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# Cyril Auburtin caub

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Last active Aug 29, 2015
Kernel Recursive Least Square using JSAT https://code.google.com/p/java-statistical-analysis-tool/, and test on Santa Fe laser data
View KLRS.md

``````NMSE = 0.039
``````
Last active Aug 29, 2015
Neural Net test on Santa Fe laser data with code.google.com/p/java-statistical-analysis-tool and sourceforge.net/projects/jarbm
View NNet.md

``````nmse = 0.095
``````
Last active Aug 29, 2015
Small MT4 (http://docs.mql4.com/) script for orders copying between 2 accounts
View Http-test.mq4
 #include 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();
Last active Aug 29, 2015
View 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)]) )
Last active Aug 29, 2015
Santa-Fe regression with matlab
View KRLS plot.md

nmse = 0.042

Last active Aug 29, 2015
an interview question
View 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
Last active Aug 29, 2015
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
Last active Aug 29, 2015
View 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)
Last active Aug 29, 2015
Simplified SMO from coursera's ml-class converted from matlab
View 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)
Created Apr 28, 2014
Combinatorics for generating polynomial terms of degree <=k
View 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