import numpy as np
with open('table.csv') as f:
    lines = f.readlines()

labels = lines[0]
lines = lines[1:]
data = []
for line in lines:
    p = list(map(float,line.split(',')[1:]))
    data.append(p)
data = np.array(data)
adj_close = data[:, data.shape[1]-1]
mean, median, last, start, mid = np.mean(adj_close), np.median(adj_close), adj_close[0], adj_close[-1], adj_close[adj_close.shape[0]//2]
std = np.std(adj_close)
print("adj close mean: {} adj close median: {} adj close first: {} adj close last: {} adj closemid: {}".format(mean,median,start,last,mid))
print("adj closestd: {}".format(std))
print("adj close variance: {}".format(std**2))
open_price = data[:,0]
close_price = data[:,3]
print(close_price[0])
daily_returns_pct = 100*(close_price - open_price)/(open_price)
#print(daily_returns_pct)
daily_returns = close_price - open_price
mean_dr, median_dr, std_dr = np.mean(daily_returns), np.median(daily_returns), np.std(daily_returns)
print("mean daily returns: {} median daily returns: {} std daily returns (volatility): {}".format(mean_dr, median_dr, std_dr))
monthlies = np.array(np.split(daily_returns,24))
mean_monthlies = np.mean(monthlies, axis = 1)
print(mean_monthlies)
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(list(range(1,25)), mean_monthlies)
plt.xlabel('Month, with first month March 20 - April 20, 2015')
plt.ylabel('Average monthly return')
cp = np.array(np.split(close_price, 2))