agentic trading research copilot

The problem

Trading research often suffers from confirmation bias, hidden assumptions, and a lack of systematic evidence tracking. Traders struggle to turn raw market data into falsifiable hypotheses and objective decision briefs.

What it does

• Generates 2 to 4 distinct market scenarios with specific input assumptions and market responses.
• Tests falsifiable hypotheses using historical analogs, statistical analysis, and model simulations.
• Maintains a traceable evidence log that distinguishes direct observations from inferences.
• Produces structured decision-ready briefs including risk assessments and confidence levels.
• Quantifies uncertainty by identifying data gaps and contradictory signals.

Why this beats prompting it yourself

This skill enforces a rigorous scientific method that generic prompts lack. It prevents the model from hallucinating certainty and mandates a comparative mindset, ensuring you see the bear case as clearly as the bull case.

Use cases

• Comparing macro scenarios for interest rate shifts and their impact on equity sectors.
• Testing short-term price action hypotheses against recent volume profiles.
• Building structural investment briefs for long-term portfolio adjustments.
• Auditing existing trading theses for survivorship bias and data limitations.

Known limitations

Requires access to external market data tools for real-time analysis. Briefs are limited to 1500 words to maintain density and focus.