def __init__(self,

symbol = 'SPY'

, mycomparesym = '^VIX'

, numberofweeksahead = 8

, expirationday = 'friday' #'friday' #'wednesday for index'

, daysbackmid = 0

, myspreadindollars = 1

, mycumprobthreshold = 80 #Percent in whole number 80 = 80%

, mycumprob_to_sell_price_lowrange = 0

, mycumprob_to_sell_price_highrange = 100 # was 95

, numberofweekstolookback = 150

, RollingNumberOfPeriods = 120

, showresults = 0

):

self.find(symbol

, mycomparesym

, numberofweeksahead

, expirationday

, daysbackmid

, myspreadindollars

, mycumprobthreshold

, mycumprob_to_sell_price_lowrange

, mycumprob_to_sell_price_highrange

, numberofweekstolookback

, RollingNumberOfPeriods

, showresults

)

def find(self

, symbol = 'SPY'

, mycomparesym = '^VIX'

, numberofweeksahead = 8

, expirationday = 'friday' #'friday' #'wednesday for index'

, daysbackmid = 0

, myspreadindollars = 1

, mycumprobthreshold = 80 #Percent in whole number 80 = 80%

, mycumprob_to_sell_price_lowrange = 0

, mycumprob_to_sell_price_highrange = 100 # was 95

, numberofweekstolookback = 150

, RollingNumberOfPeriods = 120

, showresults = 0

):

import config

# ##########

# Parameters

# symbol = 'SPY'

# mycomparesym = '^VIX'

# numberofweeksahead = 8

# expirationday = 'friday' #'friday' #'wednesday for index'

# daysbackmid = 0

# myspreadindollars = 1

# mycumprobthreshold = 80 #Percent in whole number 80 = 80%

# mycumprob_to_sell_price_lowrange = 0

# mycumprob_to_sell_price_highrange = 100 # was 95

# numberofweekstolookback = 150

# RollingNumberOfPeriods = 120

# showresults = 0

ThreshholdAbove = 0.0001 #Percent change above

ThreshholdBelow = -0.0001 #Percent change below

mycandidatesfolder = config.mycandidatesfolder #'C:\\Batches\\rts\\output\\condor\\candidates'

mysourcedatafolder = config.mysourcedatafolder #'C:\\Batches\\rts\\output\\condor\\candidates'

myselectedcandidatesfolder = config.myselectedcandidatesfolder #'C:\\Batches\\rts\\output\\condor\\selectedcandidates'

mycachefolder = config.mycachefolder #'C:\\Batches\\rts\\output\\cache'

print('mycandidatesfolder',mycandidatesfolder)

#mycandidatesfolder = 'C:\\Batches\\MyPython\\active\\output'

#mycandidatesfolder = 'C:\\Documents and Settings\\jmalinchak\\My Documents\\My Python\\Active\\New Folder\\output'

import ntpath

def path_leaf(path):

head, tail = ntpath.split(path)

return tail or ntpath.basename(head)

def path_base(path):

head, tail = ntpath.split(path)

return head or ntpath.basename(head)

#def getdictitem(thedict,key):

def dayofweek_int(dayofweek_word):

rv = int(-1)

if dayofweek_word.lower() == 'friday':

rv = int(4)

if dayofweek_word.lower() == 'saturday':

rv = int(5)

if dayofweek_word.lower() == 'sunday':

rv = int(6)

if dayofweek_word.lower() == 'monday':

rv = int(0)

if dayofweek_word.lower() == 'tuesday':

rv = int(1)

if dayofweek_word.lower() == 'wednesday':

rv = int(2)

if dayofweek_word.lower() == 'thursday':

rv = int(3)

return rv

#import csv

import os

candidatesfolderwithsymbol = os.path.join(mycandidatesfolder,symbol)

sourcedatafolderwithsymbol = os.path.join(mysourcedatafolder,symbol)

import mytools

mygeneral = mytools.general()

mygeneral.make_sure_path_exists(candidatesfolderwithsymbol) #candidatesfolderwithsymbol

mygeneral.make_sure_path_exists(sourcedatafolderwithsymbol) #sourcedatafolderwithsymbol

## ##########

## Date setup

#import datetime

#

#today_datetime = datetime.datetime.today()

#today_date = datetime.date.today()

# ##########

# Date setup

import datetime

today_datetime = datetime.datetime.today()

today_date = datetime.date.today()

iter_date = today_date

for expirationcounter in range(numberofweeksahead):

while iter_date.weekday() != dayofweek_int(expirationday):

iter_date += datetime.timedelta(1)

expirationdate_string = str(iter_date)

iter_date += datetime.timedelta(1)

print('Doing...',expirationcounter,expirationdate_string)

while True :

# today_date = today_date

expire_date = datetime.datetime.strptime(expirationdate_string,'%Y-%m-%d').date()

if today_date != expire_date:

break

today_date = today_date - datetime.timedelta(hours=24)

delta = expire_date - today_date

# ####################################################

# Get Option Prices

pricingsymbol = symbol

if pricingsymbol in ['VIX','RUT']:

pricingsymbol = '^'+symbol

import pullprices as pp

df_optionpricescurrent = pp.options_to_dataframe(pricingsymbol,expirationdate_string,0)

#print('$$$$$$$$$$$$$ cvcvcvcvc $$$$$$$$$$$$$$$$$')

#print(df_optionpricescurrent)

if showresults == 1:

# ==========

print('-----',symbol,'Option Prices','-----')

#print(df_optionpricescurrent)

# ==========

if len(df_optionpricescurrent) == 0:

print('-:-:-:-:-:--:-:-:-:-:--:-:-:-:-:- no option prices found for',pricingsymbol,expirationdate_string)

else:

# ########

# Initialize notes

print('Initialized:','calculatecumulativeprobabilityofpricechangebasedonexpiration.py')

print('-----------')

print('Symbol:',symbol)

print(' Compared to:',mycomparesym)

print(' Today:',today_date)

print(' Expire Date:',expire_date )

print(' Number of Days to Expiration:',delta.days)

# ##########

# Date setup

import time

millis = int(round(time.time() * 1000))

datestringforfilename = today_datetime.strftime('%Y-%m-%d %H.%M.%S ') + str(millis)

datestringforcsv = today_datetime.strftime('%Y-%m-%d %H:%M:%S.%f')

datestringforprinting = today_datetime.strftime('%Y-%m-%d %H:%M')

daysbackfar = delta.days

import builddataframeofrefdateminusd2tod1stockpricechanges

df_stockpricechanges_unfiltered = builddataframeofrefdateminusd2tod1stockpricechanges.perform(pricingsymbol,numberofweekstolookback,daysbackmid,daysbackfar,showresults).DataFrameResult

df_stockpricechanges = df_stockpricechanges_unfiltered.dropna(subset=['priceDaysBackFar'])

#print(df_stockpricechanges)

number_of_observations = len(df_stockpricechanges.index)

print(' Number Of Observations Found',number_of_observations)

print('runtime_delta',datetime.datetime.today() - today_datetime)

# ---------------------------------------------------------------------------------

comparesym = mycomparesym

#import datetime

#today_date = datetime.date.today()

#today_datetime = datetime.datetime.today()

#print('today_date',today_date)

datedelta = datetime.timedelta(weeks=numberofweekstolookback+3)

startdatecalculatedf_datetime = today_datetime - datedelta

startdatecalculatedf_string = str(startdatecalculatedf_datetime.date())

print(' Start Date:',startdatecalculatedf_string)

# ###########################

# Get VIX or comparable stock

import pullprices

df_comparestockpricehistory = pullprices.stockhistorybackfilledtodatframeofstockhistoryinstances(comparesym, startdatecalculatedf_string, str(today_date))

compare_stock_price = pullprices.stock(mycomparesym)

print('runtime_delta',datetime.datetime.today() - today_datetime)

#print(f.loc[f.index == '2015-06-18'])

#f1 = f.loc[f.index.isin(['2015-06-18'])][['Adj Close']]

# ################################################################################################################

# performs some general statistics

import pandas as pd

df_std = pd.rolling_std(df_stockpricechanges[['DrawDownPctChange', 'DrawUpPctChange']], RollingNumberOfPeriods)

df_mean = pd.rolling_mean(df_stockpricechanges[['DrawDownPctChange', 'DrawUpPctChange']], RollingNumberOfPeriods)

# #########################################################################

# Adds a column to dataframe to Compare data to something (VIX for example)

df_stockpricechanges['comppratfar'] = float('NaN')

df_stockpricechanges['breachedaboveorbelow'] = int(0)

#print(df_stockpricechanges)

# #######################################################################################

# Counts number of observations that hit above and below threshold during trading period

idrawbeyondf_upabove = 0

idrawbeyondf_downbelow = 0

#icountfartomidbeyondf_above = 0

#icountfartomidbeyondf_below = 0

#zzzzz error was here

#print df_comparestockpricehistory

for index, row in df_stockpricechanges.iterrows():

try:

fartomidpricechangedelta = (row['priceDaysBackMid'] - row['priceDaysBackFar']) / row['priceDaysBackFar']

except:

fartomidpricechangedelta = float('NaN')

if row['DrawUpPctChange'] > ThreshholdAbove:

idrawbeyondf_upabove = idrawbeyondf_upabove + 1

if row['DrawDownPctChange'] > abs(ThreshholdBelow):

idrawbeyondf_downbelow = idrawbeyondf_downbelow + 1

# #################################################

# Populates the comppratfar field (VIX for example)

#row['comppratfar'] = df_comparestockpricehistory.ix[row.index, 'Adj Close']

#df_stockpricechanges['comppratfar'][str(index.date())] = df_comparestockpricehistory.ix[row['dateDaysBackFar'], 'Adj Close']

#print row['dateDaysBackFar'],str(index.date())

#zzzzz error was here

if row['dateDaysBackFar'] in df_comparestockpricehistory.index:

df_stockpricechanges['comppratfar'][str(index.date())] = df_comparestockpricehistory['Adj Close'][row['dateDaysBackFar']]

#df_stockpricechanges.set_value(str(index.date()), 'comppratfar', df_comparestockpricehistory[df_comparestockpricehistory.'Adj Close' == row['dateDaysBackFar']])

# df_stockpricechanges['comppratfar'][str(index.date())] =

#str(index.date())

# if showresults == 1:

# # ==========

# print(df_stockpricechanges)

# # ==========

if showresults == 1:

print('Last DrawUpPctChange Mean',df_mean.ix[len(df_mean.index)-1,'DrawUpPctChange'])

print('Last DrawUpPctChange Std',df_std.ix[len(df_std.index)-1,'DrawUpPctChange'])

print('---------------------------------')

print('Percent Beyond Draw Up')

print('---------------------------------')

print(' ',symbol

,'{percent:.2%}'.format(percent=idrawbeyondf_upabove/len(df_stockpricechanges.index))

,'of the'

,len(df_stockpricechanges.index)

,'observations closed above the '

,'{percent:.2%}'.format(percent=ThreshholdAbove)

,'threshold between t-',daysbackfar,'and t-',daysbackmid,', a total of'

,idrawbeyondf_upabove

,'observations'

)

print('---------------------------------')

print('Percent Beyond Draw Down')

print('---------------------------------')

print(' ',symbol

,'{percent:.2%}'.format(percent=idrawbeyondf_downbelow/len(df_stockpricechanges.index))

,'of the'

,len(df_stockpricechanges.index)

,'observations closed below the'

,'{percent:.2%}'.format(percent=ThreshholdBelow)

,'threshold between t-',daysbackfar,'and t-',daysbackmid,', a total of'

,idrawbeyondf_downbelow

,'observations'

)

#////////////////////////////////////////////////////

# Draw Up and Draw Down analysis

import scipy.stats as ss

import numpy as np

import matplotlib.pyplot as plt

#////////////////////////////////////////////////////

# Draw Up analysis

print('')

print(symbol,'Draw Up:',len(df_stockpricechanges.index), 'observations',idrawbeyondf_upabove,'breached',ThreshholdAbove)

print(' ','DaysBackFar',daysbackfar,' DaysBackMid:',daysbackmid)

serDrawUp = pd.Series(df_stockpricechanges['DrawUpPctChange'])

serDrawUp.hist(cumulative=True, normed=1, bins=idrawbeyondf_upabove)

maxpercent_drawup = 0

for n in np.linspace(0,1,1000,endpoint=False):

cumprob_to_sell_price = ss.percentileofscore(serDrawUp, n)

if cumprob_to_sell_price >= mycumprobthreshold:

maxpercent_drawup = n

print(' ',round(cumprob_to_sell_price,1),'percent of observations closed up inside of','{percent:.2%}'.format(percent=n)),'percent'

break

plt.show()

#////////////////////////////////////////////////////

# Draw Down analysis

print('')

print(symbol,'Draw Down:',len(df_stockpricechanges.index), 'observations',idrawbeyondf_upabove,'breached',ThreshholdBelow)

print(' ','DaysBackFar',daysbackfar,' DaysBackMid:',daysbackmid)

serDrawDown = pd.Series(df_stockpricechanges['DrawDownPctChange'])

serDrawDown.hist(cumulative=True, normed=1, bins=idrawbeyondf_downbelow)

maxpercent_drawdown = 0

for n in np.linspace(0,1,1000,endpoint=False):

cumprob_to_sell_price = ss.percentileofscore(serDrawDown, n)

if cumprob_to_sell_price >= mycumprobthreshold:

maxpercent_drawdown = n

print(' ',round(cumprob_to_sell_price,1),'percent of observations closed down inside of','{percent:.2%}'.format(percent=(-1.0)*n)),'percent'

break

plt.show()

stockprice = pp.stock(pricingsymbol)

# ##########################

# Calculate Analysis Results

#import mytools

osymbol = mytools.get_from_optionsymbol()

rows_optionpricescurrent = []

#rows_optionpricescurrent.append(['optionsymbol','stockprice','strike','pdeltapct_to_sell_price','cumprob_to_sell_price','bid','ask','last'])

rows_optionpricescurrent.append(['optionsymbol','exdate','symbol','ty','st','strike_at_sell_price','strike_at_buy_price','pdeltapct_to_sell_price','cumprob_to_sell_price','cumprob_to_buy_price','bid','ask','iv','iscandidate'])

#df_candidates = {}

pdeltapct_atthreshold_calloption = float('NaN')

pdeltapct_atthreshold_putoption = float('NaN')

previous_pdeltapct_for_put = float('NaN')

# ##################################

# Loop through current option prices

#print(df_optionpricescurrent)

#print('++++++++++++++++++++++++++++++++++++++++++ df_optionpricescurrent')

if df_optionpricescurrent is None:

print('There are no option prices for ' + symbol + ' expdate: '+expirationdate_string)

else:

df_optionpricescurrent['optiontype'] = 'X'

for index, row in df_optionpricescurrent.iterrows():

optionsymbol = row['optionsymbol']

df_optionpricescurrent['optiontype'][index] = mytools.get_from_optionsymbol().optiontype(optionsymbol)

#print(optionsymbol)

strike_at_sell_price = float(row['strike'])

strike_at_sell_price_formatted = "%.2f" % strike_at_sell_price

optiontype = mytools.get_from_optionsymbol().optiontype(row['optionsymbol'])

# if optiontype == 'C':

# strike_at_buy_price = float(strike_at_sell_price) + float(myspreadindollars)

# else:

# strike_at_buy_price = float(strike_at_sell_price) - float(myspreadindollars)

if optiontype == 'C':

strike_at_buy_price = float(strike_at_sell_price) + float(1)

else:

strike_at_buy_price = float(strike_at_sell_price) - float(1)

exdate = osymbol.expirationdate(row['optionsymbol'])

vsymbol = osymbol.symbol(row['optionsymbol'])

#print('stockprice',stockprice)

pdeltapct_to_sell_price = (float(strike_at_sell_price) - float(stockprice)) / float(stockprice)

#print('pdeltapct_to_sell_price',pdeltapct_to_sell_price)

pdeltapct_to_buy_price = (float(strike_at_buy_price) - float(stockprice)) / float(stockprice)

cumprob_to_sell_price = float('NaN')

cumprob_to_buy_price = float('NaN')

iscandidate = 0

# ========================

# if abs(pdeltapct_to_sell_price) > 0.10:

# print('++++++++++++++++++++++++more than 10',row)

# ========================

#print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')

if optiontype == 'C':

if pdeltapct_to_sell_price > 0:

cumprob_to_sell_price = ss.percentileofscore(serDrawUp, pdeltapct_to_sell_price)

cumprob_to_buy_price = ss.percentileofscore(serDrawUp, pdeltapct_to_buy_price)

#print('cumprob_to_sell_price',cumprob_to_sell_price,mycumprobthreshold)

if str(pdeltapct_atthreshold_calloption).lower() == 'nan':

if float(cumprob_to_sell_price) >= float(mycumprobthreshold):

print('Yes Call threshold found %%%%%%%%%%%%%%%%%%%%%%%%%%',cumprob_to_sell_price)

pdeltapct_atthreshold_calloption = pdeltapct_to_sell_price

#capturedspreadf_at_call_thresholdf_cross

print('Call',str(pdeltapct_atthreshold_calloption),'cumprob_to_sell_price > mycumprobthreshold',str(cumprob_to_sell_price),str(mycumprobthreshold),pdeltapct_atthreshold_calloption,pdeltapct_atthreshold_putoption)

# # #####################

else:

print(' No Call threshold found :::::::::::','float(cumprob_to_sell_price) >= float(mycumprobthreshold)',cumprob_to_sell_price,mycumprobthreshold)

#bbbbbb

elif optiontype == 'P':

if pdeltapct_to_sell_price < 0:

cumprob_to_sell_price = ss.percentileofscore(serDrawUp,(-1.0) * pdeltapct_to_sell_price)

cumprob_to_buy_price = ss.percentileofscore(serDrawUp,(-1.0) * pdeltapct_to_buy_price)

if str(pdeltapct_atthreshold_putoption) == 'nan':

if float(round(cumprob_to_sell_price,2)) <= float(round(mycumprobthreshold,2)):

print('Yes Put threshold found $$$$$$$$$$$$$$$$$$$$$$$$$$',cumprob_to_sell_price)

pdeltapct_atthreshold_putoption = previous_pdeltapct_for_put

print('Put',str(pdeltapct_atthreshold_putoption),'cumprob_to_sell_price > mycumprobthreshold',str(cumprob_to_sell_price),str(mycumprobthreshold))

# # #####################

previous_pdeltapct_for_put = pdeltapct_to_sell_price

if cumprob_to_sell_price != float('NaN'):

if cumprob_to_sell_price > mycumprob_to_sell_price_lowrange and cumprob_to_sell_price < mycumprob_to_sell_price_highrange:

iscandidate = 1

# ##########

# candidates are found and put into a dictionary, you might want some other data storage

#df_candidates[str(strike_at_sell_price)+optiontype] = row,rows[len(rows)-1]

#vvvvvvv

rows_optionpricescurrent.append([optionsymbol,exdate,vsymbol,optiontype,stockprice,strike_at_sell_price,strike_at_buy_price,'{percent:.2%}'.format(percent=pdeltapct_to_sell_price),'{percent:.2%}'.format(percent=cumprob_to_sell_price/100),'{percent:.2%}'.format(percent=cumprob_to_buy_price/100),row['bid'],row['ask'],row['impliedvolatility'],iscandidate])

#df_candidates[strike_at_sell_price,optiontype] = [symbol,optiontype,stockprice,strike_at_sell_price,'{percent:.2%}'.format(percent=pdeltapct_to_sell_price),round(cumprob_to_sell_price,1),row['bid'],row['ask'],row['impliedvolatility'],iscandidate]

#print(symbol,'price change from ',stockprice,'to strike_at_sell_price',strike_at_sell_price,'(','{percent:.2%}'.format(percent=pdeltapct_to_sell_price),') exp',exdate,cumprob_to_sell_price)

headers = rows_optionpricescurrent.pop(0)

df_cumprobsbystrikeranges = pd.DataFrame(rows_optionpricescurrent,columns=headers)

# qqqqqq

#print(df_cumprobsbystrikeranges)

print('----------------------------- ok got here -------------------------------------')

# #####################

# Based on mycumprobthreshold, how many breached total, up and down

df_stockpricechanges['breachedaboveorbelow'] = int(0)

breachedmycumprobthresholdf_total = 0

breachedmycumprobthresholdf_up = 0

breachedmycumprobthresholdf_down = 0

icountfartomidbeyondf_above = 0

icountfartomidbeyondf_below = 0

for index, row in df_stockpricechanges.iterrows():

#fartomidpricechangedelta = (row['priceDaysBackMid'] - row['priceDaysBackFar']) / row['priceDaysBackFar']

priceToBreachFarToMidf_FinishUp = float(row['priceDaysBackFar']) + (float(row['priceDaysBackFar']) * pdeltapct_atthreshold_calloption)

priceToBreachFarToMidf_FinishDown = float(row['priceDaysBackFar']) + (float(row['priceDaysBackFar']) * pdeltapct_atthreshold_putoption)

if float(row['priceDaysBackMid']) > priceToBreachFarToMidf_FinishUp:

icountfartomidbeyondf_above = icountfartomidbeyondf_above + 1

if float(row['priceDaysBackMid']) < priceToBreachFarToMidf_FinishDown:

icountfartomidbeyondf_below = icountfartomidbeyondf_below + 1

#print('priceToBreachFarToMid',round(float(row['priceDaysBackMid']),2),'by',round(priceToBreachFarToMidf_FinishUp,2),round(priceToBreachFarToMidf_FinishDown,2))

isbeyondmycumprobthresholdf_total = 0

if row['DrawUpPctChange'] > pdeltapct_atthreshold_calloption:

isbeyondmycumprobthresholdf_total = 1

breachedmycumprobthresholdf_up = breachedmycumprobthresholdf_up + 1

if row['DrawDownPctChange'] > abs(pdeltapct_atthreshold_putoption):

isbeyondmycumprobthresholdf_total = 1

breachedmycumprobthresholdf_down = breachedmycumprobthresholdf_down + 1

if isbeyondmycumprobthresholdf_total != 0:

breachedmycumprobthresholdf_total = breachedmycumprobthresholdf_total + 1

#df_stockpricechanges['breachedaboveorbelow'][str(index.date())] = 'breached mycumprobthreshold (' + str(mycumprobthreshold) + '%) ' + str(pdeltapct_atthreshold_calloption) + ' ' + str(pdeltapct_atthreshold_putoption) + ' ' + str(isbeyondmycumprobthresholdf_total) #breachedaboveorbelow

df_stockpricechanges['breachedaboveorbelow'][str(index.date())] = isbeyondmycumprobthresholdf_total

df_stockpricechanges['pdeltapct_atthreshold_calloption'] = pdeltapct_atthreshold_calloption

df_stockpricechanges['pdeltapct_atthreshold_putoption'] = pdeltapct_atthreshold_putoption

'''

/////////////////////////////////////////////////////////////////////////////////////////

SourceData CSV

/////////////////////////////////////////////////////////////////////////////////////////

'''

print('Len of df_stockpricechanges',len(df_stockpricechanges))

df_stockpricechanges.to_csv(sourcedatafolderwithsymbol + "\\ironcondor sourcedata (" + expirationdate_string + ') '+ symbol + " " + datestringforfilename + ".csv",columns=('dateDaysBackMid','dateDaysBackFar','priceRefDate','priceDaysBackMid','priceDaysBackFar','DeltaFartoMid','DrawDownMax','DrawUpMax','DrawDownPctChange','DrawUpPctChange','comppratfar','pdeltapct_atthreshold_calloption','pdeltapct_atthreshold_putoption','breachedaboveorbelow'))

# ==========

#print(df_cumprobsbystrikeranges)

# ==========

print('================= ok got here dummy#2345245 ==================')

# ####################################################

# Here is where we find the value of the credit spread

# still need to build the Put credit spread

candidaterows = []

'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

Candidate Header Row

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''

candidateheader = ['openshortoptsym','openlongoptsym','ty','stkp','spdel','sstrk','bstrk','scump','bcump','sbid','bask','capt','myexdate','currdate','daysbackmid','pricecompare','obsv','siv','sel']

candidaterows.append(candidateheader)

lastmycompareprice = df_comparestockpricehistory['Adj Close'][len(df_comparestockpricehistory)-1]

#print df_cumprobsbystrikeranges

crossedthresholdf_call = 0

crossedthresholdf_put = 0

sum_of_iter_capt_at_cumprob_cross = float(0)

previousrow = None

for index,row in df_cumprobsbystrikeranges.iterrows():

sel = ''

if float(row['iscandidate']) == 1:

openshortoptsym = row['optionsymbol']

myexdate = row['exdate']

myoptiontype = row['ty']

sellstockprice = row['st']

sellstrike = row['strike_at_sell_price']

sellcumprob = row['cumprob_to_sell_price']

buycumprob = row['cumprob_to_buy_price']

pdeltapct_to_sell_price = row['pdeltapct_to_sell_price']

sellbidprice = row['bid']

buyaskprice = float('NaN')

buystrike = float('NaN')

siv = row['iv']

row_inner_found = []

openlongoptsym = ''

iter_scump = float(row['cumprob_to_sell_price'].replace('%',''))

if row['ty'] == 'C' and crossedthresholdf_call == 0 and iter_scump >= mycumprobthreshold:

crossedthresholdf_call = 1

sel = 'x'

#print '&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&'

if row['ty'] == 'P' and crossedthresholdf_put == 0 and iter_scump <= mycumprobthreshold:

crossedthresholdf_put = 1

prevcandidaterow = candidaterows[len(candidaterows)-1]

a_indices = [i for i, x in enumerate(candidaterows[0]) if x == 'sel']

prevcandidaterow[a_indices[0]] = 'x'

#print 'tttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt'

#print prevcandidaterow

if row['ty'] == 'C':

for index_inner,row_inner in df_cumprobsbystrikeranges.iterrows():

if row_inner['ty'] == row['ty']:

if float(row_inner['strike_at_sell_price']) == float(row['strike_at_sell_price'])+myspreadindollars:

row_inner_found = row_inner

buyaskprice = row_inner['ask']

#buystrike = row_inner['strike_at_sell_price']

buystrike = mytools.get_from_optionsymbol().strike(row_inner['optionsymbol'])

openlongoptsym = row_inner['optionsymbol']

#print('CALL >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')

if row['ty'] == 'P':

for index_inner,row_inner in df_cumprobsbystrikeranges.iterrows():

if row_inner['ty'] == row['ty']:

if float(row_inner['strike_at_sell_price']) == float(row['strike_at_sell_price'])-myspreadindollars:

row_inner_found = row_inner

buyaskprice = row_inner['ask']

#buystrike = row_inner['strike_at_sell_price']

buystrike = mytools.get_from_optionsymbol().strike(row_inner['optionsymbol'])

openlongoptsym = row_inner['optionsymbol']

#print('PUT >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')

previousrow = row

'''

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

Candidate Value Rows

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

'''

# row_sel = [x for i, x in enumerate(candidaterows) if x[a_columnidx[0]] == 'x']

# print(row_sel)

# row_sel[0][a_columnidx] = round(float(sellbidprice)-float(buyaskprice),3)

# row_sel[1][a_columnidx] = round(float(sellbidprice)-float(buyaskprice),3)

# sum_of_iter_capt_at_cumprob_cross = sum_of_iter_capt_at_cumprob_cross - float(prevcandidaterow[a_indices[0]])

#candidaterows.append(candidateheader)

if len(openshortoptsym) > 0 and len(openlongoptsym) > 0:

candidaterows.append([openshortoptsym,openlongoptsym,myoptiontype,sellstockprice,pdeltapct_to_sell_price,sellstrike,buystrike,sellcumprob,buycumprob,sellbidprice,buyaskprice,round(float(sellbidprice)-float(buyaskprice),3),myexdate.strftime('%Y-%m-%d'),datestringforcsv,daysbackmid,compare_stock_price,number_of_observations,siv,sel])

#print(myexdate.strftime('%Y-%m-%d'),myoptiontype,sellstockprice,pdeltapct_to_sell_price,sellstrike,buystrike,sellcumprob,sellbidprice,buyaskprice,round(float(sellbidprice)-float(buyaskprice),3))

#print(row)

#print('row_inner_found ------------------------------------------------')

#print(row_inner_found)

## #############################

## Example of List Comprehension

#a_columnidx = [i for i, x in enumerate(candidaterows[0]) if x == 'sel']

#print('x[a_columnidx[0]]',a_columnidx[0])

#for r in candidaterows:

# if r[a_columnidx[0]] == 'x':

# print(r)

print('================= ok got here dummy#42231 ==================')

headers = candidaterows.pop(0)

# This might not work - 9999999999999999

print('len(candidaterows)',len(candidaterows))

if len(candidaterows) > 1:

if len(candidaterows[1]) > 0:

print('jkjkjkjkjkjkj candidaterows.count',len(candidaterows[1]))

df_candidates = pd.DataFrame(candidaterows,columns=headers)

'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

Candidate Value CSV

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''

df_candidates.to_csv(candidatesfolderwithsymbol + "\\ironcondor candidates (" + expirationdate_string + ') ' + symbol + " " + datestringforfilename + ".csv",columns=('openshortoptsym','openlongoptsym','ty','stkp','spdel','sstrk','bstrk','scump','bcump','sbid','bask','capt','myexdate','currdate','daysbackmid','pricecompare','obsv','siv','sel'))

# #################################################

# Use Dictionary Comprehension to get selected rows

selectedcandidaterows = []

list_selectedcandidaterows_1 = []

list_selectedcandidaterows_1.append(candidateheader)

#dict_selectedcandidaterows = { k:r for k,r in df_candidates.iterrows() if r['sel'] == 'x'}

#list_selectedcandidaterows_2 = [ r for k,r in df_candidates.items() if r['sel'] == 'x']

#print('777777777777777777777')

#print(list_selectedcandidaterows_2)

#selectedcandidaterows = list_selectedcandidaterows_2.extend(list_selectedcandidaterows_1)

# ################################

# The above is equivalent to below

for k,candidaterow in df_candidates.iterrows():

if candidaterow['sel'] == 'x':

list_selectedcandidaterows_1.append(candidaterow)

print('!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')

print('!! Found a selected strike from candidate list')

headers = list_selectedcandidaterows_1.pop(0)

df_selectedcandidates = pd.DataFrame(list_selectedcandidaterows_1,columns=headers)

#print('2 ##########################################')

#print(df_selectedcandidates)

#print('1 ##########################################')

#print(list_selectedcandidaterows)

#print('Try dict',type(list_selectedcandidaterows) is dict)

#print(list(list_selectedcandidaterows.items()))

#print('2 ##########################################')

filepath_to_selectedcandidates = os.path.join(myselectedcandidatesfolder,symbol,'selectedcandidates ' + expirationdate_string + ' ' + symbol + '.csv')

mygeneral.make_sure_path_exists(path_base(filepath_to_selectedcandidates)) #sourcedatafolderwithsymbol

#import os

#Python 2-3 differences

#df_selectedcandidaterows = pd.DataFrame(df_selectedcandidates, columns=['ty','stkp','spdel','sstrk','bstrk','scump','bcump','sbid','bask','capt','myexdate','currdate','daysbackmid','pricecompare','obsv','sel'])

#print(df_selectedcandidaterows)

#df_selectedcandidaterows = pd.DataFrame(list_selectedcandidaterows.items(), columns=['ty','stkp','spdel','sstrk','bstrk','scump','bcump','sbid','bask','capt','myexdate','currdate','daysbackmid','pricecompare','obsv','sel'])

#print('3 ##########################################')

'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

Selected Candidates CSV

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''

print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')

print('>> here are the current contents of the df_selectedcandidates dataframe')

print(df_selectedcandidates)

if os.path.isfile(filepath_to_selectedcandidates) != True:

df_selectedcandidates.to_csv(filepath_to_selectedcandidates,columns=('openshortoptsym','openlongoptsym','ty','stkp','spdel','sstrk','bstrk','scump','bcump','sbid','bask','capt','myexdate','currdate','daysbackmid','pricecompare','obsv','siv','sel'))

elif len(df_selectedcandidates) > 0:

#df_fromcsv = pd.read_csv(filepath_to_selectedcandidates, index_col=0)

existingcsvfile = open(filepath_to_selectedcandidates, 'a') # Open file as append mode

df_selectedcandidates.to_csv(existingcsvfile, header = False)

existingcsvfile.close()

#print(df_candidates)

crossedthresholdf_call = 0

crossedthresholdf_put = 0

capturedspreadf_at_call_thresholdf_cross = float('Nan')

capturedspreadf_at_put_thresholdf_cross = float('Nan')

sellstrike_at_call_thresholdf_cross = float('Nan')

sellstrike_at_put_thresholdf_cross = float('Nan')

previousrow = None

for index,row in df_candidates.iterrows():

iter_scump = float(row['scump'].replace('%',''))

if row['ty'] == 'C' and crossedthresholdf_call == 0 and iter_scump >= mycumprobthreshold:

crossedthresholdf_call = 1

capturedspreadf_at_call_thresholdf_cross = float(row['capt'])

sellstrike_at_call_thresholdf_cross = float(row['sstrk'])

if row['ty'] == 'P' and crossedthresholdf_put == 0 and iter_scump <= mycumprobthreshold:

crossedthresholdf_put = 1

if previousrow != None:

capturedspreadf_at_put_thresholdf_cross = float(previousrow['capt'])

sellstrike_at_put_thresholdf_cross = float(previousrow['sstrk'])

# previousrow need only to be done for put side

previousrow = row

#print('scump=',iter_scump)

print('-- ------------------------------------------')

print('-- here are some result written out')

print('Symbol:',symbol)

print(' Current stock price:',round(stockprice,2))

print(' Today:',today_date)

print(' Expire Date:',expire_date )

print(' Number of Days to Expiration:',delta.days)

print(' Price changes start date:',startdatecalculatedf_string)

print(' Number Of Observations Found',number_of_observations)

print(' Price delta % for strike to meet',str(mycumprobthreshold)+'%','cumprob threshold (Call):','{percent:.2%}'.format(percent=pdeltapct_atthreshold_calloption))

print(' Price delta % for strike to meet',str(mycumprobthreshold)+'%','cumprob threshold (Put):','{percent:.2%}'.format(percent=pdeltapct_atthreshold_putoption))

print('DRAW UP & DOWN ---- What happened during')

print(' Breaches at',mycumprobthreshold,'% cumulative prob threshold (Far to Mid)')

print(' Count of Draw UP breaches: ',breachedmycumprobthresholdf_up)

print(' Count of Draw DOWN breaches:',breachedmycumprobthresholdf_down)

print(' Count of Draw TOTAL breaches: ',breachedmycumprobthresholdf_total,'of',number_of_observations)

print(' Prcnt of Draw TOTAL breaches: ','{percent:.2%}'.format(percent=(breachedmycumprobthresholdf_total)/float(number_of_observations)))

print('FINISH ---- What happened during')

print(' Count finishes.... Above price set by cumprob threshold=',icountfartomidbeyondf_above)

print(' Count finishes.... Below price set by cumprob threshold=',icountfartomidbeyondf_below)

print(' Count finishes.... Above or below price set by cumprob =',icountfartomidbeyondf_above+icountfartomidbeyondf_below,'of',number_of_observations)

print(' Prcnt finishes.... Above or below price set by cumprob =','{percent:.2%}'.format(percent=(icountfartomidbeyondf_above+icountfartomidbeyondf_below)/float(number_of_observations)))

print('Condor Specs--------')

print(' sellstrike_at_call_thresholdf_cross=',round(sellstrike_at_call_thresholdf_cross,2))

print(' sellstrike_at_put_thresholdf_cross=',round(sellstrike_at_put_thresholdf_cross,2))

print(' Capture at Call threshold:',capturedspreadf_at_call_thresholdf_cross)

print(' Capture at Put threshold:',capturedspreadf_at_put_thresholdf_cross)

print(' Capture Total Amt=',round(capturedspreadf_at_call_thresholdf_cross+capturedspreadf_at_put_thresholdf_cross,2))

print(' sum_of_iter_capt_at_cumprob_cross',sum_of_iter_capt_at_cumprob_cross)

print(' Note: candidates occur where stock price does not close at levels set by cumprob sell price, meaning your condor was a success')

print(' ',mycomparesym+':',round(compare_stock_price,2))

print('-----------------')

#for k,v in df_candidates.items():

# print(k)

# print(v)

'''

rows = []

rows.append(['strike','optionsymbol','last','bid','ask','change','pctchange','volume','openinterest','impliedvolatility'])

for tr in table:

d = [td.text_content().strip().replace(',','') for td in tr.xpath('./td')]

rows.append(d)

stockprice=stock(symbol)

headers = rows.pop(0)

import pandas as pd

df_optionpricescurrent = pd.DataFrame(rows, columns=headers)

#import numpy as np

df_optionpricescurrent['stockprice'] = stockprice