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

dir_path = os.path.dirname(os.path.realpath(__file__))

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']
close = goog_data_signal['price']
exe_start_time = time.time()

import statistics as stats
import math as math

time_period = 20

history = [] # history of prices
sma_values = [] # to tracking sma
stddev_values = [] # history of computed stdev values

for close_price in close:
    history.append(close_price)
    if len(history) > time_period: # track at most 20 prices
        del(history[0])

    sma = stats.mean(history)
    sma_values.append(sma)

    variance = 0
    for hist_price in history:
        variance = variance + ((hist_price - sma) ** 2)
    
    stdev = math.sqrt(variance / len(history))
    stddev_values.append(stdev)


goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
goog_data = goog_data.assign(StandardDeviationOver20Days=pd.Series(stddev_values, index=goog_data.index))

""" Visualization """

import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='Google price in $')
goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
ax2 = fig.add_subplot(212, ylabel='Stddev in $')
goog_data['StandardDeviationOver20Days'].plot(ax=ax2, color='b', lw=2., legend=True)
plt.savefig(dir_path + '/stdev.png')
plt.show()