QTL
AI Quant Blueprint

Build smarter trading systems with AI.

A step-by-step guide to using ChatGPT, LLMs and Python to research, build, backtest and deploy quantitative trading strategies across FX, crypto and commodities.
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AI Quant Blueprint research workspace showing Python code, AI-assisted strategy research, quantitative dashboards, research notebooks and validation materials
300+
Pages of guided instruction
8
Chapters, one connected workflow
30+
Worked Python examples
150+
AI prompts and workflows
25+
Strategies tested and documented
Lifetime
Access, including future editions
Chapter Structure

Eight chapters, one connected workflow.

  1. 01The Quant Research Process
  2. 02Prompting LLMs for Strategy Ideation
  3. 03Data Sourcing & Preparation
  4. 04Python Backtesting Fundamentals
  5. 05Position Sizing & Risk Management
  6. 06Robustness Testing & Overfitting
  7. 07Interpreting Performance Metrics
  8. 08From Backtest to Live Implementation
Quant Trading Labs Research Visual
Chapter spread showing an annotated Python backtest workflow and prompt sequence
Ref · QTL-VISStatus · Commissioned
Chapter Spread
A representative spread: annotated prompt sequence alongside the Python backtest it produces.
Learning Outcomes

What you'll take away.

  • Understand the full quant process end-to-end
  • Use AI to generate and refine trading ideas
  • Code strategies confidently in Python
  • Backtest with statistical rigor
  • Avoid the most common analytical pitfalls
  • Deploy with an honest view of live-vs-backtest gap

Included with Founding Membership.

The Blueprint ships alongside the Research Vault, the Daily Quant Brief and a new backtested strategy every month.

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