Retrieve intraday stock data from Google Finance.

google_finance_intraday.py

#!/usr/bin/env python 
"""
Retrieve intraday stock data from Google Finance.
"""

import csv
import datetime
import re

import pandas as pd
import requests

def get_google_finance_intraday(ticker, period=60, days=1):
    """
    Retrieve intraday stock data from Google Finance.

    Parameters
    ----------
    ticker : str
        Company ticker symbol.
    period : int
        Interval between stock values in seconds.
    days : int
        Number of days of data to retrieve.

    Returns
    -------
    df : pandas.DataFrame
        DataFrame containing the opening price, high price, low price,
        closing price, and volume. The index contains the times associated with
        the retrieved price values.
    """

    uri = 'http://www.google.com/finance/getprices' \
          '?i={period}&p={days}d&f=d,o,h,l,c,v&df=cpct&q={ticker}'.format(ticker=ticker,
                                                                          period=period,
                                                                          days=days)
    page = requests.get(uri)
    reader = csv.reader(page.content.splitlines())
    columns = ['Open', 'High', 'Low', 'Close', 'Volume']
    rows = []
    times = []
    for row in reader:
        if re.match('^[a\d]', row[0]):
            if row[0].startswith('a'):
                start = datetime.datetime.fromtimestamp(int(row[0][1:]))
                times.append(start)
            else:
                times.append(start+datetime.timedelta(seconds=period*int(row[0])))
            rows.append(map(float, row[1:]))
    if len(rows):
        return pd.DataFrame(rows, index=pd.DatetimeIndex(times, name='Date'),
                            columns=columns)
    else:
        return pd.DataFrame(rows, index=pd.DatetimeIndex(times, name='Date'))

import requests
import pandas as pd
import arrow
import datetime

def get_quote_data(symbol='SBIN.NS', data_range='1d', data_interval='1m'):
    res = requests.get('https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?range={data_range}&interval={data_interval}'.format(**locals()))
    data = res.json()
    body = data['chart']['result'][0]    
    dt = datetime.datetime
    dt = pd.Series(map(lambda x: arrow.get(x).to('Asia/Calcutta').datetime.replace(tzinfo=None), body['timestamp']), name='Datetime')
    df = pd.DataFrame(body['indicators']['quote'][0], index=dt)
    dg = pd.DataFrame(body['timestamp'])    
    df = df.loc[:, ('open', 'high', 'low', 'close', 'volume')]
    df.dropna(inplace=True)     #removing NaN rows
    df.columns = ['OPEN', 'HIGH','LOW','CLOSE','VOLUME']    #Renaming columns in pandas
    
    return df

data = get_quote_data('KPIT.NS', '1d', '1m')
print(data)

Works great, but is there a way to save this data to a CSV file?

import requests
import pandas as pd
import arrow
import datetime

def get_quote_data(symbol='SBIN.NS', data_range='1d', data_interval='1m'):
    res = requests.get('https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?range={data_range}&interval={data_interval}'.format(**locals()))
    data = res.json()
    body = data['chart']['result'][0]    
    dt = datetime.datetime
    dt = pd.Series(map(lambda x: arrow.get(x).to('Asia/Calcutta').datetime.replace(tzinfo=None), body['timestamp']), name='Datetime')
    df = pd.DataFrame(body['indicators']['quote'][0], index=dt)
    dg = pd.DataFrame(body['timestamp'])    
    df = df.loc[:, ('open', 'high', 'low', 'close', 'volume')]
    df.dropna(inplace=True)     #removing NaN rows
    df.columns = ['OPEN', 'HIGH','LOW','CLOSE','VOLUME']    #Renaming columns in pandas
    
    return df

data = get_quote_data('KPIT.NS', '1d', '1m')
print(data)

Works great, but is there a way to save this data to a CSV file?

data.to_csv('output.csv')

To sum up this string..
Step 1: Install python: https://www.python.org/downloads/
Step 2: Install panda: pip install panda
Step 3: Save this code:
'
import requests
import pandas as pd
import arrow
import datetime

def get_quote_data(symbol='SBIN.NS', data_range='1d', data_interval='1m'):
res = requests.get('https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?range={data_range}&interval={data_interval}'.format(**locals()))
data = res.json()
body = data['chart']['result'][0]
dt = datetime.datetime
dt = pd.Series(map(lambda x: arrow.get(x).to('Asia/Calcutta').datetime.replace(tzinfo=None), body['timestamp']), name='Datetime')
df = pd.DataFrame(body['indicators']['quote'][0], index=dt)
dg = pd.DataFrame(body['timestamp'])

return df.loc[:, ('open', 'high', 'low', 'close', 'volume')]

data = get_quote_data('SBIN.NS', '5d', '1m')
data.dropna(inplace=True) #removing NaN rows
print(data)
data.to_csv('output.csv')
'
Step 4: Drag the saved python file to CMD and an output CSV will be saved.

what is the symbol for NIFTY50 data

what is the symbol for NIFTY50 data

Yahoo finance symbol for nifty 50 = '^NSEI'

import requests
import pandas as pd
import arrow
import datetime

def get_quote_data(symbol , data_range='1d', data_interval='1m'):
res = requests.get('https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?range={data_range}&interval={data_interval}'.format(**locals()))
data = res.json()
body = data['chart']['result'][0]
dt = datetime.datetime
dt = pd.Series(map(lambda x: arrow.get(x).to('Asia/Calcutta').datetime.replace(tzinfo=None), body['timestamp']), name='Datetime')
df = pd.DataFrame(body['indicators']['quote'][0], index=dt)
dg = pd.DataFrame(body['timestamp'])
df = df.loc[:, ('open', 'high', 'low', 'close', 'volume')]
df.dropna(inplace=True)
df.columns = ['OPEN', 'HIGH','LOW','CLOSE','VOLUME']

return df

for s in symbol:
data = get_quote_data(s,'1d','1m')
data.to_csv(s.strip(".NS")+'.csv')

this is for multiple stock symbols