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

Produkt bewerten

Liam Everly

Verlag:

Independently Published

Unsere-Artikel-Nr.: P36155156

EAN: 9798189196713

Erhältlich:

Nicht auf Lager

Zustellung: Do, 03.09.2026

Versand: Kostenlos

CHF 28.25

Beschreibung

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.

Spezifikationen

Sprache

Englisch

Autor

Liam Everly

Erscheinungsjahr

2026

Format

Buch (Softcover)

Anzahl Seiten