Copyright 2016 Ramanathan Perumal. Licensed under the MIT license. ❄️⛄
nse1minR is available through GitHub.
To install the latest version from GitHub:
install.packages("devtools")
devtools::install_github("ramamet/nse1minR")
It contains huge amount of datasets (223 mb / ~ 40,000,000 rows of data!), this process takes a while (around 120secs).
Have patience. All things are difficult before they become easy 📥 ☕ 😇
We'll first load the package, and then see how all other datasources to be used.
library("nse1minR")
The NIFTY 50 index is National Stock Exchange of India's benchmark
stock market index for Indian equity market.
It is a well diversified 50 stock index accounting for 22 sectors
of the economy. It is used for a variety of purposes such as benchmarking
fund portfolios, index based derivatives and index funds.
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).
head(nifty_1min)
index date time open high low close id
1 NIFTY 20130401 09:16 5701.15 5704.65 5694.30 5697.00 2013APRNIFTY.txt
2 NIFTY 20130401 09:17 5697.05 5698.35 5695.65 5697.50 2013APRNIFTY.txt
3 NIFTY 20130401 09:18 5697.90 5697.90 5690.60 5692.15 2013APRNIFTY.txt
4 NIFTY 20130401 09:19 5691.65 5694.70 5691.65 5693.90 2013APRNIFTY.txt
5 NIFTY 20130401 09:20 5694.40 5695.05 5693.35 5694.55 2013APRNIFTY.txt
6 NIFTY 20130401 09:21 5694.10 5700.05 5694.10 5697.20 2013APRNIFTY.txt
Bank Nifty represents the 12 most liquid and large capitalised stocks from the banking sector which trade on the National Stock Exchange (NSE). It provides investors and market intermediaries a benchmark that captures the capital market performance of Indian banking sector.
head(bank_1min)
index date time open high low close id
1 BANKNIFTY 20121203 09:16 12125.70 12161.70 12125.70 12160.95 2012DECBNF.txt
2 BANKNIFTY 20121203 09:17 12161.75 12164.80 12130.40 12130.40 2012DECBNF.txt
3 BANKNIFTY 20121203 09:18 12126.85 12156.10 12126.85 12156.10 2012DECBNF.txt
4 BANKNIFTY 20121203 09:19 12157.25 12164.75 12151.60 12164.20 2012DECBNF.txt
5 BANKNIFTY 20121203 09:20 12162.80 12162.80 12148.20 12151.15 2012DECBNF.txt
6 BANKNIFTY 20121203 09:21 12152.95 12159.90 12151.90 12158.15 2012DECBNF.txt
It contains the 502 company name list and their id for accessing the datasource. Company performance interms of 1 Min chart data can be further loaded from their corresponding file name. A data frame with 3 variables: company, id, and file
head(companyList)
company id data
1 3i Infotech 3IINFOTECH nse_AA
2 8K Miles Software Services 8KMILES nse_AA
3 Aban Offshore Limited ABAN nse_AA
4 Aban Offshore Limited ABAN nse_AA
5 A B B Limited ABB nse_AA
6 Abbott India Limited ABBOTINDIA nse_AA
Choosing individual stocks from other companies can have
advantages over fund allocation for the investors.
Predicting an individual stock price is not a easy task,
while it depends on many external sources such as company performance,
government policy, public expectation and media focus, etc.
Stock price are considered to be
very dynamic and some dependency with technical indicators.
These datasets are aggregated with their alphabetical order (groups).
for example, if you want to search BAJAJCORP, then you need to use nse_BB database.
head(nse_BB)
id date time open high low close volume
1 BAJAJCORP 20150703 09:16:00 436.10 437.85 436.10 436.65 309
2 BAJAJCORP 20150703 09:17:00 437.60 437.60 436.80 437.00 83
3 BAJAJCORP 20150703 09:18:00 436.85 437.40 436.00 437.05 485
4 BAJAJCORP 20150703 09:19:00 437.60 437.65 437.10 437.55 170
5 BAJAJCORP 20150703 09:20:00 436.25 437.90 436.25 437.90 345
6 BAJAJCORP 20150703 09:21:00 437.85 438.70 437.85 438.70 1013
The datasets were captured from the following website using Wget.
http://www.keralabanking.com/intraday-one-minute-historical-data-free-download/
Initial raw data sets are very complex and mixed datatypes.
These are processed properly using R libraries like dplyr,stringr and other data munging packages.
The desired outputs are then converted into a .RData format to use in our package.
If you would like to contribute further on this package or bugs, please mail me by [email protected].