Metadata-Version: 2.4
Name: therminal-py
Version: 0.1.0
Summary: Python SDK for Therminal — Kalshi temperature prediction markets + NWS weather data
Project-URL: Homepage, https://github.com/Tarabcak/therminal-py
Project-URL: API, https://api.mostlyright.xyz
Author: Tarabcak
License-Expression: MIT
Keywords: METAR,OHLCV,kalshi,prediction-markets,trading,weather
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: httpx>=0.27
Provides-Extra: cli
Requires-Dist: rich>=13.0; extra == 'cli'
Requires-Dist: typer>=0.12; extra == 'cli'
Provides-Extra: dev
Requires-Dist: pandas>=2.0; extra == 'dev'
Requires-Dist: pyarrow>=15.0; extra == 'dev'
Requires-Dist: pytest-httpx>=0.35; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: rich>=13.0; extra == 'dev'
Requires-Dist: ruff>=0.5; extra == 'dev'
Requires-Dist: typer>=0.12; extra == 'dev'
Provides-Extra: pandas
Requires-Dist: pandas>=2.0; extra == 'pandas'
Requires-Dist: pyarrow>=15.0; extra == 'pandas'
Description-Content-Type: text/markdown

# therminal-py

Python SDK for [Therminal](https://api.mostlyright.xyz) — Kalshi temperature prediction markets + NWS weather data.

## Install

```bash
pip install therminal-py            # core (returns dicts)
pip install therminal-py[pandas]    # + DataFrame support
pip install therminal-py[cli]       # + CLI tool
pip install therminal-py[dev]       # everything (for development)
```

## Quick Start

```python
from therminal import TherminalClient

client = TherminalClient()

# Get candles as a Pandas DataFrame
df = client.candles(
    market="KXHIGHNY-26MAR20-T50",
    from_date="2026-03-01",
    interval=5,
    as_dataframe=True,
)

# Get NYC weather observations in metric units
obs = client.observations(station="NYC", units="metric", limit=10)

# Download a parquet file
client.download_parquet("observations", station="NYC", year=2024)
```

## API Reference

See the full [API documentation](https://api.mostlyright.xyz/health).
