# cuemacro/findatapy

Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.

Repository: https://github.com/cuemacro/findatapy
Canonical: https://ross.abutalabs.com/products/findatapy
Language: Python
License: Apache-2.0
License Family: permissive
Topics: market-data, quandl, bloomberg, dukascopy, arctic, fred, python-api, eikon
Last push: 2026-07-02T14:29:12+00:00

## Health v2 (maintenance only)
Score: 83/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 90, release rhythm 63, longevity 100
- inputs: {"age_days": 3682, "days_push": 62, "days_rel": 166, "gap_med": 34, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2108, forks 221 (observed 2026-08-28T04:06:14.060163+00:00)

## What it is
findatapy is a Python library providing a unified high-level API for downloading market data from sources like Bloomberg, Refinitiv Eikon, Quandl, Yahoo, FRED/ALFRED, and Dukascopy. It supports custom ticker configuration and is particularly useful for FX market data retrieval.

## Use cases
- download historical FX rates from multiple sources with one API
- fetch tick data from Dukascopy
- pull economic data from FRED/ALFRED
- download Bloomberg market data in Python
- get stock prices from Yahoo Finance
- compute FX crosses automatically from USD pairs

## When to choose
- you need a single unified interface to many market data providers
- you work with FX data and need cross-rate calculation
- you want pandas DataFrames of market data for analysis

## When to avoid
- you need a fully documented production-grade library
- you only use one data source with its own official SDK
- you need real-time streaming data

## Facets
- artifact type: library
- maturity: active
- function: http-client, etl, data-science
- domain: fintech, analytics
- platform: python, cross-platform
- tags: market-data, bloomberg, quandl, fred, dukascopy, eikon, yahoo-finance, fx-data, financial-data, pandas, data-engineering

## Member repositories
- cuemacro/findatapy (main) score 83

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:14.060163+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:53.152010+00:00, confidence not recorded.
  - readme: https://github.com/cuemacro/findatapy (fetched 2026-08-28T04:06:14.060163+00:00, sha ae7b5fa9dfb2)
  - registry_pypi: https://pypi.org/pypi/findatapy/json (fetched 2026-08-29T10:33:59.140590+00:00, sha a525bd1df52a)
- Data as of 2026-08-30T08:39:29.467469+00:00.