Name: NSE - Nifty 50 Index Minute Data (2015 To 2024)
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

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.

Tables

Nifty 100 Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_100_minute

19.43 MB
908,897 rows
6 columns

CREATE TABLE nifty_100_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty 200 Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_200_minute

17.15 MB
908,898 rows
6 columns

CREATE TABLE nifty_200_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty 500 Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_500_minute

11.52 MB
496,658 rows
6 columns

CREATE TABLE nifty_500_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty 50 Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_50_minute

19.44 MB
909,321 rows
6 columns

CREATE TABLE nifty_50_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Alpha 50 Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_alpha_50_minute

13.12 MB
496,331 rows
6 columns

CREATE TABLE nifty_alpha_50_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Auto Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_auto_minute

18.57 MB
908,897 rows
6 columns

CREATE TABLE nifty_auto_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Bank Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_bank_minute

22.75 MB
909,275 rows
6 columns

CREATE TABLE nifty_bank_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Commodities Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_commodities_minute

15.33 MB
847,752 rows
6 columns

CREATE TABLE nifty_commodities_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Consr Durbl Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_consr_durbl_minute

5.95 MB
203,621 rows
6 columns

CREATE TABLE nifty_consr_durbl_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Consumption Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_consumption_minute

12.38 MB
623,702 rows
6 columns

CREATE TABLE nifty_consumption_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Cpse Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_cpse_minute

9.53 MB
496,658 rows
6 columns

CREATE TABLE nifty_cpse_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Energy Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_energy_minute

21.57 MB
908,896 rows
6 columns

CREATE TABLE nifty_energy_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Fin Service Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_fin_service_minute

20.91 MB
908,896 rows
6 columns

CREATE TABLE nifty_fin_service_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Fmcg Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_fmcg_minute

22.31 MB
908,897 rows
6 columns

CREATE TABLE nifty_fmcg_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Gs Compsite Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_gs_compsite_minute

7.41 MB
484,107 rows
6 columns

CREATE TABLE nifty_gs_compsite_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Healthcare Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_healthcare_minute

4.9 MB
203,621 rows
6 columns

CREATE TABLE nifty_healthcare_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Ind Digital Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_ind_digital_minute

4.44 MB
203,621 rows
6 columns

CREATE TABLE nifty_ind_digital_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty India Mfg Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_india_mfg_minute

4.93 MB
203,621 rows
6 columns

CREATE TABLE nifty_india_mfg_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Infra Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_infra_minute

15.84 MB
908,898 rows
6 columns

CREATE TABLE nifty_infra_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty It Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_it_minute

20.37 MB
909,276 rows
6 columns

CREATE TABLE nifty_it_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nifty Largemid250 Minute

@kaggle.debashis74017_nifty_50_minute_data.nifty_largemid250_minute

4.99 MB
203,621 rows
6 columns

CREATE TABLE nifty_largemid250_minute (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);