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#get very helpful Ken French data | |
#for this project we will look at Industry Portfolios | |
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip | |
require(latticeExtra) | |
require(PerformanceAnalytics) | |
require(quantmod) | |
#my.url will be the location of the zip file with the data | |
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip" |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
__author__ = "Deepak.G.R." | |
__license__ = 'Public Domain' | |
""" | |
usage: | |
Go to command line and type | |
python ai-class.py "topic-name" |
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## Credit: Taken from: http://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session | |
# improved list of objects | |
.ls.objects <- function (pos = 1, pattern, order.by, | |
decreasing=FALSE, head=FALSE, n=5) { | |
napply <- function(names, fn) sapply(names, function(x) | |
fn(get(x, pos = pos))) | |
names <- ls(pos = pos, pattern = pattern) | |
obj.class <- napply(names, function(x) as.character(class(x))[1]) | |
obj.mode <- napply(names, mode) | |
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class) |