marketgoblin 0.6.0

Market data platform for downloading and storing financial OHLCV data

marketgoblin

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

marketgoblin is a lightweight market data platform built on Polars and yfinance. It fetches multiple datasets (OHLCV, shares-outstanding, dividends), slices them into monthly Parquet files, writes JSON sidecars with metadata, and lets you load them back with a single call.

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Features

• Multi-dataset — OHLCV, shares-outstanding, and dividends selected via a Dataset enum; per-source dispatch makes it easy to add more
• Tidy stacked OHLCV — adjusted and raw prices live in one frame, distinguished by an is_adjusted bool column; one network call per symbol covers both
• 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 before any I/O; unsupported (provider, dataset) pairs raise at the dispatch boundary
• Pluggable providers — subclass BaseSource, implement _build_dispatch(), register in one line; YahooSource and TiingoSource 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

import polars as pl
from marketgoblin import Dataset, MarketGoblin

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

# Fetch and persist OHLCV — tidy stacked frame: each trading day appears
# twice (is_adjusted=True / False). Filter to pick a variant.
lf = goblin.fetch("AAPL", "2024-01-01", "2024-03-31", parse_dates=True)
adjusted = lf.filter(pl.col("is_adjusted")).collect()
print(adjusted)

# Load back from disk (no network call)
lf = goblin.load("AAPL", "2024-01-01", "2024-03-31", parse_dates=True)
print(lf.collect())

# Shares outstanding — sparse, corporate-action-driven series
shares = goblin.fetch("AAPL", "2024-01-01", "2024-03-31", dataset=Dataset.SHARES, parse_dates=True)
print(shares.collect())

# Dividends — event-driven (typically quarterly)
dividends = goblin.fetch("AAPL", "2024-01-01", "2024-03-31", dataset=Dataset.DIVIDENDS, parse_dates=True)
print(dividends.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, **source_kwargs)

Method	Description
fetch(symbol, start, end, dataset=Dataset.OHLCV, parse_dates=False)	Download, save to disk (if save_path set), return LazyFrame
load(symbol, start, end, dataset=Dataset.OHLCV, parse_dates=False)	Load from disk; raises RuntimeError if no save_path
fetch_many(symbols, start, end, dataset=Dataset.OHLCV, parse_dates=False, max_workers=8, requests_per_second=2.0)	Batch fetch via ThreadPoolExecutor, rate-limited
supported_datasets (property)	frozenset[Dataset] of datasets the configured provider supports

Datasets

Dataset	Provider support	Columns
Dataset.OHLCV	yahoo, tiingo	date (int32), open / high / low / close (float32), volume (int64), is_adjusted (bool), symbol
Dataset.SHARES	yahoo, tiingo	date (int32), shares (int64), symbol
Dataset.DIVIDENDS	yahoo, tiingo	date (int32), dividend (float32), symbol
Dataset.SPLITS	tiingo	date (int32), split_factor (float32), symbol
Dataset.FUNDAMENTALS_DAILY	tiingo	date (int32), market_cap / enterprise_val (int64), pe_ratio / pb_ratio / trailing_peg_1y (float32), symbol
Dataset.FUNDAMENTALS_STATEMENTS	tiingo	date (int32), fiscal_year (int16), fiscal_quarter (int8), ~76 income/balance/cash-flow/overview line items each as <field>_{as_reported,adjusted} (float64 for $/share-counts, float32 for per-share & ratios), symbol

OHLCV is returned as a tidy stacked frame: each trading day appears twice (is_adjusted=True and is_adjusted=False). Filter downstream (.filter(pl.col("is_adjusted"))) to pick a variant. Adjusted Open/High/Low are derived locally from the Adj Close / Close ratio — verified to match yfinance's auto_adjust=True output exactly while halving network calls.

Data on disk

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

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Adding a Provider

import polars as pl

from marketgoblin import Dataset
from marketgoblin.sources.base import BaseSource, Fetcher

class MySource(BaseSource):
    name = "mysource"

    def _build_dispatch(self) -> dict[Dataset, Fetcher]:
        return {Dataset.OHLCV: self._fetch_ohlcv}

    def _fetch_ohlcv(self, symbol: str, start: str, end: str) -> pl.LazyFrame:
        ...  # return a normalized LazyFrame with an is_adjusted column

Per-dataset fetchers all share the (symbol, start, end) signature — there is no adjusted toggle, since OHLCV variants are stacked into a single frame distinguished by the is_adjusted column.

Then register it in goblin.py:

_SOURCES = {"yahoo": YahooSource, "tiingo": TiingoSource, "mysource": MySource}

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Running Tests

pytest
pytest --cov=marketgoblin   # with coverage

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License

MIT © Antônio Salomão