Featured
The research behind the desk.
How TradingAgents, an open multi-agent AI framework, turns news, macro and sentiment into a single decision — and how we point it at three risk-tiered funds.
3 Sep 2026 · 18 min read
Longer-form ideas and investment notes.
Featured
How TradingAgents, an open multi-agent AI framework, turns news, macro and sentiment into a single decision — and how we point it at three risk-tiered funds.
3 Sep 2026 · 18 min read
Our goal is to use AI agents to build funds that outperform the S&P 500. Not tips, not signals: three real, fully managed portfolios you can watch, with the reasoning behind every decision published the same day.
TradingAgents is open research that runs a small team of AI agents the way a real investment desk works. Instead of asking one model to guess, each agent has a job, they argue it out, and a manager makes the final call.
The point of the arguing is simple. Ask one model "should I buy this?" and you get a confident, shallow yes. Make a bull and a bear fight over it, then make a risk team sign off, and the weak spots come straight to the surface.
Most people who pick up TradingAgents point it at a single stock and ask whether to buy. We do something different. We funnel that research into three managed funds, so the agents are not just picking one name, they are running whole portfolios.
Every trading day at market close, the full team runs again: the news, macro and sentiment agents gather what happened, the bull and bear argue it out, the risk team checks it against each fund's mandate, and the portfolio manager rebalances. Nothing sits still by accident. Every position is re-examined, and whatever changes get written up in plain English on the Commentary page that same day.
Same engine, same daily process, three different appetites for risk. See how each one is performing.
Every position starts as a five-step relay. Nothing skips a stage, and each one can send the decision back down the line.
The heavier version: TradingAgents is built on LangGraph, which wires the agents into a graph they pass their findings through. It pulls live prices and fundamentals, recent news and macro data, runs each agent as its own language-model call, and lets the researcher and risk stages run several rounds of back-and-forth before the manager commits. It is open research from Tauric Research, so anyone can check exactly how it reaches a call: read the paper on arXiv or the code on GitHub.
LiquidAssets is run by a finance and computer science student who has traded the US and New Zealand markets for years. The academic work does the reasoning. The judgement about how to point it, and at what risk, is ours.
Model portfolios only. Not financial advice. No real trades are placed by this prototype.
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