AI Quant Lab
Turn trading questions into testable research, inspect backtest limits, and use AI-assisted code while keeping evidence, risk, and human judgment explicit.
- Ask testable questions
- Understand AI limits
- Spot data biases
Formulate testable hypotheses, then evaluate evidence.
Testable Hypotheses
Reframe trading ideas into data-verifiable research questions.
Understand AI Limits
Use AI for code and research assistance while keeping human risk judgment in the loop.
Recognize Data Biases
Identify overfitting, sample leakage, execution friction, and out-of-sample failure.
Research Review
Review assumptions, evidence, and failure modes before treating a result as useful.
5 planned modules. 25 planned research lessons. Evidence you can defend.
The AI Quant Lab is a future research layer, not the next numbered block after Pro.
- 015 planned lessons
Microstructure & Order Flow
Understand price formation through order flow and the order book.
- 025 planned lessons
Global Macro & Liquidity
Build a research view from cross-asset and policy variables.
- 035 planned lessons
Multi-Strategy Portfolios
Build portfolios, correlation, and market-regime frameworks.
- 045 planned lessons
Quant Infrastructure
Set up data, backtesting, validation, and research workflows.
- 055 planned lessons
Capital & Risk Research
Close the research loop with risk budgeting and portfolio management.
Finish with a research project you can explain and defend.
Put one research question, the evidence, the data and model limitations, risk boundaries, and review conclusions into a single inspectable project.
- Define one falsifiable research question
- Document data, assumptions, and validation limits
- Write down risk boundaries and failure conditions
- Finish with a review conclusion — no live orders or return targets