Confirmation of Trend using Neural Networks (by kind permission of Leo)

• 09/02/2018 at 12:30 PM #79564
  Stefanb

  My fault, copy past went wrong.

  Like GraHal says, line 37-46 are duplicated.

   

  Comments on the code otherwise?

  

  Stefanb

  Participant

  Topics: 15

  Replies: 74

  Been thanked: 22 times
  09/02/2018 at 7:03 PM #79578
  GraHal

  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.

  // 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

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319

  // 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

   

  

  GraHal

  Participant

  Topics: 214

  Replies: 8830

  Been thanked: 2201 times
  09/02/2018 at 7:09 PM #79581
  Nicolas

  @stefanb

  You’re off topic…and over optimized 😉

  @grahal

  Thanks for posting it. Now the topic is full of different stuffs. I let you manage the versioning 🙂

  

  Nicolas

  Keymaster

  Topics: 81

  Replies: 20863

  Been thanked: 4774 times
  09/02/2018 at 7:35 PM #79583
  GraHal

  Ha I think it’s a lost cause!?

  I have tried in the past on several Threads to encourage use of version numbers etc, but – whilst we still have folks making elementary faux pas like posting unformatted code (not using the Insert PRT Code button) then I think we have to accept we may never have Configuration Control?

  But I think now this Topic can justifiably be renamed 🙂 …

  Confirmation of Trend using Neural Networks (by kind permission of Leo)

  

  GraHal

  Participant

  Topics: 214

  Replies: 8830

  Been thanked: 2201 times
  09/03/2018 at 8:36 AM #79605
  GraHal

  Leo posted a later version Neural Network (v1.2) so here is the System based on Leo’s latest self-learning neural algorithm.

  Tested with Spread = 4

   

  1 user thanked author for this post.

  4everTrading

  

  GraHal

  Participant

  Topics: 214

  Replies: 8830

  Been thanked: 2201 times
  09/03/2018 at 8:52 AM #79613
  Leo

  Wow GraHal!

  It looks awesome!

  thanks for testing in an strategy.

  A lot of work ahead! But such a rustic neural network proves than it works, imagine all the possibilities!

  2 users thanked author for this post.

  4everTrading, GraHal

  

  Leo

  Participant

  Topics: 17

  Replies: 357

  Been thanked: 144 times
  09/09/2018 at 10:39 PM #80056
  Leo

  Hi GraHal,

  I posted a little improved version of the neural network… but the inputs totally different.

  In case you feel curious.

  NOTE:

  please add in your codes

  DEFPARAM PreLoadBars = 10000

  1	DEFPARAM PreLoadBars = 10000

  then you give time to the algorithm for learn

  Thanks in advance

  As you know, while I am coding with one hand in the other one my baby is sleeping otherwise she will cry, haha 🙂

   

  1 user thanked author for this post.

  GraHal

  

  Leo

  Participant

  Topics: 17

  Replies: 357

  Been thanked: 144 times
  09/10/2018 at 7:35 AM #80060
  GraHal

  Leo wrote:

  in the other one my baby is sleeping

  I recall similar scenarios, can be frustrating at the time, but it passes all too quick then the precious moments will be only in your mind also, so enjoy if you can!? 🙂

  Make sure she not swallow a memory stick full of code or she may end up like Nicolas!! 🙂

  

  GraHal

  Participant

  Topics: 214

  Replies: 8830

  Been thanked: 2201 times
• Author
  Posts