Torch-Trade/btcusdt_perp_1m_05_2021_to_02_2026

BTCUSDT Perpetual Futures 1-Minute OHLCV (1 2021 - Mar 2026)

Overview

1-minute OHLCV candlestick data for the BTC/USDT USDT-margined perpetual futures contract on Binance, covering May 1, 2021 to February 28, 2026.

• Rows: 2,541,592
• Completeness: ~100% (only 7 single-bar month-boundary gaps from data packaging)

Why futures OHLCV?

Perpetual futures are the most actively traded crypto instruments, with volume typically 3-10x higher than spot. This dataset provides several signals not available in spot data:

• Futures volume: Higher liquidity and more representative of actual trading interest
• Taker buy/sell ratio: taker_buy_base_volume / volume shows whether aggressive buyers or sellers dominate each candle (>0.5 = buyer-dominated)
• Number of trades: Trade count as a proxy for market activity independent of order size
• Quote volume: Notional value traded, useful for comparing activity across price levels

Columns

Column	Type	Description
timestamp	datetime64[ns]	Candle open time (UTC)
open	float64	Opening price (USDT)
high	float64	Highest price in the candle
low	float64	Lowest price in the candle
close	float64	Closing price (USDT)
volume	float64	Trading volume (base asset)
quote_volume	float64	Trading volume (USDT notional)
num_trades	int64	Number of trades
taker_buy_base_volume	float64	Taker buy volume (base asset)
taker_buy_quote_volume	float64	Taker buy volume (USDT notional)

Statistics

Metric	Value
Start price	$57,550.00
End price	$66,937.10
Min price	$15,502.00
Max price	$126,086.80
Return	+16.3%
Avg daily volume	345,284 BTC
Mean taker buy ratio	0.4971
Mean trades/candle	2,600

Data Quality

The only gaps are single missing bars at month boundaries (23:59 -> 00:01, missing the 00:00 candle). This is a Binance data packaging artifact, not actual missing trading data. Completeness is effectively 100%.

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

futures = load_dataset("Torch-Trade/btcusdt_perp_1m_05_2021_to_02_2026")["train"].to_pandas()
futures["timestamp"] = pd.to_datetime(futures["timestamp"])

# Merge — rename futures columns to avoid clash
futures = futures.rename(columns={
    "open": "fut_open", "high": "fut_high", "low": "fut_low", "close": "fut_close",
    "volume": "fut_volume", "quote_volume": "fut_quote_volume",
    "num_trades": "fut_num_trades",
    "taker_buy_base_volume": "fut_taker_buy_volume",
    "taker_buy_quote_volume": "fut_taker_buy_quote_volume",
})
df = spot.merge(futures, on="timestamp", how="left")

# Derive taker buy ratio
df["taker_buy_ratio"] = df["fut_taker_buy_volume"] / df["fut_volume"]

Usage

from datasets import load_dataset
import pandas as pd

ds = load_dataset("Torch-Trade/btcusdt_perp_1m_05_2021_to_02_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape)  # (2541592, 10)
print(df.head())

License

MIT -- data sourced from Binance Data Collection.