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binary-Q1.Rmd
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binary-Q1.Rmd
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---
title: "<img src='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"
author: "[<span style='color:blue'>®γσ, Lian Hu</span>](https://englianhu.github.io/) <img src='www/ENG.jpg' width='24'> <img src='www/RYO.jpg' width='24'>®"
date: "`r lubridate::now()`"
output:
html_document:
number_sections: yes
toc: yes
toc_depth: 4
toc_float:
collapsed: no
smooth_scroll: no
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, warning = FALSE, include = FALSE}
suppressPackageStartupMessages(library('BBmisc'))
pkgs <- c('knitr', 'kableExtra', 'tint', 'devtools', 'lubridate', 'plyr', 'stringr', 'magrittr', 'dplyr', 'tidyr', 'tidyverse', 'tidyquant', 'turner', 'readr', 'quantmod', 'htmltools', 'highcharter', 'googleVis', 'formattable', 'ggfortify', 'DT', 'forecast', 'MCMCpack', 'PerformanceAnalytics', 'broom', 'microbenchmark', 'doParallel', 'Boruta', 'fBasics', 'fPortfolio', 'rugarch', 'parma', 'rmgarch')#, 'Mcomp', 'bsts')
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', 'simStakesAutoArima.R', 'simETS.R', 'simStakesETS.R', 'plotChart2.R', 'armaSearch.R', 'simGarch.R', 'simStakesGarch.R'), function(pkg) source(paste0('./function/', pkg))))
## Directly install the developing packages.
#'@ require(devtools)
#'@ install_bitbucket("rugarch","alexiosg")
#'@ install_bitbucket("rmgarch","alexiosg")
#'@ install_bitbucket("racd","alexiosg")
#'@ install_bitbucket("twinkle","alexiosg")
#'@ install_bitbucket("spd","alexiosg")
#'@ install_bitbucket("parma","alexiosg")
## 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(16)
# invalidate cache when the package version changes
#'@ knitr::opts_chunk$set(tidy = FALSE, cache.extra = packageVersion('tint'))
rm(pkgs)
#'@ options(htmltools.dir.version = FALSE)
```
# Introduction
Below are the questionaire. Here I created this file to apply `MCMCpack` and `forecast` to compelete the questions prior to completed the `Ridge`, `ElasticNet` and `LASSO` regression (quite alot of models for comparison)^[We can use `cv.glmnet()` in `glmnet` package or `caret` package for cross validation models. You can refer to [<span style='color:blue'>Algorithmic Trading</span>](https://robotwealth.com/caterory/algorithmic-trading/page/4/) and [<span style='color:blue'>Successful Algorithmic Trading</span>](https://raw.githubusercontent.com/englianhu/binary.com-interview-question/fcad2844d7f10c486f3601af9932f49973548e4b/reference/Successful%20Algorithmic%20Trading.pdf) which applied cross-validation in focasting in financial market. You can buy the ebook with full Python coding of [<span style='color:blue'>Successful Algorithmic Trading</span>](https://www.quantstart.com/successful-algorithmic-trading-ebook) as well.].
<iframe width="560" height="315" src="https://www.youtube.com/embed/Aw77aMLj9uM" frameborder="0" allowfullscreen></iframe>
<center><iframe src="https://raw.githubusercontent.com/englianhu/binary.com-interview-question/ff20ee95aa60ef5cca3cf797066089103eb62acf/reference/quant-analyst-skills-test.pdf" width="600" height="900"></iframe></center>
# Content
## Question 1
### Read Data
I use 3 years data for the question as experiment, 1st year data is burn-in data for statistical modelling and prediction purpose while following 2 years data for forecasting and staking. There have 252 trading days within a year.
```{r read-data, echo = FALSE, warning = FALSE, eval = FALSE}
## ================== eval = FALSE =============================
## Do not execute...
##
## Remove all objects include hidden objects.
rm(list = ls(all.names = TRUE))
## get currency dataset online.
getFX('USD/JPY', from = '2014-01-01', to = '2017-01-20') #oanda only provides 180 days data. getSymbols()
#'@ USDJPY <- readRDS('./data/USDJPY.rds')
USDJPY <- xts(USDJPY[, -1], order.by = USDJPY$Date)
## dateID
dateID <- index(mbase)
dateID0 <- ymd('2015-01-01')
dateID <- dateID[dateID > dateID0]
obs.data <- USDJPY[index(USDJPY) > dateID0]
## Now we try to use the daily mean value which is (Hi + Lo) / 2.
pred.data <- ldply(dateID, function(dt) {
smp = USDJPY
dtr = last(index(smp[index(smp) < dt]))
smp = smp[paste0(dtr %m-% years(1), '/', dtr)]
frd = as.numeric(difftime(dt, dtr), units = 'days')
fit = ets(smp) #https://www.otexts.org/fpp/7/7
data.frame(Date = dt, forecast(fit, h = frd)) %>% tbl_df
}, .parallel = FALSE) %>% tbl_df
cmp.data <- xts(pred.data[, -1], order.by = pred.data$Date)
cmp.data <- cbind(cmp.data, obs.data)
rm(obs.data, pred.data)
# Test the models
lm(Point.Forecast~ USD.JPY, data = cmp.data)
MCMCregress(Point.Forecast~ USD.JPY, data = cmp.data)
plot(forecast(fit))
forecast(fit, h = 4)
```
```{r read-data2, warning = FALSE}
## get currency dataset online.
## https://stackoverflow.com/questions/24219694/get-symbols-quantmod-ohlc-currency-data
#'@ getFX('USD/JPY', from = '2014-01-01', to = '2017-01-20')
## getFX() doesn't shows Op, Hi, Lo, Cl price but only price. Therefore no idea to place bets.
#'@ USDJPY <- getSymbols('JPY=X', src = 'yahoo', from = '2014-01-01',
#'@ to = '2017-01-20', auto.assign = FALSE)
#'@ names(USDJPY) <- str_replace_all(names(USDJPY), 'JPY=X', 'USDJPY')
#'@ USDJPY <- xts(USDJPY[, -1], order.by = USDJPY$Date)
#'@ saveRDS(USDJPY, './data/USDJPY.rds')
USDJPY <- read_rds(path = './data/USDJPY.rds')
mbase <- USDJPY
## dateID
dateID <- index(mbase)
dateID0 <- ymd('2015-01-01')
dateID <- dateID[dateID > dateID0]
```
```{r data-summary, warning = FALSE}
dim(mbase)
summary(mbase) %>%
kable %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive'))
```
### Statistical Modelling
#### ARIMA vs ETS
<span style='color:red'>**Remarks :** *Here I try to predict the sell/buy price and also settled price. However just noticed the question asking about prediction of the variance^[The profit is made based on the range of variance Hi-Lo price but not the accuracy of the highest, lowest or closing price.] based on mean price. I can also use the focasted highest and forecasted lowest price for variance prediction as well. However I will conduct another study and answer for the variance with Garch models.*</span>
Below are some articles with regards exponential smoothing.
- [<span style='color:blue'>Recent Advances in Robust Statistics: Theory and Applications</span>](https://books.google.com.my/books?id=ntR5DQAAQBAJ&pg=PA174&lpg=PA174&dq=exponential+smoothing+mcmc&source=bl&ots=QANf4o9oFh&sig=JWov-64qeFTcOScG2pYj9OVCl2k&hl=ja&sa=X&redir_esc=y#v=onepage&q=exponential%20smoothing%20mcmc&f=false)
- [<span style='color:blue'>Error, trend, seasonality - ets and its forecast model friends</span>](https://ellisp.github.io/blog/2016/11/27/ets-friends)
- [<span style='color:blue'>A study of outliers in the exponential smoothing approach to forecasting</span>](https://www.statindex.org/articles/258660)
- [<span style='color:blue'>8.10 ARIMA vs ETS</span>](https://www.otexts.org/fpp/8/10)
- [<span style='color:blue'>Introduction to ARIMA : nonseasonal models</span>](https://people.duke.edu/~rnau/411arim.htm#les)
It is a common myth that ARIMA models are more general than exponential smoothing. While linear exponential smoothing models are all special cases of ARIMA models, the non-linear exponential smoothing models have no equivalent ARIMA counterparts. There are also many ARIMA models that have no exponential smoothing counterparts. In particular, every ETS model^[[<span style='color:blue'>**forecast::ets()**</span>](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/ets) : Usually a three-character string identifying method using the framework terminology of Hyndman et al. (2002) and Hyndman et al. (2008). The first letter denotes the error type ("A", "M" or "Z"); the second letter denotes the trend type ("N","A","M" or "Z"); and the third letter denotes the season type ("N","A","M" or "Z"). In all cases, "N"=none, "A"=additive, "M"=multiplicative and "Z"=automatically selected. So, for example, "ANN" is simple exponential smoothing with additive errors, "MAM" is multiplicative Holt-Winters' method with multiplicative errors, and so on.
It is also possible for the model to be of class "ets", and equal to the output from a previous call to ets. In this case, the same model is fitted to y without re-estimating any smoothing parameters. See also the use.initial.values argument.] is non-stationary, while ARIMA models can be stationary.
The ETS models with seasonality or non-damped trend or both have two unit roots (i.e., they need two levels of differencing to make them stationary). All other ETS models have one unit root (they need one level of differencing to make them stationary).
The following table gives some equivalence relationships for the two classes of models.
| ETS model | ARIMA model | Parameters |
|:-------------------:|:-------------------------------------:|:-----------------------------:|
| $ETS(A, N, N)$ | $ARIMA(0, 1, 1)$ | $θ_{1} = α − 1$ |
| $ETS(A, A, N)$ | $ARIMA(0, 2, 2)$ | $θ_{1} = α + β − 2$ |
| | | $θ_{2} = 1 − α$ |
| $ETS(A, A_{d}, N)$ | $ARIMA(1, 1, 2)$ | $ϕ_{1} = ϕ$ |
| | | $θ_{1} = α + ϕβ − 1 − ϕ$ |
| | | $θ_{2} = (1 − α)ϕ$ |
| $ETS(A, N, A)$ | $ARIMA(0, 0, m)(0, 1, 0)_{m}$ | |
| $ETS(A, A, A)$ | $ARIMA(0, 1, m+1)(0, 1, 0)_{m}$ | |
| $ETS(A, A_{d}, A)$ | $ARIMA(1, 0, m+1)(0, 1, 0)_{m}$ | |
For the seasonal models, there are a large number of restrictions on the ARIMA parameters.
Kindly refer to [<span style='color:blue'>*8.10 ARIMA vs ETS*</span>](https://www.otexts.org/fpp/8/10) for further details.
```{r build-AutoArima, warning = FALSE}
## Modelling Auto Arima focasting data.
#'@ fitAutoArima.op <- suppressAll(simAutoArima(USDJPY, .prCat = 'Op')) #will take a minute
#'@ saveRDS(fitAutoArima.op, './data/fitAutoArima.op.rds')
#'@ fitAutoArima.hi <- suppressAll(simAutoArima(USDJPY, .prCat = 'Hi')) #will take a minute
#'@ saveRDS(fitAutoArima.hi, './data/fitAutoArima.hi.rds')
#'@ fitAutoArima.mn <- suppressAll(simAutoArima(USDJPY, .prCat = 'Mn')) #will take a minute
#'@ saveRDS(fitAutoArima.mn, './data/fitAutoArima.mn.rds')
#'@ fitAutoArima.lo <- suppressAll(simAutoArima(USDJPY, .prCat = 'Lo')) #will take a minute
#'@ saveRDS(fitAutoArima.lo, './data/fitAutoArima.lo.rds')
#'@ fitAutoArima.cl <- suppressAll(simAutoArima(USDJPY, .prCat = 'Cl')) #will take a minute
#'@ saveRDS(fitAutoArima.cl, './data/fitAutoArima.cl.rds')
fitAutoArima.op <- readRDS('./data/fitAutoArima.op.rds')
fitAutoArima.hi <- readRDS('./data/fitAutoArima.hi.rds')
fitAutoArima.mn <- readRDS('./data/fitAutoArima.mn.rds')
fitAutoArima.lo <- readRDS('./data/fitAutoArima.lo.rds')
fitAutoArima.cl <- readRDS('./data/fitAutoArima.cl.rds')
```
```{r build-ETS, warning = FALSE}
## Modelling ETS focasting data.
#'@ fitETS.op <- suppressAll(simETS(USDJPY, .prCat = 'Op')) #will take a minute
#'@ saveRDS(fitETS.op, './data/fitETS.op.rds')
#'@ fitETS.hi <- suppressAll(simETS(USDJPY, .prCat = 'Hi')) #will take a minute
#'@ saveRDS(fitETS.hi, './data/fitETS.hi.rds')
#'@ fitETS.mn <- suppressAll(simETS(USDJPY, .prCat = 'Mn')) #will take a minute
#'@ saveRDS(fitETS.mn, './data/fitETS.mn.rds')
#'@ fitETS.lo <- suppressAll(simETS(USDJPY, .prCat = 'Lo')) #will take a minute
#'@ saveRDS(fitETS.lo, './data/fitETS.lo.rds')
#'@ fitETS.cl <- suppressAll(simETS(USDJPY, .prCat = 'Cl')) #will take a minute
#'@ saveRDS(fitETS.cl, './data/fitETS.cl.rds')
fitETS.op <- readRDS('./data/fitETS.op.rds')
fitETS.hi <- readRDS('./data/fitETS.hi.rds')
fitETS.mn <- readRDS('./data/fitETS.mn.rds')
fitETS.lo <- readRDS('./data/fitETS.lo.rds')
fitETS.cl <- readRDS('./data/fitETS.cl.rds')
```
**Application of MCMC**
Need to refer to MCMC since I am using exponential smoothing models...
- [<span style='color:blue'>Markov Chain Monte Carlo Method</span>](https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_introbayes_sect007.htm)
- [<span style='color:blue'>Burn-In is Unnecessary</span>](https://users.stat.umn.edu/~geyer/mcmc/burn.html)
- [<span style='color:blue'>**Nice R Code** *Punning code better since 2013*</span>](https://nicercode.github.io/guides/mcmc/)
```{r lm-ETS, warning = FALSE}
## Here I test the accuracy of forecasting of ets ZZZ model 1.
## Test the models
## opened price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitETS.op))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitETS.op))
## highest price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitETS.hi))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitETS.hi))
## mean price fit data (mean price of daily highest and lowest price)
summary(lm(Point.Forecast~ USDJPY.Close, data = fitETS.mn))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitETS.mn))
## lowest price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitETS.lo))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitETS.lo))
## closed price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitETS.cl))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitETS.cl))
```
**Mean Squared Error**
```{r combine-dataAutoArima, warning = FALSE}
fcdataAA <- do.call(cbind, list(USDJPY.FPOP.Open = fitAutoArima.op$Point.Forecast,
USDJPY.FPHI.High = fitAutoArima.hi$Point.Forecast,
USDJPY.FPMN.Mean = fitAutoArima.mn$Point.Forecast,
USDJPY.FPLO.Low = fitAutoArima.lo$Point.Forecast,
USDJPY.FPCL.Close = fitAutoArima.cl$Point.Forecast,
USDJPY.Open = fitAutoArima.op$USDJPY.Open,
USDJPY.High = fitAutoArima.op$USDJPY.High,
USDJPY.Low = fitAutoArima.op$USDJPY.Low,
USDJPY.Close = fitAutoArima.op$USDJPY.Close))
fcdataAA %<>% na.omit
names(fcdataAA) <- c('USDJPY.FPOP.Open', 'USDJPY.FPHI.High', 'USDJPY.FPMN.Mean',
'USDJPY.FPLO.Low', 'USDJPY.FPCL.Close', 'USDJPY.Open',
'USDJPY.High', 'USDJPY.Low', 'USDJPY.Close')
## Mean Squared Error : comparison of accuracy
paste('Open = ', mean((fcdataAA$USDJPY.FPOP.Open - fcdataAA$USDJPY.Open)^2))
paste('High = ', mean((fcdataAA$USDJPY.FPHI.High - fcdataAA$USDJPY.High)^2))
paste('Mean = ', mean((fcdataAA$USDJPY.FPMN.Mean - (fcdataAA$USDJPY.High + fcdataAA$USDJPY.Low)/2)^2))
paste('Low = ', mean((fcdataAA$USDJPY.FPLO.Low - fcdataAA$USDJPY.Low)^2))
paste('Close = ', mean((fcdataAA$USDJPY.FPCL.Close - fcdataAA$USDJPY.Close)^2))
```
```{r combine-dataETS, warning = FALSE}
fcdata <- do.call(cbind, list(USDJPY.FPOP.Open = fitETS.op$Point.Forecast,
USDJPY.FPHI.High = fitETS.hi$Point.Forecast,
USDJPY.FPMN.Mean = fitETS.mn$Point.Forecast,
USDJPY.FPLO.Low = fitETS.lo$Point.Forecast,
USDJPY.FPCL.Close = fitETS.cl$Point.Forecast,
USDJPY.Open = fitETS.op$USDJPY.Open,
USDJPY.High = fitETS.op$USDJPY.High,
USDJPY.Low = fitETS.op$USDJPY.Low,
USDJPY.Close = fitETS.op$USDJPY.Close))
fcdata %<>% na.omit
names(fcdata) <- c('USDJPY.FPOP.Open', 'USDJPY.FPHI.High', 'USDJPY.FPMN.Mean',
'USDJPY.FPLO.Low', 'USDJPY.FPCL.Close', 'USDJPY.Open',
'USDJPY.High', 'USDJPY.Low', 'USDJPY.Close')
## Mean Squared Error : comparison of accuracy
paste('Open = ', mean((fcdata$USDJPY.FPOP.Open - fcdata$USDJPY.Open)^2))
paste('High = ', mean((fcdata$USDJPY.FPHI.High - fcdata$USDJPY.High)^2))
paste('Mean = ', mean((fcdata$USDJPY.FPMN.Mean - (fcdata$USDJPY.High + fcdata$USDJPY.Low)/2)^2))
paste('Low = ', mean((fcdata$USDJPY.FPLO.Low - fcdata$USDJPY.Low)^2))
paste('Close = ', mean((fcdata$USDJPY.FPCL.Close - fcdata$USDJPY.Close)^2))
```
#### Garch vs EWMA
Basically for volatility analyzing, we can using RSY Volatility mesure, kindly refer to *Analyzing Financial Data and Implementing Financial Models using R*^[paper 22] for more information. Well, Garch model is designate for forecast volatility.
Now we look at Garch model, *Figlewski (2004)*^[*Paper 19th*] applied few models and also using different length of data for comparison. Now I use daily Hi-Lo and 365 days data in order to predict the next market price. The author applid Garch on SAP200, 10-years-bond and 20-years-bond and concludes that the Garch model is better than eGarch but implied volatility model better than Garch and eGarch, and the monthly Hi-Lo data is better accurate than daily Hi-Lo for long term investment.
<iframe width="560" height="315" src="https://www.youtube.com/embed/wsYXKh_xmSs" frameborder="0" allowfullscreen></iframe>
$$\begin{equation}
h_{t} = {\omega} + \sum_{i=1}^q{{\alpha}_{i} {\epsilon}_{t-i}^2} + \sum_{j=1}^p{{\gamma}_{j} h_{t-j}}\ \dots equation\ 2.1.2.2.1
\end{equation}$$
- [<span style='color:blue'>Volatility Forecasting Using GARCH(1,1)</span>](https://quantumfinancier.wordpress.com/2010/08/26/volatility-forecasting-using-garch11/)
- [<span style='color:blue'>Regime Switching System Using Volatility Forecast</span>](https://quantumfinancier.wordpress.com/2010/08/27/regime-switching-system-using-volatility-forecast/)
- [<span style='color:blue'>Normalized Price Spread Strategy</span>](https://quantumfinancier.wordpress.com/2010/06/17/normalized-price-spread-strategy/)
- [<span style='color:blue'>Volatility Autocorrelation in Different Markets</span>](https://quantumfinancier.wordpress.com/2010/05/27/volatility-autocorrelation-in-different-markets/)
- [<span style='color:blue'>Basic Introduction to GARCH and EGARCH (part 1)</span>](https://quantumfinancier.wordpress.com/2010/09/12/381/)
- [<span style='color:blue'>Basic Introduction to GARCH and EGARCH (part 2)</span>](https://quantumfinancier.wordpress.com/2010/09/14/basic-introduction-to-garch-and-egarch-part-2/)
- [<span style='color:blue'>Basic Introduction to GARCH and EGARCH (Part 3)</span>](https://quantumfinancier.wordpress.com/2010/09/23/basic-introduction-to-garch-and-egarch-part-3/amp)^[Using this EGARCH model, we can epect a better estimate the volatility for assets returns due to how the EGARCH counteracts the limitations on the classic GARCH model.Here is the final part of the series of posts on the volatility modelling where I will briefly talk about one of the many variant of the GARCH model: the exponential GARCH (abbreviated EGARCH). I chose this variant because it improves the GARCH model and better model some market mechanics. In the GARCH post, I didn’t mention any of the limitation of the model as I kept them for today’s post. First of all, the GARCH model assume that only the magnitude of unanticipated excess returns determines $\sigma^2_t$. Intuitively, we can question this assumption; I, for one, would argue that not only the magnitude but also the direction of the returns affects volatility.]
- [<span style='color:blue'>Analysis List</span>](https://vlab.stern.nyu.edu/doc?topic=mdls)
- Volatility Analysis
+ [<span style='color:blue'>AGARCH](https://vlab.stern.nyu.edu/doc/6?topic=mdls</span>)
+ [<span style='color:blue'>APARCH</span>](https://vlab.stern.nyu.edu/doc/5?topic=mdls)
+ [<span style='color:blue'>Asy. MEM</span>](https://vlab.stern.nyu.edu/doc/10?topic=mdls)
+ [<span style='color:blue'>Asy. Power MEM</span>](https://vlab.stern.nyu.edu/doc/11?topic=mdls)
+ [<span style='color:blue'>CDS-GARCH</span>](https://vlab.stern.nyu.edu/doc/25?topic=mdls)
+ [<span style='color:blue'>CDS-GARCH-DYN</span>](https://vlab.stern.nyu.edu/doc/26?topic=mdls)
+ [<span style='color:blue'>EGARCH</span>](https://vlab.stern.nyu.edu/doc/4?topic=mdls)
+ [<span style='color:blue'>GARCH</span>](https://vlab.stern.nyu.edu/doc/2?topic=mdls)
+ [<span style='color:blue'>GAS-GARCH Student T</span>](https://vlab.stern.nyu.edu/doc/23?topic=mdls)
+ [<span style='color:blue'>GJR-GARCH</span>](https://vlab.stern.nyu.edu/doc/3?topic=mdls)
+ [<span style='color:blue'>MEM</span>](https://vlab.stern.nyu.edu/doc/9?topic=mdls)
+ [<span style='color:blue'>Spline-GARCH</span>](https://vlab.stern.nyu.edu/doc/7?topic=mdls)
+ [<span style='color:blue'>Zero Slope Spline-GARCH</span>](https://vlab.stern.nyu.edu/doc/8?topic=mdls)
- Correlation Analysis
+ [<span style='color:blue'>EWMA Covariance</span>](https://vlab.stern.nyu.edu/doc/12?topic=mdls)
+ [<span style='color:blue'>GARCH-DCC</span>](https://vlab.stern.nyu.edu/doc/13?topic=mdls)
+ [<span style='color:blue'>GARCH-DECO</span>](https://vlab.stern.nyu.edu/doc/20?topic=mdls)
+ [<span style='color:blue'>GJR-DCC</span>](https://vlab.stern.nyu.edu/doc/14?topic=mdls)
+ [<span style='color:blue'>GJR-DECO</span>](https://vlab.stern.nyu.edu/doc/21?topic=mdls)
- Systemic Risk Analysis
+ [<span style='color:blue'>Domestic MES</span>](https://vlab.stern.nyu.edu/doc/40?topic=mdls)
+ [<span style='color:blue'>Dynamic MES</span>](https://vlab.stern.nyu.edu/doc/16?topic=mdls)
+ [<span style='color:blue'>Dynamic MES with Simulation</span>](https://vlab.stern.nyu.edu/doc/22?topic=mdls)
+ [<span style='color:blue'>Global Dynamic MES</span>](https://vlab.stern.nyu.edu/doc/17?topic=mdls)
- Long Run Value at Risk
+ [<span style='color:blue'>Long Term GJR-GARCH Forecast</span>](https://vlab.stern.nyu.edu/doc/18?topic=mdls)
+ [<span style='color:blue'>Long Term GJR-GARCH Forecast with Options</span>](https://vlab.stern.nyu.edu/doc/19?topic=mdls)
- Liquidity Analysis
+ [<span style='color:blue'>Asymmetric ILLIQ</span>](https://vlab.stern.nyu.edu/doc/28?topic=mdls)
+ [<span style='color:blue'>Historical ILLIQ</span>](https://vlab.stern.nyu.edu/doc/29?topic=mdls)
+ [<span style='color:blue'>Spline ILLIQ</span>](https://vlab.stern.nyu.edu/doc/27?topic=mdls)
- Fixed Income Analysis
+ [<span style='color:blue'>ESR Interest Rate Forecast</span>](https://vlab.stern.nyu.edu/doc/39?topic=mdls)
- [<span style='color:blue'>Extensions of the GARCH Model</span>](https://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/sfehtmlnode67.html)^[Comparison among the GARCH, TGARCH (Threshold GARCH) and EGARCH (Exponential GARCH) models.]
- [<span style='color:blue'>How to fit ARMA+GARCH Model In R?</span>](https://quant.stackexchange.com/questions/4948/how-to-fit-armagarch-model-in-r?answertab=votes#tab-top)
- [<span style='color:blue'>A short introduction to the rugarch package</span>](https://unstarched.net/r-examples/rugarch/a-short-introduction-to-the-rugarch-package/)
- [<span style='color:blue'>A practical introduction to garch modeling</span>](https://www.r-bloggers.com/a-practical-introduction-to-garch-modeling/)
- [<span style='color:blue'>ARCH-GARCH Example with R</span>](https://yunus.hacettepe.edu.tr/~iozkan/eco665/archgarch.html)
- [<span style='color:blue'>Financial Econometrics Practical *Practical 6: Univariate Volatility Modelling*</span>](https://curiousquant.com/ClassNotes/FinMetrics/Practicals/Practical_6/Practical_6.html)
- [<span style='color:blue'>Multivariate GARCH(1,1) in R</span>](https://stackoverflow.com/questions/35035857/multivariate-garch1-1-in-r?answertab=votes#tab-top)
- [<span style='color:blue'>R - Modelling Multivariate GARCH (rugarch and ccgarch)</span>](https://stackoverflow.com/questions/16874375/r-modelling-multivariate-garch-rugarch-and-ccgarch?answertab=votes#tab-top)
- [<span style='color:blue'>Introduction to some R package</span>](./reference/Introduction to some R packages.pdf)
- [<span style='color:blue'>Introduction to the ruGarch package</span>](./reference/Introduction to the rugarch package.pdf)
- [<span style='color:blue'>The rmgarch models: Background and properties. - R Project</span>](./reference/The rmgarch models - Background and Properties.pdf)
- [<span style='color:blue'>rmgarch - How to Multivariate GARCH Models in R | R-How.com</span>](https://r-how.com/packages/rmgarch)
- [<span style='color:blue'>Volatility forecast evaluation in R</span>](https://eranraviv.com/volatility-forecast-evaluation-in-r/)
Firstly we use `rugarch` <s>and then `rmgarch`</s>^[Due to file loading heavily, here I leave the multivariate Garch models for future works.] to compare the result.
```{r intro-Garch, warning = FALSE}
## https://www.unstarched.net/r-examples/rugarch/a-short-introduction-to-the-rugarch-package/
ugarchspec()
## This defines a basic ARMA(1,1)-GARCH(1,1) model, though there are many more options to choose from ranging from the type of GARCH model, the ARFIMAX-arch-in-mean specification and conditional distribution. In fact, and considering only the (1,1) order for the GARCH and ARMA models, there are 13440 possible combinations of models and model options to choose from:
## possible Garch models.
nrow(expand.grid(GARCH = 1:14, VEX = 0:1, VT = 0:1, Mean = 0:1, ARCHM = 0:2, ARFIMA = 0:1, MEX = 0:1, DISTR = 1:10))
spec = ugarchspec(variance.model = list(model = 'eGARCH', garchOrder = c(2, 1)), distribution = 'std')
```
There will be `r nrow(expand.grid(GARCH = 1:14, VEX = 0:1, VT = 0:1, Mean = 0:1, ARCHM = 0:2, ARFIMA = 0:1, MEX = 0:1, DISTR = 1:10))` possible combination Garch models. Here I tried to filter few among them.
Now we try to build a Garch model and will build some Garch models to get the best fit in later section.
<iframe width="560" height="315" src="https://www.youtube.com/embed/0q3gSJKJUs8" frameborder="0" allowfullscreen></iframe>
```{r build-Garch, warning = FALSE}
## Modelling Garch ('sGarch' model) focasting data.
#'@ fitGM.op <- suppressAll(simGarch(USDJPY, .prCat = 'Op')) #will take a minute
#'@ saveRDS(fitGM.op, './data/fitGM.op.rds')
#'@ fitGM.hi <- suppressAll(simGarch(USDJPY, .prCat = 'Hi')) #will take a minute
#'@ saveRDS(fitGM.hi, './data/fitGM.hi.rds')
#'@ fitGM.mn <- suppressAll(simGarch(USDJPY, .prCat = 'Mn')) #will take a minute
#'@ saveRDS(fitGM.mn, './data/fitGM.mn.rds')
#'@ fitGM.lo <- suppressAll(simGarch(USDJPY, .prCat = 'Lo')) #will take a minute
#'@ saveRDS(fitGM.lo, './data/fitGM.lo.rds')
#'@ fitGM.cl <- suppressAll(simGarch(USDJPY, .prCat = 'Cl')) #will take a minute
#'@ saveRDS(fitGM.cl, './data/fitGM.cl.rds')
fitGM.op <- readRDS('./data/fitGM.op.rds')
fitGM.hi <- readRDS('./data/fitGM.hi.rds')
fitGM.mn <- readRDS('./data/fitGM.mn.rds')
fitGM.lo <- readRDS('./data/fitGM.lo.rds')
fitGM.cl <- readRDS('./data/fitGM.cl.rds')
```
```{r intro-EWMA, warning = FALSE}
## ======================== eval = FALSE ==============================
## Exponential Weighted Moving Average model - EWMA fixed parameters
#'@ ewma.spec.fixed <- llply(pp, function(y) {
#'@ z = simStakesGarch(mbase, .solver = .solver.par[1], .prCat = y[1],
#'@ .prCat.method = 'CSS-ML', .baseDate = ymd('2015-01-01'),
#'@ .parallel = FALSE, .progress = 'text',
#'@ .setPrice = y[2], .setPrice.method = 'CSS-ML',
#'@ .initialFundSize = 1000, .fundLeverageLog = FALSE,
#'@ .filterBets = FALSE, .variance.model = list(
#'@ model = .variance.model.par[6], garchOrder = c(1, 1),
#'@ submodel = NULL, external.regressors = NULL,
#'@ variance.targeting = FALSE),
#'@ .mean.model = list(armaOrder = c(1, 1),
#'@ include.mean = TRUE,
#'@ archm = FALSE, archpow = 1,
#'@ arfima = FALSE,
#'@ external.regressors = NULL,
#'@ archex = FALSE),
#'@ .dist.model = .dist.model.par[1], start.pars = list(),
#'@ fixed.pars = list(alpha1 = 1 - 0.94, omega = 0))
#'@
#'@ txt1 <- paste0('saveRDS(z', ', file = \'./data/',
#'@ .variance.model.par[6], '.EWMA.fixed.',
#'@ y[1], '.', y[2], '.', .dist.model.par[1], '.',
#'@ .solver.par[1], '.rds\')')
#'@ eval(parse(text = txt1))
#'@ cat(paste0(txt1, ' done!', '\n'))
#'@ rm(z)
#'@ })
## Exponential Weighted Moving Average model - EWMA estimated parameters
#'@ ewma.spec.est <- llply(pp, function(y) {
#'@ z = simStakesGarch(mbase, .solver = .solver.par[1], .prCat = y[1],
#'@ .prCat.method = 'CSS-ML', .baseDate = ymd('2015-01-01'),
#'@ .parallel = FALSE, .progress = 'text',
#'@ .setPrice = y[2], .setPrice.method = 'CSS-ML',
#'@ .initialFundSize = 1000, .fundLeverageLog = FALSE,
#'@ .filterBets = FALSE, .variance.model = list(
#'@ model = .variance.model.par[6], garchOrder = c(1, 1),
#'@ submodel = NULL, external.regressors = NULL,
#'@ variance.targeting = FALSE),
#'@ .mean.model = list(armaOrder = c(1, 1),
#'@ include.mean = TRUE,
#'@ archm = FALSE, archpow = 1,
#'@ arfima = FALSE,
#'@ external.regressors = NULL,
#'@ archex = FALSE),
#'@ .dist.model = .dist.model.par[1], start.pars = list(),
#'@ fixed.pars = list(omega = 0))
#'@
#'@ txt1 <- paste0('saveRDS(z', ', file = \'./data/',
#'@ .variance.model.par[6], '.EWMA.est.',
#'@ y[1], '.', y[2], '.', .dist.model.par[1], '.',
#'@ .solver.par[1], '.rds\')')
#'@ eval(parse(text = txt1))
#'@ cat(paste0(txt1, ' done!', '\n'))
#'@ rm(z)
#'@ })
## itegration Garch model - iGarch
#'@ igarch.spec <- llply(pp, function(y) {
#'@ z = simStakesGarch(mbase, .solver = .solver.par[1], .prCat = y[1],
#'@ .prCat.method = 'CSS-ML', .baseDate = ymd('2015-01-01'),
#'@ .parallel = FALSE, .progress = 'text',
#'@ .setPrice = y[2], .setPrice.method = 'CSS-ML',
#'@ .initialFundSize = 1000, .fundLeverageLog = FALSE,
#'@ .filterBets = FALSE, .variance.model = list(
#'@ model = .variance.model.par[6], garchOrder = c(1, 1),
#'@ submodel = NULL, external.regressors = NULL,
#'@ variance.targeting = FALSE),
#'@ .mean.model = list(armaOrder = c(1, 1),
#'@ include.mean = TRUE,
#'@ archm = FALSE, archpow = 1,
#'@ arfima = FALSE,
#'@ external.regressors = NULL,
#'@ archex = FALSE),
#'@ .dist.model = .dist.model.par[1], start.pars = list(),
#'@ fixed.pars = list())
#'@
#'@ txt1 <- paste0('saveRDS(z', ', file = \'./data/',
#'@ .variance.model.par[6], '.', y[1], '.', y[2], '.',
#'@ .dist.model.par[1], '.', .solver.par[1], '.rds\')')
#'@ eval(parse(text = txt1))
#'@ cat(paste0(txt1, ' done!', '\n'))
#'@ rm(z)
#'@ })
```
**Application of MCMC**
Need to refer to MCMC since I am using Garch models...
- [<span style='color:blue'>Markov Chain Monte Carlo Method</span>](https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_introbayes_sect007.htm)
- [<span style='color:blue'>Burn-In is Unnecessary</span>](https://users.stat.umn.edu/~geyer/mcmc/burn.html)
- [<span style='color:blue'>**Nice R Code** *Punning code better since 2013*</span>](https://nicercode.github.io/guides/mcmc/)
```{r lm-Garch, warning = FALSE}
## Here I test the accuracy of forecasting of univariate Garch ('sGarch' model) models.
## Test the models
## opened price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitGM.op))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitGM.op))
## highest price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitGM.hi))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitGM.hi))
## mean price fit data (mean price of daily highest and lowest price)
summary(lm(Point.Forecast~ USDJPY.Close, data = fitGM.mn))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitGM.mn))
## lowest price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitGM.lo))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitGM.lo))
## closed price fit data
summary(lm(Point.Forecast~ USDJPY.Close, data = fitGM.cl))
summary(MCMCregress(Point.Forecast~ USDJPY.Close, data = fitGM.cl))
```
**Mean Squared Error**
```{r combine-dataGM, warning = FALSE}
## Univariate Garch models.
fcdataGM <- do.call(cbind, list(USDJPY.FPOP.Open = fitGM.op$Point.Forecast,
USDJPY.FPHI.High = fitGM.hi$Point.Forecast,
USDJPY.FPMN.Mean = fitGM.mn$Point.Forecast,
USDJPY.FPLO.Low = fitGM.lo$Point.Forecast,
USDJPY.FPCL.Close = fitGM.cl$Point.Forecast,
USDJPY.Open = fitGM.op$USDJPY.Open,
USDJPY.High = fitGM.op$USDJPY.High,
USDJPY.Low = fitGM.op$USDJPY.Low,
USDJPY.Close = fitGM.op$USDJPY.Close))
fcdataGM %<>% na.omit
names(fcdataGM) <- c('USDJPY.FPOP.Open', 'USDJPY.FPHI.High', 'USDJPY.FPMN.Mean',
'USDJPY.FPLO.Low', 'USDJPY.FPCL.Close', 'USDJPY.Open',
'USDJPY.High', 'USDJPY.Low', 'USDJPY.Close')
## Mean Squared Error : comparison of accuracy
paste('Open = ', mean((fcdataGM$USDJPY.FPOP.Open - fcdataGM$USDJPY.Open)^2))
paste('High = ', mean((fcdataGM$USDJPY.FPHI.High - fcdataGM$USDJPY.High)^2))
paste('Mean = ', mean((fcdataGM$USDJPY.FPMN.Mean - (fcdataGM$USDJPY.High + fcdataGM$USDJPY.Low)/2)^2))
paste('Low = ', mean((fcdataGM$USDJPY.FPLO.Low - fcdataGM$USDJPY.Low)^2))
paste('Close = ', mean((fcdataGM$USDJPY.FPCL.Close - fcdataGM$USDJPY.Close)^2))
```
#### MCMC vs Bayesian Time Series
Generally, we can write a Bayesian structural model like this:
$$\begin{equation}
Y_{t} = \mu_{t} + x_{t}\beta + S_{t} + \varepsilon_{t}\ ;\ e_{t}\sim N(0,\sigma_{e}^{2})
\end{equation}$$
$$\begin{equation}
\mu_{t} + 1 = \mu_{t} + \nu_{t}\ ,\ \nu_{t}\sim N(0,\sigma_{\nu}^{2})
\end{equation}$$
For bayesian and MCMC, I leave it for future works.
```{r bsts}
## Sorry ARIMA, but I’m Going Bayesian (packages : bsts, arm)
## https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/
## https://stackoverflow.com/questions/11839886/r-predict-glm-equivalent-for-mcmcpackmcmclogit
```
#### MIDAS
For Midas, I also leave it for future works. Kindly refer to [<span style='color:blue'>Mixed Frequency Data Sampling Regression Models - The R Package midasr</span>](https://raw.githubusercontent.com/englianhu/binary.com-interview-question/757b27e1e93132368b0898152078be4961b05a28/reference/Mixed%20Frequency%20Data%20Sampling%20Regression%20Models%20-%20The%20R%20Package%20midasr.pdf) for more information about Midas.
### Data Visualization
Plot graph.
#### ARIMA vs ETS
```{r plotETS-1, echo = FALSE, warning = FALSE}
## Plot the models
## opened price fit data
autoplot(forecast(ets(fitETS.op$Point.Forecast), h = 4), facets = TRUE) + geom_forecast(color = '#ffcccc', show.legend = FALSE) + labs(x = 'Day', y = 'Forex Price', title = 'Forecasts from ETS model [A,N,N]', subtitle = 'Opening Price Forecast', caption = 'Source : USDJPY')
#'@ ggplot(data = pd, aes(x = date, y = observed)) + geom_line(color = 'red') + geom_line(aes(y = fitted), color = "blue") + geom_line(aes(y = forecast)) + geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25) + scale_x_date(name = "Time in Decades") + scale_y_continuous(name = "GDP per capita (current US$)") + theme(axis.text.x = element_text(size = 10), legend.justification=c(0,1), legend.position=c(0,1)) + ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)") + scale_color_manual(values = c("Blue", "Red"), breaks = c("Fitted", "Data", "Forecast")) + ggsave((filename = "gdp_forecast_ggplot.pdf"), width=330, height=180, units=c("mm"), dpi = 300, limitsize = TRUE)
## highest price fit data
autoplot(forecast(ets(fitETS.hi$Point.Forecast), h = 4), facets = TRUE) + geom_forecast(color = '#FFCCCC', show.legend = FALSE) + labs(x = 'Day', y = 'Forex Price', title = 'Forecasts from ETS model [A,N,N]', subtitle = 'Highest Price Forecast', caption = 'Source : USDJPY')
## mean price fit data (mean price of daily highest and lowest price)
autoplot(forecast(ets(fitETS.mn$Point.Forecast), h = 4), facets = TRUE) + geom_forecast(color = '#FFCCCC', show.legend = FALSE) + labs(x = 'Day', y = 'Forex Price', title = 'Forecasts from ETS model [A,N,N]', subtitle = 'Mean Price Forecast', caption = 'Source : USDJPY')
## lowest price fit data
autoplot(forecast(ets(fitETS.lo$Point.Forecast), h = 4), facets = TRUE) + geom_forecast(color = '#FFCCCC', show.legend = FALSE) + labs(x = 'Day', y = 'Forex Price', title = 'Forecasts from ETS model [A,N,N]', subtitle = 'Lowest Price Forecast', caption = 'Source : USDJPY')
## opened price fit data
autoplot(forecast(ets(fitETS.cl$Point.Forecast), h = 4), facets = TRUE) + geom_forecast(color = '#FFCCCC', show.legend = FALSE) + labs(x = 'Day', y = 'Forex Price', title = 'Forecasts from ETS model [A,N,N]', subtitle = 'Closing Price Forecast', caption = 'Source : USDJPY')
```
```{r plotETS-2, echo = FALSE, warning = FALSE}
#'@ source('./function/plotChart2.R', local = TRUE)
suppressAll(rm(fitETS.op, fitETS.hi, fitETS.mn, fitETS.lo, fitETS.cl))
plotChart2(fcdata, initialName = 'FP', chart.type = 'FP', graph.title = 'ETS Model : USDJPY')
```
#### Garch vs EWMA
```{r plotGM-1, echo = FALSE, warning = FALSE}
#'@ source('./function/plotChart2.R', local = TRUE)
suppressAll(rm(fitGM.op, fitGM.hi, fitGM.mn, fitGM.lo, fitGM.cl))
plotChart2(fcdataGM, initialName = 'FP', chart.type = 'FP', graph.title = 'Garch Model : USDJPY')
```
#### MCMC vs Bayesian Time Series
#### MIDAS
### Staking Model
#### ARIMA vs ETS
Staking function. Here I apply Kelly criterion as the betting strategy. I don't pretend to know the order of price flutuation flow from the Hi-Lo price range, therefore I just using Closing price for settlement while the staking price restricted within the variance (Hi-Lo) to made the transaction stand. The settled price can only be closing price unless staking price is opening price which sellable within the Hi-Lo range.
Due to we cannot know the forecasted sell/buy price and also forecasted closing price which is coming first solely from Hi-Lo data, therefore the Profit&Loss will slidely different (sell/buy price = forecasted sell/buy price).
- Forecasted profit = edge based on forecasted sell/buy price - forecasted settled price.
- If the forecasted sell/buy price doesn't exist within the Hi-Lo price, then the transaction is not stand.
- If the forecasted settled price does not exist within the Hi-Lo price, then the settled price will be the real closing price.
Kindly refer to [<span style='color:blue'>**Quintuitive** ARMA Models for Trading</span>](https://www.quintutive.com2012/08/22/arma-models-for-trading) to know how to determine PULL or CALL with ARMA models^[The author compare the ROI between **Buy-and-Hold** with **GARCH** model.].
Here I set an application of leverage while it is very risky (the variance of ROI is very high) as we can know from later comparison.
**Staking Model**
For Buy-Low-Sell-High tactic, I placed two limit order for tomorrow now, which are buy and sell. The transaction will be standed once the price hit in tomorrow. If the buy price doesn't met, there will be no transaction made, while sell price doesn't occur will use closing price for settlement.^[Using Kelly criterion staking model]
For variance betting, I used both focasted highest minus the forecasted lowest price to get the range. After that placed two limit orders as well. If one among the buy or sell price doesn't appear will use closing price as final settlement.^[Place $100 for every single bet.]
#### Garch vs EWMA
The staking models same with what I applied onto ETS modelled dataset.
#### MCMC vs Bayesian Time Series
#### MIDAS
### Return of Investment
#### ARIMA vs ETS
```{r simStaking-AutoArima, eval = FALSE, warning = FALSE, include = FALSE}
##============================ EVAL = FALSE ================================
##
## Model 1 without leverage.
##
## Placed orders - Fund size without log
#'@ mbase <- USDJPY
## settled with highest price.
fundAutoArimaOPHI <- simStakesAutoArima(mbase, .prCat = 'Op', .setPrice = 'Hi', .initialFundSize = 1000)
saveRDS(fundAutoArimaOPHI, file = './data/fundAutoArimaOPHI.rds')
fundAutoArimaHIHI <- simStakesAutoArima(mbase, .prCat = 'Hi', .setPrice = 'Hi', .initialFundSize = 1000)
saveRDS(fundAutoArimaHIHI, file = './data/fundAutoArimaHIHI.rds')
fundAutoArimaMNHI <- simStakesAutoArima(mbase, .prCat = 'Mn', .setPrice = 'Hi', .initialFundSize = 1000)
saveRDS(fundAutoArimaMNHI, file = './data/fundAutoArimaMNHI.rds')
fundAutoArimaLOHI <- simStakesAutoArima(mbase, .prCat = 'Lo', .setPrice = 'Hi', .initialFundSize = 1000)
saveRDS(fundAutoArimaLOHI, file = './data/fundAutoArimaLOHI.rds')
fundAutoArimaCLHI <- simStakesAutoArima(mbase, .prCat = 'Cl', .setPrice = 'Hi', .initialFundSize = 1000)
saveRDS(fundAutoArimaCLHI, file = './data/fundAutoArimaCLHI.rds')
## settled with mean price.
fundAutoArimaOPMN <- simStakesAutoArima(mbase, .prCat = 'Op', .setPrice = 'Mn', .initialFundSize = 1000)
saveRDS(fundAutoArimaOPMN, file = './data/fundAutoArimaOPMN.rds')
fundAutoArimaHIMN <- simStakesAutoArima(mbase, .prCat = 'Hi', .setPrice = 'Mn', .initialFundSize = 1000)
saveRDS(fundAutoArimaHIMN, file = './data/fundAutoArimaHIMN.rds')
fundAutoArimaMNMN <- simStakesAutoArima(mbase, .prCat = 'Mn', .setPrice = 'Mn', .initialFundSize = 1000)
saveRDS(fundAutoArimaMNMN, file = './data/fundAutoArimaMNMN.rds')
fundAutoArimaLOMN <- simStakesAutoArima(mbase, .prCat = 'Lo', .setPrice = 'Mn', .initialFundSize = 1000)
saveRDS(fundAutoArimaLOMN, file = './data/fundAutoArimaLOMN.rds')
fundAutoArimaCLMN <- simStakesAutoArima(mbase, .prCat = 'Cl', .setPrice = 'Mn', .initialFundSize = 1000)
saveRDS(fundAutoArimaCLMN, file = './data/fundAutoArimaCLMN.rds')
## settled with opening price.
fundAutoArimaOPLO <- simStakesAutoArima(mbase, .prCat = 'Op', .setPrice = 'Lo', .initialFundSize = 1000)
saveRDS(fundAutoArimaOPLO, file = './data/fundAutoArimaOPLO.rds')
fundAutoArimaHILO <- simStakesAutoArima(mbase, .prCat = 'Hi', .setPrice = 'Lo', .initialFundSize = 1000)
saveRDS(fundAutoArimaHILO, file = './data/fundAutoArimaHILO.rds')
fundAutoArimaMNLO <- simStakesAutoArima(mbase, .prCat = 'Mn', .setPrice = 'Lo', .initialFundSize = 1000)
saveRDS(fundAutoArimaMNLO, file = './data/fundAutoArimaMNLO.rds')
fundAutoArimaLOLO <- simStakesAutoArima(mbase, .prCat = 'Lo', .setPrice = 'Lo', .initialFundSize = 1000)
saveRDS(fundAutoArimaLOLO, file = './data/fundAutoArimaLOLO.rds')
fundAutoArimaCLLO <- simStakesAutoArima(mbase, .prCat = 'Cl', .setPrice = 'Lo', .initialFundSize = 1000)
saveRDS(fundAutoArimaCLLO, file = './data/fundAutoArimaCLLO.rds')
## settled with closing price.
fundAutoArimaOPCL <- simStakesAutoArima(mbase, .prCat = 'Op', .setPrice = 'Cl', .initialFundSize = 1000)
saveRDS(fundAutoArimaOPCL, file = './data/fundAutoArimaOPCL.rds')
fundAutoArimaHICL <- simStakesAutoArima(mbase, .prCat = 'Hi', .setPrice = 'Cl', .initialFundSize = 1000)
saveRDS(fundAutoArimaHICL, file = './data/fundAutoArimaHICL.rds')
fundAutoArimaMNCL <- simStakesAutoArima(mbase, .prCat = 'Mn', .setPrice = 'Cl', .initialFundSize = 1000)
saveRDS(fundAutoArimaMNCL, file = './data/fundAutoArimaMNCL.rds')
fundAutoArimaLOCL <- simStakesAutoArima(mbase, .prCat = 'Lo', .setPrice = 'Cl', .initialFundSize = 1000)
saveRDS(fundAutoArimaLOCL, file = './data/fundAutoArimaLOCL.rds')
fundAutoArimaCLCL <- simStakesAutoArima(mbase, .prCat = 'Cl', .setPrice = 'Cl', .initialFundSize = 1000)
saveRDS(fundAutoArimaCLCL, file = './data/fundAutoArimaCLCL.rds')
## Placed orders - Fund size without log
fundList <- list(fundOPHI = fundAutoArimaOPHI, fundHIHI = fundAutoArimaHIHI, fundMNHI = fundAutoArimaMNHI, fundLOHI = fundAutoArimaLOHI, fundCLHI = fundAutoArimaCLHI,
fundOPMN = fundAutoArimaOPMN, fundHIMN = fundAutoArimaHIMN, fundMNMN = fundAutoArimaMNMN, fundLOMN = fundAutoArimaLOMN, fundCLMN = fundAutoArimaCLMN,
fundOPLO = fundAutoArimaOPLO, fundHILO = fundAutoArimaHILO, fundMNLO = fundAutoArimaMNLO, fundLOLO = fundAutoArimaLOLO, fundCLLO = fundAutoArimaCLLO,
fundOPCL = fundAutoArimaOPCL, fundHICL = fundAutoArimaHICL, fundMNCL = fundAutoArimaMNCL, fundLOCL = fundAutoArimaLOCL, fundCLCL = fundAutoArimaCLCL)
ldply(fundList, function(x) { x %>% mutate(StartDate = first(Date), LatestDate = last(Date), InitFund = first(BR), LatestFund = last(Bal), Profit = sum(Profit), RR = LatestFund/InitFund) %>% dplyr::select(StartDate, LatestDate, InitFund, LatestFund, Profit, RR) %>% unique }) %>% tbl_df
# A tibble: 20 x 7
# .id StartDate LatestDate InitFund LatestFund Profit RR
# <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
# 1 fundOPHI 2015-01-02 2017-01-20 1000 1325.983 325.98313 1.325983
# 2 fundHIHI 2015-01-02 2017-01-20 1000 1000.000 0.00000 1.000000
# 3 fundMNHI 2015-01-02 2017-01-20 1000 1236.199 236.19900 1.236199
# 4 fundLOHI 2015-01-02 2017-01-20 1000 1716.985 716.98492 1.716985
# 5 fundCLHI 2015-01-02 2017-01-20 1000 1323.688 323.68809 1.323688
# 6 fundOPMN 2015-01-02 2017-01-20 1000 1304.819 304.81916 1.304819
# 7 fundHIMN 2015-01-02 2017-01-20 1000 1363.714 363.71443 1.363714
# 8 fundMNMN 2015-01-02 2017-01-20 1000 1000.000 0.00000 1.000000
# 9 fundLOMN 2015-01-02 2017-01-20 1000 1440.170 440.16965 1.440170
# 10 fundCLMN 2015-01-02 2017-01-20 1000 1292.947 292.94694 1.292947
# 11 fundOPLO 2015-01-02 2017-01-20 1000 1307.610 307.60951 1.307610
# 12 fundHILO 2015-01-02 2017-01-20 1000 1637.251 637.25113 1.637251
# 13 fundMNLO 2015-01-02 2017-01-20 1000 1250.375 250.37547 1.250375
# 14 fundLOLO 2015-01-02 2017-01-20 1000 1000.000 0.00000 1.000000
# 15 fundCLLO 2015-01-02 2017-01-20 1000 1261.157 261.15684 1.261157
# 16 fundOPCL 2015-01-02 2017-01-20 1000 1047.563 47.56281 1.047563
# 17 fundHICL 2015-01-02 2017-01-20 1000 1401.694 401.69378 1.401694
# 18 fundMNCL 2015-01-02 2017-01-20 1000 1158.790 158.79028 1.158790
# 19 fundLOCL 2015-01-02 2017-01-20 1000 1499.818 499.81773 1.499818
# 20 fundCLCL 2015-01-02 2017-01-20 1000 1000.000 0.00000 1.000000
```
```{r ROI-AutoArima, warning = FALSE, echo = FALSE}
## load the pre-run and saved models.
## Profit and Loss of Arima models.
flsAutoArima <- dir('./data', pattern = 'fundAutoArima')
fundList <- llply(flsAutoArima, function(dt) {
cbind(Model = str_replace_all(dt, '.rds', ''),
readRDS(file = paste0('./data/', dt))) %>% tbl_df
})
names(fundList) <- sapply(fundList, function(x) xts::first(x$Model))
## Summary of ROI
aa.tbl <- ldply(fundList, function(x) { x %>% mutate(StartDate = xts::first(Date), LatestDate = last(Date), InitFund = xts::first(BR), LatestFund = last(Bal), Profit = sum(Profit), RR = LatestFund/InitFund) %>% dplyr::select(StartDate, LatestDate, InitFund, LatestFund, Profit, RR) %>% unique }) %>% tbl_df
aa.tbl %>%
kable %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%', height = '400px')
```
The return of investment from best fitted Auto Arima model.
```
7 fundAutoArimaHILO 2015-01-02 2017-01-20 1000 1637.251 637.25113 1.637251
10 fundAutoArimaLOHI 2015-01-02 2017-01-20 1000 1716.985 716.98492 1.716985
```
Profit and Loss of default `ZZZ` ets models.
```{r simStaking-woutLog, warning = FALSE}
##
## Model 1 without leverage.
##
## Placed orders - Fund size without log
#'@ mbase <- USDJPY
## settled with highest price.
#'@ fundOPHI <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Hi', .initialFundSize = 1000)
#'@ saveRDS(fundOPHI, file = './data/fundOPHI.rds')
#'@ fundHIHI <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Hi', .initialFundSize = 1000)
#'@ saveRDS(fundHIHI, file = './data/fundHIHI.rds')
#'@ fundMNHI <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Hi', .initialFundSize = 1000)
#'@ saveRDS(fundMNHI, file = './data/fundMNHI.rds')
#'@ fundLOHI <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Hi', .initialFundSize = 1000)
#'@ saveRDS(fundLOHI, file = './data/fundLOHI.rds')
#'@ fundCLHI <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Hi', .initialFundSize = 1000)
#'@ saveRDS(fundCLHI, file = './data/fundCLHI.rds')
## settled with mean price.
#'@ fundOPMN <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Mn', .initialFundSize = 1000)
#'@ saveRDS(fundOPMN, file = './data/fundOPMN.rds')
#'@ fundHIMN <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Mn', .initialFundSize = 1000)
#'@ saveRDS(fundHIMN, file = './data/fundHIMN.rds')
#'@ fundMNMN <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Mn', .initialFundSize = 1000)
#'@ saveRDS(fundMNMN, file = './data/fundMNMN.rds')
#'@ fundLOMN <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Mn', .initialFundSize = 1000)
#'@ saveRDS(fundLOMN, file = './data/fundLOMN.rds')
#'@ fundCLMN <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Mn', .initialFundSize = 1000)
#'@ saveRDS(fundCLMN, file = './data/fundCLMN.rds')
## settled with opening price.
#'@ fundOPLO <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Lo', .initialFundSize = 1000)
#'@ saveRDS(fundOPLO, file = './data/fundOPLO.rds')
#'@ fundHILO <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Lo', .initialFundSize = 1000)
#'@ saveRDS(fundHILO, file = './data/fundHILO.rds')
#'@ fundMNLO <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Lo', .initialFundSize = 1000)
#'@ saveRDS(fundMNLO, file = './data/fundMNLO.rds')
#'@ fundLOLO <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Lo', .initialFundSize = 1000)
#'@ saveRDS(fundLOLO, file = './data/fundLOLO.rds')
#'@ fundCLLO <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Lo', .initialFundSize = 1000)
#'@ saveRDS(fundCLLO, file = './data/fundCLLO.rds')
## settled with closing price.
#'@ fundOPCL <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Cl', .initialFundSize = 1000)
#'@ saveRDS(fundOPCL, file = './data/fundOPCL.rds')
#'@ fundHICL <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Cl', .initialFundSize = 1000)
#'@ saveRDS(fundHICL, file = './data/fundHICL.rds')
#'@ fundMNCL <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Cl', .initialFundSize = 1000)
#'@ saveRDS(fundMNCL, file = './data/fundMNCL.rds')
#'@ fundLOCL <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Cl', .initialFundSize = 1000)
#'@ saveRDS(fundLOCL, file = './data/fundLOCL.rds')
#'@ fundCLCL <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Cl', .initialFundSize = 1000)
#'@ saveRDS(fundCLCL, file = './data/fundCLCL.rds')
## Placed orders - Fund size without log
#'@ fundList <- list(fundOPHI = fundOPHI, fundHIHI = fundHIHI, fundMNHI = fundMNHI, fundLOHI = fundLOHI, fundCLHI = fundCLHI,
#'@ fundOPMN = fundOPMN, fundHIMN = fundHIMN, fundMNMN = fundMNMN, fundLOMN = fundLOMN, fundCLMN = fundCLMN,
#'@ fundOPLO = fundOPLO, fundHILO = fundHILO, fundMNLO = fundMNLO, fundLOLO = fundLOLO, fundCLLO = fundCLLO,
#'@ fundOPCL = fundOPCL, fundHICL = fundHICL, fundMNCL = fundMNCL, fundLOCL = fundLOCL, fundCLCL = fundCLCL)
#'@ ldply(fundList, function(x) { x %>% mutate(StartDate = first(Date), LatestDate = last(Date), InitFund = first(BR), LatestFund = last(Bal), Profit = sum(Profit), RR = LatestFund/InitFund) %>% dplyr::select(StartDate, LatestDate, InitFund, LatestFund, Profit, RR) %>% unique }) %>% tbl_df
## A tibble: 20 × 5
# .id StartDate LatestDate InitFund LatestFund Profit RR
# <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
#1 fundOPHI 2015-01-02 2017-01-20 1000 326.83685 1326.837 1.326837
#2 fundHIHI 2015-01-02 2017-01-20 1000 0.00000 1000.000 1.000000
#3 fundMNHI 2015-01-02 2017-01-20 1000 152.30210 1152.302 1.152302
#4 fundLOHI 2015-01-02 2017-01-20 1000 816.63808 1816.638 1.816638
#5 fundCLHI 2015-01-02 2017-01-20 1000 323.18564 1323.186 1.323186
#6 fundOPMN 2015-01-02 2017-01-20 1000 246.68001 1246.680 1.246680
#7 fundHIMN 2015-01-02 2017-01-20 1000 384.90915 1384.909 1.384909
#8 fundMNMN 2015-01-02 2017-01-20 1000 0.00000 1000.000 1.000000
#9 fundLOMN 2015-01-02 2017-01-20 1000 529.34170 1529.342 1.529342
#10 fundCLMN 2015-01-02 2017-01-20 1000 221.03926 1221.039 1.221039
#11 fundOPLO 2015-01-02 2017-01-20 1000 268.31155 1268.312 1.268312
#12 fundHILO 2015-01-02 2017-01-20 1000 649.35074 1649.351 1.649351
#13 fundMNLO 2015-01-02 2017-01-20 1000 298.28509 1298.285 1.298285
#14 fundLOLO 2015-01-02 2017-01-20 1000 0.00000 1000.000 1.000000
#15 fundCLLO 2015-01-02 2017-01-20 1000 208.85690 1208.857 1.208857
#16 fundOPCL 2015-01-02 2017-01-20 1000 30.55969 1030.560 1.030560
#17 fundHICL 2015-01-02 2017-01-20 1000 400.59057 1400.591 1.400591
#18 fundMNCL 2015-01-02 2017-01-20 1000 117.96808 1117.968 1.117968
#19 fundLOCL 2015-01-02 2017-01-20 1000 530.68975 1530.690 1.530690
#20 fundCLCL 2015-01-02 2017-01-20 1000 0.00000 1000.000 1.000000
## load fund files which is from chunk `r simStaking-woutLog`.
fundOPHI <- readRDS('./data/fundOPHI.rds')
fundHIHI <- readRDS('./data/fundHIHI.rds')
fundMNHI <- readRDS('./data/fundMNHI.rds')
fundLOHI <- readRDS('./data/fundLOHI.rds')
fundCLHI <- readRDS('./data/fundCLHI.rds')
fundOPMN <- readRDS('./data/fundOPMN.rds')
fundHIMN <- readRDS('./data/fundHIMN.rds')
fundMNMN <- readRDS('./data/fundMNMN.rds')
fundLOMN <- readRDS('./data/fundLOMN.rds')
fundCLMN <- readRDS('./data/fundCLMN.rds')
fundOPLO <- readRDS('./data/fundOPLO.rds')
fundHILO <- readRDS('./data/fundHILO.rds')
fundMNLO <- readRDS('./data/fundMNLO.rds')
fundLOLO <- readRDS('./data/fundLOLO.rds')
fundCLLO <- readRDS('./data/fundCLLO.rds')
fundOPCL <- readRDS('./data/fundOPCL.rds')
fundHICL <- readRDS('./data/fundHICL.rds')
fundMNCL <- readRDS('./data/fundMNCL.rds')
fundLOCL <- readRDS('./data/fundLOCL.rds')
fundCLCL <- readRDS('./data/fundCLCL.rds')
## Placed orders - Fund size without log
fundList <- list(fundOPHI = fundOPHI, fundHIHI = fundHIHI, fundMNHI = fundMNHI, fundLOHI = fundLOHI, fundCLHI = fundCLHI,
fundOPMN = fundOPMN, fundHIMN = fundHIMN, fundMNMN = fundMNMN, fundLOMN = fundLOMN, fundCLMN = fundCLMN,
fundOPLO = fundOPLO, fundHILO = fundHILO, fundMNLO = fundMNLO, fundLOLO = fundLOLO, fundCLLO = fundCLLO,
fundOPCL = fundOPCL, fundHICL = fundHICL, fundMNCL = fundMNCL, fundLOCL = fundLOCL, fundCLCL = fundCLCL)
```
```{r simStaking-withLog, eval = FALSE, warning = FALSE, include = FALSE}
##============================ EVAL = FALSE ================================
##
## Leveraged model 2
##
## Placed orders - Fund size with log
fundOPHI <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Hi', .initialFundSize = log(1000))
fundHIHI <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Hi', .initialFundSize = log(1000))
fundMNHI <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Hi', .initialFundSize = log(1000))
fundLOHI <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Hi', .initialFundSize = log(1000))
fundCLHI <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Hi', .initialFundSize = log(1000))
fundOPMN <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Mn', .initialFundSize = log(1000))
fundHIMN <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Mn', .initialFundSize = log(1000))
fundMNMN <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Mn', .initialFundSize = log(1000))
fundLOMN <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Mn', .initialFundSize = log(1000))
fundCLMN <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Mn', .initialFundSize = log(1000))
fundOPLO <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Lo', .initialFundSize = log(1000))
fundHILO <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Lo', .initialFundSize = log(1000))
fundMNLO <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Lo', .initialFundSize = log(1000))
fundLOLO <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Lo', .initialFundSize = log(1000))
fundCLLO <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Lo', .initialFundSize = log(1000))
fundOPCL <- simStakesETS(mbase, .prCat = 'Op', .setPrice = 'Cl', .initialFundSize = log(1000))
fundHICL <- simStakesETS(mbase, .prCat = 'Hi', .setPrice = 'Cl', .initialFundSize = log(1000))
fundMNCL <- simStakesETS(mbase, .prCat = 'Mn', .setPrice = 'Cl', .initialFundSize = log(1000))
fundLOCL <- simStakesETS(mbase, .prCat = 'Lo', .setPrice = 'Cl', .initialFundSize = log(1000))
fundCLCL <- simStakesETS(mbase, .prCat = 'Cl', .setPrice = 'Cl', .initialFundSize = log(1000))
## Placed orders - Fund size with log
#'@ fundList <- list(fundOPHI = fundOPHI, fundHIHI = fundHIHI, fundMNHI = fundMNHI, fundLOHI = fundLOHI, fundCLHI = fundCLHI,
#'@ fundOPMN = fundOPMN, fundHIMN = fundHIMN, fundMNMN = fundMNMN, fundLOMN = fundLOMN, fundCLMN = fundCLMN,
#'@ fundOPLO = fundOPLO, fundHILO = fundHILO, fundMNLO = fundMNLO, fundLOLO = fundLOLO, fundCLLO = fundCLLO,
#'@ fundOPCL = fundOPCL, fundHICL = fundHICL, fundMNCL = fundMNCL, fundLOCL = fundLOCL, fundCLCL = fundCLCL)
#'@
#'@ ldply(fundList, function(x) { x %>% mutate(StartDate = first(Date), LatestDate = last(Date), InitFund = first(BR), LatestFund = last(Bal), Profit = sum(Profit), RR = LatestFund/InitFund) %>% dplyr::select(StartDate, LatestDate, InitFund, LatestFund, Profit, RR) %>% unique }) %>% tbl_df
## A tibble: 20 × 7
# .id StartDate LatestDate InitFund LatestFund Profit RR