You can deploy this model and start earning money today!

Sahana/
optiona-cnt21-nifty

Name: optiona-cnt21-nifty
Availability: InStock

endpoints_compatible

Views

No views yet

CNT 2.1 — HF Inference Endpoint

Price-only credit spread profitability predictor for NIFTY options.

60 close-price candles → Chronos-2 base (768D) → MLP classifiers → bull/bear probabilities

Serves both spread widths per direction: width-100 (legacy keys, primary bull_probability/bear_probability) and width-200 (_w200 keys). One embedding pass feeds all classifiers.

Files

handler.py — Endpoint handler
requirements.txt — Python dependencies
model_store/ — Classifier weights, scalers, metadata (built from the MLflow registry)
model_store/deployment_manifest.json — registry versions, run IDs, hashes of this build
.hfignore — Excluded from upload

No adapter directory needed — loads amazon/chronos-2 base model from HF hub.

Prepare & Upload

bash

python -m deploy.build_model_store   # pulls @production versions from MLflow registry
cd hf_inference && python test_handler.py   # local smoke test

Then, from the project root, python upload_to_hf.py. A push is an immediate production release — the endpoint auto-tracks the latest revision. Verify locally first; after pushing run python test_hf_point.py and compare thresholds against deployment_manifest.json. (sync_assets.sh is the legacy path-based sync — deprecated, see DEPLOYMENT_AUDIT_CNT21.md for why.)

Input contract

POST JSON body:

json

{
  "inputs": [
    {"date": "2025-06-30 09:15:00", "close": 24150.5},
    {"date": "2025-06-30 09:18:00", "close": 24148.2}
  ],
  "parameters": {"context_length": 60}
}

Required columns

Column	Type	Description
`date`	str	Datetime (e.g. `"2025-06-30 09:15:00"`)
`close`	float	NIFTY 50 spot close price

Minimum 60 rows (3-min candles), sorted ascending by date.

Parameters

Key	Type	Default	Description
`context_length`	int	60	Number of trailing candles for context window

Output contract