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Re: DeepLearn, introduce new feature [Re: NewtraderX] #488051
01/03/24 07:19 01/03/24 07:19

Joined: Sep 2017
Posts: 82

TipmyPip

Junior Member

TipmyPip

Junior Member

Joined: Sep 2017
Posts: 82

And this one will be even more sophisticated :-) but the limits of your imagination for the inputs, and the structures of the networks are only a function of time :-)

Code

#define INPUTS_PER_MODEL 10
#define TOTAL_INPUTS 50  // Adjusted for additional volume data
#define NUM_MODELS 5     // Number of models in the first layer
#define FINAL_LAYER_INPUTS NUM_MODELS * 2  // Each model contributes two signals (long and short)

function run() {
    set(PARAMETERS);
    StartDate = 20190101;
    BarPeriod = 60;
    Capital = 2000;
    LookBack = 20; 

    vars Open = series(priceOpen());
    vars High = series(priceHigh());
    vars Low = series(priceLow());
    vars Close = series(priceClose());
    vars Volume = series(marketVol());

    // Define your inputs including volume
    vars Inputs = series(TOTAL_INPUTS);
	 int i;
    for(i = 0; i < 10; ++i) {
        Inputs[i] = Open[i + 1];
        Inputs[i + 10] = High[i + 1];
        Inputs[i + 20] = Low[i + 1];
        Inputs[i + 30] = Close[i + 1];
        Inputs[i + 40] = Volume[i + 1];
    }

    // Divide inputs among neural networks
    var ModelInputs[NUM_MODELS][INPUTS_PER_MODEL];
	 int i;
    for(i = 0; i < TOTAL_INPUTS; ++i) {
        ModelInputs[i / INPUTS_PER_MODEL][i % INPUTS_PER_MODEL] = Inputs[i];
    }

    vars ModelLongOutputs = series(NUM_MODELS);
    vars ModelShortOutputs = series(NUM_MODELS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        ModelLongOutputs[i] = adviseLong(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
        ModelShortOutputs[i] = adviseShort(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
    }

    // Second layer of neural network
    vars FinalInputs = series(FINAL_LAYER_INPUTS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        FinalInputs[i] = ModelLongOutputs[i];
        FinalInputs[i + NUM_MODELS] = ModelShortOutputs[i]; // Offset by NUM_MODELS
    }

    var FinalLongSignal = adviseLong(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS);
    var FinalShortSignal = adviseShort(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS);
    var Threshold = 0.5;

    set(LOGFILE | PLOTNOW);
    if (FinalLongSignal > Threshold)
        enterLong();
    if (FinalShortSignal > Threshold)
        enterShort();

    plot("Final Long Signal", FinalLongSignal, NEW|LINE, BLACK);
    plot("Final Short Signal", FinalShortSignal, LINE, RED);
}

In addition, may be Inputting the data in a different manner will cause the networks to respond differently:

Code

// Define your inputs including volume
    vars Inputs = series(TOTAL_INPUTS);
	 int i;
    for(i = 0; i < LookBack; ++i) {
        Inputs[i * 5 + 0] = Open[i];
        Inputs[i * 5 + 1] = High[i];
        Inputs[i * 5 + 2] = Low[i];
        Inputs[i * 5 + 3] = Close[i];
        Inputs[i * 5 + 4] = Volume[i];
    }

Last edited by TipmyPip; 01/03/24 07:33.

ZorroTraderGPT - https://bit.ly/3Gbsm4S

Re: DeepLearn, introduce new feature [Re: NewtraderX] #488083
01/09/24 11:47 01/09/24 11:47

Joined: Sep 2017
Posts: 82

TipmyPip

Junior Member

TipmyPip

Junior Member

Joined: Sep 2017
Posts: 82

Deep Learning idea with Multi-Layer Neural Network:

Code

#define INPUTS_PER_MODEL 10
#define TOTAL_INPUTS 50  // Adjusted for additional volume data
#define NUM_MODELS 5     // Number of models in the first layer
#define FINAL_LAYER_INPUTS NUM_MODELS * 2  // Each model contributes two signals (long and short)
#define SECOND_LAYER_INPUTS 6 // Inputs for the second layer

var change(int n) {
    return scale((priceClose(0) - priceClose(n)) / priceClose(0), 100) / 100;
}

var range(int n) {
    return scale((HH(n) - LL(n)) / priceClose(0), 100) / 100;
}

function run() {
    set(PARAMETERS);
    StartDate = 20190101;
    BarPeriod = 60;
    Capital = 2000;
    LookBack = 120;

    vars Open = series(priceOpen());
    vars High = series(priceHigh());
    vars Low = series(priceLow());
    vars Close = series(priceClose());
    vars Volume = series(marketVol());

    // First layer: NEURAL models
    vars Inputs = series(TOTAL_INPUTS);
	 int i;
    for(i = 0; i < LookBack; ++i) {
        Inputs[i * 5 + 0] = Open[i];
        Inputs[i * 5 + 1] = High[i];
        Inputs[i * 5 + 2] = Low[i];
        Inputs[i * 5 + 3] = Close[i];
        Inputs[i * 5 + 4] = Volume[i];
    }

    var ModelInputs[NUM_MODELS][INPUTS_PER_MODEL];
	 int i;
    for(i = 0; i < TOTAL_INPUTS; ++i) {
        ModelInputs[i % NUM_MODELS][i / NUM_MODELS] = Inputs[i];
    }

    vars ModelLongOutputs = series(NUM_MODELS);
    vars ModelShortOutputs = series(NUM_MODELS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        ModelLongOutputs[i] = adviseLong(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
        ModelShortOutputs[i] = adviseShort(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
    }

    // Second layer: change, range, and pattern-based signals
    vars SecondLayerInputs = series(SECOND_LAYER_INPUTS);
    SecondLayerInputs[0] = change(2);
    SecondLayerInputs[1] = range(2);
    SecondLayerInputs[2] = adviseLong(PATTERN + 2 + RETURNS, 0, priceH(2), priceL(2), priceC(2), priceH(1), priceL(1), priceC(1), priceH(1), priceL(1), priceC(1), priceH(0), priceL(0), priceC(0));

    // Third layer: Final decision neural network
    vars FinalInputs = series(FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        FinalInputs[i * 2 + 0] = ModelLongOutputs[i];
        FinalInputs[i * 2 + 1] = ModelShortOutputs[i];
    }
    for(i = 0; i < SECOND_LAYER_INPUTS; ++i) {
        FinalInputs[FINAL_LAYER_INPUTS + i] = SecondLayerInputs[i];
    }

    var FinalLongSignal = adviseLong(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
    var FinalShortSignal = adviseShort(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
    var Threshold = 0.5;

    set(LOGFILE | PLOTNOW);
    if (FinalLongSignal > Threshold)
        enterLong();
    if (FinalShortSignal > Threshold)
        enterShort();

    plot("Final Long Signal", FinalLongSignal, NEW|LINE, BLACK);
    plot("Final Short Signal", FinalShortSignal, LINE, RED);
}

ZorroTraderGPT - https://bit.ly/3Gbsm4S

Re: DeepLearn, introduce new feature [Re: NewtraderX] #488084
01/10/24 10:57 01/10/24 10:57

Joined: Sep 2017
Posts: 82

TipmyPip

Junior Member

TipmyPip

Junior Member

Joined: Sep 2017
Posts: 82

Here is a Neural Combination of Fuzzy Trading, Which can be improved : (Please note that memory is required and more optimization and testing is needed)

Code

#define INPUTS_PER_MODEL 10
#define TOTAL_INPUTS 50  // Adjusted for additional volume data
#define NUM_MODELS 5     // Number of models in the first layer
#define FINAL_LAYER_INPUTS NUM_MODELS * 2  // Each model contributes two signals (long and short)
#define SECOND_LAYER_INPUTS 18 // Adjusted for additional fuzzy inputs

var change(int n) {
    return scale((priceClose(0) - priceClose(n)) / priceClose(0), 100) / 100;
}

var range(int n) {
    return scale((HH(n) - LL(n)) / priceClose(0), 100) / 100;
}

function run() {
    set(PARAMETERS);
    StartDate = 20190101;
    BarPeriod = 60;
    Capital = 2000;
    LookBack = 120;

    vars Open = series(priceOpen());
    vars High = series(priceHigh());
    vars Low = series(priceLow());
    vars Close = series(priceClose());
    vars Volume = series(marketVol());

    // First layer: NEURAL models
    vars Inputs = series(TOTAL_INPUTS);
	 int i;
    for(i = 0; i < LookBack; ++i) {
        Inputs[i * 5 + 0] = Open[i];
        Inputs[i * 5 + 1] = High[i];
        Inputs[i * 5 + 2] = Low[i];
        Inputs[i * 5 + 3] = Close[i];
        Inputs[i * 5 + 4] = Volume[i];
    }

    var ModelInputs[NUM_MODELS][INPUTS_PER_MODEL];
	 int i;
    for(i = 0; i < TOTAL_INPUTS; ++i) {
        ModelInputs[i % NUM_MODELS][i / NUM_MODELS] = Inputs[i];
    }

    vars ModelLongOutputs = series(NUM_MODELS);
    vars ModelShortOutputs = series(NUM_MODELS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        ModelLongOutputs[i] = adviseLong(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
        ModelShortOutputs[i] = adviseShort(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
    }

    // Second layer: change, range, pattern-based signals, and fuzzy logic
    vars SecondLayerInputs = series(SECOND_LAYER_INPUTS);
    SecondLayerInputs[0] = change(2);
    SecondLayerInputs[1] = range(2);
    SecondLayerInputs[2] = adviseLong(PATTERN + 2 + RETURNS, 0, priceH(2), priceL(2), priceC(2), priceH(1), priceL(1), priceC(1), priceH(1), priceL(1), priceC(1), priceH(0), priceL(0), priceC(0));
    // Fuzzy logic signals
    SecondLayerInputs[3] = equalF(Close[0], Close[1]);
    SecondLayerInputs[4] = aboveF(Close[0], Close[1]);
    SecondLayerInputs[5] = belowF(Close[0], Close[1]);
    SecondLayerInputs[6] = betweenF(Close[0], LL(10), HH(10));
    SecondLayerInputs[7] = peakF(Close);
    SecondLayerInputs[8] = valleyF(Close);
    SecondLayerInputs[9] = risingF(Close);
    SecondLayerInputs[10] = fallingF(Close);
    SecondLayerInputs[11] = crossOverF(Close, SMA(Close, 10));
    SecondLayerInputs[12] = crossUnderF(Close, SMA(Close, 10));
    SecondLayerInputs[13] = andF(risingF(Close), fallingF(Open));
    SecondLayerInputs[14] = orF(risingF(Close), fallingF(Open));
    SecondLayerInputs[15] = notF(risingF(Close));
    SecondLayerInputs[16] = crossOverF(Close, Open);
    SecondLayerInputs[17] = crossUnderF(Close, Open);

    // Third layer: Final decision neural network
    vars FinalInputs = series(FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        FinalInputs[i * 2 + 0] = ModelLongOutputs[i];
        FinalInputs[i * 2 + 1] = ModelShortOutputs[i];
    }
    for( i = 0; i < SECOND_LAYER_INPUTS; ++i) {
        FinalInputs[FINAL_LAYER_INPUTS + i] = SecondLayerInputs[i];
    }

    var FinalLongSignal = adviseLong(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
    var FinalShortSignal = adviseShort(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
    var Threshold = 0.5;

    set(LOGFILE | PLOTNOW);
    if (FinalLongSignal > Threshold)
        enterLong();
    if (FinalShortSignal > Threshold)
        enterShort();

    plot("Final Long Signal", FinalLongSignal, NEW|LINE, BLACK);
    plot("Final Short Signal", FinalShortSignal, LINE, RED);
}

Last edited by TipmyPip; 01/10/24 10:58.

ZorroTraderGPT - https://bit.ly/3Gbsm4S

Re: DeepLearn, introduce new feature [Re: NewtraderX] #488085
01/10/24 11:31 01/10/24 11:31

Joined: Sep 2017
Posts: 82

TipmyPip

Junior Member

TipmyPip

Junior Member

Joined: Sep 2017
Posts: 82

Some ideas of the different Network architectures can perform at a higher accuracy:

Code

#define INPUTS_PER_MODEL 10
#define TOTAL_INPUTS 50  // Adjusted for additional volume data
#define NUM_MODELS 5     // Number of models in the first layer
#define FINAL_LAYER_INPUTS NUM_MODELS * 2  // Each model contributes two signals (long and short)
#define SECOND_LAYER_INPUTS 18 // Adjusted for additional fuzzy inputs

var change(int n) {
    return scale((priceClose(0) - priceClose(n)) / priceClose(0), 100) / 100;
}

var range(int n) {
    return scale((HH(n) - LL(n)) / priceClose(0), 100) / 100;
}

function run() {
    set(PARAMETERS);
    StartDate = 20190101;
    BarPeriod = 60;
    Capital = 2000;
    LookBack = 120;

    vars Open = series(priceOpen());
    vars High = series(priceHigh());
    vars Low = series(priceLow());
    vars Close = series(priceClose());
    vars Volume = series(marketVol());

    // First layer: NEURAL models
    vars Inputs = series(TOTAL_INPUTS);
	 int i;
    for(i = 0; i < LookBack; ++i) {
        Inputs[i * 5 + 0] = Open[i];
        Inputs[i * 5 + 1] = High[i];
        Inputs[i * 5 + 2] = Low[i];
        Inputs[i * 5 + 3] = Close[i];
        Inputs[i * 5 + 4] = Volume[i];
    }

    var ModelInputs[NUM_MODELS][INPUTS_PER_MODEL];
	 int i;
    for(i = 0; i < TOTAL_INPUTS; ++i) {
        ModelInputs[i % NUM_MODELS][i / NUM_MODELS] = Inputs[i];
    }

    vars ModelLongOutputs = series(NUM_MODELS);
    vars ModelShortOutputs = series(NUM_MODELS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        ModelLongOutputs[i] = adviseLong(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
        ModelShortOutputs[i] = adviseShort(NEURAL + BALANCED, 0, &ModelInputs[i][0], INPUTS_PER_MODEL);
    }

    // Second layer: change, range, pattern-based signals, and fuzzy logic based on change and range
    vars SecondLayerInputs = series(SECOND_LAYER_INPUTS);
    var ChangeValue = change(2);
    var RangeValue = range(2);

    SecondLayerInputs[0] = ChangeValue;
    SecondLayerInputs[1] = RangeValue;
    SecondLayerInputs[2] = adviseLong(PATTERN + 2 + RETURNS, 0, priceH(2), priceL(2), priceC(2), priceH(1), priceL(1), priceC(1), priceH(1), priceL(1), priceC(1), priceH(0), priceL(0), priceC(0));
    SecondLayerInputs[3] = equalF(ChangeValue, RangeValue);
    SecondLayerInputs[4] = aboveF(ChangeValue, RangeValue);
    SecondLayerInputs[5] = belowF(ChangeValue, RangeValue);
    SecondLayerInputs[6] = betweenF(ChangeValue, RangeValue - 0.1, RangeValue + 0.1);
    SecondLayerInputs[7] = peakF(series(ChangeValue));
    SecondLayerInputs[8] = valleyF(series(ChangeValue));
    SecondLayerInputs[9] = risingF(series(ChangeValue));
    SecondLayerInputs[10] = fallingF(series(ChangeValue));
    SecondLayerInputs[11] = crossOverF(series(ChangeValue), series(RangeValue));
    SecondLayerInputs[12] = crossUnderF(series(ChangeValue), series(RangeValue));
    SecondLayerInputs[13] = andF(aboveF(ChangeValue, 0), belowF(RangeValue, 0.5));
    SecondLayerInputs[14] = orF(aboveF(ChangeValue, 0), belowF(RangeValue, 0.5));
    SecondLayerInputs[15] = notF(aboveF(ChangeValue, 0));
    SecondLayerInputs[16] = crossOverF(series(ChangeValue), series(0));
    SecondLayerInputs[17] = crossUnderF(series(ChangeValue), series(0));

    // Third layer: Final decision neural network
    vars FinalInputs = series(FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
	 int i;
    for(i = 0; i < NUM_MODELS; ++i) {
        FinalInputs[i * 2 + 0] = ModelLongOutputs[i];
        FinalInputs[i * 2 + 1] = ModelShortOutputs[i];
    }
	 int i;
    for(i = 0; i < SECOND_LAYER_INPUTS; ++i) {
        FinalInputs[FINAL_LAYER_INPUTS + i] = SecondLayerInputs[i];
    }

    var FinalLongSignal = adviseLong(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
    var FinalShortSignal = adviseShort(NEURAL + BALANCED, 0, FinalInputs, FINAL_LAYER_INPUTS + SECOND_LAYER_INPUTS);
    var Threshold = 0.5;

    set(LOGFILE | PLOTNOW);
    if (FinalLongSignal > Threshold)
        enterLong();
    if (FinalShortSignal > Threshold)
        enterShort();

    plot("Final Long Signal", FinalLongSignal, NEW|LINE, BLACK);
    plot("Final Short Signal", FinalShortSignal, LINE, RED);
}

ZorroTraderGPT - https://bit.ly/3Gbsm4S

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