Metadata-Version: 2.4
Name: options-chain-features
Version: 1.0.0
Summary: Deterministic feature engineering for options chain data
Author: Vansh Ashok Setpal
License: MIT
Project-URL: Homepage, https://vansh0016.github.io/options-chain-features/
Project-URL: Documentation, https://vansh0016.github.io/options-chain-features/
Project-URL: Repository, https://github.com/vansh0016/options-chain-features
Project-URL: Issues, https://github.com/vansh0016/options-chain-features/issues
Keywords: options,derivatives,feature-engineering,quantitative-finance,volatility,greeks,options-chain,machine-learning
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: polars>=1.30
Requires-Dist: numpy>=1.24
Requires-Dist: duckdb>=0.9
Requires-Dist: pyarrow>=10.0
Requires-Dist: python-dateutil>=2.8
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == "dev"
Requires-Dist: mkdocs>=1.5; extra == "dev"
Requires-Dist: mkdocs-material>=9.5; extra == "dev"
Dynamic: license-file

# Options Chain Features (OCF)

Options Chain Features (OCF) is a modular, research-grade Python library for building model-ready features from options chain data.

OCF provides a deterministic, schema-driven pipeline that converts raw vendor data (e.g. Bloomberg-style extracts) into structured, machine-learning-friendly feature tables without imposing trading logic, forecasting assumptions, or model constraints.

---

## Installation

Install via pip:

```bash
pip install options-chain-features
```

---

## What OCF Provides

At a high level, the library includes:

* Canonical schemas for underlying data and option chains
* Normalization layers for vendor-style raw inputs
* Deterministic feature blocks operating on aligned daily data
* Implied volatility surface & term-structure features
* Liquidity and positioning features
* Option-level Greeks and aggregate exposure representations
* End-to-end pipelines with strict validation and explicit configuration

All components are usable independently or as part of a full pipeline.

---

## Design Principles

OCF is built around a few core principles:

* No hidden state - All transformations are explicit and deterministic.

* No implicit joins or IO - File handling is separated from computation.

* Schema-first design - Every stage operates on clearly defined canonical tables.

* Feature isolation - Each feature block can be tested, enabled, or disabled independently.

* Research-friendly - Outputs are flat, numeric, and directly consumable by ML models.

---

## Documentation

Detailed documentation is available [here](https://vansh0016.github.io/options-chain-features/), including data schemas, feature definitions, pipeline architecture, Greeks and exposure construction, and configuration options.

---

## Release Status

Current release: **v1.0.0**

This project follows Semantic Versioning (MAJOR.MINOR.PATCH).

---

## License

This project is released under the MIT License.
See [`LICENSE`](./LICENSE) for details.

---

## Citation

If you use OCF in research or production systems, attribution is appreciated.

```bibtex
@software{setpalocf2026,
  author = {Vansh Ashok Setpal},
  title  = {Options Chain Features (OCF)},
  year   = {2026},
  url    = {https://github.com/vansh0016/options-chain-features}
}
