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Building a Multi-Agent Financial AI That Gets Graded by the Market
Explore a multi-agent AI that analyzes stocks, generates trade plans, and is graded by actual market outcomes, revealing practical insights for building decisive AI systems.
I built a multi-agent evaluation pipeline for stock analysis where AI-generated trade plans are automatically scored against future market outcomes. For each stock, four specialized agent teams (technical, fundamental, sentiment, and risk) independently analyze the opportunity before a portfolio manager agent decides whether to produce a trade setup or reject it entirely.
The key idea is that every prediction becomes a future evaluation. Entry, stop loss, and take-profit levels are stored, monitored, and later compared against what actually happened in the market. Closed signals become structured evaluation data that can be used to measure agent quality and improve prompts, workflows, and decision logic.
I’ll show the live architecture, agent orchestration pipeline, trade outcome tracking system, evaluation dashboards, and the feedback loop that turns market results into iterative improvements.
TradeFlare deploys multi-agent AI to analyze overnight stock data.
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