01
You ask the question
Start with the market behaviour, hypothesis or constraint you want to investigate. You do not need to begin with code.
AI Quant Blueprint
Build smarter trading systems with AI.
A structured guide to using AI and Python across the quantitative research workflow — from hypothesis and data preparation through backtesting, validation and deployment.
8
Structured chapters
30+
Worked Python examples
104
Governed AI prompts
Research → Code
Connected workflow
Backtest → Deploy
Production pathway
Annual
Member access
Chapter Structure
Eight chapters, one connected workflow.
- 01The Quant Research Process
- 02Prompting LLMs for Strategy Ideation
- 03Data Sourcing & Preparation
- 04Python Backtesting Fundamentals
- 05Position Sizing & Risk Management
- 06Robustness Testing & Overfitting
- 07Interpreting Performance Metrics
- 08From Backtest to Live Implementation
AI-Assisted Research
You do not need to arrive as a quant developer.
AI can handle much of the technical translation between a research question and the code needed to investigate it. QTL shows you how to use ChatGPT and other LLMs to explain concepts, generate Python, debug analysis and challenge results.
The objective is not to remove judgement. It is to remove the unnecessary technical barrier between a good research question and a properly tested answer.
QTL Principle
AI lowers the technical barrier. It does not lower the research standard.
02
AI translates
Use ChatGPT or another LLM to explain concepts, structure prompts, generate Python, debug errors and turn plain-English questions into testable research steps.
03
The workflow tests
Run the analysis against data, backtest the logic, inspect failure modes and apply robustness and validation controls.
04
You decide
The human role remains the important one: judge the evidence, reject weak ideas, refine promising ones and decide what deserves promotion.
Research questionAI-assisted promptPython analysisEvidence & decision
Learning Outcomes
What you'll take away.
- Understand the quantitative research process end-to-end
- Use AI as a structured research assistant rather than an authority
- Translate research questions into reproducible Python workflows
- Build and assess backtests with appropriate controls
- Recognise common sources of overfitting and analytical error
- Understand the gap between research results and production deployment
Included with annual membership.
The Blueprint sits inside the wider Quant Trading Labs research environment alongside the Member Library, Research Vault, Daily Quant Brief, governed prompt resources and current strategy research updates.