Download Examples

Path-based access with wildcard pattern support

Wildcard Patterns

Select exactly the data you need using path patterns

All data for specific symbol
crypto_trades/date=*/hour=*/exchange=*/
  instrument_type=*/symbol=BTCUSDT/**
Specific date range
crypto_trades/date=2025-01-*/hour=*/
  exchange=binance/**
All Binance data
crypto_trades/date=*/hour=*/
  exchange=binance/**
Specific trading hours
crypto_trades/date=2025-01-15/
  hour=1[4-6]/**
Only perpetual futures
crypto_trades/date=*/hour=*/exchange=*/
  instrument_type=perpetual/**
Complex query
crypto_trades/date=2025-01-*/hour=*/
  exchange=binance/instrument_type=spot/
  symbol=BTC*/**
DuckDB Query with Wildcards

Query directly without downloading

import duckdb

con = duckdb.connect()

# Query all BTCUSDT trades from January 2025
df = con.execute("""
  SELECT * FROM read_parquet(
    's3://quantum-edge/crypto_trades/
     date=2025-01-*/hour=*/exchange=*/
     instrument_type=*/symbol=BTCUSDT/**/*.parquet'
  )
  WHERE price > 90000
""").df()

print(f"Rows: {len(df):,}")
Polars Scan with Wildcards

Lazy evaluation for large datasets

import polars as pl

# Lazy scan with wildcard pattern
df = pl.scan_parquet(
    "s3://quantum-edge/crypto_trades/\
date=2025-01-*/hour=*/exchange=binance/**"
)

# Filter and aggregate (evaluated lazily)
result = (df
    .filter(pl.col("symbol") == "BTCUSDT")
    .group_by("date")
    .agg([
        pl.col("volume").sum(),
        pl.col("price").mean()
    ])
    .collect()
)

print(result)
Bulk Download with wget

Download files matching pattern

# First, get your API token from /tokens page
TOKEN="your_api_token_here"

# Download all files for a specific day
wget --header="Authorization: Bearer $TOKEN" \
  --recursive --no-parent --no-host-directories \
  --cut-dirs=2 \
  "https://api.quantum-edge.app/download/\
crypto_trades/date=2025-01-15/"
Python with Path Patterns

Programmatic access with filtering

import requests
import pandas as pd
from pathlib import Path

TOKEN = "your_api_token"
API_BASE = "https://api.quantum-edge.app"

# List files matching pattern
resp = requests.get(
    f"{API_BASE}/api/datasets/crypto_trades/files",
    headers={"Authorization": f"Bearer {TOKEN}"},
    params={
        "date": "2025-01-15",
        "exchange": "binance",
        "symbol": "BTCUSDT"
    }
)

files = resp.json()["files"]
print(f"Found {len(files)} files")

# Download first file
file_url = files[0]["downloadUrl"]
df = pd.read_parquet(file_url)
print(df.head())
▶
Wildcard Support: Use * for any characters, ** for recursive directory matching
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No Throttling: Download as much data as you need without restrictions
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Authentication: Create API tokens at /tokens for programmatic access
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Efficient Queries: Path patterns are evaluated server-side - only matching files are scanned