Datasetpublic

vishnun0027/indian-market-historical-ohlcv

Indian Market Data (NSE/BSE) Production-grade historical OHLCV dataset for Indian financial markets. Auto-updated daily via GitHub Actions. Dataset Summary Metric Value Total Files 2674 Total Size 287.9 MB Last Updated 2026-09-27 06:34 UTC Update Frequency Daily (weekdays) Source Yahoo Finance via yfinance Asset Coverage Asset Type Symbols Size Stocks 2624 279.3 MB Indices 17 3.1 MB Etfs 17 2.0 MB Commodities… See the full description on the dataset page: https://huggingface.co/datasets/vishnun0027/indian-market-historical-ohlcv.

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Indian Market Data (NSE/BSE)

Production-grade historical OHLCV dataset for Indian financial markets. Auto-updated daily via GitHub Actions.

Dataset Summary

Metric	Value
Total Files	2674
Total Size	287.9 MB
Last Updated	2026-09-27 06:34 UTC
Update Frequency	Daily (weekdays)
Source	Yahoo Finance via yfinance

Asset Coverage

Asset Type	Symbols	Size
Stocks	2624	279.3 MB
Indices	17	3.1 MB
Etfs	17	2.0 MB
Commodities	8	1.7 MB
Forex	8	1.8 MB

Schema

Each Parquet file contains daily OHLCV data with the following columns:

Column	Type	Description
`date`	`date32`	Trading date
`open`	`float64`	Opening price
`high`	`float64`	Day high
`low`	`float64`	Day low
`close`	`float64`	Closing price
`adj_close`	`float64`	Adjusted close (splits & dividends)
`volume`	`int64`	Trading volume
`dividends`	`float64`	Dividend amount
`stock_splits`	`float64`	Stock split ratio
`symbol`	`string`	Ticker symbol

Usage

python

import pandas as pd

# Load a single stock
df = pd.read_parquet("hf://datasets/vishnun0027/indian-market-historical-ohlcv/stocks/RELIANCE.parquet")

# Load all stocks
from datasets import load_dataset
ds = load_dataset("vishnun0027/indian-market-historical-ohlcv", data_dir="stocks")

File Structure

├── stocks/          # NSE/BSE equities (*.parquet)
├── indices/         # Market indices (*.parquet)
├── etfs/            # Exchange-traded funds (*.parquet)
├── commodities/     # Commodity futures (*.parquet)
├── forex/           # Currency pairs (*.parquet)
├── metadata/        # Asset universe & catalog
└── sync_status.json # Last sync timestamp & stats

Data Quality

Each sync run validates data for:

—Duplicate dates
—OHLC consistency (High ≥ Low, bounds checks)
—Missing values
—Volume anomalies
—Trading day gaps

License

MIT