Dataset
indian-stock-market-minute-data
Indian equity market price history covering NSE-listed stocks and indices from 2000 through 2026, offered in Parquet shards of roughly 1.5GB each for efficient streaming on the Hugging Face hub. The collection draws on more than 2,500 tickers and supports both minute-level and end-of-day intervals.
Data preview
Dataset card
Indian Stock Market Minute Data is an MIT-licensed Hugging Face dataset of historical price history for 2,500+ NSE-listed stocks and indices from 2000 through 2026. It is published by user GalacticWanderer under the slug indian-stock-market-minute-data and is partitioned into minute and daily Parquet shards.
What this dataset contains
The dataset bundles historical OHLCV records plus an open-interest field for more than 2,500 NSE stocks and indices, sourced via the Upstox platform and tagged with finance, NSE, India, stock-market, and quantitative-finance labels. Coverage spans 2000 to 2026 and is split into a minute-frequency stream of roughly 10.5 GB and a much smaller daily-frequency split. The full table contains around 720 million rows.
Structure and fields
Files are organized as Parquet shards that group symbols alphabetically, e.g. train-00000 of the minute split covering stocks A–C, with a separate shard layout for the daily split. Each row carries symbol (large_string), timestamp (timestamp in UTC microseconds), open, high, low, close (float32), volume (int64) and oi (int64). The dataset can be loaded through the Hugging Face datasets library using split='minute' or split='day', with streaming supported for the larger minute split.
How analysts use it
Quantitative researchers can use the minute-level timestamp, open, high, low, close, volume and oi fields to backtest intraday strategies, study NSE liquidity, or train time-series forecasting models on Indian equities. The long 2000–2026 history and inclusion of open interest also makes the data suitable for event studies around corporate actions and macro announcements. Because the dataset is openly licensed and Shard-optimized for streaming, it lowers the cost of building large-sample studies of emerging-market equities.
Documentation & references 1
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