File size: 1,541 Bytes
68f1463 6a0be47 68f1463 6a0be47 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | ---
license: cc-by-4.0
language:
- en
tags:
- finance
- cryptocurrency
- ohlcv
- candles
- solana
- sol
- trading
- time-series
pretty_name: SOL-USDT 1-Minute Candles (2026-01-01 to 2026-07-20)
size_categories:
- 100K<n<1M
task_categories:
- time-series-forecasting
dataset_info:
features:
- name: timestamp
dtype: int64
- name: open
dtype: float64
- name: high
dtype: float64
- name: low
dtype: float64
- name: close
dtype: float64
- name: volume
dtype: float64
splits:
- name: train
num_examples: 289334
---
# SOL-USDT 1-Minute Candles
Minute-resolution OHLCV candlestick data for the **SOL-USDT** trading pair, from
**2026-01-01T00:00:00Z** to **2026-07-20T23:59:00Z** (289,334 candles, UTC).
## Columns
| Column | Type | Description |
|---|---|---|
| `timestamp` | int64 | Candle open time, Unix epoch milliseconds (UTC) |
| `open` | float | Open price |
| `high` | float | High price |
| `low` | float | Low price |
| `close` | float | Close price |
| `volume` | float | Base asset volume (SOL) |
Each row is one 1-minute candle. Prices are quoted in USDT.
## Load
```python
from datasets import load_dataset
ds = load_dataset("<your-username>/sol-usdt-1m-candles", split="train")
```
Or directly from the JSONL file:
```python
from datasets import load_dataset
ds = load_dataset("json", data_files="data/sol_usdt_1m.jsonl", split="train")
```
## License
Released under CC-BY-4.0. Provided as-is, without warranty of any kind.
|