Overview

Build robust, leakage-free trading systems powered by deep sequence models in Python. This hands-on guide shows how to turn raw market data into deployable signals using Transformers, LSTMs, and Temporal Convolutional Networks, then carry those signals through evaluation, execution, and portfolio construction. Written for quants, researchers, and systematic traders who demand reproducible results and rigorous validation, it focuses on practical techniques that hold up out of sample. Every chapter includes a full Python code demo that moves from data construction to model training and trading-aligned evaluation. You will learn how to design predictive targets that match holding periods, prevent look-ahead, optimize with cost-aware losses, and monitor models in production. The emphasis is on causality, efficiency, and reliability across regimes and asset classes. What you will learn: Engineer event-based datasets with tick, volume, and dollar bars, synchronize cross-asset panels, and clean microstructure noise Create triple-barrier labels and meta-labels aligned with tradable horizons Eliminate leakage using purged and embargoed cross-validation and walk-forward splits Build rolling and exponentially weighted normalizations, volatility targeting, and fractional differentiation for stationarity Construct sliding windows and batching strategies for causal training without overlap bias Optimize with trading-aware objectives including differentiable Sharpe, quantile losses, and turnover penalties Train LSTMs and GRUs with truncated BPTT, stateful inference, and robust regularization Implement TCNs with causal dilated convolutions and receptive fields tuned to holding periods Apply causal Transformers with masked self-attention, time encodings, and efficient long-context variants Pretrain with self-supervised objectives tailored to market sequences and transfer to downstream tasks Model cross-sectional signals with shared encoders, cross-asset attention, and ranking heads Adapt to regime shifts via drift detection, mixture-of-experts, and lightweight adapters Backtest with realistic costs, slippage, and latency, then map signals to execution and position sizing Construct portfolios with constraints, risk models, and neutralization, and quantify uncertainty with ensembles and conformal prediction Interpret sequence models and monitor live calibration, drift, and performance Why this book: Code-first and production-minded: every chapter ships with a complete Python demo Causal and trading-aligned design choices from data to decisions Scales from daily bars to tick data and across equities, futures, crypto, and FX Includes: Full Python code demos in every chapter Reusable pipelines for labeling, windowing, loss functions, and evaluation Practical tips for latency, memory efficiency, and deployment

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