Torch-Trade/bnbusdt_perp_funding_8h_10_2021_to_02_2026

BNBUSDT Perpetual Funding Rate (1 2021 - Mar 2026)

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Overview

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

• —Rows: 4,836
• —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

Statistics

Joining with spot OHLCV

This dataset is designed to complement the spot OHLCV dataset `Torch-Trade/bnbusdt_spot_1m_10_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/bnbusdt_spot_1m_10_2021_to_03_2026")["train"].to_pandas()
spot["timestamp"] = pd.to_datetime(spot["timestamp"])

funding = load_dataset("Torch-Trade/bnbusdt_perp_funding_8h_10_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/bnbusdt_perp_funding_8h_10_2021_to_02_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape)  # (4836, 3)
print(df.head())

License

MIT -- data sourced from Binance Data Collection.