Quick Start Guide

Get up and running with CryptoHFTData in just a few minutes. Follow these steps to start accessing high-frequency cryptocurrency market data.

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1. Get Your API Key

Sign up for a CryptoHFTData account
Navigate to your dashboard
Click "Generate API Key" to create your first API key
Copy and securely store your API key

2. Install the Python SDK

bash

pip install cryptohftdata

3. 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. Explore Different 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

Arbitrage Detection

Compare prices across multiple exchanges to identify 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()

Market Making Analytics

Analyze order book depth and 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)