Metadata-Version: 2.4
Name: feather-data-fetcher
Version: 0.1.0
Summary: Production-grade data ingestion engine for Quantitative Finance and AI.
Home-page: https://github.com/featherai/feather-data-fetcher
Author: Feather AI
Author-email: hello@featherai.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests>=2.25.1
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: ccxt>=4.0.0
Requires-Dist: yfinance>=0.2.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 🪶 Feather Data Fetcher

**Production-grade financial data ingestion for Python.**

`feather-data-fetcher` is an open-source data pipeline extracted directly from the core of the **Feather AI** institutional intelligence engine. 

Building trading bots and AI models is hard enough without having to write broken retry-loops for terrible financial APIs. This package handles rate limits, exponential backoff, time-zone normalization, and alternative data aggregation out of the box.

## Why use this?
* **Zero "Dirty Data":** We automatically clean and normalize OHLCV data from Yahoo Finance and CoinGecko.
* **Built-in Resilience:** Uses `urllib3` Retry adapters to silently survive API 500s and 429 Rate Limits without crashing your script.
* **Alternative Data Unlocked:** Fetch Congressional trading, Insider selling, Crypto Whale tracking, and Dark Pool volume with single function calls.

## Installation
```bash
pip install feather-data-fetcher
```

## Quickstart

```python
from feather_fetcher import fetch_stock_history_yfinance, fetch_crypto_whale_signals

# 1. Fetch clean, normalized OHLCV data
df = fetch_stock_history_yfinance("NVDA", period="3mo")
print(df.head())

# 2. Track institutional Crypto whales (Requires CCXT/Binance)
whales = fetch_crypto_whale_signals("BTC/USDT", large_usd=100000)
print(f"Net Whale Flow: ${whales['weighted_net']}")
```

---

## ⚡ Want the Data Analyzed Automatically?

Raw data is just the beginning. 

If you want this data automatically fed through custom HuggingFace Sentiment Transformers, Quant Volatility Matrices, and Herfindahl-Hirschman (HHI) concentration scoring to predict market crashes in **under 25 seconds**...

**[Join the Waitlist for the Feather AI Platform](https://featherai.com)**
*Terminal-native wealth management, powered by advanced ML.*
