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"EXIT STRATEGY 1" MEANS EXIT AT CALCULATED PERCENTILE D7-D10 OR OTHER DEFINED EXIT TIME FRAME; IF THE EXIT PERCENTILE/PERCENTAGE IS NOT ACHIEVED THEN EXIT AT CLOSE D10.
"EXIT STRATEGY 2" MEANS LOOK FOR EXIT POINT D7-10 OR OTHER DEFINED EXIT TIME FRAME WHERE THE LOSS IS NO MORE THAN 5%
"EXIT STRATEGY 3" MEANS THAT IF THE CALC SELL IS NOT ACHIEVED DURING THE DEFINED WIN TIME, THEN THE POSITION IS CLOSED AT CLOSE ON D6 OR OTHER DEFINED WIN TIME.
"EXIT STRATEGY 4" Means that if win is not reached by day 10, then position is held 'til day 20/expiration
########################################################################################################################################
def trade_analyzer(data,sellpc,buypc,exstr,exitpc,mmgmt,strategyformula,acct):
trpl = ''.join([strategyformula,'_Trade_Profit/Loss'])
profit = 1 + (sellpc-buypc)
entries = pd.Series(data=(data['D2Lo/D2op'] <= buypc),index=data.index,name=''.join([strategyformula,'_Got_In?']))
if exstr == 'EX4':
wins = pd.Series(data=((data['Trade_Hi'] >= sellpc) & (entries == True)),index=data.index,name=''.join([strategyformula,'_Wins?']))
else:
wins = pd.Series(data=((data['per1hi_percent'] >= sellpc) & (entries == True)),index=data.index,name=''.join([strategyformula,'_Wins?']))
if exstr == 'EX1':
successfulexits = pd.Series(data=((wins == False) & (entries == True) & (data['per2hi_percent'] >= exitpc)),index=data.index,name=''.join([strategyformula,'_Successful_Exits']))
elif exstr == 'EX2':
successfulexits = pd.Series(data=(wins == False) & (entries == True) & (data['per2hi_percent'] >= (data['d2op']*.95)),index=data.index,name=''.join([strategyformula,'_Successful_Exits']))
elif exstr == 'EX3' or exstr == 'EX4':
successfulexits = pd.Series(data=False,index=data.index,name=''.join([strategyformula,'_Successful_Exits']))
if exstr == 'EX3':
escape = pd.Series(data=np.where(((wins == False) & (entries == True) & (successfulexits == False)),exstr, np.nan),index=data.index,name=''.join([strategyformula,'_Escapes?']))
elif exstr == 'EX1':
escape = pd.Series(data=(np.where((wins == False) & (entries == True) & (successfulexits == False),exstr, np.nan)),index=data.index,name=''.join([strategyformula,'_Escapes?']))
elif exstr == 'EX2':
escape = pd.Series(data=(np.where((wins == False) & (entries == True) & (successfulexits == False),exstr, np.nan)),index=data.index,name=''.join([strategyformula,'_Escapes?']))
elif exstr == 'EX4':
escape = pd.Series(data=(np.where((wins == False) & (entries == True) & (successfulexits == False),exstr, np.nan)),index=data.index,name=''.join([strategyformula,'_Escapes?']))
tradepl = pd.Series(data=(np.where(wins == True,(acct * (sellpc-buypc)),
np.where(successfulexits == True,(acct * (exitpc-buypc)),
np.where(escape == 'EX3',(acct * (data['per1cl_percent']-buypc)),
np.where(((escape == 'EX2') & (successfulexits == False)),((acct * (data['per2cl_percent']-buypc)),
np.where(((escape == 'EX1') & (successfulexits == False)),((acct * (data['per2cl_percent']-buypc)-acct),
np.where((escape == 'EX4'),(acct * (data['per3cl_percent']-buypc)),np.nan))))))),index=data.index,name=trpl)
ev = ''.join([strategyformula,'_Evaluation'])
eval = pd.Series(data=(np.where(((wins == False) | ((wins == True) & (tradepl == ((acct * profit)-acct)))),True,False)),index=data.index, name =''.join([strategyformula,'_Win_Calc_Evaluation'])) #Discuss this calc with Dad
datah = pd.concat([entries,wins,successfulexits,escape,tradepl,eval],axis=1)
return datah
def decoder(strategy):
#"20/10.GLB,7-10.GLB,EX1,MGMT1"
st = strategy.split(',',3)
buysell = st[0].split('/',1)
buyperc = buysell[0]
buysell = buysell[1].split('.',1)
sellperc = buysell[0]
sellset = buysell[1]
if len(st[1]) != 0:
exit = st[1].split('.',1)
exitperiod = exit[0]
exitset = exit[1]
else:
exitperiod = np.nan
exitset = np.nan
exitstrategy = st[2]
mmgmt = st[3]
return buyperc,sellperc,sellset,exitperiod,exitset,exitstrategy,mmgmt
def reporter(data,strategy,option,account):
buyperc,sellperc,sellset,exitperiod,exitset,exstr,mmgmt = decoder(strategy)
report = pd.DataFrame(index=np.arange(1),columns=criteria)
strategyformula = ''.join([strategy,',',option.replace('.csv','')])
report['Strategy_Formula'] = strategyformula
report['#_in_Dataset'] = len(data)
buypc = data['D2Lo/D2op'].quantile(q=int(buyperc)/100)
report['Buy_Target_%'] = buypc
acct = account * .5
semiset = data[data['D2Lo/D2op'] <= buypc] #Semi-Restricted Data Set
if exstr == 'EX4':
if sellset == 'SEMI':
sellpc = semiset['Trade_Hi'].quantile(q=int(sellperc)/100)
report['Win_Period_10th'] = semiset['Trade_Hi'].quantile(q=.1)
report['Win_Period_20th'] = semiset['Trade_Hi'].quantile(q=.2)
report['Win_Period_40th'] = semiset['Trade_Hi'].quantile(q=.4)
report['Win_Period_50th'] = semiset['Trade_Hi'].quantile(q=.5)
report['Win_Period_60th'] = semiset['Trade_Hi'].quantile(q=.6)
else:
sellpc = data['Trade_Hi'].quantile(q=int(sellperc)/100)
report['Win_Period_10th'] = data['Trade_Hi'].quantile(q=.1)
report['Win_Period_20th'] = data['Trade_Hi'].quantile(q=.2)
report['Win_Period_40th'] = data['Trade_Hi'].quantile(q=.4)
report['Win_Period_50th'] = data['Trade_Hi'].quantile(q=.5)
report['Win_Period_60th'] = data['Trade_Hi'].quantile(q=.6)
else:
if sellset == 'SEMI':
sellpc = semiset['per1hi_percent'].quantile(q=int(sellperc)/100)
report['Win_Period_10th'] = semiset['per1hi_percent'].quantile(q=.1)
report['Win_Period_20th'] = semiset['per1hi_percent'].quantile(q=.2)
report['Win_Period_40th'] = semiset['per1hi_percent'].quantile(q=.4)
report['Win_Period_50th'] = semiset['per1hi_percent'].quantile(q=.5)
report['Win_Period_60th'] = semiset['per1hi_percent'].quantile(q=.6)
else:
sellpc = data['per1hi_percent'].quantile(q=int(sellperc)/100)
report['Win_Period_10th'] = data['per1hi_percent'].quantile(q=.1)
report['Win_Period_20th'] = data['per1hi_percent'].quantile(q=.2)
report['Win_Period_40th'] = data['per1hi_percent'].quantile(q=.4)
report['Win_Period_50th'] = data['per1hi_percent'].quantile(q=.5)
report['Win_Period_60th'] = data['per1hi_percent'].quantile(q=.6)
restset = semiset[semiset['per1hi_percent'] >= sellpc] #Restricted Data Set
report['Sell_Target_%'] = sellpc
#Exit Percentage Target Based On Exit Set Specification
if exitset == 'GLB':
exitpc = data['per2hi_percent'].quantile(q=.1)
elif exitset == 'SEMI':
exitpc = semiset['per2hi_percent'].quantile(q=.1)
elif exitset == 'REST':
exitpc = restset['per2hi_percent'].quantile(q=.1)
#Generate Column Names
wins = ''.join([strategyformula,'_Wins?'])
trpl = ''.join([strategyformula,'_Trade_Profit/Loss'])
ev = ''.join([strategyformula,'_Win_Calc_Evaluation'])
biglosstx = ''.join([strategyformula,'_TXs_Loss>50%'])
cumprofit = ''.join([strategyformula,'_Cumulative_Profit/Loss'])
entries = ''.join([strategyformula,'_Got_In?'])
successfulexits = ''.join([strategyformula,'_Successful_Exits'])
report['Calc_Profit%'] = sellpc-buypc
pft = 1 + (sellpc-buypc)
datah = trade_analyzer(data,sellpc=sellpc,buypc=buypc,exstr=exstr,exitpc=exitpc,mmgmt=mmgmt,strategyformula=strategyformula,acct=acct)
#Final Calcs
report['D2_Investible_Volume'] = data.ix[-20:]['d2ivst'].mean()#test
report['#_of_Transactions'] = np.sum(datah[entries])
datah[biglosstx] = datah[trpl] <= (acct * -.5)
report['#_OF_"FALSE"_IN_WIN_CALC'] = len(data) - np.sum(datah[ev]) #Investigate: Clarify the meaning of the Win Calc and its calculation
report['%_of_TXs_w/_LOSS>50%'] = np.sum(datah[biglosstx])/len(semiset)
datah[cumprofit] = datah[trpl].cumsum()
report['Win%'] = np.sum(datah[wins])/np.sum(datah[entries])
report['Fail%'] = 1-report['Win%']
report['CALC._PROF/LOSS_ON_Fd_TXS'] = exitpc-buypc
report['Highest_Hi'] = datah[cumprofit].max()
report[´Biggest_Loser'] = datah[trpl].min()
report['Max_Drawdown(Acct_Min)'] = datah[cumprofit].min()
report['Max_%_Drawdown'] = datah[cumprofit].min()/acct
net = datah[datah[entries] == True]
report['Net_Profit/Loss'] = net.ix[-1][cumprofit]
report['Ratio_Net/Highest_Hi'] = report['Net_Profit/Loss']/report['Highest_Hi']
report['Hist_Profit/Loss_per_Tx'] = (report['Net_Profit/Loss']/report['#_of_Transactions'])
report['#_TX>0'] = np.sum(datah[trpl]>0)
report['%_of_TX>0'] = np.sum(datah[trpl]>0)/len(semiset)
report['Catastrophic_Fail_%_(-80%)'] = np.sum(datah[trpl]<=(acct*(-.8)))
report['AMT_AT_RISK'] = acct
report['#_of_Exit_Attempts'] = np.sum(datah[entries]) - np.sum(datah[wins])
report['%_Successful_Exits'] = np.sum(datah[successfulexits])/(np.sum(datah[entries]) - np.sum(datah[wins]))
if (mmgmt == 'MGMT1') & (report.ix[0]['#_of_Transactions'] > 9) & (report.ix[0]['Ratio_Net/Highest_Hi'] >= .9499) & (report.ix[0]['Hist_Profit/Loss_per_Tx'] >= acct * .099) & (report.ix[0]['#_OF_"FALSE"_IN_WIN_CALC'] < 2) & (report.ix[0]['%_of_TXs_w/_LOSS>50%'] < .06) & (report.ix[0]['Win%'] >= .8) & (report.ix[0]['Catastrophic_Fail_%_(-80%)'] == 0):
report['Level'] = 1
else:
report['Level'] = 0
return report, datah
criteria = ('Form_#','Level','Strategy_Formula','#_of_Transactions','#_in_Dataset','D2_Investible_Volume','Calc_Profit%','Win%','Net_Profit/Loss',
'Highest_Hi','Ratio_Net/Highest_Hi','Hist_Profit/Loss_per_Tx','#_TX>0','%_of_TX>0', '#_OF_"FALSE"_IN_WIN_CALC',
'Fail%','CALC._PROF/LOSS_ON_Fd_TXS','#_of_Exit_Attempts','%_Successful_Exits',´Biggest_Loser','Max_Drawdown(Acct_Min)','Max_%_Drawdown','%_of_TXs_w/_LOSS>50%',
'Catastrophic_Fail_%_(-80%)','AMT_AT_RISK','Buy_Target_%','Sell_Target_%','Win_Period_10th','Win_Period_20th','Win_Period_40th','Win_Period_50th','Win_Period_60th')
strategies = ["20/10.GLB,7-10.GLB,EX1,MGMT1",
"20/10.GLB,7-10.GLB,EX1,MGMT2",
"20/10.GLB,7-10.GLB,EX2,MGMT1",
"20/10.GLB,7-10.GLB,EX2,MGMT2",
"20/10.GLB,7-10.GLB,EX3,MGMT1",
"20/10.GLB,7-10.GLB,EX3,MGMT2",
"20/10.GLB,7-10.SEMI,EX1,MGMT1",
"20/10.GLB,7-10.SEMI,EX1,MGMT2",
"20/10.GLB,7-10.REST,EX1,MGMT1",
"20/10.GLB,7-10.REST,EX1,MGMT2",
"20/10.SEMI,7-10.GLB,EX1,MGMT1",
"20/10.SEMI,7-10.GLB,EX1,MGMT2",
"20/10.SEMI,7-10.GLB,EX2,MGMT1",
"20/10.SEMI,7-10.GLB,EX2,MGMT2",
"20/10.SEMI,7-10.GLB,EX3,MGMT1",
"20/10.SEMI,7-10.GLB,EX3,MGMT2",
"20/10.SEMI,7-10.SEMI,EX1,MGMT1",
"20/10.SEMI,7-10.SEMI,EX1,MGMT2",
"20/10.SEMI,7-10.REST,EX1,MGMT1",
"20/10.SEMI,7-10.REST,EX1,MGMT2",
"20/5.GLB,7-10.GLB,EX1,MGMT1",
"20/5.GLB,7-10.GLB,EX1,MGMT2",
"20/5.GLB,7-10.GLB,EX2,MGMT1",
"20/5.GLB,7-10.GLB,EX2,MGMT2",
"20/5.GLB,7-10.GLB,EX3,MGMT1",
"20/5.GLB,7-10.GLB,EX3,MGMT2",
"20/5.GLB,7-10.SEMI,EX1,MGMT1",
"20/5.GLB,7-10.SEMI,EX1,MGMT2",
"20/5.GLB,7-10.REST,EX1,MGMT1",
"20/5.GLB,7-10.REST,EX1,MGMT2",
"20/5.SEMI,7-10.GLB,EX1,MGMT1",
"20/5.SEMI,7-10.GLB,EX1,MGMT2",
"20/5.SEMI,7-10.GLB,EX2,MGMT1",
"20/5.SEMI,7-10.GLB,EX2,MGMT2",
"20/5.SEMI,7-10.GLB,EX3,MGMT1",
"20/5.SEMI,7-10.GLB,EX3,MGMT2",
"20/5.SEMI,7-10.SEMI,EX1,MGMT1",
"20/5.SEMI,7-10.SEMI,EX1,MGMT2",
"20/5.SEMI,7-10.REST,EX1,MGMT1",
"20/5.SEMI,7-10.REST,EX1,MGMT2",
"40/10.SEMI,7-10.GLB,EX1,MGMT1",
"40/10.SEMI,7-10.GLB,EX1,MGMT2",
"40/10.SEMI,7-10.GLB,EX2,MGMT1",
"40/10.SEMI,7-10.GLB,EX2,MGMT2",
"40/10.SEMI,7-10.GLB,EX3,MGMT1",
"40/10.SEMI,7-10.GLB,EX3,MGMT2",
"40/10.SEMI,7-10.SEMI,EX1,MGMT1",
"40/10.SEMI,7-10.SEMI,EX1,MGMT2",
"40/10.SEMI,7-10.REST,EX1,MGMT1",
"40/10.SEMI,7-10.REST,EX1,MGMT2",
"40/5.GLB,7-10.GLB,EX1,MGMT1",
"40/5.GLB,7-10.GLB,EX1,MGMT2",
"40/5.GLB,7-10.GLB,EX2,MGMT1",
"40/5.GLB,7-10.GLB,EX2,MGMT2",
"40/5.GLB,7-10.GLB,EX3,MGMT1",
"40/5.GLB,7-10.GLB,EX3,MGMT2",
"40/5.GLB,7-10.SEMI,EX1,MGMT1",
"40/5.GLB,7-10.SEMI,EX1,MGMT2",
"40/5.GLB,7-10.REST,EX1,MGMT1",
"40/5.GLB,7-10.REST,EX1,MGMT2",
"40/5.SEMI,7-10.GLB,EX1,MGMT1",
"40/5.SEMI,7-10.GLB,EX1,MGMT2",
"40/5.SEMI,7-10.GLB,EX2,MGMT1",
"40/5.SEMI,7-10.GLB,EX2,MGMT2",
"40/5.SEMI,7-10.GLB,EX3,MGMT1",
"40/5.SEMI,7-10.GLB,EX3,MGMT2",
"40/5.SEMI,7-10.SEMI,EX1,MGMT1",
"40/5.SEMI,7-10.SEMI,EX1,MGMT2",
"40/5.SEMI,7-10.REST,EX1,MGMT1",
"40/5.SEMI,7-10.REST,EX1,MGMT2",
"20/10.GLB,11-20.GLB,EX4,MGMT1",
"20/10.SEMI,11-20.GLB,EX4,MGMT1",
"20/10.GLB,11-20.SEMI,EX4,MGMT1",
"20/10.SEMI,11-20.SEMI,EX4,MGMT1"]
symbs = ('QQQ','SPY','AAPL','IWM') #symbs = ('SPY','VXX','XIV','SPY','AAPL','QQQ','IWM','X','NFLX','AMZN','GLD','GDX','C')
targetdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Sort_Reports\\Sorts\\' + d.strftime('%Y-%m-%d') + '\\Sorts\\'
for symb in symbs:
os.makedirs(targetdir, exist_ok=True)
targetdirdata = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Sort_Reports\\Sorts\\' + d.strftime('%Y-%m-%d') + '\\Data\\' + symb + '\\'
os.makedirs(targetdirdata, exist_ok=True)
sortdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Sort_Reports\\Sort_Data\\' + symb + '\\Summary\\' + d.strftime('%Y-%m-%d') + '\\'
sortdirs = list(os.listdir(sortdir)) #gives list of each sort's summary report folder
account = 20000
for dir in sortdirs: #looks at each folder in summary report folder for each sort folder in list 'sortdirs'
sorts = list(os.listdir(''.join([sortdir,dir,'\\']))) #gives list of all files in each sort report summary folder
for sor in sorts:
s = sor.split('_',3)
option = s[3]
srt = s[1]
sortsummary = pd.read_csv(''.join([sortdir,dir,'\\',sor]),'rb',delimiter=',',parse_dates=['Trade_Date'],infer_datetime_format=True).set_index(['Trade_Date'])
sortsummary.dropna(axis=0,subset=['d2op'],inplace=True)
rprtpath = ''.join([targetdir,symb,'_Sort_Report_',srt,'_',d.strftime('%Y-%m-%d'),'.csv'])
datapath = ''.join([targetdirdata,symb,'_Sort_Report_Data_',srt,'_',option,'_',d.strftime('%Y-%m-%d'),'.csv'])
data = sortsummary
datae = pd.DataFrame()
sort_report = pd.DataFrame()
frm = 0
for strategy in strategies:
frm =+ 1
if strategy.split(',',3)[3] == 'MGMT2':
continue
else:
report, datah = reporter(data=data,strategy=strategy,option=option,account=account)
datae = pd.concat([datae,datah],axis=1)
report['Form_#'] = ''.join([str(frm),'_',option])
sort_report = pd.concat([sort_report,report],axis=0)
sort_report.to_csv(rprtpath,mode='w',header=report.columns)
if 'Unnamed: 0' in data.columns:
data.drop(['Unnamed: 0'], axis=1,inplace=True)
data = pd.concat([data,datae],axis=1)
data.to_csv(datapath,mode='w',header=data.columns)
def option_n_strike(data):
option = pd.Series((x.split(',',4)[4].split('.',1)[0] for x in data['Strategy_Formula']),index=data.index,name='Option')
strike = pd.Series((x.split(',',4)[4].split('.',1)[1] for x in data['Strategy_Formula']),index=data.index,name='Strike')
buy_sell = pd.Series((x.split(',',4)[0].split('.',1)[0] for x in data['Strategy_Formula']),index=data.index,name='Buy/Sell')
data = pd.concat([data,strike,option,buy_sell],axis=1)
return data
lcrcolumns = ['Stock_Symbol','Sort','Form_#','Level','Strategy_Formula','#_of_Transactions','#_in_Dataset','D2_Investible_Volume','Calc_Profit%','Win%','Net_Profit/Loss',
'Highest_Hi','Ratio_Net/Highest_Hi','Hist_Profit/Loss_per_Tx','#_TX>0','%_of_TX>0', '#_OF_"FALSE"_IN_WIN_CALC',
'Fail%','CALC._PROF/LOSS_ON_Fd_TXS','#_of_Exit_Attempts','%_Successful_Exits','Max_Drawdown','Max_%_Drawdown','%_of_TXs_w/_LOSS>50%',
'Catastrophic_Fail_%_(-80%)','AMT_AT_RISK','Buy_Target_%','Sell_Target_%','Win_Period_10th','Win_Period_20th','Win_Period_40th','Win_Period_50th','Win_Period_60th']
targetdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Level_Class_Reports\\' +d.strftime('%Y-%m-%d')+ '\\'
sortsdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Sort_Reports\\Sorts\\' +d.strftime('%Y-%m-%d')+ '\\Sorts\\'
sortdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Sort_Reports\\Sorts\\' +d.strftime('%Y-%m-%d')+ '\\'
path = ''.join([targetdir,'Level_Class_Report_',d.strftime('%Y_%m_%d'),'.csv'])
os.makedirs(targetdir, exist_ok=True)
sorts = os.listdir(sortsdir)
lcr = pd.DataFrame(columns=lcrcolumns)
for sort in sorts:
lcreport = pd.read_csv(''.join([sortsdir,sort]),'rb',delimiter=',')
lcreport = lcreport[lcreport['Level']==1]
s = sort.split('_',4)
lcreport['Stock_Symbol'] = s[0]
lcreport['Sort'] = s[3]
lcr = pd.concat([lcr,lcreport],axis=0)
lcr = option_n_strike(lcr)
if 'Unnamed: 0' in lcreport.columns:
lcreport.drop(['Unnamed: 0'], axis=1,inplace=True)
lcr = lcr[['Stock_Symbol','Option','Strike','Sort','Form_#','Level','Strategy_Formula','Buy/Sell','#_of_Transactions','#_in_Dataset','D2_Investible_Volume','Calc_Profit%','Win%','Net_Profit/Loss',
'Highest_Hi','Ratio_Net/Highest_Hi','Hist_Profit/Loss_per_Tx','#_TX>0','%_of_TX>0', '#_OF_"FALSE"_IN_WIN_CALC',
'Fail%','CALC._PROF/LOSS_ON_Fd_TXS','#_of_Exit_Attempts','%_Successful_Exits','Max_Drawdown','Max_%_Drawdown','%_of_TXs_w/_LOSS>50%',
'Catastrophic_Fail_%_(-80%)','AMT_AT_RISK','Buy_Target_%','Sell_Target_%','Win_Period_10th','Win_Period_20th','Win_Period_40th','Win_Period_50th','Win_Period_60th']]
if os.path.isfile(path):
lcr.to_csv(path,mode='a',header=None)
else:
lcr.to_csv(path,mode='w',header=lcr.columns)
c = c + 1
##################################################################Trades & Plays Logger##################################################################################################################
'''
tnpcolumns = [['Trade_#','Log_Date','Trade_Date','Option_Type','Option_Symbol','Expiration','Underlying_Symbol','Strike_Position(stx)',
'Strategy_Formula','Buy/Sell','Buy_Target_%','Sell_Target_%','D2_Investible_Volume','Strategy_1',
'Strategy_2','Strategy_3','Strategy_4','Strategy_5','Strategy_6',
'Strategy_7','Strategy_8','Strategy_9','Strategy_10','Strategy_11',
'Strategy_12','Strategy_13','Strategy_14','Strategy_15','Strategy_16',
'Strategy_17','Strategy_18','Strategy_19','Strategy_20']]
# tnplog index name = 'Transaction_#', 1 based index
def next_monthlies(d): # must test len of option price history to ensure 1: that purchase is possible on d2 and 2: that there is at least 12 days of data
f = d + dt.timedelta(days=30)
g = d + dt.timedelta(days=60)
g = dt.date(g.year, g.month, 15)
f = dt.date(f.year, f.month, 15)
g = (g + dt.timedelta(days=(calendar.FRIDAY - g.weekday()) % 7))
f = (f + dt.timedelta(days=(calendar.FRIDAY - f.weekday()) % 7))
e = f - dt.timedelta(weeks=2)
return e,f,g
def find_strike(array,value,shift):
idx = np.abs(array-value).argmin()
stx = array[idx+shift]
return stx
def nearest_exp(array,dat,start):
strt = dt.date(start.year, start.month, start.day)
array.reset_index(drop=True,inplace=True)
idx = np.abs(array-dat).argmin()
exp = aray[idx]
return exp
def TnPLogger(data,d):
tnplogger = pd.DataFrame(columns = tnpcolumns)
stocks = data['Stock_Symbol'].unique()
i = -1
for symb in stocks:
#stk = web.DataReader(symb, 'yahoo', start, d)
#center = stk.ix[d]['open']
trades = data[data['Stock_Symbol']==symb]
calls = trades[trades['Option']=='call']
puts = trades[trades['Option']=='put']
cstx = calls['Strike'].unique()
pstx = puts['Strike'].unique()
if len(cstx) > 0:
for x in cstx:
optc = calls[calls['Strike']==x]
buy_sells = optc['Buy/Sell'].unique()
for bs in buy_sells:
i = i + 1
#min_exp,exp,max_exp = next_monthlies(d)
#opt = option_finder(symb=symb,center=center,t='c',min_exp=min_exp,exp=exp,max_exp=max_exp,m=x)
tnplogger.at[i,'Strategy_Formula'] = optc.ix[optc['Hist_Profit/Loss_per_Tx'].idxmax()]['Strategy_Formula']
tnplogger.at[i,'D2_Investible_Volume'] = optc['D2_Investible_Volume'].mean()
tnplogger.at[i,'Buy/Sell'] = bs
tnplogger.at[i,'Trade_Date'] = d
tnplogger.at[i,'Option_Type'] = 'Call'
tnplogger.at[i,'Strike_Position(stx)'] = x
tnplogger.at[i,'Underlying_Symbol'] = symb
optc.sort_values(by='Win%',ascending=True,axis=0,inplace=True)
strtgs = optc['Strategy_Formula'].unique()
strtgs = strtgs[:20]
#tnplogger['Suggested_Option_Symbol'] = opt
#tnplogger['Expiration'] = opt.split(',',3)[0]
z = -1
for s in strtgs:
z = z + 1
col = ''.join(['Strategy_',str(z+1)])
tnplogger.at[i,col] = s
else:
pass
if len(pstx) > 0:
for x in pstx:
optp = puts[puts['Strike']==x]
buy_sells = optp['Buy/Sell'].unique()
for bs in buy_sells:
i = i + 1
#min_exp,exp,max_exp = next_monthlies(d)
#opt = option_finder(symb=symb,center=center,t='p',min_exp=min_exp,exp=exp,max_exp=max_exp,m=x)
tnplogger.at[i,'Strategy_Formula'] = optp.ix[optp['Hist_Profit/Loss_per_Tx'].idxmax()]['Strategy_Formula']
tnplogger.at[i,'D2_Investible_Volume'] = optp['D2_Investible_Volume'].mean()
tnplogger.at[i,'Buy/Sell'] = bs
tnplogger.at[i,'Trade_Date'] = d
tnplogger.at[i,'Option_Type'] = 'Put'
tnplogger.at[i,'Strike_Position(stx)'] = x
tnplogger.at[i,'Underlying_Symbol'] = symb
optp.sort_values(by='Win%',ascending=True,axis=0,inplace=True)
strtgs = optp['Strategy_Formula'].unique()
strtgs = strtgs[:20]
#tnplogger['Option_Symbol'] = opt
#tnplogger['Expiration'] = opt.split(',',3)[0]
z = -1
for s in strtgs:
z = z + 1
col = ''.join(['Strategy_',str(z+1)])
tnplogger.at[i,col] = s
else:
pass
tnplogger['Trade_#'] = tnplogger.index + 1
tnplogger['Log_Date'] = dt.date.today()
return tnplogger
runlogcolumns = ['Run_Dates','LCR_Dates']
runlogpath = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Logs\\TnPRunLog.csv'
tnplogpath = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Logs\\Trades_&_Plays_Log.csv'
lcrdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Level_Class_Reports\\' #+d.strftime('%Y-%m-%d')+ '\\Level_Class_Report_' +d.strftime('%Y-%m-%d') + '.csv'
tempdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Templates\\'
if os.path.isfile(runlogpath):
runlog = pd.read_csv(runlogpath, 'rb', delimiter=',',parse_dates=['Run_Dates'],infer_datetime_format=True)
tnplog = pd.read_csv(tnplogpath,'rb',delimiter=',',parse_dates=['Log_Date'],infer_datetime_format=True).sort(columns='Log_Date',ascending=False,axis=0)
last_run = runlog.iloc[-1]['Run_Dates'].date()
lcrs = os.listdir(lcrdir)
remaining = pd.to_datetime([i for i in lcrs if last_run < dt.datetime.strptime(i, '%Y-%m-%d').date()],infer_datetime_format=True)
tx=1
else:
lcrs = os.listdir(lcrdir)
remaining = [dt.datetime.strptime(i, '%Y-%m-%d').date() for i in lcrs]
runlog = pd.DataFrame(columns = runlogcolumns)
tnplog = pd.DataFrame(columns = tnpcolumns)
tx = 0
for d in remaining:
lcrpath = ''.join([lcrdir,d.strftime('%Y-%m-%d'),'\\Level_Class_Report_',d.strftime('%Y_%m_%d'),'.csv'])
lcr = pd.read_csv(lcrpath,'rb',delimiter=',')
log = TnPLogger(lcr,d)
if not tx == 0:
log['Trade_#'] = log['Trade_#'] + tnplog.iloc[-1]['Trade_#']
tnplog = pd.concat([tnplog,log],axis=0)
tx = 1
#tnplog.sort(columns='Log_Date', axis=0, ascending=True, inplace=True)
#tnplog.reset_index(drop=True,inplace=True)
tnplog.to_csv(tnplogpath,mode='w',delimiter=',',index=False)
daysrunlog = pd.DataFrame(data={'Run_Dates' : dt.date.today(),'LCR_Dates' : remaining}, columns = runlogcolumns)
runlog = pd.concat([runlog,daysrunlog],axis=0)
runlog.to_csv(runlogpath, mode='w',index=False,header=runlog.columns)
#############################################################################################################################################
def next_monthlies(d): # must test len of option price history to ensure 1: that purchase is possible on d2 and 2: that there is at least 12 days of data
f = d + dt.timedelta(days=30)
g = d + dt.timedelta(days=60)
g = dt.date(g.year, g.month, 15)
f = dt.date(f.year, f.month, 15)
g = (g + dt.timedelta(days=(calendar.FRIDAY - g.weekday()) % 7))
f = (f + dt.timedelta(days=(calendar.FRIDAY - f.weekday()) % 7))
e = f - dt.timedelta(weeks=2)
return e,f,g
def find_strike(array,value,shift):
idx = np.abs(array-value).argmin()
stx = array[idx+shift]
return stx
def nearest_exp(array,dat,start):
strt = dt.date(start.year, start.month, start.day)
array.reset_index(drop=True,inplace=True)
idx = np.abs(array-dat).argmin()
exp = array[idx]
return exp
def option_finder(opt,symb,strike,start,opt_type,min_exp,exp,max_exp,m): # open opt_key outside of func in begining of loop and pass df as argument
try:
opt = (opt[opt['expiration'] <= max_exp])
opt = (opt[opt['expiration'] >= min_exp])
opt = (opt[opt['quote_date'] >= start])
stx = find_strike(opt['strike'],strike,m)
opt = (opt[opt['strike'] == stx])
opt.reset_index(drop=False, inplace=True)
if len(opt['expiration'].unique()) > 1:
x = nearest_exp(opt['expiration'],exp,start)
mask = opt['expiration'] == x
opt = opt[opt['expiration'] == x]
duples = opt['quote_date'].duplicated()
if duples.isin([True]).values.any():
opt = opt[duples == False]
option = ''.join([str(opt.loc[opt.index[0]]['expiration'].date()),',',opt_type,',',str(stx),',',symb])
opt = opt[['underlying_symbol', 'quote_date','root', 'expiration', 'strike', 'option_type',
'open', 'high', 'low', 'close', 'trade_volume', 'bid_size_1545',
'bid_1545', 'ask_size_1545', 'ask_1545', 'underlying_bid_1545',
'underlying_ask_1545', 'implied_underlying_price_1545',
'active_underlying_price_1545', 'implied_volatility_1545', 'delta_1545',
'gamma_1545', 'theta_1545', 'vega_1545', 'rho_1545', 'bid_size_eod',
'bid_eod', 'ask_size_eod', 'ask_eod', 'underlying_bid_eod',
'underlying_ask_eod', 'vwap', 'open_interest', 'delivery_code']]
except:
opt = 'NA'
option = 'NA'
return opt, option
current_time = dt.datetime.now()
last_friday = (current_time.date()
- dt.timedelta(days=current_time.weekday())
+ dt.timedelta(days=4))
logdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Logs\\Trades_&_Plays_Log.csv'
tnplog = pd.read_csv(logdir, 'rb', delimiter=',',parse_dates=['Trade_Date'],infer_datetime_format=True).set_index(['Trade_#'])
#tnplog = tnplog.replace(' ',np.nan, regex=True)
to_get_option_symbols = tnplog[tnplog['Option_Symbol'].isnull()]
tnplog['Option_Symbol'] = tnplog['Option_Symbol'].astype('str')
tnplog['Expiration'] = tnplog['Expiration'].astype('str')
for i in to_get_option_symbols.index:
dat = tnplog.loc[i]['Trade_Date'] #datetime.strptime(tnplog.loc[x]['Trade_Date'], '%m/%d/%Y')
if (dat.date() == last_friday) & (dt.date.today().weekday() >= 5) | (dat.date() == dt.date.today()):
continue
#pass
symb = tnplog.loc[i]['Underlying_Symbol']
dir = 'C:\\Users\\asus\\Documents\\Quant\\Database\\' + symb + '\\Options\\'
targetpath = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Fore-Tester\\Option_Data\\' + symb + '\\'
os.makedirs(targetpath, exist_ok=True)
m = tnplog.loc[i]['Strike_Position(stx)']
dirstk = 'C:\\Users\\asus\\Documents\\Quant\\Database\\' + symb + '\\Stock\\' + symb + '.csv'
stk = pd.read_csv(dirstk,'r',',',parse_dates=['Date'],infer_datetime_format=True).set_index(['Date'])
y = pd.Index(stk.index).get_loc(dat)
d = stk.index[y+1]
opt_type = tnplog.loc[i]['Option_Type']
min_exp, exp, max_exp = next_monthlies(d=dat)
strike = np.round(stk.loc[d][0],decimals=0)
diropt = ''.join([dir,symb,'_',str(exp.year),'_',opt_type,'s.csv'])
try:
optdata = pd.read_csv(diropt,'rb',delimiter=',',parse_dates=['expiration','quote_date'],infer_datetime_format=True).sort_index(axis=0)
except:
optdata = 'NA'
opt,option_symbol = option_finder(opt=optdata,symb=symb,strike=strike,start=dat,opt_type=opt_type,min_exp=min_exp,exp=exp,max_exp=max_exp,m=m)
if len(option_symbol) > 5:
tnplog.at[i,'Option_Symbol'] = option_symbol
tnplog.at[i,'Expiration'] = option_symbol.split(',',1)[0]
tnplog.at[i,'Log_Date'] = dt.date.today()
optpath = ''.join([targetpath,option_symbol,'.csv'])
opt.to_csv(optpath,mode='w',header=opt.columns)
else:
continue
tnplog.to_csv(logdir,mode='w',header=tnplog.columns)
'''
import pandas as pd
import numpy as np
import os
from datetime import datetime
import datetime as dt
import calendar
def next_monthlies(d): # must test len of option price history to ensure 1: that purchase is possible on d2 and 2: that there is at least 12 days of data
f = d + dt.timedelta(days=30)
g = d + dt.timedelta(days=60)
g = dt.date(g.year, g.month, 15)
f = dt.date(f.year, f.month, 15)
g = (g + dt.timedelta(days=(calendar.FRIDAY - g.weekday()) % 7))
f = (f + dt.timedelta(days=(calendar.FRIDAY - f.weekday()) % 7))
e = f - dt.timedelta(weeks=2)
return e,f,g
def find_strike(array,value,shift):
idx = np.abs(array-value).argmin()
stx = array[idx+shift]
return stx
def nearest_exp(array,dat,start):
strt = dt.date(start.year, start.month, start.day)
array.reset_index(drop=True,inplace=True)
idx = np.abs(array-dat).argmin()
exp = array[idx]
return exp
def option_finder(opt,symb,strike,start,opt_type,min_exp,exp,max_exp,m): # open opt_key outside of func in begining of loop and pass df as argument
try:
opt = (opt[opt['expiration'] <= max_exp])
opt = (opt[opt['expiration'] >= min_exp])
opt = (opt[opt['quote_date'] >= start])
stx = find_strike(opt['strike'],strike,m)
opt = (opt[opt['strike'] == stx])
opt.reset_index(drop=False, inplace=True)
if len(opt['expiration'].unique()) > 1:
x = nearest_exp(opt['expiration'],exp,start)
mask = opt['expiration'] == x
opt = opt[opt['expiration'] == x]
duples = opt['quote_date'].duplicated()
if duples.isin([True]).values.any():
opt = opt[duples == False]
option = ''.join([str(opt.loc[opt.index[0]]['expiration'].date()),',',opt_type,',',str(stx),',',symb])
opt = opt[['underlying_symbol', 'quote_date','root', 'expiration', 'strike', 'option_type',
'open', 'high', 'low', 'close', 'trade_volume', 'bid_size_1545',
'bid_1545', 'ask_size_1545', 'ask_1545', 'underlying_bid_1545',
'underlying_ask_1545', 'implied_underlying_price_1545',
'active_underlying_price_1545', 'implied_volatility_1545', 'delta_1545',
'gamma_1545', 'theta_1545', 'vega_1545', 'rho_1545', 'bid_size_eod',
'bid_eod', 'ask_size_eod', 'ask_eod', 'underlying_bid_eod',
'underlying_ask_eod', 'vwap', 'open_interest', 'delivery_code']]
except:
opt = 'NA'
option = 'NA'
return opt, option
current_time = dt.datetime.now()
last_friday = (current_time.date()
- dt.timedelta(days=current_time.weekday())
+ dt.timedelta(days=4, weeks=-1))
logdir = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Logs\\Trades_&_Plays_Log.csv'
tnplog = pd.read_csv(logdir, 'rb', delimiter=',',parse_dates=['Trade_Date'],infer_datetime_format=True).set_index(['Trade_#'])
#tnplog = tnplog.replace(' ',np.nan, regex=True)
to_get_option_symbols = tnplog[tnplog['Option_Symbol'].isnull()]
tnplog['Option_Symbol'] = tnplog['Option_Symbol'].astype('str')
tnplog['Expiration'] = tnplog['Expiration'].astype('str')
for i in to_get_option_symbols.index:
dat = tnplog.loc[i]['Trade_Date'] #datetime.strptime(tnplog.loc[x]['Trade_Date'], '%m/%d/%Y')
if (dat.date() == last_friday) & (dt.date.today().weekday() >= 5) | (dat.date() == (dt.date.today() + dt.timedelta(days=1))):
continue
#pass
symb = tnplog.loc[i]['Underlying_Symbol']
dir = 'C:\\Users\\asus\\Documents\\Quant\\Database\\' + symb + '\\Options\\'
targetpath = 'C:\\Users\\asus\\Dropbox\\Outlines\\MTAUTO-PYTHON\\Option_Data\\' + symb + '\\'
os.makedirs(targetpath, exist_ok=True)
m = tnplog.loc[i]['Strike_Position(stx)']
dirstk = 'C:\\Users\\asus\\Documents\\Quant\\Database\\' + symb + '\\Stock\\' + symb + '.csv'
stk = pd.read_csv(dirstk,'r',',',parse_dates=['Date'],infer_datetime_format=True).set_index(['Date'])
y = pd.Index(stk.index).get_loc(dat)
d = stk.index[y+1]
opt_type = tnplog.loc[i]['Option_Type']
min_exp, exp, max_exp = next_monthlies(d=dat)
strike = np.round(stk.loc[d][0],decimals=0)
diropt = ''.join([dir,symb,'_',str(exp.year),'_',opt_type,'s.csv'])
try:
optdata = pd.read_csv(diropt,'rb',delimiter=',',parse_dates=['expiration','quote_date'],infer_datetime_format=True).sort_index(axis=0)
except:
optdata = 'NA'
opt,option_symbol = option_finder(opt=optdata,symb=symb,strike=strike,start=dat,opt_type=opt_type,min_exp=min_exp,exp=exp,max_exp=max_exp,m=m)
if len(option_symbol) > 5:
tnplog.at[i,'Option_Symbol'] = option_symbol
tnplog.at[i,'Expiration'] = option_symbol.split(',',1)[0]
tnplog.at[i,'Log_Date'] = dt.date.today()
optpath = ''.join([targetpath,option_symbol,'.csv'])
opt.to_csv(optpath,mode='w',header=opt.columns)
else:
continue
tnplog.to_csv(logdir,mode='w',header=tnplog.columns)