Metadata-Version: 2.4
Name: oq-backtest
Version: 0.1.0
Summary: Honest, vectorized backtester for Indian equities with full STT/brokerage/GST/slippage/tax cost modeling.
Project-URL: Homepage, https://github.com/revorhq/openquant
Project-URL: Repository, https://github.com/revorhq/openquant
Project-URL: Issues, https://github.com/revorhq/openquant/issues
Author: OpenQuant India Contributors
License: Apache-2.0
Keywords: backtesting,finance,india,nse,quant,trading
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.11
Requires-Dist: numpy>=1.24
Requires-Dist: oq-core
Requires-Dist: pandas>=2.0
Description-Content-Type: text/markdown

# oq-backtest

Honest, vectorized backtester for Indian equities.

Models every cost an Indian retail trader actually pays — STT, brokerage,
exchange charges, GST, stamp duty, SEBI fees, slippage, and STCG/LTCG —
with broker presets for Zerodha, Upstox, Fyers, Dhan. Outputs gross vs net
equity curves side-by-side with a full cost attribution breakdown.

```bash
pip install oq-backtest
```

```python
import oq_backtest as ob
result = ob.backtest(signals, prices, costs="zerodha")
print(result.tearsheet())
```

Includes walk-forward / out-of-sample utilities and an intraday layer for
1–60 min bars with session square-off.

Part of [OpenQuant India](https://github.com/revorhq/openquant) — honest, open
source quant infrastructure for Indian markets. Apache 2.0.
