更新时间：2021-06-11 18:48:01
封面
版权信息
Why subscribe?
Contributors About the author
About the reviewers
Preface
1 Machine Learning for Trading – From Idea to Execution
The rise of ML in the investment industry
Designing and executing an ML-driven strategy
ML for trading – strategies and use cases
Summary
2 Market and Fundamental Data – Sources and Techniques
Market data reflects its environment
Working with high-frequency data
API access to market data
How to work with fundamental data
Efficient data storage with pandas
3 Alternative Data for Finance – Categories and Use Cases
The alternative data revolution
Sources of alternative data
Criteria for evaluating alternative data
The market for alternative data
Working with alternative data
4 Financial Feature Engineering – How to Research Alpha Factors
Alpha factors in practice – from data to signals
Building on decades of factor research
Engineering alpha factors that predict returns
From signals to trades – Zipline for backtests
Separating signal from noise with Alphalens
Alpha factor resources
5 Portfolio Optimization and Performance Evaluation
How to measure portfolio performance
How to manage portfolio risk and return
Trading and managing portfolios with Zipline
Measuring backtest performance with pyfolio
6 The Machine Learning Process
How machine learning from data works
The machine learning workflow
7 Linear Models – From Risk Factors to Return Forecasts
From inference to prediction
The baseline model – multiple linear regression
How to run linear regression in practice
How to build a linear factor model
Regularizing linear regression using shrinkage
How to predict returns with linear regression
Linear classification
8 The ML4T Workflow – From Model to Strategy Backtesting
How to backtest an ML-driven strategy
Backtesting pitfalls and how to avoid them
How a backtesting engine works
backtrader – a flexible tool for local backtests
Zipline – scalable backtesting by Quantopian
9 Time-Series Models for Volatility Forecasts and Statistical Arbitrage
Tools for diagnostics and feature extraction
How to diagnose and achieve stationarity
Univariate time-series models
Multivariate time-series models
Cointegration – time series with a shared trend
Statistical arbitrage with cointegration
10 Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading
How Bayesian machine learning works
Probabilistic programming with PyMC3
Bayesian ML for trading
11 Random Forests – A Long-Short Strategy for Japanese Stocks
Decision trees – learning rules from data
Random forests – making trees more reliable
Long-short signals for Japanese stocks
12 Boosting Your Trading Strategy
Getting started – adaptive boosting
Gradient boosting – ensembles for most tasks
Using XGBoost LightGBM and CatBoost
A long-short trading strategy with boosting
Boosting for an intraday strategy
13 Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning
Dimensionality reduction
PCA for trading
Clustering
Hierarchical clustering for optimal portfolios
14 Text Data for Trading – Sentiment Analysis
ML with text data – from language to features
From text to tokens – the NLP pipeline
Counting tokens – the document-term matrix
NLP for trading
15 Topic Modeling – Summarizing Financial News
Learning latent topics – Goals and approaches
Probabilistic latent semantic analysis
Latent Dirichlet allocation
