Multi-agent equity research for public markets.
TradeMarketAgents combines specialized AI research agents, structured market data, news, fundamentals and alternative sentiment signals to produce traceable, evidence-based equity research.
A research system built around independent agents and conflicting evidence.
Specialized agents independently examine market data, fundamentals, news and sentiment, then construct bullish, bearish and risk assessments. Their outputs are compared and reconciled into research reports designed to preserve the evidence behind each conclusion.
Multi-agent reasoning
Independent research roles analyze the same asset from different perspectives rather than collapsing everything into a single prompt.
Alternative data
Market and sentiment signals can be integrated alongside traditional financial and news data.
Evidence traceability
Research outputs are designed to retain the chain of evidence and make disagreements between agents visible.
Systematic evaluation
Historical and forward out-of-sample evaluation is used to measure research quality and model calibration.
Reasoning first. Prediction second.
The project focuses on evidence-based analysis, model comparison, multi-agent debate and out-of-sample evaluation rather than treating language-model output as a black-box price prediction engine. The goal is to build a more inspectable and testable research workflow.
Active development and evaluation.
TradeMarketAgents is currently an early-stage project under active development, with ongoing work on research orchestration, data integration, evaluation and model calibration.