Everything on this site is one experiment: can a frontier AI, improving its own trading rules every day, beat a fixed rulebook that never changes? Here is the whole machine, step by step, with nothing hidden.
Every account is paper — simulated money on real market prices. Nothing here is investment advice.
Each account trades from a playbook (in our code it's called the genome): a written set of entry and exit rules composed from classic, named trading setups — breakouts, pullbacks, mean-reversion, squeeze patterns and the like. The AI doesn't type raw code or click buy on a whim; it writes rules, and the engine executes those rules mechanically all day.
That's why every trade on the public record shows the exact trigger that fired it, printed verbatim. If a rule didn't fire, no trade happened. The fixed System rulebook is the control: same engine, same setups — but no AI ever edits it.
Before the market opens, each model is shown its own recent trades — wins, losses, what fired and what it cost — and writes a short self-review plus, if it wants one, a proposed new playbook. That proposal is a complete set of rules, not a vibe: "buy this pattern under these conditions, exit here."
This is the "learning" on the site: not retraining the model, but the model rewriting its own rules from its own evidence, in public, every day.
A proposed playbook never goes live on its author's say-so. It runs a tryout: both the new playbook and the current one are simulated over recent real market days, trade by trade, and compared. To take over, the newcomer must:
Most proposals lose the tryout. When you see "kept the incumbent" in our notes, that's the gate doing its job — the AI wanted to change, and the evidence said no.
Markets have weather — trending up, trending down, choppy, volatile. A playbook written for one kind of weather can look terrible when tested on another, which used to mean a defensive idea proposed during a downturn was graded mostly on last week's sunshine.
Two things address that now. First: if the market's character genuinely flips mid-day (a sustained shift, not a blip), the AIs get an extra same-day review instead of waiting for tomorrow morning. Second: that tryout is graded on a window built from recent days that looked like the market being entered — plus today — so a down-market idea is finally examined on down-market days. The passing bar itself never moves; only the terrain does.
The same models also play live chess and poker, reviewing those games nightly the same way they review trades. The Crossover — our live experiment: do lessons from the game table make a better trader? Unproven; verdict published either way.