Torch-Trade/bnbusdt_perp_1m_10_2021_to_02_2026

---
license: mit
task_categories:
- time-series-forecasting
- reinforcement-learning
tags:
- finance
- cryptocurrency
- bnb
- trading
- ohlcv
- perpetual-futures
- derivatives
- binance
size_categories:
- 1M<n<10M
---

# BNBUSDT Perpetual Futures 1-Minute OHLCV (1 2021 - Mar 2026)


## Overview

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

- **Rows**: 2,321,277
- **Completeness**: ~100% (only 2 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 | $387.35 |
| End price | $617.14 |
| Min price | $183.83 |
| Max price | $1,372.68 |
| Return | +59.3% |
| Avg daily volume | 1,282,729 BNB |
| Mean taker buy ratio | 0.4954 |
| Mean trades/candle | 672 |

## 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/bnbusdt_spot_1m_10_2021_to_03_2026`](https://huggingface.co/datasets/Torch-Trade/bnbusdt_spot_1m_10_2021_to_03_2026). To join at training time:

```python
from datasets import load_dataset
import pandas as pd

# Load both
spot = load_dataset("Torch-Trade/bnbusdt_spot_1m_10_2021_to_03_2026")["train"].to_pandas()
spot["timestamp"] = pd.to_datetime(spot["timestamp"])

futures = load_dataset("Torch-Trade/bnbusdt_perp_1m_10_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

```python
from datasets import load_dataset
import pandas as pd

ds = load_dataset("Torch-Trade/bnbusdt_perp_1m_10_2021_to_02_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape) # (2321277, 10)
print(df.head())
```

## License

MIT -- data sourced from [Binance Data Collection](https://data.binance.vision/).

许可证：MIT
任务类别：
- 时间序列预测
- 强化学习
标签：
- 金融
- 加密货币
- BNB
- 交易
- OHLCV（开盘-最高-最低-收盘-成交量）
- 永续合约（perpetual futures）
- 衍生品
- 币安（Binance）
数据规模：1M<n<10M

# BNBUSDT永续合约1分钟OHLCV（开盘-最高-最低-收盘-成交量）数据（2021年10月 - 2026年2月）


## 概述

本数据集包含币安（Binance）上BNB/USDT USDT保证金永续合约的1分钟OHLCV蜡烛图数据，覆盖时段为2021年10月1日至2026年2月28日。

- **数据行数**：2,321,277
- **数据完整性**：近100%（仅存在2处因数据打包产生的月边界单K线缺口）

## 为何选用永续合约OHLCV数据？

永续合约是当前交易最活跃的加密货币工具，其交易量通常为现货市场的3-10倍。本数据集提供多项现货数据无法获取的交易信号：
- **合约交易量**：流动性更高，更能反映真实的市场交易兴趣
- **主动买卖盘比例**：`taker_buy_base_volume / volume` 可用于判断每根K线内主动买盘还是卖盘占据主导（数值大于0.5则代表买方主导市场）
- **交易笔数**：以成交笔数作为市场活跃度的代理指标，不受订单规模的影响
- **报价交易量**：名义交易金额，便于跨不同价格层级对比市场活跃度

## 字段说明

| 列名 | 数据类型 | 描述 |
|--------|------|-------------|
| `timestamp` | `datetime64[ns]` | K线开盘时间（UTC时区） |
| `open` | `float64` | 开盘价格（单位：USDT） |
| `high` | `float64` | K线内最高价格 |
| `low` | `float64` | K线内最低价格 |
| `close` | `float64` | 收盘价格（单位：USDT） |
| `volume` | `float64` | 交易量（基础资产单位） |
| `quote_volume` | `float64` | 报价交易量（USDT名义金额） |
| `num_trades` | `int64` | 成交笔数 |
| `taker_buy_base_volume` | `float64` | 主动买盘交易量（基础资产单位） |
| `taker_buy_quote_volume` | `float64` | 主动买盘交易量（USDT名义金额） |

## 统计信息

| 统计指标 | 数值 |
|--------|-------|
| 起始价格 | 387.35美元 |
| 结束价格 | 617.14美元 |
| 最低价格 | 183.83美元 |
| 最高价格 | 1,372.68美元 |
| 累计收益率 | +59.3% |
| 日均交易量 | 1,282,729 BNB |
| 平均主动买盘比例 | 0.4954 |
| 单K线平均交易笔数 | 672 |

## 数据质量

本数据集仅有的缺口为月边界处的单根缺失K线（即23:59 -> 00:01时段缺失00:00时刻的K线），该现象为币安数据打包的固有特征，并非真实交易数据缺失，数据完整性实际可达100%。

## 与现货OHLCV数据集合并

本数据集可与现货OHLCV数据集 [`Torch-Trade/bnbusdt_spot_1m_10_2021_to_03_2026`](https://huggingface.co/datasets/Torch-Trade/bnbusdt_spot_1m_10_2021_to_03_2026) 互补使用。若需在训练时合并二者，可参考以下代码：

python
from datasets import load_dataset
import pandas as pd

# 加载两个数据集
spot = load_dataset("Torch-Trade/bnbusdt_spot_1m_10_2021_to_03_2026")["train"].to_pandas()
spot["timestamp"] = pd.to_datetime(spot["timestamp"])

futures = load_dataset("Torch-Trade/bnbusdt_perp_1m_10_2021_to_02_2026")["train"].to_pandas()
futures["timestamp"] = pd.to_datetime(futures["timestamp"])

# 合并前重命名永续合约数据集的列以避免命名冲突
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")

# 计算主动买盘比例
df["taker_buy_ratio"] = df["fut_taker_buy_volume"] / df["fut_volume"]

## 使用示例

python
from datasets import load_dataset
import pandas as pd

ds = load_dataset("Torch-Trade/bnbusdt_perp_1m_10_2021_to_02_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape) # (2321277, 10)
print(df.head())

## 许可证

MIT协议——数据源自[币安数据集合平台（Binance Data Collection）](https://data.binance.vision/)。