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mgei / sic_structure.csv
Created October 2, 2023 13:30
SIC structure (division, major group, major industry, industry) as sourced from https://www.osha.gov/data/sic-manual on 2023-10-02
We can make this file beautiful and searchable if this error is corrected: Unclosed quoted field in line 9.
"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
mgei / pca2.R
Created February 8, 2021 16:04
# 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)
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)
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
mgei / SR_calc.R
Created September 25, 2020 07:35
Calculating the return, standard deviation, and SR for the SPX
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,
library(shiny)
ui <- fluidPage(
fluidRow(
column(width = 12,
tabsetPanel(
tabItem("main", "MENU"),
tabItem("more", uiOutput("hello"))
))
)
@mgei
mgei / app.R
Created February 24, 2020 15:14
persistent data storage of user input in R Shiny
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
mgei / letf.R
Created January 17, 2020 10:43
leveraged etf compounding performance lag
library(tidyverse)
library(ggplot2)
periods <- 10
trend <- 0
vola <- 0.1
leverage <- 2
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")

What I've done thus far

Install QMKL

But before:

Install Py-videocore

Increase video memory!