MarketLab: Reproducible Market Experiment Platform

Problem

Financial experiments often become messy notebooks with unclear assumptions, weak validation, and unreproducible results.

Solution

Built a package-first research toolkit for market experiments, including data preparation, baselines, ML model training, walk-forward evaluation, diagnostics, reports, and paper-trading workflows.

Deliverables

• Experiment configs
• Data preparation pipeline
• Baseline strategies
• ML model training workflows
• Walk-forward folds
• Diagnostics
• Reports and plots
• Local paper-trading MVP
• Docker/MCP server path

Why it matters

• Package-first structure means experiments are reproducible — not just notebooks that ran once
• Walk-forward evaluation avoids look-ahead bias that invalidates most notebook backtests
• YAML configs make it easy to hand off experiments to another engineer or stakeholder