def monthlyreturns(self,
list_of_symbols,
startdate_string='2005-01-01',
showresults=0):
#'^GSmr ^OEX ^VIX ^OEX ^MID ^RUT ^DJI
import pullreturns as pr
dict_of_dfs = {}
mysymbolslist = list_of_symbols #['^GSPC','^DJI','^MID','^OEX','AAPL','LEO']
for symbol in mysymbolslist:
df = pr.monthlyreturnsusingyahoosymbol(symbol, startdate_string)
dict_of_dfs[symbol] = df
#df = pr.monthlyreturnsusingyahoosymbol('^GSPC','2005-01-01')
#dict_of_dfs['^GSPC'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^DJI','2005-01-01')
#dict_of_dfs['^DJI'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^MID','2005-01-01')
#dict_of_dfs['^MID'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^VIX','2005-01-01')
#dict_of_dfs['^VIX'] = df
passed = 0
for k, v in dict_of_dfs.items():
if passed == 0:
df_align = v[['b_monthend', 'e_pctchange']]
df_align = df_align.set_index('b_monthend')
df_align.columns = [k]
#sLength = len(df_align[k])
#originalid = k
else:
df_new = v[['b_monthend', 'e_pctchange']]
df_new = df_new.set_index('b_monthend')
df_new.columns = [k]
df_new.sort_index
#print df_new
#df_align[k] = df_new.loc[k].shape[0]
#print df_new
#df_align[k] = pd.Series(df_new, index=df_align.index)
#df_align[k] = df_align[originalid].map(lambda x: df_new[k])
df_align[k] = df_new[k]
passed = 1
if showresults == 1:
print '----------------------------------------------------'
print ' monthly returns'
print '----------------------------------------------------'
print df_align
#self.DataFrameMonthlyReturns = df_align
return df_align
#x = np.array([[0, 2], [1, 1], [2, 0]]).T
#print np.cov(x)
#import pandas as pd
#'^GSmr ^OEX ^VIX ^OEX ^MID ^RUT ^DJI
import numpy as np
import pandas as pd
import pullreturns as pr
dict_of_dfs = {}
mysymbolslist = ['^GSPC', '^DJI', '^MID', '^OEX', 'AAPL', 'LEO']
for symbol in mysymbolslist:
df = pr.monthlyreturnsusingyahoosymbol(symbol, '2005-01-01')
dict_of_dfs[symbol] = df
#df = pr.monthlyreturnsusingyahoosymbol('^GSPC','2005-01-01')
#dict_of_dfs['^GSPC'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^DJI','2005-01-01')
#dict_of_dfs['^DJI'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^MID','2005-01-01')
#dict_of_dfs['^MID'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^VIX','2005-01-01')
#dict_of_dfs['^VIX'] = df
passed = 0
# [5, 500, 0],
# [1, 100, 7]])
#
#print np.cov(x)
import pandas as pd
import numpy as np
x = np.array([[0, 2], [1, 1], [2, 0]]).T
print np.cov(x)
#import pandas as pd
#'^GSmr ^OEX ^VIX ^OEX ^MID ^RUT ^DJI
import pullreturns as pr
dict_of_dfs = {}
df = pr.monthlyreturnsusingyahoosymbol('^GSPC', '2005-01-01')
dict_of_dfs['^GSPC'] = df
df = pr.monthlyreturnsusingyahoosymbol('^DJI', '2005-01-01')
dict_of_dfs['^DJI'] = df
df = pr.monthlyreturnsusingyahoosymbol('^MID', '2005-01-01')
dict_of_dfs['^MID'] = df
df = pr.monthlyreturnsusingyahoosymbol('^VIX', '2005-01-01')
dict_of_dfs['^VIX'] = df
passed = 0
for k, v in dict_of_dfs.items():
if passed == 0:
df_align = v[['b_monthend', 'e_pctchange']]
def monthlyreturns(self,
list_of_symbols,
startdate_string='2005-01-01',
showresults=0):
#'^GSmr ^OEX ^VIX ^OEX ^MID ^RUT ^DJI
import pullreturns as pr
dict_of_dfs = {}
mysymbolslist = list_of_symbols #['^GSPC','^DJI','^MID','^OEX','AAPL','LEO']
for symbol in mysymbolslist:
df = pr.monthlyreturnsusingyahoosymbol(symbol, startdate_string)
dict_of_dfs[symbol] = df
#df = pr.monthlyreturnsusingyahoosymbol('^GSPC','2005-01-01')
#dict_of_dfs['^GSPC'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^DJI','2005-01-01')
#dict_of_dfs['^DJI'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^MID','2005-01-01')
#dict_of_dfs['^MID'] = df
#
#df = pr.monthlyreturnsusingyahoosymbol('^VIX','2005-01-01')
#dict_of_dfs['^VIX'] = df
#passed = 0
#import datetime
import pandas as pd
#import numpy as np
#todays_date = datetime.datetime.now().date()
#index = pd.date_range(todays_date-datetime.timedelta(10), periods=10, freq='D')
index = ['X']
columns = ['A', 'B', 'C']
df_largest = pd.DataFrame(index=index, columns=columns)
df_largest = df_largest.fillna(0) # with 0s rather than NaNs
#print df_largest
#while len(dict_of_dfs_bysize) < len(dict_of_dfs):
keyoflargestdf = ''
for k, v in dict_of_dfs.items():
if len(v) > len(df_largest):
df_largest = v
keyoflargestdf = k
#break
df_align = df_largest[['b_monthend', 'e_pctchange']]
df_align = df_align.set_index('b_monthend')
df_align.columns = [keyoflargestdf]
df_align.sort_index
#print df_align
#print df_largest
# if passed == 0:
# df_align = v[['b_monthend','e_pctchange']]
# df_align = df_align.set_index('b_monthend')
# df_align.columns = [k]
# df_align.sort_index
# #sLength = len(df_align[k])
# #originalid = k
#
# else:
for k, v in dict_of_dfs.items():
if not k == keyoflargestdf:
df_new = v[['b_monthend', 'e_pctchange']]
df_new = df_new.set_index('b_monthend')
df_new.columns = [k]
df_new.sort_index
#print df_new
#df_align[k] = df_new.loc[k].shape[0]
#print df_new
#df_align[k] = pd.Series(df_new, index=df_align.index)
#df_align[k] = df_align[originalid].map(lambda x: df_new[k])
df_align[k] = df_new[k]
if showresults == 1:
print '----------------------------------------------------'
print ' monthly returns'
print '----------------------------------------------------'
print df_align
#self.DataFrameMonthlyReturns = df_align
return df_align
