zachmayer

#!/usr/bin/env python
# encoding: utf-8

#Constants.R

#Created by Kenneth J. Shackleton on 14 June 2011.
#Copyright (c) 2011 Ringo Limited.
#All rights reserved.
#R Port by Zach Mayer on 4 December 2011

zachmayer

#Option 1 - install openblas with homebrew and link to CRAN installed R
brew tap homebrew/science
brew install openblas
ln -sf /usr/local/Cellar/openblas/0.2.12/lib/libopenblas.dylib /Library/Frameworks/R.framework/Resources/lib/libRblas.dylib

#Option 2 - install r with openblas through homebrew
brew tap homebrew/science
brew install r --with-openblas

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#Code to re-create John Hussman's Recession warning index
#http://www.hussmanfunds.com/wmc/wmc110801.htm
#R code by Zach Mayer

rm(list = ls(all = TRUE)) #CLEAR WORKSPACE
library(quantmod)

#################################################
# 1. Credit spreads
#################################################

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#Directory
setwd('~/wherever')

#Load Required Packages
library('caret')
library('glmnet')
library('ipred')
library('e1071')
library('caTools')

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#Load Data
rm(list = ls(all = TRUE))
library(quantmod)
library(PerformanceAnalytics)

symbols <- c('XLE','XLV','XLI','XLU','XLP','IYZ','XLK','XLY','XLF','XLB','GLD','SLV','EFA','EEM','FXA','FXE','FXY','HYG','LQD')

getSymbols(symbols,from='2007-01-01')
getSymbols('SPY',from='2007-01-01')

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#Multi-Class Summary Function
#Based on caret:::twoClassSummary
require(compiler)
multiClassSummary <- cmpfun(function (data, lev = NULL, model = NULL){
  
  #Load Libraries
  require(Metrics)
  require(caret)
  
  #Check data

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#################################################
# Build a model
#################################################

#Choose Differencing window
#Hussman uses 6 months
Diff <- 6		
Data$PAYEMS <- (Data$PAYEMS-Lag(Data$PAYEMS,Diff))/Lag(Data$PAYEMS,Diff)
Data$UNRATE <- (Data$UNRATE-Lag(Data$UNRATE,Diff))/Lag(Data$UNRATE,Diff)
Data$SP500 <- (Data$SP500-Lag(Data$SP500,Diff))/Lag(Data$SP500,Diff)

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def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

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setClass("parDEoptim", representation(optim="list", member="list"))

parDEoptim <- function (fn, lower, upper, n=1, control = DEoptim.control(), .packages=NULL, ...) {
  library("DEoptim")
  library("foreach")
  segSizes <- (upper-lower)/n  
  segments <- lapply(1:n, function(x){
    lower+segSizes*(x-1)
  })

zachmayer

getNews <- function(symbol, number){

	# Warn about length
	if (number>300) {
		warning("May only get 300 stories from google")
	}

	# load libraries
	library(XML); library(plyr); library(stringr); library(lubridate);
	library(xts); library(RDSTK)