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Forked from lebedov/google_finance_intraday.py
Created August 14, 2018 01:35
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Retrieve intraday stock data from Google Finance.
#!/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'))
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