NSE - Nifty 50 Index Minute Data (2015 To 2024)

UPDATED EVERY WEEK
Last Update - 9th NOV 2024

Context

• The NIFTY 50 is a well-diversified 50 stock index and it represents 13 important sectors of the economy.
• It is used for a variety of purposes such as benchmarking fund portfolios, index-based derivatives, and index funds.
• NIFTY 50 is owned and managed by NSE Indices Limited.
• The NIFTY 50 index has shaped up to be the largest single financial product in India.

This data contains all the indices of NSE.

NIFTY 50,
NIFTY BANK, 
NIFTY 100, 
NIFTY COMMODITIES,
NIFTY CONSUMPTION, 
NIFTY FIN SERVICE, 
NIFTY IT, 
NIFTY INFRA, 
NIFTY ENERGY, 
NIFTY FMCG, 
NIFTY AUTO, 
NIFTY 200, 
NIFTY ALPHA 50, 
NIFTY 500, 
NIFTY CPSE, 
NIFTY GS COMPSITE, 
NIFTY HEALTHCARE, 
NIFTY CONSR DURBL, 
NIFTY LARGEMID250, 
NIFTY INDIA MFG, 
NIFTY IND DIGITAL

File Information and Column Descriptions.

Nifty 50 index data with 1 minute data. The dataset contains OHLC (Open, High, Low, and Close) prices from Jan 2015 to Aug 2024.

• This dataset can be used for time series analysis, regression problems, and time series forecasting both for one step and multi-step ahead in the future.
• Options data can be integrated with this minute data, to get more insight about this data.
• Different backtesting strategies can be built on this data.

File Information

• This dataset contains 6 files, each file contains nifty 50 data with different intervals.
• Different intervals are - 1 min, 3 min, 5 min, 15 min, and 1 hour, Daily data from intervals of 2015 Jan to 2024 August.

Column Descriptors

• Each file contains OHLC (Open, High, Low, and Close) prices and Data time information. Since these are Nifty 50 index data, so volume is not present.

Inspiration

Time series forecasting - Predict stock price

• Predict future stock price one step ahead and multi-step ahead in time.
• Use different time series forecasting techniques for forecasting the future stock price.

Machine learning and Deep learning techniques

• Possible ML and DL models include Neural networks, RNNs, LSTMs, Transformers, Attention networks, etc.
• Different error functions can be considered like RMSE, MAE, RMSEP etc.

Feature engineering

• Different augmented features can be created and that can be used for forecasting.
• Correlation analysis, Feature importance to justify the important features.