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NMSE = 0.039

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nmse = 0.095

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#include <http51.mqh>
extern string       url = "http://localhost:8080/orders";

//this scripts sends an http post containing orders info each tick to this url

int start() {
    string params [0,2];
  
    int status[1];          // HTTP Status code
    int total=OrdersTotal();

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require(nnet)
require(caret)

y = read.csv('http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe/A.dat', header=F)
y2 = read.csv('http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe/A.cont', header=F)
k = 40
n=100
y = y$V1/256
y2 = y2$V1/256
dat = sapply(1:k, function(a) c(rep(NA,a),y[1:(length(y)-a)]) )

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% problem: we have a result, and many elements, find all combinations that sum up to the result


function sol = knapsack01(maxCapacity, items)
 
% knapsack problem with variables in {0,1}
 
% Naive solution is O(n!), knapsack implementation is O(n*m) where n is 
% items length and m is weights length
 

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from scipy import *

M = 22 #ma for rsi
N = 14 #rsi loopback
thresh = [20,80] #rsi thresholds
cost = 0.0001 # cost per trade (spread)

price = 1.3 + 0.1*randn(100) + sin(linspace(0,10,100))
ma = ema(price, M)
ri = rsindex(price-ma, N)

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function [ as_ ] = polyMultiFeatures( items, k )
    as = [];
    function recurse(a, i)
        % we should optimize and early stop a with length>k 
        if i>size(items,2)
            if size(a,2)<=k
                as{end+1} = a;
            end
            return;
        end

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in green without padding, in red with symmetric padding

% generate a  noisy signal
y = flipud( 3*sin(0.14*(1:128)')-1/20*(1:128)'+ (((1:128)'-64)/20).^2 + randn(128,1));
w=[-21;14;39;54;59;54;39;14;-21]/231; % http://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter#Tables_of_selected_convolution_coefficients

z = zeros(length(y)+10,1); % bigger
z(5:end-4) = y; 

z(1:5) = flipud(z(6:10)); % symmetric padding

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% classic case, feature are by column
function [Xnorm, Xmean, Xsigma] = normalizeFeature(X)
Xmean = mean(X);
Xnorm = bsxfun(@minus, X, Xmean);
Xsigma = sqrt(sum(Xnorm.^2)/(size(Xnorm,1)-1)); 
Ynorm = bsxfun(@rdivide, Xnorm, Xsigma);

% all this is equivalent to zscore(X) :)

% for example collaborative filtering sets, classes are in row, and users in col