Using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and Pyfolio, leverage machine learning to design and back-test automated trading strategies for real-world markets. A complimentary PDF eBook is included with every print or Kindle book purchase.
Machine Learning for Algorithmic Trading PDF by Stefan Jansen Predictive models to extract signals from market and alternative data for systematic trading strategies with Python 2nd ed. Edition PDF.
Machine Learning for Algorithmic Trading PDF
Book Description :
The end-to-end machine learning workflow for trading is introduced in this book, covering everything from concept and feature engineering to model optimization, strategy creation, and backtesting. Examples from tree-based ensembles and linear models to deep learning methods from state-of-the-art research are used to demonstrate this.
Machine Learning for Algorithmic Trading PDF
This version demonstrates how to create tradeable signals using market, fundamental, and alternative data, including tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, and satellite photos. It provides an example of how to design financial features.
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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.
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
Who is This Book For: Machine Learning For algorithmic Trading:
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.
Table Of Contents
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
(N.B. Please use the Look Inside option to see further chapters)
