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Hi Today i am excited and thought to check if i can read the share market data , so i am going to
demonstrate how to read stock data from Julia program. To do this there are few things we need to
do , so i will navigate to share market website https://www.nseindia.com and try to select one of the
sample stock, that is i am going to select "State Bank Of India" its code is SBIN , and after the data
opens up i will go to historical data of the share or stock market. Here there is a download option . I
will try to download the data of 3 months old , i will try to save the file as
"sbi-share-market-stock-price.csv".
The above file i need to save in a directory from where i can read. So i will try to check my present
working directory, to do this we had seen in our previous tutorial , if you are not aware of it , try to
go to main menu and check out this step. I am storing the "sbi-share-market-stock-price.csv" file
in the present working directory.
After doing this or after saving the data i will try to see if i can read this from Julia Lang Program
using DataFrames
Here we are using a library DataFrames. DataFrames package is available through the Julia package system.
readStockMarketData = readtable("sbi-share-market-stock-price.csv",separator=',')
The next line we are declaring a variable readStockMarketData and try to read the csv file with a
separator , we are using , as the separator here.
When we execute this program i can read the stock market data from the csv file and display on my
screen
Output:
> using DataFrames
readStockMarketData = readtable("sbi-share-market-stock-price.csv",separator=',')
64×10 DataFrame. Omitted printing of 2 columns
Output :
│ Row │ Date │ Symbol │ Series │ Open_Price │ High_Price │ Low_Price │ Last_Traded_Price │ Close_Price │
│ │ String⍰ │ String⍰ │ String⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
├─────┼─────────────┼─────────┼─────────┼────────────┼────────────┼───────────┼───────────────────┼─────────────┤
│ 1 │ 19-Feb-2019 │ SBIN │ EQ │ 260.9 │ 267.5 │ 260.3 │ 262.75 │ 262.9 │
│ 2 │ 18-Feb-2019 │ SBIN │ EQ │ 264.4 │ 265.5 │ 258.8 │ 259.5 │ 259.95 │
│ 3 │ 15-Feb-2019 │ SBIN │ EQ │ 268.85 │ 268.85 │ 261.3 │ 262.8 │ 262.95 │
│ 4 │ 14-Feb-2019 │ SBIN │ EQ │ 268.0 │ 268.35 │ 263.65 │ 267.5 │ 267.1 │
│ 5 │ 13-Feb-2019 │ SBIN │ EQ │ 276.5 │ 276.7 │ 266.7 │ 267.0 │ 268.2 │
│ 6 │ 12-Feb-2019 │ SBIN │ EQ │ 280.65 │ 282.2 │ 274.65 │ 275.1 │ 275.4 │
│ 7 │ 11-Feb-2019 │ SBIN │ EQ │ 285.95 │ 285.95 │ 280.0 │ 280.45 │ 280.5 │
│ 8 │ 08-Feb-2019 │ SBIN │ EQ │ 285.6 │ 288.0 │ 282.2 │ 286.55 │ 286.0 │
│ 9 │ 07-Feb-2019 │ SBIN │ EQ │ 289.4 │ 294.0 │ 286.6 │ 287.0 │ 287.4 │
│ 10 │ 06-Feb-2019 │ SBIN │ EQ │ 285.85 │ 289.75 │ 283.5 │ 289.45 │ 289.05 │
⋮
│ 54 │ 05-Dec-2018 │ SBIN │ EQ │ 282.45 │ 283.2 │ 277.05 │ 280.2 │ 280.15 │
│ 55 │ 04-Dec-2018 │ SBIN │ EQ │ 287.95 │ 287.95 │ 281.9 │ 282.6 │ 282.55 │
│ 56 │ 03-Dec-2018 │ SBIN │ EQ │ 286.7 │ 287.9 │ 283.25 │ 287.1 │ 286.8 │
│ 57 │ 30-Nov-2018 │ SBIN │ EQ │ 288.8 │ 290.9 │ 283.2 │ 284.05 │ 284.65 │
│ 58 │ 29-Nov-2018 │ SBIN │ EQ │ 289.3 │ 289.65 │ 284.25 │ 288.0 │ 286.4 │
│ 59 │ 28-Nov-2018 │ SBIN │ EQ │ 289.4 │ 292.45 │ 283.0 │ 284.0 │ 284.55 │
│ 60 │ 27-Nov-2018 │ SBIN │ EQ │ 284.6 │ 290.5 │ 284.05 │ 289.0 │ 289.4 │
│ 61 │ 26-Nov-2018 │ SBIN │ EQ │ 284.1 │ 287.25 │ 279.6 │ 286.5 │ 286.4 │
│ 62 │ 22-Nov-2018 │ SBIN │ EQ │ 285.95 │ 287.05 │ 281.6 │ 282.65 │ 282.7 │
│ 63 │ 21-Nov-2018 │ SBIN │ EQ │ 283.45 │ 289.55 │ 283.05 │ 286.5 │ 287.05 │
│ 64 │ 20-Nov-2018 │ SBIN │ EQ │ 285.9 │ 288.55 │ 282.5 │ 282.6 │ 283.45 │
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