StockAI

The project

Project overview

StockAI is experimental quantitative software research focused on testing ideas and evaluating evidence. Its scope includes backtesting, validation, risk controls, and the auditability of research and system behavior.

Problem or objective

A favorable historical result can depend on assumptions that fail outside the original test. The research objective is to challenge those assumptions and examine whether data quality, validation design, or operating conditions undermine a conclusion.

Solution approach

I explore validation through backtesting, walk-forward analysis, holdouts, and Monte Carlo analysis. Canary validation and operational controls add further checks around behavior, while stale-data rules and risk locks define conditions in which a system should stop or restrict activity.

Key capabilities

• Backtesting and walk-forward validation
• Monte Carlo analysis and holdout evaluation
• Canary validation and stale-data rules
• Risk locks and auditing

Architecture & technical elements

The research separates strategy evaluation, evidence validation, and operational controls. Data freshness rules, risk locks, and auditing make the conditions behind a result as important as the result itself.

• Backtesting
• Walk-forward validation
• Monte Carlo analysis
• Holdouts
• Risk controls
• Auditing

What the work demonstrates

A disciplined approach to uncertainty: testing assumptions, separating research evidence from operational permission, and designing explicit controls for unreliable inputs or unexpected behavior.