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beebots

imikerussell/beebots · Homepage

Three AI trading bees on OKX, every decision by Jev.

Finance & Trading0.99Runner-up: Agent Decisions
Automated README summary

Automatically summarized from the README on 26 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

An open-source experiment where three AI trading bees trade OKX perpetual futures with simulated money by default, each with its own AI-designed style. Built for developers curious about AI-driven trading; the experiment concluded at -21.4% and is no longer maintained.

How it uses Jev

Jev is one of two selectable decision models (with Gemini 4 Argon). When asked and answering, it picks each bee's move; between paced calls, over the daily spend cap, without a key, or on failure, a deterministic code chooser decides instead. Every order passes through a plain-code risk layer.

Primitives:choice

Technique worth stealing

Paced model calls with a deterministic fallback chooser and a hard-coded risk layer around every order.

Try it

Run `docker compose up -d`, open the server IP, complete Setup, and press Start paper trading.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.14.0
Project signals
jev-1.14.0
Category
jev-1.14.0
Pattern tags
jev-1.14.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectFound in material0.87
  • Shows a System One patternnot found in the excerpt0.19
  • Describes handling uncertaintynot found in the excerpt0.03
  • Reports measured numbersFound in material0.99
  • Is a runnable toolunclear0.45
  • Top-10 learning shortlist signalnot found in the excerpt0.14
  • Is a replica of the modelnot found in the excerpt0.16
  • Solves a common problem (0–2)score on a 0–2 scale0.06 / 2
  • About JevFound in material0.99
Signals
Jev is core to the project0.87Shows a System One pattern0.19Describes handling uncertainty0.03Reports measured numbers0.99Is a runnable tool0.45Top-10 learning shortlist signal0.14Is a replica of the model0.1600.51Solves a common problem (0–2)0.06 / 2012

no-judgment band (0.40–0.60)

Design patterns
Funnel0.61Fail open, fail closed0.33Check-up0.2700.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 26 Oct 2026.