Torch-Trade/btcusdt_perp_basis_1m_05_2021_to_02_2026

BTCUSDT Perpetual Futures-Spot Basis (1 2021 - Mar 2026)

Overview

1-minute premium index (futures-spot basis) data for the BTC/USDT perpetual futures contract on Binance, covering May 1, 2021 to February 28, 2026.

• Rows: 2,531,443
• Completeness: 99.60%

What is the premium index (basis)?

The premium index measures the instantaneous percentage difference between the perpetual futures price and the spot price:

basis = (futures_price - spot_price) / spot_price

• Positive basis (contango): Futures trading at a premium to spot -- bullish sentiment, longs willing to pay more
• Negative basis (backwardation): Futures at a discount -- bearish sentiment, panic selling, or liquidation cascades
• Large spikes: Indicate sudden shifts in derivatives positioning, often preceding spot price moves

Values are typically small decimals (e.g., 0.001 = 0.1% premium). The basis drives the funding rate: sustained positive basis leads to positive funding, and vice versa.

Columns

Column	Type	Description
timestamp	datetime64[ns]	Candle open time (UTC)
basis_open	float64	Opening basis as decimal fraction
basis_high	float64	Highest basis in the candle
basis_low	float64	Lowest basis in the candle
basis_close	float64	Closing basis as decimal fraction

Statistics

Metric	Value
Mean basis	-0.000228 (-0.0228%)
Median basis	-0.000382 (-0.0382%)
Min basis	-0.081181 (-8.1181%)
Max basis	0.061010 (6.1010%)
Std	0.000489

Data Quality

The premium index is computed by Binance internally and has small gaps that cannot be backfilled from external sources. These are exchange-wide infrastructure events shared across all perpetual pairs.

Period	Duration	Type
2021-07-01 (full day)	24h (1,441 bars)	Premium index data gap
2021-07-24 — 2021-07-27	96h (5,760 bars)	Premium index data gap
2022-07-12 12:56-14:32	~1.5h (50 bars)	Scattered missing bars
2022-10-02 (full day)	24h (1,440 bars)	Premium index data gap
2023-02-24 (full day)	24h (1,440 bars)	Premium index data gap
2023-11-10 03:37-04:09	~30min (14 bars)	Scattered missing bars
2024-08-12 10:01-10:04	3min (2 bars)	Minor gap
Month boundaries	1 bar each (x9)	Data packaging artifact

Total missing bars: 10,156 out of ~2,541,599

Recommended handling: Forward-fill missing bars at training time. The basis changes slowly relative to 1-minute resolution, so forward-filling introduces negligible error.

Joining with spot OHLCV

This dataset complements the spot OHLCV dataset Torch-Trade/btcusdt_spot_1m_05_2021_to_03_2026. To join at training time:

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"])

basis = load_dataset("Torch-Trade/btcusdt_perp_basis_1m_05_2021_to_02_2026")["train"].to_pandas()
basis["timestamp"] = pd.to_datetime(basis["timestamp"])

# Merge on timestamp, forward-fill gaps
df = spot.merge(basis, on="timestamp", how="left")
df[["basis_open", "basis_high", "basis_low", "basis_close"]] = df[["basis_open", "basis_high", "basis_low", "basis_close"]].ffill()

Usage

from datasets import load_dataset
import pandas as pd

ds = load_dataset("Torch-Trade/btcusdt_perp_basis_1m_05_2021_to_02_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape)  # (2531443, 5)
print(df.head())

License

MIT -- data sourced from Binance Data Collection.