marketgoblin 0.1.1

Market data platform for downloading and storing financial OHLCV data

marketgoblin

Download, store, and load financial OHLCV data — fast and without fuss.

marketgoblin is a lightweight market data platform built on Polars and yfinance. It fetches OHLCV data, slices it into monthly Parquet files, writes JSON sidecars with metadata, and lets you load it back with a single call.

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Features

• Single-symbol and batch fetch — fetch() and fetch_many() with thread-pool concurrency
• Disk persistence — monthly .pq slices with atomic writes; JSON sidecar per slice
• Lazy evaluation — all data paths return pl.LazyFrame (Polars)
• Date flexibility — dates stored as int32 YYYYMMDD on disk; use parse_dates=True to get pl.Date
• Retry logic — YahooSource retries transient failures with exponential backoff (3 attempts)
• Rate limiting — fetch_many() respects a configurable requests-per-second cap (default: 2 req/s)
• Input validation — dates are validated for format and ordering before any I/O
• Pluggable providers — subclass BaseSource and register in one line; CSVSource included

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Installation

pip install marketgoblin

Or with uv:

uv add marketgoblin

For development:

git clone https://github.com/aexsalomao/marketgoblin
cd marketgoblin
uv sync --extra dev

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Quick Start

from marketgoblin import MarketGoblin

goblin = MarketGoblin(provider="yahoo", save_path="./data")

# Fetch and persist
lf = goblin.fetch("AAPL", "2024-01-01", "2024-03-31", parse_dates=True)
print(lf.collect())

# Load back from disk
lf = goblin.load("AAPL", "2024-01-01", "2024-03-31", parse_dates=True)
print(lf.collect())

# Batch fetch — failed symbols are logged, never crash the batch
results = goblin.fetch_many(["AAPL", "MSFT", "GOOGL"], "2024-01-01", "2024-03-31")
for symbol, lf in results.items():
    print(f"{symbol}: {lf.collect().height} rows")

Run the full walkthrough:

python example.py

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API

MarketGoblin

MarketGoblin(provider: str, api_key: str | None = None, save_path: str | Path | None = None)

Method	Description
fetch(symbol, start, end, adjusted=True, parse_dates=False)	Download, save to disk (if save_path set), return LazyFrame
load(symbol, start, end, adjusted=True, parse_dates=False)	Load from disk; raises RuntimeError if no save_path
fetch_many(symbols, start, end, adjusted=True, parse_dates=False, max_workers=8, requests_per_second=2.0)	Batch fetch via ThreadPoolExecutor, rate-limited

Data on disk

Property	Detail
Date column	int32 YYYYMMDD (e.g. 20240101); parse_dates=True → pl.Date
OHLC columns	float32
Volume column	int64
Parquet path	{save_path}/{provider}/ohlcv/{adjusted|raw}/{SYMBOL}/{SYMBOL}_{YYYY-MM}.pq
JSON sidecar	Same path, .json extension — row count, date range, OHLCV stats, missing trading days

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Adding a Provider

from marketgoblin.sources.base import BaseSource
import polars as pl

class MySource(BaseSource):
    name = "mysource"

    def fetch(self, symbol, start, end, adjusted=True) -> pl.LazyFrame:
        ...  # return a normalized LazyFrame

Then register it in goblin.py:

_SOURCES = {"yahoo": YahooSource, "csv": CSVSource, "mysource": MySource}

A CSVSource is included out of the box for loading local CSV files:

goblin = MarketGoblin(provider="csv", data_dir="./csv_files")
lf = goblin.fetch("AAPL", "2024-01-01", "2024-03-31")

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Running Tests

pytest
pytest --cov=marketgoblin   # with coverage

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License

MIT © Antônio Salomão