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# coding: utf-8

# In[1]:

import numpy as np

import matplotlib.pyplot as plt

# from pylab import *

import pandas as pd

from pandas.io.data import DataReader

from datetime import date, timedelta

# import vincent

# vincent.core.initialize_notebook()

# In[32]:

# %matplotlib inline

# In[18]:

end = date.today()

start = end - pd.DateOffset(years=10)

periods = [1,2,3,5,10] # in years

periods = [x*12 for x in periods] # in months

# stock list

L = ['AAPL', 'AMZN', 'ATVI', 'BBBY', 'BRK.B','COST', 'DDD', 'DIS',

'DISCK', 'EPD', 'ETP', 'KMP', 'MMP', 'PAA', 'SBUX', 'VDE', 'VNQ', 'VCIT']

# transaction list

T = []

#set up DataFrames

# daily_close = pd.DataFrame(index=pd.date_range(start, end))

daily_share = pd.DataFrame(index=pd.bdate_range(start, end), columns=L)

daily_value = pd.DataFrame(index=pd.bdate_range(start, end), columns=L)

#monthly_return = pd.DataFrame(index=pd.date_range(start, end))

irr_table = pd.DataFrame(index=periods, columns=L)

# In[19]:

# get daily equity "Adj Close" from start to end

# would like to build a database of SP500 stocks instead

daily_close = DataReader(L, 'yahoo', start, end)['Adj Close']

# In[20]:

print(daily_close.tail(10))

# In[21]:

def add_trans(trans_date, action, shares, ticker, cash_flow):

if action == 'buy':

if daily_share[ticker][trans_date] is np.nan: daily_share[ticker][trans_date] = 0 # for first buy

daily_share[ticker][trans_date:] = daily_share[ticker][trans_date] + shares

elif action == 'sell':

daily_share[ticker][trans_date:] = daily_share[ticker][trans_date] - shares

elif action == 'split':

daily_share[ticker][trans_date:] = daily_share[ticker][trans_date] + shares

else:

print('action not recognized as "buy", "sell" or "split"')

# In[22]:

add_trans(date(2006, 2, 24), 'buy', 195, 'AAPL',0)

add_trans(date(2006, 7, 6), 'buy', 700, 'AAPL',0)

add_trans(date(2007, 6, 8), 'buy', 160, 'AAPL',0)

add_trans(date(2008, 2, 6), 'buy', 150, 'AAPL',0)

add_trans(date(2008, 9, 12), 'buy', 260, 'AAPL',0)

add_trans(date(2010, 10, 18), 'sell', 1020, 'AAPL',0)

add_trans(date(2012, 5, 21), 'buy', 190, 'AAPL',0)

# In[34]:

plt.plot(daily_share['AAPL'])

plt.figure()

# In[27]:

daily_value = daily_close * daily_share

plt.plot(daily_value['AAPL'])

plt.figure()

# In[28]:

monthly_close = daily_close.resample('M')

monthly_return = np.log(monthly_close / monthly_close.shift(1))

avg_yearly_return = (monthly_return.mean()+1)**12-1

plt.scatter(monthly_return.mean(), monthly_return.std())

plt.figure()

print(monthly_return.corr())

# In[10]:

def xirr(transactions):

years = [(ta[0] - transactions[0][0]).days / 365.0 for ta in transactions]

residual = 1

step = 0.05

guess = 0.05

epsilon = 0.00001

limit = 10000

while abs(residual) > epsilon and limit > 0:

limit -= 1

residual = 0.0

for i, ta in enumerate(transactions):

residual += ta[1] / pow(guess, years[i])

if abs(residual) > epsilon:

if residual > 0:

guess += step

else:

guess -= step

step /= 2.0

return guess-1

# In[11]:

months = 60

begin = monthly_close.AMZN[-1-months]

end = monthly_close.AMZN[-1]

irr = ((end-begin)/begin + 1)**(12/months) - 1

print("{:%}".format(irr))

# In[12]:

tas2 = [(monthly_close.index[-1-months], -monthly_close.AMZN[-1-months]),

(monthly_close.index[-1], monthly_close.AMZN[-1])]

print("{:%}".format(xirr(tas2)))

# In[29]:

def build_IRR (periods, monthly_close):

for stock in monthly_close.iteritems():

for period in periods:

begin = stock[1][-1-period]

end = stock[1][-1]

irr = ((end-begin)/begin + 1)**(12/period) - 1

irr_table[stock[0]][period] = irr

return

build_IRR(periods, monthly_close)

print(irr_table)

# In[30]:

# writer = pd.ExcelWriter('output.xlsx')

# daily_close.to_excel(writer, 'daily close')

# monthly_close.to_excel(writer, 'monthly close')

# monthly_return.to_excel(writer, 'monthly return')

# irr_table.to_excel(writer, 'IRR table')

# writer.save()

# In[33]:

# line = vincent.Line(monthly_close)

# line.axis_titles(x='Date', y='Price')

# line.legend(title='Daily Prices')

# line.display()

plt.plot(np.log(monthly_close))

plt.show()