# -*- coding: utf-8 -*-
"""
AKdf create
@author: Dazhuang
"""
# -*- coding: utf-8 -*-
"""
Count days
@author: Dazhuang
"""
import requests
import re
import json
import pandas as pd
from datetime import date
import time
from pylab import *
from scipy.cluster.vq import *
def retrieve_quotes_historical(stock_code):
quotes = []
url = 'https://finance.yahoo.com/quote/%s/history?p=%s' % (stock_code, stock_code)
r = requests.get(url)
m = re.findall('"HistoricalPriceStore":{"prices":(.*?),"isPending"', r.text)
if m:
quotes = json.loads(m[0])
quotes = quotes[::-1]
return [item for item in quotes if not 'type' in item]
def create_df(stock_code):
quotes = retrieve_quotes_historical(stock_code)
list1 = []
for i in range(len(quotes)):
x = date.fromtimestamp(quotes[i]['date'])
y = date.strftime(x,'%Y-%m-%d')
list1.append(y)
quotesdf_ori = pd.DataFrame(quotes, index = list1)
listtemp = []
for i in range(len(quotesdf_ori)):
temp = time.strptime(quotesdf_ori.index[i],"%Y-%m-%d")
listtemp.append(temp.tm_mon)
tempdf = quotesdf_ori.copy()
tempdf['month'] = listtemp
totalclose = tempdf.groupby('month').close.mean()
df_totalclose = pd.DataFrame(totalclose)
df_totalclose['code'] = stock_code
return df_totalclose
dfAXP_totalclose = create_df('AXP')
dfKO_totalclose = create_df('KO')
AKdf = dfAXP_totalclose.append(dfKO_totalclose)
AKdf['month'] = AKdf.index
