Metadata-Version: 2.4
Name: marketkit
Version: 0.1.3
Summary: Reliable, clean market data and basic analytics — pure Python, no C dependencies.
Project-URL: Homepage, https://github.com/aditya33agrawal/marketkit
Project-URL: Documentation, https://aditya33agrawal.github.io/marketkit
Project-URL: Issues, https://github.com/aditya33agrawal/marketkit/issues
Author-email: Aditya Agrawal <aditya33agrawal@gmail.com>
License-Expression: MIT
License-File: LICENSE
Keywords: finance,market-data,ohlcv,stocks,technical-analysis,trading
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.9
Requires-Dist: pandas>=1.5
Requires-Dist: platformdirs>=3
Requires-Dist: pyarrow>=12
Requires-Dist: requests>=2.28
Provides-Extra: dev
Requires-Dist: build; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: pytest-cov; extra == 'dev'
Requires-Dist: responses; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Provides-Extra: docs
Requires-Dist: mkdocs-material; extra == 'docs'
Requires-Dist: mkdocstrings[python]; extra == 'docs'
Description-Content-Type: text/markdown

# marketkit

Reliable, clean market data and basic analytics, pure Python, no C dependencies.

## Install

pip install marketkit

## Quick start

import marketkit as mk

# Fetch clean, adjusted OHLCV data
df = mk.get("AAPL")

# Analytics
print(mk.sharpe(df))
print(mk.drawdown(df))

# Indicators
df["rsi"] = mk.rsi(df)
df["sma50"] = mk.sma(df, window=50)

# One-shot summary
mk.summary("AAPL")

## Why marketkit?

- Pure Python — installs with plain `pip install`, no compiler needed
- Doesn't break — automatic source fallback + caching so one bad day from Yahoo doesn't crash your script
- Clean output — flat columns, predictable dtypes, adjusted prices by default
- Beginner-friendly — sensible defaults, clear errors, great docs

## Disclaimer

Not affiliated with any data provider. Data is for personal/research use only.
Users must comply with each source's terms of service. Not financial advice.
