3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machines

This is a discussion on 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machines within the Trading Software forums, part of the Commercial category; Originally Posted by joseph1986 you can pm me pat for the info, combined cost would be around $4000 for a ...

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Old Jan 25, 2017, 5:30pm   #273
 
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Originally Posted by joseph1986 View Post
you can pm me pat for the info, combined cost would be around $4000
for a 6 month license. Made that money back in 2.5 months as a newbie
beats any course dollar for dollar....only problem is that you'll be addicted to a corporate product....which has updates that you need to adjust to once in a while.
On the subject of learning the software, think it would take someone with no statistical background 3 or 4 months to master.....provided you devote countless hours of testing,
Thanks
Think I will save my pennies just now.
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Old Jan 25, 2017, 9:11pm   #274
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Thanks
Think I will save my pennies just now.
yeah, I understand with the price......few years back when I was in college it costs less than $300 now they jacked up the price since they realized its power to dynamically predict consumer behavior. The most I think is one from north carolina went from $169 in 2008 to $18000 last annum.....yearly license.
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Old Jan 28, 2017, 12:54am   #275
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Krzysztof,

What is your cluster service you use for this intensive calculation ?
Regards
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Old Feb 7, 2017, 12:40am   #276
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Krzysiaczek99 started this thread
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Originally Posted by surfeur View Post
Krzysztof,

What is your cluster service you use for this intensive calculation ?
Regards

MATLAB pararell toolbox

Krzysztof
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Old Feb 9, 2017, 11:54pm   #277
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Originally Posted by Krzysiaczek99 View Post
MATLAB pararell toolbox

Krzysztof
Krzysztof, i speak about of your cloud cluster/server where you rent your server ? have you happy for the service ?

Regards
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Old Feb 10, 2017, 12:23am   #278
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Originally Posted by surfeur View Post
Krzysztof, i speak about of your cloud cluster/server where you rent your server ? have you happy for the service ?

Regards
I don't rent, Its my private. I created it from a few old servers, you can buy them really cheap now. Before I was using a few 8core PCs but it was not enough.

Krzysztof
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Old Apr 13, 2017, 1:22pm   #279
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backtest report

Krzysiaczek99 started this thread Here is a backtest report for a period of 11 months (Apr 2016 - Jan 2017) for 8 symbols. Clearly period July - Oct was a down period for portfolio, however some symbols were profitable. Live test is ongoing. As per today prediction of the system for next days/weeks is strengthening of USD (sell of EURUSD, GBPUSD, buy USDJPY).
Let's see if it will be right.

Krzysztof
Attached Files
File Type: pdf report.pdf (1.04 MB, 43 views)
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Old Apr 22, 2017, 11:37pm   #280
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MMI filtering

Krzysiaczek99 started this thread Hello everybody,

Recently I got inspired by post about Market Meanness Index from Financial Hacker. More info about it is here:

http://www.financial-hacker.com/the-...eanness-index/
http://www.financial-hacker.com/boos...ade-filtering/

As I have big database from my system (>6 millions of trades for lats year) i decided to filter them using MMI. Here is my implementation

Code:
function [profit_tot, possum, negsum, winner, loser, alltrades] = MMISim(lotsize, spread, freq, tradesize, trades, currency, Smooth, Length)
    % for each pair
    % 1) calculate raw MMI    
    % 2) smooth MMI
    
    [startidx, stopidx] = findidxs(trades, currency);
       
    i=1; % index to trade file
       
    alltrades = repmat(trades,1);
    inew = 1; % index to new trade file
    
    rawMMI = calc_rawMMI(currency(:,6), Length);
    MMI = indicators(rawMMI, 'sma', Smooth);
    
    % pSmoothed = indicators(currency, 'sma', Smooth);
       
    for k=startidx:stopidx       
                         
           if round(currency(k,1)+currency(k,2),4) >= round(datenum(trades(i,2)),4) % currency in sync with trade list
           if i < size(trades,1)
             i=i+1;  
           else
             break;
           end
           if (MMI(k)<MMI(k-1)) % MMI is falling
           alltrades(inew,:)= trades(i,:); % copy trade
           inew = inew +1;
           end
           end
                  
    end
    
     % delete superfluus of trade file
        alltrades(inew+1:end,:) = [];
        
        profit_tot = sum(cell2num(alltrades(:,14)))*tradesize*lotsize/freq;
        
        
possum=0;
negsum=0.0001;
winner = 0;
loser = 0;

for ii=1:size(alltrades,1);
            
        if cell2num(alltrades(ii,14))>0
            possum = possum + cell2num(alltrades(ii,14));
            winner = winner + 1;
        end
        if cell2num(alltrades(ii,14))<0
            negsum = negsum + cell2num(alltrades(ii,14));
            loser = loser + 1;
        end;
end
           
     % PFs = abs(sum(possum)/sum(negsum));
     % PPs = sum(winner)/(sum(winner)+sum(loser))*100;   
                
end

function [rawMMI] = calc_rawMMI(Data, Length)

m = movmedian(Data,Length);

  rawMMI = zeros(size(Data,1),1);
for k =size(Data,1):-1:Length+2
       nh=0; nl=0;
  for i=k:-1:k-Length
    if(Data(i) > m(k) && Data(i) > Data(i-1))
      nl=nl+1;
    else if(Data(i) < m(k) && Data(i) < Data(i-1))
      nh=nh+1;
        end
    end
  end
  
  rawMMI(k) = 100.*(nl+nh)/(Length-1);
  
  end

end
and here are the trade files for 8 symbols for 2 different algos. SDAE and Pegassos SVM

Code:
QAtrades_Peg__PF=1.05_Profit=33282336.4_PP=63
QAtrades_SDAE__PF=1.19_Profit=108194086.9_PP=65.4
so initial PF are 1.05 and 1.19.

Here are the results of filtering for different Smooth and Length

Code:
whatifQAtrades_Peg_PF=1.05_Profit=8529502416.6_PP=63.14_freq=1_tradesize=0.01_Smooth=600_Length=50_TP=350392_FP=204589
whatifQAtrades_SDAE_PF=1.2_Profit=23894165020.55_PP=65.63_freq=1_tradesize=0.01_Smooth=600_Length=50_TP=326980_FP=171238

whatifQAtrades_SDAE_PF=1.2_Profit=22116171802.7_PP=65.61_freq=1_tradesize=0.01_Smooth=100_Length=100_TP=330259_FP=173129
whatifQAtrades_SDAE_PF=1.19_Profit=23293250055.9_PP=65.79_freq=1_tradesize=0.01_Smooth=200_Length=100_TP=329397_FP=171282
whatifQAtrades_SDAE_PF=1.18_Profit=22808654859.55_PP=65.71_freq=1_tradesize=0.01_Smooth=300_Length=100_TP=331200_FP=172836
whatifQAtrades_SDAE_PF=1.2_Profit=26104137089.45_PP=65.93_freq=1_tradesize=0.01_Smooth=400_Length=100_TP=333457_FP=172280
whatifQAtrades_SDAE_PF=1.2_Profit=27012799035_PP=65.8_freq=1_tradesize=0.01_Smooth=500_Length=100_TP=332532_FP=172810
whatifQAtrades_SDAE_PF=1.2_Profit=27447013953.5_PP=65.97_freq=1_tradesize=0.01_Smooth=600_Length=100_TP=331464_FP=170994

whatifQAtrades_SDAE_PF=1.2_Profit=22963529441.75_PP=65.72_freq=1_tradesize=0.01_Smooth=100_Length=200_TP=331773_FP=173076
whatifQAtrades_SDAE_PF=1.17_Profit=23790659466.55_PP=65.5_freq=1_tradesize=0.01_Smooth=200_Length=200_TP=336169_FP=177035
whatifQAtrades_SDAE_PF=1.17_Profit=24303021213.8_PP=65.51_freq=1_tradesize=0.01_Smooth=300_Length=200_TP=334265_FP=175962
whatifQAtrades_SDAE_PF=1.18_Profit=25439141483.15_PP=65.54_freq=1_tradesize=0.01_Smooth=400_Length=200_TP=334190_FP=175750
whatifQAtrades_SDAE_PF=1.19_Profit=27360164418.65_PP=65.75_freq=1_tradesize=0.01_Smooth=500_Length=200_TP=337621_FP=175866
whatifQAtrades_SDAE_PF=1.17_Profit=27445995139.35_PP=65.49_freq=1_tradesize=0.01_Smooth=600_Length=200_TP=338051_FP=178145

whatifQAtrades_Peg_PF=1.05_Profit=7349564747_PP=63.13_freq=1_tradesize=0.01_Smooth=600_Length=200_TP=359971_FP=210249
so it seems no big impact on PF, just number of trades cut in half (TP+FP).

as the price data was 1 min but maybe trends are more visible on higher TF i made small modification to simulate 15M TF. I changed

Data(i-1) to Data(i-16) in MMI caclulation function but also not impact....

Code:
whatifQAtrades_Peg_PF=1.06_Profit=6456268137.3_PP=63.63_freq=1_tradesize=0.01_Smooth=600_Length=200_TP=375900_FP=214844
whatifQAtrades_SDAE_PF=1.17_Profit=27481160172.8_PP=65.71_freq=1_tradesize=0.01_Smooth=600_Length=200_TP=349375_FP=182356

So it looks that this method of filtering does not improve or worsen the performance, the timing of filtering seems to be completely random. In next step I will try to make more detailed filtering to see the impact on different symbols, maybe any of them has more 'trendy' characteristics. However this type of selection will introduce higher variance of final result (less trades) and selection bias so it will be risky to say if any improvement in performance is real.

and here is a link to original trades files. If someone has an idea how to filter them to improve their performance let me know. I know already Hidden Markov Models method to switch between regimes but i think this method is not much better than ordinary MA cross.

https://www.mediafire.com/?2bcrqpb3boh23w9

Krzysztof

Last edited by Krzysiaczek99; Apr 23, 2017 at 3:17pm.
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