Analyze funding rates in Python with the Optiom API.

Fetch and annualize

One GET request returns funding points as {time, value} pairs, where value is the raw 8-hour rate. Multiply by 3 × 365 to annualize. The script prints distribution stats, the most negative intervals, and the share of time funding was positive — a crude long-bias gauge.

import pandas as pd
import requests

BASE = "https://www.getoptiom.com/api/tradinghub"
SYMBOL = "BTCUSDT"
INTERVALS_PER_YEAR = 3 * 365  # funding settles every 8h

res = requests.get(
    f"{BASE}/funding",
    params={"symbol": SYMBOL, "max_rows": 20_000},
    timeout=30,
)
res.raise_for_status()

df = pd.DataFrame(res.json())
df["dt"] = pd.to_datetime(df["time"], unit="s", utc=True)
df["annualized_pct"] = df["value"] * INTERVALS_PER_YEAR * 100
df = df.set_index("dt").sort_index()

print(df["annualized_pct"].describe().round(2))
print("\nMost negative intervals (shorts pay longs):")
print(df.nsmallest(5, "value")[["value", "annualized_pct"]])

positive_share = (df["value"] > 0).mean() * 100
print(f"\nFunding was positive {positive_share:.1f}% of the time.")

Reading the output

A mean annualized funding of a few percent is normal in neutral markets. Sustained readings above 30-50% annualized flag crowded longs and often precede mean reversion; deeply negative funding marks capitulation lows. Combine with open interest (GET /oi) to distinguish new positioning from forced unwinds.

Going further

Swap the symbol for any listed pair, or schedule this script and alert yourself when annualized funding crosses a threshold. The same endpoint powers the funding views in the Optiom terminal, so your numbers always match what you see on screen.