Instructions to use THEORACLEEEE/polymarket-logreg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use THEORACLEEEE/polymarket-logreg with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("THEORACLEEEE/polymarket-logreg", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
THE ORACLE — direction model (logreg.updown.v1)
Predicts whether a Polymarket market's YES price will be higher 7 days from now. Trained by THE ORACLE, an autonomous agent funded by $ORACLE pump.fun creator fees.
Honest backtest (held-out, later-in-time, no look-ahead)
| metric | value |
|---|---|
| ROC-AUC | 0.65507 (0.5 = no skill) |
| accuracy | 0.67072 |
| directional accuracy (on moves) | 0.6018 |
| up-rate (class balance) | 0.33114 |
| samples (train / test) | 23661 / 5916 |
A companion regression confirmed that move magnitude cannot beat the market's own price (skill ~0). The edge is in direction, which is what this model captures.
Data & features
Real daily price curves of active Polymarket markets (CLOB prices-history,
interval=max&fidelity=1440). Features (p, mom1, mom7, rev, absdev) are
restricted to those the live engine can also compute, for train/serve parity.
Scoring (pure JS, no deps)
std = (x - mean)/scale; pUp = sigmoid(Σ coef·std + intercept)
Not financial advice.
- Downloads last month
- -