Cyril Auburtin caub

## KLRS.md

      
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              August 29, 2015 13:55
            
              
                Kernel Recursive Least Square using JSAT https://code.google.com/p/java-statistical-analysis-tool/, and test on Santa Fe laser data
              
          
NMSE = 0.039

  
## NNet.md

      
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                / NNet.md
            
            
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              August 29, 2015 13:55
            
              
                Neural Net test on Santa Fe laser data with code.google.com/p/java-statistical-analysis-tool and sourceforge.net/projects/jarbm
              
          
nmse = 0.095

  
## Http-test.mq4
#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();

## santa-fe.r
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)]) )

## KRLS plot.md

      
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                / KRLS plot.md
            
            
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              April 10, 2023 07:33
            
              
                Santa-Fe regression with matlab
              
          
nmse = 0.042

  
## knapsack01.m
% 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


## Zigzag.md

      
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                caub
                / Zigzag.md
            
            
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              August 29, 2015 13:58
            
              
                zigzag chart indicator, a piecewise linear curve fit with alternate slopes (up, down, up, down...), used here to detect double-top and double-bottom patterns http://en.wikipedia.org/wiki/Chart_pattern
              
          
## rsi-bt.py
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)

## svm.py
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)

## polyMultiFeatures.m
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
	#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();
	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)]) )
	% 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
	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)
	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)
	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