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Machine Learning for Algorithmic Trading
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Theexplosivegrowthofdigitaldatahasboostedthedemandforexpertiseintradingstrategiesthatusemachinelearning(ML).Thisrevisedandexpandedsecondeditionenablesyoutobuildandevaluatesophisticatedsupervised,unsupervised,andreinforcementlearningmodels.Thisbookintroducesend-to-endmachinelearningforthetradingworkflow,fromtheideaandfeatureengineeringtomodeloptimization,strategydesign,andbacktesting.Itillustratesthisbyusingexamplesrangingfromlinearmodelsandtree-basedensemblestodeep-learningtechniquesfromcuttingedgeresearch.Thiseditionshowshowtoworkwithmarket,fundamental,andalternativedata,suchastickdata,minuteanddailybars,SECfilings,earningscalltranscripts,financialnews,orsatelliteimagestogeneratetradeablesignals.ItillustrateshowtoengineerfinancialfeaturesoralphafactorsthatenableanMLmodeltopredictreturnsfrompricedataforUSandinternationalstocksandETFs.ItalsoshowshowtoassessthesignalcontentofnewfeaturesusingAlphalensandSHAPvaluesandincludesanewappendixwithoveronehundredalphafactorexamples.Bytheend,youwillbeproficientintranslatingMLmodelpredictionsintoatradingstrategythatoperatesatdailyorintradayhorizons,andinevaluatingitsperformance.
目錄(155章)
倒序
- 封面
- 版權(quán)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 更新時(shí)間：2021-06-11 18:48:01
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