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)
'''
print('')
print(symbol,'Draw Down:',len(df_0.index), 'observations',ibeyondabove,'breached',ThreshholdBelow)
print(' ','DaysBackFar',daysbackfar,' DaysBackMid:',daysbackmid)
serDrawDown = pd.Series(df_0['DrawDownPctChange'])
serDrawDown.hist(cumulative=True, normed=1, bins=ibeyondbelow)
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()
import pullprices as pp
df = pp.options_to_dataframe(symbol,expirationdate_string,0)
if showresults == 1:
# ==========
print('-----',symbol,'Option Prices','-----')
print(df)
# ==========
stockprice = pp.stock(symbol)
# ##########################
# Calculate Analysis Results
import mytools
osymbol = mytools.get_from_optionsymbol()
rows = []
def find(
self,
symbol='SPY',
minimum_midspread=0.2,
numberofweeksahead=1,
expirationday='friday' #'friday' #'wednesday for index'
):
import config
import pandas as pd
# ##########
# 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
myoutputfolder = config.myoutputfolder #'C:\\Batches\\rts\\output\\condor\\candidates'
#mycandidatesfolder = config.mycandidatesfolder #'C:\\Batches\\rts\\output\\condor\\candidates'
#mysourcedatafolder = config.mysourcedatafolder #'C:\\Batches\\rts\\output\\condor\\candidates'
#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()
today_datestring = today_date.strftime('%Y-%m-%d')
newdatetime = today_date
iter_date = today_date
print 'numberofweeksahead', numberofweeksahead
for expirationcounter in range(numberofweeksahead):
print 'Range counter', expirationcounter, range(numberofweeksahead)
newdatetime += datetime.timedelta(7 * expirationcounter)
print 'newdatetime', newdatetime
iter_date = newdatetime
if expirationcounter + 1 == numberofweeksahead:
print('expirationcounter', expirationcounter)
print(' range(numberofweeksahead)', range(numberofweeksahead))
while iter_date.weekday() != self.dayofweek_int(expirationday):
iter_date += datetime.timedelta(1)
expirationdate_string = str(iter_date)
expirationdatetime_string = str(iter_date) + ' 16:00'
#print('expirationdatetime_string',expirationdatetime_string)
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()
expire_datetime = datetime.datetime.strptime(
expirationdatetime_string, '%Y-%m-%d %H:%M')
#print('expire_datetime',expire_datetime)
if today_date != expire_date:
break
today_date = today_date - datetime.timedelta(hours=24)
delta_precise = expire_datetime - today_datetime
delta_precise_in_seconds = delta_precise.total_seconds()
delta_precise_in_minutes = delta_precise_in_seconds / 60
delta_precise_in_hours = delta_precise_in_seconds / 60.0 / 60.0
#print('delta_precise',delta_precise)
print('delta_precise_in_seconds', delta_precise_in_seconds)
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)
#df_stockprice_realtime_to_dataframe = pp.stockprice_realtime_to_dataframe(pricingsymbol)
#print('$$$$$$$$$$$$$ cvcvcvcvc $$$$$$$$$$$$$$$$$')
#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(' Now:', today_datetime)
today_datetimestring = today_datetime.strftime(
'%Y-%m-%d %H:%M:%S')
print(' Now string', today_datetimestring)
print(' Expire Date:', expire_date)
print(' Number of Days to Expiration:', delta.days)
print(' Number of Hours to Expiration:',
delta_precise_in_hours)
print(' Number of minutes to expiration:',
delta_precise_in_minutes)
#lastprice = df_stockprice_realtime_to_dataframe.iloc[0]['last']
#time = df_stockprice_realtime_to_dataframe.iloc[0]['time']
# print(df_optionpricescurrent)
# print(df_optionpricescurrent.iloc[0])
# print(type(df_optionpricescurrent.iloc[0]))
stockprice = df_optionpricescurrent.iloc[0]['stockprice']
time = df_optionpricescurrent.iloc[0]['time']
print(' Last stock trade(price+time):', stockprice, time)
#Evaluate spreads
#print(df_optionpricescurrent)
maxstrikecall = 0
maxstrikeput = 10000
df_optionpricesindexed = df_optionpricescurrent.set_index(
['optionsymbol'])
ls_bestcall = []
ls_bestput = []
ofromsymbol = mytools.get_from_optionsymbol()
for index, row in df_optionpricesindexed.iterrows():
seriesshort = row
#print(row)
optiontype = ofromsymbol.optiontype(index)
optionsymbolshort = ''
if optiontype == 'C':
iter_offset = 1
if float(row.strike) > float(stockprice):
optionsymbolshort = index
if optiontype == 'P':
iter_offset = -1
if float(row.strike) < float(stockprice):
optionsymbolshort = index
if len(optionsymbolshort) > 0:
optionsymbollong = ofromsymbol.offsetstrikeoptionsymbol(
index, iter_offset)
if optionsymbollong in df_optionpricesindexed.index:
serieslong = df_optionpricesindexed.loc[
optionsymbollong]
shortmid = (float(seriesshort.bid) +
float(seriesshort.ask)) / 2
longmid = (float(serieslong.bid) +
float(serieslong.ask)) / 2
midspread = round(shortmid - longmid, 2)
deltapct = round(
(float(seriesshort.strike) -
float(stockprice)) / float(stockprice), 3)
#print('maxstrikecall',maxstrikecall,seriesshort.strike,midspread,seriesshort.bid)
if midspread > minimum_midspread:
if optiontype == 'C':
if float(seriesshort.strike
) > maxstrikecall:
maxstrikecall = float(
seriesshort.strike)
ls_bestcall = [
optionsymbolshort,
seriesshort.strike,
serieslong.strike, midspread,
deltapct
]
if optiontype == 'P':
if float(seriesshort.strike
) < maxstrikeput:
maxstrikeput = float(
seriesshort.strike)
ls_bestput = [
optionsymbolshort,
seriesshort.strike,
serieslong.strike, midspread,
deltapct
]
print(ls_bestcall)
print(ls_bestput)
# for optionsymbol in df_optionpricescurrent['optionsymbol']:
# #df.append(Function(x))
# iter_strike = mytools.get_from_optionsymbol.strike(optionsymbol)
# iter_optiontype = mytools.get_from_optionsymbol.optiontype(optionsymbol)
# if iter_optiontype == 'C':
# if iter_strike > stockprice:
# print(iter_optiontype,iter_strike,optionsymbol)
# print(df_optionpricesindexed.loc[optionsymbol])
callsymbol = ''
putsymbol = ''
callstrike = ''
putstrike = ''
capturecall = ''
captureput = ''
deltapctcall = ''
deltapctput = ''
print('ls_bestcall', ls_bestcall)
if len(ls_bestcall) >= 3:
callsymbol = ls_bestcall[0]
callstrike = ls_bestcall[1]
capturecall = ls_bestcall[3]
deltapctcall = ls_bestcall[4]
if len(ls_bestput) >= 3:
putsymbol = ls_bestput[0]
putstrike = ls_bestput[1]
captureput = ls_bestput[3]
deltapctput = ls_bestput[4]
'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
make dataframe
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''
dataframerows = []
dataframeheader = [
'datestamp', 'symbol', 'minimum_midspread',
'stockprice', 'time', 'callsymbol', 'putsymbol',
'callstrike', 'putstrike', 'capturecall', 'captureput',
'deltapctcall', 'deltapctput', 'mintoexp'
]
dataframerows.append(dataframeheader)
dataframerows.append([
today_datetimestring,
symbol,
minimum_midspread,
stockprice,
time
# , ls_bestcall[0],ls_bestput[0]
# , ls_bestcall[3],ls_bestput[3]
# , ls_bestcall[4],ls_bestput[4]
,
callsymbol,
putsymbol,
callstrike,
putstrike,
capturecall,
captureput,
deltapctcall,
deltapctput,
delta_precise_in_minutes
])
headers = dataframerows.pop(0)
df = pd.DataFrame(dataframerows, columns=headers)
df_2 = df.set_index('datestamp')
print(df_2)
'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
output to CSV
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''
filelocation_string = myoutputfolder + "\\intraday " + today_datestring + ' ' + symbol + '.csv'
if os.path.exists(filelocation_string) != True:
df_2.to_csv(
filelocation_string,
columns=(
# 'datestamp',
'symbol',
'minimum_midspread',
'stockprice',
'time',
'callsymbol',
'putsymbol',
'callstrike',
'putstrike',
'capturecall',
'captureput',
'deltapctcall',
'deltapctput',
'mintoexp'))
else:
with open(filelocation_string, 'a') as f:
df_2.to_csv(f, header=False)
print 'file location:', filelocation_string
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()
# ####################################################
# Get Option Prices
import pullprices as pp
df_optionpricescurrent = pp.options_to_dataframe(symbol, expirationdate_string,
0)
if showresults == 1:
# ==========
print('-----', symbol, 'Option Prices', '-----')
print(df_optionpricescurrent)
# ==========
stockprice = pp.stock(symbol)
# ##########################
# Calculate Analysis Results
#import mytools
osymbol = mytools.get_from_optionsymbol()
rows = []
#rows.append(['optionsymbol','stockprice','strike','pdeltapct_to_sell_price','cumprob_to_sell_price','bid','ask','last'])
serDrawUp = pd.Series(df_0['DrawUpPctChange'])
serDrawUp.hist(cumulative=True, normed=1, bins=ibeyondabove)
plt.show()
import numpy as np
import scipy.stats as ss
for n in np.linspace(0, 1, 1000, endpoint=False):
cumprob = ss.percentileofscore(serDrawUp, n)
if cumprob >= 80:
print(round(cumprob, 1), 'percent of observations closed up inside of',
'{percent:.2%}'.format(percent=n)), 'percent'
break
print('')
print(symbol, 'Draw Down:', len(df_0.index), 'observations')
print(' ', 'DaysBackFar', daysbackfar, ' DaysBackMid:', daysbackmid)
serDrawDown = pd.Series(df_0['DrawDownPctChange'])
serDrawDown.hist(cumulative=True, normed=1, bins=ibeyondbelow)
plt.show()
for n in np.linspace(0, 1, 1000, endpoint=False):
cumprob = ss.percentileofscore(serDrawDown, n)
if cumprob >= 80:
print(round(cumprob,
1), 'percent of observations closed down inside of',
'{percent:.2%}'.format(percent=(-1) * n)), 'percent'
break
import pullprices as pp
df = pp.options_to_dataframe(symbol, '2015-06-19', 0)
print(df)
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()
# ####################################################
# Get Option Prices
import pullprices as pp
df_optionpricescurrent = pp.options_to_dataframe(pricingsymbol,expirationdate_string,0)
if showresults == 1:
# ==========
print('-----',symbol,'Option Prices','-----')
#print(df_optionpricescurrent)
# ==========
stockprice = pp.stock(symbol)
# ##########################
# 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'])
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 3 12:16:45 2015
@author: jmalinchak
"""
import pullprices as pp
df = pp.options_to_dataframe('SPY','2015-07-17',0)
df['optiontype'] = 'X'
import mytools
for index, row in df.iterrows():
optionsymbol = row['optionsymbol']
df['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
dummy_ask_at_sell_price = row['ask']
optiontype = mytools.get_from_optionsymbol().optiontype(row['optionsymbol'])
if optiontype == 'C':
strike_at_buy_price = float(strike_at_sell_price) + float(1)
#Decimal('3.214').quantize(TWOPLACES)
else:
strike_at_buy_price = float(strike_at_sell_price) - float(1)
df['optiontype'][index] = mytools.get_from_optionsymbol().optiontype(row['optionsymbol'])
serDrawDown.hist(cumulative=True, normed=1, bins=ibeyondbelow)
plt.show()
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 >= 80:
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
import pullprices as pp
df = pp.options_to_dataframe(symbol, expirationdate_string, 0)
if showresults == 1:
# ==========
print(df)
# ==========
stockprice = pp.stock(symbol)
print(stockprice)
import mytools
osymbol = mytools.get_from_optionsymbol()
rows = []
#rows.append(['optionsymbol','stockprice','strike','pdeltapct_to_sell_price','cumprob_to_sell_price','bid','ask','last'])
rows.append([
'exdate', 'symbol', 'ty', 'st', 'strike_at_sell_price',
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 3 12:16:45 2015
@author: jmalinchak
"""
import pullprices as pp
df = pp.options_to_dataframe('SPY', '2015-07-17', 0)
df['optiontype'] = 'X'
import mytools
for index, row in df.iterrows():
optionsymbol = row['optionsymbol']
df['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
dummy_ask_at_sell_price = row['ask']
optiontype = mytools.get_from_optionsymbol().optiontype(
row['optionsymbol'])
if optiontype == 'C':
strike_at_buy_price = float(strike_at_sell_price) + float(1)
#Decimal('3.214').quantize(TWOPLACES)
else:
strike_at_buy_price = float(strike_at_sell_price) - float(1)
import scipy.stats as ss
print(ss.percentileofscore([1, 2, 3, 4], 3))
import numpy as np
print(np.arange(0, 1, 0.1))
print(np.linspace(0, 1, 11))
for n in np.linspace(0, .3, 1000, endpoint=False):
print(n)
import pullprices as pp
df = pp.options_to_dataframe('MSFT', '2015-06-19', 0)
print(df)
import datetime
today_date = datetime.datetime.now()
print 'from time: ', today_date.strftime('%Y-%m-%d')
print 'from time: ', today_date.strftime('%Y%m%d%H%M%S')
print(today_date)
expire_date = datetime.datetime.strptime('2015-06-19', '%Y-%m-%d').date()
print(expire_date)
delta = expire_date - today_date.date()
print(delta.days)
import time
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 15 15:23:29 2015
@author: justin.malinchak
"""
# ##########
# Date setup
import pullprices as pp
import datetime
today_datetime = datetime.datetime.today()
today_date = datetime.date.today()
iter_date = today_date
for i in range(8):
while iter_date.weekday() != 4:
iter_date += datetime.timedelta(1)
iter_date_string = iter_date
iter_date += datetime.timedelta(1)
print i,iter_date_string
df_optionpricescurrent = pp.options_to_dataframe('SPY',iter_date_string,0)
if not df_optionpricescurrent is None:
print df_optionpricescurrent
else:
print 'Ha, caugnt the issue'
import pandas as pd
print('pandas verion',pd.__version__)
import scipy.stats as ss
print(ss.percentileofscore([1, 2, 3, 4], 3))
import numpy as np
print(np.arange(0,1,0.1))
print(np.linspace(0,1,11))
for n in np.linspace(0,.3,1000,endpoint=False):
print(n)
import pullprices as pp
df = pp.options_to_dataframe('MSFT','2015-06-19',0)
print(df)
import datetime
today_date = datetime.datetime.now()
print 'from time: ',today_date.strftime('%Y-%m-%d')
print 'from time: ',today_date.strftime('%Y%m%d%H%M%S')
print(today_date)
expire_date = datetime.datetime.strptime('2015-06-19','%Y-%m-%d').date()
print(expire_date )
delta = expire_date - today_date.date()
print(delta.days)
import time
millis = int(round(time.time() * 1000))
print millis
def find(self
, symbol = 'SPY'
, numberofweeksahead = 1
, expirationday = 'friday' #'friday' #'wednesday for index'
):
import config
import pandas as pd
# ##########
# 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
myoutputfolder = config.myoutputfolder #'C:\\Batches\\rts\\output\\condor\\candidates'
#mycandidatesfolder = config.mycandidatesfolder #'C:\\Batches\\rts\\output\\condor\\candidates'
#mysourcedatafolder = config.mysourcedatafolder #'C:\\Batches\\rts\\output\\condor\\candidates'
#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()
today_datestring = today_date.strftime('%Y-%m-%d')
iter_date = today_date
for expirationcounter in range(numberofweeksahead):
if expirationcounter + 1 == numberofweeksahead:
print('expirationcounter',expirationcounter)
print(' range(numberofweeksahead)', range(numberofweeksahead))
while iter_date.weekday() != self.dayofweek_int(expirationday):
iter_date += datetime.timedelta(1)
expirationdate_string = str(iter_date)
expirationdatetime_string = str(iter_date)+' 16:00'
#print('expirationdatetime_string',expirationdatetime_string)
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()
expire_datetime = datetime.datetime.strptime(expirationdatetime_string,'%Y-%m-%d %H:%M')
#print('expire_datetime',expire_datetime)
if today_date != expire_date:
break
today_date = today_date - datetime.timedelta(hours=24)
delta_precise = expire_datetime - today_datetime
delta_precise_in_seconds = delta_precise.total_seconds()
delta_precise_in_minutes = delta_precise_in_seconds/60
delta_precise_in_hours = delta_precise_in_seconds/60.0/60.0
#print('delta_precise',delta_precise)
print('delta_precise_in_seconds',delta_precise_in_seconds)
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)
#df_stockprice_realtime_to_dataframe = pp.stockprice_realtime_to_dataframe(pricingsymbol)
#print('$$$$$$$$$$$$$ cvcvcvcvc $$$$$$$$$$$$$$$$$')
#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(' Now:',today_datetime)
today_datetimestring = today_datetime.strftime('%Y-%m-%d %H:%M:%S')
print(' Now string',today_datetimestring)
print(' Expire Date:',expire_date )
print(' Number of Days to Expiration:',delta.days)
print(' Number of Hours to Expiration:',delta_precise_in_hours)
print(' Number of minutes to expiration:',delta_precise_in_minutes)
#lastprice = df_stockprice_realtime_to_dataframe.iloc[0]['last']
#time = df_stockprice_realtime_to_dataframe.iloc[0]['time']
# print(df_optionpricescurrent)
# print(df_optionpricescurrent.iloc[0])
# print(type(df_optionpricescurrent.iloc[0]))
stockprice = df_optionpricescurrent.iloc[0]['stockprice']
time = df_optionpricescurrent.iloc[0]['time']
print(' Last stock trade(price+time):',stockprice,time)
#Evaluate spreads
#print(df_optionpricescurrent)
maxstrikecall = 0
maxstrikeput = 10000
df_optionpricesindexed = df_optionpricescurrent.set_index(['optionsymbol'])
for index, row in df_optionpricesindexed.iterrows():
seriesshort = row
#print(row)
optiontype = mytools.get_from_optionsymbol.optiontype(index)
optionsymbolshort = ''
if optiontype == 'C':
iter_offset = 1
if float(row.strike) > float(stockprice):
optionsymbolshort = index
if optiontype == 'P':
iter_offset = -1
if float(row.strike) < float(stockprice):
optionsymbolshort = index
if len(optionsymbolshort) > 0:
optionsymbollong = mytools.get_from_optionsymbol.offsetstrikeoptionsymbol(index,iter_offset)
if optionsymbollong in df_optionpricesindexed.index:
serieslong = df_optionpricesindexed.loc[optionsymbollong]
shortmid = (float(seriesshort.bid) + float(seriesshort.ask))/2
longmid = (float(serieslong.bid) + float(serieslong.ask))/2
midspread = round(shortmid - longmid,2)
deltapct = round((float(seriesshort.strike) - float(stockprice)) / float(stockprice),3)
if midspread > 0.20:
if optiontype == 'C':
if float(seriesshort.strike) > maxstrikecall:
maxstrikecall = float(seriesshort.strike)
ls_bestcall = [optionsymbolshort,seriesshort.strike, serieslong.strike,midspread,deltapct]
if optiontype == 'P':
if float(seriesshort.strike) < maxstrikeput:
maxstrikeput = float(seriesshort.strike)
ls_bestput = [optionsymbolshort,seriesshort.strike, serieslong.strike,midspread,deltapct]
print(ls_bestcall)
print(ls_bestput)
# for optionsymbol in df_optionpricescurrent['optionsymbol']:
# #df.append(Function(x))
# iter_strike = mytools.get_from_optionsymbol.strike(optionsymbol)
# iter_optiontype = mytools.get_from_optionsymbol.optiontype(optionsymbol)
# if iter_optiontype == 'C':
# if iter_strike > stockprice:
# print(iter_optiontype,iter_strike,optionsymbol)
# print(df_optionpricesindexed.loc[optionsymbol])
'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
make dataframe
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''
dataframerows = []
dataframeheader = [
'datestamp',
'symbol','stockprice','time'
, 'callsymbol','putsymbol'
, 'capturecall','captureput'
, 'deltapctcall','deltapctput'
, 'mintoexp'
]
dataframerows.append(dataframeheader)
dataframerows.append([
today_datetimestring,symbol,stockprice,time
, ls_bestcall[0],ls_bestput[0]
, ls_bestcall[3],ls_bestput[3]
, ls_bestcall[4],ls_bestput[4]
, delta_precise_in_minutes
])
headers = dataframerows.pop(0)
df = pd.DataFrame(dataframerows,columns=headers)
df_2 = df.set_index('datestamp')
print(df_2)
'''@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
output to CSV
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'''
filelocation_string = myoutputfolder + "\\intraday " + today_datestring + ' ' + symbol +'.csv'
if os.path.exists(filelocation_string) != True:
df_2.to_csv(filelocation_string,columns=(
# 'datestamp',
'symbol','stockprice','time'
, 'callsymbol','putsymbol'
, 'capturecall','captureput'
, 'deltapctcall','deltapctput'
, 'mintoexp'
))
else:
with open(filelocation_string, 'a') as f:
df_2.to_csv(f, header=False)
print('')
print(symbol,'Draw Up:',len(df_0.index), 'observations')
print(' ','DaysBackFar',daysbackfar,' DaysBackMid:',daysbackmid)
serDrawUp = pd.Series(df_0['DrawUpPctChange'])
serDrawUp.hist(cumulative=True, normed=1, bins=ibeyondabove)
plt.show()
import numpy as np
import scipy.stats as ss
for n in np.linspace(0,1,1000,endpoint=False):
cumprob = ss.percentileofscore(serDrawUp, n)
if cumprob >= 80:
print(round(cumprob,1),'percent of observations closed up inside of','{percent:.2%}'.format(percent=n)),'percent'
break
print('')
print(symbol,'Draw Down:',len(df_0.index), 'observations')
print(' ','DaysBackFar',daysbackfar,' DaysBackMid:',daysbackmid)
serDrawDown = pd.Series(df_0['DrawDownPctChange'])
serDrawDown.hist(cumulative=True, normed=1, bins=ibeyondbelow)
plt.show()
for n in np.linspace(0,1,1000,endpoint=False):
cumprob = ss.percentileofscore(serDrawDown, n)
if cumprob >= 80:
print(round(cumprob,1),'percent of observations closed down inside of','{percent:.2%}'.format(percent=(-1)*n)),'percent'
break
import pullprices as pp
df = pp.options_to_dataframe(symbol,'2015-06-19',0)
print(df)
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 18 09:53:32 2015
@author: jmalinchak
"""
import pullprices as pp
df = pp.options_to_dataframe('^VIX','2015-07-24')
#df = pp.options_to_dataframe('SPY','2015-07-24')
print('len(df)',len(df))
