# Same method as in sma.py, used pandas.DataFrame.rolling for faster calculation

import time
import statistics
import pandas as pd
import numpy as np
from pandas_datareader import data

exe_start_time = time.time()

start_date = '2014-01-01'
end_date = '2018-01-01'
SRC_DATA_FILENAME = 'goog_data.pkl'

try:
    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
    print('File data found...reading GOOG data')
except FileNotFoundError:
    print('File not found...downloading the GOOG data')
    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
    goog_data.to_pickle(SRC_DATA_FILENAME)

goog_data_signal = pd.DataFrame(index=goog_data.index)
goog_data_signal['price'] = goog_data['Adj Close']

import statistics as stats

time_period = 20 # number of days over which to average
history = [] # to track a history of prices
sma_values = [] # to track simple moving average values



goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
goog_data = goog_data.assign(Simple20DayMovinigAverage=pd.Series(result, index=goog_data.index))
print(result)


# for close_price in close:
#     history.append(close_price)
#     if len(history) > time_period: # we remove oldest price because we only average over last 'time_period' prices
#         del(history[0])
#     sma_values.append(stats.mean(history))

# goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
# goog_data = goog_data.assign(Simple20DayMovingAverage=pd.Series(sma_values, index=goog_data.index))
# close_price = goog_data['ClosePrice']
# sma = goog_data['Simple20DayMovingAverage']

# print("--- %s seconds ---" % (time.time() - exe_start_time))

# import matplotlib.pyplot as plt

# fig = plt.figure()
# ax1 = fig.add_subplot(111, ylabel='Google price in $')
# close_price.plot(ax=ax1, color='g', lw=2., legend=True)
# sma.plot(ax=ax1, color='r', lw=2., legend=True)
# plt.savefig('sma.png')