bestdan

#!/usr/bin/env sh


# # These functions make it easy to look at the output of a dart
# # test with large non-matching objects. 
# 
# # To use: 
# # One and done: 
# auditDartTest test/example_test.dart 'this test fails'
# 

bestdan

#' @title S&P 500 heatmap
#' @author Daniel Egan
#' @description Creates real and nominal triangle heatmaps based on the S&P 500.
#' @details Last update 2012-06-01
### ----

library(quantmod)
library(RColorBrewer)

#Some useful matrix functions

bestdan

#' @title Log-means versus Medians
#' @author Daniel Egan
#' @description When data has a power law or extremely skewed distribution,
#' using a log-mean usually results in more stable and useful central estimates
#' compared to a mean or a median.
#' https://towardsdatascience.com/on-average-youre-using-the-wrong-average-geometric-harmonic-means-in-data-analysis-2a703e21ea0

library(dplyr)
library(tidyr)
library(ggplot2)

bestdan

library(ggplot2)
library(tidyr)
library(dplyr)


# Normalize
data(mtcars)
mtcars_norm <- as.data.frame(apply(mtcars, 2, scale))
# Make long

bestdan

# Version 1: LOC incentive --> inflated code
add_up = function(inputs){
  total = inputs[1]

  for i in 2:length(inputs){
    total = total + inputs[i]
    }
    
  return(total)
}

bestdan

def cs_service_bot():
    welcome_message = """Hello! Welcome to the DNS Cable Company's Service Portal. Are you a new or existing customer?
                         \n[1] New Customer
                         \n[2] Existing Customer
                         \n"""

    response = input(welcome_message)

    if response == "1":
        new_customer()

bestdan

Keybase proof

I hereby claim:

• I am bestdan on github.
• I am dpegan271 (https://keybase.io/dpegan271) on keybase.
• I have a public key ASBCTT8RIBBl4GrD_mXa50Qkb8gxy2XmNULJld4SBNfB2wo

To claim this, I am signing this object:

bestdan

rm(list=ls())
library(quantmod)
library(lubridate)
library(PerformanceAnalytics)

#' Required 'drawdown' size
#' Default: -6% return
drawdownHurdle<- -0.06

# Grab data -----------------------------------------------------------------------------------

bestdan

effFrontier = function (data, nports = 20, shorts=T,wmin=0, wmax=1) 
{
  rcov<-cov(data)
  averet<-colMeans(data)
  mxret = max((colMeans(data)))
  mnret = 0#-mxret Long only...
  n.assets = ncol(data)
  reshigh = rep(wmax,n.assets)
  if( shorts )
  {