| --- |
| license: cc-by-4.0 |
| tags: |
| - hackathon |
| - alphahack |
| - factor-model |
| --- |
| |
| # 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`](https://huggingface.co/datasets/xenosaac/alphahack-devpost) |
| - **Source code**: [`xenosaac/Alpha-Hack`](https://github.com/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 |
|
|
| ```bash |
| 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`. |
|
|
