Torch-Trade/xrpusdt_spot_1m_05_2021_to_03_2026

---
license: mit
task_categories:
- time-series-forecasting
tags:
- finance
- trading
- cryptocurrency
- xrp
- ohlcv
- binance
size_categories:
- 1M<n<10M
---

# XRPUSDT Spot 1-Minute OHLCV Dataset

1-minute OHLCV candlestick data for the **XRP/USDT** spot pair on Binance,
covering **May 1, 2021** to **February 28, 2026**.

- **Rows:** 2,541,600
- **Completeness:** 100.00%
- **Time Range:** May 1, 2021 — February 28, 2026


## 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 (XRP) |

## Statistics

| Metric | Value |
|--------|-------|
| Start price | $1.5945 |
| End price | $1.3762 |
| Min price | $0.2884 |
| Max price | $3.6537 |
| Return | -13.7% |

## Data Quality

**No gaps** — the dataset is 100% complete with no missing minutes.

### Backfilled bars

47085 bars across Binance maintenance windows were sourced from Bybit XRPUSDT spot.

## Usage

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

ds = load_dataset("Torch-Trade/xrpusdt_spot_1m_05_2021_to_03_2026")
df = ds["train"].to_pandas()
df["timestamp"] = pd.to_datetime(df["timestamp"])

print(df.shape) # (2541600, 6)
print(df.head())
```

## License

**MIT** — price data sourced from Binance and Bybit public data repositories.