Freqtrade Hyperliquid historical data

"""Write Ironflow's free Hyperliquid history as Freqtrade futures data."""
from pathlib import Path

import pandas as pd
from freqtrade.data.history import get_datahandler
from freqtrade.enums import CandleType

COINS = ["BTC", "ETH"]
MONTHS = pd.period_range("2025-06", "2026-10", freq="M").strftime("%Y-%m")
URL = "https://huggingface.co/datasets/ironflow/hyperliquid-{}/resolve/main/data/{}.parquet"


def load(dataset):
    df = pd.concat(pd.read_parquet(URL.format(dataset, m)) for m in MONTHS)
    return df.rename(columns={"timestamp": "date"})


candles = load("candles-1h")
funding = load("funding")
funding["date"] = funding["date"].dt.floor("h")

datadir = Path("user_data/data/hyperliquid")
(datadir / "futures").mkdir(parents=True, exist_ok=True)
store = get_datahandler(datadir, "feather")
for coin in COINS:
    pair = f"{coin}/USDC:USDC"
    store.ohlcv_store(pair, "1h", candles[candles.coin == coin].sort_values("date"), CandleType.FUTURES)
    store.ohlcv_store(pair, "1h", funding[funding.coin == coin].sort_values("date"), CandleType.FUNDING_RATE)
    print(pair, len(candles[candles.coin == coin]), "candles")

"""Write recent Hyperliquid candles and funding as Freqtrade data, using your key."""
import os
import time
from pathlib import Path

import pandas as pd
import requests
from freqtrade.data.history import get_datahandler
from freqtrade.enums import CandleType

API = "https://api.ironflow.sh/v1"
HEADERS = {"Authorization": f"Bearer {os.environ['IRONFLOW_KEY']}"}
COINS = ["BTC"]
TIMEFRAME = "5m"
DAYS = 13  # free key: 14 days, Explorer: 90, Builder: 365

end = pd.Timestamp.now("UTC").floor("h")
span = {"from": int((end - pd.Timedelta(days=DAYS)).floor("D").timestamp() * 1000), "to": int(end.timestamp() * 1000)}


def fetch(path, **params):
    rows = []
    params.update(span, limit=1000)
    while True:
        r = requests.get(f"{API}/{path}", params=params, headers=HEADERS, timeout=30)
        if r.status_code == 429:
            time.sleep(int(r.headers.get("Retry-After", 5)))
            continue
        r.raise_for_status()
        body = r.json()
        rows += body["data"]
        if not body["pagination"]["has_more"]:
            df = pd.DataFrame(rows)
            df["date"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
            return df
        params["cursor"] = body["pagination"]["next_cursor"]


def candles(market, interval):
    df = fetch("candles", market=market, interval=interval)
    df[["open", "high", "low", "close", "volume"]] = df[["open", "high", "low", "close", "volume"]].astype(float)
    return df


datadir = Path("user_data/data/hyperliquid")
(datadir / "futures").mkdir(parents=True, exist_ok=True)
store = get_datahandler(datadir, "feather")
for coin in COINS:
    pair, market = f"{coin}/USDC:USDC", f"{coin}-PERP"
    store.ohlcv_store(pair, TIMEFRAME, candles(market, TIMEFRAME), CandleType.FUTURES)
    store.ohlcv_store(pair, "1h", candles(market, "1h"), CandleType.FUTURES)  # Freqtrade needs 1h for funding
    funding = fetch("funding", market=market)
    funding["date"] = funding["date"].dt.floor("h")
    funding = funding.drop_duplicates("date")  # first reading of each hour
    funding["funding_rate"] = funding["rate"].astype(float)
    store.ohlcv_store(pair, "1h", funding, CandleType.FUNDING_RATE)
    print(pair, "done")