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Thread: Probabilistic Perceptron Neural Network



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  1. 05-06-2009 08:20 AM #1
    doshur
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    Probabilistic Perceptron Neural Network

    Some crazy idea just pop up in my head creating a Probabilistic Perceptron Neural Network

    this is how it works.
    every new bar, it will open a buy or a sell
    the perceptron will adjust the weights and learn

    give me your thoughts

    How to use
    Use the trainer to train,
    inputs 0 - 200, step 1 for all
    then sort by profit factor
    Last edited by doshur; 06-24-2009 at 07:58 AM.
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  2. 05-06-2009 08:34 AM #2
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    SIMBA
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    Most suitable settings

    Quote Originally Posted by doshur View Post
    Some crazy idea just pop up in my head creating a Probabilistic Perceptron Neural Network

    this is how it works.
    every new bar, it will open a buy or a sell
    the perceptron will adjust the weights and learn

    give me your thoughts

    How to use
    Use the trainer to train,
    inputs 0 - 200, step 1 for all
    then sort by profit factor
    Doshur,

    Thanks for the perceptron,I have just downloaded it,so my comments are preliminary.

    By looking at this code:

    double ATR = iATR(NULL, 0, 13, 0);

    double StopLoss = ATR * 3;
    double TakeProfit = ATR;


    I suggest that you modify it to

    double ATR = iATR(NULL, 0, M,0);

    double StopLoss = ATR * N;
    double TakeProfit = ATR*P;

    This way the perceptron will be able to find the most suitable ATR period and ATR multipliers for both stoploss and Take Profit.
    I would use a range for M,N,P from 0.5 to 10 in steps of 0.5...as an alternative, you could make them external doubles and then optimize,but I believe that the fun of using the perceptron is for it to do the optimization on a continuous basis.If this is too wide a range and it slows the process ,you cold then make them external doubles,optimize to find where the best settings range for several different pairs and then reduce the "range of search" of the perceptron accordingly(if,for example,trough optimization of several pairs,you find optimal M(ATR period) ranges from 5 to 8..optimal N(Stoploss ATR multiplier) between 0.5 and 2.5..and optimalP(Atr multiplier for take profit) from 1.5 to 5.0..you can use those limits and program the perceptron accordingly).

    Simba
    Last edited by SIMBA; 05-06-2009 at 08:43 AM.
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    http://www.dukascopy.com/traderconte...rader=Werewolf
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  3. 05-06-2009 08:53 AM #3
    doshur
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    actually im not try to find the ATR, TP and SL
    rather i can input the weights to the indicator and aids in manual / ea trading as some filter
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  4. 05-06-2009 10:20 AM #4
    myaron
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    Did you took it from the Artificial Intelegence EA

    he is using the same method.
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  5. 05-06-2009 10:49 AM #5
    doshur
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    Quote Originally Posted by myaron View Post
    he is using the same method.
    It doesn't really matter where I took it from because the implementation of perceptron is the same, weight X input ...

    i only made some modification to it to be a Probabilistic one.
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  6. 05-06-2009 03:21 PM #6
    richest
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    can you tell me when will the train EA produces the suitable result? also, what's the use of the perceptron_close indicator?
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  7. 05-06-2009 03:25 PM #7
    doshur
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    Quote Originally Posted by richest View Post
    can you tell me when will the train EA produces the suitable result? also, what's the use of the perceptron_close indicator?
    u can try on H1 or H4 for a few weeks
    the indicator is for visual check on the chart (remember to input the optimize settings)
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  8. 05-06-2009 04:59 PM #8
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    biddick
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    Have you or anyone tried membrain?:Membrain NeuralNet - MQL4 forum
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  9. 05-09-2009 04:26 AM #9
    doshur
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    Perceptron in action, see the forecast period. Seems pretty isn't it?
    Attached Thumbnails Attached Thumbnails Click image for larger version. 

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  10. 05-10-2009 01:17 AM #10
    oR4z0r
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    Be careful with what you're seeing there. The weighted-fitting approach is actually forecasting the past. What you need is the correlation between a clearly defined span of out-of-sample future compared to the last weight adjustment using a certain amount of latest historical data.
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