MÔ TẢ SẢN PHẨM
1.Mô tả sản phẩm
Key Features

•     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

Book 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

        
2. Tác giả
Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems.