dictParam['stoploss'] = dictDataSpec['decimalStoploss']
if dictParam['stoploss'] == 0.99:
dictParam['boolStoploss'] = False
else:
dictParam['boolStoploss'] = True
# set time start, end, step
dtDataStart = dfAll.index[0]
dtDataEnd = dfAll.index[-1]
NDayTrain = dictDataSpec['NDayTrain']
NDayTest = dictDataSpec['NDayTest']
NthFriday = dictDataSpec['NthFriday']
NDayShift = dictDataSpec['NDayShift']
NMonthStart = dictDataSpec['NMonthStart']
listDTTestStart = Utils.generateNthFriday(NthFriday, NMonthStart,
NDayShift)
listDTTestStart = [x for x in listDTTestStart if x <= Utils.dtLastData]
dtDataStart = datetime.datetime.combine(dfAll.index[0], dtDataStart.time())
dtTrainStart = dtDataStart
seriesIndicatorAll = pd.Series()
seriesYAll = pd.Series()
seriesIndicatorAll = dfAll['Close'].copy()
seriesIndicatorAll = seriesIndicatorAll.apply(lambda x: np.nan)
# back test this data spec
if dtEnter > listDTTestStart[-1]:
listDTTestStart.append(dtEnter)
for nWindow, dtTestStart in enumerate(listDTTestStart):
# the start & end of test window
def funcTopPort(strParamSweep):
# param
strMethodVolatility = 'EWMAN2'
# dtBackTestStart & dtBackTestEnd
dtBackTestStart = Utils.dtBackTestStart
#dtBackTestEnd = Utils.dtBackTestEnd
#########################################################
# read backtest result of rquired cases
#########################################################
# read stored results
listDictPerTopPort = Utils.getFileAddressForTopPort(strParamSweep)
#---------------- clear fake tomorrow
dictDataSpec = listDictPerTopPort[0]
strTB = dictDataSpec['strModelName'][0]
try:
Utils.UtilsDB.clearTBFakeTomorrow(Utils.UtilsDB.DB_NAME_POSITION,
strTB)
Utils.UtilsDB.clearTBFakeTomorrow(Utils.UtilsDB.DB_NAME_PERFORMANCE,
strTB)
except:
pass
#---------------- iterate
for dictPerTopPort in listDictPerTopPort:
gc.collect()
listDictDataSpec = []
for strFileAddress in dictPerTopPort['listFileAddress']:
listDictDataSpec.append(pd.read_pickle(strFileAddress))
#########################################################
# prepare directory for saving
#########################################################
strDirParamSweep = Utils.dirResultPerCase + '/' + strParamSweep
if os.path.exists(strDirParamSweep) is False:
os.mkdir(strDirParamSweep)
strDirAddressCase = strDirParamSweep + '/' + dictPerTopPort[
'strCase'] + '/'
if os.path.exists(strDirAddressCase) is False:
os.mkdir(strDirAddressCase)
strFileAddressPrefix = strDirAddressCase
# save config file of the port
dictPerTopPortOut = dict(
(key, value[0]) for key, value in dictPerTopPort.iteritems()
if key not in ['strCase', 'listFileAddress', 'Secu'])
pd.Series(dictPerTopPortOut).to_pickle(strFileAddressPrefix + '/' +
'portconfig.pickle')
NDayTrain = dictPerTopPort['NDayTrain'][0]
NDayTest = dictPerTopPort['NDayTest'][0]
NDayShift = dictPerTopPort['NDayShift'][0]
strModelName = dictPerTopPort['strModelName'][0]
#########################################################
# determine the rebalance datetime
#########################################################
if strModelName in ['TS', 'TSC']:
listDTTestStart = Utils.generateNthFriday(
dictPerTopPort['NthFriday'][0],
dictPerTopPort['NMonthStart'][0])
elif strModelName in ['TSM', 'XSM']:
listDTTestStart = Utils.generateDTTestStartCalendarDay(
NDayTest, NDayShift, dictPerTopPort['NWeekStart'][0])
else:
print 'incorrect model name: %s' % strModelName
listDTTestStart = [x for x in listDTTestStart if x <= Utils.dtLastData]
dtEnter = Utils.UtilsDB.dtTomorrow
listDTTestStart.append(dtEnter)
seriesDTRebalance = pd.Series(listDTTestStart)
#########################################################
# calculate the portfolio return
#########################################################
dtBackTestStart = seriesDTRebalance[
seriesDTRebalance >= Utils.dtBackTestStart].values[0]
if strModelName in ['TSM', 'TS', 'TSC', 'XSM']:
dfOut = UtilsPortSync.funcShowStrategyPortSum(
dictPerTopPort,
listDictDataSpec,
seriesDTRebalance,
strMethodVolatility,
NDayTest,
dtBackTestStart,
#dtBackTestEnd,
strFileAddressPrefix)
dfOut = dfOut[dfOut.index >= dtBackTestStart]
# plot & savefig
dfOut = dfOut[['Cum Return', 'Max DD', 'Position']].ffill().dropna()
#ax = dfOut[['Cum Return', 'Max DD', 'Position']].plot(secondary_y=['Position'])
#ax.set_ylabel('Cum Return')
#ax.set_ylabel('Position')
#plt.savefig(strFileAddressPrefix + 'figure.png', format='png')
#plt.close()
print strFileAddressPrefix
# output
dfOut.to_pickle(strFileAddressPrefix + 'dfOut.pickle')