def __init__(
self,
symbol,
startdate_string='2004-12-31',
enddate_string='',
period='monthly' #or daily
,
source='Yahoo'
#, enddate_string='xxx'
):
print('Initialized class pullreturns.perform')
#today_date = datetime.date.today()
self.Symbol = symbol
self.StartDateString = startdate_string
self.EndDateString = enddate_string
self.Period = period
import datetime
#today_datetime = datetime.datetime.today()
startdate_date = datetime.datetime.strptime(startdate_string,
"%Y-%m-%d")
my_enddate_string = enddate_string
yesterday_date = datetime.date.fromordinal(
datetime.date.today().toordinal() - 1)
if len(my_enddate_string) == 0:
my_enddate_string = str(yesterday_date)
enddate_date = datetime.datetime.strptime(my_enddate_string,
"%Y-%m-%d")
if enddate_date <= startdate_date:
my_enddate_string = str(yesterday_date)
import pullprices as pp
if period == 'monthly':
self._StockHistoryDataframe = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(
symbol, startdate_string,
my_enddate_string) #,str(today_date))
#self.MonthDayCharacter = 'M'
else:
self._StockHistoryDataframe = pp.stockhistorynobackfilltodataframeusingcache(
symbol, startdate_string,
my_enddate_string) #,str(today_date))
#self.MonthDayCharacter = 'D'
#Append todays price if no end date specified
appendtodaysprice = False
if enddate_string == '':
appendtodaysprice = True
self.ReturnsDataframe = self.calculatereturns(
period=period, appendtodaysprice=appendtodaysprice)
self.PriceHistoryDataframe = self.getpricehistory(
period=period, appendtodaysprice=appendtodaysprice)
self.FirstDateOfPriceHistory = self.PriceHistoryDataframe.index.tolist(
)[0]
def __init__(
self,
symbol,
startdate_string='2004-12-31'
#, period = 'monthly' #or daily
,
source='Yahoo'
#, enddate_string='xxx'
):
print('Initialized class pullreturns.perform')
self.Symbol = symbol
self.StartDateString = startdate_string
#self.Period = period
import datetime
#today_datetime = datetime.datetime.today()
#today_date = datetime.date.today()
yesterday_date = datetime.date.fromordinal(
datetime.date.today().toordinal() - 1)
import pullprices as pp
self.StockHistoryDataframe = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(
symbol, startdate_string, str(yesterday_date)) #,str(today_date))
self.ReturnsDataframe = self.monthlyreturns()
def __init__(
self,
symbol,
startdate_string='2004-12-31',
period='monthly' #or daily
,
source='Yahoo'
#, enddate_string='xxx'
):
print('Initialized class pullreturns.perform')
self.Symbol = symbol
self.StartDateString = startdate_string
self.Period = period
import datetime
#today_datetime = datetime.datetime.today()
#today_date = datetime.date.today()
yesterday_date = datetime.date.fromordinal(
datetime.date.today().toordinal() - 1)
import pullprices as pp
if period == 'monthly':
self._StockHistoryDataframe = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(
symbol, startdate_string,
str(yesterday_date)) #,str(today_date))
#self.MonthDayCharacter = 'M'
else:
self._StockHistoryDataframe = pp.stockhistorynobackfilltodataframeusingcache(
symbol, startdate_string,
str(yesterday_date)) #,str(today_date))
#self.MonthDayCharacter = 'D'
self.ReturnsDataframe = self.calculatereturns(period)
self.PriceHistoryDataframe = self.getpricehistory(period)
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
# Parameters
startdate_string = '1980-01-01'
symbol = '^GSPC' # ^GSPC ^OEX ^VIX ^OEX ^MID ^RUT
# ##########
# 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', '2015-12', 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()
import pullprices as pp
df1 = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache('FB','2011-01-01','2015-07-17')
df2 = df1.dropna(subset=['Open'])
print df2
import pullprices as pp
df1 = pp.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(
'FB', '2011-01-01', '2015-07-17')
df2 = df1.dropna(subset=['Open'])
print df2
def build(self, symbol, param_numberofweeks, daysbackmid, daysbackfar,
showresults):
import pandas as pd
import numpy as np
if showresults == 1:
print('mysymbol', symbol, '------------------------------------')
#param_numberofweeks = 4
numberofweekstorun = param_numberofweeks
from datetime import datetime, timedelta
dStart = datetime.now().date() + timedelta(weeks=-numberofweekstorun)
#print(dStart)
index = pd.date_range(dStart + timedelta(days=0),
periods=numberofweekstorun,
freq='W-Fri')
#print(index)
columns = [
'dateDaysBackMid', 'dateDaysBackFar', 'priceRefDate',
'priceDaysBackMid', 'priceDaysBackFar', 'DeltaFartoMid',
'DrawDownMax', 'DrawUpMax', 'DrawDownPctChange', 'DrawUpPctChange'
]
df_ = pd.DataFrame(index=index, columns=columns)
df_ = df_.fillna(0) # with 0s rather than NaNs
#df_ = df_.fillna(method='pad')
# ------------------------------------------------------------
# Change this number after * if changing the number of columns
data = np.array([np.arange(len(index))] * 10).T
# ------------------------------------------------------------
df = pd.DataFrame(data, index=index, columns=columns)
#-------------------------------------------------------------------------------
import pullprices
fmt = '%Y-%m-%d'
dEnd = index[-1]
dEnd_string = dEnd.strftime(fmt)
dDataStart = dStart + timedelta(days=(-1 * daysbackfar) - 1)
dDataStart_string = dDataStart.strftime(fmt)
#f = pullprices.stockhistory(symbol, dDataStart, dEnd)
df_backfilled = pullprices.stockhistorybackfilledtodatframeofstockhistoryinstancesusingcache(
symbol, dDataStart_string, dEnd_string)
#df_backfilled_nonan = df_backfilled.dropna(subset=['Open'])
#print(f)
for idx, row in df.iterrows():
pDraw_downmax = 0
pDraw_upmax = 0
#print(idx)
dRef = idx
ddaysbackmid = dRef + timedelta(days=-1 * daysbackmid)
ddaysbackfar = dRef + timedelta(days=-1 * daysbackfar)
dRef_string = dRef.strftime(fmt)
ddaysbackmid_string = ddaysbackmid.strftime(fmt)
ddaysbackfar_string = ddaysbackfar.strftime(fmt)
try:
pdaysbackfar = df_backfilled.ix[ddaysbackfar_string][
'Adj Close']
#print(ddaysbackfar_string,pdaysbackfar)
except:
pdaysbackfar = float('NaN')
#print('no data')
pass
try:
pdaysbackmid = df_backfilled.ix[ddaysbackmid_string][
'Adj Close']
#print(ddaysbackmid_string,pdaysbackmid)
except:
pdaysbackmid = float('NaN')
#print('no data')
pass
try:
pRef = df_backfilled.ix[dRef_string]['Adj Close']
#print(dRef_string,pRef)
except:
pRef = float('NaN')
#print('no data')
pass
try:
pdeltadaysbackfartodaysbackmid = pdaysbackmid - pdaysbackfar
#print('pricedelta',pdeltadaysbackfartodaysbackmid)
except:
pdeltadaysbackfartodaysbackmid = float('NaN')
#print('no delta')
for iday in range(1, daysbackfar - daysbackmid + 1):
dDraw = ddaysbackfar + timedelta(days=iday)
dDraw_string = dDraw.strftime(fmt)
#print(dDraw_string)
try:
pDraw = df_backfilled.ix[dDraw_string]['Adj Close']
pDelta = pDraw - pdaysbackfar
if pDelta < pDraw_downmax:
pDraw_downmax = pDelta
if pDelta > pDraw_upmax:
pDraw_upmax = pDelta
if pdaysbackfar == float('NaN'):
pDraw_downmax = float('NaN')
pDraw_upmax = float('NaN')
except:
pDraw = float('Nan')
#print(dRef_string,ddaysbackmid_string,ddaysbackfar_string)
#print('inside builddataframeofrefdateminusd2tod1stockpricechanges.py','WAWAWAWAWAW pDraw_downmax,pdaysbackfar',pDraw_downmax,pdaysbackfar)
try:
pctchangeDraw_downmax = pDraw_downmax / pdaysbackfar
except:
pctchangeDraw_downmax = float('NaN')
try:
pctchangeDraw_upmax = pDraw_upmax / pdaysbackfar
except:
pctchangeDraw_upmax = float('NaN')
df.loc[dRef] = pd.Series({
'dateDaysBackMid':
ddaysbackmid.strftime('%Y-%m-%d'),
'dateDaysBackFar':
ddaysbackfar.strftime('%Y-%m-%d'),
'priceRefDate':
pRef,
'priceDaysBackMid':
pdaysbackmid,
'priceDaysBackFar':
pdaysbackfar,
'DeltaFartoMid':
pdeltadaysbackfartodaysbackmid,
'DrawDownMax':
pDraw_downmax,
'DrawUpMax':
pDraw_upmax,
'DrawDownPctChange':
abs(pctchangeDraw_downmax),
'DrawUpPctChange':
pctchangeDraw_upmax
})
#print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
#df = df.fillna(method='pad')
if showresults == 1:
print(df)
print(len(data))
print(len(df))
print('------------------------')
print(dStart)
print(dEnd)
print('dRef', dRef)
self.DataFrameResult = df
