Machine Learning for Algorithmic Trading

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

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Chapter 1: Machine Learning for Trading – From Idea to Execution 1

The rise of ML in the investment industry 2

From electronic to high-frequency trading 3

Factor investing and smart beta funds 5

Algorithmic pioneers outperform humans 7

ML and alternative data 10

Crowdsourcing trading algorithms 11

Designing and executing an ML-driven strategy 12

Sourcing and managing data 13

From alpha factor research to portfolio management 13

Strategy backtesting 15

ML for trading – strategies and use cases 15

The evolution of algorithmic strategies 15

Use cases of ML for trading 16

Summary 19

Chapter 2: Market and Fundamental Data – Sources and Techniques 21

Market data reflects its environment 22

Market microstructure – the nuts and bolts 23

How to trade – different types of orders 23

Where to trade – from exchanges to dark pools 24

Working with high-frequency data 26

How to work with Nasdaq order book data 26

Communicating trades with the FIX protocol 27

The Nasdaq TotalView-ITCH data feed 27

From ticks to bars – how to regularize market data 35

AlgoSeek minute bars – equity quote and trade data 40

API access to market data 44

Remote data access using pandas 44

yfinance – scraping data from Yahoo! Finance 46

Quantopian 48

Zipline 48

Quandl 50

Other market data providers 50

How to work with fundamental data 51

Financial statement data 51

Other fundamental data sources 56

Efficient data storage with pandas 57

Summary 58

Chapter 3: Alternative Data for Finance – Categories and Use Cases 59

The alternative data revolution 60

Sources of alternative data 62

Individuals 62

Business processes 63

Sensors 63

Criteria for evaluating alternative data 65

Quality of the signal content 65

Quality of the data 67

Technical aspects 68

The market for alternative data 69

Data providers and use cases 70

Working with alternative data 72

Scraping OpenTable data 72

Scraping and parsing earnings call transcripts 77

Summary 80

Chapter 4: Financial Feature Engineering – How to Research

Alpha Factors 81

Alpha factors in practice – from data to signals 82

Building on decades of factor research 84

Momentum and sentiment – the trend is your friend 84

Value factors – hunting fundamental bargains 88

Volatility and size anomalies 90

Quality factors for quantitative investing 92

Engineering alpha factors that predict returns 94

How to engineer factors using pandas and NumPy 94

How to use TA-Lib to create technical alpha factors 99

Denoising alpha factors with the Kalman filter 100

How to preprocess your noisy signals using wavelets 104

From signals to trades – Zipline for backtests 106

How to backtest a single-factor strategy 106

Combining factors from diverse data sources 109

Separating signal from noise with Alphalens 111

Creating forward returns and factor quantiles 112

Predictive performance by factor quantiles 113

The information coefficient 115

Factor turnover 117

Alpha factor resources 118

Alternative algorithmic trading libraries 118

Summary 119

Chapter 5: Portfolio Optimization and Performance Evaluation 121

How to measure portfolio performance 122

Capturing risk-return trade-offs in a single number 122

The fundamental law of active management 124

How to manage portfolio risk and return 125

The evolution of modern portfolio management 125

Mean-variance optimization 127

Alternatives to mean-variance optimization 131

Risk parity 134

Risk factor investment 135

Hierarchical risk parity 135

Trading and managing portfolios with Zipline 136

Scheduling signal generation and trade execution 137

Implementing mean-variance portfolio optimization 138

Measuring backtest performance with pyfolio 140

Creating the returns and benchmark inputs 141

Walk-forward testing – out-of-sample returns 142

Summary 146

Chapter 6: The Machine Learning Process 147

How machine learning from data works 148

The challenge – matching the algorithm to the task 149

Supervised learning – teaching by example 149

Unsupervised learning – uncovering useful patterns 150

Reinforcement learning – learning by trial and error 152

The machine learning workflow 153

Basic walkthrough – k-nearest neighbors 154

Framing the problem – from goals to metrics 154

Collecting and preparing the data 160

Exploring, extracting, and engineering features 160

Selecting an ML algorithm 162

Design and tune the model 162

How to select a model using cross-validation 165

How to implement cross-validation in Python 166

Challenges with cross-validation in finance 168

Parameter tuning with scikit-learn and Yellowbrick 170

Summary 172

Chapter 7: Linear Models – From Risk Factors to Return Forecasts 173

From inference to prediction 174

The baseline model – multiple linear regression 175

How to formulate the model 175

How to train the model 176

The Gauss–Markov theorem 179

How to conduct statistical inference 180

How to diagnose and remedy problems 181

How to run linear regression in practice 184

OLS with statsmodels 184

Stochastic gradient descent with sklearn 186

How to build a linear factor model 187

From the CAPM to the Fama–French factor models 188

Obtaining the risk factors 189

Fama–Macbeth regression 191

Regularizing linear regression using shrinkage 194

How to hedge against overfitting 194

How ridge regression works 195

How lasso regression works 196

How to predict returns with linear regression 197

Preparing model features and forward returns 197

Linear OLS regression using statsmodels 203

Linear regression using scikit-learn 205

Ridge regression using scikit-learn 208

Lasso regression using sklearn 210

Comparing the quality of the predictive signals 212

Linear classification 212

The logistic regression model 213

How to conduct inference with statsmodels 215

Predicting price movements with logistic regression 217

Summary 219

Chapter 8: The ML4T Workflow –

From Model to Strategy Backtesting 221

How to backtest an ML-driven strategy 222

Backtesting pitfalls and how to avoid them 223

Getting the data right 224

Getting the simulation right 225

Getting the statistics right 226

How a backtesting engine works 227

Vectorized versus event-driven backtesting 228

Key implementation aspects 230

backtrader – a flexible tool for local backtests 232

Key concepts of backtrader's Cerebro architecture 232

How to use backtrader in practice 235

backtrader summary and next steps 239

Zipline – scalable backtesting by Quantopian 239

Calendars and the Pipeline for robust simulations 240

Ingesting your own bundles with minute data 242

The Pipeline API – backtesting an ML signal 245

How to train a model during the backtest 250

Instead of How to use 254

Summary 254

Chapter 9: Time-Series Models for Volatility Forecasts and

Statistical Arbitrage 255

Tools for diagnostics and feature extraction 256

How to decompose time-series patterns 257

Rolling window statistics and moving averages 258

How to measure autocorrelation 259

How to diagnose and achieve stationarity 260

Transforming a time series to achieve stationarity 261

Handling instead of How to handle 261

Time-series transformations in practice 263

Univariate time-series models 265

How to build autoregressive models 266

How to build moving-average models 267

How to build ARIMA models and extensions 268

How to forecast macro fundamentals 270

How to use time-series models to forecast volatility 272

Multivariate time-series models 276

Systems of equations 277

The vector autoregressive (VAR) model 277

Using the VAR model for macro forecasts 278

Cointegration – time series with a shared trend 281

The Engle-Granger two-step method 282

The Johansen likelihood-ratio test 282

Statistical arbitrage with cointegration 283

How to select and trade comoving asset pairs 283

Pairs trading in practice 285

Preparing the strategy backtest 288

Backtesting the strategy using backtrader 292

Extensions – how to do better 294

Summary 294

Chapter 10: Bayesian ML – Dynamic Sharpe Ratios

and Pairs Trading 295

How Bayesian machine learning works 296

How to update assumptions from empirical evidence 297

Exact inference – maximum a posteriori estimation 298

Deterministic and stochastic approximate inference 301

Probabilistic programming with PyMC3 305

Bayesian machine learning with Theano 305

The PyMC3 workflow: predicting a recession 305

Bayesian ML for trading 317

Bayesian Sharpe ratio for performance comparison 317

Bayesian rolling regression for pairs trading 320

Stochastic volatility models 323

Summary 326

Chapter 11: Random Forests – A Long-Short Strategy

for Japanese Stocks 327

Decision trees – learning rules from data 328

How trees learn and apply decision rules 328

Decision trees in practice 330

Overfitting and regularization 336

Hyperparameter tuning 338

Random forests – making trees more reliable 345

Why ensemble models perform better 345

Bootstrap aggregation 346

How to build a random forest 349

How to train and tune a random forest 350

Feature importance for random forests 352

Out-of-bag testing 352

Pros and cons of random forests 353

Long-short signals for Japanese stocks 353

The data – Japanese equities 354

The ML4T workflow with LightGBM 355

The strategy – backtest with Zipline 362

Summary 364

Chapter 12: Boosting Your Trading Strategy 365

Getting started – adaptive boosting 366

The AdaBoost algorithm 367

Using AdaBoost to predict monthly price moves 368

Gradient boosting – ensembles for most tasks 370

How to train and tune GBM models 372

How to use gradient boosting with sklearn 374

Using XGBoost, LightGBM, and CatBoost 378

How algorithmic innovations boost performance 379

A long-short trading strategy with boosting 383

Generating signals with LightGBM and CatBoost 383

Inside the black box - interpreting GBM results 391

Backtesting a strategy based on a boosting ensemble 399

Lessons learned and next steps 401

Boosting for an intraday strategy 402

Engineering features for high-frequency data 402

Minute-frequency signals with LightGBM 404

Evaluating the trading signal quality 405

Chapter 13: Data-Driven Risk Factors and Asset Allocation with

Unsupervised Learning 407

Dimensionality reduction 408

The curse of dimensionality 409

Linear dimensionality reduction 411

Manifold learning – nonlinear dimensionality reduction 418

PCA for trading 421

Data-driven risk factors 421

Eigenportfolios 424

Clustering 426

k-means clustering 427

Hierarchical clustering 429

Density-based clustering 431

Gaussian mixture models 432

Hierarchical clustering for optimal portfolios 433

How hierarchical risk parity works 433

Backtesting HRP using an ML trading strategy 435

Summary 438

Chapter 14: Text Data for Trading – Sentiment Analysis 439

ML with text data – from language to features 440

Key challenges of working with text data 440

The NLP workflow 441

Applications 443

From text to tokens – the NLP pipeline 443

NLP pipeline with spaCy and textacy 444

NLP with TextBlob 448

Counting tokens – the document-term matrix 449

The bag-of-words model 450

Document-term matrix with scikit-learn 451

Key lessons instead of lessons learned 455

NLP for trading 455

The naive Bayes classifier 456

Classifying news articles 457

Sentiment analysis with Twitter and Yelp data 458

Summary 462

Chapter 15: Topic Modeling – Summarizing Financial News 463

Learning latent topics – Goals and approaches 464

Latent semantic indexing 465

How to implement LSI using sklearn 466

Strengths and limitations 468

Probabilistic latent semantic analysis 469

How to implement pLSA using sklearn 470

Latent Dirichlet allocation 471

How LDA works 471

How to evaluate LDA topics 473

How to implement LDA using sklearn 475

How to visualize LDA results using pyLDAvis 475

How to implement LDA using Gensim 476

Modeling topics discussed in earnings calls 478

Data preprocessing 478

Model training and evaluation 479

Running experiments 480

Topic modeling for with financial news 481

Summary 482

Chapter 16: Word Embeddings for Earnings Calls and SEC Filings 483

How word embeddings encode semantics 484

How neural language models learn usage in context 485

word2vec – scalable word and phrase embeddings 485

Evaluating embeddings using semantic arithmetic 487

How to use pretrained word vectors 489

GloVe – Global vectors for word representation 489

Custom embeddings for financial news 491

Preprocessing – sentence detection and n-grams 492

The skip-gram architecture in TensorFlow 2 493

Visualizing embeddings using TensorBoard 496

How to train embeddings faster with Gensim 497

word2vec for trading with SEC filings 499

Preprocessing – sentence detection and n-grams 500

Model training 501

Sentiment analysis using doc2vec embeddings 503

Creating doc2vec input from Yelp sentiment data 503

Training a doc2vec model 504

Training a classifier with document vectors 505

Lessons learned and next steps 507

New frontiers – pretrained transformer models 507

Attention is all you need 508

BERT – towards a more universal language model 509

Trading on text data – lessons learned and next steps 511

Summary 511

Chapter 17: Deep Learning for Trading 513

Deep learning – what's new and why it matters 514

Hierarchical features tame high-dimensional data 515

DL as representation learning 516

How DL relates to ML and AI 517

Designing an NN 518

A simple feedforward neural network architecture 519

Key design choices 520

How to regularize deep NNs 522

Training faster – optimizations for deep learning 523

Summary – how to tune key hyperparameters 525

A neural network from scratch in Python 526

The input layer 526

The hidden layer 527

The output layer 528

Forward propagation 529

The cross-entropy cost function 529

How to implement backprop using Python 529

Popular deep learning libraries 534

Leveraging GPU acceleration 534

How to use TensorFlow 2 535

How to use TensorBoard 537

How to use PyTorch 1.4 538

Alternative options 541

Optimizing an NN for a long-short strategy 542

Engineering features to predict daily stock returns 542

Defining an NN architecture framework 542

Cross-validating design options to tune the NN 543

Evaluating the predictive performance 545

Backtesting a strategy based on ensembled signals 547

How to further improve the results 549

Summary 549

Chapter 18: CNNs for Financial Time Series and Satellite Images 551

How CNNs learn to model grid-like data 552

From hand-coding to learning filters from data 553

How the elements of a convolutional layer operate 554

The evolution of CNN architectures: key innovations 558

CNNs for satellite images and object detection 559

LeNet5 – The first CNN with industrial applications 560

AlexNet – reigniting deep learning research 563

Transfer learning – faster training with less data 565

Object detection and segmentation 573

Object detection in practice 573

CNNs for time-series data – predicting returns 577

An autoregressive CNN with 1D convolutions 577

CNN-TA – clustering time series in 2D format 581

Summary 589

Chapter 19: RNNs for Multivariate Time Series and

Sentiment Analysis 591

How recurrent neural nets work 592

Unfolding a computational graph with cycles 594

Backpropagation through time 594

Alternative RNN architectures 595

How to design deep RNNs 596

The challenge of learning long-range dependencies 597

Gated recurrent units 599

RNNs for time series with TensorFlow 2 599

Univariate regression – predicting the S&P 500 600

How to get time series data into shape for an RNN 600

Stacked LSTM – predicting price moves and returns 605

Multivariate time-series regression for macro data 611

RNNs for text data 614

LSTM with embeddings for sentiment classification 614

Sentiment analysis with pretrained word vectors 617

Predicting returns from SEC filing embeddings 619

Summary 624

Chapter 20: Autoencoders for Conditional Risk Factors

and Asset Pricing 625

Autoencoders for nonlinear feature extraction 626

Generalizing linear dimensionality reduction 626

Convolutional autoencoders for image compression 627

Managing overfitting with regularized autoencoders 628

Fixing corrupted data with denoising autoencoders 628

Seq2seq autoencoders for time series features 629

Generative modeling with variational autoencoders 629

Implementing autoencoders with TensorFlow 2 630

How to prepare the data 630

One-layer feedforward autoencoder 631

Feedforward autoencoder with sparsity constraints 634

Deep feedforward autoencoder 634

Convolutional autoencoders 636

Denoising autoencoders 637

A conditional autoencoder for trading 638

Sourcing stock prices and metadata information 639

Computing predictive asset characteristics 641

Creating the conditional autoencoder architecture 643

Lessons learned and next steps 648

Summary 648

Chapter 21: Generative Adversarial Networks for Synthetic

Time-Series Data 649

Creating synthetic data with GANs 650

Comparing generative and discriminative models 651

Adversarial training – a zero-sum game of trickery 651

The rapid evolution of the GAN architecture zoo 652

GAN applications to images and time-series data 653

How to build a GAN using TensorFlow 2 655

Building the generator network 655

Creating the discriminator network 656

Setting up the adversarial training process 657

Evaluating the results 660

TimeGAN for synthetic financial data 660

Learning to generate data across features and time 661

Implementing TimeGAN using TensorFlow 2 663

Evaluating the quality of synthetic time-series data 672

Lessons learned and next steps 678

Summary 678

Chapter 22: Deep Reinforcement Learning –

Building a Trading Agent 679

Elements of a reinforcement learning system 680

The policy – translating states into actions 681

Rewards – learning from actions 681

The value function – optimal choice for the long run 682

With or without a model – look before you leap? 682

How to solve reinforcement learning problems 682

Key challenges in solving RL problems 683

Fundamental approaches to solving RL problems 683

Solving dynamic programming problems 684

Finite Markov decision problems 684

Policy iteration 687

Value iteration 688

Generalized policy iteration 688

Dynamic programming in Python 689

Q-learning – finding an optimal policy on the go 694

Exploration versus exploitation – ???????? -greedy policy 695

The Q-learning algorithm 695

How to train a Q-learning agent using Python 695

Deep RL for trading with the OpenAI Gym 696

Value function approximation with neural networks 697

The Deep Q-learning algorithm and extensions 697

Introducing the OpenAI Gym 699

How to implement DDQN using TensorFlow 2 700

Creating a simple trading agent 704

How to design a custom OpenAI trading environment 705

Deep Q-learning on the stock market 709

Lessons learned 711

Summary 711

Chapter 23: Conclusions and Next Steps 713

Key takeaways and lessons learned 714

Data is the single most important ingredient 715

Domain expertise – telling the signal from the noise 716

ML is a toolkit for solving problems with data 717

Beware of backtest overfitting 719

How to gain insights from black-box models 719

ML for trading in practice 720

Data management technologies 720

ML tools 722

Online trading platforms 722

Conclusion 723

Appendix: Alpha Factor Library 725

Common alpha factors implemented in TA-Lib 726

A key building block – moving averages 726

Overlap studies – price and volatility trends 729

Momentum indicators 733

Volume and liquidity indicators 741

Volatility indicators 743

Fundamental risk factors 744

WorldQuant's quest for formulaic alphas 745

Cross-sectional and time-series functions 745

Formulaic alpha expressions 747

Bivariate and multivariate factor evaluation 749

Information coefficient and mutual information 749

Feature importance and SHAP values 750

Comparison – the top 25 features for each metric 750

Financial performance – Alphalens 752

References 753

Index 769

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