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
Summary
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
Summary
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
Summary
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
Summary
6 The Machine Learning Process
How machine learning from data works
The machine learning workflow
Summary
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
Summary
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
Summary
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
Summary
10 Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading
How Bayesian machine learning works
Probabilistic programming with PyMC3
Bayesian ML for trading
Summary
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
Summary
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
Summary
13 Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning
Dimensionality reduction
PCA for trading
Clustering
Hierarchical clustering for optimal portfolios
Summary
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
Summary
15 Topic Modeling – Summarizing Financial News
Learning latent topics – Goals and approaches
Probabilistic latent semantic analysis
Latent Dirichlet allocation
Modeling topics discussed in earnings calls
Topic modeling for with financial news
Summary
16 Word Embeddings for Earnings Calls and SEC Filings
How word embeddings encode semantics
How to use pretrained word vectors
Custom embeddings for financial news
word2vec for trading with SEC filings
Sentiment analysis using doc2vec embeddings
New frontiers – pretrained transformer models
Summary
17 Deep Learning for Trading
Deep learning – what's new and why it matters
Designing an NN
A neural network from scratch in Python
Popular deep learning libraries
Optimizing an NN for a long-short strategy
Summary
18 CNNs for Financial Time Series and Satellite Images
How CNNs learn to model grid-like data
CNNs for satellite images and object detection
CNNs for time-series data – predicting returns
Summary
19 RNNs for Multivariate Time Series and Sentiment Analysis
How recurrent neural nets work
RNNs for time series with TensorFlow 2
RNNs for text data
Summary
20 Autoencoders for Conditional Risk Factors and Asset Pricing
Autoencoders for nonlinear feature extraction
Implementing autoencoders with TensorFlow 2
A conditional autoencoder for trading
Summary
21 Generative Adversarial Networks for Synthetic Time-Series Data
Creating synthetic data with GANs
How to build a GAN using TensorFlow 2
TimeGAN for synthetic financial data
Summary
22 Deep Reinforcement Learning – Building a Trading Agent
Elements of a reinforcement learning system
How to solve reinforcement learning problems
Solving dynamic programming problems
Q-learning – finding an optimal policy on the go
Deep RL for trading with the OpenAI Gym
Summary
23 Conclusions and Next Steps
Key takeaways and lessons learned
ML for trading in practice
Conclusion
Alpha Factor Library
Common alpha factors implemented in TA-Lib
WorldQuant's quest for formulaic alphas
Bivariate and multivariate factor evaluation
References
Index
更新时间：2021-06-11 18:48:01
完结共155章
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