References: The trader

For education only. Not investment, financial, or trading advice.

Source material

Primary source (open-source, Apache-2.0):
- TradingAgents, an open-source multi-agent LLM framework
  Authors: Yijia Xiao, Edward Sun, Di Luo, Wei Wang
  Organization: TauricResearch
  Repository: https://github.com/TauricResearch/TradingAgents
  Paper: "TradingAgents: Multi-Agents LLM Financial Trading Framework",
         arXiv:2412.20138 (https://arxiv.org/abs/2412.20138)
  License: Apache-2.0
  Pinned snapshot for this course: 7e9e7b8 (the framework as of 2026-05-01)

All code shown in this lesson is quoted from the framework at the pinned
snapshot above, under the Apache-2.0 license, with attribution. Clawdemy's
lesson prose is original. We teach the architecture only; we make no
investment, financial, or trading claims, and we report no performance results.

How to open it (no account or Git needed)

The framework is public and free to read. You do not need a GitHub account, the tool called Git, or any programming knowledge: open the link below in your browser and read the files like ordinary web pages.

• github.com/TauricResearch/TradingAgents (pinned snapshot). This link points to one frozen version of the project, marked by the short code 7e9e7b8.

The files this lesson reads

At the pinned snapshot:

• tradingagents/agents/trader/trader.py: the trader reads the judge’s plan from shared state, is told to anchor on it, and writes a concrete proposal back as its own field.
• tradingagents/graph/setup.py: the one line that builds the trader on the quick (cheaper) model, while the judge is built on the deep one.

Read this next

• TradingAgents repository (TauricResearch). The full source, at the pinned snapshot. The two files above are the ones this lesson quotes.
• TradingAgents paper (arXiv:2412.20138) by Yijia Xiao, Edward Sun, Di Luo, and Wei Wang. The framework’s own description of its design.

Adjacent topics

Where this sits inside this track.

• The bull and the bear. The previous lesson: the debate and the judge that committed to the verdict this lesson operationalizes.
• The risk gate. The next lesson: the trader’s proposal is stress-tested by a risk layer (several voices that pull in different directions) and a final manager who can temper or override.
• Why split one AI into many. Lesson 1 introduced the deep-versus-fast model split that this lesson reads straight off the trader’s wiring.