Is anyone making money trading with Jev? Not that I could find. I read every public
Jev project in finance I could locate, 19 of them, in the week of 24 September 2026.
Most are demos or paper trading. Four put Jev up against a simpler alternative, and in
none of those did Jev come out ahead. The biggest and most serious one, QuantDinger,
became the subject of the rest of this series.

Why I went looking

In mid-September 2026 my X bookmarks filled up with Jev projects. Jev playing Super
Mario from emulator RAM. Jev playing Doom. Jev flying a simulated drone from a camera.
A browser agent built on it with 19,940 GitHub stars. All of them had been updated
within the same ten days.

Jev is TypeSafe AI's model: you send it a JSON "state" plus a few typed questions, and
it answers with probabilities instead of prose, fast and cheaply ($0.042 per million
input tokens). That shape, structured data in and a probability out, looks made for
trading. And sure enough, trading bots started appearing next to the games.

I build and test trading tools for a living, so the question was obvious: does any of
this work on a market?

What did I find?

A public gist that tracks Jev projects listed 18 in finance, counting its comments. I added one from my own
bookmarks and read the code of each one. Here's how they split:

Group Count What they are
Trading or price prediction 12 bots, paper funds, signal labs, prediction experiments
Finance, not trading 3 classifying bank-customer intents (two) and IRS tax documents
No working Jev code 4 two empty repos, one deleted, one data backend with Jev switched off

The 12 that touch trading:

Project What Jev does How it runs
OpenByteInc/QuantDinger (12,065★) approves or blocks a strategy's entry orders live and paper, self-hosted platform
jarrodwatts/jev-trader (2,192★) decides buy or sell on every block (~300 ms) public version is a dry run
aowang-ai/jev-trade (127★) decides direction, action and leverage every 2 s testnet
irfndi/prism-liquidity-agent (107★) shadow judgements for a liquidity bot; rules decide paper
sosopop/jev_stock (13★) predicts up / flat / down for Hong Kong stocks research only
buberlo/jev-trader (6★) six judgements feeding a market-making policy design stage, synthetic data
erboland/jev-fund (3★) buy / sell / hold for a 10-stock paper fund paper
zzsong1023/jev-market-reflex (2★) buy / sell / hold every 0.5 s on BTC, ETH, SOL paper
UditJain2622004/Jev-Trading (1★) filters scalping entries on SOL backtest
Gamma-Software/jev-signals-lab (1★) 12 questions per market snapshot proof of concept
IslamBaraka90/jev-…-financial-use-cases 50 finance demos, some on price demo
maxlibin/moomoo-jev-trader comments on or filters a US-stock bot live-capable, filter off by default

Several of these say plainly that they're demos, and that's fair. A demo isn't a claim of
profit. But a demo is also not evidence that it works.

Did any of them show Jev has an edge?

Four compared Jev with something simpler. None came out clearly ahead:

Project Comparison Result
UditJain2622004/Jev-Trading 18 experiments, Jev filtering SOL 1-minute scalps, each with a no-Jev control no experiment profitable; Jev-picked trades won at most 45.7%, and a simple rule matched Jev's best
sosopop/jev_stock 120 next-day predictions on HK stocks Jev 45% correct; always predicting "down" scored 51.7%
IslamBaraka90 (50 finance demos) Jev against a few lines of rules the rules matched or beat Jev in 40 of 47 demos; price calls were 42–52%
irfndi/prism-liquidity-agent author reports PF 3.53 with Jev vs 1.74 without the data behind it isn't in the repo

To be clear about what those numbers are: they come from each repo's own logs, or from
simple baselines I computed against them. Each is small. But the direction is the same
every time, and nobody has published the opposite: Jev beating a plain model on data it
hadn't seen, after costs.

The non-trading ones look better, for what it's worth. On banking-intent classification
Jev scored 92.4% against a fine-tuned BERT's 93.66% (with 24 retrieved examples in each
prompt), at about $0.00014 a call. Sorting
pages into the right form type, reading a label off a known list: that's the kind of
job where it does well.

Why QuantDinger?

Because it's the one that matters most if it's wrong. It has more stars than all the
other trading projects combined, it's a full platform rather than a demo, it can trade
real money on real exchanges, and its hosted version charges credits for each Jev check. It also uses
Jev in the most sensible-sounding way: not letting the model trade, only letting it veto
entries.

So that's where I went next:

  • Part 2 reviews QuantDinger as a platform: what's solid and what's risky with real
    money.
  • Part 3 covers its trading bots, and the exit structure I'd copy into an EA.
  • Part 4 opens up the Jev gate itself, and the question it left me with.
  • Part 5 is the experiment I ran to answer it, on 181,290 Bitcoin candles.
  • Part 6 is what I'd conclude.

What I didn't check

  • I read code and published logs; I didn't run every project.
  • The list is a snapshot of one week in September 2026. New projects appear daily.
  • Star counts and commits are as of 24–25 September 2026.
  • A project with no published comparison may still work. It just hasn't shown it.