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Machine Learning for Markets

Machine learning for noisy, non-stationary markets: labels and validation, boosting and neural networks, order-book sequence models, text and large language models, reinforcement learning for execution and hedging, and the engineering to train, serve and monitor models.

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Foundations for Noisy, Non-Stationary Data

  1. 1 Why Financial Machine Learning Is Different 8 solved exercises
  2. 2 Targets, Labels and Sample Weights 8 solved exercises
  3. 3 Validation 8 solved exercises
  4. 4 Linear and Regularised Baselines 8 solved exercises
  5. 5 Trees and Boosting 8 solved exercises
  6. 6 Feature Engineering, Selection and Importance 8 solved exercises

Deep Learning

  1. 7 Neural Networks for Noisy Tabular Data 8 solved exercises
  2. 8 Sequence Models on Order Books 8 solved exercises
  3. 9 Cross-Sectional Deep Models 8 solved exercises
  4. 10 Representation Learning 8 solved exercises
  5. 11 Probabilistic Models and Uncertainty 8 solved exercises
  6. 12 Online Learning and Drift 8 solved exercises

Text, Alternative Data and Generative Models

  1. 13 Text: From Bag of Words to Embeddings 8 solved exercises
  2. 14 Large Language Models in Finance 8 solved exercises
  3. 15 Alternative-Data Pipelines 8 solved exercises
  4. 16 Generative Models and Synthetic Data 8 solved exercises

Decisions

  1. 17 Reinforcement Learning Foundations 8 solved exercises
  2. 18 Reinforcement Learning for Execution and Market Making 8 solved exercises
  3. 19 Deep Hedging and Machine Learning in Pricing 8 solved exercises
  4. 20 Clustering, Regimes and Anomaly Detection 8 solved exercises
  5. 21 Interpretability and Model Governance 8 solved exercises
  6. 22 From Prediction to Portfolio 8 solved exercises

Machine-Learning Engineering

  1. 23 Training Infrastructure 8 solved exercises
  2. 24 Data and Feature Stores 8 solved exercises
  3. 25 Experiment Tracking and Reproducibility 8 solved exercises
  4. 26 Low-Latency Inference 8 solved exercises
  5. 27 Monitoring and Retraining 8 solved exercises
  6. 28 The Machine-Learning Team 8 solved exercises
  7. 29 Build: An Order-Book Model, End to End 8 solved exercises

29 chapters — 29 online so far, and more publishing regularly. The complete book is already available as a free PDF you can read online.