mgei

"sicD","sic2","sic3","sic4","division","major_group","industry_group","industry"
"A","01","011","0111","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Cash Grains","Wheat"
"A","01","011","0112","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Cash Grains","Rice"
"A","01","011","0115","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Cash Grains","Corn"
"A","01","011","0116","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Cash Grains","Soybeans"
"A","01","011","0119","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Cash Grains","Cash Grains, Not Elsewhere Classified"
"A","01","013","0131","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Field Crops, Except Cash Grains","Cotton"
"A","01","013","0132","Agriculture, Forestry, And Fishing","Agricultural Production Crops","Field Crops, Except Cash Grains","Tobacco"
"A","01","013","0133","Agriculture, Forestry, And Fishing","Agricultural Pro

mgei

# What is PCA?
# - PCA is a form of multi-dimensional scaling. 
# - It transforms the data into a lower dimensional space while keeping the maxiumum of information. 
# Book: Multi-Dimensional Diversification

# get data https://www.msci.com/end-of-day-data-search

library(tidyverse)
library(tidyquant)
library(readxl)

mgei

library(tidyquant)

ty10 <- tq_get("DGS10", get = "economic.data", from = as.Date("1950-01-01"))


mod_dur <- function(yield) {
  # out <- (1-(1/(1+0.5*yield/100)^(2*10)))/(yield/100)
  out <- (1-(1/(1+0.5*yield)^(2*10)))/(yield)
  
  return(out)

mgei

library(tidyverse)
library(jsonlite)

get_earnings <- function(symbol, av_key, get = "quarterly", cache_dir = "cache") {
  if (!(cache_dir %in% list.dirs(full.names = F, recursive = F))) {
    dir.create(cache_dir)
  }
  
  if (!(paste0(symbol, ".json") %in% list.files(cache_dir))) {
    print("downloading")

mgei

library(tidyverse)
library(tidyquant)
library(lubridate)

# get data from Yahoo ----
spx <- tq_get("^GSPC")

# calculate returns ----
ret_daily <- spx %>% 
  mutate(ret = adjusted/lag(adjusted)-1,

mgei

library(shiny)

ui <- fluidPage(
  fluidRow(
    column(width = 12,
           tabsetPanel(
             tabItem("main", "MENU"),
             tabItem("more", uiOutput("hello"))
           ))
  )

mgei

library(shiny)
library(shinyWidgets)
library(tidyverse)

# read persistent data or create a new file if it does not exist yet
if ("favs.csv" %in% list.files()) {
  favs <- read_file("favs.csv") %>% 
    strsplit(", ") %>% 
    .[[1]] %>% 
    as.numeric() %>% 

mgei

library(tidyverse)

library(ggplot2)

periods <- 10
trend <- 0
vola <- 0.1

leverage <- 2

mgei

library(shiny)
library(tidyverse)

# data <- readRDS("persistent_data/data.RDS")
data <- as_tibble(mtcars) %>% head()

ui <- fluidPage(
  tableOutput("mytable"),
  actionButton("go", "filter it"),
  actionButton("save", "save it")

mgei

What I've done thus far

Install QMKL

But before:

Install Py-videocore

Increase video memory!