AlphaHack Trained Models
Three trained scikit-learn artifacts for the AlphaHack quantitative hackathon-strategy engine. Each lives in its own subdirectory with a dedicated model card.
| Subdirectory | Artifact | Purpose | Size |
|---|---|---|---|
model1-regime-classifier/ |
regime_classifier.pkl |
Event → winner-archetype classifier (Model 1) | 2.2 MB |
model2-winner-predictor/ |
winner_predictor_final.pkl |
Project → ex-ante prize-probability classifier (Model 2) | 493 KB |
text-embedder/ |
text_embedder.pkl |
TF-IDF + TruncatedSVD text embedder (auxiliary) | 4.2 MB |
Companion artifacts:
- Dataset:
xenosaac/alphahack-devpost - Source code:
xenosaac/Alpha-Hack
Honest framing
These models were validated retrospectively (Model 2 sponsor-prize AUC 0.908, 95% CI [0.859, 0.947]) and tested in one prospective trial in April 2026 that did not produce a prize. Treat as a research artifact, not a guaranteed winning strategy. Each model card documents the known failure modes specific to that artifact.
Quickstart
pip install hackalpha
python -c "
import joblib
m2 = joblib.load('model2-winner-predictor/winner_predictor_final.pkl')
print(m2['features']) # the 23 features it expects
"
The text-embedder/text_embedder.pkl requires the hackalpha package
to be importable at load time (it pickles a hackalpha.research.text_embeddings.TextEmbeddingFeatures
instance). The other two models are pure scikit-learn and load without
any project-specific deps.
License
CC BY 4.0 — see LICENSE.