Would a reasonable person ever guess that a 25 year old energy company could trade at over 1000 P/E?
Would a reasonable AI model?
Teaching LLMs to manage assets means helping them understand how liquidity, volatility, and social factors combine to create unintuitive outcomes in the market.
If you remember the COVID toilet paper shortage, you know how people act when critical resources start to become scarce.
Markets can act the same way: part of our job is to teach LLMs to look beyond the technical fundamentals of a market & understand the human psychology that drives them.
To accomplish this, we evaluate models not based on knowledge, but on intuition.
We reward models for making surprising decisions that lead to positive outcomes.
We reward models for choosing unique data sources to look for invalidation.
We reward models for acknowledging exotic risks.
This results in models that can express trades creatively and find the strange correlations that lead to 25 year old energy companies trading at over 1000 P/E.
Agentic finance went from "trading bots" to a category serious people track quarterly.
@CambrianNetwork's Q2 Landscape is a good snapshot of how fast it's maturing.
Cod3x is in there tooπ
Two new models just landed on Cod3x.
@Alibaba_Qwen's Qwen 3.7 Max
@MiniMax_AI's MiniMax M3
Deeper reasoning, better tool use, more firepower.
Time to out-trade the rest of the desk π
American traders had access to trillions of dollars in AI upside since the launch of ChatGPT.
Most didn't see a penny of it.
@uv asks: how can we teach AI to navigate long-horizon macro trades like this?
The hard part is - if you showed an AI model one of these charts and told it to trade, it would cheat by mapping data or news to the trading outcome.
All of this information is already contained in the model.
So to teach AI how to trade long-horizon outcomes, you need to:
1. Enrich these charts with every possible detail
2. Study them to understand their fundamental nature
3. Create new, imaginary charts that are sufficiently realistic for models to learn without cheating.
This is how reinforcement learning can help models navigate real markets - backed by tremendous amounts of human labor!