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binary-forex-trading-Q1E.Rmd
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binary-forex-trading-Q1E.Rmd
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---
title: "<img src='https://raw.githubusercontent.com/englianhu/binary.com-interview-question/757b27e1e93132368b0898152078be4961b05a28/www/binary-logo-resize.jpg' width='240'>"
subtitle: "[<span style='color:blue'>binary.com</span>](https://github.com/englianhu/binary.com-interview-question) Interview Question I (Extention)"
author: "[<span style='color:blue'>®γσ, Lian Hu</span>](https://englianhu.github.io/) <img src='https://raw.githubusercontent.com/englianhu/binary.com-interview-question/master/www/ENG.jpg' width='24'> <img src='https://raw.githubusercontent.com/englianhu/binary.com-interview-question/master/www/RYO.jpg?raw=true' width='24'>白戸則道®"
date: "`r Sys.Date()`"
output:
tufte::tufte_html:
toc: yes
toc_depth: 4
self_contained: no
tufte::tufte_book:
citation_package: natbib
latex_engine: xelatex
tufte::tufte_handout:
citation_package: natbib
latex_engine: xelatex
link-citations: yes
---
```{r setup, include = FALSE}
#'@ rm(list = ls(all = TRUE))
## load chunks `saveQ1E`.
#'@ load('./Q1E.RData')
load('./Q1E-read-data3.RData')
suppressPackageStartupMessages(library('BBmisc'))
pkgs <- c('knitr', 'kableExtra', 'tint', 'devtools', 'lubridate', 'data.table', 'plyr', 'stringr', 'magrittr', 'dplyr', 'tidyr', 'tidyverse', 'ff', 'ffbase', 'tidyquant', 'sparklyr', 'readr', 'quantmod', 'htmltools', 'highcharter', 'googleVis', 'formattable', 'ggfortify', 'DT', 'forecast', 'Mcomp', 'MCMCpack', 'PerformanceAnalytics', 'broom', 'microbenchmark', 'doParallel', 'Boruta', 'fBasics', 'fPortfolio', 'rugarch', 'parma', 'rmgarch')
suppressAll(lib(pkgs))
#'@ install.packages(pkgs, lib = 'C:/Program Files/R/R-3.4.0/library')
suppressAll(l_ply(c('last.R', 'Mn.R', 'has.Mn.R', 'simAutoArima.R', 'simAutoArima2.R', 'simETS.R', 'simETS2.R', 'plotChart2.R', 'armaSearch.R', 'simGarch.R', 'simGarch2.R', 'quantitative-finance.R'), function(pkg) source(paste0('./function/', pkg))))
## Set option to below if you want to plot an independent webpage with graph
#'@ op <- options(gvis.plot.tag=NULL)
op <- options(gvis.plot.tag = 'chart')
options(gvis.plot.tag = 'chart', warn = -1)
#'@ options(rpubs.upload.method = 'internal')
## R: llply fully reproducible results in parallel
## https://stackoverflow.com/questions/34946177/r-llply-fully-reproducible-results-in-parallel
cl <- makeCluster(detectCores())
registerDoParallel(cl)
## Create a cluster object to be used for rugarcgh and rmgarch models.
cluster = makePSOCKcluster(15)
## Connect to Spark.
#'@ spark_install()
#'@ sc <- spark_connect(master = 'local')
rm(pkgs)
```
# 1. Introduction
By refer to *Ryo Eng Lian Hu (2017)*^[reference paper 1], I tried to compare few models for financial trading. Today I am using <s>1 minute, 3 minutes, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 1 hour and also daily data instead of</s>^[Since the datasets contain few observation within a minute and also some data time period empty more than 30 minutes. Test all time period but eventually error...] only daily data. By the way, I try to compare the accuracy of forecasting instead of invesmnent.
Besides, this paper will modelling multivariate Garch compare to previous paper which applied univariate Garch models.
# 2. Data
I select the data from 2013-01-01 to 2017-08-31^[Only get 2013-01-01 to 2017-05-15 in csv format data.] via [**the Bonnot Gang** *1 Minute Historical Data Download*](http://thebonnotgang.com/tbg/historical-data/)^[Note: The CSV file is using ; (semicolon) as a separator and , (comma) as a decimal separator.].
```{r read-data, eval = FALSE, include = FALSE}
curr <- c('USDJPY', 'USDEUR', 'USDGBP', 'USDAUD', 'USDCAD', 'USDCHF')
prd <- paste0(1:1500, 'd')
curr.minute <- llply(curr[], function(cr) {
llply(prd, function(dy) {
f.get.google.intraday(cr, 60, dy)
}) %>% do.call(rbind, .)
curr.minute$High <- curr.minute$Open
curr.minute$Low <- curr.minute$Open
curr.minute$Close <- curr.minute$Open
})
names(curr.minute) <- curr
saveRDS(curr.minute, './data/USDJPY.Minute.rds')
USDJPY.Minute3 <- to.minutes3(USDJPY.Minute)
```
```{python, eval = FALSE, include = FALSE}
#!/usr/bin/env python
"""
Retrieve intraday stock data from Google Finance.
"""
# https://gist.github.com/lebedov/f09030b865c4cb142af1
import './function/get_google_finance_intraday.py'
```
```{r read-data2, eval = FALSE, include = FALSE}
## Download the datasets from http://thebonnotgang.com/tbg/historical-data/ (2013-01-01 to 2017-08-31)
fls <- dir('./data', pattern = 'USD+.+.csv') %>% str_replace_all('_1m.csv', '')
mbase <- llply(fls, function(fl) {
#'@ z = spark_read_csv(sc, path = paste0('./data/', fl), sep = ';') %>% dplyr::tbl_df %>%
z = data.table::fread(paste0('./data/', fl, '_1m.csv')) %>% dplyr::tbl_df %>% dplyr::select(symbol, timestamp, close) %>%
mutate(Symbol = factor(symbol),
#'@ Date = lubridate::ymd_hms(timestamp),
#'@ Date = as.POSIXct(strptime(timestamp, "%Y-%m-%d %H:%M:%S")), #filter as minute as the smallest unit
Date = as.POSIXct(strptime(timestamp, "%Y-%m-%d %H:%M")),
Close = as.numeric(stringr::str_replace_all(close, ',', '.')))
z$symbol = NULL
z$timestamp = NULL
z$close = NULL
names(z) = c('Symbol' , 'Date', paste0(unique(z$Symbol), c('.Close'))) #only get closing price
z$Symbol = NULL
#'@ z$Date = z$Date %>% stringr::str_replace_all(':[0-9]{2}$', '') %>%
#'@ lubridate::ymd_hm
z %<>% unique
z = xts(z[, -1], order.by = z$Date) %>% to.minutes
## Create a column which contain the "first date 00:00:00" until the "last date time".
tmp = xts(, seq.POSIXt(
from = as.POSIXct(strptime(index(z)[1], '%Y-%m-%d')),
to = as.POSIXct(strptime(xts::last(index(z)), '%Y-%m-%d %H:%M')), by = 'mins'))
res = cbind(tmp, z) #cbind() will be faster than merge()
res = na.locf(res)
#'@ satsun = sort(c(seq(as.Date('2013-01-05'), as.Date('2017-08-31'), by = 7),
#'@ seq(as.Date('2013-01-06'), as.Date('2017-08-31'), by = 7)))
na.omit(res)
})#, .parallel = TRUE)
names(mbase) <- fls %>% str_replace_all('_1m.csv', '')
```
```{r read-data3, eval = FALSE, include = FALSE}
## Due to minutes nor hours dataset not completed and always happen error, here only read daily data but not minutes, hours etc.
fls <- dir('./data', pattern = 'USD+.+.csv') %>% str_replace_all('_1m.csv', '')
mbase <- llply(fls, function(fl) {
#'@ z = spark_read_csv(sc, path = paste0('./data/', fl), sep = ';') %>% dplyr::tbl_df %>%
z = data.table::fread(paste0('./data/', fl, '_1m.csv')) %>% dplyr::tbl_df %>% dplyr::select(symbol, timestamp, close) %>%
mutate(Symbol = factor(symbol), Date = lubridate::ymd_hms(timestamp),
Close = as.numeric(stringr::str_replace_all(close, ',', '.')))
z$symbol = NULL
z$timestamp = NULL
z$close = NULL
names(z) = c('Symbol' , 'Date', paste0(unique(z$Symbol), c('.Close'))) #only get closing price
z$Symbol = NULL
z$Date %<>% stringr::str_replace_all(':[0-9]{2}$', '') %>%
lubridate::ymd_hm
z %<>% unique
xts(z[, -1], order.by = z$Date)
})
names(mbase) <- fls %>% str_replace_all('_1m.csv', '')
## Save read-data2 dataset.
save.image('./Q1E-read-data3.RData')
```
```{r get-yahooData, eval = FALSE, include = FALSE}
## Due to minutes nor hours dataset not completed and always happen error, here only read daily data but not minutes, hours etc.
library(quantmod)
getSymbols('AUD=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
getSymbols('EUR=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
getSymbols('GBP=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
getSymbols('CAD=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
getSymbols('CHF=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
getSymbols('CNY=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
getSymbols('JPY=X', src = 'yahoo', from = '2012-01-01', to = '2017-08-31')
USDAUD <- `AUD=X` %>% na.omit; rm(`AUD=X`)
USDEUR <- `EUR=X` %>% na.omit; rm(`EUR=X`)
USDGBP <- `GBP=X` %>% na.omit; rm(`GBP=X`)
USDCAD <- `CAD=X` %>% na.omit; rm(`CAD=X`)
USDCHF <- `CHF=X` %>% na.omit; rm(`CHF=X`)
USDCNY <- `CNY=X` %>% na.omit; rm(`CNY=X`)
USDJPY <- `JPY=X` %>% na.omit; rm(`JPY=X`)
names(USDAUD) <- names(USDAUD) %>% str_replace_all('AUD=X', 'USDAUD')
names(USDEUR) <- names(USDEUR) %>% str_replace_all('EUR=X', 'USDEUR')
names(USDGBP) <- names(USDGBP) %>% str_replace_all('GBP=X', 'USDGBP')
names(USDCAD) <- names(USDCAD) %>% str_replace_all('CAD=X', 'USDCAD')
names(USDCHF) <- names(USDCHF) %>% str_replace_all('CHF=X', 'USDCHF')
names(USDCNY) <- names(USDCNY) %>% str_replace_all('CNY=X', 'USDCNY')
names(USDJPY) <- names(USDJPY) %>% str_replace_all('JPY=X', 'USDJPY')
## Save yahooData dataset.
saveRDS(USDAUD, './data/USDAUD.rds')
saveRDS(USDEUR, './data/USDEUR.rds')
saveRDS(USDGBP, './data/USDGBP.rds')
saveRDS(USDCAD, './data/USDCAD.rds')
saveRDS(USDCHF, './data/USDCHF.rds')
saveRDS(USDCNY, './data/USDCNY.rds')
saveRDS(USDJPY, './data/USDJPY.rds')
```
```{r dateID, eval = FALSE, include = FALSE}
## dateID
#'@ dateID <- llply(mbase, function(x) index(x) %>% as.character) %>%
#'@ unlist %>% unique %>% stringr::str_replace_all(':[0-9]{2}$', '') %>%
#'@ lubridate::ymd_hm %>% sort
#'@ dateID <- llply(mbase, function(x) index(x) %>% as.character) %>%
#'@ unlist %>% unique %>% lubridate::ymd_hms %>% sort
#'@ dateID <- llply(mbase, function(x) index(x) %>% as.character) %>% unlist %>% unique
#'@ dateID <- as.POSIXct(strptime(dateID, "%Y-%m-%d %H:%M")) %>% sort
#'@ dateID0 <- ymd('2014-01-01')
#'@ dateID <- dateID[dateID > dateID0]
```
```{r saveQ1E, eval = FALSE, include = FALSE}
## Save read-data2 dataset.
save.image('./Q1E.RData')
```
```{r data-summ, warning = FALSE, echo = FALSE}
## 1 minute
mbase_min1 <- mbase
#'@ mbase_min1 <- llply(mbase, function(x) {
#'@ z = to.minutes(x)
#'@ nn = names(x) %>% str_replace_all('.Close', '')
#'@ names(z) = names(z) %>% str_replace_all('x', nn)
#'@ z})
## 3 minutes
mbase_min3 <- llply(mbase, function(x) {
z = to.minutes3(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
## 5 minutes
mbase_min5 <- llply(mbase, function(x) {
z = to.minutes5(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
## 10 minutes
mbase_min10 <- llply(mbase, function(x) {
z = to.minutes10(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
## 15 minutes
mbase_min15 <- llply(mbase, function(x) {
z = to.minutes15(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
## 30 minutes
mbase_min30 <- llply(mbase, function(x) {
z = to.minutes30(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
## 1 hour
mbase_hrs1 <- llply(mbase, function(x) {
z = to.hourly(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
## 1 day
mbase_day1 <- llply(mbase, function(x) {
#'@ z %<>% mutate(Date = as.POSIXct(strptime(Date, "%Y-%m-%d %H")))
z = to.daily(x)
nn = names(x) %>% str_replace_all('.Close', '')
names(z) = names(z) %>% str_replace_all('x', nn)
z})
#'@ fx <- ldply(mbase, dim)
#'@ names(fx) <- c('Currency', 'Rows.all', 'Cols.all')
rm(mbase)
fx_1m <- ldply(mbase_min1, dim)
names(fx_1m) <- c('Currency', 'Rows.1m', 'Cols.1m')
fx_3m <- ldply(mbase_min3, dim)
names(fx_3m) <- c('Currency', 'Rows.3m', 'Cols.3m')
fx_5m <- ldply(mbase_min5, dim)
names(fx_5m) <- c('Currency', 'Rows.5m', 'Cols.5m')
fx_10m <- ldply(mbase_min10, dim)
names(fx_10m) <- c('Currency', 'Rows.10m', 'Cols.10m')
fx_15m <- ldply(mbase_min15, dim)
names(fx_15m) <- c('Currency', 'Rows.15m', 'Cols.15m')
fx_30m <- ldply(mbase_min30, dim)
names(fx_30m) <- c('Currency', 'Rows.30m', 'Cols.30m')
fx_1h <- ldply(mbase_hrs1, dim)
names(fx_1h) <- c('Currency', 'Rows.1h', 'Cols.1h')
fx_1d <- ldply(mbase_day1, dim)
names(fx_1d) <- c('Currency', 'Rows.1d', 'Cols.1d')
do.call(cbind, list(fx_1m, fx_3m, fx_5m, fx_10m, fx_15m, fx_30m,
fx_1h, fx_1d)) %>% kable(width = 'auto')
llply(mbase_day1, summary)
```
Due to the dataset always happened errors and not completed somemore wrong figures, here I get the daily data from 2012-01-01 to 2017-08-31 via Yahoo. However only take the data from 2014-01-01 to 2017-08-31 as experiments.
```{r read-yahooData}
## Save yahooData dataset.
USDAUD <- readRDS('./data/USDAUD.rds')
USDEUR <- readRDS('./data/USDEUR.rds')
USDGBP <- readRDS('./data/USDGBP.rds')
USDCAD <- readRDS('./data/USDCAD.rds')
USDCHF <- readRDS('./data/USDCHF.rds')
USDCNY <- readRDS('./data/USDCNY.rds')
USDJPY <- readRDS('./data/USDJPY.rds')
mbase <- list(USDAUD = USDAUD, USDEUR = USDEUR, USDGBP = USDGBP,
USDCAD = USDCAD, USDCHF = USDCHF, USDCNY = USDCNY,
USDJPY = USDJPY)
rm(USDAUD, USDEUR, USDGBP, USDCAD, USDCHF, USDCNY, USDJPY)
llply(mbase, summary)
```
# 3. Statiscal Modelling
## 3.1 Auto Arima Models
```{r sparklyr, eval = FALSE, include = FALSE}
## http://bigdata-madesimple.com/tools-in-the-data-armoury-r-vs-spark/
> sc <- sparkR.init(master="local")
Launching java with spark-submit command spark-submit2.cmd sparkr-shell C:\Users\scibr\AppData\Local\Temp\RtmpOI78eA\backend_port1f7051615e1b
Error in sparkR.sparkContext(master, appName, sparkHome, convertNamedListToEnv(sparkEnvir), :
JVM is not ready after 10 seconds
In addition: Warning message:
'sparkR.init' is deprecated.
Use 'sparkR.session' instead.
See help("Deprecated")
> library(sparklyr)
> sc <- spark_connect(master = "local")
* Using Spark: 2.1.0
Error in get_java(throws = TRUE) :
Java is required to connect to Spark. JAVA_HOME is set but does not point to a valid version. Please fix JAVA_HOME or reinstall from: https://www.java.com/en/
```
```{r minute1-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.min1.R')
AUDUSD.AutoArima.min1 <- readRDS('./data/AUDUSD.AutoArima.min1.rds')
EURUSD.AutoArima.min1 <- readRDS('./data/EURUSD.AutoArima.min1.rds')
GBPUSD.AutoArima.min1 <- readRDS('./data/GBPUSD.AutoArima.min1.rds')
USDCAD.AutoArima.min1 <- readRDS('./data/USDCAD.AutoArima.min1.rds')
USDCHF.AutoArima.min1 <- readRDS('./data/USDCHF.AutoArima.min1.rds')
USDCNY.AutoArima.min1 <- readRDS('./data/USDCNY.AutoArima.min1.rds')
USDJPY.AutoArima.min1 <- readRDS('./data/USDJPY.AutoArima.min1.rds')
```
```{r minute3-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.min3.R')
AUDUSD.AutoArima.min3 <- readRDS('./data/AUDUSD.AutoArima.min3.rds')
EURUSD.AutoArima.min3 <- readRDS('./data/EURUSD.AutoArima.min3.rds')
GBPUSD.AutoArima.min3 <- readRDS('./data/GBPUSD.AutoArima.min3.rds')
USDCAD.AutoArima.min3 <- readRDS('./data/USDCAD.AutoArima.min3.rds')
USDCHF.AutoArima.min3 <- readRDS('./data/USDCHF.AutoArima.min3.rds')
USDCNY.AutoArima.min3 <- readRDS('./data/USDCNY.AutoArima.min3.rds')
USDJPY.AutoArima.min3 <- readRDS('./data/USDJPY.AutoArima.min3.rds')
```
```{r minute5-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.min5.R')
AUDUSD.AutoArima.min5 <- readRDS('./data/AUDUSD.AutoArima.min5.rds')
EURUSD.AutoArima.min5 <- readRDS('./data/EURUSD.AutoArima.min5.rds')
GBPUSD.AutoArima.min5 <- readRDS('./data/GBPUSD.AutoArima.min5.rds')
USDCAD.AutoArima.min5 <- readRDS('./data/USDCAD.AutoArima.min5.rds')
USDCHF.AutoArima.min5 <- readRDS('./data/USDCHF.AutoArima.min5.rds')
USDCNY.AutoArima.min5 <- readRDS('./data/USDCNY.AutoArima.min5.rds')
USDJPY.AutoArima.min5 <- readRDS('./data/USDJPY.AutoArima.min5.rds')
```
```{r minute10-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.min10.R')
AUDUSD.AutoArima.min10 <- readRDS('./data/AUDUSD.AutoArima.min10.rds')
EURUSD.AutoArima.min10 <- readRDS('./data/EURUSD.AutoArima.min10.rds')
GBPUSD.AutoArima.min10 <- readRDS('./data/GBPUSD.AutoArima.min10.rds')
USDCAD.AutoArima.min10 <- readRDS('./data/USDCAD.AutoArima.min10.rds')
USDCHF.AutoArima.min10 <- readRDS('./data/USDCHF.AutoArima.min10.rds')
USDCNY.AutoArima.min10 <- readRDS('./data/USDCNY.AutoArima.min10.rds')
USDJPY.AutoArima.min10 <- readRDS('./data/USDJPY.AutoArima.min10.rds')
```
```{r minute15-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.min15.R')
AUDUSD.AutoArima.min15 <- readRDS('./data/AUDUSD.AutoArima.min15.rds')
EURUSD.AutoArima.min15 <- readRDS('./data/EURUSD.AutoArima.min15.rds')
GBPUSD.AutoArima.min15 <- readRDS('./data/GBPUSD.AutoArima.min15.rds')
USDCAD.AutoArima.min15 <- readRDS('./data/USDCAD.AutoArima.min15.rds')
USDCHF.AutoArima.min15 <- readRDS('./data/USDCHF.AutoArima.min15.rds')
USDCNY.AutoArima.min15 <- readRDS('./data/USDCNY.AutoArima.min15.rds')
USDJPY.AutoArima.min15 <- readRDS('./data/USDJPY.AutoArima.min15.rds')
```
```{r minute30-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.min30.R')
AUDUSD.AutoArima.min30 <- readRDS('./data/AUDUSD.AutoArima.min30.rds')
EURUSD.AutoArima.min30 <- readRDS('./data/EURUSD.AutoArima.min30.rds')
GBPUSD.AutoArima.min30 <- readRDS('./data/GBPUSD.AutoArima.min30.rds')
USDCAD.AutoArima.min30 <- readRDS('./data/USDCAD.AutoArima.min30.rds')
USDCHF.AutoArima.min30 <- readRDS('./data/USDCHF.AutoArima.min30.rds')
USDCNY.AutoArima.min30 <- readRDS('./data/USDCNY.AutoArima.min30.rds')
USDJPY.AutoArima.min30 <- readRDS('./data/USDJPY.AutoArima.min30.rds')
```
```{r hour1-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.h1.R')
AUDUSD.AutoArima.h1 <- readRDS('./data/AUDUSD.AutoArima.h1.rds')
EURUSD.AutoArima.h1 <- readRDS('./data/EURUSD.AutoArima.h1.rds')
GBPUSD.AutoArima.h1 <- readRDS('./data/GBPUSD.AutoArima.h1.rds')
USDCAD.AutoArima.h1 <- readRDS('./data/USDCAD.AutoArima.h1.rds')
USDCHF.AutoArima.h1 <- readRDS('./data/USDCHF.AutoArima.h1.rds')
USDCNY.AutoArima.h1 <- readRDS('./data/USDCNY.AutoArima.h1.rds')
USDJPY.AutoArima.h1 <- readRDS('./data/USDJPY.AutoArima.h1.rds')
```
```{r day3m-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.d3m.R')
AUDUSD.AutoArima.d3m <- readRDS('./data/AUDUSD.AutoArima.d3m.rds')
EURUSD.AutoArima.d3m <- readRDS('./data/EURUSD.AutoArima.d3m.rds')
GBPUSD.AutoArima.d3m <- readRDS('./data/GBPUSD.AutoArima.d3m.rds')
USDCAD.AutoArima.d3m <- readRDS('./data/USDCAD.AutoArima.d3m.rds')
USDCHF.AutoArima.d3m <- readRDS('./data/USDCHF.AutoArima.d3m.rds')
USDCNY.AutoArima.d3m <- readRDS('./data/USDCNY.AutoArima.d3m.rds')
USDJPY.AutoArima.d3m <- readRDS('./data/USDJPY.AutoArima.d3m.rds')
```
```{r day6m-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.d6m.R')
AUDUSD.AutoArima.d6m <- readRDS('./data/AUDUSD.AutoArima.d6m.rds')
EURUSD.AutoArima.d6m <- readRDS('./data/EURUSD.AutoArima.d6m.rds')
GBPUSD.AutoArima.d6m <- readRDS('./data/GBPUSD.AutoArima.d6m.rds')
USDCAD.AutoArima.d6m <- readRDS('./data/USDCAD.AutoArima.d6m.rds')
USDCHF.AutoArima.d6m <- readRDS('./data/USDCHF.AutoArima.d6m.rds')
USDCNY.AutoArima.d6m <- readRDS('./data/USDCNY.AutoArima.d6m.rds')
USDJPY.AutoArima.d6m <- readRDS('./data/USDJPY.AutoArima.d6m.rds')
```
```{r day1y-AutoArima, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/AutoArima.d1y.R')
AUDUSD.AutoArima.d1y <- readRDS('./data/AUDUSD.AutoArima.d1y.rds')
EURUSD.AutoArima.d1y <- readRDS('./data/EURUSD.AutoArima.d1y.rds')
GBPUSD.AutoArima.d1y <- readRDS('./data/GBPUSD.AutoArima.d1y.rds')
USDCAD.AutoArima.d1y <- readRDS('./data/USDCAD.AutoArima.d1y.rds')
USDCHF.AutoArima.d1y <- readRDS('./data/USDCHF.AutoArima.d1y.rds')
USDCNY.AutoArima.d1y <- readRDS('./data/USDCNY.AutoArima.d1y.rds')
USDJPY.AutoArima.d1y <- readRDS('./data/USDJPY.AutoArima.d1y.rds')
```
Below I use arima model to analyse Yahoo data.
```{r yahooAutoArima, eval = FALSE}
## Using closing price to forecast.
.baseDate = '2014-01-01 00:00:00'
.baseDate = ymd(ymd_hms(.baseDate))
## 3 months
yahooD3M <- llply(mbase, function(x) {
z = simAutoArima(x, .prCat = 'Cl',
.baseDate = .baseDate, .verbose = TRUE,
.maPeriod = 'months', .unit = 3)
nm = names(Cl(x)) %>% str_replace_all('.Close', '')
saveRDS(z, paste0('./data/', nm, '.yahooAutoArima.d3m.rds'))
cat(paste0('Saved... ./data/', nm, '.yahooAutoArima.d3m.rds\n'))
})
## 6 months
yahooD6M <- llply(mbase, function(x) {
z = simAutoArima(x, .prCat = 'Cl',
.baseDate = .baseDate, .verbose = TRUE,
.maPeriod = 'months', .unit = 6)
nm = names(Cl(x)) %>% str_replace_all('.Close', '')
saveRDS(z, paste0('./data/', nm, '.yahooAutoArima.d6m.rds'))
cat(paste0('Saved... ./data/', nm, '.yahooAutoArima.d6m.rds\n'))
})
## 1 year
yahooD1Y <- llply(mbase, function(x) {
z = simAutoArima(x, .prCat = 'Cl',
.baseDate = .baseDate, .verbose = TRUE,
.maPeriod = 'years', .unit = 1)
nm = names(Cl(x)) %>% str_replace_all('.Close', '')
saveRDS(z, paste0('./data/', nm, '.yahooAutoArima.d1y.rds'))
cat(paste0('Saved... ./data/', nm, '.yahooAutoArima.d1y.rds\n'))
})
## 18 months
yahooD18M <- llply(mbase, function(x) {
z = simAutoArima(x, .prCat = 'Cl',
.baseDate = .baseDate, .verbose = TRUE,
.maPeriod = 'months', .unit = 18)
nm = names(Cl(x)) %>% str_replace_all('.Close', '')
saveRDS(z, paste0('./data/', nm, '.yahooAutoArima.d18m.rds'))
cat(paste0('Saved... ./data/', nm, '.yahooAutoArima.d18m.rds\n'))
})
## 2 years
yahooD2Y <- llply(mbase, function(x) {
z = simAutoArima(x, .prCat = 'Cl',
.baseDate = .baseDate, .verbose = TRUE,
.maPeriod = 'years', .unit = 2)
nm = names(Cl(x)) %>% str_replace_all('.Close', '')
saveRDS(z, paste0('./data/', nm, '.yahooAutoArima.d2y.rds'))
cat(paste0('Saved... ./data/', nm, '.yahooAutoArima.d2y.rds\n'))
})
```
## 3.2 Exponential Smoothing Models
```{r multi-etsM, eval = FALSE, include = FALSE}
## set all models provided by ets function.
ets.m1 <- c('A', 'M', 'Z')
ets.m2 <- c('N', 'A', 'M', 'Z')
ets.m3 <- c('N', 'A', 'M', 'Z')
ets.m <- do.call(paste0, expand.grid(ets.m1, ets.m2, ets.m3))
rm(ets.m1, ets.m2, ets.m3)
```
```{r multi-ETS, eval = FALSE, include = FALSE}
## ----- Due to error, here I use simAutoArima -----------------------------
## Using closing price to forecast.
.baseDate = '2014-01-01 00:00:00'
.baseDate = ymd(ymd_hms(.baseDate))
## 3 months
res <- llply(ets.m, function(x) {
llply(mbase_day1, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'months', .unit = 3, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.',
x, '.Cl.d3m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 6 months
res <- llply(ets.m, function(x) {
llply(mbase_day1, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'months', .unit = 6, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.',
x, '.Cl.d6m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 1 years
res <- llply(ets.m, function(x) {
llply(mbase_day1, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'years', .unit = 1, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.',
x, '.Cl.d1y.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
```
```{r day3m-ETS, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/ETS.d3m.R')
AUDUSD.ETS.d3m <- readRDS('./data/AUDUSD.ETS.d3m.rds')
EURUSD.ETS.d3m <- readRDS('./data/EURUSD.ETS.d3m.rds')
GBPUSD.ETS.d3m <- readRDS('./data/GBPUSD.ETS.d3m.rds')
USDCAD.ETS.d3m <- readRDS('./data/USDCAD.ETS.d3m.rds')
USDCHF.ETS.d3m <- readRDS('./data/USDCHF.ETS.d3m.rds')
USDCNY.ETS.d3m <- readRDS('./data/USDCNY.ETS.d3m.rds')
USDJPY.ETS.d3m <- readRDS('./data/USDJPY.ETS.d3m.rds')
```
```{r day6m-ETS, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/ETS.d6m.R')
AUDUSD.ETS.d6m <- readRDS('./data/AUDUSD.ETS.d6m.rds')
EURUSD.ETS.d6m <- readRDS('./data/EURUSD.ETS.d6m.rds')
GBPUSD.ETS.d6m <- readRDS('./data/GBPUSD.ETS.d6m.rds')
USDCAD.ETS.d6m <- readRDS('./data/USDCAD.ETS.d6m.rds')
USDCHF.ETS.d6m <- readRDS('./data/USDCHF.ETS.d6m.rds')
USDCNY.ETS.d6m <- readRDS('./data/USDCNY.ETS.d6m.rds')
USDJPY.ETS.d6m <- readRDS('./data/USDJPY.ETS.d6m.rds')
```
```{r day1y-ETS, eval = FALSE, include = FALSE}
## Using closing price to forecast.
#'@ source('./function/ETS.d1y.R')
AUDUSD.ETS.d1y <- readRDS('./data/AUDUSD.ETS.d1y.rds')
EURUSD.ETS.d1y <- readRDS('./data/EURUSD.ETS.d1y.rds')
GBPUSD.ETS.d1y <- readRDS('./data/GBPUSD.ETS.d1y.rds')
USDCAD.ETS.d1y <- readRDS('./data/USDCAD.ETS.d1y.rds')
USDCHF.ETS.d1y <- readRDS('./data/USDCHF.ETS.d1y.rds')
USDCNY.ETS.d1y <- readRDS('./data/USDCNY.ETS.d1y.rds')
USDJPY.ETS.d1y <- readRDS('./data/USDJPY.ETS.d1y.rds')
```
Now I analyse the Yahoo data by using Exponential Time Series Smoothing models.
```{r multi-yahooETS, eval = FALSE, include = FALSE}
## ----- Due to error, here I use simAutoArima -----------------------------
## Using closing price to forecast.
.baseDate = '2014-01-01 00:00:00'
.baseDate = ymd(ymd_hms(.baseDate))
## 3 months
res <- llply(ets.m, function(x) {
llply(mbase, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'months', .unit = 3, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.yahoo.',
x, '.Cl.d3m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 6 months
res <- llply(ets.m, function(x) {
llply(mbase, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'months', .unit = 6, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.yahoo.',
x, '.Cl.d6m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 1 year
res <- llply(ets.m, function(x) {
llply(mbase, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'years', .unit = 1, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.yahoo.',
x, '.Cl.d1y.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 18 months
res <- llply(ets.m, function(x) {
llply(mbase, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'months', .unit = 18, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.yahoo.',
x, '.Cl.d18m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 2 years
res <- llply(ets.m, function(x) {
llply(mbase, function(y) {
z = simETS(y, .model = x, .prCat = 'Cl', .baseDate = .baseDate,
.maPeriod = 'years', .unit = 2, .verbose = TRUE,
.simulate = TRUE, .bootstrap = TRUE)
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 <- paste0('saveRDS(z', ', file = \'./data/', nm, '.yahoo.',
x, '.Cl.d2y.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
```
## 3.3 Univariate Garch Models and EWMA
```{r multi-garchM, eval = FALSE, include = FALSE}
## Multiple Garch models inside `rugarch` package.
.variance.model.par <- c('sGARCH', 'fGARCH', 'eGARCH', 'gjrGARCH', 'apARCH', 'iGARCH', 'csGARCH', 'realGARCH')
## set all models provided by Garch function.
.solver.par <- c('hybrid', 'solnp', 'nlminb', 'gosolnp', 'nloptr', 'lbfgs')
.sub.fGarch.par <- c('GARCH', 'TGARCH', 'AVGARCH', 'NGARCH', 'NAGARCH', 'APARCH', 'GJRGARCH', 'ALLGARCH')
.dist.model.par <- c('norm', 'snorm', 'std', 'sstd', 'ged', 'sged', 'nig', 'ghyp', 'jsu')
##Bootstrap parameters
.boot.method = c('Partial', 'Full')
.boot.solver = c('solnp', 'nlminb')
```
```{r multi-Garch, eval = FALSE, include = FALSE}
## ----- Due to error, here I use simAutoArima -----------------------------
## Using closing price to forecast.
.baseDate = '2014-01-01 00:00:00'
.baseDate = ymd(ymd_hms(.baseDate))
## 3 months
res <- llply(Garch.m, function(x) {
llply(mbase_day1, function(y) {
z = simGarch(y, .prCat = 'Cl', .baseDate = .baseDate,
.variance.model = list(model = x, garchOrder = c(1, 1),
submodel = NULL,
external.regressors = NULL,
variance.targeting = FALSE),
.maPeriod = 'months', .unit = 3, .verbose = TRUE)#,
#.bootstrap = TRUE, .boot.method = 'Partial',
#.boot.solver = 'solnp')
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 = paste0('saveRDS(z', ', file = \'./data/', nm, '.',
x, '.hybrid.Cl.d3m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 6 months
res <- llply(Garch.m, function(x) {
llply(mbase_day1, function(y) {
z = simGarch(y, .prCat = 'Cl', .baseDate = .baseDate,
.variance.model = list(model = x, garchOrder = c(1, 1),
submodel = NULL,
external.regressors = NULL,
variance.targeting = FALSE),
.maPeriod = 'months', .unit = 6, .verbose = TRUE)#,
#.bootstrap = TRUE, .boot.method = 'Partial',
#.boot.solver = 'solnp')
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 = paste0('saveRDS(z', ', file = \'./data/', nm, '.',
x, '.hybrid.Cl.d6m.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
## 1 years
res <- llply(Garch.m, function(x) {
llply(mbase_day1, function(y) {
z = simGarch(y, .prCat = 'Cl', .baseDate = .baseDate,
.variance.model = list(model = x, garchOrder = c(1, 1),
submodel = NULL,
external.regressors = NULL,
variance.targeting = FALSE),
.maPeriod = 'years', .unit = 1, .verbose = TRUE)#,
#.bootstrap = TRUE, .boot.method = 'Partial',
#.boot.solver = 'solnp')
nm = names(Cl(z)) %>% str_replace_all('.Close', '')
txt1 = paste0('saveRDS(z', ', file = \'./data/', nm, '.',
x, '.hybrid.Cl.d1y.rds\'); rm(z)')
eval(parse(text = txt1))
cat(paste0(txt1, ' done!', '\n'))
})
cat(paste(x, 'done', '\n'))
})
```
```{r day3m-Garch, eval = FALSE}
## Using closing price to forecast.
#'@ source('./function/Garch.d3m.R')
AUDUSD.Garch.d3m <- readRDS('./data/AUDUSD.Garch.d3m.rds')
EURUSD.Garch.d3m <- readRDS('./data/EURUSD.Garch.d3m.rds')
GBPUSD.Garch.d3m <- readRDS('./data/GBPUSD.Garch.d3m.rds')
USDCAD.Garch.d3m <- readRDS('./data/USDCAD.Garch.d3m.rds')
USDCHF.Garch.d3m <- readRDS('./data/USDCHF.Garch.d3m.rds')
USDCNY.Garch.d3m <- readRDS('./data/USDCNY.Garch.d3m.rds')
USDJPY.Garch.d3m <- readRDS('./data/USDJPY.Garch.d3m.rds')
```
```{r day6m-Garch, eval = FALSE}
## Using closing price to forecast.
#'@ source('./function/Garch.d6m.R')
AUDUSD.Garch.d6m <- readRDS('./data/AUDUSD.Garch.d6m.rds')
EURUSD.Garch.d6m <- readRDS('./data/EURUSD.Garch.d6m.rds')
GBPUSD.Garch.d6m <- readRDS('./data/GBPUSD.Garch.d6m.rds')
USDCAD.Garch.d6m <- readRDS('./data/USDCAD.Garch.d6m.rds')
USDCHF.Garch.d6m <- readRDS('./data/USDCHF.Garch.d6m.rds')
USDCNY.Garch.d6m <- readRDS('./data/USDCNY.Garch.d6m.rds')
USDJPY.Garch.d6m <- readRDS('./data/USDJPY.Garch.d6m.rds')
```
```{r day1y-Garch, eval = FALSE}
## Using closing price to forecast.
#'@ source('./function/Garch.d1y.R')
AUDUSD.Garch.d1y <- readRDS('./data/AUDUSD.Garch.d1y.rds')
EURUSD.Garch.d1y <- readRDS('./data/EURUSD.Garch.d1y.rds')
GBPUSD.Garch.d1y <- readRDS('./data/GBPUSD.Garch.d1y.rds')
USDCAD.Garch.d1y <- readRDS('./data/USDCAD.Garch.d1y.rds')
USDCHF.Garch.d1y <- readRDS('./data/USDCHF.Garch.d1y.rds')
USDCNY.Garch.d1y <- readRDS('./data/USDCNY.Garch.d1y.rds')
USDJPY.Garch.d1y <- readRDS('./data/USDJPY.Garch.d1y.rds')
```
## 3.4 Multivariate Garch Models
# 4. Mean Squared Error (MSE)
## 4.1 Auto Arima Models
Now we look at the accuracy of prediction.
```{r res-AutoArima, warning = FALSE, echo = FALSE}
## 3 months data for daily price.
AutoArima.day.3m <- dir('./data/', pattern = '^[A-Z]{6}.AutoArima.d3m.rds$') %>% str_replace_all('.rds', '')
d3m <- ldply(AutoArima.day.3m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d3m', ''), MSE.3M = mse)
})
## 6 months data for daily price.
AutoArima.day.6m <- dir('./data/', pattern = '^[A-Z]{6}.AutoArima.d6m.rds$') %>% str_replace_all('.rds', '')
d6m <- ldply(AutoArima.day.6m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d6m', ''), MSE.6M = mse)
})
## 1 year data for daily price.
AutoArima.day.1y <- dir('./data/', pattern = '^[A-Z]{6}.AutoArima.d1y.rds$') %>% str_replace_all('.rds', '')
d1y <- ldply(AutoArima.day.1y, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d1y', ''), MSE.1Y = mse)
})
mse.arima <- suppressAll(join_all(list(d3m, d6m, d1y), type = 'full'))
mse.arima <- suppressAll(rbind(mse.arima, c('Mean = ', colMeans(mse.arima[-1]))))
mse.arima %<>% tbl_df %>% mutate(
Currency = as.character(Currency),
MSE.3M = as.numeric(MSE.3M),
MSE.6M = as.numeric(MSE.6M),
MSE.1Y = as.numeric(MSE.1Y))
mse.arima$Currency[length(mse.arima$Currency)] <- 'Mean = '
mse.arima %>% kable(width = 'auto')
#|Currency | MSE.3M| MSE.6M| MSE.1Y|
#|:----------------|---------:|---------:|---------:|
#|AUDUSD.AutoArima | 0.0000252| 0.0000245| 0.0000243|
#|EURUSD.AutoArima | 0.0000390| 0.0000393| 0.0000385|
#|GBPUSD.AutoArima | 0.0000722| 0.0000689| 0.0000676|
#|USDCAD.AutoArima | 0.0000434| 0.0000423| 0.0000416|
#|USDCHF.AutoArima | 0.0000589| 0.0000586| 0.0000564|
#|USDCNY.AutoArima | 0.0002294| 0.0002117| 0.0002072|
#|USDJPY.AutoArima | 0.4468217| 0.4273710| 0.4172619|
#|Mean = | 0.0638985| 0.0611166| 0.0596711|
```
Below is the summary of Yahoo data.
```{r res-yahooAutoArima, warning = FALSE, echo = FALSE}
## 3 months data for daily price.
yahooAutoArima.day.3m <- dir('./data/', pattern = '^[A-Z]{6}.yahooAutoArima.d3m.rds$') %>% str_replace_all('.rds', '')
d3m <- ldply(yahooAutoArima.day.3m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d3m', ''), MSE.3M = mse)
})
## 6 months data for daily price.
yahooAutoArima.day.6m <- dir('./data/', pattern = '^[A-Z]{6}.yahooAutoArima.d6m.rds$') %>% str_replace_all('.rds', '')
d6m <- ldply(yahooAutoArima.day.6m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d6m', ''), MSE.6M = mse)
})
## 1 year data for daily price.
yahooAutoArima.day.1y <- dir('./data/', pattern = '^[A-Z]{6}.yahooAutoArima.d1y.rds$') %>% str_replace_all('.rds', '')
d1y <- ldply(yahooAutoArima.day.1y, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d1y', ''), MSE.1Y = mse)
})
## 18 months data for daily price.
yahooAutoArima.day.18m <- dir('./data/', pattern = '^[A-Z]{6}.yahooAutoArima.d18m.rds$') %>% str_replace_all('.rds', '')
d18m <- ldply(yahooAutoArima.day.18m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d18m', ''), MSE.18M = mse)
})
## 2 years data for daily price.
yahooAutoArima.day.2y <- dir('./data/', pattern = '^[A-Z]{6}.yahooAutoArima.d2y.rds$') %>% str_replace_all('.rds', '')
d2y <- ldply(yahooAutoArima.day.2y, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.d2y', ''), MSE.2Y = mse)
})
## Summary
mse.arima <- suppressAll(join_all(list(d3m, d6m, d1y, d18m, d2y), type = 'full'))
mse.arima <- suppressAll(rbind(mse.arima, c('Mean = ', colMeans(mse.arima[-1]))))
mse.arima %<>% tbl_df %>% mutate(
Currency = as.character(Currency),
MSE.3M = as.numeric(MSE.3M),
MSE.6M = as.numeric(MSE.6M),
MSE.1Y = as.numeric(MSE.1Y),
MSE.18M = as.numeric(MSE.18M),
MSE.2Y = as.numeric(MSE.2Y))
mse.arima$Currency[length(mse.arima$Currency)] <- 'Mean = '
mse.arima %>% kable(width = 'auto')
#|Currency | MSE.3M| MSE.6M| MSE.1Y| MSE.18M| MSE.2Y|
#|:---------------------|---------:|---------:|---------:|---------:|---------:|
#|USDAUD.yahooAutoArima | 0.0000725| 0.0000691| 0.0000680| 0.0000683| 0.0000674|
#|USDCAD.yahooAutoArima | 0.0000426| 0.0000418| 0.0000411| 0.0000411| 0.0000412|
#|USDCHF.yahooAutoArima | 0.0000581| 0.0000562| 0.0000569| 0.0000564| 0.0000554|
#|USDCNY.yahooAutoArima | 0.0002035| 0.0001980| 0.0001953| 0.0001981| 0.0001988|
#|USDEUR.yahooAutoArima | 0.0000269| 0.0000263| 0.0000261| 0.0000268| 0.0000259|
#|USDGBP.yahooAutoArima | 0.0000219| 0.0000203| 0.0000190| 0.0000194| 0.0000193|
#|USDJPY.yahooAutoArima | 0.4893502| 0.4700268| 0.4593798| 0.4691871| 0.4666987|
#|Mean = | 0.0699680| 0.0672055| 0.0656837| 0.0670853| 0.0667295|
```
## 4.2 Exponential Smoothing Models
Similar with univariate `auto.arima` model, here we try to compare the the accuracy of prediction of univariate `ets` models among the portfolios where each portfolio contains USD exchanged with other currencies.
```{r res-ETS, warning = FALSE, echo = FALSE}
## 3 months data for daily price.
ETS.day.3m <- dir('./data/', pattern = '^[A-Z]{6}.[A-Z]{3}.Cl.d3m.rds$') %>% str_replace_all('.rds', '')
d3m <- ldply(ETS.day.3m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.Cl.d3m', ''), MSE.3M = mse)
})
## 6 months data for daily price.
ETS.day.6m <- dir('./data/', pattern = '^[A-Z]{6}.[A-Z]{3}.Cl.d6m.rds$') %>% str_replace_all('.rds', '')
d6m <- ldply(ETS.day.6m, function(x) {
z = readRDS(paste0('./data/', x, '.rds')) %>% na.omit
#data.frame(x$Point.Forecast, Cl(x))
mse = mean((z$Point.Forecast - Cl(z))^2)
data.frame(Currency = str_replace_all(x, '.Cl.d6m', ''), MSE.6M = mse)
})