xiaodaigh

# to run this firstly set up the MinIO server
# on Windows download this https://dl.min.io/server/minio/release/windows-amd64/minio.exe
# download("https://dl.min.io/server/minio/release/windows-amd64/minio.exe")

# to run minio make sure the executable is in the PATH
# then run
# ;minio.exe server /path/to/minio/data
# e.g. minio.exe server c:/minio-data/

using Minio, Parquet, Parquet2, DataFrames, AWSS3

joshuaulrich

require(quantmod)

do_chart <- function(symbol) {
   quote <- getQuote(symbol)
   quote$Close <- quote$Last
   xts(OHLCV(quote), quote[,"Trade Time"],
       pct_change = quote[,"% Change"])
}

filename <- "intraday-sp500.rds"

dirkschumacher

library(armacmp)
# Arnold, T., Kane, M., & Lewis, B. W. (2019). A Computational Approach to Statistical Learning. CRC Press.
# logistic regression using the Newton-Raphson
log_reg <- armacmp(function(X, y) {
  beta <- rep.int(0, ncol(X))
  for (i in seq_len(25)) {
    b_old <- beta
    alpha <- X %*% beta
    p <- 1 / (1 + exp(-alpha))

whizzzkid

# Instructions for 4.14 and cuda 9.1
# If upgrading from 4.13 and cuda 9.0
$ sudo apt-get purge --auto-remove libcud*
$ sudo apt-get purge --auto-remove cuda*
$ sudo apt-get purge --auto-remove nvidia*
# also remove the container directory direcotory at /usr/local/cuda-9.0/

# Important libs required with 4.14.x with Cuda 9.X
$ sudo apt install libelf1 libelf-dev

abarbour

# Add rowwise summary lines to ggplot2::facet_grid style plots.
# Andy Barbour
# geokook.wordpress.com
# December 2012

## clear workspace
rm(list=ls())

library(multitaper)
library(rbenchmark)

gwd999

svmComputeOneForecast = function(
      id,
      data,
      response,
      startPoints,
      endPoints,
      len,
      history=500,
      trace=FALSE,
      kernel="radial",

dsparks

# Starting with categorical data, ending with a table of log odds ratios

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("plyr", "reshape2")
if(doInstall){install.packages(toInstall,
                               repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Canonical example of categorical data
HEC <- melt(HairEyeColor)

dsparks

# Let's say you want to display a table of model coefficients
# in order of their significance,
# and you want to plot the distribution of model residuals,
# but you don't know how to access these values.
# Use str().

# Generate some random data
NN <- 1000
theData <- data.frame(Alpha = rnorm(NN),
                      Beta = rnorm(NN))

timelyportfolio

require(PerformanceAnalytics)
require(quantmod)


getSymbols("^GSPC", from = "1900-01-01")
#get return series from closing price
ret.bh <- ROC(GSPC[,4],n = 1,type = "discrete")
#change first value from NA to 0
ret.bh[1,] <- 0

timelyportfolio

#analyze asset allocation experience in Spain

require(lattice)
require(latticeExtra)
require(reshape2)
require(directlabels)
require(quantmod)
require(PerformanceAnalytics)
require(RQuantLib)