THULab/crypto_data

CryptoData Dataset (TsFile format)

CryptoData is a collection of cryptocurrency market data intended for price prediction, market trend analysis, and historical analysis. The source card describes default, close, indicators, and sequences configurations. At the pinned source revision, the available data files are the daily candle CSVs; the converted artifact therefore documents and contains the available candle data only.

Modalities: Time-series

Source and scale

• Original dataset: sebdg/crypto_data
• Source revision: 9767bde1e557d1aef9bb70808ce5642493c11574
• Source layout: 298 files under candles/*.csv; 294 contain observations.
• Converted scale: 216,226 rows from 294 markets, with a dominant daily cadence.
• There is no predefined train/validation/test split.
• Four files are header-only: ASTR-EUR.csv, BLZ-EUR.csv, ONDO-EUR.csv, and SSV-EUR.csv. They contribute zero rows and are not represented as empty devices.

TsFile schema

Column	Role	TsFile type	Source meaning
Time	TIME	INT64 (ms)	Source time, epoch milliseconds
market	TAG	STRING	Market identifier, for example BTC-EUR
open	FIELD	DOUBLE	Opening price
high	FIELD	DOUBLE	High price
low	FIELD	DOUBLE	Low price
close	FIELD	DOUBLE	Closing price
volume	FIELD	DOUBLE	Traded volume

Conversion notes

• Every non-empty candle file is combined into one logical table and sorted by market, then ascending Time.
• Source integer epoch-millisecond time is renamed to Time without changing its value. The source market column is the device TAG.
• OHLCV values are retained as numeric fields; no rows or semantic measurements are dropped. Header-only files are excluded solely because they contain no observations.
• Indicators and sequence arrays described in the source card are not claimed because their generated files are absent at this pinned revision.

Files and usage

• crypto_data.tsfile

from pathlib import Path
from tsfile import TsFileReader

path = Path("crypto_data.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    table_name = next(iter(schemas))
    with reader.query_table(table_name, ["close", "volume"], batch_size=1024) as result:
        batch = result.read_arrow_batch()
        if batch is not None:
            print(batch.to_pandas().head())

License and attribution

The source dataset is released under the Apache-2.0 license. See the original dataset card for the source description and usage context.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("crypto_data.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())