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timestamp timestamp[ms]date 2021-10-01 00:00:00 2026-02-28 15:59:59 | funding_rate float64 -0 0 | funding_interval_hours int64 8 8 |
|---|---|---|
2021-10-01T00:00:00.012000 | -0.001328 | 8 |
2021-10-01T08:00:00.008000 | -0.000506 | 8 |
2021-10-01T16:00:00 | -0.000103 | 8 |
2021-10-02T00:00:00 | -0.000987 | 8 |
2021-10-02T08:00:00.002000 | -0.000173 | 8 |
2021-10-02T16:00:00.011000 | -0.000075 | 8 |
2021-10-03T00:00:00.019000 | -0.000021 | 8 |
2021-10-03T08:00:00.011000 | -0.000595 | 8 |
2021-10-03T16:00:00.004000 | -0.000955 | 8 |
2021-10-04T00:00:00.004000 | -0.000206 | 8 |
2021-10-04T08:00:00 | -0.000299 | 8 |
2021-10-04T16:00:00 | -0.000516 | 8 |
2021-10-05T00:00:00.007000 | -0.000022 | 8 |
2021-10-05T08:00:00.007000 | -0.000263 | 8 |
2021-10-05T16:00:00.004000 | -0.000006 | 8 |
2021-10-06T00:00:00.002000 | -0.000586 | 8 |
2021-10-06T08:00:00 | -0.000528 | 8 |
2021-10-06T16:00:00.003000 | -0.00019 | 8 |
2021-10-07T00:00:00.007000 | 0 | 8 |
2021-10-07T08:00:00.002000 | -0.000001 | 8 |
2021-10-07T16:00:00 | 0 | 8 |
2021-10-08T00:00:00.011000 | 0 | 8 |
2021-10-08T08:00:00.003000 | -0.000156 | 8 |
2021-10-08T16:00:00.002000 | 0.000498 | 8 |
2021-10-09T00:00:00.006000 | 0.000026 | 8 |
2021-10-09T08:00:00.003000 | 0 | 8 |
2021-10-09T16:00:00 | 0.000073 | 8 |
2021-10-10T00:00:00 | 0.000063 | 8 |
2021-10-10T08:00:00.008000 | 0.000352 | 8 |
2021-10-10T16:00:00 | 0.000357 | 8 |
2021-10-11T00:00:00.002000 | 0 | 8 |
2021-10-11T08:00:00.001000 | 0.000191 | 8 |
2021-10-11T16:00:00.006000 | 0.000244 | 8 |
2021-10-12T00:00:00.008000 | 0 | 8 |
2021-10-12T08:00:00.013000 | 0 | 8 |
2021-10-12T16:00:00.003000 | 0 | 8 |
2021-10-13T00:00:00.003000 | 0.000134 | 8 |
2021-10-13T08:00:00.015000 | 0.000223 | 8 |
2021-10-13T16:00:00 | 0 | 8 |
2021-10-14T00:00:00.011000 | -0.000033 | 8 |
2021-10-14T08:00:00.017000 | -0.000519 | 8 |
2021-10-14T16:00:00 | -0.000106 | 8 |
2021-10-15T00:00:00.005000 | -0.000051 | 8 |
2021-10-15T08:00:00.002000 | 0 | 8 |
2021-10-15T16:00:00.005000 | -0.000309 | 8 |
2021-10-16T00:00:00.009000 | -0.000327 | 8 |
2021-10-16T08:00:00 | -0.000206 | 8 |
2021-10-16T16:00:00.003000 | -0.000466 | 8 |
2021-10-17T00:00:00.001000 | -0.000732 | 8 |
2021-10-17T08:00:00.002000 | -0.000615 | 8 |
2021-10-17T16:00:00.002000 | -0.000467 | 8 |
2021-10-18T00:00:00.006000 | -0.001554 | 8 |
2021-10-18T08:00:00.004000 | -0.000215 | 8 |
2021-10-18T16:00:00.001000 | -0.00002 | 8 |
2021-10-19T00:00:00.002000 | -0.000787 | 8 |
2021-10-19T08:00:00 | -0.000024 | 8 |
2021-10-19T16:00:00 | -0.000813 | 8 |
2021-10-20T00:00:00.017000 | -0.000656 | 8 |
2021-10-20T08:00:00.004000 | -0.000429 | 8 |
2021-10-20T16:00:00.020000 | -0.000195 | 8 |
2021-10-21T00:00:00 | -0.000223 | 8 |
2021-10-21T08:00:00 | -0.000883 | 8 |
2021-10-21T16:00:00.008000 | 0.000949 | 8 |
2021-10-22T00:00:00.005000 | 0.000181 | 8 |
2021-10-22T08:00:00.005000 | 0.0002 | 8 |
2021-10-22T16:00:00.018000 | 0.000601 | 8 |
2021-10-23T00:00:00.005000 | 0.000444 | 8 |
2021-10-23T08:00:00.001000 | 0.00023 | 8 |
2021-10-23T16:00:00.004000 | 0.00032 | 8 |
2021-10-24T00:00:00.006000 | 0.000393 | 8 |
2021-10-24T08:00:00.012000 | 0.000294 | 8 |
2021-10-24T16:00:00.001000 | 0.000529 | 8 |
2021-10-25T00:00:00.012000 | 0.000317 | 8 |
2021-10-25T08:00:00.002000 | 0.000442 | 8 |
2021-10-25T16:00:00.022000 | 0.000369 | 8 |
2021-10-26T00:00:00.005000 | 0.000453 | 8 |
2021-10-26T08:00:00.016000 | 0.000526 | 8 |
2021-10-26T16:00:00 | 0.000426 | 8 |
2021-10-27T00:00:00.004000 | 0.00024 | 8 |
2021-10-27T08:00:00 | 0.00043 | 8 |
2021-10-27T16:00:00.002000 | 0 | 8 |
2021-10-28T00:00:00.002000 | 0 | 8 |
2021-10-28T08:00:00 | 0 | 8 |
2021-10-28T16:00:00.005000 | 0 | 8 |
2021-10-29T00:00:00 | 0 | 8 |
2021-10-29T08:00:00.007000 | 0 | 8 |
2021-10-29T16:00:00.001000 | 0 | 8 |
2021-10-30T00:00:00 | 0.000358 | 8 |
2021-10-30T08:00:00.001000 | 0.000203 | 8 |
2021-10-30T16:00:00.013000 | 0.00015 | 8 |
2021-10-31T00:00:00.008000 | 0 | 8 |
2021-10-31T08:00:00.010000 | 0 | 8 |
2021-10-31T16:00:00.001000 | 0 | 8 |
2021-11-01T00:00:00.009000 | -0.000365 | 8 |
2021-11-01T08:00:00.014000 | 0 | 8 |
2021-11-01T16:00:00.001000 | 0 | 8 |
2021-11-02T00:00:00 | 0 | 8 |
2021-11-02T08:00:00.007000 | -0.000018 | 8 |
2021-11-02T16:00:00.010000 | 0 | 8 |
2021-11-03T00:00:00.019000 | 0 | 8 |
End of preview. Expand
in Data Studio
BNBUSDT Perpetual Funding Rate (1 2021 - Mar 2026)
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
| 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 | -0.00005811 (-0.0058%) |
| Median | 0.00000000 (0.0000%) |
| Min | -0.00208684 (-0.2087%) |
| Max | 0.00100171 (0.1002%) |
| Std | 0.00023554 |
| Positive % | 17.4% |
| Negative % | 29.1% |
| Annualized mean | -6.36% |
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.
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