We gave an AI trader 40,000 calls on Base. It did worse than a coin flip.
Viral posts show Jev, a fast decision model, trading crypto every block. None of them checked whether its calls are right. We did: pre-registered, 90 days, four markets on Base, every decision scored against what the price did next.
Data table
What the viral posts actually claim
The posts sell speed and cost. We went looking for the profit.
| Source | What it shows | Profit shown |
|---|
Does Jev know where the price is going?
No. In every market and horizon its probabilities rank outcomes about as well as chance, and usually a little worse. Free rules do as well or better.
Data table
Data table
One decision, as Jev saw it
State sent to Jev
Jev's answer: probability the price is higher after
The 15-minute question
How we measured it
Then what is it doing?
Following the trend. Its probability of a rise tracks the last 15 to 60 minutes of price and order flow. On Base, pool prices tend to reverse over that horizon, so following the trend loses.
Data table
Data table
Did we get something wrong?
We tried to. The pipeline finds skill when it exists, the result survives a change of price source, and two independent projects found the same pattern.
What if you trade it anyway, or do the opposite?
Trading it loses to holding. Flipping it gives a weaker copy of a free linear model, still far from paying for its trades.
Data table
The gap no prompt can close
Each trade on a Base pool pays the pool fee, price impact and gas both ways. Given how far prices move in 15 minutes, breaking even takes the hit rate on the right. The best signal we found reaches the one on the left.
Is there money around it without predicting anything?
Yes, but it belongs to specialists who were already there. We checked three places and Jev adds nothing to any of them.
Where Jev might still help
Jev is cheap and quick at judging text. That turns into an edge only where text is the bottleneck, nobody else is reading it fast, and the price is slow to react. In liquid crypto those three rarely meet. The candidates left are narrow: long-tail tokens reacting to unstructured posts, filtering rugs at launch, and reading prediction-market rules. None is tested here.
Test the window before the model. For example, first measure how long a pool takes to reprice after a listing announcement. If it is one block, bots already own it and no classifier will help.
Run it yourself
Everything is in the repository: the pre-registered protocol, the code, and the reports. You need Node 23.6 or later and a Vercel AI Gateway key. The public Base RPC works, just slowly.
git clone https://github.com/r4topunk/jev-trading-study
cd jev-trading-study && pnpm install
node scripts/backfill.ts --days 90
node scripts/backtest.ts --market eth --days 90 --every 15 --max-usd 0.8
node scripts/compare.ts
node scripts/audit/positive.ts