AI-Powered Algorithmic Trading with Python: A Practical Machine Learning Playbook to Build Leak-Proof Signals, Backtest Realistically, Manage Portfoli

By Liam Everly

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.

• Format: Paperback
• Language: English
• ISBN: B0HBNVF7QB
• ISBN13: 9798189196713
• Release Date: July 2026
• Publisher: Independently Published
• Length: 180 Pages
• Weight: 0.55 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"