cryptohftdata 0.3.0

Python SDK for accessing cryptocurrency high-frequency trading data

CryptoHFTData Python SDK

cryptohftdata is a Python SDK for downloading high-frequency cryptocurrency market data as pandas DataFrames.

It is designed for research scripts, notebooks, and production ingestion jobs that need a simple interface over the CryptoHFTData parquet dataset.

Installation

pip install cryptohftdata

Quick Start

Dataset downloads require an API key. Export CRYPTOHFTDATA_API_KEY or pass api_key= explicitly.

import os

import cryptohftdata as chd

chd.configure_client(api_key=os.environ["CRYPTOHFTDATA_API_KEY"])

exchange = chd.exchanges.BINANCE_FUTURES
symbols = chd.list_symbols(exchange, data_type="trades")

if "BTCUSDT" not in symbols:
    raise RuntimeError("BTCUSDT is not currently listed for Binance futures trades")

trades = chd.get_trades(
    symbol="BTCUSDT",
    exchange=exchange,
    start_date="2025-08-01",
    end_date="2025-08-01",
    max_workers=4,
)

print(trades.head())
print(f"rows={len(trades)} columns={list(trades.columns)}")

Authentication Model

• list_symbols(), list_exchanges(), and get_exchange_info() query API metadata and can be used without an API key.
• Dataset download helpers such as get_trades() and get_mark_price() download parquet files and require an API key.
• The convenience helpers also accept client configuration kwargs such as api_key, base_url, timeout, max_retries, rate_limit, and use_jwt.

Native S3 Access

If you want raw flat-file access instead of DataFrame helpers, exchange your existing CryptoHFTData API key for short-lived R2 S3 credentials:

import os
import requests

response = requests.post(
    "https://api.cryptohftdata.com/s3-credentials",
    headers={"X-API-Key": os.environ["CRYPTOHFTDATA_API_KEY"]},
    timeout=30,
)
response.raise_for_status()

payload = response.json()
creds = payload["credentials"]

print(payload["bucket"])
print(payload["endpoint"])
print(payload["expires_at"])

You can then pass the returned credentials into boto3:

import boto3

s3 = boto3.client(
    "s3",
    endpoint_url=payload["endpoint"],
    region_name=payload["region"],
    aws_access_key_id=creds["access_key_id"],
    aws_secret_access_key=creds["secret_access_key"],
    aws_session_token=creds["session_token"],
)

objects = s3.list_objects_v2(Bucket=payload["bucket"])
for item in objects.get("Contents", []):
    print(item["Key"])

Public API

Convenience helpers

Use the top-level helpers when you want the shortest path from notebook code to DataFrame output:

import cryptohftdata as chd

chd.configure_client(api_key="your-api-key")

trades = chd.get_trades("BTCUSDT", chd.exchanges.BINANCE_FUTURES, "2025-08-01", "2025-08-01")
mark_price = chd.get_mark_price("BTCUSDT", chd.exchanges.BINANCE_FUTURES, "2025-08-01", "2025-08-01")

Explicit client usage

Use CryptoHFTDataClient when you want explicit configuration, context-manager usage, or cache inspection:

from cryptohftdata import CryptoHFTDataClient, exchanges

with CryptoHFTDataClient(api_key="your-api-key", timeout=60, rate_limit=10) as client:
    info = client.get_exchange_info(exchanges.BYBIT_FUTURES)
    trades = client.get_trades(
        "ETHUSDT",
        exchanges.BYBIT_FUTURES,
        "2025-08-01",
        "2025-08-01",
        max_workers=2,
    )
    print(info["supported_data_types"])
    print(client.get_cache_info())

Data Sets

All dataset download helpers return pandas DataFrames. Column names can vary by exchange, but these are the typical shapes:

Helper	Typical columns
get_klines()	open_time, open, high, low, close, volume
get_orderbook()	timestamp, side, level, price, size
get_trades()	timestamp, trade_id, price, quantity, side
get_ticker()	timestamp, open, high, low, close, volume
get_mark_price()	timestamp, mark_price, index_price, funding_rate, next_funding_time
get_open_interest()	timestamp, symbol, exchange, open_interest
get_liquidations()	timestamp, side, price, quantity, order_id

You can inspect the in-package schema reference if you want a structured summary at runtime:

from cryptohftdata import get_dataset_schema

schema = get_dataset_schema("trades")
print(schema.typical_columns)

Supported Exchanges

Use the SDK itself to discover supported exchanges and dataset coverage instead of hard-coding assumptions:

import cryptohftdata as chd

for exchange in chd.list_exchanges():
    info = chd.get_exchange_info(exchange)
    print(exchange, info["type"], info["supported_data_types"])

The package also exposes constants through chd.exchanges, for example:

• chd.exchanges.BINANCE_SPOT
• chd.exchanges.BINANCE_FUTURES
• chd.exchanges.BYBIT_SPOT
• chd.exchanges.BYBIT_FUTURES
• chd.exchanges.KRAKEN_FUTURES

Error Handling

The most common exceptions are:

• ValidationError for invalid symbols, exchange identifiers, or date ranges
• ConfigurationError when a dataset download is attempted without an API key
• AuthenticationError when credentials are rejected
• APIError for API-side failures or malformed responses

Example:

import cryptohftdata as chd

try:
    chd.get_trades("BTCUSDT", chd.exchanges.BINANCE_FUTURES, "2025-08-02", "2025-08-01")
except chd.ValidationError as exc:
    print(f"invalid request: {exc}")

Examples and Docs

• Example scripts live in sdk/python/examples/
• Documentation source files live in sdk/python/docs/
• Package docstrings are available through help(cryptohftdata) and help(cryptohftdata.CryptoHFTDataClient)

Development

From a source checkout:

cd sdk/python
pip install -e ".[dev,docs,test]"
pytest
sphinx-build -b html docs docs/_build/html

Support

• Documentation: https://cryptohftdata.com/docs
• Support: https://cryptohftdata.com/support
• Homepage: https://cryptohftdata.com