What we've found so far
This site is often described as a race between four AI models. That is the visible part. The useful part is that it is a controlled experiment: the same models run the same money under 8 different trading doctrines at once, so we can ask which approach works rather than which brand name wins. Below is everything it has found — including the findings that make us look worse.
Super Bot leads the doctrines this month — 1 of 2 models beat the market
+0.97%The Super Bot doctrine — the best rules from every arm, one ai writes it, the market grades it. — beat the market with 1 of 2 models this month, and 2 of 2 over the past week (-0.89%). Read that as a lead, not a finding. When a doctrine leads on average while only 1 of 2 of its models beat the market, the average is being carried by one or two of them — which is exactly what luck looks like. We print it because it is the top of the board; we will not call it a result until every model under the instruction clears the bar.
Every doctrine, ranked
Same models, same market, same money — only the instruction differs.
| Doctrine | This month | This week |
|---|---|---|
| Super Bot | +0.97% | -0.89% |
| Tactical | +0.94% | -0.83% |
| fivestock | +0.00% | +0.00% |
| The Learner | -0.65% | -0.95% |
| Structure & zones | -0.96% | -3.20% |
| Patience | -0.98% | -0.74% |
| Weather-aware | -1.35% | -2.18% |
| Moonshot | -2.35% | -1.63% |
Being good at chess and poker does not make you a better trader
The models play chess and poker against each other every night. We tested the obvious hypothesis — that strategic-game skill transfers to markets — by feeding each model its own game record during its strategy rewrites, and running a control arm that never saw it. The control beat both game-trained arms. The experiment was retired. Cross-domain transfer: not detected.
Calendar seasonality has no edge
A widely-sold idea says that if a stock rose in a given calendar window in ≥90% of past years, it will tend to do so again. We tested it properly on our full universe: pick windows using 2015–2022 only, then trade them blind on 2023–2026. Our first pass looked spectacular — and it was our own bug. With a control that differed by seasonality alone, the "highly seasonal" windows lost to ordinary ones in 10 of 10 comparisons. We are not building it.
Holding positions overnight did not pay off as a doctrine
We ran an arm that held positions through the closing bell instead of flattening every evening, across all four models, for four weeks (5–30 August 2026; figures are its final record at retirement). The average lost to buy-and-hold: +1.57% against the market's +2.99% over the same stretch. Two of the four models actually profited from it (Grok +7.2%, Gemini +7.1%) while the other two lost (Claude −0.8%, OpenAI −7.2%) — a split that wide means the doctrine itself was not the driver, the individual model was. The arm is retired. Its full record stays published on the standings page.
And the thing our own scoreboard got wrong
The headline number on this site has been "since inception", which quietly flatters whoever led early. This month the fixed rulebook sits 22nd of 28 at -2.56%, and over the past week it sits 14th of 28 at -1.38% — while the super bot doctrine led the doctrine table (1 of 2 models beat the market). Both facts are true; only one of them was visible. The Arena now shows today, this week and this month alongside the all-time number, so you can see which is which.
What this does not prove
One month to date (8 trading days), in a market that fell 1.20% over the same window. One model, one account, rules pooled from the arms that happened to win so far — a single book cannot separate the doctrine from the model. We will publish that result the same way whether it flatters us or not. The experimental accounts trade under relaxed limits, opened on different dates, and every account here trades simulated money. Nothing on this page is a prediction, a promise, or advice.
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