Skip to content
The Algotrading Book
Search
Ctrl
K
Cancel
GitHub
Select theme
Dark
Light
Auto
Select language
English
Русский
Overview
About the Book
Block 1 — Foundations
Chapter 1: Algorithmic Intelligence in Digital Asset Markets
Chapter 2: Crypto Market Data: Order Books, Candles, and On-Chain Signals
Chapter 3: Beyond Price: Sourcing Unconventional Crypto Signals
Chapter 4: Crafting Predictive Features for Crypto Returns
Chapter 5: Portfolio Construction and Risk Budgeting for Digital Assets
Chapter 6: The Practitioner's Guide to Training ML Models on Financial Data
Chapter 7: Linear Methods for Crypto Return Prediction and Risk Decomposition
Chapter 8: End-to-End Strategy Simulation: From Signal to PnL
Chapter 9: Temporal Dynamics: Modeling Crypto Volatility and Cross-Asset Relationships
Chapter 10: Probabilistic Reasoning: Bayesian Approaches to Crypto Strategy Assessment
Глава 10: Regulatory и Risk Management
Chapter 11: Tree-Based Learning: Extracting Nonlinear Patterns from Crypto Markets
Chapter 12: Gradient Boosting Mastery: High-Performance Crypto Signal Generation
Chapter 13: Discovering Hidden Structure: Unsupervised Learning for Crypto Markets
Chapter 14: Processing Crypto Text: From Tweets to Trading Signals
Chapter 15: Uncovering Themes in Crypto Discourse with Topic Models
Chapter 16: Semantic Representations: Embeddings for Crypto Language
Chapter 17: Neural Network Foundations for Quantitative Crypto Strategies
Chapter 18: Convolutional Architectures: Treating Crypto Data as Images
Chapter 19: Sequential Intelligence: RNNs for Crypto Time Series and Sentiment
Chapter 20: Autoencoders: Learning Latent Crypto Market Structure
Chapter 21: Synthetic Market Generation: GANs for Crypto Data Augmentation
Chapter 22: Autonomous Trading Agents: Reinforcement Learning for Crypto Execution
Chapter 23: From Research to Production: Deploying ML Strategies on Bybit
Chapter 24: The Crypto Signal Compendium: A Comprehensive Factor Reference
Block 2 — Trading Strategies
Chapter 28: Hidden Markov Models — Regime-Switching Trading Strategy
Chapter 29: Earnings Surprise Prediction — Event-Driven Strategy
Chapter 30: Order Flow Imbalance — Intraday Microstructure Strategy
Chapter 31: Optimal Execution with Reinforcement Learning — Beating TWAP/VWAP
Chapter 32: Cross-Asset Momentum — Global Tactical Asset Allocation
Chapter 33: Anomaly Detection — Market Regime Detection, Unusual Pattern Recognition & Tail Risk Hedging
Chapter 34: Online Learning — Adaptive Momentum with Continuous Retraining
Chapter 35: Multi-Agent Reinforcement Learning — Market Simulation and Competitive Strategies
Chapter 36: Crypto DEX Arbitrage — AMM and Cross-Exchange Strategies
Chapter 37: Sentiment-Momentum Fusion — Social Media Enhanced Momentum
Chapter 38: Statistical Arbitrage and Pairs Trading for Crypto Markets
Chapter 39: Conformal Prediction — Trading with Calibrated Uncertainty
Chapter 40: Options Greeks Prediction — Delta-Neutral Volatility Trading
Chapter 42: Limit Order Book Reconstruction and Feature Engineering
Block 3 — Attention & Transformers
Chapter 26: Temporal Fusion Transformers — Multi-horizon Portfolio Allocation
Chapter 41: Higher Order Transformers for Cryptocurrency Trading
Chapter 43: Stockformer — Multivariate Stock Prediction with Cross-Asset Attention
Chapter 44: ProbSparse Attention for Trading
Chapter 45: Deep Convolutional Transformer (DCT) for Stock Movement Prediction
Chapter 46: Temporal Attention Networks for Financial Time-Series
Chapter 47: Cross-Attention for Multi-Asset Trading
Chapter 48: Positional Encoding for Time Series
Chapter 49: Multi-Scale Attention for Financial Time Series
Chapter 50: Memory-Augmented Transformers for Trading
Chapter 51: Linformer — Self-Attention with Linear Complexity for Long Sequences
Chapter 52: Performer — Efficient Attention with FAVOR+
Chapter 53: BigBird — Sparse Attention for Long Sequences in Trading
Chapter 54: Reformer - Locality-Sensitive Hashing (LSH) Attention
Chapter 55: FNet — Fourier Transform for Efficient Token Mixing
Chapter 56: Nyströmformer for Trading
Chapter 57: Longformer for Financial Analysis
Chapter 58: FlashAttention for Algorithmic Trading
Chapter 59: Grouped Query Attention for Algorithmic Trading
Chapter 60: KV-Cache Optimization for Algorithmic Trading
Block 4 — Large Language Models
Chapter 61: FinGPT — Open-Source Financial Large Language Model
Chapter 62: BloombergGPT for Trading — Financial LLM Applications
Chapter 63: LLM Alpha Mining — Generating Trading Factors with Large Language Models
Chapter 64: Multi-Agent LLM Trading — Collaborative AI Systems for Financial Markets
Chapter 65: Retrieval-Augmented Generation (RAG) for Trading
Chapter 66: Chain-of-Thought Trading — Explainable LLM Trading Decisions
Chapter 67: LLM Sentiment Analysis for Trading
Chapter 68: LLM News Interpretation for Trading
Chapter 69: LLM Earnings Call Analysis — Extracting Trading Signals from Corporate Communications
Chapter 70: Fine-tuning LLM for Finance — LoRA, QLoRA, and Prefix-Tuning
Chapter 71: Prompt Engineering for Trading — LLM Optimization Techniques
Chapter 72: LLM Market Simulation — Testing Financial Theories with AI Agents
Chapter 73: LLM Risk Assessment for Trading
Chapter 74: LLM Portfolio Construction
Chapter 75: LLM Factor Discovery for Trading
Chapter 76: LLM Anomaly Detection in Financial Markets
Chapter 77: LLM Regime Classification for Financial Markets
Chapter 78: LLM Trade Execution
Chapter 79: LLM Backtesting Assistant
Chapter 80: LLM Compliance Check
Block 5 — NLP & Pretraining
Chapter 241: FinBERT Sentiment
Chapter 242: BERT for Financial NLP
Chapter 243: RoBERTa for Trading
Chapter 244: XLNet for Finance
Chapter 245: ALBERT for Trading
ELECTRA for Finance: Efficient Pre-training for Financial Text Analysis
Chapter 247: DeBERTa for Trading
Chapter 248: Abstractive Summarization of Financial Documents for Trading Signals
Chapter 249: Cross-Lingual NLP for Global Crypto Market Signals
Chapter 250: GPT Financial Analysis
Chapter 251: Named Entity Recognition for Finance
Chapter 252: Relation Extraction in Finance
Chapter 253: Document Classification in Finance
Chapter 254: Text Summarization Finance
Chapter 255: Question Answering for Finance
Chapter 256: Aspect-Based Sentiment Analysis for Finance
Chapter 257: Event Extraction Trading
Chapter 258: Temporal Reasoning in Finance
Chapter 259: Multimodal NLP Trading
Chapter 260: Knowledge Graph Trading
Chapter 284: Domain-Adaptive Pretraining for Financial Language Models
Chapter 285: Instruction Tuning and RLHF for Financial LLMs
Chapter 286: T5 Pretraining for Finance
Block 6 — Meta-Learning
Chapter 81: Model-Agnostic Meta-Learning (MAML) for Trading
Chapter 82: Reptile Algorithm for Algorithmic Trading
Chapter 83: Prototypical Networks for Finance
Chapter 84: Matching Networks for Finance
Chapter 85: Zero-Shot Trading
Chapter 86: Few-Shot Market Prediction
Chapter 87: Task-Agnostic Trading
Chapter 88: Meta-Reinforcement Learning (Meta-RL) for Trading
Chapter 89: Continual Meta-Learning for Trading
Chapter 90: Meta-Gradient Optimization for Trading
Block 7 — Transfer Learning
Chapter 91: Transfer Learning for Trading
Chapter 92: Domain Adaptation for Finance
Chapter 93: Multi-Task Learning for Trading
Chapter 94: QuantNet Transfer Trading
Chapter 95: Meta-Volatility Prediction
Block 8 — Causal Inference
Chapter 96: Granger Causality Trading
Chapter 97: PCMCI Causal Discovery for Trading
Chapter 98: Transfer Entropy for Trading
Chapter 99: VarLiNGAM Markets
Chapter 100: DAG Learning for Finance
Chapter 101: Instrumental Variables Trading
Chapter 102: Double ML Trading
Chapter 103: Causal Forests Finance
Chapter 104: Synthetic Control Method for Trading
Chapter 105: Difference-in-Differences (DiD) for Trading
Chapter 106: Regression Discontinuity Design for Trading
Chapter 107: Propensity Score Methods for Trading
Chapter 108: Mediation Analysis for Finance
Chapter 109: Causal Factor Discovery
Chapter 110: Counterfactual Trading
Block 9 — Explainability
Chapter 111: SHAP Trading Interpretability
LIME for Trading Explanation: Local Interpretable Model-agnostic Explanations
Chapter 113: Integrated Gradients for Finance
Chapter 114: Attention Visualization for Trading
Chapter 115: Feature Attribution Trading - Explainable AI for Financial Markets
Chapter 116: Counterfactual Explanations for Trading
Chapter 117: Concept Bottleneck Trading
Prototype Learning for Market State Classification
Chapter 119: Rule Extraction Trading — Extracting Interpretable Rules from Black-Box Models
Saliency Maps for Trading: Visualizing Model Interpretability in Financial Predictions
Chapter 121: Layer-wise Relevance Propagation (LRP)
Chapter 122: DeepLift Trading - Neural Network Attribution for Explainable Trading Signals
Grad-CAM for Financial Markets: Visual Explanations from Deep Networks
Chapter 124: Attention Rollout Trading
Decision Tree Distillation for Trading: Extracting Interpretable Rules from Complex Models
Block 10 — State Space Models
Chapter 126: Mamba for Trading
Chapter 127: S4 Trading - Structured State Space Models for Financial Markets
Liquid State Machines for Trading: Reservoir Computing for Financial Time Series
Chapter 129: MambaTS for Time Series Forecasting
Chapter 130: Mamba4Cast Zero-Shot Forecasting
Chapter 131: C-Mamba Channel Correlation
Chapter 132: TimeParticle SSM
Chapter 133: HiPPO Framework for Trading
Chapter 134: Linear Attention and State Space Models (SSM)
Chapter 135: Bidirectional Mamba
Chapter 136: Hierarchical State Space Models (HiSS) for Trading
Chapter 137: Gated State Space Models (Gated SSM)
Chapter 138: Diagonal State Space Models for Trading
Chapter 139: SSM-GNN Hybrid Models for Trading
Chapter 140: SSM-Transformer Hybrid for Trading
Block 11 — Physics-Informed NNs
Chapter 141: Physics-Informed Neural Networks for Black-Scholes
Chapter 142: PINN for American Option Pricing
Chapter 143: PINN for Jump Diffusion Option Pricing
Chapter 144: Physics-Informed Neural Networks for the Heston Stochastic Volatility Model
Chapter 145: Physics-Informed Neural Networks for Hull-White Interest Rate Model
Chapter 146: Physics-Informed Neural Networks for the CIR Model
Chapter 147: Neural SDE Trading
Chapter 148: Neural ODE Trading
Chapter 149: Hamiltonian Neural Networks for Trading
Chapter 150: Lagrangian Neural Networks for Trading
Chapter 151: Physics-Constrained GAN for Trading
Chapter 152: Operator Learning for Finance
Chapter 153: DeepONet for Finance
Chapter 154: Fourier Neural Operator (FNO) in Algorithmic Trading
Chapter 155: Neural Implicit Finance (INRs & SIREN)
Block 12 — Generative Models
Diffusion Models for Synthetic Time Series and Forecasting
Chapter 231: VAE Factor Model for Financial Markets
Chapter 232: Disentangled VAE for Latent Factor Discovery in Crypto Markets
Chapter 233: RVRAE Dynamic Factor Model
Chapter 234: Beta-VAE Trading
Chapter 235: VQ-VAE Trading
Chapter 236: Conditional VAE Trading
Chapter 237: Hierarchical VAE Trading
Chapter 238: Disentangled VAE Trading
Chapter 239: VAE Portfolio Generation
Chapter 240: VAE Volatility Surface
Chapter 332: Normalizing Flows for Finance
Chapter 333: RealNVP Trading — Invertible Transformations for Market Distribution Learning
Chapter 334: GLOW (Generative Flow) for Trading
Chapter 335: Neural Spline Flows — Flexible Density Estimation for Trading
Chapter 336: Continuous Normalizing Flows — Modeling Market Dynamics with Neural ODEs
Chapter 337: Score Matching Trading — Learning Data Distributions for Market Prediction
Chapter 338: Energy-Based Models for Trading
Chapter 339: Hopfield Networks for Trading
Chapter 340: Associative Memory Trading — Retrieval-Based Predictions with Dense Associative Networks
Block 13 — Contrastive & SSL
Chapter 156: SimCLR for Stocks (Self-Supervised Contrastive Learning)
Chapter 157: MoCo Trading (Momentum Contrast for Finance)
Chapter 158: BYOL Trading (Bootstrap Your Own Latent)
Chapter 159: Barlow Twins Finance
Chapter 160: SwAV for Algorithmic Trading
VICReg Trading: Variance-Invariance-Covariance Regularization
Triplet Learning for Stocks
Temporal Contrastive Learning for Stocks
Cross-Modal Contrastive Learning for Trading
Chapter 165: Hard Negative Mining for Contrastive Learning
Chapter 166: Contrastive Predictive Coding (CPC)
Chapter 167: InfoNCE Trading
Chapter 168: NT-Xent Loss and Temperature Scaling
Chapter 169: Supervised Contrastive Learning for Trading
Chapter 170: Prototypical Contrastive Learning (PCL)
Chapter 281: Self-Supervised Learning for Trading
Chapter 282: Masked Autoencoders for Financial Time Series
Chapter 283: Self-Supervised Learning for Financial Time Series
Chapter 287: CLIP Multimodal Trading
Chapter 288: Data Augmentation for Trading
Chapter 289: Curriculum Learning for Trading
Chapter 290: Active Learning for Trading
Block 14 — Microstructure & LOB
Chapter 261: LOB Deep Learning
Chapter 262: Market Making ML
Chapter 263: Order Flow Prediction
Chapter 264: Price Impact Modeling with Machine Learning
Chapter 265: Tick Data Forecasting with Machine Learning
Chapter 266: Trade Classification with Machine Learning
Chapter 267: Quote Update Prediction
Chapter 268: Spread Modeling with ML for Trading
Chapter 269: Liquidity Prediction for Trading
Chapter 270: Microstructure Features for Trading ML
Chapter 271: DeepLOB Forecasting
Chapter 272: LOBFrame Benchmark Trading
Chapter 273: LIT Transformer for Limit Order Book Modeling
Chapter 274: CNN LOB Prediction
Chapter 275: LSTM LOB Forecasting
Chapter 276: Attention LOB (Limit Order Book) Trading
Chapter 277: GNN LOB -- Graph Neural Network for Limit Order Book Trading
Chapter 278: Variational LOB (Limit Order Book) Trading
Chapter 279: Generative LOB (Limit Order Book) Trading
Chapter 280: Reinforcement Learning for LOB (Limit Order Book) Trading
Block 15 — Reinforcement Learning
Chapter 291: TD3 Portfolio Trading
Chapter 292: SAC (Soft Actor-Critic) for Trading
Chapter 293: PPO Portfolio Trading
Chapter 294: A2C (Advantage Actor-Critic) for Trading
Chapter 295: DDPG for Trading
Chapter 296: DQN Portfolio Trading
Chapter 297: Rainbow DQN for Trading
Chapter 298: C51 Distributional RL for Trading
Chapter 299: IQN (Implicit Quantile Networks) for Trading
Chapter 300: Hierarchical Reinforcement Learning for Trading
Chapter 301: Model-Based RL Trading
Chapter 302: World Models for Trading
Chapter 303: Dreamer Trading
Chapter 304: MuZero Trading
Chapter 305: Inverse Reinforcement Learning for Trading
Chapter 306: Imitation Learning for Trading
Chapter 307: Behavioral Cloning for Trading
Chapter 308: GAIL Trading - Generative Adversarial Imitation Learning for Trading
Chapter 309: Reward Shaping for Trading
Chapter 310: Curiosity-Driven Trading
Chapter 311: Monte Carlo Tree Search for Trading
Chapter 312: Planning RL Trading
Chapter 313: Offline Reinforcement Learning for Trading
Chapter 314: Conservative Q-Learning for Trading
Chapter 315: Decision Transformer for Trading
Chapter 316: Trajectory Transformer for Trading
Chapter 317: Goal-Conditioned Reinforcement Learning for Trading
Chapter 318: Option Framework RL Trading
Chapter 319: Population Based Training for Trading
Chapter 320: Multi-Objective Reinforcement Learning for Trading
Block 16 — Uncertainty & Bayesian
Chapter 321: Bayesian Optimization for Trading Strategy Hyperparameters
Chapter 322: Gaussian Process Trading
Chapter 323: Uncertainty Quantification for Trading
Chapter 324: Ensemble Uncertainty for Trading
Chapter 325: MC Dropout Trading
Chapter 326: Deep Ensembles Trading
Chapter 327: Bayesian Neural Networks for Trading
Chapter 328: Variational Inference Trading
Chapter 329: Probabilistic Forecasting for Trading
Chapter 330: Prediction Intervals and Uncertainty Bounds for Trading Forecasts
Chapter 331: Copula Models for Crypto Portfolio Dependence
Block 17 — Graph Neural Networks
Chapter 27: Graph Neural Networks — Sector Momentum via Correlation Networks
Chapter 341: Graph Transformer Trading
Chapter 342: Equivariant Graph Neural Networks for Cryptocurrency Trading
Chapter 343: Message Passing Neural Networks for Trading
Chapter 344: Graph Attention Networks for Trading
Chapter 345: Heterogeneous Graph Neural Networks for Trading
Chapter 346: Temporal Graph Neural Networks for Trading
Chapter 347: Dynamic Graph Neural Networks for Trading
Chapter 348: Graph Generation for Trading
Chapter 349: Graph Pooling for Trading — Hierarchical Market Representations
Chapter 350: Subgraph Mining for Trading — Discovering Hidden Patterns in Financial Networks
Block 18 — CNN Time Series
Chapter 351: WaveNet for Trading
Chapter 352: Temporal Convolutional Networks (TCN) for Trading
Chapter 353: Dilated Convolutions for Trading
Chapter 354: InceptionTime for Trading — Time Series Classification with Inception Networks
Chapter 355: ResNet for Time Series — Deep Residual Networks for Financial Forecasting
Chapter 356: DenseNet for Cryptocurrency Trading
Chapter 357: EfficientNet for Algorithmic Trading
Chapter 358: ConvNeXt for Trading — Modern ConvNets Competing with Transformers
Chapter 359: Depthwise Separable Convolutions for Trading
Chapter 360: Squeeze-and-Excitation Networks for Algorithmic Trading
Block 19 — Federated Learning
Chapter 171: Federated Averaging (FedAvg)
Chapter 172: FedProx for Finance
Chapter 173: Secure Aggregation for Trading
Chapter 174: Differential Privacy for Trading
Chapter 175: Blockchain Federated Learning for Trading
Chapter 176: Personalized Federated Learning for Trading
Chapter 177: Hierarchical Federated Learning for Trading
Chapter 178: Asynchronous Federated Learning for Trading
Chapter 179: Communication-Efficient Federated Learning
Chapter 180: Ternary Gradient Compression for Trading
Chapter 181: Byzantine-Robust Federated Learning for Trading
Chapter 182: Fair Federated Learning for Trading
Chapter 183: Knowledge Distillation in Federated Learning for Trading
Chapter 184: Cross-Silo Federated Learning for Trading
Chapter 185: Edge Federated Learning for Trading
Block 20 — Quantum Computing
Chapter 186: VQE Portfolio Optimization
Chapter 187: QAOA Trading - Quantum Approximate Optimization for Portfolio Selection
Chapter 188: Quantum GAN for Finance
Chapter 189: Quantum Kernel Trading
Chapter 190: Quantum SVM Trading
Chapter 191: Variational Quantum Classifier for Trading
Chapter 192: Quantum Boltzmann Trading
Chapter 193: Grover Search Trading
Chapter 194: HHL Algorithm for Finance
Chapter 195: Quantum Feature Map
Chapter 196: Quantum Error Correction for Trading
Chapter 197: Hybrid Quantum-Classical Computing for Trading
Chapter 198: NISQ Trading Algorithms
Chapter 199: Quantum Annealing Trading
Chapter 200: Tensor Network Trading
Block 21 — Model Efficiency
Chapter 201: Knowledge Distillation Trading
Chapter 202: Model Compression Finance
Chapter 203: Pruning Trading Models
Chapter 204: Quantization Trading Models
Chapter 205: Lottery Ticket Trading
Chapter 206: Self-Distillation Trading
Chapter 207: Teacher-Student Trading
Chapter 208: Progressive Distillation
Chapter 209: Data Distillation Trading
Chapter 210: Feature Distillation
Chapter 211: Neural Architecture Search (NAS) for Trading
Chapter 212: AutoML Finance
Chapter 213: Hyperparameter Optimization for Trading Models
Chapter 214: Evolutionary NAS Trading
Chapter 215: Differentiable NAS (DARTS)
Chapter 216: One-Shot Neural Architecture Search (NAS)
Chapter 217: Hardware-Aware Neural Architecture Search (NAS)
Chapter 218: Multi-Objective Neural Architecture Search (NAS)
Chapter 219: DARTS Trading — Differentiable Architecture Search for Financial Time Series
Chapter 220: EXAMM Trading — Evolutionary Exploration of Augmenting Memory Models for Financial Markets
Block 22 — Adversarial Robustness
Chapter 221: Adversarial Training for Robust Trading Models
Chapter 222: Adversarial Attack Detection
Chapter 223: Robust Trading Models
Chapter 224: Input Perturbation Defense
Chapter 225: Certified Robustness
Chapter 226: Adversarial Examples in Finance
Chapter 227: Evasion Attacks in Trading
Chapter 228: Poisoning Attacks in Trading
Chapter 229: Model Inversion Finance
Chapter 230: Membership Inference
Block 23 — Frontier Methods
Chapter 361: Koopman Operator Trading — Linearizing Nonlinear Market Dynamics
Chapter 362: Reservoir Computing for Trading
Chapter 363: Liquid Neural Networks for Algorithmic Trading
Chapter 364: Neuromorphic Trading — Brain-Inspired Computing for Ultra-Low-Latency Markets
Chapter 365: Foundation Models for Algorithmic Trading
GitHub
Select theme
Dark
Light
Auto
Select language
English
Русский
Глава 10: Regulatory и Risk Management
