Jev makes 77 trading decisions vs an LLM's 3, 105x faster
On the same MON-USDC order book, Jev made quoting decisions every 300ms block and answered in 78ms, while the chat model took 8.2 seconds and 27 blocks per decision and was swept twice.
Do you realize Jev trades circles around any chat LLM. I still can't figure out why so few people are using it. Both ran side by side on the same MON-USDC order book, same flow, same quoting rule. If your trading bot still runs on a chat model, you are someone else's exit liquidity. Opus 5.5 can write the whole bot from a spec, and Jev makes the calls inside it. The setup has 7 parts: 1. Every 300ms Monad block, code turns the L2 book into under 400 tokens of numbers like mid, spread, imbalance, vol and inventory. 2. Six typed questions about those numbers go to Jev in one request, and the answers come back in 78ms. 3. The LLM took 8.2 seconds per decision. 27 blocks went by while it thought, and it got picked off twice on prices that were already gone. 4. Jev pulled every quote before the sweep when toxic flow hit 0.83, and widened the spread when liquidity stress hit 0.78. 5. Pricing stays Avellaneda-Stoikov math in code, Jev only decides if the book is worth quoting into. 6. Max position, daily loss and drawdown live in code Jev cannot touch, and a late answer means the bot sits out that block. 7. Final count was 77 decisions vs 3 and 105x faster, at $0.00001 per block. The article below walks through each layer, Python included. Save it before it gets buried:
This post quotes another post: “Building an HFT trading bot with Jev: one buy/sell decision per block”