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pullreturns.py
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pullreturns.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jul 21 14:40:39 2015
@author: justin.malinchak
"""
import datetime
import pandas as pd
import numpy as np
def validate_date(date_text):
try:
datetime.datetime.strptime(date_text, '%Y-%m-%d')
return True
except ValueError:
#raise ValueError("Incorrect data format, should be YYYY-MM-DD")
return False
def is_number(s):
try:
if np.isnan(s) == True:
return False
float(s)
return True
except ValueError:
return False
def test_builddataframe():
import pandas as pd
import numpy as np
df = pd.DataFrame({'a':np.random.randn(5),
'b':np.random.randn(5),
'c':np.random.randn(5),
'd':np.random.randn(5)})
cols_to_keep = ['a', 'c', 'd']
dummies = ['d']
not_dummies = [x for x in cols_to_keep if x not in dummies]
data = df[not_dummies]
print(data)
class perform:
def set_SymbolsList(self,SymbolsList):
self._SymbolsList = SymbolsList
def get_SymbolsList(self):
return self._SymbolsList
SymbolsList = property(get_SymbolsList, set_SymbolsList)
def set_StartDateString(self,StartDateString):
self._StartDateString = StartDateString
def get_StartDateString(self):
return self._StartDateString
StartDateString = property(get_StartDateString, set_StartDateString)
def set_EndDateString(self,EndDateString):
self._EndDateString = EndDateString
def get_EndDateString(self):
return self._EndDateString
EndDateString = property(get_EndDateString, set_EndDateString)
def set_HistoryOfAdjClosePricesDataframe(self,HistoryOfAdjClosePricesDataframe):
self._HistoryOfAdjClosePricesDataframe = HistoryOfAdjClosePricesDataframe
def get_HistoryOfAdjClosePricesDataframe(self):
return self._HistoryOfAdjClosePricesDataframe
HistoryOfAdjClosePricesDataframe = property(get_HistoryOfAdjClosePricesDataframe, set_HistoryOfAdjClosePricesDataframe)
def set_HistoryOfClosePricesDataframe(self,HistoryOfClosePricesDataframe):
self._HistoryOfClosePricesDataframe = HistoryOfClosePricesDataframe
def get_HistoryOfClosePricesDataframe(self):
return self._HistoryOfClosePricesDataframe
HistoryOfClosePricesDataframe = property(get_HistoryOfClosePricesDataframe, set_HistoryOfClosePricesDataframe)
def set_TotalReturnsDataframe(self,TotalReturnsDataframe):
self._TotalReturnsDataframe = TotalReturnsDataframe
def get_TotalReturnsDataframe(self):
return self._TotalReturnsDataframe
TotalReturnsDataframe = property(get_TotalReturnsDataframe, set_TotalReturnsDataframe)
def set_PriceChangeReturnsDataframe(self,PriceChangeReturnsDataframe):
self._PriceChangeReturnsDataframe = PriceChangeReturnsDataframe
def get_PriceChangeReturnsDataframe(self):
return self._PriceChangeReturnsDataframe
PriceChangeReturnsDataframe = property(get_PriceChangeReturnsDataframe, set_PriceChangeReturnsDataframe)
##
## def set_TotalReturnsDataframeTimes100(self,TotalReturnsDataframeTimes100):
## self._TotalReturnsDataframeTimes100 = TotalReturnsDataframeTimes100
## def get_TotalReturnsDataframeTimes100(self):
## return self._TotalReturnsDataframeTimes100
## TotalReturnsDataframeTimes100 = property(get_TotalReturnsDataframeTimes100, set_TotalReturnsDataframeTimes100)
def set_AggregatedTotalReturnsDataframe(self,AggregatedTotalReturnsDataframe):
self._AggregatedTotalReturnsDataframe = AggregatedTotalReturnsDataframe
def get_AggregatedTotalReturnsDataframe(self):
return self._AggregatedTotalReturnsDataframe
AggregatedTotalReturnsDataframe = property(get_AggregatedTotalReturnsDataframe, set_AggregatedTotalReturnsDataframe)
def set_AggregatedPriceChangeReturnsDataframe(self,AggregatedPriceChangeReturnsDataframe):
self._AggregatedPriceChangeReturnsDataframe = AggregatedPriceChangeReturnsDataframe
def get_AggregatedPriceChangeReturnsDataframe(self):
return self._AggregatedPriceChangeReturnsDataframe
AggregatedPriceChangeReturnsDataframe = property(get_AggregatedPriceChangeReturnsDataframe, set_AggregatedPriceChangeReturnsDataframe)
def __init__(self
, symbols
, startdate = '2004-12-31'
, enddate = '2005-12-31'
):
print('Initialized class pullreturns.perform')
self.SymbolsList = symbols
self.StartDateString = startdate
self.EndDateString = enddate
import datetime
import numpy as np
yesterday_date = datetime.date.fromordinal(datetime.date.today().toordinal()-1)
import pullprices as pp1
df_good,df_missing = pp1.pull().stockhistoryasdataframe(symbols,startdate,enddate)
print df_good
list_of_good_symbols = np.unique(df_good[['Ticker']])
print 'count of symbols returned from pullprices', len(list_of_good_symbols)
print 'list_of_good_symbols', list_of_good_symbols
if len(df_missing) > 0:
list_of_missing_symbols = np.unique(df_missing[['Ticker']])
print 'list_of_missing_symbols', list_of_missing_symbols
self.SymbolsList = list_of_good_symbols
df_pivotadjclose = df_good.pivot(index='Date', columns='Ticker', values='Adj Close')
df_pivotclose = df_good.pivot(index='Date', columns='Ticker', values='Close')
#print df_pivotadjclose
self.HistoryOfAdjClosePricesDataframe = df_pivotadjclose
print '--- self.HistoryOfAdjClosePricesDataframe ----'
print self.HistoryOfAdjClosePricesDataframe
self.HistoryOfClosePricesDataframe = df_pivotclose
self.TotalReturnsDataframe = self.dailyreturns('Total')
self.PriceChangeReturnsDataframe = self.dailyreturns('PriceChange')
#self.LogDailyPriceChangeReturnsDataframe = self.dailystackedreturns(totalorpricechange='PriceChange',logorarithmetic='log')
self.AggregatedTotalReturnsDataframe = self.aggregatedreturns('Total')
self.AggregatedPriceChangeReturnsDataframe = self.aggregatedreturns('PriceChange')
## def calc_beta_test02():
## df = self.AggregatedTotalReturnsDataframe
## df['mean'] = df.mean(axis=1)
## np_array = df.values
## print np_array
## m = df['mean'] # market returns are column zero from numpy array
## for s in self.SymbolsList:
## x = df[s] # stock returns are column one from numpy array
## covariance = np.cov(x,m) # Calculate covariance between stock and market
## beta = covariance[0,1]/covariance[1,1]
## return beta
##
##
## def calc_beta_test01(df):
## np_array = df.values
## m = np_array[:,0] # market returns are column zero from numpy array
## s = np_array[:,1] # stock returns are column one from numpy array
## covariance = np.cov(s,m) # Calculate covariance between stock and market
## beta = covariance[0,1]/covariance[1,1]
## return beta
def dailystackedreturns(self,totalorpricechange='PriceChange',logorarithmetic='log'):
symbols = self.SymbolsList
import datetime
today_date = datetime.date.today()
#import pullpricesusingpandas as pp
if totalorpricechange == 'PriceChange':
df_00 = self.HistoryOfClosePricesDataframe
else:
df_00 = self.HistoryOfAdjClosePricesDataframe
print '--- df_00 --- pullreturns.dailyreturns()'
print df_00
df_01 = pd.DataFrame(index=df_00.index.copy())
for s in self.SymbolsList:
if not logorarithmetic == 'log':
df_01[s] = df_00[s].pct_change()
else:
df_01[s] = np.log(1.0 + df_00[s].pct_change())
#print df_01
return df_01
#df['pct_change'] = df.price.pct_change()
#df['log_return'] = np.log(1 + df.pct_change)
def dailyreturns(self,totalorpricechange='PriceChange'):
symbols = self.SymbolsList
import datetime
today_date = datetime.date.today()
#import pullpricesusingpandas as pp
if totalorpricechange == 'PriceChange':
df_00 = self.HistoryOfClosePricesDataframe
else:
df_00 = self.HistoryOfAdjClosePricesDataframe
print '--- df_00 --- pullreturns.dailyreturns()'
print df_00
import pandas as pd
dates1 = pd.date_range('1910-01-01', str(today_date), freq='D')
dummy_date = datetime.datetime.strptime("1801-01-01", "%Y-%m-%d")
prev_date = dummy_date
prev_value = float('Nan')
rows_dailyreturns = []
rows_dailyreturnstimes100 = []
rows_dailyreturns.append(['prev_date','curr_date','ticker','prev_value','curr_value','change_pct'])
rows_dailyreturnstimes100.append(['prev_date','curr_date','ticker','prev_value','curr_value','change_pct'])
d_of_prevs = {}
prevclose = []
for dt in dates1:
if str(dt.date()) in df_00.index:
myobj = df_00.loc[str(dt.date())]
curr_date = dt.date()
for s in symbols:
if s in d_of_prevs:
if len(d_of_prevs[s]) > 0:
t,c,d = d_of_prevs[s]
#print t,c,d
if is_number(myobj[s]):
curr_value = myobj[s]
prev_value = c
if is_number(curr_value) and is_number(prev_value):
change_pct = (float(curr_value) - float(prev_value))/float(prev_value)
else:
change_pct = float('NaN')
#print str(d),str(curr_date), s, prev_value, curr_value, change_pct
rows_dailyreturns.append([str(d),str(curr_date), t, prev_value, curr_value, change_pct])
rows_dailyreturnstimes100.append([str(d),str(curr_date), t, prev_value, curr_value, change_pct * 100.0])
for s in symbols:
#print 'xxx',myobj[s]
if not s in d_of_prevs:
d_of_prevs[s] = []
if is_number(myobj[s]):
d_of_prevs[s] = [s,myobj[s],curr_date]
headers = rows_dailyreturns.pop(0)
df_dailyreturns = pd.DataFrame(rows_dailyreturns, columns = headers)
headers = rows_dailyreturnstimes100.pop(0)
df_dailyreturnstimes100 = pd.DataFrame(rows_dailyreturnstimes100, columns = headers)
return df_dailyreturns #,df_dailyreturnstimes100
## def aggregatedreturns(self):
## ar = self._aggregateddailyreturns()
##
## #if uselogreturns == False:
## # ar = self._annualizeddailyreturn()
## #else:
## # ar = self._annualizeddailyreturnusinglogreturns()
## return ar
def aggregatedreturns(self,totalorpricechange='PriceChange'):
import numpy as np
import random
import datetime
ls_final = []
ls_final.append(['symbol','start_date','end_date','annualized_return', 'cumulative_return','random_return','start_price','end_price','stdev','stdev_np','mean_np'])
i = 0
for s in self.SymbolsList:
i += 1
print 'Doing aggregated return for',totalorpricechange,i,s
if totalorpricechange == 'PriceChange':
dfr = self.PriceChangeReturnsDataframe.copy()
else:
dfr = self.TotalReturnsDataframe.copy()
#333333
#print '----- dfr ------', 'pullreturns'
#print dfr
df_dailyreturns = dfr[(dfr.ticker == s)]
df_dailyreturns = df_dailyreturns.dropna()
df_dailyreturns.sort_index(inplace = True)
#print '----- df_dailyreturns ------', 'pullreturns'
#print df_dailyreturns
# =PRODUCT(F85:F155)^(1/($B85/12))-1
# [(1.13)*(0.98)*(1.15)*(1.08)]^(12/42)-1
#df_dailyreturns = self.dailyreturns()
#df_dailyreturns = df_dailyreturns.dropna()
first_valid_index = df_dailyreturns.first_valid_index()
#print 'first_valid_index', first_valid_index
firstdate = df_dailyreturns.loc[first_valid_index]['curr_date']
start_price = df_dailyreturns.loc[first_valid_index]['curr_value']
last_valid_index = df_dailyreturns.last_valid_index()
#print 'last_valid_index',last_valid_index
lastdate = df_dailyreturns.loc[last_valid_index]['curr_date']
end_price = df_dailyreturns.loc[last_valid_index]['curr_value']
#df_dailyreturns.loc[df_dailyreturns.index,'change_pct']= df_dailyreturns.loc[df_dailyreturns.index, 'change_pct'] + float(1.0)
stdev = float(df_dailyreturns.loc[df_dailyreturns.index, 'change_pct'].std())
stdev_np = np.std(df_dailyreturns.loc[df_dailyreturns.index, 'change_pct'], ddof=1)
mean_np = np.mean(df_dailyreturns.loc[df_dailyreturns.index, 'change_pct'])
#print 'stdev', s,std_float
df_dailyreturns.loc[df_dailyreturns.index,'change_pct_unitized'] = df_dailyreturns.loc[df_dailyreturns.index, 'change_pct'] + float(1.0)
ls_dailyreturns = df_dailyreturns['change_pct_unitized'].values.tolist()
#print 'lastdate',lastdate
#print '------------------- df_dailyreturns ----------------------'
#print df_dailyreturns
listmultiplied = reduce(lambda x, y: x*y, ls_dailyreturns)
#print 'listmultiplied',listmultiplied
#years between
time1 = datetime.datetime.strptime(str(firstdate) + ' 00:00:00.00', "%Y-%m-%d %H:%M:%S.%f")
time2 = datetime.datetime.strptime(str(lastdate) + ' 23:59:59.999999', "%Y-%m-%d %H:%M:%S.%f") #datetime.datetime.now()
#print 'times',time1,time2
elapsedTime = time2 - time1
yrs = float(divmod(elapsedTime.total_seconds(), 60.0)[0]/60.0/24.0/365.0)
#print 'years',yrs
adr = listmultiplied ** (float(1)/(yrs)) - 1.0
df_dailyreturns.loc[df_dailyreturns.index,'cumulative_return'] = (1 + df_dailyreturns.change_pct).cumprod() - 1
#print df_dailyreturns
cumr = df_dailyreturns.loc[last_valid_index]['cumulative_return']
randret = random.randint(0, 200) # 0 or 1(both incl.)
randr = adr * randret/100.0
ls_final.append([s,firstdate,lastdate,adr,cumr,randr,start_price,end_price,stdev,stdev_np,mean_np])
headers = ls_final.pop(0)
import pandas as pd
df_final = pd.DataFrame(ls_final, columns = headers)
print 'pullreturns - finished def aggregatedreturns with ', len(df_final)
return df_final
## def _annualizeddailyreturnusinglogreturns_old(self,):
## # =PRODUCT(F85:F155)^(1/($B85/12))-1
## # [(1.13)*(0.98)*(1.15)*(1.08)]^(12/42)-1
## df_dailyreturns = self.dailyreturns()
## df_dailyreturns = df_dailyreturns.dropna()
##
## df_dailyreturns.sort_index(inplace = True)
## firstdate = df_dailyreturns.loc[0]['b_periodend']
## #print firstdate
## lastdate = df_dailyreturns.loc[len(df_dailyreturns)-1]['b_periodend']
## #print lastdate
## #print df_dailyreturns
## df_dailyreturns.loc[df_dailyreturns.index,'e_logreturnunitized']= df_dailyreturns.loc[df_dailyreturns.index, 'e_logreturn'] + float(1.0)
## ls_dailyreturns = df_dailyreturns['e_logreturnunitized'].values.tolist()
## listmultiplied = reduce(lambda x, y: x*y, ls_dailyreturns)
## #print listmultiplied
##
## #years between
## import datetime
## time1 = datetime.datetime.strptime(str(firstdate) + ' 16:00', "%Y-%m-%d %H:%M")
## time2 = datetime.datetime.strptime(str(lastdate) + ' 16:00', "%Y-%m-%d %H:%M")#datetime.datetime.now()
## elapsedTime = time2 - time1
## yrs = float(divmod(elapsedTime.total_seconds(), 60.0)[0]/60.0/24.0/365.0)
## #print 'yrs',yrs
##
## adr = listmultiplied ** (float(1)/(yrs)) - 1.0
## return adr
if __name__=='__main__':
#symbols = ['FB', 'MSFT', 'SPY', 'IBM', 'T', 'AMD','INTC','ACN', 'VZ', 'ORCL','DIS','BA','AMGN','MCD','CELG','LLY','COST','BIIB','MDLZ','TJX']
#symbols = ['FB', 'MSFT', 'SPY', 'IBM']
## symbols = ['GOOGL',
## 'FB',
## 'MSFT',
## 'LRCX',
## 'EVR',
## 'MASI',
## 'CELG',
## 'AOS',
## 'LPX',
## 'MRK',
## 'EVR',
## 'JNJ',
## 'INTC',
## 'GOLD',
## 'LMT',
## 'RTN',
## 'BP',
## 'T',
## 'HSBC',
## 'THO'
## ]
#symbols = ['LAZ', 'LMT', 'RTN', 'MAS', 'AMAT', 'INTC', 'LPX', 'GRMN', 'PCLN', 'KSS', 'JWN', 'M', 'GPS', 'LOW', 'PEP', 'CVS', 'CL', 'KMB', 'MO', 'PM', 'CVX', 'BAC', 'BEN', 'MS', 'AXP', 'CELG', 'AMGN', 'JNJ', 'LLY', 'MMM', 'UNP', 'CSCO', 'SWKS', 'CA', 'STX', 'LYB', 'APD', 'T', 'TGT', 'HD', 'ETR', 'AES', 'HOG', 'F', 'GPC', 'LEG', 'WHR', 'NWL', 'TRIP', 'HAS', 'BC', 'CMCSA', 'DIS', 'VIA', 'DISH', 'NWS', 'PAG', 'CRI', 'COLM', 'SKX', 'NKE', 'TAP', 'CASY', 'HRL', 'HAIN', 'SJM', 'ADM', 'KHC', 'MDLZ', 'FTI', 'SLB', 'NFX', 'KMI', 'CXO', 'MUR', 'WPX', 'EGN', 'XOM', 'LNG', 'FCNCA', 'LUK', 'Y', 'WTM', 'AXS', 'ALKS', 'MDT', 'XRAY', 'CAH', 'MD', 'PDCO', 'UHS', 'AGN', 'ARNC', 'UAL', 'AAL', 'GE', 'SNA', 'WAB', 'FLS', 'VRSK', 'GWR', 'GWW', 'VSAT', 'AVT', 'TWTR', 'AMD', 'QCOM', 'FSLR', 'OTEX', 'NUAN', 'HPE', 'RPM', 'MLM', 'VMC', 'SEE', 'SON', 'HHC', 'LVLT', 'LVLT', 'S', 'JLL']
symbols = ['MAR', 'MON', 'NOV', 'A', 'AAL', 'AAP', 'AAPL', 'ABBV', 'ABC', 'ABT', 'ACN', 'ADBE', 'ADI', 'ADM', 'ADP', 'ADS', 'ADSK', 'AEE', 'AEP', 'AES', 'AET', 'AFL', 'AGN', 'AIG', 'AIV', 'AIZ', 'AJG', 'AKAM', 'ALB', 'ALGN', 'ALK', 'ALL', 'ALLE', 'ALXN', 'AMAT', 'AMD', 'AME', 'AMG', 'AMGN', 'AMP', 'AMT', 'AMZN', 'ANDV', 'ANSS', 'ANTM', 'AON', 'AOS', 'APA', 'APC', 'APD', 'APH', 'ARE', 'ARNC', 'ATVI', 'AVB', 'AVGO', 'AVY', 'AWK', 'AXP', 'AYI', 'AZO', 'BA', 'BAC', 'BAX', 'BBT', 'BBY', 'BCR', 'BDX', 'BEN', 'BF.B', 'BHF', 'BHGE', 'BIIB', 'BK', 'BLK', 'BLL', 'BMY', 'BRK.B', 'BSX', 'BWA', 'BXP', 'C', 'CA', 'CAG', 'CAH', 'CAT', 'CB', 'CBG', 'CBOE', 'CBS', 'CCI', 'CCL', 'CDNS', 'CELG', 'CERN', 'CF', 'CFG', 'CHD', 'CHK', 'CHRW', 'CHTR', 'CI', 'CINF', 'CL', 'CLX', 'CMA', 'CMCSA', 'CME', 'CMG', 'CMI', 'CMS', 'CNC', 'CNP', 'COF', 'COG', 'COH', 'COL', 'COO', 'COP', 'COST', 'COTY', 'CPB', 'CRM', 'CSCO', 'CSRA', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTXS', 'CVS', 'CVX', 'CXO', 'D', 'DAL', 'DE', 'DFS', 'DG', 'DGX', 'DHI', 'DHR', 'DIS', 'DISCA', 'DISCK', 'DISH', 'DLPH', 'DLR', 'DLTR', 'DOV', 'DPS', 'DRE', 'DRI', 'DTE', 'DUK', 'DVA', 'DVN', 'DWDP', 'DXC', 'EA', 'EBAY', 'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMN', 'EMR', 'EOG', 'EQIX', 'EQR', 'EQT', 'ES', 'ESRX', 'ESS', 'ETFC', 'ETN', 'ETR', 'EVHC', 'EW', 'EXC', 'EXPD', 'EXPE', 'EXR', 'F', 'FAST', 'FB', 'FBHS', 'FCX', 'FDX', 'FE', 'FFIV', 'FIS', 'FISV', 'FITB', 'FL', 'FLIR', 'FLR', 'FLS', 'FMC', 'FOX', 'FOXA', 'FRT', 'FTI', 'FTV', 'GD', 'GE', 'GGP', 'GILD', 'GIS', 'GLW', 'GM', 'GOOG', 'GOOGL', 'GPC', 'GPN', 'GPS', 'GRMN', 'GS', 'GT', 'GWW', 'HAL', 'HAS', 'HBAN', 'HBI', 'HCA', 'HCN', 'HCP', 'HD', 'HES', 'HIG', 'HLT', 'HOG', 'HOLX', 'HON', 'HP', 'HPE', 'HPQ', 'HRB', 'HRL', 'HRS', 'HSIC', 'HST', 'HSY', 'HUM', 'IBM', 'ICE', 'IDXX', 'IFF', 'ILMN', 'INCY', 'INFO', 'INTC', 'INTU', 'IP', 'IPG', 'IR', 'IRM', 'ISRG', 'IT', 'ITW', 'IVZ', 'JBHT', 'JCI', 'JEC', 'JNJ', 'JNPR', 'JPM', 'JWN', 'K', 'KEY', 'KHC', 'KIM', 'KLAC', 'KMB', 'KMI', 'KMX', 'KO', 'KORS', 'KR', 'KSS', 'KSU', 'L', 'LB', 'LEG', 'LEN', 'LH', 'LKQ', 'LLL', 'LLY', 'LMT', 'LNC', 'LNT', 'LOW', 'LRCX', 'LUK', 'LUV', 'LVLT', 'LYB', 'M', 'MA', 'MAA', 'MAC', 'MAS', 'MAT', 'MCD', 'MCHP', 'MCK', 'MCO', 'MDLZ', 'MDT', 'MET', 'MGM', 'MHK', 'MKC', 'MLM', 'MMC', 'MMM', 'MNST', 'MO', 'MOS', 'MPC', 'MRK', 'MRO', 'MS', 'MSFT', 'MSI', 'MTB', 'MTD', 'MU', 'MYL', 'NAVI', 'NBL', 'NDAQ', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NLSN', 'NOC', 'NRG', 'NSC', 'NTAP', 'NTRS', 'NUE', 'NVDA', 'NWL', 'NWS', 'NWSA', 'O', 'OKE', 'OMC', 'ORCL', 'ORLY', 'OXY', 'PAYX', 'PBCT', 'PCAR', 'PCG', 'PCLN', 'PDCO', 'PEG', 'PEP', 'PFE', 'PFG', 'PG', 'PGR', 'PH', 'PHM', 'PKG', 'PKI', 'PLD', 'PM', 'PNC', 'PNR', 'PNW', 'PPG', 'PPL', 'PRGO', 'PRU', 'PSA', 'PSX', 'PVH', 'PWR', 'PX', 'PXD', 'PYPL', 'Q', 'QCOM', 'QRVO', 'RCL', 'RE', 'REG', 'REGN', 'RF', 'RHI', 'RHT', 'RJF', 'RL', 'RMD', 'ROK', 'ROP', 'ROST', 'RRC', 'RSG', 'RTN', 'SBAC', 'SBUX', 'SCG', 'SCHW', 'SEE', 'SHW', 'SIG', 'SJM', 'SLB', 'SLG', 'SNA', 'SNI', 'SNPS', 'SO', 'SPG', 'SPGI', 'SPLS', 'SRCL', 'SRE', 'STI', 'STT', 'STX', 'STZ', 'SWK', 'SWKS', 'SYF', 'SYK', 'SYMC', 'SYY', 'T', 'TAP', 'TDG', 'TEL', 'TGT', 'TIF', 'TJX', 'TMK', 'TMO', 'TRIP', 'TROW', 'TRV', 'TSCO', 'TSN', 'TSS', 'TWX', 'TXN', 'TXT', 'UA', 'UAA', 'UAL', 'UDR', 'UHS', 'ULTA', 'UNH', 'UNM', 'UNP', 'UPS', 'URI', 'USB', 'UTX', 'V', 'VAR', 'VFC', 'VIAB', 'VLO', 'VMC', 'VNO', 'VRSK', 'VRSN', 'VRTX', 'VTR', 'VZ', 'WAT', 'WBA', 'WDC', 'WEC', 'WFC', 'WHR', 'WLTW', 'WM', 'WMB', 'WMT', 'WRK', 'WU', 'WY', 'WYN', 'WYNN', 'XEC', 'XEL', 'XL', 'XLNX', 'XOM', 'XRAY', 'XRX', 'XYL', 'YUM', 'ZBH', 'ZION', 'ZTS']
#symbols = ['MAR', 'MON', 'NOV', 'A', 'AAL', 'AAP', 'AAPL',]
startdate = '2017-07-01'
enddate = '2017-08-05'
o = perform(symbols,startdate,enddate)
print o.dailystackedreturns(totalorpricechange='PriceChange',logorarithmetic='log')
#stop
## print '------ Stock History Dataframe ------'
## print o.HistoryOfAdjClosePricesDataframe
#print '------ Daily Returns Dataframe ------'
#print o.TotalReturnsDataframe
print '------ AggregatedTotalReturnsDataframe Returns ------'
print o.AggregatedTotalReturnsDataframe
print '------ AggregatedPriceChangeReturnsDataframe Returns ------'
print o.AggregatedPriceChangeReturnsDataframe