def getpricehistory(self, period=''):
# Parameters
#startdate_string = '2004-12-31'
#symbol = '^GSPC ^OEX ^VIX ^OEX ^MID ^RUT
symbol = self.Symbol
# startdate_string = self.StartDateString
# ##########
# Date setup
import datetime
#today_datetime = datetime.datetime.today()
today_date = datetime.date.today()
df_00 = self._StockHistoryDataframe
import pandas as pd
if period == '':
period = self.Period
if period == 'monthly':
dates1 = pd.date_range('1910-01', str(today_date)[:7], freq='M')
else:
dates1 = pd.date_range('1910-01-01', str(today_date), freq='D')
rows_stockhistory = []
rows_stockhistory.append(['a_symbol', 'b_periodend', 'e_adjclose'])
for dt in dates1:
if str(dt.date()) in df_00.index:
#print str(dt.date()),'ok'
myobj = df_00.loc[str(dt.date())]
#print myobj
curr_date = dt.date()
curr_value = myobj['Adj Close']
#if prev_date != dummy_date:
rows_stockhistory.append([symbol, curr_date, curr_value])
#print df_00.index
headers = rows_stockhistory.pop(0)
df_monthlyprices = pd.DataFrame(rows_stockhistory, columns=headers)
import numpy as np
df_pricehistorytotodayfinite = df_monthlyprices[np.isfinite(
df_monthlyprices['e_adjclose'])]
import pullprices as pp
stock_dataframe = pp.stock_dataframe(symbol)
curr_price = stock_dataframe['last'][0]
mydict = {}
mydict[0] = {
'a_symbol': symbol,
'b_periodend': today_date,
'e_adjclose': curr_price
}
df_curr = pd.DataFrame(mydict).T
df_pricehistorytotoday = df_pricehistorytotodayfinite.append(
df_curr, ignore_index=True)
df_pricehistorytotoday.set_index(['b_periodend'], inplace=True)
#print df_pricehistorytotoday
return df_pricehistorytotoday
def _monthlyreturnsusingyahoosymbol(self,):
# Parameters
#startdate_string = '2004-12-31'
#symbol = '^GSPC ^OEX ^VIX ^OEX ^MID ^RUT
symbol = self.Symbol
startdate_string = self.StartDateString
# ##########
# Date setup
import datetime
#today_datetime = datetime.datetime.today()
today_date = datetime.date.today()
yesterday_date = datetime.date.fromordinal(datetime.date.today().toordinal()-1)
#print str(today_date)
import pullprices as pp
df_00 = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(symbol,startdate_string,str(yesterday_date)) #,str(today_date))
#print list(df_00)#['Close','Adj Close']
#print df_00[['Close','Adj Close']]
import pandas as pd
dates1 = pd.date_range('1910-01', str(today_date)[:7], freq='M')
dummy_date = datetime.datetime.strptime("1801-01-01", "%Y-%m-%d")
prev_date = dummy_date
prev_value = float('Nan')
rows_monthlyreturns = []
#rows_optionpricescurrent.append(['optionsymbol','stockprice','strike','pdeltapct_to_sell_price','cumprob_to_sell_price','bid','ask','last'])
rows_monthlyreturns.append(['a_symbol','b_monthend','e_pctchange','d_end'])
for dt in dates1:
if str(dt.date()) in df_00.index:
#print dt.date()
myobj = df_00.loc[str(dt.date())]
#print myobj
curr_date = dt.date()
curr_value = myobj['Adj Close']
if prev_date != dummy_date:
#print 'pullreturns curr_value,prev_value',curr_value,prev_value
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 symbol,prev_date,prev_value,curr_date,curr_value,change_pct
rows_monthlyreturns.append([symbol,curr_date,change_pct,curr_value])
#'{percent:.2%}'.format(percent=pdeltapct_to_sell_price)
#print symbol,curr_date,change_pct
prev_date = dt.date()
prev_value = myobj['Adj Close']
headers = rows_monthlyreturns.pop(0)
df_monthlyreturns = pd.DataFrame(rows_monthlyreturns,columns=headers)
import numpy as np
df_monthlyreturnsfinite = df_monthlyreturns[np.isfinite(df_monthlyreturns['e_pctchange'])]
#print df_monthlyreturnsfinite
#print df_00
stock_dataframe = pp.stock_dataframe(symbol)
myobj = df_00.loc[str(prev_date)]
prev_ending = myobj['Adj Close']
curr_price = stock_dataframe['last'][0]
if is_number(curr_price) and is_number(prev_ending):
curr_pctchange = (float(curr_price) - float(prev_ending)) / float(prev_ending)
else:
curr_pctchange = float('NaN')
#df_curr = pd.DataFrame([symbol,today_date,curr_pctchange], columns=['a_symbol','b_monthend','e_pctchange'])
#newrow = np.array([symbol,today_date,curr_pctchange])
#columns=['a_symbol','b_monthend','e_pctchange','d_end']
#import numpy as np
mydict = {}
mydict[0] = {'a_symbol':symbol,'b_monthend':str(today_date),'e_pctchange':curr_pctchange,'d_end':curr_price}
df_curr = pd.DataFrame(mydict).T
#print df_curr.T
df_monthlyreturnstotoday = df_monthlyreturnsfinite.append(df_curr, ignore_index=True)
#print df_monthlyreturns
#print str(today_date)[:7]
return df_monthlyreturnstotoday
rows_monthlyreturns.append([symbol,curr_date,change_pct,curr_value])
#'{percent:.2%}'.format(percent=pdeltapct_to_sell_price)
#print symbol,curr_date,change_pct
prev_date = dt.date()
prev_value = myobj['Adj Close']
headers = rows_monthlyreturns.pop(0)
df_monthlyreturns = pd.DataFrame(rows_monthlyreturns,columns=headers)
import numpy as np
df_monthlyreturnsfinite = df_monthlyreturns[np.isfinite(df_monthlyreturns['e_pctchange'])]
#print df_monthlyreturnsfinite
#print df_00
stock_dataframe = pp.stock_dataframe(symbol)
myobj = df_00.loc[str(prev_date)]
prev_ending = myobj['Adj Close']
curr_price = stock_dataframe['last'][0]
curr_pctchange = (curr_price - prev_ending) / prev_ending
#df_curr = pd.DataFrame([symbol,today_date,curr_pctchange], columns=['a_symbol','b_monthend','e_pctchange'])
#newrow = np.array([symbol,today_date,curr_pctchange])
#columns=['a_symbol','b_monthend','e_pctchange','d_end ']
#import numpy as np
mydict = {}
mydict[0] = {'a_symbol':symbol,'b_monthend':str(today_date),'e_pctchange':curr_pctchange,'d_end ':curr_price}
df_curr = pd.DataFrame(mydict).T
#print df_curr.T
def calculatereturns(self, period='', appendtodaysprice=True):
# Parameters
#startdate_string = '2004-12-31'
#symbol = '^GSPC ^OEX ^VIX ^OEX ^MID ^RUT
symbol = self.Symbol
# startdate_string = self.StartDateString
# ##########
# Date setup
import datetime
#today_datetime = datetime.datetime.today()
today_date = datetime.date.today()
# yesterday_date = datetime.date.fromordinal(datetime.date.today().toordinal()-1)
#print str(today_date)
# import pullprices as pp
# df_00 = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(symbol,startdate_string,str(yesterday_date)) #,str(today_date))
df_00 = self.StockHistoryDataframe
import pandas as pd
#dates1 = pd.date_range('1910-01', str(today_date)[:7], freq=self.MonthDayCharacter)
if period == '':
period = self.Period
if period == 'monthly':
dates1 = pd.date_range('1910-01', str(today_date)[:7], freq='M')
else:
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_calculatereturns = []
#rows_optionpricescurrent.append(['optionsymbol','stockprice','strike','pdeltapct_to_sell_price','cumprob_to_sell_price','bid','ask','last'])
#rows_calculatereturns.append(['a_symbol','b_periodend','e_pctchange','d_end'])
rows_calculatereturns.append(
['a_symbol', 'b_periodend', 'e_pctchange', 'e_logreturn', 'd_end'])
import math
for dt in dates1:
if str(dt.date()) in df_00.index:
#print dt.date()
myobj = df_00.loc[str(dt.date())]
#print myobj
curr_date = dt.date()
curr_value = myobj['Adj Close']
if prev_date != dummy_date:
#print 'pullreturns curr_value,prev_value',curr_value,prev_value
if is_number(curr_value) and is_number(prev_value):
logreturn_pct = math.log(
float(curr_value) / float(prev_value))
change_pct = (float(curr_value) -
float(prev_value)) / float(prev_value)
else:
change_pct = float('NaN')
#print symbol,prev_date,prev_value,curr_date,curr_value,change_pct
rows_calculatereturns.append([
symbol, curr_date, change_pct, logreturn_pct,
curr_value
])
#'{percent:.2%}'.format(percent=pdeltapct_to_sell_price)
#print symbol,curr_date,change_pct
prev_date = dt.date()
prev_value = myobj['Adj Close']
headers = rows_calculatereturns.pop(0)
df_calculatereturns = pd.DataFrame(rows_calculatereturns,
columns=headers)
import numpy as np
df_calculatereturnsfinite = df_calculatereturns[np.isfinite(
df_calculatereturns['e_pctchange'])]
#print df_calculatereturnsfinite
#print df_00
#yyyyyy
if appendtodaysprice == True:
import pullprices as pp
stock_dataframe = pp.stock_dataframe(symbol)
myobj = df_00.loc[str(prev_date)]
prev_ending = myobj['Adj Close']
curr_price = stock_dataframe['last'][0]
if is_number(curr_price) and is_number(prev_ending):
curr_logreturn = math.log(
float(curr_price) / float(prev_ending))
curr_pctchange = (float(curr_price) -
float(prev_ending)) / float(prev_ending)
else:
curr_logreturn = float('NaN')
curr_pctchange = float('NaN')
#df_curr = pd.DataFrame([symbol,today_date,curr_pctchange], columns=['a_symbol','b_periodend','e_pctchange'])
#newrow = np.array([symbol,today_date,curr_pctchange])
#columns=['a_symbol','b_periodend','e_pctchange','d_end']
#import numpy as np
mydict = {}
mydict[0] = {
'a_symbol': symbol,
'b_periodend': today_date,
'e_pctchange': curr_pctchange,
'e_logreturn': curr_logreturn,
'd_end': curr_price
}
df_curr = pd.DataFrame(mydict).T
#print df_curr.T
df_calculatereturnstotoday = df_calculatereturnsfinite.append(
df_curr, ignore_index=True)
else:
df_calculatereturnstotoday = df_calculatereturnsfinite
df_calculatereturnstotoday.set_index(['b_periodend'], inplace=True)
#print df_calculatereturns
#print str(today_date)[:7]
return df_calculatereturnstotoday
def dailyreturns(self, ):
#log(p_today / p_yesterday)
symbol = self.Symbol
#startdate_string = self.StartDateString
# ##########
# Date setup
import datetime
#today_datetime = datetime.datetime.today()
today_date = datetime.date.today()
#yesterday_date = datetime.date.fromordinal(datetime.date.today().toordinal()-1)
#print str(today_date)
import pullprices as pp
#df_00 = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(symbol,startdate_string,str(yesterday_date)) #,str(today_date))
df_00 = self.StockHistoryDataframe
#print list(df_00)#['Close','Adj Close']
#print df_00[['Close','Adj Close']]
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_optionpricescurrent.append(['optionsymbol','stockprice','strike','pdeltapct_to_sell_price','cumprob_to_sell_price','bid','ask','last'])
rows_dailyreturns.append(
['a_symbol', 'b_periodend', 'e_pctchange', 'e_logreturn', 'd_end'])
import math
for dt in dates1:
if str(dt.date()) in df_00.index:
#print dt.date()
myobj = df_00.loc[str(dt.date())]
#print myobj
curr_date = dt.date()
curr_value = myobj['Adj Close']
if prev_date != dummy_date:
#print 'pullreturns curr_value,prev_value',curr_value,prev_value
if is_number(curr_value) and is_number(prev_value):
logreturn_pct = math.log(
float(curr_value) / float(prev_value))
change_pct = (float(curr_value) -
float(prev_value)) / float(prev_value)
else:
change_pct = float('NaN')
#print symbol,prev_date,prev_value,curr_date,curr_value,change_pct
rows_dailyreturns.append([
symbol, curr_date, change_pct, logreturn_pct,
curr_value
])
#'{percent:.2%}'.format(percent=pdeltapct_to_sell_price)
#print symbol,curr_date,change_pct
prev_date = dt.date()
prev_value = myobj['Adj Close']
headers = rows_dailyreturns.pop(0)
df_dailyreturns = pd.DataFrame(rows_dailyreturns, columns=headers)
import numpy as np
df_dailyreturnsfinite = df_dailyreturns[np.isfinite(
df_dailyreturns['e_pctchange'])]
#print df_dailyreturnsfinite
#print df_00
#print 'kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk'
stock_dataframe = pp.stock_dataframe(symbol)
myobj = df_00.loc[str(prev_date)]
prev_ending = myobj['Adj Close']
curr_price = stock_dataframe['last'][0]
if is_number(curr_price) and is_number(prev_ending):
curr_logreturn = math.log(float(curr_price) / float(prev_ending))
curr_pctchange = (float(curr_price) -
float(prev_ending)) / float(prev_ending)
else:
curr_logreturn = float('NaN')
curr_pctchange = float('NaN')
#df_curr = pd.DataFrame([symbol,today_date,curr_pctchange], columns=['a_symbol','b_periodend','e_pctchange'])
#newrow = np.array([symbol,today_date,curr_pctchange])
#columns=['a_symbol','b_periodend','e_pctchange','d_end']
#import numpy as np
mydict = {}
mydict[0] = {
'a_symbol': symbol,
'b_periodend': today_date,
'e_pctchange': curr_pctchange,
'e_logreturn': curr_logreturn,
'd_end': curr_price
}
df_curr = pd.DataFrame(mydict).T
#print df_curr.T
df_dailyreturnstotoday = df_dailyreturnsfinite.append(
df_curr, ignore_index=True)
#wwwww
#df_dailyreturnstotoday.to_csv('c:\\Batches\\$Work\\returnstest.csv',columns=('a_symbol','b_periodend','e_pctchange','e_logreturn','d_end'))
#print df_dailyreturns
#print str(today_date)[:7]
return df_dailyreturnstotoday
