ivannp

import glob
import os
import subprocess

def main():
    base_dir = os.path.abspath('d:/abooks')
    ffmpeg_path = os.path.abspath('C:/tools/ffmpeg/bin/ffmpeg.exe')
    
    pattern = os.path.join(base_dir, '**', '*.m4b')
    

ivannp

armaSearch = function(
   xx,  
   minOrder=c(0,0),
   maxOrder=c(5,5),
   trace=FALSE )
{
   bestAic = 1e9
   len = NROW( xx ) 
   for( p in minOrder[1]:maxOrder[1] ) for( q in minOrder[2]:maxOrder[2] )
   {

ivannp

garchAutoTryFit = function(
   ll,
   data,
   trace=FALSE,
   forecast.length=1,
   with.forecast=TRUE,
   ic="AIC",
   garch.model="garch" )
{
   formula = as.formula( paste( sep="",

ivannp

svmComputeOneForecast = function(
      id,
      data,
      response,
      startPoints,
      endPoints,
      len,
      history=500,
      trace=FALSE,
      kernel="radial",

ivannp

import dshelper as dsh
import numpy as np
import os
import pandas as pd
import sys
import time

from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA

from sqlalchemy import create_engine, MetaData

ivannp

#!/bin/bash -x

echo "Script started";

echo "Downloading the Ranger policy patch script";
wget -O /tmp/patch_policy_esp.py https://hdiconfigactions.blob.core.windows.net/linuxhdiplclusterconfigactionv01/patch_policy_esp.py
sudo python /tmp/patch_policy_esp.py

echo "Scripts Finished"

ivannp

armaTryFit = function(
   ll,  
   data,
   trace=FALSE,
   includeMean=TRUE, 
   withForecast=TRUE,
   forecastLength=1 )
{
   formula = as.formula( paste( sep="",
                                "xx ~ arma(", ll[1], ",", ll[2], ")" ) )

ivannp

require(quantmod)

prepare.indicator = function(close, indicator, roc.n, normalize=FALSE, func=mean) {
   rets = ROC(close, type="discrete", n=roc.n)
   
   if(normalize) {
      # Normalize the returns to daily
      rets = ((1 + rets) ^ (1/roc.n)) - 1
   }
   

ivannp

findBestArma = function(
   xx,  
   minOrder=c(0,0),
   maxOrder=c(5,5),
   trace=FALSE )
{
   bestAic = 1e9
   len = NROW( xx ) 
   for( p in minOrder[1]:maxOrder[1] ) for( q in minOrder[2]:maxOrder[2] )
   {

ivannp

require(quantmod)
require(SIT)

dvi.indicator = function(prices) {
   dvi = TTR:::DVI(prices)[,3]
   
   indicator.long = ifelse(dvi <= 0.5, 1, 0)
   indicator.short = ifelse(dvi > 0.5, -1, 0)
   
   indicator = reclass(cbind(indicator.short + indicator.long), prices)