def GetExpectedReturns(self,Xtot,Ytot,k,stock):
#Retrieve the dates for each adjustment close price or close price
dataMongo = self.db.Prices.find_one({'BBGTicker' : stock},{'Adj Close' : 1})
if 'Adj Close' not in list(dataMongo.keys()):
dataMongo = self.db.Prices.find_one({'BBGTicker' : stock},{'Close' : 1})
data=dataMongo['Close']
else:
data=dataMongo['Adj Close']
dates = sorted(data.keys())
#Handle incorrect start and end date entered by user
dh = DataHandler
self.startDate = dh.HandleIncorrectDate(self.startDate,'',dates)
self.endDate = dh.HandleIncorrectDate('',self.endDate,dates)
dates = dates[dates.index(self.startDate):dates.index(self.endDate)]
#Prediction must consider the window for covariance matrix
startDate = dates[k*self.rebalanceFreq + self.window]
pr = Predictor(stock,startDate,self.horizon,self.db)
#Lookup output and features and
TIsDates = Xtot[stock]["DATES"]
Xtrain = Xtot[stock]["ANALYTICS"][:TIsDates.index(startDate)]
Ytrain = Ytot[stock]["RETURNS"][:TIsDates.index(startDate)]
Xpred = Xtot[stock]["ANALYTICS"][TIsDates.index(startDate)]
mu = [stock,pr.PredictKNN(Xtrain,Ytrain,Xpred)/self.horizon]
return mu
