Forums › ProRealTime English forum › ProOrder support › Confirmation of Trend using Neural Networks (by kind permission of Leo) › Reply To: Confirmation of Trend using Neural Networks (by kind permission of Leo)
09/02/2018 at 7:03 PM
#79578
Right full Neural Network now, huge thanks to Leo!
v4.0 below, spread = 4.
Tweaks and variations welcome (better exit strategy?).
Please post full System Code and Performance Stats.
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// Hyperparameters to be optimized DEFPARAM CUMULATEORDERS = False ETA=1 //known as the learning rate candlesback=6 // for the classifier ProfitRiskRatio=2// for the classifier spread=0.9 // for the classifier ///////////////// CLASSIFIER ///////////// myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT ///////////////////////// NEURONAL NETWORK /////////////////// // ...INITIAL VALUES... once a11=1 once a12=-1 once a13=1 once a14=-1 once a21=1 once a22=1 once a23=-1 once a24=1 once a31=-1 once a32=1 once a33=-1 once a34=1 once a41=1 once a42=-1 once a43=1 once a44=-1 once a51=-1 once a52=1 once a53=-1 once a54=1 once a61=1 once a62=-1 once a63=1 once a64=-1 once Fbias1=0 once Fbias2=0 once Fbias3=0 once Fbias4=0 once Fbias5=0 once Fbias6=0 once b11=1 once b12=-1 once b13=1 once b14=-1 once b15=1 once b16=-1 once b21=-1 once b22=1 once b23=-1 once b24=1 once b25=-1 once b26=1 once Obias1=0 once Obias2=0 // ...DEFINITION OF INPUTS... SMA20=average[min(20,barindex)](close) SMA200=average[min(200,barindex)](close) SMA2400=average[min(2400,barindex)](close) //in 5 min time frame this is the value of SMA 200 periods in hourly variable1= RSI[14](close) // or to be defined variable2= (close-SMA20)/SMA20 *100 //or to be defined variable3= (SMA20-SMA200)/SMA200 *100 //or to be defined variable4= (SMA200-SMA2400)/SMA2400 *100 // to be defined // >>> LEARNING PROCESS <<< // If the classifier has detected a wining trade in the past //IF hour > 7 and hour < 21 then IF BARINDEX > 2500 THEN IF classifierlong=1 or classifiershort=1 THEN IF hour > 7 and hour < 21 then candleentry=max(candleentrylong,candleentryshort) Y1=classifierlong Y2=classifiershort // >>> INPUT FOR NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] FOR i=1 to 10 DO // THIS HAVE TO BE IMPROVED ETAi=ETA - ETA/10*(i-1) //Learning Rate // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) // >>> PARTIAL DERIVATES OF COST FUNCTION <<< // ... CROSS-ENTROPY AS COST FUCTION ... // COST = - ( (Y1*LOG(output1)+(1-Y1)*LOG(1-output1) ) - (Y2*LOG(output2)+(1-Y2)*LOG(1-output2) ) DerObias1 = (output1-Y1) * 1 DerObias2 = (output2-Y2) * 1 Derb11 = (output1-Y1) * F1 Derb12 = (output1-Y1) * F2 Derb13 = (output1-Y1) * F3 Derb14 = (output1-Y1) * F4 Derb15 = (output1-Y1) * F5 Derb16 = (output1-Y1) * F6 Derb21 = (output2-Y2) * F1 Derb22 = (output2-Y2) * F2 Derb23 = (output2-Y2) * F3 Derb24 = (output2-Y2) * F4 Derb25 = (output2-Y2) * F5 Derb26 = (output2-Y2) * F6 //Implementing BackPropagation Obias1=Obias1-ETAi*DerObias1 Obias2=Obias2-ETAi*DerObias2 b11=b11-ETAi*Derb11 b12=b12-ETAi*Derb12 b13=b11-ETAi*Derb13 b14=b11-ETAi*Derb14 b15=b11-ETAi*Derb15 b16=b11-ETAi*Derb16 b21=b11-ETAi*Derb21 b22=b12-ETAi*Derb22 b23=b11-ETAi*Derb23 b24=b11-ETAi*Derb24 b25=b11-ETAi*Derb25 b26=b11-ETAi*Derb26 // >>> PARTIAL DERIVATES OF COST FUNCTION (LAYER) <<< DerFbias1 = (output1-Y1) * b11 * F1*(1-F1) * 1 + (output2-Y2) * b21 * F1*(1-F1) * 1 DerFbias2 = (output1-Y1) * b12 * F2*(1-F2) * 1 + (output2-Y2) * b22 * F2*(1-F2) * 1 DerFbias3 = (output1-Y1) * b13 * F3*(1-F3) * 1 + (output2-Y2) * b23 * F3*(1-F3) * 1 DerFbias4 = (output1-Y1) * b14 * F4*(1-F4) * 1 + (output2-Y2) * b24 * F4*(1-F4) * 1 DerFbias5 = (output1-Y1) * b15 * F5*(1-F5) * 1 + (output2-Y2) * b25 * F5*(1-F5) * 1 DerFbias6 = (output1-Y1) * b16 * F6*(1-F6) * 1 + (output2-Y2) * b26 * F6*(1-F6) * 1 Dera11 = (output1-Y1) * b11 * F1*(1-F1) * input1 + (output2-Y2) * b21 * F1*(1-F1) * input1 Dera12 = (output1-Y1) * b11 * F1*(1-F1) * input2 + (output2-Y2) * b21 * F1*(1-F1) * input2 Dera13 = (output1-Y1) * b11 * F1*(1-F1) * input3 + (output2-Y2) * b21 * F1*(1-F1) * input3 Dera14 = (output1-Y1) * b11 * F1*(1-F1) * input4 + (output2-Y2) * b21 * F1*(1-F1) * input4 Dera21 = (output1-Y1) * b12 * F2*(1-F2) * input1 + (output2-Y2) * b22 * F2*(1-F2) * input1 Dera22 = (output1-Y1) * b12 * F2*(1-F2) * input2 + (output2-Y2) * b22 * F2*(1-F2) * input2 Dera23 = (output1-Y1) * b12 * F2*(1-F2) * input3 + (output2-Y2) * b22 * F2*(1-F2) * input3 Dera24 = (output1-Y1) * b12 * F2*(1-F2) * input4 + (output2-Y2) * b22 * F2*(1-F2) * input4 Dera31 = (output1-Y1) * b13 * F3*(1-F3) * input1 + (output2-Y2) * b23 * F3*(1-F3) * input1 Dera32 = (output1-Y1) * b13 * F3*(1-F3) * input2 + (output2-Y2) * b23 * F3*(1-F3) * input2 Dera33 = (output1-Y1) * b13 * F3*(1-F3) * input3 + (output2-Y2) * b23 * F3*(1-F3) * input3 Dera34 = (output1-Y1) * b13 * F3*(1-F3) * input4 + (output2-Y2) * b23 * F3*(1-F3) * input4 Dera41 = (output1-Y1) * b14 * F4*(1-F4) * input1 + (output2-Y2) * b24 * F4*(1-F4) * input1 Dera42 = (output1-Y1) * b14 * F4*(1-F4) * input2 + (output2-Y2) * b24 * F4*(1-F4) * input2 Dera43 = (output1-Y1) * b14 * F4*(1-F4) * input3 + (output2-Y2) * b24 * F4*(1-F4) * input3 Dera44 = (output1-Y1) * b14 * F4*(1-F4) * input4 + (output2-Y2) * b24 * F4*(1-F4) * input4 Dera51 = (output1-Y1) * b15 * F5*(1-F5) * input1 + (output2-Y2) * b25 * F5*(1-F5) * input1 Dera52 = (output1-Y1) * b15 * F5*(1-F5) * input2 + (output2-Y2) * b25 * F5*(1-F5) * input2 Dera53 = (output1-Y1) * b15 * F5*(1-F5) * input3 + (output2-Y2) * b25 * F5*(1-F5) * input3 Dera54 = (output1-Y1) * b15 * F5*(1-F5) * input4 + (output2-Y2) * b25 * F5*(1-F5) * input4 Dera61 = (output1-Y1) * b16 * F6*(1-F6) * input1 + (output2-Y2) * b26 * F6*(1-F6) * input1 Dera62 = (output1-Y1) * b16 * F6*(1-F6) * input2 + (output2-Y2) * b26 * F6*(1-F6) * input2 Dera63 = (output1-Y1) * b16 * F6*(1-F6) * input3 + (output2-Y2) * b26 * F6*(1-F6) * input3 Dera64 = (output1-Y1) * b16 * F6*(1-F6) * input4 + (output2-Y2) * b26 * F6*(1-F6) * input4 //Implementing BackPropagation Fbias1=Fbias1-ETAi*DerFbias1 Fbias2=Fbias2-ETAi*DerFbias2 Fbias3=Fbias3-ETAi*DerFbias3 Fbias4=Fbias4-ETAi*DerFbias4 Fbias5=Fbias5-ETAi*DerFbias5 Fbias6=Fbias6-ETAi*DerFbias6 a11=a11-ETAi*Dera11 a12=a12-ETAi*Dera12 a13=a13-ETAi*Dera13 a14=a14-ETAi*Dera14 a21=a21-ETAi*Dera21 a22=a22-ETAi*Dera22 a23=a23-ETAi*Dera23 a24=a24-ETAi*Dera24 a31=a31-ETAi*Dera31 a32=a32-ETAi*Dera32 a33=a33-ETAi*Dera33 a34=a34-ETAi*Dera34 a41=a41-ETAi*Dera41 a42=a42-ETAi*Dera42 a43=a43-ETAi*Dera43 a44=a44-ETAi*Dera44 a51=a51-ETAi*Dera51 a52=a52-ETAi*Dera52 a53=a53-ETAi*Dera53 a54=a54-ETAi*Dera54 a61=a61-ETAi*Dera61 a62=a62-ETAi*Dera62 a63=a63-ETAi*Dera63 a64=a64-ETAi*Dera64 //GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64) NEXT ENDIF ENDIF //ENDIF /////////////////// NEW PREDICTION /////////////////// // >>> INPUT NEURONS <<< input1=variable1 input2=variable2 input3=variable3 input4=variable4 // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) ENDIF //return output1 as "prediction long", output2 as "prediction short", threshold as "threshold" If Output1 > 0.9 and output2 < 1 Then Buy at Market Endif If Output2 > 0.7 and output1 < 0.7 Then Sellshort at Market endif If LongonMarket then SET TARGET PPROFIT 390 SET STOP PLOSS 50 Endif If ShortonMarket then SET TARGET PPROFIT 100 SET STOP PLOSS 60 Endif |
