NMSE = 0.039
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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 |
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 |
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