[ Documentation ]

> Quick Start

MarketParquet serves daily-partitioned OHLCV data as Apache Parquet files. Each file contains all symbols for a single trading day.

1. Sign up for a free account
2. Browse available data by asset type and timeframe
3. Download the parquet files you need (web or API)
4. Load into pandas, polars, DuckDB, or any tool that reads Parquet

> File Format

Files are named by trading date and stored under by_date/{asset}_{timeframe}/YYYY/YYYY-MM-DD.parquet.

Intraday schema (1min, 5min, 30min, 1hour)

  timestamp    TIMESTAMP[us]   -- bar open time (US/Eastern)
  symbol       STRING          -- ticker symbol
  asset_type   STRING          -- "Stock", "ETF", or contract code
  open         FLOAT64
  high         FLOAT64
  low          FLOAT64
  close        FLOAT64
  volume       FLOAT64

Daily (EOD) schema

  date         DATE            -- trading date
  symbol       STRING          -- ticker symbol
  asset_type   STRING
  open         FLOAT64
  high         FLOAT64
  low          FLOAT64
  close        FLOAT64
  volume       FLOAT64

Stock and ETF prices are split & dividend adjusted. Futures use continuous ratio adjustment. Compression: Snappy.

> Python

pandas

  import pandas as pd

  # Load one day of stock 1-min bars
  df = pd.read_parquet("stock_1min_2024-01-15.parquet")

  # Filter to a single ticker
  spy = df[df.symbol == "SPY"]

  # Compute VWAP
  spy["vwap"] = (spy.close * spy.volume).cumsum() / spy.volume.cumsum()

polars (faster, recommended)

  import polars as pl

  df = pl.read_parquet("stock_daily_2024-01-15.parquet")

  # Top 10 by volume
  top = df.sort("volume", descending=True).head(10)

  # Lazy load multiple files
  df = pl.scan_parquet("stock_daily_2024-*.parquet")
  monthly_avg = df.group_by("symbol").agg(pl.col("close").mean()).collect()

DuckDB (SQL on Parquet)

  import duckdb

  con = duckdb.connect()

  # Query parquet files directly with SQL
  result = con.execute("""
      SELECT symbol, AVG(close) AS avg_close, SUM(volume) AS total_vol
      FROM 'stock_daily_*.parquet'
      WHERE symbol IN ('AAPL', 'MSFT', 'GOOGL')
      GROUP BY symbol
  """).fetchdf()

> REST API

The API is available on the free plan -- full daily history, intraday limited to the last 30 days. Pro unlocks full intraday history. Generate an API key from your account page, then authenticate with the Authorization header.

List available assets

  GET https://marketparquet.com/api/v1/assets

  curl -H "Authorization: Bearer bt_YOUR_KEY" \
    https://marketparquet.com/api/v1/assets

  # Returns:
  {
    "plan": "pro",
    "assets": [
      {
        "asset_type": "stock_1min",
        "asset": "stock",
        "timeframe": "1min",
        "file_count": 6690,
        "earliest_date": "2000-01-03",
        "latest_date": "2026-04-06",
        "access": "full"
      },
      ...
    ]
  }

List dates for an asset

  GET /api/v1/dates/{asset_type}?page=1

  curl -H "Authorization: Bearer bt_YOUR_KEY" \
    https://marketparquet.com/api/v1/dates/stock_daily

  # Returns up to 100 dates per page (newest first)
  {
    "asset_type": "stock_daily",
    "plan": "pro",
    "page": 1,
    "count": 100,
    "dates": [
      {
        "date": "2026-04-06",
        "file_size_bytes": 232488,
        "download_url": "/api/v1/download/stock_daily/2026-04-06"
      },
      ...
    ]
  }

Download a file

  GET /api/v1/download/{asset_type}/{date}

  curl -H "Authorization: Bearer bt_YOUR_KEY" \
    https://marketparquet.com/api/v1/download/stock_daily/2026-04-06

  # Returns a presigned R2 URL (valid 60 seconds)
  {
    "asset_type": "stock_daily",
    "date": "2026-04-06",
    "file_size_bytes": 232488,
    "download_url": "https://...r2.cloudflarestorage.com/...?X-Amz-Signature=...",
    "expires_in": 60
  }

Asset types

  stock_1min     stock_5min     stock_30min     stock_1hour     stock_daily
  etf_1min       etf_5min       etf_30min       etf_1hour       etf_daily
  futures_1min   futures_5min   futures_30min   futures_1hour   futures_daily

> Examples

Python: download a file via the API

  import requests
  import pandas as pd

  API_KEY = "bt_YOUR_KEY"
  BASE = "https://marketparquet.com/api/v1"
  headers = {"Authorization": f"Bearer {API_KEY}"}

  # 1. Get a presigned URL
  resp = requests.get(
      f"{BASE}/download/stock_daily/2026-04-06",
      headers=headers,
  ).json()

  # 2. Download the parquet file
  parquet_bytes = requests.get(resp["download_url"]).content

  # 3. Load with pandas
  import io
  df = pd.read_parquet(io.BytesIO(parquet_bytes))
  print(df.head())

Bash: download a date range

  API_KEY="bt_YOUR_KEY"
  for d in 2026-04-01 2026-04-02 2026-04-03; do
    URL=$(curl -s -H "Authorization: Bearer $API_KEY" \
      "https://marketparquet.com/api/v1/download/stock_daily/$d" \
      | python3 -c "import sys,json; print(json.load(sys.stdin)['download_url'])")
    curl -s -o "stock_daily_$d.parquet" "$URL"
    echo "Downloaded $d"
  done

Python: backtest a simple strategy

  import pandas as pd
  import glob

  # Load all daily files for 2024
  files = sorted(glob.glob("stock_daily_2024-*.parquet"))
  df = pd.concat([pd.read_parquet(f) for f in files])

  # SPY-only momentum strategy
  spy = df[df.symbol == "SPY"].sort_values("date").reset_index(drop=True)
  spy["return"] = spy.close.pct_change()
  spy["sma20"] = spy.close.rolling(20).mean()
  spy["signal"] = (spy.close > spy.sma20).astype(int)
  spy["strategy_return"] = spy.signal.shift(1) * spy["return"]

  cumret = (1 + spy.strategy_return).cumprod()
  print(f"Final value: {cumret.iloc[-1]:.2f}")

> Need more help?

Check the FAQ for data quality details, or browse the interactive API docs at /docs.