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Perpetual Funding Rates
Funding rates, mark prices, and open interest from HyperLiquid perps
Category
States
Point-in-time snapshots
Granularity
~5 second updates per coin
Format
Apache Parquet
Snappy compression, Hive-partitioned
Nodes
1 (EU-Central)
Frankfurt — low-latency to HyperLiquid & Ethereum
About This Dataset
Per-coin funding rates, mark prices, open interest, and funding velocity from HyperLiquid perpetual futures. Updated every ~5 seconds for BTC, ETH, SOL, and HYPE.
Partitions
exchange/coin:
hyperliquid/BTC, hyperliquid/ETH, hyperliquid/SOL, hyperliquid/HYPESchema
| Column | Type | Description |
|---|---|---|
time_chain | timestamp | Rate update timestamp (UTC) |
time_local | timestamp | Ingestion timestamp (UTC) |
coin | string | Asset symbol |
funding_rate | float64 | Current funding rate |
mark_price | float64 | Mark price (USD) |
open_interest | float64 | Open interest (USD) |
funding_velocity | float64 | Rate of change of funding |
Use Cases
- Backtest funding rate arbitrage strategies
- Analyze funding rate vs price correlation
- Detect extreme funding (squeeze conditions)
- Calculate annualized basis trade yields
R2 Path
s3://algotick-data-lake/states/funding/exchange=hyperliquid/coin={coin}/year=YYYY/month=MM/day=DD/node={node}/data.parquet
Replace
YYYY, MM, DD with the target date.
Data is collected from node=eu-central.
Query with DuckDB
import duckdb df = duckdb.sql(""" SELECT * FROM read_parquet( 's3://algotick-data-lake/states/funding/exchange=hyperliquid/coin=BTC/year=2026/month=04/day=20/node=eu-central/data.parquet' ) LIMIT 100 """).df() print(f"Rows: {len(df)}, Columns: {list(df.columns)}")
Download via API
import requests resp = requests.get( "https://algotick.dev/v2/history/raw?dataset=funding&coin=BTC&date=2026-04-20", stream=True, ) with open("funding.parquet", "wb") as f: for chunk in resp.iter_content(8192): f.write(chunk) # Then query locally import duckdb df = duckdb.sql("SELECT * FROM 'funding.parquet' LIMIT 100").df() print(df)
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