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 = '2001-01-01'
end_date = '2018-01-01'
SRC_DATA_FILENAME = dir_path + '/goog_data_large.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()

""" Calculate mean of each month's return """

goog_monthly_return = goog_data['Adj Close'].pct_change().groupby(
    [goog_data['Adj Close'].index.year, goog_data['Adj Close'].index.month]).mean()
print(goog_monthly_return)

goog_monthly_return_list = []

for i in range(len(goog_monthly_return)):
    goog_monthly_return_list.append({'month':goog_monthly_return.index[i][1],'monthly_return': goog_monthly_return[i]})

goog_monthly_return_list=pd.DataFrame(goog_monthly_return_list,\
    columns=('month','monthly_return'))
print(goog_monthly_return_list)

goog_monthly_return_list.boxplot(column='monthly_return', by='month')

""" Visulization """

import matplotlib.pyplot as plt

ax = plt.gca()
labels = [item.get_text() for item in ax.get_xticklabels()]
print(labels)
labels = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
ax.set_xticklabels(labels)
ax.set_ylabel('GOOG return')
plt.tick_params(axis='both', which='major', labelsize=7)
plt.title("GOOG Monthly return 2001-2018")
plt.suptitle("")
plt.savefig(dir_path + '/seasonality.png')
plt.show()