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findpaircrosscount_test08.py
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findpaircrosscount_test08.py
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#C:\Batches\GitStuff\$work\correlation_sample.csv
import pandas as pd
import numpy as np
class find:
def set_SymbolsList(self,SymbolsList):
self._SymbolsList = SymbolsList
def get_SymbolsList(self):
return self._SymbolsList
SymbolsList = property(get_SymbolsList, set_SymbolsList)
def set_PairRunningMaxDiffDictionary(self,PairRunningMaxDiffDictionary):
self._PairRunningMaxDiffDictionary = PairRunningMaxDiffDictionary
def get_PairRunningMaxDiffDictionary(self):
return self._PairRunningMaxDiffDictionary
PairRunningMaxDiffDictionary = property(get_PairRunningMaxDiffDictionary, set_PairRunningMaxDiffDictionary)
def set_PairRunningMinDiffDictionary(self,PairRunningMinDiffDictionary):
self._PairRunningMinDiffDictionary = PairRunningMinDiffDictionary
def get_PairRunningMinDiffDictionary(self):
return self._PairRunningMinDiffDictionary
PairRunningMinDiffDictionary = property(get_PairRunningMinDiffDictionary, set_PairRunningMinDiffDictionary)
#dict_pairdiff_betweenmaxmin
def set_PairBetweenMaxMinDiffDictionary(self,PairBetweenMaxMinDiffDictionary):
self._PairBetweenMaxMinDiffDictionary = PairBetweenMaxMinDiffDictionary
def get_PairBetweenMaxMinDiffDictionary(self):
return self._PairBetweenMaxMinDiffDictionary
PairBetweenMaxMinDiffDictionary = property(get_PairBetweenMaxMinDiffDictionary, set_PairBetweenMaxMinDiffDictionary)
def set_PairRunningPctDiffDictionary(self,PairRunningPctDiffDictionary):
self._PairRunningPctDiffDictionary = PairRunningPctDiffDictionary
def get_PairRunningPctDiffDictionary(self):
return self._PairRunningPctDiffDictionary
PairRunningPctDiffDictionary = property(get_PairRunningPctDiffDictionary, set_PairRunningPctDiffDictionary)
#PairPricesDiffDictionary
def set_PairPricesDiffDictionary(self,PairPricesDiffDictionary):
self._PairPricesDiffDictionary = PairPricesDiffDictionary
def get_PairPricesDiffDictionary(self):
return self._PairPricesDiffDictionary
PairPricesDiffDictionary = property(get_PairPricesDiffDictionary, set_PairPricesDiffDictionary)
def set_ClosePricesDataframe(self,ClosePricesDataframe):
self._ClosePricesDataframe = ClosePricesDataframe
def get_ClosePricesDataframe(self):
return self._ClosePricesDataframe
ClosePricesDataframe = property(get_ClosePricesDataframe, set_ClosePricesDataframe)
def set_PairDollarizedDiffDictionary(self,PairDollarizedDiffDictionary):
self._PairDollarizedDiffDictionary = PairDollarizedDiffDictionary
def get_PairDollarizedDiffDictionary(self):
return self._PairDollarizedDiffDictionary
PairDollarizedDiffDictionary = property(get_PairDollarizedDiffDictionary, set_PairDollarizedDiffDictionary)
def set_PairMovingAverageDiffDictionary(self,PairMovingAverageDiffDictionary):
self._PairMovingAverageDiffDictionary = PairMovingAverageDiffDictionary
def get_PairMovingAverageDiffDictionary(self):
return self._PairMovingAverageDiffDictionary
PairMovingAverageDiffDictionary = property(get_PairMovingAverageDiffDictionary, set_PairMovingAverageDiffDictionary)
def set_PairMovingStdevDiffDictionary(self,PairMovingStdevDiffDictionary):
self._PairMovingStdevDiffDictionary = PairMovingStdevDiffDictionary
def get_PairMovingStdevDiffDictionary(self):
return self._PairMovingStdevDiffDictionary
PairMovingStdevDiffDictionary = property(get_PairMovingStdevDiffDictionary, set_PairMovingStdevDiffDictionary)
def __init__(self,
closepricesfilepath, movingaveragewindow):
#self.PairMovingAverageDiffDictionary, columns = self.setclassdictionaries(closepricesfilepath)
b = self.setclassdictionaries(closepricesfilepath = closepricesfilepath,movingaveragewindow = movingaveragewindow)
df_extreme = self.PairRunningPctDiffDictionary['AAPL']['ABBV'].loc[self.PairRunningPctDiffDictionary['AAPL']['ABBV'].isin([0.0,1.0])].to_frame()
print 'df_extreme',df_extreme
my_tradedate = df_extreme.iloc[len(df_extreme)-2].name
my_triggervalue = df_extreme['ABBV'].iloc[len(df_extreme)-2]
print 'latest opportunity', my_tradedate, my_triggervalue
self.testone('AAPL','ABBV',my_tradedate,my_triggervalue)
for idx ,row in self.PairRunningPctDiffDictionary['AAPL'].iterrows():
print idx, row['ABBV'], self.PairPricesDiffDictionary['AAPL']['ABBV'][idx]
#stop
stop
#------- Testing
## df_1a = self.PairPricesDiffDictionary['A']
## df_1b = self.PairMovingAverageDiffDictionary['A']
## for idx,row in self.PairMovingAverageDiffDictionary['A']['AAL'].to_frame().iterrows():
## print idx, row['AAL']
## if idx > '2016-03-02':
## stop
## df_1c = self.PairMovingStdevDiffDictionary['A']
##
## df_2a = df_1a['AAL'].to_frame()
## df_2a.columns.values[0]='diff'
## #print df_2a
## #stop
## df_2b = df_1b['AAL'].to_frame()
## df_2b.columns.values[0]='diff_ma'
##
## df_2c = df_1c['AAL'].to_frame()
## df_2c.columns.values[0]='diff_mstdev'
##
## df_x = pd.concat([df_2a, df_2b, df_2c], axis=1)
## for idx,row in df_x.iterrows():
## print idx, row['diff'],row['diff_ma'],row['diff_mstdev']
## if idx > '2016-03-02':
## stop
## stop
#rowbegin = self.PairDollarizedDiffDictionary.index[self.PairDollarizedDiffDictionary.iloc[0]]
#print rowbegin
#stop
i = 0
for column in self.SymbolsList:
print 'Doing',column, i, 'of',len(self.SymbolsList)
if column == 'CA':
df = self.runbyticker(column)
if i == 0:
df1 = df.copy()
else:
df1 = pd.concat([df1, df], ignore_index=True)
i +=1
#if i >= 6:
# df2 = df1.sort_values('crosscount')
# print df2
#print df1
df2 = df1.sort_values('crosscount')
cachedfilepathname = 'C:\\Batches\\GitStuff\\$work\\paircrosscount_sample.csv'
df1.to_csv(cachedfilepathname,columns=(list(df1.columns.values)))
def testone(self,ticker1,ticker2,tradedate,triggervalue, maxgain=1000.0):
print ticker1,ticker2,tradedate,triggervalue
df_closeprices = self.ClosePricesDataframe
print df_closeprices[[ticker1]][(df_closeprices.index == tradedate)][ticker1][0]
#stop
df_closeprices = df_closeprices[(df_closeprices.index >= tradedate)]
#df_diffprices = self.PairPricesDiffDictionary[ticker1][ticker2]
df2 = pd.DataFrame(index=df_closeprices.index.copy())
print '----------------- ++++'
df_closeprices = df_closeprices[[ticker1,ticker2]]
#print df
opendiffprice = self.PairPricesDiffDictionary[ticker1][ticker2][tradedate]
print 'opendiffprice','xxx',opendiffprice
columns = list(df_closeprices.columns.values)
print 'columns',columns
df_openprices= df_closeprices.iloc[[0]]
print 'df_openprices', df_openprices
df_openshares = 10000.0 / df_closeprices.iloc[[0]]
print 'df_openshares',df_openshares
print '--------------------------kkkk'
#df_opendiffprice = df_diffprices.iloc[[0]][0]
#print 'df_opendiffprice',df_opendiffprice
df_blotter = df_closeprices * df_openshares.loc[tradedate]
if triggervalue < 0.5:
sign_for_ticker1 = -1
sign_for_ticker2 = 1
else:
sign_for_ticker1 = 1
sign_for_ticker2 = -1
list_of_my_dicts = []
for idx,row in df_blotter.iterrows():
#print 'a', idx, sign_for_ticker1 * row[ticker1], sign_for_ticker2 * row[ticker2], ( sign_for_ticker1 * row[ticker1] ) + ( sign_for_ticker2 * row[ticker2] ), triggervalue, self.PairPricesDiffDictionary[ticker1][ticker2][idx]
my_dict = {
'date':idx
, '01_dollarized1':sign_for_ticker1 * row[ticker1]
, '01_dollarized2':sign_for_ticker2 * row[ticker2]
, '02_pl':( sign_for_ticker1 * row[ticker1] ) + ( sign_for_ticker2 * row[ticker2] )
, '03_triggervalue':triggervalue
, '04_opendiffprice':opendiffprice
, '05_currentpricediff':self.PairPricesDiffDictionary[ticker1][ticker2][idx]
##, '05_maxpricediff':self.PairRunningMaxDiffDictionary[ticker1][ticker2][idx]
##, '05_minpricediff':self.PairRunningMinDiffDictionary[ticker1][ticker2][idx]
, '06_ticker1':ticker1
, '06_ticker2':ticker2
, '07_openshares_ticker1':df_openshares.iloc[0][ticker1]
, '07_openshares_ticker2':df_openshares.iloc[0][ticker2]
, '08_openprice_ticker1':df_openprices.iloc[0][ticker1]
, '08_openprice_ticker2':df_openprices.iloc[0][ticker2]
, '09_currentprice_ticker1':self.ClosePricesDataframe[[ticker1]][(self.ClosePricesDataframe.index == idx)][ticker1][0]
, '09_currentprice_ticker2':self.ClosePricesDataframe[[ticker2]][(self.ClosePricesDataframe.index == idx)][ticker2][0]
, '10_opentradedate':tradedate
, '11_currenttradedate':idx
}
list_of_my_dicts.append(my_dict)
df_final = pd.DataFrame(list_of_my_dicts)
df_final.set_index("date", drop=True, inplace=True)
print df_final
print '-------------------------------------------------'
df_entrytrade = df_final.iloc[:1]
print df_entrytrade.iloc[0]
#if idx > '2016-03-02':
# break
print '-------------------------------------------------'
df_exittrade = df_final[(df_final['02_pl'] >= maxgain)].iloc[:1]
print df_exittrade.iloc[0]
stop
def runbyticker(self,ticker):
import pandas as pd
import numpy as np
ticker1 = ticker
df_dollarized = self.PairDollarizedDiffDictionary[ticker1]
df_runningmax = self.PairRunningMaxDiffDictionary[ticker]
df_close = self.ClosePricesDataframe
df_pricediff = self.PairPricesDiffDictionary[ticker1]
df_ma = self.PairMovingAverageDiffDictionary[ticker1]
df_stdev = self.PairMovingStdevDiffDictionary[ticker1]
df_currdiffminusma = df_pricediff.sub(df_ma, axis=0)
df_howmanystdevsout = df_currdiffminusma.div(df_stdev, axis=0)
columns_ma = list(df_ma.columns.values)
for column in columns_ma:
if column == 'CHD':
df_runningmax = df_pricediff[column].to_frame()
stop
for column in columns_ma:
if column == 'CHD':
print ticker, column
df_extreme = df_howmanystdevsout[column].loc[df_howmanystdevsout[column].abs() >= 2.9].to_frame()
print '-------------------------------df_extreme'
print df_extreme
df_full = pd.concat([
df_close[[ticker,column]]
, df_ma[column].to_frame('ma')
, df_pricediff[column].to_frame('pricediff')
, df_currdiffminusma[column].to_frame('currdiffminusma')
, df_howmanystdevsout[column].to_frame('howmanystdevsout')
], axis=1)
print 'full!!!!!'
#print df_full
#stop
for idx,row in df_full.iterrows():
print idx, round(row[ticker],2),round(row[column],2),'| ',round(row['ma'],2),' |',round(row['pricediff'],2),round(row['currdiffminusma'],2), round(row['howmanystdevsout'],2)
#if idx > '2016-03-02':
# break
selected_opportunity = 4
triggervalue = df_extreme.iloc[selected_opportunity][column]
tradedate = df_extreme.iloc[selected_opportunity].name
self.testone(ticker,column,tradedate,triggervalue)
stop
stop
if 1 == 1:
print ' ---------------------------------------------------- df_pricediff'
#print df_pricediff
for idx,row in df_pricediff.iterrows():
print idx, row['AAL']
if idx > '2016-03-02':
break
print ' ---------------------------------------------------- df_ma'
#print df_ma
for idx,row in df_ma.iterrows():
print idx, row['AAL']
if idx > '2016-03-02':
break
print ' ---------------------------------------------------- df_currdiffminusma'
#print df_currdiffminusma
for idx,row in df_currdiffminusma.iterrows():
print idx, row['AAL']
if idx > '2016-03-02':
break
#np.divide
print ' ---------------------------------------------------- df_howmanystdevsout'
#print df_currdiffminusma
for idx,row in df_howmanystdevsout.iterrows():
print idx, row['AAL']
if idx > '2016-03-02':
break
stop
return columns_ma
def testone_old(self,ticker1,ticker2,tradedate,triggervalue):
print ticker1,ticker2,tradedate,triggervalue
df_closeprices = self.ClosePricesDataframe
df_closeprices = df_closeprices[(df_closeprices.index >= tradedate)]
df2 = pd.DataFrame(index=df_closeprices.index.copy())
print '----------------- ++++'
df_closeprices = df_closeprices[[ticker1,ticker2]]
#print df
columns = list(df_closeprices.columns.values)
print 'columns',columns
df_openshares = 10000.0 / df_closeprices.iloc[[0]]
print 'df_openshares',df_openshares
print '--------------------------kkkk'
df_blotter = df_closeprices * df_openshares.loc[tradedate]
if triggervalue < 0:
sign_for_ticker1 = -1
sign_for_ticker2 = 1
else:
sign_for_ticker1 = 1
sign_for_ticker2 = -1
for idx,row in df_blotter.iterrows():
print 'a', idx, sign_for_ticker1 * row[ticker1],sign_for_ticker2 * row[ticker2], ( sign_for_ticker1 * row[ticker1] ) + ( sign_for_ticker2 * row[ticker2] )
#if idx > '2016-03-02':
# break
print '-------------------------------------------------'
## for idx,row in df_closeprices.iterrows():
## print 'x',idx, row['A'],row['AAL']
## #if idx > '2016-03-02':
## # break
stop
df_shares2 = df_openshares.append([df_openshares]*(len(df)-1),ignore_index=True)
df_shares3 = pd.concat([df2, df_shares2], axis=1)
print 'df_shares2',df_shares2
df_shares3.set_index("Date", drop=True, inplace=True)
print df_shares3
stop
df_dollarized = df.multiply(df_shares3, axis=1)
print df_dollarized
stop
def runbyticker_old2(self,ticker):
#print mydict
import pandas as pd
import numpy as np
#dict_pairdiff = self.PairMovingAverageDiffDictionary
ticker1 = ticker
#df = self.PairMovingAverageDiffDictionary[ticker1]
#print df
df_close = self.ClosePricesDataframe
df_pricediff = self.PairPricesDiffDictionary[ticker1]
df_ma = self.PairMovingAverageDiffDictionary[ticker1]
df_stdev = self.PairMovingStdevDiffDictionary[ticker1]
columns_ma = list(df_ma.columns.values)
columns_stdev = list(df_stdev.columns.values)
print ticker1, '--------------------------------------------------------------------------- ma'
print df_ma
print ticker1, '--------------------------------------------------------------------------- stdev'
print df_stdev
df_add = df_ma[columns_ma].add(df_stdev[columns_stdev], axis=0)
print ticker1, '--------------------------------------------------------------------------- added'
print df_add
my_dictionary_of_list_of_dicts = {}
for column in columns_ma:
print 'calculating',column
my_list_of_dicts = []
for index, row in df_ma.iterrows():
mydict = {'date':index,'ticker1':ticker,'ticker2':column,'a_date':index, 'b_ma':row[column], 'c_stdev':df_stdev[column].loc[index], 'd_currdiff':df_pricediff[column].loc[index], 'e_cdminusma':df_pricediff['AAPL'].loc[index] - row['AAPL'], 'g_close_1':df_close[ticker].loc[index], 'h_close_2':df_close[column].loc[index]}
my_list_of_dicts.append(mydict)
my_dictionary_of_list_of_dicts[column] = my_list_of_dicts
df_result = pd.DataFrame(my_list_of_dicts)
df_result['f_currstdaway'] = df_result['e_cdminusma'] / df_result['c_stdev']
df_result.set_index('a_date', drop=True, inplace=True)
for index, row in df_result.iterrows():
print index, row
#print index, row['g_close_1'],row['h_close_2'],row['b_ma'], row['c_stdev'],row['f_currstdaway'], 'AddTrade!!!'
#print df_result
stop
i = 0
mylist = []
for column in df:
i +=1
#print df[column]
df1 = pd.DataFrame({'value1':df[column]})
#df1['ticker2'] = column
df1['value2'] = df1.value1.shift(-1)
df1['value31'] = df1.value1/abs(df1.value1)
df1['value32'] = df1.value2/abs(df1.value2)
df1['value4'] = df1['value31'] * df1['value32']
#print df1
#stop
#df1['cross'] = np.where( np.logical_or(np.logical_and(df1['value2']>0,df1['value1']<0), np.logical_and(df1['value1']>0,df1['value2']<0)),'yes', 'no')
#df1['cross'] = np.where( abs(df1['value1'])>0,df1['value1']<0), np.logical_and(df1['value1']>0,df1['value2']<0)),'yes', 'no')
df2 = df1.loc[df1['value4'] == -1]
prevvalue = -9999.99
if len(df2) > 30 and len(df2) < 50 :
#print df
print 'Comparing',ticker1, column
rowbegin = df1.iloc[0]
#print 'rowbegin',rowbegin
#print 'dataframeendx', df1.iloc[len(df)-1]['value1']
dataframeend = df1.iloc[len(df)-1]
dataframeendindex = dataframeend.name
dataframeendvalue = df1.iloc[len(df)-1]['value1']
#print 'dataframeend', df1.tail(1)
#stop
rowbeginindex = rowbegin.name
#print 'rowbegin index', rowbeginindex
j = 0
for idx,rowend in df2.iterrows():
j += 1
rowendindex = rowend.name
#print 'rowend index', rowendindex
mask = (df1.index > rowbeginindex) & (df1.index <= rowendindex)
df3 = df1.loc[mask]
#print df3
if df3.iloc[0]['value1'] > 0:
currvalue = df3.loc[df3['value1'].idxmax()]['value1']
else:
currvalue = df3.loc[df3['value1'].idxmin()]['value1']
if not prevvalue == -9999.99:
print ticker1, column, rowbeginindex,rowendindex,'diff', round(prevvalue,2), '-', round(currvalue,2) , '=', round(abs(prevvalue - currvalue),2)
prevvalue = currvalue
rowbeginindex = rowendindex
#if j >= 10:
# stop
mask = (df1.index > rowbeginindex) & (df1.index <= dataframeendindex)
df3 = df1.loc[mask]
print df3
print 'check where investment got out of hand here'
print 'add the sum of the inflexions to the crosscount, perhaps mean, average days between crosses?'
#print 'lastvalues',prevvalue, valueend
stop
dict1 = {'ticker1':ticker1,'ticker2':column,'crosscount':len(df2)}
mylist.append(dict1)
df_final1 = pd.DataFrame(mylist)
return df_final1
def setclassdictionaries(self,closepricesfilepath,movingaveragewindow):
print 'started def findpairstdev'
import pandas as pd
myfile = closepricesfilepath #'C:\Batches\GitStuff\$work\closeprices_sample.csv'
df = pd.read_csv(myfile)
#print df
df2 = df["Date"]
df.set_index("Date", drop=True, inplace=True)
self.ClosePricesDataframe = df
columns = list(df.columns.values)
#columns = ['CA', 'CHD']
df_openshares = 10000.0 / df.iloc[[0]]
#print df_openshares
#stop
df_shares2 = df_openshares.append([df_openshares]*(len(df)-1),ignore_index=True)
df_shares3 = pd.concat([df2, df_shares2], axis=1)
df_shares3.set_index("Date", drop=True, inplace=True)
df_dollarized = df.multiply(df_shares3, axis=1)
dict_pairdiff_runningmax = {}
dict_pairdiff_runningmin = {}
dict_pairdiff_betweenmaxmin = {}
dict_pairdiff_runningpct = {}
dict_pairdiff_prices = {}
dict_pairdiff_dollarized = {}
dict_pairdiff_movingaverage = {}
dict_pairdiff_standarddeviation = {}
print 'started creating class dictionaries...'
i2 = 0
for column in columns:
df_diff_runningmax = pd.DataFrame(index = df.index)
df_diff_runningmin = pd.DataFrame(index = df.index)
df_diff_movingaverage = pd.DataFrame(index = df.index)
df_diff_stdev = pd.DataFrame(index = df.index)
print 'setclassdictionaries',column
df_diff = df[columns].sub(df[column], axis=0)
i3 = 0
for column1 in columns:
df_diff1 = df_diff[column1].to_frame(column1)
df_diff_runningmax[column1] = df_diff1[column1].cummax().to_frame(column1)
df_diff_runningmin[column1] = df_diff1[column1].cummin().to_frame(column1)
df_diff_movingaverage[column1] = df_diff1.rolling(window=movingaveragewindow).mean()
df_diff_stdev[column1] = df_diff1.rolling(window=movingaveragewindow).std()
i3 += 1
df_diff_prices = df[columns].sub(df[column], axis=0)
df_diff_betweenmaxmin = df_diff_runningmax[columns].sub(df_diff_runningmin[columns], axis=0)
df_diff_runningpct = ( df_diff_prices - df_diff_runningmin ) / ( df_diff_runningmax - df_diff_runningmin)
df_diff_dollarized = df_dollarized[columns].sub(df_dollarized[column], axis=0)
dict_pairdiff_prices[column] = df_diff_prices
dict_pairdiff_runningmax[column] = df_diff_runningmax
dict_pairdiff_runningmin[column] = df_diff_runningmin
dict_pairdiff_betweenmaxmin[column] = df_diff_betweenmaxmin
dict_pairdiff_runningpct[column] = df_diff_runningpct
dict_pairdiff_dollarized[column] = df_diff_dollarized
dict_pairdiff_movingaverage[column] = df_diff_movingaverage
dict_pairdiff_standarddeviation[column] = df_diff_stdev
i2 +=1
if i2 >= 6:
break
print 'finished creating class dictionaries...'
self.PairPricesDiffDictionary = dict_pairdiff_prices
self.PairRunningMaxDiffDictionary = dict_pairdiff_runningmax
self.PairRunningMinDiffDictionary = dict_pairdiff_runningmin
self.PairBetweenMaxMinDiffDictionary = dict_pairdiff_betweenmaxmin
self.PairRunningPctDiffDictionary = dict_pairdiff_runningpct
self.PairDollarizedDiffDictionary = dict_pairdiff_dollarized
self.PairMovingAverageDiffDictionary = dict_pairdiff_movingaverage
self.PairMovingStdevDiffDictionary = dict_pairdiff_standarddeviation
self.SymbolsList = columns
return True
if __name__=='__main__':
o = find(closepricesfilepath = 'C:\\Batches\\GitStuff\\$work\\closeprices_sample.csv', movingaveragewindow = 50)