Datasets:

humblefool06
/

btc-usdt-4h-candles

license: cc-by-4.0
language:
  - en
tags:
  - finance
  - cryptocurrency
  - ohlcv
  - candles
  - trading
  - time-series
  - bitcoin
  - btc
pretty_name: BTC-USDT 4-Hour Candles (2017-08-17 to 2026-08-13)
size_categories:
  - 10K<n<100K
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: 19686
  download_size: 2176974
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.jsonl

BTC-USDT 4-Hour Candles

Full-history 4-Hour-resolution OHLCV candlestick data for the BTC-USDT trading pair (Bitcoin), from 2017-08-17T04:00:00Z to 2026-08-13T20:00:00Z (19,686 candles, UTC). Covers the pair's entire listed history from first traded candle to the most recent completed daily session.

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 (BTC)

Each row is one 4h candle. Prices are quoted in USDT.

Data Quality

Timestamps normalized to millisecond precision (UTC), strictly increasing, deduplicated.
OHLC sanity-checked: high >= max(open, close, low) and low <= min(open, close, high).
Missing candles correspond to periods with no traded activity (no candle was produced for those intervals).

Load

from datasets import load_dataset

ds = load_dataset("humblefool06/btc-usdt-4h-candles", split="train")

Or directly from the JSONL file:

from datasets import load_dataset

ds = load_dataset("json", data_files="data/btc_usdt_4h.jsonl", split="train")

License

Released under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). You are free to share and adapt this data for any purpose, including commercially, provided you give appropriate credit. Provided as-is, without warranty of any kind.