metapyle 0.1.2

Early-stage unified interface for querying financial time-series data

Metapyle

Early-stage research framework — Not for production use.

A unified interface for querying financial time-series data from multiple sources.

Requires Python 3.12+

Overview

Financial data lives in many places—Bloomberg terminals, internal APIs, CSV exports—each with its own ticker syntax, authentication, and quirks. Metapyle provides a YAML-based catalog that maps human-readable names to source-specific details, giving you a single client.get() interface regardless of where the data comes from.

Currently supports Bloomberg (via xbbg), Macrobond (via macrobond-data-api), GS Marquee (via gs-quant), and local files (CSV/Parquet).

Installation

uv add metapyle

Or with pip:

pip install metapyle

Note: Bloomberg requires a Bloomberg Terminal. GS Quant requires GS Marquee access. Macrobond requires the desktop app or Web API credentials.

Quick Start

1. Create a catalog file (catalog.yaml):

# Bloomberg
- my_name: sp500_close
  source: bloomberg
  symbol: SPX Index
  field: PX_LAST

# Macrobond
- my_name: us_gdp
  source: macrobond
  symbol: usnaac0169

# GS Quant
- my_name: eurusd_vol
  source: gsquant
  symbol: EURUSD
  field: FXIMPLIEDVOL::impliedVolatility
  params:
    tenor: 1m
    deltaStrike: DN

# Local file
- my_name: gdp_forecast
  source: localfile
  symbol: GDP_FORECAST    # column name in the file
  path: /data/macro.csv   # file path

2. Query data:

from metapyle import Client

with Client(catalog="catalog.yaml") as client:
    # end defaults to today
    df = client.get(["sp500_close", "us_gdp"], start="2024-01-01")

Documentation

See the User Guide for complete documentation, including:

• Catalog configuration
• Creating catalogs from CSV
• Frequency alignment
• Caching
• Available data sources
• Error handling

License

MIT License - see LICENSE for details.