6 min read

Introducing nse2r

2020/01/17

We are excited to announce the nse2r package. NSE (National Stock Exchange) is the leading stock exchange of India, located in the city of Mumbai. While users can manually download data from NSE through a browser, importing this data into R becomes cumbersome. The nse2r R package implements the retrieval of data from NSE and aims to reduce the pre-processing steps needed in analyzing such data.

nse2r is inspired by and a port of the Python package nsetools. The authors and contributors for this R package are not affiliated with NSE and NSE does not offer support for this R package.

With nse2r, you can fetch the following data related to:

  • stocks
    • quote for a given stock
    • stock description
    • validate stock symbol/ticker
    • most actively traded stocks in a month
    • 52 week high/low
    • top gainers/losers for the last trading session
  • index
    • list of NSE indices
    • validate index symbol/ticker
    • quote for a given index
  • futures & options
    • top gainers/losers for the last trading session
  • pre open market data
    • nifty
    • nifty bank
  • indices advances & declines

Installation

# Install release version from CRAN
install.packages("nse2r")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/nse2r")

Usage

nse2r uses consistent prefix nse_ for easy tab completion.

  • nse_index_ for index
  • nse_stock_ for stocks
  • nse_fo_ for futures and options
  • nse_preopen_ for preopen data

Preprocessing

nse2r does basic data preprocessing which are listed below:

  • modify column data types from character to numeric and Date
  • modify column names
    • make them more descriptive
    • to snake_case from camelCase

Users can retain the names and format as returned by NSE using the clean_names argument and setting it to FALSE.

Quick Overview

Fetch Indices Quote

nse_index_quote()
## # A tibble: 55 x 4
##    index_name        last_traded_price  change percent_change
##    <chr>                         <dbl>   <dbl>          <dbl>
##  1 NIFTY 50 Pre Open           12195.   -29.2           -0.24
##  2 NIFTY 50                    12170.   -54.7           -0.45
##  3 NIFTY NEXT 50               28617.  -137.            -0.48
##  4 NIFTY100 LIQ 15              3700.   -32.4           -0.87
##  5 NIFTY BANK                  30948.  -133.            -0.43
##  6 INDIA VIX                      15.8    0.44           2.85
##  7 NIFTY 100                   12287.   -56.0           -0.45
##  8 NIFTY 500                    9988.   -38.2           -0.38
##  9 NIFTY MIDCAP 100            17952.   -20.7           -0.12
## 10 NIFTY MIDCAP 50              4941.    -4.25          -0.09
## # ... with 45 more rows
# retain original column names as returned by NSE
nse_index_quote(clean_names = FALSE)
## # A tibble: 55 x 4
##    name              lastPrice  change pChange
##    <chr>                 <dbl>   <dbl>   <dbl>
##  1 NIFTY 50 Pre Open   12195.   -29.2    -0.24
##  2 NIFTY 50            12170.   -54.7    -0.45
##  3 NIFTY NEXT 50       28617.  -137.     -0.48
##  4 NIFTY100 LIQ 15      3700.   -32.4    -0.87
##  5 NIFTY BANK          30948.  -133.     -0.43
##  6 INDIA VIX              15.8    0.44    2.85
##  7 NIFTY 100           12287.   -56.0    -0.45
##  8 NIFTY 500            9988.   -38.2    -0.38
##  9 NIFTY MIDCAP 100    17952.   -20.7    -0.12
## 10 NIFTY MIDCAP 50      4941.    -4.25   -0.09
## # ... with 45 more rows

Top gainers for the last trading session.

nse_stock_top_gainers()
## # A tibble: 10 x 12
##    symbol series last_corp_annou~ last_corp_annou~ open_price high_price
##    <chr>  <chr>  <date>           <chr>                 <dbl>      <dbl>
##  1 INFRA~ EQ     2019-12-19       Interim Dividen~       224.       244.
##  2 ZEEL   EQ     2019-07-15       Annual General ~       269        287.
##  3 BPCL   EQ     2019-08-21       Dividend - Rs 8~       458.       466.
##  4 COALI~ EQ     2019-08-09       Annual General ~       201.       205.
##  5 KOTAK~ EQ     2019-07-12       Annual General ~      1605       1631.
##  6 HDFC   EQ     2019-07-19       Annual General ~      2443       2486.
##  7 ULTRA~ EQ     2019-07-10       Annual General ~      4455       4520 
##  8 BRITA~ EQ     2019-08-22       Scheme Of Arang~      3100       3130 
##  9 RELIA~ EQ     2019-08-02       Annual General ~      1529.      1546.
## 10 TECHM  EQ     2019-07-25       Annual General ~       777.       783.
## # ... with 6 more variables: low_price <dbl>, last_traded_price <dbl>,
## #   prev_close_price <dbl>, percent_change <dbl>, traded_quantity <dbl>,
## #   turnover_in_lakhs <dbl>
# retain original column names as returned by NSE
nse_stock_top_gainers(clean_names = FALSE)
## # A tibble: 10 x 12
##    symbol series lastCorpAnnounc~ lastCorpAnnounc~ openPrice highPrice
##    <chr>  <chr>  <date>           <chr>                <dbl>     <dbl>
##  1 INFRA~ EQ     2019-12-19       Interim Dividen~      224.      244.
##  2 ZEEL   EQ     2019-07-15       Annual General ~      269       287.
##  3 BPCL   EQ     2019-08-21       Dividend - Rs 8~      458.      466.
##  4 COALI~ EQ     2019-08-09       Annual General ~      201.      205.
##  5 KOTAK~ EQ     2019-07-12       Annual General ~     1605      1631.
##  6 HDFC   EQ     2019-07-19       Annual General ~     2443      2486.
##  7 ULTRA~ EQ     2019-07-10       Annual General ~     4455      4520 
##  8 BRITA~ EQ     2019-08-22       Scheme Of Arang~     3100      3130 
##  9 RELIA~ EQ     2019-08-02       Annual General ~     1529.     1546.
## 10 TECHM  EQ     2019-07-25       Annual General ~      777.      783.
## # ... with 6 more variables: lowPrice <dbl>, ltp <dbl>,
## #   previousPrice <dbl>, netPrice <dbl>, tradedQuantity <dbl>,
## #   turnoverInLakhs <dbl>

Stocks that have touched their 52 week highs during the day

nse_stock_year_high()
## # A tibble: 44 x 10
##    symbol symbol_desc date       new_high  year last_traded_pri~ prev_high
##    <chr>  <chr>       <date>        <dbl> <dbl>            <dbl>     <dbl>
##  1 AGCNET AGC Networ~ 2020-01-20     223.  223.             223.      212.
##  2 ALKYL~ Alkyl Amin~ 2020-01-17    1300  1300             1295      1300 
##  3 APLAP~ APL Apollo~ 2020-01-15    2022. 2022.            1993.     1977.
##  4 AUBANK AU Small F~ 2020-01-20     892   892              890.      892 
##  5 BANSW~ Banswara S~ 2020-01-20     123   123              118.      123 
##  6 BHART~ Bharti Air~ 2020-01-20     515   515              508.      513.
##  7 CANFI~ Can Fin Ho~ 2019-11-05     467.  467.             467.      434.
##  8 CDSL   Central De~ 2020-01-20     284.  284.             281.      282 
##  9 FINEO~ Fine Organ~ 2020-01-20    2266  2266             2200      2238.
## 10 FORCE~ FORCE MOTO~ 2020-01-20    1504  1504             1470.     1487 
## # ... with 34 more rows, and 3 more variables: prev_close <dbl>,
## #   change <dbl>, percent_change <dbl>
# retain original column names as returned by NSE
nse_stock_year_high(clean_names = FALSE)
## # A tibble: 44 x 10
##    symbol symbolDesc dt         value  year   ltp value_old  prev change
##    <chr>  <chr>      <date>     <dbl> <dbl> <dbl>     <dbl> <dbl>  <dbl>
##  1 AGCNET AGC Netwo~ 2020-01-20  223.  223.  223.      212.  212.  10.6 
##  2 ALKYL~ Alkyl Ami~ 2020-01-17 1300  1300  1295      1300  1272.  22.5 
##  3 APLAP~ APL Apoll~ 2020-01-15 2022. 2022. 1993.     1977. 1943.  50   
##  4 AUBANK AU Small ~ 2020-01-20  892   892   890.      892   886.   4.05
##  5 BANSW~ Banswara ~ 2020-01-20  123   123   118.      123   121.  -3.2 
##  6 BHART~ Bharti Ai~ 2020-01-20  515   515   508.      513.  509.  -0.35
##  7 CANFI~ Can Fin H~ 2019-11-05  467.  467.  467.      434.  389.  77.8 
##  8 CDSL   Central D~ 2020-01-20  284.  284.  281.      282   273.   7.85
##  9 FINEO~ Fine Orga~ 2020-01-20 2266  2266  2200      2238. 2220. -20.3 
## 10 FORCE~ FORCE MOT~ 2020-01-20 1504  1504  1470.     1487  1466.   4.2 
## # ... with 34 more rows, and 1 more variable: pChange <dbl>

Learning More

Feedback

All feedback is welcome. Issues (bugs and feature requests) can be posted to github tracker. For help with code or other related questions, feel free to reach out to us at .