# -*- coding: utf-8 -*-

"""

Created on Tue Jul 9 20:25:07 2019

@author: Darrell

"""

#%% CAPSTONE 1

#USER DECISIONS

ticker='VOO' # watchlist: CRM, VOO, SPY

hp = 5 # holding period (years)

#%% import libraries

import datetime as dt

import matplotlib.pyplot as plt

from matplotlib import style

from pandas.plotting import register_matplotlib_converters

register_matplotlib_converters()

import numpy as np

import pandas as pd

import pandas_datareader.data as web

from scipy import mean, std

from scipy.stats import skew, kurtosis

style.use('ggplot')

#%% download data

## from web

start = dt.datetime(2000,1,1)#2000,1,1

end = dt.datetime.today()

df = web.DataReader(ticker,'yahoo',start,end)

df_benchmark = web.DataReader('SPY','yahoo',df.index[1],end)

#df.to_csv('TSLA.csv')

## from csv

#df = pd.read_csv('VOO.csv', parse_dates = True, index_col = 0)

#%% moving average

df['20ma'] = df['Adj Close'].rolling(window = 20).mean()

df.dropna(inplace = True)

# historical rate of return

#eR = (df['20ma']['2019'][0]/df['20ma']['2012'][0])**(1/7)

#%% rolling return

returns_cum = df['Adj Close'].pct_change(periods=(252*hp), axis=0)

returns_cum.dropna(inplace = True)

returns_1year = (1.0 + returns_cum)**(1/hp) -1

# benchmark

returns_cum_b = df_benchmark['Adj Close'].pct_change(periods=(252*hp), axis=0)

returns_cum_b.dropna(inplace = True)

returns_1year_b = (1.0 + returns_cum_b)**(1/hp) -1

eR = mean(returns_1year)

sR = std(returns_1year)

eRL = eR - 1.96*sR

eRU = eR + 1.96*sR

#%% figures

#df['Adj Close'].plot()

#plt.show

ax1 = plt.subplot2grid((10,1),(0,0),rowspan=5)

ax2 = plt.subplot2grid((10,1),(6,0),rowspan=5,sharex=ax1)

ax1.plot(df.index, df['Adj Close'])

ax1.plot(df.index, df['20ma'])

ax1.set_ylabel('Price (USD)')

ax1.set_title(ticker)

if ticker=='VOO':

cost_basis=272.91

diff=dt.date.today()-dt.date(2019,7,2)

ndays=diff.days

ax1.axhline(y=cost_basis, color='grey', linestyle='-')

ax1.axvline(x='2019-07-02', color='grey', linestyle='-')

print('Your current earnings gross of fees =',5*(df['Adj Close'][-1]-cost_basis))

print('Your current EAR gross of fees =',

100*((df['Adj Close'][-1]/cost_basis)**(365/ndays)-1),

'%')

#ax2.bar(df.index, df['Volume'])

ax2.plot(returns_1year*100, color = 'b')

ax2.axhline(y=eR*100, color='r', linestyle='-')

ax2.axhline(y=0, color='k', linestyle='-')

# benchmark

ax2.plot(returns_1year_b*100, color = 'grey')

ax2.set_ylabel('Trailing Return (%)')

ax2.set_xlabel('Date')

#ax2.xlabel('Date')

#ax2.legend(['rolling return','mean level'])

#ax2.ylabel('Effective Annual Return')

#plt.figure(0)

plt.show()

#plt.plot(returns_1year, color = 'b')

#plt.axhline(y=eR, color='r', linestyle='-')

#plt.axhline(y=0, color='k', linestyle='-')

#plt.xlabel('Date')

#plt.legend(['rolling return','mean level'])

#plt.ylabel('Effective Annual Return')

#plt.figure(1)