{-------------------------------------------------------------------------------
 Trading Strategy Code
 Population member: 156
 Max bars back: 100

 Created by: Adaptrade Builder version 1.7.2.1
 Created:    2/19/2015 5:46:07 AM

 Scripting language: TradeStation 6 or newer

 Price files:  EURUSD-240-TS.txt
               EURUSD240.csv
 Build dates: 11/14/2011 to 12/12/2013

 Project file: C:\...\Adaptrade\HybridNN\HybridNN.gpstrat
-------------------------------------------------------------------------------}

{ Strategy inputs }
Inputs: NL1 (16),            { Indicator look-back length (bars), long trades }
        NL2 (5),             { Day of week (0 to 6 for Sunday to Saturday), long trades }
        NL3 (18),            { Indicator look-back length (bars), long trades }
        NL4 (81),            { Indicator look-back length (bars), long trades }
        NL5 (4),             { Indicator look-back length (bars), long trades }
        NL6 (100),           { Indicator look-back length (bars), long trades }
        NL7 (57),            { Indicator look-back length (bars), long trades }
        NL8 (62),            { Indicator look-back length (bars), long trades }
        NL9 (67),            { Indicator look-back length (bars), long trades }
        NL10 (34),           { Indicator look-back length (bars), long trades }
        NL11 (4),            { Day of week (0 to 6 for Sunday to Saturday), long trades }
        XL1 (3.0000),        { Number of standard deviations, long trades }
        ShiftL1 (13),        { Indicator shift value (bars), long trades }
        ShiftL2 (17),        { Indicator shift value (bars), long trades }
        ShiftL3 (14),        { Indicator shift value (bars), long trades }
        ShiftL4 (15),        { Indicator shift value (bars), long trades }
        ShiftL5 (19),        { Indicator shift value (bars), long trades }
        NS1 (59),            { Indicator look-back length (bars), short trades }
        NS2 (5),             { Price pattern look-back length (bars), short trades }
        Wgt1 (-0.1480),      { Neural network weight value (-1 to 1) }
        Wgt2 (-0.2172),      { Neural network weight value (-1 to 1) }
        Wgt3 (-0.4254),      { Neural network weight value (-1 to 1) }
        Wgt4 (-0.5144),      { Neural network weight value (-1 to 1) }
        Wgt5 (-0.0287),      { Neural network weight value (-1 to 1) }
        Wgt6 (0.2224),       { Neural network weight value (-1 to 1) }
        Wgt7 (-0.3087),      { Neural network weight value (-1 to 1) }
        Wgt8 (-0.5567),      { Neural network weight value (-1 to 1) }
        Wgt9 (0.2203),       { Neural network weight value (-1 to 1) }
        Wgt10 (-0.5853),     { Neural network weight value (-1 to 1) }
        PSParam (100000.00), { Position sizing parameter value }
        RoundPS (true),      { Round-to-nearest (true/false) }
        RoundTo (1),         { Round-to position size value }
        MinSize (1),         { Minimum allowable position size }
        SizeLimit (100000);  { Maximum allowable position size }

{ Variables for entry and exit conditions }
Var:    VarL1     (0),
        VarL2     (0),
        VarL3     (0),
        VarL10     (0),
        VarL11     (0),
        VarL15     (0),
        VarS1     (0),
        VarS2     (0),
        VarS3     (0),
        VarS4     (0),
        EntCondL  (false),
        EntCondS  (false);

{ Variables for position sizing }
Var:    NShares  (0);

{ Entry and exit conditions }
VarL1 = TrueRange;
VarL2 = StandardDev(H, NL1, 1)[ShiftL1];
VarS1 = AvgTrueRange(NS1);
VarS2 = H[NS2];
VarS3 = VarS1 + VarS2;
VarS4 = LowD(0);
EntCondL = VarL1 < VarL2;
EntCondS = VarS3 > VarS4;

{ Neural network inputs }
Array: NNInputs[10](0);

VarL3 = DayOfWeek(date);
VarL10 = ZLTrend(H, NL8)[ShiftL4];
VarL11 = HighD(0);
VarL15 = DayOfWeek(date);

NNInputs[0] = VarL3 - NL2;
NNInputs[1] = InvFisherCycle(O, NL3);
NNInputs[2] = BollingerBand(L, NL4, XL1);
NNInputs[3] = InvFisherRSI(L, NL5)[ShiftL2];
NNInputs[4] = InvFisherCycle(L, NL6);
NNInputs[5] = ZLTrend(H, NL7)[ShiftL3];
NNInputs[6] = VarL10 - VarL11;
NNInputs[7] = StandardDev(O, NL9, 1);
NNInputs[8] = InvFisherRSI(O, NL10)[ShiftL5];
NNInputs[9] = VarL15 - NL11;

{ Evaluate neural network function }
Array: NNWeights[10](0);
Var: NNOutput(0);

NNWeights[0] = Wgt1;
NNWeights[1] = Wgt2;
NNWeights[2] = Wgt3;
NNWeights[3] = Wgt4;
NNWeights[4] = Wgt5;
NNWeights[5] = Wgt6;
NNWeights[6] = Wgt7;
NNWeights[7] = Wgt8;
NNWeights[8] = Wgt9;
NNWeights[9] = Wgt10;

NNOutput = NNCompute(NNInputs, 10, NNWeights, 10, 100);

{ Position sizing calculations }
NShares = PSParam;

If RoundPS and RoundTo > 0 then
    NShares = IntPortion(NShares/RoundTo) * RoundTo;

NShares = MaxList(NShares, MinSize);
NShares = MinList(NShares, SizeLimit);

{ Entry orders }
If EntCondL and NNOutput >= 0.5 then begin
    Buy("EnMark-L") NShares shares next bar at market;
end;

If EntCondS and NNOutput <= -0.5 then begin
    Sell short("EnMark-S") NShares shares next bar at market;
end;