Torch-Trade/btcusdt_perp_funding_8h_05_2021_to_02_2026

BTCUSDT Perpetual Funding Rate (1 2021 - Mar 2026)

Overview

8-hour funding rate data for the BTC/USDT perpetual futures contract on Binance, covering May 1, 2021 to February 28, 2026.

• Rows: 5,295
• Completeness: 100.00%
• Frequency: Every 8 hours (00:00, 08:00, 16:00 UTC)

What is the funding rate?

The funding rate is a periodic payment between long and short holders of perpetual futures contracts. It keeps the perpetual price anchored to the spot price:

• Positive rate: Longs pay shorts -- market is net long (bullish positioning)
• Negative rate: Shorts pay longs -- market is net short (bearish positioning)
• High positive: Overleveraged longs, contrarian bearish signal
• Near zero: Balanced positioning

The default rate is 0.01% (1 bps) per 8 hours. Deviations indicate directional conviction.

Columns

Column	Type	Description
timestamp	datetime64[ns]	Funding rate calculation time (UTC)
funding_rate	float64	Funding rate as decimal (0.0001 = 0.01% = 1 bps)
funding_interval_hours	float64	Hours between payments (always 8)

Statistics

Metric	Value
Mean	0.00007416 (0.0074%)
Median	0.00007826 (0.0078%)
Min	-0.00119172 (-0.1192%)
Max	0.00098653 (0.0987%)
Std	0.00010552
Positive %	86.5%
Negative %	13.5%
Annualized mean	8.12%

Joining with spot OHLCV

This dataset is designed to complement the spot OHLCV dataset Torch-Trade/btcusdt_spot_1m_05_2021_to_03_2026. To join at training time, forward-fill the 8h funding rate to 1-minute resolution:

from datasets import load_dataset
import pandas as pd

# Load both datasets
spot = load_dataset("Torch-Trade/btcusdt_spot_1m_05_2021_to_03_2026")["train"].to_pandas()
spot["timestamp"] = pd.to_datetime(spot["timestamp"])

funding = load_dataset("Torch-Trade/btcusdt_perp_funding_8h_05_2021_to_02_2026")["train"].to_pandas()
funding["timestamp"] = pd.to_datetime(funding["timestamp"])

# Forward-fill funding rate to 1m
df = spot.merge(funding[["timestamp", "funding_rate"]], on="timestamp", how="left")
df["funding_rate"] = df["funding_rate"].ffill()

Usage

from datasets import load_dataset
import pandas as pd

ds = load_dataset("Torch-Trade/btcusdt_perp_funding_8h_05_2021_to_02_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape)  # (5295, 3)
print(df.head())

License

MIT -- data sourced from Binance Data Collection.