Quick Start Guide

Do the steps on this page to start with CryptoHFTData. The steps take a few minutes. Then you can download high-frequency cryptocurrency market data.

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1. Get Your API Key

Create a CryptoHFTData account.
Go to your dashboard.
Click "Generate API Key" to create your first API key.
Copy your API key.
Keep your API key in a safe location.

2. Install the Python SDK

Terminal

pip install cryptohftdata

3. Make Your First API Call

Python

1import cryptohftdata as chd
2from datetime import datetime, timedelta
3
4# Initialize the client
5client = chd.CryptoHFTDataClient(api_key="your-api-key-here")
6
7# Get Bitcoin price data from Binance
8df = client.get_trades(
9    symbol="BTCUSDT",
10    exchange=chd.exchanges.BINANCE_SPOT,
11    start_date="2025-08-01",
12    end_date="2025-08-01"
13)
14
15# Display the data
16print(df.head())

4. Use Other Data Types

Python

1# Some of the different data types you can access:
2# Get order book data
3orderbook_df = client.get_orderbook(
4    symbol="ETHUSDT",
5    exchange=chd.exchanges.BINANCE_SPOT,
6    start_date="2025-08-01",
7    end_date="2025-08-01"
8)
9
10# Get individual trades
11trades_df = client.get_trades(
12    symbol="BTCUSDT", 
13    exchange=chd.exchanges.BINANCE_FUTURES,
14    start_date="2025-08-01",
15    end_date="2025-08-01"
16)
17
18# Get open interest data
19oi_df = client.get_open_interest(
20    symbol="BTCUSDT", 
21    exchange=chd.exchanges.BYBIT_FUTURES,
22    start_date="2025-08-01",
23    end_date="2025-08-01"
24)
25
26# Get mark price data, including funding rates
27mark_price_df = client.get_mark_price(
28    symbol="BTCUSDT",
29    exchange=chd.exchanges.BINANCE_FUTURES,
30    start_date="2025-08-01", 
31    end_date="2025-08-01"
32)

5. Common Use Cases

Find Arbitrage Opportunities

Compare the prices on different exchanges to find arbitrage opportunities.

Python

1import matplotlib.pyplot as plt
2
3exchanges = [
4    chd.exchanges.BINANCE_SPOT,
5    chd.exchanges.BYBIT_SPOT,
6    chd.exchanges.KRAKEN_SPOT
7]
8
9plt.figure(figsize=(12, 6))
10
11for exchange in exchanges:
12    symbol = "BTCUSDT" if exchange != chd.exchanges.KRAKEN_SPOT else "BTC_USD"
13    df = client.get_trades(
14        symbol=symbol,
15        exchange=exchange,
16        start_date="2025-08-01",
17        end_date="2025-08-01"
18    )
19
20    # Plot event_time vs price
21    plt.plot(df['event_time'], df['price'].astype(float), label=exchange)
22
23plt.title("BTCUSDT Price on Different Exchanges")
24plt.xlabel("Event Time")
25plt.ylabel("Price (USDT)")
26plt.legend()
27plt.xticks(rotation=45)
28plt.tight_layout()
29plt.show()

Analysis for Market Making

Analyze the order book depth and the spread patterns for market making strategies.

Python

1import matplotlib.pyplot as plt
2import tqdm
3
4# Get order book snapshots
5ob_df = client.get_orderbook(
6    symbol="ETHUSDT",
7    exchange=chd.exchanges.BINANCE_FUTURES,
8    start_date="2025-08-01",
9    end_date="2025-08-01"
10)
11
12# ... (Full code example available in repo)