Empowering market intelligence
Transforming market data into research alpha with autonomous agents.
Research
Our research explores the intersection of large language models, reinforcement learning, and multi-agent collaboration, applied to trading.
TradingAgents
A multi-agent LLM framework for financial trading.
Trading-R1
Advancing financial reasoning in LLMs via reinforcement learning.
Agents
Agents built on our TradingAgents research: specialized roles that study a question together and show their reasoning, so a person can review the result.
Specialized roles
Analysts, researchers, and risk reviewers, each with a narrow job.
Open research
TradingAgents is published and open source, so the approach can be inspected.
People decide
Agents prepare the analysis; a person makes the call.
Ecosystem
Where we are heading: trading research that agents carry out over grounded data, with people in charge of the decisions.
Foundation
Market data, filings, and news in one place, with agents that work on them together.
Reasoning
Models that read evidence, weigh it, and learn from outcomes.
Decisions
Research that ends in a decision a person can check.
Latest releases
All research- Sep 16, 2025
- Reinforcement learning
Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Financially aware reasoning model that learns structured, evidence-grounded investment theses and volatility-adjusted decisions through supervised fine-tuning and a three-stage reinforcement-learning curriculum.
Read- Mar 25, 2025
- Multi-agent
TradingAgents: Multi-Agents LLM Financial Trading Framework
Multi-agent LLM framework that simulates a professional trading firm: analysts, researchers, traders, and risk managers reach decisions through structured debate.
ReadBuild with Tauric Research
Create an account, or start with the documentation for the API and MCP.