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binary-Q1Inter-HFT-RV2.Rmd
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---
title: "<img src='www/deriv.jpg' width='240'>"
subtitle: "[<span style='color:#DE5D83; background-color:black;'>Deriv.com</span> - Interday & Intraday High Frequency Trading Models Comparison <span style='color:#4E79A7'>**Review (Part II)**</span>](https://github.com/englianhu/binary.com-interview-question)"
author: "[®γσ, Lian Hu](https://englianhu.github.io/) <img src='www/quantitative trader 1.jpg' width='13'> <img src='www/RYU.jpg' width='15'> <img src='www/ENG.jpg' width='24'> ® <img src='www/xueba1.jpg' width='14'>"
date: "`r lubridate::today('Asia/Tokyo')`"
output:
html_document:
mathjax: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
number_sections: yes
toc: yes
toc_depth: 4
toc_float:
collapsed: yes
smooth_scroll: yes
code_folding: hide
css: CSSBackgrounds.css
---
<br>
<span style='color:green'>**Theme Song**</span>
<br>
<audio src="music/Mika - Elle me dit (happy song to be millionaire).mp3" controls></audio>
<br>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:white'>Elle me dit</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>她对我说</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>She told me</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>彼女は私に言う</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Μου λέει</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>Ella me dijo</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Sie sagte mir</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Lei mi dice</span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>그녀는 나에게 말한다</span>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:white'>écris une chanson contente</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>写一首欢快的歌</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>Write a happy song</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>幸せな歌を書く</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Γράψτε ένα χαρούμενο τραγούδι</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>Escribe una canción alegre</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Schreibe ein fröhliches Lied</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Scrivi una canzone allegra</span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>쾌활한 노래 쓰기</span>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:white'>Pas une chanson déprimante</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>而不是悲伤的歌</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>Not a depressing song</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>気のめいるような歌ではない</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Όχι θλιβερό τραγούδι</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>No es una canción triste</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Kein trauriges Lied</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Canzone non triste</span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>슬픈 노래가 아님</span>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:white'>Une chanson que tout le monde aime</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>一首让所有人都喜欢的歌</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>A song that everyone loves</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>みんなが大好きな曲</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Ένα τραγούδι που αρέσει σε όλους</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>Una cancion que a todos les gusta</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Ein Lied, das jeder mag</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Una canzone che piace a tutti</span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>모두가 좋아하는 노래</span>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:white'>Elle me dit</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>她对我说</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>She told me</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>彼女は私に言う</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Μου λέει</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>Ella me dijo</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Sie sagte mir</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Lei mi dice</span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>그녀는 나에게 말한다</span>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:azure'>Tu deviendras milliardaire</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>你将成为亿万富翁</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>You will become a millionaire</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>あなたは億万長者になります</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Θα γίνετε δισεκατομμυριούχος</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>Te convertirás en multimillonario</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Sie werden Milliardär</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Diventerai un miliardario</span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>당신은 억만 장자가 될 것입니다</span>
------
<span style='color:blue'>**F**</span><span style='color:white'>**R**</span><span style='color:red'>**A**</span> : <span style='color:white'>Tu auras de quoi être fier</span>
<span style='color:red'>**C**</span><span style='color:yellow'>**H**</span><span style='color:red'>**N**</span> : <span style='color:white'>你将为此感到骄傲</span>
<span style='color:blue'>**E**</span><span style='color:red'>**N**</span><span style='color:blue'>**G**</span> : <span style='color:white'>You will be proud</span>
<span style='color:white'>**J**</span><span style='color:red'>**P**</span><span style='color:white'>**N**</span> : <span style='color:white'>あなたは誇りに思うでしょう</span>
<span style='color:green'>**G**</span><span style='color:white'>**R**</span><span style='color:red'>**E**</span> : <span style='color:white'>Θα είσαι περήφανος για αυτό</span>
<span style='color:red'>**S**</span><span style='color:yellow'>**P**</span><span style='color:red'>**N**</span> : <span style='color:white'>Estarás orgulloso de ello</span>
<span style='color:black'>**G**</span><span style='color:red'>**E**</span><span style='color:yellow'>**R**</span> : <span style='color:white'>Sie werden stolz darauf sein</span>
<span style='color:green'>**I**</span><span style='color:white'>**T**</span><span style='color:red'>**A**</span> : <span style='color:white'>Ne sarai orgoglioso/span>
<span style='color:white'>**K**</span><span style='color:blue'>**R**</span><span style='color:red'>**O**</span> : <span style='color:white'>당신은 그것을 자랑스러워 할 것입니다</span>
------
<br>
# Introduction
Due to below issues from [<span style='color:#DE5D83; background-color:black;'>*Deriv.com*</span> - *Interday High Frequency Trading Models Comparison* <span style='color:red'>*Blooper*</span>](https://rpubs.com/englianhu/binary-Q1Inter-HFT), here I review the research by using same dataset as we can know from below **Part I**.
- [<span style='color:#DE5D83; background-color:black;'>*Deriv.com*</span> - *Interday High Frequency Trading Models Comparison* <span style='color:RoyalBlue'>*Review (Part I)*</span>](https://rpubs.com/englianhu/binary-Q1Inter-HFT-RV1) (in RPubs.com)
- [<span style='color:#DE5D83; background-color:black;'>*Deriv.com*</span> - *Interday High Frequency Trading Models Comparison* <span style='color:RoyalBlue'>*Review (Part I)*</span>](https://beta.rstudioconnect.com/content/16240/binary-Q1Inter-HFT-RV1.html) (in RStudioConnect.com)
Here I use the <span style='color:white; background-color:#DE5D83;'>**β**est model</span> (ETS `MNZ` & `MNZ`) from above study but adjust the data size as we can know from next section.
The purpose of adjust the data size to be intraday :
- compare if improve the accuracy.
- easier the workload for other models comparison (use minimum data size to get optimal model).
<span style='color:green'>**Load Packages**</span>
```{r setup, warning = FALSE, message = FALSE}
if(!suppressPackageStartupMessages(require('BBmisc'))) {
install.packages('BBmisc', dependencies = TRUE, INSTALL_opts = '--no-lock')
}
suppressPackageStartupMessages(require('BBmisc'))
# suppressPackageStartupMessages(require('rmsfuns'))
pkgs <- c('devtools', 'knitr', 'kableExtra', 'tint', 'furrr', 'tidyr',
'devtools','readr', 'lubridate', 'data.table', 'reprex',
'feather', 'purrr', 'quantmod', 'tidyquant', 'tibbletime',
'timetk', 'plyr', 'dplyr', 'stringr', 'magrittr', 'tdplyr',
'tidyverse', 'memoise', 'htmltools', 'formattable', 'dtplyr',
'zoo', 'forecast', 'seasonal', 'seasonalview', 'rjson',
'rugarch', 'rmgarch', 'mfGARCH', 'sparklyr', 'jcolors',
'microbenchmark', 'dendextend', 'lhmetools', 'ggthemr',
'stringr', 'pacman', 'profmem', 'ggthemes', 'flyingfox',
'htmltools', 'echarts4r', 'viridis', 'hrbrthemes', 'gtools',
'fable', 'fabletools', 'Rfast', 'Metrics', 'MLmetrics')
suppressAll(lib(pkgs))
# load_pkg(pkgs)
.dtr <- 'C:/Users/User/Documents/GitHub/binary.com-interview-question-data/'
## Set the timezone but not change the datetime
Sys.setenv(TZ = 'Asia/Tokyo')
## options(knitr.table.format = 'html') will set all kableExtra tables to be 'html', otherwise need to set the parameter on every single table.
options(warn = -1, knitr.table.format = 'html')#, digits.secs = 6)
## https://stackoverflow.com/questions/39417003/long-vectors-not-supported-yet-abnor-in-rmd-but-not-in-r-script
knitr::opts_chunk$set(warning = FALSE, #cache = TRUE,
message = FALSE, cache.lazy = FALSE)
rm(pkgs)
```
# Data
## Read Data
```{r read-data, warning = FALSE, message = FALSE}
## check if data path set
if(!exists('dtr')) {
dtr <- 'C:/Users/User/Documents/GitHub/binary.com-interview-question-data/'}
## save files if not exists
if(!file.exists(paste0(dtr, 'data/fx/USDJPY/dsmp.rds')) & exists('dsmp')) {
saveRDS(dsmp, paste0(dtr, 'data/fx/USDJPY/dsmp.rds'))}
## read files if not exists
if(!exists('dsmp')) {
dsmp <- readRDS(paste0(dtr, 'data/fx/USDJPY/dsmp.rds'))}
```
```{r smp-data, warning = FALSE, message = FALSE, results = 'asis'}
## plot sample data
dsmp[c(1:3, (nrow(dsmp)-3):nrow(dsmp)),] %>%
kbl(caption = '1 min Close Price Dataset', escape = FALSE) %>%
## https://www.w3schools.com/cssref/css_colors.asp
## https://public.tableau.com/en-us/gallery/100-color-palettes?gallery=votd
row_spec(0, background = 'DimGrey', color = 'gold', bold = TRUE) %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = 'Gray') %>%
column_spec(3, background = 'DarkGrey') %>%
column_spec(4, background = 'Gray') %>%
column_spec(5, background = 'DarkGrey') %>%
column_spec(6, background = '#4897D8') %>%
column_spec(7, background = '#556DAC') %>%
column_spec(8, background = '#92AAC7') %>%
column_spec(9, background = '#556DAC') %>%
column_spec(10, background = '#375E97') %>%
column_spec(11, background = 'CornflowerBlue') %>%
column_spec(12, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>% ##`full_width = FALSE` will auto adjust every single columns width to fit the table full width.
scroll_box(width = '100%', fixed_thead = TRUE, height = '400px')
```
*source : `r paste0(dim(dsmp), collapse = ' x ')`*
From above table, we can know the dataset gather from `r paste(range(dsmp$date), collapse = ' to ')` which is used in [<span style='color:#DE5D83; background-color:black;'>*Deriv.com*</span> - *Interday High Frequency Trading Models Comparison* <span style='color:RoyalBlue'>*Review (Part I)*</span>](https://rpubs.com/englianhu/binary-Q1Inter-HFT-RV1).
# `ETS` Modelling
## Seasonal Data Modeling
Here I start my 1st forecast date from 1st trading datetime of 2016 (`r dsmp[year == 2016]$date[1]` which is 2nd year in dataset) which is used in [<span style='color:#DE5D83; background-color:black;'>*Deriv.com*</span> - *Interday High Frequency Trading Models Comparison* <span style='color:RoyalBlue'>*Review (Part I)*</span>](https://rpubs.com/englianhu/binary-Q1Inter-HFT-RV1).
The default data size `7200` to forecast `1440` as we can know that's the optimal volume of observation data as we know from [<span style='color:#DE5D83; background-color:black;'>*Deriv.com*</span> - *Interday High Frequency Trading Models Comparison* <span style='color:RoyalBlue'>*Review (Part I)*</span>](https://rpubs.com/englianhu/binary-Q1Inter-HFT-RV1) to compare with below seasonality.
## Daily Seasonal Data
### Modelling
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `1440` minutes (`1440` minutes is a trading day).
- [<span style='color:white; background-color:LightSlateGrey;'>*8.7 ARIMA modelling in R*</span> in **Forecasting: Principles and Practice (2nd Edition)**](https://otexts.com/fpp2/arima-r.html)
- [<span style='color:white; background-color:LightSlateGrey;'>*8.9 Seasonal ARIMA models*</span> in **Forecasting: Principles and Practice (2nd Edition)**](https://otexts.com/fpp2/seasonal-arima.html)
- [Seasonality in `auto.arima()` from `forecast` package](https://stackoverflow.com/a/37400899/3806250)
Refer to above function or load below r function.
### Dy >> Dy
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `1440 mins` (`24 hrs * 60 mins = 1440 mins` is a trading day).
```{r tseas, eval = FALSE}
tseas <- function(timeID, data = dsmp, data_len,
hrz1 = c(1440, 7200), hrz2 = 1440, .model) {
if(hrz1 == 1440) {
tmp <- llply(1:length(timeID), function(i) {
if(i == 1) {
cat('\n===========================================\n')
cat('train[', i, ']\n')
print(train <- dsmp[date < timeID[i]][(.N - (data_len - 1)):.N])
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
cat('\n-------------------------------------------\n')
cat('train_test[', i, ']\n')
print(train_test <- dsmp[sq %in% ctr])
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl %>%
dplyr::mutate(index = train_test[(.N - hrz1 + 1):.N,]$index,
mk.price = train_test[(.N - hrz1 + 1):.N,]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
cat('\n-------------------------------------------\n')
cat('forecast[', i, ']\n')
print(sets %>% as.data.table)
fl_pth <- paste0(.dtr, 'data/fx/USDJPY/ts_ets_', data_len,
'_', hrz1, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '=', paste0('~/data/fx/USDJPY/ts_ets_', .model, '_',
data_len, '_', hrz1, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
} else if(i %in% seq(1, length(timeID), by = 6)[-1]) {
} else if(i == length(timeID)) {
} else {
lst_sq <- dsmp[date < timeID[i],][.N]$sq + 1
cat('\n===========================================\n')
cat('train[', i, ']\n')
print(train <- dsmp[(lst_sq - data_len + 1):lst_sq])
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
cat('\n-------------------------------------------\n')
cat('train_test[', i, ']\n')
print(train_test <- dsmp[sq %in% ctr])
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl %>%
dplyr::mutate(index = train_test[(.N - hrz1 + 1):.N,]$index,
mk.price = train_test[(.N - hrz1 + 1):.N,]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
cat('\n-------------------------------------------\n')
cat('forecast[', i, ']\n')
print(sets %>% as.data.table)
fl_pth <- paste0(.dtr, 'data/fx/USDJPY/ts_ets_', data_len,
'_', hrz1, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '=', paste0('~/data/fx/USDJPY/ts_ets_', .model, '_',
data_len, '_', hrz1, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
}
})
} else if(hrz1 == 7200) {
tmp <- llply(1:length(timeID), function(i) {
if(i == 1) {
cat('\n===========================================\n')
cat('train[', i, ']\n')
print(train <- dsmp[date < timeID[i]][(.N - (data_len - 1)):.N])
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
cat('\n-------------------------------------------\n')
cat('train_test[', i, ']\n')
print(train_test <- dsmp[sq %in% ctr])
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl %>%
dplyr::mutate(index = train_test[(.N - hrz1 + 1):.N,]$index,
mk.price = train_test[(.N - hrz1 + 1):.N,]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
cat('\n-------------------------------------------\n')
cat('forecast[', i, ']\n')
print(sets %>% as.data.table)
fl_pth <- paste0(.dtr, 'data/fx/USDJPY/ts_ets_', data_len,
'_', hrz1, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '=', paste0('~/data/fx/USDJPY/ts_ets_', .model, '_',
data_len, '_', hrz1, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
} else if(i > (length(timeID) - hrz1/hrz2) & i != length(timeID)) {
lst_sq <- dsmp[date < timeID[i],][.N]$sq + 1
## filter the length of forecasted data to fit with train_test data
## when the length of forecasted data more then length of test data.
#lst_date <- timeID[(length(timeID) - (hrz1/hrz2)):length(timeID)]
lst_date <- timeID[timeID >= timeID[i]]
lst_date_sq <- grep(
timeID[i], timeID[
(length(timeID) - (hrz1/hrz2 - 1)):length(timeID)])
cat('\n===========================================\n')
cat('train[', i, ']\n')
print(train <- dsmp[(lst_sq - data_len + 1):lst_sq])
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
cat('\n-------------------------------------------\n')
cat('train_test[', i, ']\n')
print(train_test <- dsmp[sq %in% ctr])
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl
sets <- sets[1:(hrz1 - (hrz2 * lst_date_sq)),] %>%
dplyr::mutate(index = train_test[
(.N - (hrz1 - (hrz2 * lst_date_sq)) + 1):.N, ]$index,
mk.price = train_test[
(.N - (hrz1 - (hrz2 * lst_date_sq)) + 1):.N, ]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
cat('\n-------------------------------------------\n')
cat('forecast[', i, ']\n')
print(sets %>% as.data.table)
fl_pth <- paste0(.dtr, 'data/fx/USDJPY/ts_ets_', data_len,
'_', hrz1, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '=', paste0('~/data/fx/USDJPY/ts_ets_', .model, '_',
data_len, '_', hrz1, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
} else if(i %in% seq(1, length(timeID), by = 6)[-1]) {
} else if(i == length(timeID)) {
} else {
lst_sq <- dsmp[date < timeID[i],][.N]$sq + 1
cat('\n===========================================\n')
cat('train[', i, ']\n')
print(train <- dsmp[(lst_sq - data_len + 1):lst_sq])
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
cat('\n-------------------------------------------\n')
cat('train_test[', i, ']\n')
print(train_test <- dsmp[sq %in% ctr])
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl %>%
dplyr::mutate(index = train_test[(.N - hrz1 + 1):.N,]$index,
mk.price = train_test[(.N - hrz1 + 1):.N,]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
cat('\n-------------------------------------------\n')
cat('forecast[', i, ']\n')
print(sets %>% as.data.table)
fl_pth <- paste0(.dtr, 'data/fx/USDJPY/ts_ets_', data_len,
'_', hrz1, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '=', paste0('~/data/fx/USDJPY/ts_ets_', .model, '_',
data_len, '_', hrz1, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
}
})
} else {
}
return(tmp)
}
```
```{r intra_1440, eval = FALSE}
intra_1440 <- function(timeID, data = dsmp, data_len,
hrz1 = 60, .model, vb = TRUE) {
## data_len 1440; hrz1 = 60
intr <- data_len/hrz1
tmp <- llply(1:length(timeID), function(i) {
if(i == 1) {
tmp2 <-llply(1:intr, function(j) {
if(j == 1) {
train <- dsmp[date < timeID[i]][(.N - (data_len - 1)):.N]
} else {
lst_sq <- dsmp[date < timeID[i]][(.N - (hrz1 * (intr - j + 1) - 1))]$sq
train <- dsmp[lst_sq:(lst_sq + data_len - 1)]
}
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
if(vb == TRUE) {
cat('\n===========================================\n')
cat('train[', i, '-', j, ']\n')
print(train)
}
train_test <- dsmp[sq %in% ctr]
if(vb == TRUE) {
cat('\n-------------------------------------------\n')
cat('train_test[', i, '-', j, ']\n')
print(train_test)
}
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl %>%
dplyr::mutate(index = train_test[(.N - hrz1 + 1):.N,]$index,
mk.price = train_test[(.N - hrz1 + 1):.N,]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
if(vb == TRUE) {
cat('\n-------------------------------------------\n')
cat('forecast[', i, '-', j, ']\n')
print(sets %>% as.data.table)
}
fl_pth <- paste0(
.dtr, 'data/fx/USDJPY/intraday/ts_ets_', .model, '_', data_len,
'_', hrz1, '.p', j, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '-', j, '=',
paste0('~/data/fx/USDJPY/intraday/ts_ets_', .model, '_',
data_len, '_', hrz1, '.p', j, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
})
return(tmp2)
} else if(i %in% seq(1, length(timeID), by = 6)[-1]) {
} else if(i == length(timeID)) {
} else {
tmp2 <-llply(1:intr, function(j) {
if(j == 1) {
lst_sq <- dsmp[date < timeID[i],][.N]$sq + 1
} else {
lst_sq <- dsmp[date < timeID[i],][.N]$sq + 1 + hrz1 * (j - 1)
}
train <- dsmp[(lst_sq - data_len + 1):lst_sq]
ctr <- train$sq[1]:(range(train$sq)[2] + hrz1)
if(vb == TRUE) {
cat('\n===========================================\n')
cat('train[', i, '-', j, ']\n')
print(train)
}
train_test <- dsmp[sq %in% ctr]
if(vb == TRUE) {
cat('\n-------------------------------------------\n')
cat('train_test[', i, '-', j, ']\n')
print(train_test)
}
sets <- train[, .(index, close)] %>%
as_tibble %>%
tk_ts(frequency = hrz1) %>%
ets(model = .model) %>%
forecast(h = hrz1) %>%
tk_tbl %>%
dplyr::mutate(index = train_test[(.N - hrz1 + 1):.N,]$index,
mk.price = train_test[(.N - hrz1 + 1):.N,]$close) %>%
dplyr::rename(fc.price = `Point Forecast`) %>%
dplyr::select(index, mk.price, fc.price)
if(vb == TRUE) {
cat('\n-------------------------------------------\n')
cat('forecast[', i, '-', j, ']\n')
print(sets %>% as.data.table)
}
fl_pth <- paste0(
.dtr, 'data/fx/USDJPY/intraday/ts_ets_', .model, '_', data_len,
'_', hrz1, '.p', j, '.', as_date(sets$index[1]), '.rds')
saveRDS(sets, fl_pth)
cat('\n', i, '-', j, '=',
paste0('~/data/fx/USDJPY/intraday/ts_ets_', .model, '_',
data_len, '_', hrz1, '.p', j, '.',
as_date(sets$index[1]), '.rds saved!'))
cat('\n\n')
rm(sets)
})
return(tmp2)
}
})
return(tmp)
}
```
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/tseas.R')
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 1440
hrz2 <- 1440
llply(ets.m, function(md) {
tseas(timeID = timeID, dsmp,
data_len = data_len, hrz1 = hrz1,
hrz2 = hrz2, .model = md)
})
```
```{r dy-dy, warning = FALSE, message = FALSE, results = 'asis'}
fl <- list.files(paste0(.dtr, 'data/fx/USDJPY/'), pattern = '^ts_ets_MNN_1440_1440.[0-9]{4}')[1]
if(all(is.na(fl))){
smp <- NA
} else {
smp <- readRDS(paste0(.dtr, 'data/fx/USDJPY/', fl))
data.frame(smp)[
c(1:3, (nrow(smp)-2):nrow(smp)),] %>%
kbl(caption = 'Data Sample (ETS MNN 1440 forecast 1440)', escape = FALSE) %>%
row_spec(0, background = 'DimGrey', color = 'yellow') %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = '#556DAC') %>%
#column_spec(3, background = 'LightSlateGrey') %>%
#column_spec(3, background = '#556DAC') %>%
column_spec(3, background = 'Gainsboro', color = 'goldenrod') %>%
column_spec(4, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>%
scroll_box(width = '100%', fixed_thead = TRUE)
}
```
*source : `r paste0(dim(smp), collapse = ' x ')`*
```{r include = FALSE}
if(exists('smp')) rm(smp)
if(exists('fl')) rm(fl)
```
### Dy >> 12Hr
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `720 mins` (`12 hrs * 60 mins = 720 mins` is half trading day).
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 720
llply(ets.m, function(md) {
intra_1440(timeID = timeID, dsmp,
data_len = data_len, hrz1 = hrz1,
.model = md)
})
```
```{r dy-12hr, warning = FALSE, message = FALSE, results = 'asis'}
fl <- list.files(paste0(.dtr, 'data/fx/USDJPY/intraday/'), pattern = '^ts_ets_MNN_1440_720.p_[0-9]{0,}.[0-9]{4}')[1]
if(all(is.na(fl))){
smp <- NA
} else {
smp <- readRDS(paste0(.dtr, 'data/fx/USDJPY/intraday/', fl))
data.frame(smp)[
c(1:3, (nrow(smp)-2):nrow(smp)),] %>%
kbl(caption = 'Data Sample (ETS MNN 1440 forecast 720)', escape = FALSE) %>%
row_spec(0, background = 'DimGrey', color = 'yellow') %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = '#556DAC') %>%
#column_spec(3, background = 'LightSlateGrey') %>%
#column_spec(3, background = '#556DAC') %>%
column_spec(3, background = 'Gainsboro', color = 'goldenrod') %>%
column_spec(4, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>%
scroll_box(width = '100%', fixed_thead = TRUE)
}
```
*source : `r paste0(dim(smp), collapse = ' x ')`*
```{r include = FALSE}
if(exists('smp')) rm(smp)
if(exists('fl')) rm(fl)
```
### Dy >> 8Hr
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `480 mins` (`8 hrs * 60 mins = 480 mins` is 8 hours in a trading day).
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 480
llply(ets.m, function(md) {
intra_1440(timeID = timeID, dsmp,
data_len = data_len, hrz1 = hrz1,
.model = md)
})
```
```{r dy-8hr, warning = FALSE, message = FALSE, results = 'asis'}
fl <- list.files(paste0(.dtr, 'data/fx/USDJPY/intraday/'), pattern = '^ts_ets_MNN_1440_480.p_[0-9]{0,}.[0-9]{4}')[1]
if(all(is.na(fl))){
smp <- NA
} else {
smp <- readRDS(paste0(.dtr, 'data/fx/USDJPY/intraday/', fl))
data.frame(smp)[
c(1:3, (nrow(smp)-2):nrow(smp)),] %>%
kbl(caption = 'Data Sample (ETS MNN 1440 forecast 480)', escape = FALSE) %>%
row_spec(0, background = 'DimGrey', color = 'yellow') %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = '#556DAC') %>%
#column_spec(3, background = 'LightSlateGrey') %>%
#column_spec(3, background = '#556DAC') %>%
column_spec(3, background = 'Gainsboro', color = 'goldenrod') %>%
column_spec(4, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>%
scroll_box(width = '100%', fixed_thead = TRUE)
}
```
*source : `r paste0(dim(smp), collapse = ' x ')`*
```{r include = FALSE}
if(exists('smp')) rm(smp)
if(exists('fl')) rm(fl)
```
### Dy >> 6Hr
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `360 mins` (`6 hrs * 60 mins = 360 mins` is 6 hours in a trading day).
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 360
llply(ets.m, function(md) {
intra_1440(timeID = timeID, dsmp,
data_len = data_len, hrz1 = hrz1,
.model = md)
})
```
```{r dy-6hr, warning = FALSE, message = FALSE, results = 'asis'}
fl <- list.files(paste0(.dtr, 'data/fx/USDJPY/intraday/'), pattern = '^ts_ets_MNN_1440_360.p_[0-9]{0,}.[0-9]{4}')[1]
if(all(is.na(fl))){
smp <- NA
} else {
smp <- readRDS(paste0(.dtr, 'data/fx/USDJPY/intraday/', fl))
data.frame(smp)[
c(1:3, (nrow(smp)-2):nrow(smp)),] %>%
kbl(caption = 'Data Sample (ETS MNN 1440 forecast 360)', escape = FALSE) %>%
row_spec(0, background = 'DimGrey', color = 'yellow') %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = '#556DAC') %>%
#column_spec(3, background = 'LightSlateGrey') %>%
#column_spec(3, background = '#556DAC') %>%
column_spec(3, background = 'Gainsboro', color = 'goldenrod') %>%
column_spec(4, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>%
scroll_box(width = '100%', fixed_thead = TRUE)
}
```
*source : `r paste0(dim(smp), collapse = ' x ')`*
```{r include = FALSE}
if(exists('smp')) rm(smp)
if(exists('fl')) rm(fl)
```
### Dy >> 4Hr
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `240 mins` (`4 hrs * 60 mins = 240 mins` is 4 hours in a trading day).
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 240
llply(ets.m, function(md) {
intra_1440(timeID = timeID, dsmp,
data_len = data_len, hrz1 = hrz1,
.model = md)
})
```
```{r dy-4hr, warning = FALSE, message = FALSE, results = 'asis'}
fl <- list.files(paste0(.dtr, 'data/fx/USDJPY/intraday/'), pattern = '^ts_ets_MNN_1440_240.p_[0-9]{0,}.[0-9]{4}')[1]
if(all(is.na(fl))){
smp <- NA
} else {
smp <- readRDS(paste0(.dtr, 'data/fx/USDJPY/intraday/', fl))
data.frame(smp)[
c(1:3, (nrow(smp)-2):nrow(smp)),] %>%
kbl(caption = 'Data Sample (ETS MNN 1440 forecast 240)', escape = FALSE) %>%
row_spec(0, background = 'DimGrey', color = 'yellow') %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = '#556DAC') %>%
#column_spec(3, background = 'LightSlateGrey') %>%
#column_spec(3, background = '#556DAC') %>%
column_spec(3, background = 'Gainsboro', color = 'goldenrod') %>%
column_spec(4, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>%
scroll_box(width = '100%', fixed_thead = TRUE)
}
```
*source : `r paste0(dim(smp), collapse = ' x ')`*
```{r include = FALSE}
if(exists('smp')) rm(smp)
if(exists('fl')) rm(fl)
```
### Dy >> 3Hr
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `180 mins` (`3 hrs * 60 mins = 180 mins` is 3 hours in a trading day).
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 180
llply(ets.m, function(md) {
intra_1440(timeID = timeID, dsmp,
data_len = data_len, hrz1 = hrz1,
.model = md)
})
```
```{r dy-3hr, warning = FALSE, message = FALSE, results = 'asis'}
fl <- list.files(paste0(.dtr, 'data/fx/USDJPY/intraday/'), pattern = '^ts_ets_MNN_1440_180.p_[0-9]{0,}.[0-9]{4}')[1]
if(all(is.na(fl))){
smp <- NA
} else {
smp <- readRDS(paste0(.dtr, 'data/fx/USDJPY/intraday/', fl))
data.frame(smp)[
c(1:3, (nrow(smp)-2):nrow(smp)),] %>%
kbl(caption = 'Data Sample (ETS MNN 1440 forecast 180)', escape = FALSE) %>%
row_spec(0, background = 'DimGrey', color = 'yellow') %>%
column_spec(1, background = 'CornflowerBlue') %>%
column_spec(2, background = '#556DAC') %>%
#column_spec(3, background = 'LightSlateGrey') %>%
#column_spec(3, background = '#556DAC') %>%
column_spec(3, background = 'Gainsboro', color = 'goldenrod') %>%
column_spec(4, background = 'LightGray', color = 'goldenrod') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
kable_material(full_width = FALSE) %>%
scroll_box(width = '100%', fixed_thead = TRUE)
}
```
*source : `r paste0(dim(smp), collapse = ' x ')`*
```{r include = FALSE}
if(exists('smp')) rm(smp)
if(exists('fl')) rm(fl)
```
### Dy >> 2Hr
I set the length of data as Daily (`1440 mins` which is 1 trading days) to forecast `120 mins` (`2 hrs * 60 mins = 120 mins` is 2 hours in a trading day).
```{r, eval = FALSE}
# --------- eval=FALSE ---------
ets.m <- 'MNN'
source('function/intra_1440.R')
timeID <- unique(dsmp$date)
bse <- dsmp[year == 2016]$date[1] #"2016-01-04" #1st trading date in 2nd year
timeID %<>% .[. >= bse]
#timeID %<>% .[. >= as_date('2016-01-04')]
data_len <- 1440
hrz1 <- 120
llply(ets.m, function(md) {