xxparthparekhxx/indian-stock-market-minute-data

🇮🇳 Indian Stock Market Data: Minute & Daily (2000 - 2026)

📌 Overview

This is a high-performance financial dataset containing the historical price history of 2,500+ NSE Stocks and Indices.

The dataset has been sharded and optimized for high-speed training. Instead of thousands of tiny files, it is grouped into large ~1.5GB Parquet shards, making it ideal for fast streaming with the Hugging Face datasets library.

📊 Dataset Stats

• Total Rows: ~715 Million
• Size: ~10.5 GB (Compressed Snappy Parquet) / ~125 GB (Uncompressed)
• Coverage: 99.4% of active/suspended NSE Equities & Indices
• Granularity: - Minute: 1-minute intraday candles (2022-2026)
  • Day: Daily candles (2000-2026)
• Schema: symbol, timestamp (UTC), open, high, low, close, volume, oi

📂 Directory Structure

The data is partitioned by frequency to allow for efficient loading.

/minute/
    train-00000.parquet  (Stocks A-C)
    train-00001.parquet  (Stocks C-H)
    ...
/day/
    train-00000.parquet  (All Daily Data)

Note: The files are sorted by Symbol then Timestamp. This means all data for a specific stock (e.g., RELIANCE) is contiguous within a single shard, maximizing compression and read speed.

💻 Usage (Python)

🚀 Option 1: Using Hugging Face Datasets (Recommended)

This method automatically handles downloading, caching, and iterating over the shards.

from datasets import load_dataset

# 1. Load ALL Minute-Level Data (Streams 10.5 GB in shards)
# Use split="minute" to get the high-res intraday data
ds_minute = load_dataset("xxparthparekhxx/indian-stock-market-minute-data", split="minute")

# 2. Filter for a specific stock
# (The library efficiently scans the Arrow table in RAM)
reliance = ds_minute.filter(lambda x: x['symbol'] == 'RELIANCE')

print(reliance[0])

⚡ Option 2: Streaming (No Download)

If you don't want to download the full 10.5 GB to disk, you can stream it on-the-fly.

from datasets import load_dataset

dataset = load_dataset(
    "xxparthparekhxx/indian-stock-market-minute-data", 
    split="minute", 
    streaming=True
)

# Iterate through the dataset without downloading everything
# Since data is sorted by Symbol, you will see all rows for a stock sequentially
for row in dataset:
    if row['symbol'] == 'TATASTEEL':
        print(row)
        # Stop after finding the first row to prove it works
        break

📉 Option 3: Load Daily Data Only

If you only need daily timeframe data (2000-2026), you can load just the daily split (~100MB).

from datasets import load_dataset

ds_day = load_dataset("xxparthparekhxx/indian-stock-market-minute-data", split="day")
print(ds_day[0])

🐼 Option 4: Using Pandas

You can read individual shards directly if you prefer manual control.

import pandas as pd

# Load the first shard of minute data (Contains stocks starting with A-B approx)
df = pd.read_parquet("hf://datasets/xxparthparekhxx/indian-stock-market-minute-data/minute/train-00000.parquet")

print(df.head())

📝 Schema & Data Types

Column	Type	Description
symbol	String	NSE Trading Symbol (e.g., RELIANCE, NIFTY_50)
timestamp	Datetime (ns)	UTC Timezone. (Add +5:30 for IST)
open	Float32	Opening Price
high	Float32	High Price
low	Float32	Low Price
close	Float32	Closing Price
volume	Int64	Volume Traded
oi	Int64	Open Interest (0 if not applicable)

⚠️ Disclaimer

This dataset is intended for research, educational, and backtesting purposes only.

• It is not a live feed.
• Do not use this as the primary basis for live financial trading.
• The authors are not responsible for any financial losses incurred from using this data.

📄 License

This dataset is released under the MIT License.