DATA FACTORY™ API v1.1

# Standard Python with requests & pandas
import requests
import pandas as pd

API_KEY = "df_trial_YOUR_API_KEY"
URL = "https://www.techplayground.in/api/v1/crypto/candles"
headers = {"X-API-Key": API_KEY}
params = {"symbol": "BTCUSDT", "interval": "1m", "limit": 100}

response = requests.get(URL, headers=headers, params=params)
data = response.json()

df = pd.DataFrame(data["data"])
print(f"Loaded {len(df)} bars. Latest BTC Price: ${df['close'].iloc[-1]:,.2f}")
print(df[['timestamp', 'close', 'volume', 'volume_delta', 'cvd']].tail(5))

// Node.js 18+ native fetch
const apiKey = "df_trial_YOUR_API_KEY";
const url = "https://www.techplayground.in/api/v1/crypto/candles?symbol=BTCUSDT&interval=1m&limit=10";

async function fetchCryptoCandles() {
  const res = await fetch(url, {
    headers: { "X-API-Key": apiKey }
  });
  const json = await res.json();
  console.log(`Fetched ${json.count} bars in ${json.query_time_ms}ms:`);
  console.table(json.data.map(d => ({ time: d.timestamp, close: d.close, cvd: d.cvd })));
}

fetchCryptoCandles();

-- Zero-Copy In-Memory Scan of Downloaded Parquet Files (Python or DuckDB CLI)
import duckdb

con = duckdb.connect()

-- Scan 23 million rows in under 2 seconds without loading into RAM
con.execute("""
    SELECT 
        strftime(timestamp, '%Y-%m-%d %H:%M') AS bar_time,
        close,
        volume,
        taker_buy_volume - (volume - taker_buy_volume) AS volume_delta,
        cvd
    FROM read_parquet('S:/DataFactory_Warehouse/crypto/BTCUSDT/*.parquet')
    WHERE cvd > 100.0
    ORDER BY timestamp DESC
    LIMIT 10;
""").show()