caub

NMSE = 0.039

caub

nmse = 0.095

caub

#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();

caub

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)]) )

caub

nmse = 0.042

caub

% 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
 

caub

caub

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)

caub

from scipy import *
#from scipy.linalg import *
from pylab import *


class SVM:

  def train(self, X, Y, kernel, C, tol = 1e-3, max_passes = 5):
    m = size(X, 0)
    n = size(X, 1)

caub

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