\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={A Dynamic Level Technical Indicator Model for Oil Price Forecasting},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2021-10-15 (revised: 15 October 2021)}
\def\TheID{10.34257/GJCSTGVOL21IS1PG5\makeatother }
\def\TheDate{2021-10-15}
\title{A Dynamic Level Technical Indicator Model for Oil Price Forecasting}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{David Ademola  Oyemade}

             \author[2]{David  Enebeli}

\renewcommand\Authands{ and }

\date{\small \em Received: 9 September 2021 Accepted: 3 October 2021 Published: 15 October 2021}

\maketitle


\begin{abstract}
        


Investment in commodities and stock requires a nearly accurate prediction of price to make profit and to prevent losses. Technical indicators are usually employed on the software platforms for commodities and stock for such price prediction and forecasting. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital. We propose a dynamic level technical indicator model for the forecasting of commodities? prices. The proposed model creates dynamic price supports and resistances levels in different time frames of the price chart using a novel algorithm and employs them for price forecasting. In this study, the proposed model was applied to predict the prices of the United Kingdom (UK) Oil. It was compared with the combination of two popular and widely accepted technical indicators, the Moving Average Convergence and Divergence (MACD) and Stochastic Oscillator. The results showed that the proposed dynamic level technical indicator model outperformed MACD and Stochastic Oscillator in terms of profit.

\end{abstract}


\keywords{technical indicator, commodities, price forecasting, UK oil, MACD, stochastic oscillator}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\begin{textblock*}{10cm}(1.05cm,3cm)
{{\textit{CrossRef DOI of original article:}} \underline{10.34257/GJCSTGVOL21IS1PG5}}
\end{textblock*}\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he price of oil affects the global economy and geographical events, making oil price uncertain and unstable, because oil is a major source of energy \hyperref[b26]{[28]}. In the application of computer science and time series mathematical theories to the oil and gas industries , the prediction of oil prices is still a challenge because oil falls under the categories of commodities which are easily affected by change in government policies and unpredictable natural or unnatural events. The oil market is complicated because, like the stock market, its features are neither linear nor stationary \hyperref[b2]{[3]} \hyperref[b3]{[4]}. Oil, an already volatile market, reached a flash point in 2020 accentuated by the coronavirus (COVID 19) pandemic which resulted in a sharp drop of price that affected the oil exporting countries. Fig. \hyperref[fig_0]{1} is a pictorial view of an instance in the sharp drop in global oil prices as a result of the COVID 19 pandemic. Fig. \hyperref[fig_0]{1} shows that oil collapsed to the lowest price in 18 years. Such a price drop negatively impacted on the economy of the nations that depend on oil as a major part of their gross domestic product (GDP) and nations that depend on oil for economy sustenance and survival. Such inadvertent and sharp drops in oil prices also have adverse effects on the performance of software systems designed for the forecasting of oil prices. Therefore, developing a proactive model capable of automation for the prediction of oil prices is of high significance and is worth the efforts.  \hyperref[b11]{[12]}. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital, in spite of the claims of their designers and developers. In this paper, a dynamic level technical indicator model for the forecasting of commodities' prices is proposed. The remaining part of this paper is organized as follows the next section is the background of study. This is, followed by the review of related works, the methodology and the implementation. Finally, the results and discussion are presented and followed by the conclusion and future works. 
\section[{II.}]{II.} 
\section[{Background of Study}]{Background of Study}\par
Technical Indicators are primarily intended for displaying some graphical signals on a security charts for the purpose of guiding traders and users on appropriate trading decisions. These graphical signals are displayed through some calculated dependencies achievable through programming codes in a programming language suitable for the terminal employed for the security or commodity.    
\section[{Related Works}]{Related Works}\par
Although many technical indicators exist, only few are documented publicly in the research community. Previous works relating to technical indicators are discussed in this section. Bartolucci et al \hyperref[b1]{[2]} proposed a generalized version of moving average convergence and divergence by adopting the martingale and applied the indicator for the monitoring of crude oil prices. Nazário et al \hyperref[b2]{[3]} gave the classification of technical analysis on stock market in a literature review. Using the combination of technical indicators and news articles as inputs, Vargas et al \hyperref[b4]{[5]} applied deep learning for the prediction of daily directional stock price movement. They compared the performance of a hybrid model composed of a Convolutional Neural Network (CNN) for the financial news with Long Short-Term Memory (LSTM) for technical indicators. Chan and Teong \hyperref[b5]{[6]} applied neural networks to enhance technical analysis positing that false breakout had been previously experienced with the use of technical analysis. Oriani and Coelho \hyperref[b6]{[7]} evaluated the impact of a number of technical indicators on the stock market using multilayer perceptrons (MLP) but presented no model. Gholamiangonabadi et al \hyperref[b12]{[14]} combined Principal Component Analysis, Stepwise Regression Analysis and Artificial Neural Networks for the performance evaluation of the technical indicators of an electrical industry stock exchange. Thawornwong et al \hyperref[b15]{[17]} also focused on the application of neural networks for decision making in the stock market. Stankovi? \hyperref[b13]{[15]} investigated the effectiveness of least square support vector machine and some traditional technical indicators such as MACD and Relative Strength Index (RSI) for financial series stock trend prediction and investment strategy optimization. Chong and Ng \hyperref[b16]{[18]} simply tested and compared MACD with RSI using the Financial Times -Institute of Actuaries 30 (FT30) index of Mills. Rosillo \hyperref[b17]{[19]} also simply tested the RSI, MACD, momentum and stochastic rules for technical analysis using the Spanish stock market. Almeida et al \hyperref[b14]{[16]} analyzed some technical indicators using an algorithm based on differential evolution to generate Pareto fronts for each technical indicator to achieve multi-objective optimization. Chi and Peng \hyperref[b18]{[20]} studied the relationship among various technical indicators and using self-organizing map and fuzzy neural network. On the prediction of oil prices, the diverse approaches proposed by other members of the research community include: the use of sentiment on news article \hyperref[b19]{[21]}, autoregressive integrated moving average (ARIMA) model \hyperref[b20]{[22]}, a hybrid of wavelet or Commodity Futures Prices and artificial neural networks \hyperref[b21]{[23]}  \hyperref[b23]{[25]}, deep learning based models \hyperref[b22]{[24]}, statistical learning method \hyperref[b24]{[26]}, time-varying approach \hyperref[b25]{[27]}, gray wave forecasting method and optimization via bagging ensemble models \hyperref[b27]{[29]}.\par
Most of the existing technical indicator models adopt the statistical approach while recent ones adopt deep learning methodology and they are limited in the diversity of application. Our proposed model creates programmable dynamic levels for price supports and resistances. The proposed model can be used both for trending and hedging markets. In addition, while most of the existing technical indicators were used for stock decision making, our proposed model focuses on the prediction of oil prices. These are some of the main contributions and novelty of this paper.\par
IV. 
\section[{Methodology}]{Methodology}\par
The proposed model leverages on the overriding impact of support and resistance levels of the terminal charts and their effects on the system's profit. An algorithm was developed to capture, establish and indicate the support and resistance for different timeframes of the terminal charts and to dynamically move these levels as the price of the commodity changes. The relative movements of one minute (M1), five minutes (M5), fifteen minutes (M15), one hour (H1), four hour (H4) and daily (D1) timeframes during price trending, reversal and breakout were observed and studied over a period of time. The result of the research observation was then recommended for order placements and other trading decisions. 
\section[{a) The Dynamic Level Technical Indicator Model}]{a) The Dynamic Level Technical Indicator Model}\par
The    The graphical component displays the various indicators with different object properties. This component sets the line color, width and style. While the changing values of the dynamic levels can be captured with program codes for auto-trading, manual trading depending on the positioning of the indicator lines for trading decision. The algorithm of the graphical component is shown below. 
\section[{i. Algorithm for the graphical component}]{i. Algorithm for the graphical component}\par
The algorithm of the graphical component of the dynamic level component given below for M15 resistance for(int b=0; b<2; b+=2) \{ ObjectDelete("LineNameLabel"+b); ObjectCreate("LineNameLabel"+b,OBJ\textunderscore HLINE,0,0,MaxBuyPriceHigh15M); //MaxBuyPriceClose1H ObjectSet("LineNameLabel"+b,OBJPROP\textunderscore COLOR,Aqua); ObjectSet("LineNameLabel"+b,OBJPROP\textunderscore WIDTH,2); ObjectSet("LineNameLabel"+b,OBJPROP\textunderscore RAY,False); \}\par
The algorithm of the graphical component of the traditional technical indicator is given below for M15 resistance. 
\section[{//---plot MaxBuyPriceHigh15M indicator\textunderscore label "MaxBuyPriceHigh15M" indicator\textunderscore type DRAW\textunderscore LINE indicator\textunderscore color clrAqua indicator\textunderscore style STYLE\textunderscore SOLID indicator\textunderscore width 2 e) Research Observations and Model applications}]{//---plot MaxBuyPriceHigh15M indicator\textunderscore label "MaxBuyPriceHigh15M" indicator\textunderscore type DRAW\textunderscore LINE indicator\textunderscore color clrAqua indicator\textunderscore style STYLE\textunderscore SOLID indicator\textunderscore width 2 e) Research Observations and Model applications}\par
It was observed in the course of this study that trending in the bullish direction occurs when the M5 line moves above the M15 line or the M15 line moves above the H1 line at the resistance level. Similarly, trending in the bearish direction occurs when the M5 line moves below the M15 line or the M15 line moves below the H1 line at the support level. Price breakout in the bullish direction occurs when the M15 line moves above the H4 line at the resistance level. In the same way, price breakout in the bearish direction occurs when the M15 line moves below the H4 line at the resistance level. These observations, which has not been stated in previous studies by the research community, produced positive results when implemented. They therefore, form part of the contribution to knowledge of this paper. 
\section[{f) Materials}]{f) Materials}\par
The experiments carried out in this study were performed with Meta Quote programming language installed on Intel(R) Core(TM) i3-2330M CPU @ 2.20GHz, 4 GB RAM, 64-bit Windows 8 operating system. The program was run on MetaTrader 4 terminal installed on a US based virtual private server.\par
V. 
\section[{Implementation}]{Implementation}\par
The proposed model was implemented for M5, M15, H1, H4 and D1 timeframes. The various properties of the indicator lines in the different timeframes implemented are shown in Table \hyperref[tab_1]{1}. The values of the dynamic level active array size, the line variable names at supports and resistances as well as the line color, line type and line width are shown in Table \hyperref[tab_1]{1}. Fig. \hyperref[fig_8]{5} illustrates how the proposed model captured the exact support of the H4 with orange color, displayed using the H4 timeframe chart. The proposed technical indicator is implemented with the name "Dynamic Level 1" as shown in Fig. \hyperref[fig_8]{5}. The file name used for saving the indicator's program codes is displayed as "Dynamic Level 1.mq4" under indicators files' Listing in Fig. \hyperref[fig_3]{3}.    The price of oil affects the global economy because oil is a major source of energy. However, the features of the oil market is neither linear nor stationary, making the prediction of oil price a challenge. In this paper, a dynamic level indicator model has been proposed for the forecasting of oil prices. The proposed model was deployed for the UK Oil on live trading for a period of three months and compared with MACD/ Stochastic Oscillator technical indicators which ran at the same period. The result showed that the proposed model is more profitable than MACD/Stochastic Oscillator indicators and therefore can be adopted for oil price prediction. In addition, the research observation of this paper introduces a novel method of price trend prediction based on the relative movements of the dynamic levels in the terminal charts.\par
Future works shall focus on the investigation of further possible profit optimization of the dynamic level technical indicator model.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}A}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Fig. 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-9.png}
\caption{\label{fig_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Fig. 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Fig. 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{78}\includegraphics[]{image-12.png}
\caption{\label{fig_10}Fig. 7 :Fig. 8 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.03575525812619503\textwidth}P{0.06988527724665393\textwidth}P{0.08451242829827917\textwidth}P{0.2519120458891013\textwidth}P{0.029254302103250477\textwidth}P{0.1869024856596558\textwidth}P{0.19177820267686424\textwidth}}
Timeframe\tabcellsep Dynamic Level Active Array Size\tabcellsep Object Property Color\tabcellsep Object Type\tabcellsep Object Width\tabcellsep Line Variable Name at Support\tabcellsep Line Variable Name at Resistance\\
M1\tabcellsep 4\tabcellsep \tabcellsep OBJ\textunderscore HLINE\tabcellsep 2\tabcellsep \tabcellsep \\
M5\tabcellsep 5\tabcellsep LawnGreen\tabcellsep OBJ\textunderscore HLINE\tabcellsep 2\tabcellsep MinSellPriceLow5M\tabcellsep MaxBuyPriceHigh5M\\
M15\tabcellsep 60\tabcellsep Aqua\tabcellsep OBJ\textunderscore HLINE\tabcellsep 2\tabcellsep MinSellPriceLow15M\tabcellsep MaxBuyPriceHigh15M\\
H1\tabcellsep 72\tabcellsep Yellow\tabcellsep OBJ\textunderscore HLINE\tabcellsep 2\tabcellsep MinSellPriceLow1H\tabcellsep MaxBuyPriceHigh1H\\
H4\tabcellsep 126\tabcellsep Orange\tabcellsep OBJ\textunderscore HLINE\tabcellsep 2\tabcellsep MinSellPriceLow4H\tabcellsep MaxBuyPriceHigh4H\\
D1\tabcellsep 300\tabcellsep Violet\tabcellsep OBJ\textunderscore HLINE\tabcellsep 2\tabcellsep MinSellPriceLowD1\tabcellsep MaxBuyPriceHighD1\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 			\footnote{© 2021 Global Journals} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Thawornwong]{b15}\label{b15} 	 		\textit{17] also focused on the application of neural networks for decision making in the stock market},  		 			Thawornwong 		.  		 	 
\bibitem[Bartolucci et al. ()]{b1}\label{b1} 	 		\textit{A Generalized Moving Average Convergence/ Divergence for Testing Semi-strong Market Efficiency},  		 			F Bartolucci 		,  		 			A Cardinali 		,  		 			Fulvia Pennoni 		.  		2018. Springer International Publishing AG. p. .  	 	 (part of Springer Nature) 
\bibitem[Bisoi and Dash (2014)]{b3}\label{b3} 	 		‘A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter’.  		 			R Bisoi 		,  		 			P Dash 		.  	 	 		\textit{Applied Computing}  		June(1. 2014. 19 p. .  	 
\bibitem[Nazário et al. ()]{b2}\label{b2} 	 		‘A literature review of technical analysis on stock markets’.  		 			R T F Nazário 		,  		 			J L Silva 		,  		 			V A Sobreiro 		,  		 			H Kimura 		.  	 	 		\textit{The Quaterly Review of Economics and Finance}  		2017. 2017. Elsevier. 66 p. .  	 
\bibitem[Brockwell and Davis ()]{b11}\label{b11} 	 		 			P J Brockwell 		,  		 			R A Davis 		.  		 \url{https://book.mql4.com/samples/icustom}  		\textit{Time Series: Theory and Methods},  				 (New York)  		1991. Springer-Verlag. 2 p. .  	 	 (Springer Series in Statistics) 
\bibitem[Jessin and Kiruthiga ()]{b20}\label{b20} 	 		‘Crude Oil Price Forecasting using ARIMA model’.  		 			P A S Jessin 		,  		 			Kiruthiga 		.  	 	 		\textit{International Research Journal of Engineering and Technology}  		2020. p. .  	 
\bibitem[Shabri and Samsudin ()]{b21}\label{b21} 	 		\textit{Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model},  		 			A Shabri 		,  		 			R Samsudin 		.  		201402. 2014. 2014 p. 10.  	 	 (Mathematical Problems in Engineering) 
\bibitem[Vargas et al. ()]{b4}\label{b4} 	 		\textit{Deep Learning for Stock Market Prediction Using Technical Indicators and Financial News},  		 			M R Vargas 		,  		 			C E M Anjo 		,  		 			G L G Bichara 		,  		 			A G Evsukoff 		.  		2018. p. 8.  		 			Loughborough University, IEEE Xplore 		 	 
\bibitem[Chan and Teon ()]{b5}\label{b5} 	 		‘Enhancing Technical Analysis in the Forex Market Using Neural Networks’.  		 			K C C Chan 		,  		 			F K Teon 		.  	 	 		\textit{Proceedings of ICNN'95 -International Conference on Neural Networks , IEEE Xplore},  				 (ICNN'95 -International Conference on Neural Networks , IEEE Xplore)  		2002.  	 
\bibitem[Gerlein and Mcginnity ()]{b9}\label{b9} 	 		‘Evaluating machine learning classification for financial trading: An empirical approach’.  		 			E A Gerlein 		,  		 			Martin Mcginnity 		.  		 \xref{http://dx.doi.org/10.1016/j.eswa.2016.01.018}{10.1016/j.eswa.2016.01.018}.  		 \url{http://dx.doi.org/10.1016/j.eswa.2016.01.018}  	 	 		\textit{Expert Systems with Applications}  		2016. Elsevier. 54 p. .  	 
\bibitem[Oriani_ et al. ()]{b6}\label{b6} 	 		\textit{Evaluating the Impact of Technical Indicators on Stock Forecasting},  		 			F B Oriani\textunderscore  		,  		 			G P Coelho 		,  		 			; Ieee Xplore 		.  		2016.  	 
\bibitem[Sezer and Gudelek ()]{b10}\label{b10} 	 		‘Financial time series forecasting with deep learning: A systematic literature review’.  		 			O B Sezer 		,  		 			M U Gudelek 		.  		 \xref{http://dx.doi.org/10.1016/j.asoc.2020.106181}{10.1016/j.asoc.2020.106181}.  		 \url{https://doi.org/10.1016/j.asoc.2020.106181}  	 	 		\textit{Applied Soft Computing Journal}  		2005-2019. 2020. 2020. Elsevier. 90 p. .  	 
\bibitem[Kulkarni and Haidar ()]{b23}\label{b23} 	 		‘Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices’.  		 			S Kulkarni 		,  		 			I Haidar 		.  	 	 		\textit{International Journal of Computer Science and Information Security}  		2009. 2  (1) .  	 
\bibitem[Li et al. ()]{b19}\label{b19} 	 		‘Forecasting Oil Price Trends with Sentiment of Online News Articles’.  		 			J Li 		,  		 			Z Xu 		,  		 			L Yu 		,  		 			L Tang 		.  	 	 		\textit{Procedia Computer Science},  				 (edia Computer Science)  		2016. 91 p. .  	 
\bibitem[Chen et al. ()]{b22}\label{b22} 	 		‘Forecasting Oil Prices: a Deep Learning Based Model’.  		 			Y Chen 		,  		 			K He 		,  		 			K F T Geoffrey 		.  	 	 		\textit{Science Direct},  				2017. 2017. 122 p. .  	 
\bibitem[Hoff1 and Olsvik ()]{b26}\label{b26} 	 		\textit{Forecasting the Price of Crude Oil: The Predictive Power of Futures Prices and Realized Volatility},  		 			M Hoff1 		,  		 			Olsvik 		.  		2015. Trondheim. p. .  		 			Norwegian University of Science and Technology, Department of Industrial Economics and Technology Management 		 	 
\bibitem[Slim ()]{b24}\label{b24} 	 		‘Improved Crude Oil Price Forecasting With Statistical Learning Methods’.  		 			C Slim 		.  	 	 		\textit{Journal of Modern Accounting and Auditing}  		2015. 11  (1)  p. .  	 
\bibitem[Li et al. ()]{b7}\label{b7} 	 		\textit{Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding},  		 			Z Li 		,  		 			L Yang 		,  		 			J Zhao 		,  		 			T Bian 		,  		 			T Qin 		,  		 			Liu 		.  		August 4-8, 2019. 2019. Anchorage, AK, USA, ACM. 19 p. .  	 
\bibitem[Gholamiangonabadi et al. ()]{b12}\label{b12} 	 		‘Investigating the Performance of Technical Indicators in Electrical Industry in Tehran's Stock Exchange Using Hybrid Methods of SRA, PCA and Neural Networks’.  		 			D Gholamiangonabadi 		,  		 			S D M Taheri 		,  		 			A Mohammadi 		,  		 			M B Menhaj 		.  	 	 		\textit{The 5th Conference on Thermal Power Plants (lPGC2014)},  				 (Tehran, Iran, IEEE)  		June 10-11,2014. 2014. p. .  		 			Shahid Beheshti University 		 	 
\bibitem[Stankovi ? et al. ()]{b13}\label{b13} 	 		‘Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging Markets’.  		 			J Stankovi ? 		,  		 			I Markovi ? 		,  		 			M Stojanovi 		.  	 	 		\textit{Procedia Economics and Finance},  				 (edia Economics and Finance)  		2015. 19 p. .  	 
\bibitem[Almeida et al. ()]{b14}\label{b14} 	 		‘Multi-objective Optimization Approach to Stock Market Technical Indicators’.  		 			R Almeida 		,  		 			G Reynoso-Meza 		,  		 			M T A Steiner 		.  	 	 		\textit{IEEE Congress on Evolutionary Computation (CEC)}  		2016. 2016. IEEE Xplore. p. .  	 
\bibitem[Zhao et al.]{b25}\label{b25} 	 		\textit{Oil Price Forecasting Using a Time-Varying Approach},  		 			L Zhao 		,  		 			S Wang 		,  		 			Z Zhang 		.  		 	 	 (Energies 2020, 13, 1403, pp. 8, 2020) 
\bibitem[Gabralla1 and Abraham ()]{b27}\label{b27} 	 		‘Prediction of Oil Prices Using Bagging and Random Subspace’.  		 			L A Gabralla1 		,  		 			A Abraham 		.  	 	 		\textit{Advances in Intelligent Systems and Computing 303},  				2014. 2014. Springer International Publishing Switzerland. p. .  	 	 (Proceedings of the Fifth Intern. Conf. on Innov) 
\bibitem[Chong and Ng ()]{b16}\label{b16} 	 		‘Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30’.  		 			T T Chong 		,  		 			W Ng 		.  		 \xref{http://dx.doi.org/10.1080/13504850600993598}{10.1080/13504850600993598}.  	 	 		\textit{Applied Economics Letters}  		2008. 15  (14)  p. .  	 
\bibitem[Rosillo et al. ()]{b17}\label{b17} 	 		‘Technical Analysis and the Spanish Stock Exchange: Testing the RSI, MACD, Momentum and Stochastic Rules Using Spanish Market Companies’.  		 			R Rosillo 		,  		 			D De La Fuente 		,  		 			J A L Brugos 		.  		 \xref{http://dx.doi.org/10.1080/00036846.2011.631894}{10.1080/00036846.2011.631894}.  	 	 		\textit{Applied Economics}  		2013. 45 p. .  	 
\bibitem[Chi and Peng ; Ieee Xplore ()]{b18}\label{b18} 	 		\textit{The Study on the Relationship among Technical Indicators and the Development of Stock Index Prediction System},  		 			S Chi 		,  		 			W Peng ; Ieee Xplore 		.  		2003. p. .  	 
\bibitem[the-impact-of-coronavirus-covid-19-and-the-globaloil-price-shock-on-the-fiscal-position-of-oil-exporting -developing-countries-8bafbd95/. Accessed: 18 th (2021)]{b8}\label{b8} 	 		\textit{the-impact-of-coronavirus-covid-19-and-the-globaloil-price-shock-on-the-fiscal-position-of-oil-exporting -developing-countries-8bafbd95/. Accessed: 18 th},  		 \url{https://www.oecd.org/coronavirus/policy-responses/}  		April 2021.  	 
\bibitem[Box and Jenkins ()]{b0}\label{b0} 	 		\textit{Time series analysis: Forecasting and control},  		 			G E Box 		,  		 			G M Jenkins 		.  		1994. p. 598.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
