Python Trading Systems With AI
A backtest handbook for building research-grade trading systems.
Covers data pipelines, signal design, risk controls, and execution mechanics with runnable Python examples.
Chapters include: market data ingestion with websockets, feature engineering for time series, walk-forward validation, position sizing with Kelly criterion, Monte Carlo drawdown simulation, and deploying strategies to paper trading environments.
Written for intermediate Python developers who want to build systematic trading systems. No finance background required — every concept is explained from first principles.
What you'll build: a complete systematic trading bot that ingests real market data, generates signals from technical and fundamental indicators, sizes positions using Kelly criterion, and executes trades with proper risk controls.
Tools covered: pandas for data manipulation, numpy for numerical computing, ccxt for exchange connectivity, matplotlib for visualization, and pytest for backtest validation.
The book includes 47 code examples, 12 complete strategies, and 3 full end-to-end systems ranging from simple moving average crossovers to multi-factor momentum models with dynamic risk budgeting.
Includes the full PDF handbook plus account-linked reading.
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