Test AI trading strategies instead of trusting impressive backtests. Use Python to detect market regimes, evaluate pairs trading, and check look-ahead bias, execution costs, and risk before paper trading.

Key Features

• Detect market regimes with forward-only HMM inference that avoids look-ahead bias
• Research pairs trading using cointegration tests and cost-aware Python backtests
• Train SAC agents and evaluate drawdowns, position sizing, and tail risk

Book Description

Algorithmic trading strategies can look profitable when backtests use future data, omit transaction costs, or assume unrealistic fills. This book helps Python developers and quant researchers examine AI trading ideas through calculations and controlled experiments. Start with market-data validation, feature engineering, and backtest accounting that preserves the order of signals and trades. Detect market regimes with hidden Markov models (HMMs), forward-only inference, Kalman filters, and cross-asset ensembles. Research statistical arbitrage by screening cointegrated stock pairs, calculating dynamic hedge ratios, and designing regime-adjusted Z-score signals. Explore machine learning for trading through a simulated reinforcement learning environment, Soft Actor-Critic (SAC) training, and Optuna tuning. Evaluate Half-Kelly position sizing, maximum drawdown, VaR, CVaR, and walk-forward validation before comparing SAC with PPO and QR-DQN. Examine paper broker backends, FastAPI monitoring, Discord alerts, and MLflow experiment records and model-serving components. Ted Park connects these methods to code examples and verification exercises, distinguishing runnable implementations from extended designs. You will finish better equipped to assess trading strategies, reproduce results, and run disciplined paper-trading research.

What you will learn

• Validate market data and engineer leak-free trading features
• Backtest Python strategies with realistic orders, fills, and fees
• Detect regimes with forward-only hidden Markov models
• Apply Kalman filters to noisy prices and trading spreads
• Select cointegrated pairs and generate regime-aware signals
• Train a SAC reinforcement learning agent in a trading simulator
• Measure Half-Kelly sizing, drawdown, and CVaR exposure
• Inspect paper-broker events and track experiments with MLflow

Who this book is for

This book is for quantitative analysts, quantitative researchers, systematic traders, and algorithmic trading developers who want to test Python trading strategies. Financial data scientists and machine learning engineers exploring HMM regime detection, statistical arbitrage, or reinforcement learning for trading will also benefit. It suits readers conducting research, backtesting, and paper-trading experiments, not those seeking ready-made live trading bots. You should know basic Python, introductory probability, and statistics; no brokerage account is required.