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AI-Powered Algorithmic Trading with Python: A Practical Machine Learning Playbook to Build Leak-Proof Signals, Backtest Realistically, Manage Portfolio Risk and Deploy AI Agents - Softcover

Everly, Liam

 
9798189196713: AI-Powered Algorithmic Trading with Python: A Practical Machine Learning Playbook to Build Leak-Proof Signals, Backtest Realistically, Manage Portfolio Risk and Deploy AI Agents

Inhaltsangabe

A profitable-looking model can still be a dangerous trading system.

AI-Powered Algorithmic Trading with Python gives you a disciplined path from idea to controlled execution through the eight-gate Evidence-to-Execution Framework: Thesis, Clock, Target, Evidence, Portfolio, Reality, Launch, and Lifecycle.

Inside, you will learn how to:

  • Build point-in-time datasets without future leakage, survivor bias, or revision errors

  • Define tradeable regression, classification, ranking, and policy-learning targets

  • Compare gradient boosting, deep learning, causal methods, and reinforcement learning with honest baselines

  • Use walk-forward validation, purging, nested search, experiment accounting, and a governed final holdout

  • Translate forecasts into positions under risk, liquidity, turnover, and uncertainty constraints

  • Model spread, impact, delay, borrow, partial fills, and strategy capacity

  • Move from research to shadow mode, paper trading, monitoring, and controlled live execution

  • Build cited RAG research copilots and bounded AI agents with human approval and audit trails


Nine compact case studies cover momentum, earnings-call text, causal events, mean reversion, execution costs, volatility targeting, regime-aware allocation, paper trading, and point-in-time RAG.

This is not a promise of easy profits. It is a practical playbook for building research that is realistic, reproducible, auditable, and designed to protect capital when the model is wrong.

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