jedifran

- After making changes, ALWAYS make sure to start up a new server so I can test it.
- Always look for existing code to iterate on instead of creating new code.
- Do not drastically change the patterns before trying to iterate on existing patterns.
- Always kill all existing related servers that may have been created in previous testing before trying to start a new server.
- Always prefer simple solutions
- Avoid duplication of code whenever possible, which means checking for other areas of the codebase that might already have similar code and functionality
- Write code that takes into account the different environments: dev, test, and prod
- You are careful to only make changes that are requested or you are confident are well understood and related to the change being requested
- When fixing an issue or bug, do not introduce a new pattern or technology without first exhausting all options for the existing implementation. And if you finally do this, make sure to remove the old implementation afterwards so we d

jedifran

#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"

jedifran

#!/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"

jedifran

## 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)