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guzus/jev-nyotti-action
jev-nyotti-action is a machine learning model from guzus. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
A per-state gradient-boosting policy that imitates the first execution action in the next 15 minutes (hold / openlong / openshort / add / reduce / close) of a user-supplied BitMEX XBTUSD execution export (2018-03 → 20…
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Updated Sep 23, 2026
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From the Hugging Face model README
A per-state gradient-boosting policy that imitates the first execution action in the next 15 minutes (hold / open_long / open_short / add / reduce / close) of a user-supplied BitMEX XBTUSD execution export (2018-03 → 2020-06 train). It is paired with a deterministic, direction-neutral decision layer (24h trend filter, 32-bar minimum hold, 8-bar cooldown, no add/reduce). It carries its own paper position and serves jev-nyotti. This is not a Qwen model or an LLM.
Pre-registered gate on Binance 15m BTC/ETH/SOL/XRP, 2022-04-02 → 12-31, a window no model saw before: passed.
| 4-coin mean, fee-inclusive (7.5 bps/unit) | Net | Fees |
|---|---|---|
| V7 | +19.0 % | 15.0 % |
| V6 model without rules | −29.2 % | 162.7 % |
| V4 | −333.0 % | 354.0 % |
| 1-unit buy & hold | −70.2 % | — |
Caveats:
It has not beaten buy-and-hold in bull markets. Treat it as a research artifact, not a strategy.
numeric_policy.json, SHA-256
95531fcda384c095101051bf689947f46177a3a97e47806d3d16bb25f157d284.inference/jev_inference/numeric_policy.py and decision_rules.py.