stockpytrendfix.py

import numpy as np
import pandas as pd
import csv
import matplotlib.pyplot as plt
import scipy.stats as scs
from matplotlib import animation
from sklearn import preprocessing
from pytrends.request import TrendReq
import datetime

date_time_str = '2018-06-29'  
date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d')

df = pd.read_csv('googletrend_stock_data.csv', index_col='date', parse_dates=['date'])
price = df.squeeze()
price.head()


df = pd.read_csv('googletrend_stock_data2.csv', index_col='date', parse_dates=['date'])
all_price = df.squeeze()
all_price.head()
# print('Date:',date_time_str[5:7])  
#Login to Google. Only need to run this once, the rest of requests will use the same session.
# pytrend = TrendReq()


# tk = pytrend.get_historical_interest2(['stock'], year_start=2004, month_start=1, day_start=1, year_end=2018, month_end=8, day_end=1, cat=0, geo='', gprop='', sleep=0)
# print(price)
# #tk.drop(['isPartial'], axis=1)
# #del tk['isPartial']
# print (tk.head())
# type(price)


def get_months( test):
    value = ""
    for i in range (0,len(all_price),1):
        if(test == str(all_price.index[i])[:7]+"-01 00:00:00"):
            value = all_price[str(all_price.index[i])[:7]+"-01 00:00:00"]
    return value

plt.figure(figsize=(16,8))
fig, ax = plt.subplots()

#preprocessing.scale(tk['stock'])
ax.plot(price)
plt.show()


# print( str(price.index[0])[:7] )
# print (price [price.index[0]])
s = pd.Series(price)

fuck = []
print (price.index)


for x in price.index:
    print (x)
    get_month = get_months(str(x)[:7]+"-01 00:00:00" )
    
    if(type(price[x]) == type(pd.Series(1))):
        fuck.append( [str(x),(np.float64(price[x][0])*(int(get_month)/100))])
    else:
        fuck.append( ([str(x),(np.float64(price[x])*(int(get_month)/100))]))

    #print (get_month/100)
    #print (price[x]*(get_month/100))

    #print (get_months(str(price.index[str(x)])[:7]+"-01 00:00:00"))

df2 = pd.DataFrame(fuck, columns = ['date', 'price'] )
df2['date'] = pd.to_datetime(df2['date'])
price2 = df2.set_index('date')


plt.figure(figsize=(16,8))

fig, ax = plt.subplots()

#preprocessing.scale(tk['stock'])
ax.plot(price2)
plt.gcf().autofmt_xdate()
plt.show()