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Guillermo Bondonno INVESTOR

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Hi everyone, Im trying to make a simple algorithm with keras work, so far it trains fine, but when I try to predict some value, it throws me a shape error, only that when I print to the debugger the shape it appears to be correct. Heres the code and the output.

for security in changes.AddedSecurities:

            symbol = security.Symbol

            new_feature = np.array(algorithm.History(security.Symbol,15, Resolution.Daily)["open"])

            algorithm.Debug(f" Its shape is {new_feature.shape}")

            

            self.mom[symbol] = self.model.predict(new_feature)

            algorithm.Debug(f"Prediction for {symbol} is {self.mom[symbol]}")

The output in the debugger:

9 | 11:52:31: 

Runtime Error: ValueError : Error when checking input: expected dense_1_input to have shape (15,) but got array with shape (1,)

  at OnSecuritiesChanged in main.py:line 63

 ValueError : Error when checking input: expected dense_1_input to have shape (15,) but got array with shape (1,)	 (Open Stacktrace)

 10 | 11:52:41: 

Algorithm Id:(200821177eb8c61af0f08a86be772a20) completed in 27.77 seconds at 0k data points per second. Processing total of 742 data points.

 11 | 11:52:41: 

 Its shape is (15,)

Do you have any idea why this maight happen?

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Guillermo Bondonno

Accepted Answer

Update Backtest

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Guillermo Bondonno

45 ,

import clr

clr.AddReference("System")

clr.AddReference("QuantConnect.Algorithm")

clr.AddReference("QuantConnect.Common")



from System import *

from QuantConnect import *

from QuantConnect.Algorithm import *



import numpy as np

from keras.models import Sequential

from keras.layers import Dense, Activation

from keras.optimizers import SGD

from sklearn.preprocessing import MinMaxScaler

from datetime import timedelta



class MOMAlphaModel(AlphaModel):

    # se puede hacer una knn aca adentro

    def __init__(self):

        self.mom = {}

        

        self.model = Sequential()

        self.model.add(Dense(10))

        self.model.add(Activation('relu'))

        self.model.add(Dense(1))

        

        

    def OnSecuritiesChanged(self, algorithm, changes):

        self.TrainNN(changes.AddedSecurities, algorithm)

        

        for security in changes.AddedSecurities:

            symbol = security.Symbol

            new_feature = np.array([algorithm.History(security.Symbol,15, Resolution.Daily)["open"]])

            algorithm.Debug(f"{new_feature}")

            algorithm.Debug(f" its shape is {new_feature.shape}")

            try:

                self.mom[symbol] = self.model.predict(new_feature, batch_size=1)

                algorithm.Debug(f"Prediction for {symbol} is {self.mom[symbol]}")

            except Exception as e:

                algorithm.Debug(f"{e}")

                pass

    

    def TrainNN(self, data, algorithm):

        Xs = []

        Ys = []

        

        if len(data) == 0:

            return None

        

        for sec in data:

            #price = [last 10 weeks prices]

            if len(algorithm.History(sec.Symbol,16, Resolution.Daily)['open']) >= 16:

                #Ys.append(algorithm.History(sec.Symbol,16, Resolution.Daily)["open"][0] / algorithm.History(sec.Symbol,16, Resolution.Daily)["open"][1] - 1)

                Xs.append(algorithm.History(sec.Symbol,15, Resolution.Daily)["open"])

        

        if len(Xs) >= 1:

            for X in Xs:

                Ys.append(X[0] / X[1] - 1)

            Xs = np.array(Xs) #shape: (number of secs , 15 )

            Ys = np.array(Ys) #shape: (number of secs, )

            

            sgd = SGD(lr = 0.05) # learning rate = 0.01



            self.model.compile(loss='mse', optimizer=sgd)



            cost = self.model.fit(x=Xs, y=Ys, batch_size=None, epochs=10)

        



    def Update(self, algorithm, data):

        

        ups = []

        downs = []

        

        for i in self.mom:

            if self.mom[i] > 0.01:

                ups.append(Insight.Price(i, timedelta(days = 1), InsightDirection.Up))

            elif self.mom[i] < -0.01:

                downs.append(Insight.Price(i, timedelta(days = 1), InsightDirection.Down))

            else:

                pass

        

        algorithm.Debug(f"Produced {len(ups)+len(downs)} insights")

        return Insight.Group(ups + downs)

        

 

class FrameworkAlgorithm(QCAlgorithm):

    

    def Initialize(self):

        self.SetStartDate(2014, 10, 1)   

        self.SetEndDate(2014, 12, 1)    

        self.SetCash(100000)           

        

        #symbols = [Symbol.Create("SPY", SecurityType.Equity, Market.USA),  Symbol.Create("BND", SecurityType.Equity, Market.USA)]

        self.UniverseSettings.Resolution = Resolution.Daily

        self.AddUniverse(self.MyCoarseFilterFunction, self.MyFineFundamentalFunction)

        

        self.SetAlpha(MOMAlphaModel())

        

        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())

        self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.02))

        

        #1. Set the Execution Model to an Immediate Execution Model

        self.SetExecution(ImmediateExecutionModel())

        #self.Debug(f"I hold these secs: {[sec.Symbol for sec in self.Securities]}")

        

    def MyCoarseFilterFunction(self, coarse):

        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)

        filtered = [ x.Symbol for x in sortedByDollarVolume if x.HasFundamentalData ]

        return filtered[:80]



    def MyFineFundamentalFunction(self, fine):

        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=False)

        return [ x.Symbol for x in sortedByPeRatio[:20] ]

Full code

Alexandre Catarino

100.5k ,

Hi Guillermo Bondonno

Sorry about the wait.
In fact, one of the history requests from line 33 does not return 15 days because the security doesn't have 15 days of data. You are not catching it with the Debug statement because the debug messages are rate limited.
I would suggest adding a check for the size of the data frame to address the issue:

history = algorithm.History(security.Symbol,15, Resolution.Daily)

if len(history.index) < 15:

    continue

new_feature = np.array([history.open])

Guillermo Bondonno INVESTOR

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