---
pretty_name: Historical Global Equity Indexes Sample
license: other
tags:
- finance
- indexes
- indices
- market-data
- time-series
- quantitative-finance
- equities
- global-markets
configs:
- config_name: daily
  data_files:
  - split: train
    path: "data/daily/*.parquet"
- config_name: minute
  data_files:
  - split: train
    path: "data/minute/*.parquet"
---

# Historical Global Equity Indexes Sample

A free evaluation sample of historical index data covering selected global, developed-market, emerging-market, and country-level equity benchmarks.

**Full historical index catalog, broader index coverage, downloadable datasets, and pricing:**  
**[https://futuresforexandsomeindexes.com/](https://futuresforexandsomeindexes.com/)**

This repository is a free evaluation sample intended for schema inspection, data-quality evaluation, integration testing, and quantitative research prototyping. It is not the complete commercial dataset.

## Sample coverage

This release contains **4 indexes**, **12 distinct normalized monthly partition codes**, and **1,341,572 observations** across daily and minute frequencies.

| Index | Symbol | Provider | Subgroup | Daily partitions | Minute partitions |
| --- | --- | --- | --- | ---: | ---: |
| S&P China BMI | `SSCH` | S&P DOW JONES INDICES | Emerging / Frontier Markets | 12 | 12 |
| S&P Developed ex-U.S. BMI | `SSCW` | S&P DOW JONES INDICES | Developed Markets | 12 | 12 |
| S&P Emerging Markets BMI | `SSCK` | S&P DOW JONES INDICES | Emerging / Frontier Markets | 12 | 12 |
| S&P Global 1200 | `SSGL` | S&P Dow Jones Indices | Large & Mid Cap | 12 | 12 |

The sample manifest determines the exact index and monthly partitions included. The complete catalog may contain substantially broader historical and index coverage.

## Filename normalization

The source manifest may contain compact monthly filenames such as `01-25.json`. Index storage and staging use a four-digit year:

`01-25.json` → `01-2025.json` → staged `01-2025.csv` → public `file_code = 01-2025`

The public catalog and sanitized manifest always use the normalized `MM-YYYY` partition code.

## Symbol normalization

Leading dollar signs in manifest symbols are removed before output. For example:

`$INX` → `INX`

The normalized symbol is what appears in the Parquet data, public manifest, catalog, and metadata.

## Package contents

- `data/daily/*.parquet` — normalized daily index observations.
- `data/minute/*.parquet` — normalized minute-level index observations.
- `catalog.csv` / `catalog.parquet` — one row per index/frequency/month source file with observation counts, timestamp bounds, output shard, and SHA-256.
- `metadata.json` — release-level coverage and file statistics.
- `manifest.json` — sanitized public sample manifest; internal source IDs are excluded.
- `schema.json` — column definitions plus timestamp, partition, symbol, and volume semantics.
- `checksums.sha256` — package integrity hashes.
- `dataset-metadata.json` — Kaggle publication metadata.
- `examples/` — Polars, pandas, and DuckDB loading examples.

## Data format

The release uses **Apache Parquet with ZSTD compression**.

Daily and minute files share the normalized columns:

`timestamp`, `open`, `high`, `low`, `close`, `volume`, `index_name`, `symbol`, `dataset`, `provider`, `group`, `subgroup`, `file_code`

Daily observations in this release: **1,043**  
Minute observations in this release: **1,340,529**

## Volume

The current index staging source does not populate volume. The `volume` field is therefore retained as nullable for schema stability and future compatibility; null volume should not be interpreted as zero activity.

## Timestamp semantics

`timestamp` is the original source timestamp integer and is preserved unchanged. The public release builder does **not** perform timezone conversion.

## Quick start

### Polars

```python
import polars as pl

minute = pl.scan_parquet("data/minute/*.parquet")

sample = (
    minute
    .filter(pl.col("symbol") == "INX")
    .select(["timestamp", "open", "high", "low", "close"])
    .collect()
)

print(sample.head())
```

### pandas

```python
from pathlib import Path
import pandas as pd

files = sorted(Path("data/minute").glob("*.parquet"))

df = pd.concat(
    (pd.read_parquet(path) for path in files),
    ignore_index=True,
)

print(df.head())
```

### DuckDB

```sql
SELECT
    index_name,
    symbol,
    COUNT(*) AS observations
FROM read_parquet('data/minute/*.parquet')
GROUP BY index_name, symbol
ORDER BY index_name;
```

## Typical uses

- Global equity-market research across developed and emerging benchmark indexes.
- Cross-market and regional index comparison.
- Intraday and daily quantitative analysis of global equity benchmarks.
- Schema and data-quality evaluation before obtaining broader historical coverage.

## Integrity and reproducibility

Every Parquet shard is referenced in the catalog and the package includes `checksums.sha256`. Before release, the builder validates required columns, normalized row identifiers, required non-null fields, basic OHLC bounds, internal source-ID uniqueness, and normalized monthly partition uniqueness.

Internal source identifiers used by the staging pipeline are intentionally excluded from public release metadata.

## Complete dataset

This package is intentionally limited to a small evaluation subset. For the complete index catalog, broader historical coverage, availability, samples, and pricing, visit:

### [https://futuresforexandsomeindexes.com/](https://futuresforexandsomeindexes.com/)

## Release information

- Release version: `2026-09-18`
- Generated: `2026-09-18T16:38:40.613086+00:00`
- Dataset family: `Global Equity Indexes`
- Compression: `zstd`
- License metadata: `other`