README.md

metadata

license: mit
language:
  - en
task_categories:
  - time-series-forecasting
  - tabular-regression
size_categories:
  - 10K<n<100K
tags:
  - tsfile
  - timeseries
  - modality:timeseries
  - format:tsfile
  - finance
  - stock-market
modality: timeseries
pretty_name: S&P 500 Index History (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: spx.tsfile

S&P 500 Index History (TsFile format)

This dataset contains the historical S&P 500 index observations published in the source repository. Each dated row provides open, high, low, close, adjusted-close, and volume values for time-series forecasting and tabular analysis.

Modalities: Time-series

Source and scale

• Original dataset: misikoff/SPX
• Source revision: 498783fd0df5ad1f13c98e42329c60b8f8f9c6c1
• Source file: ^SPX.csv (CSV)
• Converted scale: 24,167 observations in one TsFile table.
• Observed date range: 1928-01-03 through 2024-03-15.
• Source split: one table; no validation or test split is defined.

TsFile schema

Column	Role	TsFile type	Source meaning
Time	TIME	INT64 (ms)	Source Date, parsed at UTC midnight
open	FIELD	DOUBLE	Opening index value
high	FIELD	DOUBLE	Highest index value
low	FIELD	DOUBLE	Lowest index value
close	FIELD	DOUBLE	Closing index value
adj_close	FIELD	DOUBLE	Adjusted closing index value
volume	FIELD	INT64	Source volume value

There are no TAG columns because the source contains one index series.

Conversion notes

• Date is represented losslessly as integer-millisecond Time; the redundant source date text is not duplicated as a FIELD.
• Source measurement names are normalized to lowercase TsFile identifiers; Adj Close becomes adj_close.
• All 24,167 rows and all six measurements are retained. The conversion does not infer a market-data provider or provenance beyond the source card.

Files and usage

• spx.tsfile

from tsfile import TsFileReader

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

License

The source dataset is licensed under the MIT License.

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("spx.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())