Name: NSE India Stocks (companies)
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

NSE India Stocks (companies)

Context

In this study, large number of National Stock Exchange(NSE), India
stocks under different sectors are mined from various financial websites
and data analytic steps are followed. Primary goal of this work
is to explore the hidden context patterns between diverse group of
stocks and discover the predictive analytic knowledge using machine
learning algorithms.
The transaction dataset are captured for NSE stocks using statistical computing software R.
The price of the stock is determined by the market forces. Buyers and
sellers quote the preferred price, so there is a dynamic data day by day.
Though it is difficult to identify when to buy and sell the stock, technical
indicators may support us to forecast the future price

Content

A data frame with 8 variables: index, date, time, open, high, low, close and id. For each year from 2013 to 2016, the number of trading data of each minute of given each date. The currency of the price is Indian Rupee (INR).

Code : market id
Date : numerical value (Ex. 20151203- to be converted as 2015/12/03)
Time : factor (Ex. 09:16)
Open : numeric (opening price)
High : numeric (high price)
Low : numeric (low price)
Close : numeric (closing price)
Volume : numeric (total volume traded)

Acknowledgements

References
[1] Brett Lantz, Machine Learning with R . Packt Publishing Ltd., Birmingham, UK , 2015.
[2] The R Project https://www.r-project.org/
[3] https://finance.yahoo.com/
[4] https://www.google.com/finance
[5] https://www.nseindia.com/

Inspiration

machine learning (NSE stocks)

Tables

Nsecomp 1

@kaggle.ramamet4_nse_company_stocks.nsecomp_1

86.37 MB
5,803,315 rows
9 columns

CREATE TABLE nsecomp_1 (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "code" VARCHAR,
  "date" DOUBLE,
  "time" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" DOUBLE
);

Nsecomp 2

@kaggle.ramamet4_nse_company_stocks.nsecomp_2

80.67 MB
5,803,315 rows
9 columns

CREATE TABLE nsecomp_2 (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "code" VARCHAR,
  "date" BIGINT,
  "time" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nsecomp 3

@kaggle.ramamet4_nse_company_stocks.nsecomp_3

79.96 MB
5,803,315 rows
9 columns

CREATE TABLE nsecomp_3 (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "code" VARCHAR,
  "date" BIGINT,
  "time" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nsecomp 4

@kaggle.ramamet4_nse_company_stocks.nsecomp_4

82.98 MB
5,803,315 rows
9 columns

CREATE TABLE nsecomp_4 (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "code" VARCHAR,
  "date" BIGINT,
  "time" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);

Nsecomp 5

@kaggle.ramamet4_nse_company_stocks.nsecomp_5

83.6 MB
5,803,314 rows
9 columns

CREATE TABLE nsecomp_5 (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "code" VARCHAR,
  "date" BIGINT,
  "time" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT
);