Datasets:
File size: 3,336 Bytes
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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](https://huggingface.co/datasets/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`
```python
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:
```python
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())
```
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