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The Algotrading Book

The Algotrading Book

365 chapters covering the full spectrum of ML methods for cryptocurrency trading. One chapter per day for a year.
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23 Thematic Blocks

Foundations

Chapters 1–24. Crypto data pipelines, feature engineering, classical ML, deep learning basics.

Trading Strategies

Chapters 25–38. Regime detection, stat arb, execution RL, order flow, momentum.

Transformers & LLMs

Chapters 39–101. All attention variants, LLMs, BERT, NLP, pretraining.

Causal & Explainability

Chapters 102–146. Meta-learning, transfer, causal inference, SHAP, LIME.

Generative Models

Chapters 147–218. SSMs, PINNs, VAE, diffusion, flows, contrastive learning.

RL & Microstructure

Chapters 219–268. LOB deep learning, all RL algorithms from DQN to MuZero.

Uncertainty & GNNs

Chapters 269–300. Bayesian methods, graph neural networks, CNN time series.

Advanced Topics

Chapters 301–365. Federated learning, quantum, distillation, NAS, adversarial, frontier.

Tech Stack

Every chapter includes working code with:

  • Bybit API v5 — REST + WebSocket (no Binance, no Zipline)
  • Python — PyTorch, scikit-learn, pandas
  • Rust — tokio, reqwest, serde