Dataset

indian-stock-market-minute-data

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A sharded Parquet archive of NSE equities and index prices from India spanning 2000 to 2026, covering more than 2,500 tickers and organized into roughly 1.5 GB files for efficient streaming.

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This dataset provides minute- and daily-frequency historical price history for more than 2,500 NSE-listed Indian stocks and indices from 2000 to 2026, distributed as optimized Parquet shards for the Hugging Face `datasets` library. It is published by an individual contributor on Hugging Face and is tagged with an MIT license, though no explicit license file is provided.

What this dataset contains

The collection covers approximately 720 million rows of intraday and end-of-day quotes across 2,500+ NSE symbols and indices. Minute-level data is split across roughly ten ~1.5 GB Parquet shards, alphabetized by ticker, while a separate daily-frequency split contains the full 2000–2026 history in a single ~100 MB shard. The total uncompressed size is reported as falling in the 100M–1B row range, and the readme notes 10.5 GB of streaming content for the minute split.

Structure and fields

Each row is keyed by a `symbol` string and a UTC `timestamp`, giving researchers the standard pair to join intraday and daily files for the same security. Price action is captured in four float32 fields, `open`, `high`, `low`, and `close`, alongside an int64 `volume` column and an int64 `oi` (open interest) column that supports derivatives work. Shards are partitioned by frequency (`minute` vs. `day`) and then alphabetically by ticker, which lets users stream or filter on `symbol` without loading every file.

How analysts use it

Long-history intraday data on emerging-market equities is useful for backtesting short-horizon momentum, mean-reversion, and volatility strategies on Indian large- and mid-cap names, as well as for studying liquidity around NSE session opens and closes. The availability of `oi` alongside price and volume suggests the data can also support futures and options analytics, such as open-interest divergence signals. Researchers can load the daily split for lightweight factor work or stream the minute shards through `datasets`, `dask`, or `polars` for heavier time-series modeling.

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