a mobile application for stock price prediction

A MOBILE APPLICATION FOR

STOCK PRICE PREDICTION

CHOY YI TOU

A project report submitted in partial fulfilment of the

requirements for the award of Bachelor of Science

(Honours) Software Engineering

Lee Kong Chian Faculty of Engineering and Science

Universiti Tunku Abdul Rahman

April 2021

DECLARATION

I hereby declare that this project report is based on my original work except for

citations and quotations which have been duly acknowledged. I also declare that it

has not been previously and concurrently submitted for any other degree or award at

UTAR or other institutions.

Signature

:

Name : CHOY YI TOU

ID No. : 1701301

Date : 11/4/2021

APPROVAL FOR SUBMISSION

I certify that this project report entitled “A MOBILE APPLICATION FOR

STOCK PRICE PREDICTION” was prepared by CHOY YI TOU has met the

required standard for submission in partial fulfilment of the requirements for the

award of Bachelor of Science (Honours) Software Engineering at Universiti Tunku

Abdul Rahman.

Approved by,

Signature :

Supervisor : Khor Kok Chin

Date : 11/4/2021

Signature :

Co-Supervisor :

Date :

The copyright of this report belongs to the author under the terms of the

copyright Act 1987 as qualified by Intellectual Property Policy of Universiti Tunku

Abdul Rahman. Due acknowledgement shall always be made of the use of any

material contained in, or derived from, this report.

© 2021, Choy Yi Tou. All right reserved.

ACKNOWLEDGEMENTS

I would like to thank everyone who had contributed to the successful completion of

this project. I would like to express my gratitude to my research supervisor, Dr. Khor

Kok Chin for his invaluable advice, guidance and his enormous patience throughout

the development of the research.

In addition, I would also like to express my gratitude to my loving parents

and friends who had helped and given me encouragement during the whole process

of my project development.

ABSTRACT

Investing in stocks’ markets is risky and it needs a lot of research and time to make

the right decision for earning money in stock markets. Novice investors are limited to

specific investment knowledge and lack investment tools to gain wealth in the stocks’

markets. A mobile application for stock price prediction using time series algorithms

is developed to tackle the problem mentioned. There are a few prediction algorithms

being evaluated which were Long Short-Term Memory (LSTM), Holt Winter, Auto-

Regression Integrated Moving Average (ARIMA), Seasonal Auto-Regression

Integrated Moving Average (SARIMA) and Prophet. These prediction algorithms

were evaluated using 6 sectors of Bursa Malaysia stocks which are in total 216

stocks. The evaluation methods were Root Mean Square Error and Mean Absolute

Error. The results show ARIMA has the least error among all five prediction

algorithms. Therefore, ARIMA is the best prediction model to implement in the

mobile application for stock price prediction. The mobile application for stock price

prediction was developed with prototyping methodology. There were three iteration

in this project to develop and enhance the functionality of the mobile application.

Although the mobile application for stock price prediction had been developed, it

contains some limitations like the model is inaccurate for some time and users cannot

set the prediction period. All these limitations can be improved in the future. In

conclusion, this project’s objectives were achieved by developing the mobile

application for stock price prediction using the best time series algorithm evaluated,

which is ARIMA.

vii

TABLE OF CONTENTS

DECLARATION i

APPROVAL FOR SUBMISSION ii

ACKNOWLEDGEMENTS iv

ABSTRACT vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURES xiii

LIST OF SYMBOLS / ABBREVIATIONS xvi

LIST OF APPENDICES xvii

CHAPTER

1 INTRODUCTION 18

1.1 Introduction 18

1.2 Background 18

1.3 Problem Statement 19

1.4 Objectives 20

1.5 Proposed Solution 20

1.6 Proposed Approach 21

1.7 Project Scope 22

2 LITERATURE REVIEW 23

2.1 Introduction 23

2.2 Existing Mobile Applications for Stock Market 23

2.2.1 KLSE Screener (Bursa) 23

2.2.2 StockHunter – KLSE (Bursa) 25

2.2.3 BursaMKTPLC 27

2.2.4 Conclusion 28

viii

2.3 Stock Price Prediction Time Series Algorithms 29

2.4 Evaluation Methods 37

2.5 System Development Methodology 39

2.5.1 Waterfall 39

2.5.2 Rapid Application Development Model 40

2.5.3 Prototype Model 40

2.5.4 Scrum Model 41

2.5.5 Extreme Programming 42

2.5.6 Conclusion 42

3 PROJECT METHODOLOGY 43

3.1 Introduction 43

3.2 System Development Methodology 43

3.3 Project Plan 45

3.3.1 Work Breakdown Structure 45

3.3.2 Project Duration Plan 47

3.3.3 Gantt Chart 49

3.4 Development Tools 50

3.4.1 Fast API 50

3.4.2 React Native 50

3.4.3 JavaScript 50

3.4.4 Python 50

3.4.5 SQLite 50

3.5 Knowledge Discovery Process (KDD) 51

4 PROJECT SPECIFICATION 53

4.1 Introduction 53

4.2 Use Case Modelling 53

4.2.1 Use Case Diagram 53

4.2.2 Use Case Description 53

4.3 System Requirements 61

4.3.1 Functional Requirements 62

4.3.2 Non-Functional Requirements 62

ix

5 SYSTEM DESIGN 63

5.1 Introduction 63

5.2 Software Architecture Design 63

5.3 Database Design 64

5.3.1 Physical Entity Relationship Diagram 64

5.3.2 Logical Entity Relationship Diagram 65

5.3.3 Data Dictionary 65

5.4 User Interface 68

5.4.1 Log In Page 68

5.4.2 Sign Up Page 69

5.4.3 Favourite Page 70

5.4.4 Search Page 70

5.4.5 Sector Page 71

5.4.6 Individual Stock Page 72

5.4.7 Profile Page 74

6 IMPLEMENTATION 76

6.1 Introduction 76

6.2 Selection of Time Series Algorithms 76

6.2.1 Data Collection 76

6.2.2 Data Pre-Processing 76

6.2.3 Evaluate Prediction Algorithms 77

6.3 Implementation of Prediction Model 82

6.4 API Endpoints 84

7 TESTING 86

7.1 Introduction 86

7.2 Unit Testing 86

7.2.1 Sign Up Module 86

7.2.2 Log In Module 88

7.2.3 Favourite Module 88

7.2.4 Search Module 89

7.2.5 Sectors Module 90

x

7.2.6 Individual Stock Module 91

7.2.7 Profile Module 92

7.3 Integration Testing 94

7.3.1 Sign Up and Log In Module 94

7.3.2 Edit Profile and Log In Module 95

7.3.3 Store Favourite Stocks Module 95

7.3.4 Remove Favourite Stocks Module 96

7.4 System Testing 97

8 CONCLUSION AND RECOMMENDATIONS 99

8.1 Introduction 99

8.2 Limitations 99

8.3 Recommendations and Future Works 99

8.4 Conclusion 99

REFERENCES 100

APPENDICES 104

xi

LIST OF TABLES

TABLE TITLE PAGE

2.1 Table for input data, prediction techniques and

results from different papers 29

2.2 Table of results for each parameters and number of

epochs 35

2.3 Table for the model errors of ARIMA, SMA and

Holts Winter 36

3.1 Project Duration Plan 47

4.1 Use case description for creating account 54

4.2 Use case description for login account 55

4.3 Use case description for managing favourite stocks 56

4.4 Use case description for searching stocks 57

4.5 Use case description for extracting historical stock

prices from another stock portal 58

4.6 Use case description for predicting stock prices via

time series algorithms 59

4.7 Use case description for viewing predicted stock

prices 60

4.8 Use case description for viewing charts of

predicted stock prices 61

5.1 Users’ entity data dictionary 65

5.2 Sectors’ entity data dictionary 66

5.3 Stocks’ entity data dictionary 66

5.4 Favourites’ entity data dictionary 67

6.1 Average RMSE and MAE for 5 prediction

algorithms for all stocks 77

xii


algorithms for Construction sector 78


algorithms for Technology sector 78


algorithms for Plantation sector 78


algorithms for Health Care sector 79


algorithms for Real Estate Investment Trusts sector 79


algorithms for Utilities sector 79

6.8 List of FastAPI endpoints 84

6.9 List of FastAPI endpoints (continued) 85

7.1 Unit Test Cases for Sign Up Module 86

7.2 Unit Test Cases for Log In Module 88

7.3 Unit Test Cases for Favourite Module 88

7.4 Unit Test Cases for Favourite Module 89

7.5 Unit Test Cases for Sectors Module 90

7.6 Unit Test Cases for Individual Stock Module 91

7.7 Unit Test Cases for Profile Module 92

7.8 Integration Test Case for Sign Up & Log In 94

7.9 Integration Test Case for Edit Profile and Log In 95



7.12 Test Case for System Testing 97

xiii

LIST OF FIGURES

FIGURE TITLE PAGE

1.1 Overview of the proposed solution 21

1.2 System Prototyping 21

2.1 Screenshot of KLSEScreener Application Interface 23

2.2 Screenshot of StockHunter – KLSE (Bursa)

Application Interface 25

2.3 Screenshot of BursaMKTPLC Application

Interface 27

2.4 Prediction error for 10-day prediction of GOOGL

stock 33

2.5 Prediction error for 1-day prediction of GOOGL

stock 33

2.6 Predicted stock prices vs actual stock prices for

LSTM 34

2.7 Predicted stock prices vs actual stock prices for

MLP 35

2.8 Waterfall Model 39

2.9 Rapid Application Development Model 40

2.10 Prototype Model 41

2.11 Scrum Model 41

2.12 Extreme Programming Model 42

3.1 Prototyping Methodology 43

3.2 Project Gantt Chart 49

3.3 Knowledge Discovery Process overview 51

4.1 Use Case Diagram 53

5.1 Software Architecture Design 64

xiv

5.2 Physical entity relationship diagram 64

5.3 Logical entity relationship diagram 65

5.4 User interface for log in page 68

5.5 User interface for log in page when user’s

credentials are incorrect 68

5.6 User interface for sign up page 69

5.7 User interface for sign up page when duplicate

username or email entered 69

5.8 User interface for favourite page 70

5.9 User interface for search page 70

5.10 User interface for search page when keyword is

entered 71

5.11 User interface for sector page 71

5.12 User interface for clicking into sector page 72

5.13 User interface for individual stock page 72

5.14 User interface for individual stock page after

Predict button is clicked 73

5.15 User interface for individual stock page after

Historical Chart button is clicked 73

5.16 User interface for profile page 74

5.17 User interface for profile edit username page 74

5.18 User interface for profile change password page 75

6.1 Graph comparing the actual and predicted stock

prices of ARIMA 80


prices of ARIMA 80


prices of SARIMA 81


prices of Holt Winter 81

xv


prices of Prophet 82

6.6 Code for CORS in Python script 83

6.7 Code for acknowledgement of local server port in

mobile application 83

xvi

LIST OF SYMBOLS / ABBREVIATIONS

ANN Artificial Neural Network

ARIMA Auto-Regression Integrated Moving Average

BLSTM Bidirectional Long Short-Term Memory

CNX Credit Rating Information

DY dividend yield

EPS earning per share

FOREX Foreign Exchange Market

GRU Gated Recurrent Unit Networks

KDD Knowledge Discovery Process

LS-SVM Least Squares Support Vector Machine

LSTM Long Short-Term Memory Networks

MAE mean absolute error

MAPE mean absolute percentage error

MLP Multilayer Perceptron Neural Network

NB Naïve Bayes

NIFTY National Stack Exchange Fifty

NTA net tangible assets

P/E price to earnings ratio

PSO Particle Swarm Optimization

RAD Rapid Application Development

RF Random Forest

RMSE root-mean square error

RNN Recurrent Neural Networks

ROE return on equity

SLSTM Stacked Long Short-Term Memory

SMA Simple Moving Average

SVM Support Vector Machine

xvii

LIST OF APPENDICES

Appendix A: User Interface Design Iteration 1 104

18

CHAPTER 1

INTRODUCTION

1.1 Introduction

In the era of technology, people are more exposed to information. Everyone can

access the information they require anytime and anywhere with the help of

technology. It is also applied to the field of investment. People grow their wealth,

generate passive incomes and achieving financial goals through investment by doing

research online to live a decent life after retirement. There are many investment types

in the market such as properties, foreign exchange markets (Forex), bonds, fixed

deposits and others. In this project, the investment type that will be focused on is

stocks.

This chapter explores a brief introduction about the background, problem

statements, objectives, the proposed solution, the proposed approach and the project

scope.

1.2 Background

The process of investing in stock markets is easy and convenient nowadays.

Investors interested in investing in stock markets can open an account and trade

wherever they want as long as they have their mobile smart devices with them.

Technologies have made the investment in stock markets more convenient than

before for investors. There are few ways that investors can earn through investing in

stock markets which are capital gain and dividend. However, investing in stock

markets is risky as investors may lose their money if they did not do proper research.

Stocks investing requires investors to do many planning and analyses to find the right

timing and right stocks to invest in, and it is a time-consuming task.

The prediction of a stock price is one strategy for investors to identify which

stocks to invest. With predicted stock prices, investors will know whether the stock

price will rise or drop in the future. Therefore, stock price prediction is an important

aspect for investors to sort out the possible high return stocks and invest at the right

time.

19

1.3 Problem Statement

i. Mobile application for stock price prediction is less likely to be found in

the market.

According to Böhmer et al. (2011), mobile phones are used to be

communication-only devices but it have developed into multipurpose gadgets

that allow users to accomplish variation of tasks as there is a gradual growth

in the amount of mobile application. There is an increase in the usage of

mobile phones as well as mobile applications. Users can use mobile

applications anytime and anywhere when they are on their mobile phone.

There are many mobile applications for viewing stock prices that can be

found in Google Play store or App store. Some of these applications have the

function of providing buy or sell call using technical analysis to users.

However, mobile applications for stock price prediction are less likely to be

found in the market.

ii. Novice investors invest in stocks without proper investment tools.

To earn massive incomes in stock markets, planning should be carried out

and followed strictly in every single trade in the stock market. Unfortunately,

novice investors tend to invest their money in stock markets by just listening

to the rumours. Some of the investors are investing in the stock market with

emotions. They did not follow a systematic strategy for their investment

which causes them to experience a great loss in the stock market. Therefore, a

proper investment tool is essential to novice investors who wish to earn

passive income from stock markets. A proper investment tool can assist

novice users through their investment journey as it can minimise the losses

made by novice investors.

iii. Investors are limited to specific investment knowledge

The stock picking research and the techniques to find the right time to invest

require much knowledge about investing. Kiyosaki (2000) stated that

financial education is more important than money to help people build their

wealth. Novice investors are limited to investment knowledge as they might

not come from this field of study. On the other hand, investors who are still

working have limited time to learn about the knowledge for investing in the

stock market. Some expert investors realise the power of time series

algorithms which can help them to gain more profit from stock markets.

20

However, they are lack knowledge about the time series algorithms that can

apply in the investment in stock markets.

1.4 Objectives

i. To develop a mobile application for stock price prediction by the end of this

project.

ii. To provide stock price prediction using time series algorithms in the mobile

application.

iii. To select the best time series algorithms for the stock price prediction based

on empirical study.

1.5 Proposed Solution

These problems can be solved by developing a mobile application for stock price

prediction to the investors.

This system can extract the historical prices of the stocks in the Malaysian

market from a stock portal through an API. These data will be used for creating the

necessary charts and performing the prediction. This is because the future stock

prices are predicted based on historical stock prices.

There is a module that enables users to view the predicted price chart of the

stocks they selected. Users can search a particular stock and the application will

generate a predicted price chart. With the help of the visualise charts, users will

better understand the current trend of the particular stock. Thus, they can have better

planning on their investment portfolio.

The main purpose of this system is to provide stock price prediction and time

series algorithms for predicting stock prices. There are lots of time-series algorithms,

such as moving average, autoregression, autoregressive moving average, etc. The

best time-series algorithm will be chosen by comparing the performance of the

algorithms via research. Users can use this system to predict future stock prices using

the best performance among time series algorithms.

21

Figure 1.1: Overview of the proposed solution

1.6 Proposed Approach

The system development methodology proposed to be used in this project is the

prototyping methodology. Figure 1.1 shows the system development workflow in a

project.

Figure 1.2: System Prototyping

Prototyping methodology is focusing on building the system prototypes after

planning, analysis, design and implementation phases. Analysis, design and

implementation phases will be conducted simultaneously to produce the system

prototype. After the first system prototype is released, it will be shown to customers

or users to review and to give some comments to make the system better. Then the

22

analysis, design and implementation phases will be going through again to ensure the

suggestions given by users or customers are fulfilled. The process of rebuilding the

system prototype will be carried out for a few times until the prototype reaches the

satisfaction of users and customers. Then the prototype is implemented into the real

system. The prototyping methodology is a good system development methodology

for this mobile application for stock price prediction as it makes sure the system

implemented will have a reasonable rate of satisfaction among the users.

1.7 Project Scope

The project aims to develop a mobile application for stock price prediction using

time series algorithms. The platform of this system is a mobile platform. The target

users of this system are investors that invest in the Malaysian stock market. The

algorithms for predicting the stock price are time series algorithms only. This system

will help users to predict future stock prices for the stocks that they prefer. This

system can extract the history prices from a stock portal and store them as data.

There is also a module to display charts so that the users have a better view of the

stock prices. Last but not least, there is a stock price prediction module in the system.

23

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter discusses the existing mobile applications for stocks markets. The

features and functions of the existing mobile applications for stock markets will be

studied and analysed as some of the features may be implemented in this project.

Furthermore, different time series algorithms are studied to choose a suitable

algorithm for this project using an evaluation matrix.

2.2 Existing Mobile Applications for Stock Market

2.2.1 KLSE Screener (Bursa)

KLSE Screener is a free mobile application which available on both android and iOS

platforms, used by most of the stock investors in the market as it is easy to use for

analysing Malaysia stock market. KLSE Screener provides real-time stock price for

the Malaysian stock market and some popular world indices such as STI, Dow Jones,

Hang Seng Index and others. It has high ratings in android, which is 4.2 and iOS,

which is 4.3 as this means that KLSE Screener is a useful application for its users.

Figure 2.1: Screenshot of KLSEScreener Application Interface

24

Main features

• Searching of stocks

Users can search the stocks in the search module by using the symbol of

stocks, code of stocks and description of stocks.

• Details of stocks

This application shows all the details of stocks to users such as financial

information, historical charts, quarter and annual reports, dividends pay-outs,

capital changes, news related to the stock, the announcement of the company,

discussions regarding the company, company summary and underlying

warrants. With these details, users can analyse the stocks easily.

• Watchlist

Users can store the favourite stocks in some self-created list so that it is

convenient for them to watch the stocks every time. Users can create a few

watchlists with different names to store the stocks according to the categories.

If the particular stock no longer favoured by the users, they can remove it

from their watchlist.

• Screener of stocks and warrants

Users are allowed to filter the stock list shown by giving the maximum and

minimum value of the financial information of stocks such as P/E, ROE, DY,

EPS, NTA and others.

• Stock alerts

This feature is a very important and useful function for the users who are

more to trading stocks. Users can set the stock prices and when the stock

reaches the target prices, the system will send a notification to alert the users.

• Fee’s calculator

The fees of trading stocks can be costly if investors have many trades within

a short time. This feature allows users to calculate the fees by setting

brokerage, clearing fee and stamp duty according to their broker.

KLSE Screener provides a lot of detailed information for the users to do

researches on the stocks that they in favour. However, the researches that needed to

carry out to find quality stocks are time consuming. The weakness of this application

is that it does not have a function that predicts the future stock prices for investors as

25

it aims more to value investors who invest in the stocks that have strong

fundamentals.

2.2.2 StockHunter – KLSE (Bursa)

StockHunter – KLSE (Bursa) is available on android and iOS platforms and it is free

of charge, but it offers in-app purchase to let users upgrade to access more features.

StockHunter application provides Bursa stock prices with 15 minutes delayed and

some indices like Hang Seng, KOSPI, S&P 500 and others. It also shows the prices

for currency and commodity.

Figure 2.2: Screenshot of StockHunter – KLSE (Bursa) Application Interface

Main features

• Hunt

This feature is a module that allows different types of investors to sort the

stocks they like. First, users can customise their filter with StockHunter

analysis, Technical analysis and Fundamental analysis. However, the

fundamental and technical analysis of sorting stocks need to purchase for

membership to access. Other than the custom-made filter, StockHunter has a

few strategy lists for the users to take it as references. The strategy lists

contain Hot, Trend, Growth, Top, Report, Momentum and Sector. Hot is the

26

list containing high volume stocks, while trend consists of bullish up trend

and down trend stocks which categorised by stars. The stocks that increase in

profit are collected in Growth and Top will store the stocks with the highest

profit, dividend, net cash or loss. For Report, the stocks will be separated into

different categories as the report financial month. On the other hand,

Momentum consists of volume up, gap up and high turnover stocks. Lastly,

the stocks are categories into different groups according to their sector, which

stores in the Sector strategy list.

• Searching of stocks

This feature allows users to search for stocks they want using a stock name

and stock code.

• Watchlist

This application allows users to store their favourite stocks on the watchlist

they like. There is a maximum of 10 watchlists that users can use.

• Information about stocks

The information of the stock consists of the chart of the stock with many

indicators that can be applied to the chart. There is also fundamental and

technical financial information provided to the users. Quarterly results of the

stock will be shown but users cannot download the quarter report from the

application. There are news related to the stock, dividend pay-outs, and the

underlying warrant in this feature. The application also allows the users

access to the information of the stock in other financial websites such as

Malaysia Stock Biz, Bloomberg, I3 Financial and others.

StockHunter – KLSE (Bursa) is an application that very useful for traders

who like technical analysis as it contains many indicators that can be applied to price

charts and the information for technical analysis for each stock. However, it does not

provide real-time stock prices but with 15 minutes of delay. This application only

provides investors the strategy to pick the right stocks but does not let the investors

know when is the right time to invest in the stocks.

27

2.2.3 BursaMKTPLC

BursaMKTPLC is an application developed by Bursa Malaysia and is free to use.

BursaMKTPLC provides real-time Malaysia stock prices and data for the users to

make better decision when investing.

Figure 2.3: Screenshot of BursaMKTPLC Application Interface

• Screener of stocks

The screener of stocks allows users to sort the stocks using indicators like

price-earnings, dividend yield and others. However, users cannot customise

the values themselves as the values of the indicators are all set to a certain

range and let the users choose.

• Trading ideas

This feature let users know the rating of the stocks as the ratings are

calculated by Bursa Malaysia. The ratings indicate 10-8 (positive), 7-4

(neutral), 3-1 (negative) and 0/NR (no rating).

• Watchlist

This application allows users to store their favourite stocks on the watchlist

so that it is convenient for them to find the stocks next time.

• Alerts

Users can set the target prices for a stock. When the stock price reaches the

target prices, notifications will be sent to the users to inform them.

28

• Details of stocks

This feature contains the price chart of the stock, financial information, rating

of the stock, and values of balance sheets, income statements and cash flow

statements of quarter and annual reports. The news related to the stock and

the underlying warrants will also be shown to the users.

Users can get many trading ideas from BursaMKTPLC as the rating features

of this application can let users know the quality of stocks easily. Users can sort the

stocks using the rating system in each indicator such as earning, fundamental, value,

risk, etc. Although it provides a rating to let users choose the quality stocks that

suitable for them, users will not know when to invest or dispose the stocks in the

future.

2.2.4 Conclusion

There are lots of applications related to the Malaysian stock market nowadays as the

investment is very important to all people. The applications like those mentioned

above have many features that can aid investors to analyse the Malaysian stock

market and find good stocks to enhance their portfolio so as to earn more passive

income. These applications are easy to use as the menu bars are all on the bottom of

the applications. This is a norm for stock related applications. Hence, this project will

implement some features that can enhance the performance of the applications from

the review above. Since there are time limitations for this project, not all the above-

mentioned applications features will be implemented in this project.

The applications mentioned above do not provide the right timing and right

stock prices for investors to invest. This gives investors trouble to investors as they

need to do more research on the stocks, such as technical analysis and calculating the

intrinsic value of stocks for finding the suitable prices to invest. Hence, stock price

prediction, which is the main study in this project, is important and has to be

implemented to stock application.

29

2.3 Stock Price Prediction Time Series Algorithms

Stock price prediction is a challenging activity that requires analyses. Therefore, this

analysis would be easier for investors if it can analyse the time series data (Obthong

et al., 2020). An interesting variable that is in a temporal or systematic sequence is

called time series (Montgomery, Jennings and Kulahci, 2016). According to

Montgomery, Jennings and Kulahci (2016), time series is usually used for

forecasting and predicting of future data. Hence, time series algorithms will be

studied and analysed in this project.

Table 2.1: Table for input data, prediction techniques and results from different

papers

Author Problem Input Data Prediction

Techniques

Results

Chau et al.

(2019)

Provide

guidance to

retail investors

in stock market

through stock

price prediction

mobile

application. 10-

day and 1-day

stock prices will

be predicted.

S&P 500 stocks

from different

sectors.

Historical stock

prices, daily

percentage

change and

moving

averages with

22, 50, 100 and

200 days are

used.

-Dense Neural

Network

-Simple

Recurrent Neural

Networks

(RNNs)

- Long Short-

Term Memory

Networks

(LSTMs)

-Gated Recurrent

Unit Networks

(GRUs)

Refer to

Figure 2.4

and Figure

2.5

Khare et al.

(2017)

Applying deep

learning in

predicting short

term stock

prices.

10 New York

Stock Exchange

stocks.

Historical

minute stock

prices for all

-Multilayer

Perceptron

Neural Network

(MLP)

-Long Short-

Term Memory

Refer to

Figure 2.6

and Figure

2.7

30

trading days in

1-year period.

Neural Network

(LSTM)

Roondiwala,

Patel and

Varma

(2015)

To seek for best

indicators to

enhance

predicting of

stock prices

using LSTM

Daily historical

stock data which

consists of

opening, high,

low and closing

prices and

volume.

-Long Short-

Term Memory

(LSTM)

Refer to

Table 2.1

Kulkarni,

Jadha and

Dhingra

(2020)

Research on

time series data

analysis for

predicting stock

prices

High, low,

opening and

closing prices

and volume of

monthly stock

data from

January 2009 to

October 2019

retrieved from

Bombay Stock

Exchange

-Auto-Regression

Integrated

Moving Average

(ARIMA)

-Simple Moving

Average

-Holt-Winters

Method

Refer to

Table 2.2

Althelaya,

El-Alfy and

Mohammed

(2018)

Use

multivariate

analysis with

bidirectional

and stacked

LSTM for

forecasting

stock market.

Historical daily

closing stock

prices for S&P

500 stocks

-Multilayer

Perceptron

(MLP)

-Long Short-

Term Memory

(LSTM)

-Bidirectional

Long Short-Term

Memory

(BLSTM)

-Stacked Long

Short-Term

Memory

Error

Percentage:

Short-term:

BLSTM:

0.00947

SLSTM:

0.01248

LSTM:

0.01582

MLP:

0.03875

Long-term:

BLSTM:

31

(SLSTM) 0.06055

SLSTM:

0.06637

LSTM:

0.08371

MLP:

0.09369

Selvin et al.

(2017)

Short, medium

and long term

of predicting

stock prices

NIFTY-IT index

and NIFTY-

Pharma index

with minute

stock prices.

-Long Short-

Term Memory

(LSTM)

-Recurrent

Neural Network

(RNN)

-Convolutional

Neural Network

(CNN)

-Auto-Regression

Integrated

Moving Average

(ARIMA)

Error

Percentage:

CNN: 2.36,

8.96, 3.63

RNN: 3.9,

7.65, 3.83

LSTM:

4.18, 7.82,

3.94

ARIMA:

31.91,

21.16, 36.53

Adebiyi,

Adewumi

and Ayo

(2014)

To differentiate

ARIMA and

Artificial

Neural

Networks on

predicting stock

prices.

Historical daily

stock prices for

Dell index.

-Auto-Regression

Integrated

Moving Average

(ARIMA)

-Artificial Neural

Network (ANN)

Error

Percentage:

ARIMA:

0.608

ANN:

0.8614

Hegazy,

Soliman and

Salam

(2014)

Prediction of

the stock

market using

machine

learning model.

Historical daily

stock prices for

S&P 500 stocks

from different

fields.

-Least Squares

Support Vector

Machine (LS-

SVM)

-Particle Swarm

Optimization

Error

Percentage:

LS-SVM:

0.1147

PSO:

0.7417

32

(PSO)

-Artificial Neural

Network (ANN)

ANN:

1.7212

Roncoroni,

Fusai and

Cummins,

(2015)

Analysis of

different

commodity

markets.

NIFTY 50

stocks with

daily historical

stock prices

-Long Short-

Term Memory

(LSTM)

Error

Percentage:

0.00859

Patel et al.,

(2015)

Use machine

learning models

to forecast the

stock prices.

Daily historical

stock prices of

CNX NIFTY

index and S&P

Bombay Stock

Exchange

Sensex index.

-Artificial Neural

Network (ANN)

-Support Vector

Machine (SVM)

-Random Forest

(RF)

-Naïve Bayes

(NB)

Accuracy

Percentage:

NB: 90.19

RF: 89.98

SVM: 89.33

ANN: 86.69

Chau et al. (2019) conducted research on developing applications for stock

prices prediction by using evolutional optimized machine learning models. The

algorithms tested in their projects were long short-term memory networks (LSTMs),

gated recurrent unit networks (GRUs), simple recurrent neural networks (RNNs) and

dense neural network. This paper consisted of two types of stock prices predictions

which were 10-day and 1-day prediction. The data of the historical stock prices was

trained with the models above and the mean squared error was calculated. Two

models were selected and the better performed model was taken as the parent and

another one was removed. The parent model was mutated to come out with a new

network and trained it again. The process was repeated for a number of iterations set

by them. This evolution algorithm can optimise the hyperparameter search. In terms

of accuracy, the prediction errors were higher in the 10-day predict than the 1-day

predict, but the errors were increasing from time to time in the 1-day predict. In this

project, this method will not be implemented because there is time limitation and the

evolution algorithm is a complicated and time-consuming method as it requires few

members to conduct each part.

33

Figure 2.4: Prediction error for 10-day prediction of GOOGL stock

(Chau et al., 2019)

Figure 2.5: Prediction error for 1-day prediction of GOOGL stock

(Chau et al., 2019)

Another research that was done by Kumar et al. (2018) included five models

which using Random Forest, Naïve Bayes, Softmax, Support Vector Machine and K-

Nearest Neighbor. In this paper, features were extracted at first followed by training

and testing of the data set. Lastly, the results of the prediction were obtained to find

the best models. Kumar et al. (2018) reported that Random Forest had the most

accurate results in the large data set, while Naïve Bayes had the best performance in

accuracy for small datasets. This research was conducted for supervised

classification models that predicted the trend of the stock market, so it will not be

34

implemented in this project as it is not suitable for this project predicting the prices

of the stocks using time series data.

Khare et al. (2017) stated that they implemented a long short-term memory

model and a feed-forward multilayer perceptron model for predicting short term

stock prices. The multilayer perceptron model received the data a few times to

decrease the errors by altering the weights until a number of epochs were hit. Based

on Cheng, Dong and Lapata (2016), long short-term memory model adds latest data

into a memory slot gradually, and it is controlled by a gate as it will remove the

outdated data, store the latest data and show the current data. From the research

conducted by Khare et al. (2017), the long short-term memory model can predict the

stock price trend accurately, but the accuracy of predicting exact stock prices was

higher in the multilayer perceptron model. The evaluation metric to analyse the

performance of these two models was Root Mean Square Error (RMSE). The result

showed that multilayer perceptron gave less error than long short-term memory.

Although the long short-term memory model was not accurate as compared with the

multilayer perceptron model in predicting stock prices, both of the results were in the

same trend as the actual stock prices. The results can be seen in the figure below.

Hence, both of these models are suitable in predicting time series stock price data so

they will be taken into consideration to implement in this project.

Figure 2.6: Predicted stock prices vs actual stock prices for LSTM

(Khare et al., 2017)

35

Figure 2.7: Predicted stock prices vs actual stock prices for MLP

(Khare et al., 2017)

Another research that proposed long short-term memory (LSTM) model was

done by Roondiwala, Patel and Varma (2015) which used LSTM and recurrent

neural network (RNN) algorithms to build the model for predicting future stock

prices. Data preprocessing was gone through for the raw data taken from

https://www.quandl.com/data/NSE so that the data can be used to predict the results.

This paper also proposed that the data was used to train and test the models.

According to Roondiwala, Patel and Varma (2015), this paper analysed the

performance of the results using Root Mean Square Error (RMSE) which compared

the results of different parameters and number of epochs. The table below shows the

results for each parameter and the number of epochs.

Table 2.2: Table of results for each parameters and number of epochs

Parameters No. of Epochs Training RMSE Testing RMSE

Open/Close 250 0.01491 0.01358

Open/Close 500 0.01027 0.00918

High/Low/Close 250 0.01511 0.014

High/Low/Close 500 0.01133 0.01059

High/Low/Open/Close 250 0.0133 0.01236

High/Low/Open/Close 500 0.00983 0.00859

(Roondiwala, Patel and Varma, 2015)

36

From the table above, it is obvious that the model with High/Low/Open/Close

and 500 epochs had the best performance among others as it had the lowest value of

RMSE for both train and test predictions. Therefore, with such a low value of RMSE,

long short-term memory and recurrent neural network can provide an accurate result

of prediction future stock prices so this model may be implemented in this project.

Based on Kulkarni, Jadha and Dhingra (2020), a few time series algorithms

were used to build the model to predict the future stock prices based on 10 years of

historical data of the Bombay Stock Exchange. These algorithms were Auto-

Regression Integrated Moving Average (ARIMA), Simple Moving Average (SMA)

and Holt-Winters Method. The parameters of the data used by Kulkarni, Jadha and

Dhingra (2020) in this research are similar to the research conducted by Roondiwala,

Patel and Varma (2015) which are high, low, open, close and another parameter

which is volume. The predicted prices were used to plot a graph used to compare

with the actual stock prices graph. The performances of these three algorithms on

predicting the future stock prices were evaluated using RMSE, MAE and MAPE,

which can be seen from the table below.

Table 2.3: Table for the model errors of ARIMA, SMA and Holts Winter

Model Name/ Metrics RMSE MAE MAPE

ARIMA (training error) 1.101809 0.6353445 8.313343

ARIMA (test error) 1.320584 1.2807830 11.601874

SMA (training error) 0.4874397 0.2748435 3.675369

SMA (test error) 1.2990895 1.2641892 11.456808

Holts Winter (training

error)

1.106470 0.6419695 8.792462

Holts Winter (test error) 1.319503 1.2796688 11.591707

(Kulkarni, Jadha and Dhingra, 2020)

The results of this research can be seen clearly from the table above, which

SMA had the best performance among the others as it contained the lowest errors in

three of the evaluation methods. Other than SMA, Holts Winter obtained better

results than ARIMA, which had the highest value of errors in test error.

37

The time series algorithms mentioned above, such as Auto-Regression

Integrated Moving Average, which is ARIMA, Long Short-Term Memory (LSTM),

Recurrent Neural Network (RNN) and Multilayer Perceptron (MLP) can be used for

the data type of time series as well as financial time series. All these algorithms can

be used for forecasting related to the project conducted by Obthong et al. (2020).

However, all these algorithms have their advantages and disadvantages. For the

ARIMA algorithm, the pattern found on one series often does not suit another series,

so the trends or processes found in the data as a whole cannot be established (Selvin

et al., 2017). However, Obthong et al. (2020) reported that ARIMA provides more

stable and systematic predictions for short term data. According to Kumar, Goomer

and Singh (2018), LSTM is considered as a subset of RNN that can help RNN, which

is problematic in building model (Obthong et al., 2020) to overcome the problem of

slowed learning because of the gap between the different networks in the loop.

LSTM is also better than other algorithms in storing the data for a long period

(Kumar, Goomer and Singh, 2018). Although MLP provides accurate results for

difficult problems in predictions, the connection of MLP is dragging and it is

difficult to set the range (Pradeepkumar and Ravi, 2017). In the paper written by

Svetunkov and Petropoulos (2018), the Simple Moving Average algorithm is mainly

used in projects as it is easy to understand due to its simplicity. But it also has the

disadvantage of setting the sequence of the moving average first before applying the

algorithm.

In conclusion, getting suitable algorithms for a problem requires much effort

as it is difficult to identify the best algorithm for a particular problem. Therefore,

after reviewing the research mentioned above, this project evaluated Long Short-

Term Memory, Holt Winter, Auto-Regression Integrated Moving Average, Seasonal

Auto-Regression Integrated Moving Average and Prophet to find the best among

them for predicting future stock prices. This is because these algorithms are

frequently used in predicting financial time series problems, and they also have good

performance compared to other algorithms.

2.4 Evaluation Methods

According to Kulkarni, Jadha and Dhingra (2020), mean absolute error (MAE) and

root mean square error (RMSE) are used as evaluation metrics for analysing the

performance of the prediction models. Chai and Draxler (2014) also stated that both

38

MAE and RMSE are widely used for evaluating purposes, but there is no conclusion

stated that one of these methods is the best method to evaluate the prediction model.

When it comes to calculating average error using nature measure, MAE is more

unambiguous compared to RMSE as calculating MAE is not complicated (Chai and

Draxler, 2014). Therefore, some research prefered MAE rather than RMSE as the

evaluation metric. On the other hand, MAE provides all errors with the same weight

but RMSE penalises variance because RMSE provides more weight to errors with

greater absolute values than to the errors with low absolute values.

Based on Roondiwala, Patel and Varma (2015), Root Mean Square Error

(RMSE) was used to analyse and compare the models in their research. RMSE can

be used to reduce the error between the actual results and the value of the output

acquired from the models (Roondiwala, Patel and Varma, 2015). Wang and Lu (2018)

stated that RMSE is the evaluation metric that commonly used by many systems for

calculating the errors between predicted values and actual values, such as SVD

model, Recommending Based on Rating Frequencies and Slope One. Rahman et al.

(2019) who did the stock market prediction using Gated Recurrent Units research

stated that the model they used was regression model which normally uses Root

Mean Square Error to assess the performance of the models. From all the research

mentioned above, it can be clearly seen that the evaluation method to monitor the

stock prices prediction models’ performance is mainly Root Mean Square Error.

Hence, the evaluation method that will be implemented in this project to analyse the

accuracy of results of the models bulit will be Root Mean Square Error. The formula

of RMSE is as below:

RMSE = √∑(�̂�𝑖 − 𝑦𝑖)²

𝑛

𝑛

𝑖=1

where

ŷ = predicted values,

y = observed values,

n = number of observation.

39

The formula of MAE is as below:

𝑀𝐴𝐸 =∑ |𝑦𝑖 − 𝑥𝑖|𝑛

𝑖=1

𝑛

where

y = predicted values,

x = observed values,

n = total number of data points

2.5 System Development Methodology

Developing a system requires involves not only code writing but also many other

activities such as requirement gathering, document writing, testing and others.

System development is not an easy task as all the activities have to be done in a well-

planned manner. Hence, the development methodologies are guidelines or blueprint

to develop the system effectively and efficiently.

2.5.1 Waterfall

The most traditional development methodology is the waterfall model which is

developing the system through orderly manner processes (Chowdhury et al., 2018).

In the waterfall model, the development life cycle will go through phase by phase as

the development team has to complete a phase before going to the next. Basesd on

Iyawa, Herselman and Coleman (2016), the costs required in developing a system

through the waterfall model is high as well as the level of dissatisfaction of the

customers. On the other hand, the model required much time compared to other

models as the development team needs to ensure the quality of deliverables in each

phase before moving on to the next phase.

Figure 2.8: Waterfall Model

(Eason, 2016)

40

2.5.2 Rapid Application Development Model

Fatimah, Supriatna and Kurniawati (2018) reported that Rapid Application

Development Model (RAD) is a model that focuses on the development cycle which

is in a short period. Chowdhury et al. (2018) claimed that customers and users will

have high participation throughout the stages in the RAD model as this model is

adapted to the modifications in requirements. Since customers will highly participate

in developing the application in the RAD model, the time limitations may be affected

as this model is focusing on a short period in each iteration (Iyawa, Herselman and

Coleman, 2016).

Figure 2.9: Rapid Application Development Model

(Chowdhury et al., 2018)

2.5.3 Prototype Model

The prototype model requires the involvement of users or clients in the early phases,

such as the requirement gathering phase and design phase, as this can help to develop

the best design before implementation. Based on Chandra (2015), the prototype

model is suitable for projects with unclear requirements as clients can add the

requirements during the design phase. Since the prototype model will keep building

the prototype until the prototype accepted by users and customers, it is best to be

used in the projects that have less understanding of specifications so that the

development team can alter the requirements from time to time.

41

Figure 2.10: Prototype Model


2.5.4 Scrum Model

In the Software Development Life Cycle model, Scrum is an agile model which

enables frequent changes in the requirements from the customers. Srivastava,

Bhardwaj and Saraswat (2017) claimed that Scrum is a very flexible model as it does

not have strict procedures to be followed, but a development team need to conduct

meetings frequently to communicate about the project. Scrum emphasises the

communication and collaboration between team members giving them the privilege

to find solutions for the problem during development. The scrum model requires a

development team that can communicate well and team members that can meet up

frequently without obstacles.

Figure 2.11: Scrum Model


42

2.5.5 Extreme Programming

Extreme programming is also an agile model in the software development

methodology. It is a development model that fitting small development teams and it

will deploy some smaller version frequently as the development processes are easy to

be managed (Chowdhury et al., 2018). According to Iyawa, Herselman and Coleman

(2016), the documentation needed in extreme programming is relatively less

compared to other models as this will lighten the burden of development teams.

Figure 2.12: Extreme Programming Model


2.5.6 Conclusion

All of the system development methodologies have their own advantages and

disadvantages to different projects development. The development team should

choose the most suitable methods to develop the project as there is no best

methodology that can be applied to all projects. In this project, the algorithms used

for predicting future stock prices are unclear as the performance of each algorithm

needs to be evaluated before implementing it to the application. Hence, prototyping

methodology is chosen in this project to develop the prototypes of algorithms from

time to time until the best performance algorithm is designed.

43

CHAPTER 3

PROJECT METHODOLOGY

3.1 Introduction

In this chapter, this project’s system development methodology and the planning of

this project will be reviewed.

3.2 System Development Methodology

System development methodologies are used by software developers as a guideline

or blueprint to have a systematic plan to develop the system well. Among many

system development methodologies in the industry, prototyping methodology will be

used in this project as it is suitable for developing the mobile application for stock

price prediction.

Figure 3.1: Prototyping Methodology

The figure above shows that the phases included in the prototyping model are

planning, analysis, design, implementation and deployment. In prototyping

methodology, the prototype will be implemented and analyse to get the best version

of the prototype then only deploy the prototype into the system.

44

a. Planning phase

This is the phase that lists out all the activities and tasks that will be

performed in this project and set the time frames to complete it. The planning

phase is to ensure that the project can be conducted and completed smoothly.

In this project, a project plan which includes Work Breakdown Structure and

Gantt Chart will be created in this planning phase. Work Breakdown

Structure lists down all the sub-tasks of this project while Gantt Chart set the

time frames to complete each task in the Work Breakdown Structure.

b. Analysis phase

The analysis phase is to study, analyse and gather the requirements of the

project. The requirements related to the algorithm of predicting future stock

prices, are gathered through researching papers and also some advice from

the supervisor. The requirements of the time series algorithms and evaluation

methods of the algorithms will be analysed.

c. Design phase

This phase aims to design the system in detail based on the requirements

analysed in the previous phase. In this phase, the workflow of the tasks and a

brief picture of the system’s interface will be developed.

d. Implementation phase

After designing what the system should look like, it is followed by an

implementation phase to implement the prototype by referring to the design.

A prototype will be evaluated in performance and user experience. Then the

prototype will go through the analysis phase, design phase and

implementation phase again to refine the prototype based on the evaluation.

e. Deployment phase

When the prototype is being evaluated to a stage satisfaction level is high,

then it will undergo deployment phase. The prototype is implemented into a

system and testing of the system will be conducted. Lastly, the system will be

deployed.

45

There are three iterations planned to be implemented in this project. All of the

iterations will be enhanced by analysing the requirement gathered and the system’s

design. Besides that, the prototype of each iteration will be evaluated to identify the

part that can be improved to enhance the prototype from one iteration to another

iteration. After the prototype has gone through iteration 3, which is the last iteration

planned, the testing phase will be taken place. A few types of testing will be carried

out, such as unit testing for each module, integration testing for the interconnection

between modules, system testing for the reliability of the system and user acceptance

testing for getting review from users. The testing phase is to ensure that the system is

developed and deployed with minimal bugs or errors to increase user experience.

Once the system passes through all the tests in the testing phase, the system is ready

to be delivered. The user interface design for prototype iteration 1 is shown in

Appendix A.

3.3 Project Plan

In the project plan, Work Breakdown Structure, Project Duration Plan and Gantt

Chart of this project will be discussed.

3.3.1 Work Breakdown Structure

0.0 Mobile Application for Stock Price Prediction

1.0 Planning

1.1 Title Selection and Registration

1.2 Project Plan

1.3 Problem Formulation

1.4 Objectives Formulation

1.5 Scope Defining

1.6 Preliminary Report

2.0 Prototyping Iteration 1

2.1 Analysis

2.1.1 Requirement Gathering

2.1.2 Review on Requirement Gathered

2.2 Design

2.2.1 Selection of Stock Price Prediction Algorithm

46

2.2.2 Selection of Dataset

2.2.3 Interface

2.3 Implementation

2.4 Evaluation


3.1 Analysis Improvement from Iteration 1

3.2 Design Improvement from Iteration 1

3.3 Implementation

3.4 Evaluation


4.1 Analysis Improvement from Iteration 2

4.2 Design Improvement from Iteration 2

4.3 Implementation

4.4 Evaluation

4.0 Testing

4.1 Unit Testing

4.2 Integration Testing

4.3 System Testing

4.4 User Acceptance Testing

5.0 Deployment

5.1 Delivery of Project

47

3.3.2 Project Duration Plan

Table 3.1: Project Duration Plan

Task Name Start Date

(dd/mm/yyyy)

End Date

(dd/mm/yyyy)

Duration

Planning

Title Selection and Registration 15/06/2020 21/06/2020 7

Project Plan 22/06/2020 26/06/2020 5

Problem Formulation 27/06/2020 29/06/2020 3

Objectives Formulation 30/06/2020 02/07/2020 3

Scope Definition 03/07/2020 05/07/2020 3

Preliminary Report 27/06/2020 12/07/2020 16

Prototyping Iteration 1

Analysis

• Requirement Gathering 13/07/2020 26/07/2020 14

• Review on Requirement

Gathered

20/07/2020 09/08/2020 21

Design

• Selection of Stock Price

Prediction Algorithms

10/08/2020 12/08/2020 3

• Selection of Dataset 13/08/2020 23/08/2020 11

• Interface 20/08/2020 26/08/2020 7

Implementation 27/08/2020 06/09/2020 11

Evaluation 07/09/2020 13/09/2020 7


Analysis Improvement from Iteration

1

14/09/2020 27/09/2020 14

Design Improvement from Iteration 1 28/09/2020 15/11/2020 49

Implementation 16/11/2020 08/01/2021 54

Evaluation 09/01/2021 17/01/2021 9


Analysis Improvement from Iteration

1

18/01/2021 24/01/2021 7

48

Design Improvement from Iteration 1 25/01/2021 14/02/2021 21

Implementation 15/02/2021 07/03/2021 21

Evaluation 08/03/2021 14/03/2021 7

Testing

Unit Testing 15/03/2021 21/03/2021 7

Integration Testing 22/03/2021 28/03/2021 7

System Testing 29/03/2021 04/04/2021 7

User Acceptance Testing 05/04/2021 11/04/2021 7

Deployment

Delivery of Project 12/04/2021 18/04/2021 7

49

3.3.3 Gantt Chart

Figure 3.2: Project Gantt Chart

50

3.4 Development Tools

3.4.1 Fast API

Fast API is a web framework which used by developers to build API with Python 3.6

and above. In this project, the stock code is passed to the API built with Fast API and

all the retrieval of historical stock prices and the model to predict future stock prices

are all coded in the Python script which hosts in Fast API. The predicted stock prices

then pass back to the mobile application.

3.4.2 React Native

This mobile application framework which developers use to build mobile

applications with both android and iOS is called React Native. JavaScript is the

programming language used in React Native.

3.4.3 JavaScript

JavaScript is a programming scripting language that allows developers to build

applications with complicated features like updating contents, controlling multimedia,

etc. In this project, JavaScript was being used to build the user interface of the

application.

3.4.4 Python

Python is a programming language that is widely used for machine learning and data

analysis. This is because it is simple to learn and has a good data handling capacity

compared to other programming languages. The stock price prediction algorithm in

this project used Python as the machine learning language.

3.4.5 SQLite

An SQL database engine is implemented by SQLite which is a C-language library.

SQLite is frequently used by developers to build mobile applications. In this project,

the database which stored the user list, stocks list and favourite stocks list, was

developed using SQLite.

51

3.5 Knowledge Discovery Process (KDD)

Figure 3.3: Knowledge Discovery Process overview

(Ristoski and Paulheim, 2016)

In this project, the KDD process will be implemented for the part of stock price

prediction. KDD is a process that consists of several steps to transform the raw data

into knowledge and present it to end-users. These steps include selecting data, data

pre-processing, transformation of data, data mining, and the last step is interpretation

or evaluation of the data mining model.

Since this is a time series prediction project, the historical data is essential for

the predicting task in this project. The raw data of this project will be the data of the

stock in the Malaysian stock market. For the data selection process, the historical

daily prices of the stocks will be chosen as the data to be used for data mining. These

historical stock prices will be extracted from the stock portal, which is Investing.com.

After selecting the stock data from the stock portal, these data need to go

through data pre-processing and transforming process to form useful data and

maximise the information taken. First of all, the data will be cleaned by eliminating

the null or missing values, affecting the accuracy of prediction. Retrieve data from

Investing.com will get some unwanted data, such as the daily volume of the stocks.

Therefore, useful features such as daily prices will be taken out as processed data to

lower the load when conducting data mining. The dataset becomes smaller with only

52

useful data after data pre-processing. The data will be split into two categories: the

training set and the testing set.

Data mining will be carried out when the historical daily stock prices are

cleaned and can be used to obtain a more accurate result. The time series algorithms

that will be used in this project are ARIMA, LSTM, SARIMA, Holt Winter and

Prophet, which are discussed in Chapter 2 Literature Review. The models will be

built based on these five algorithms and the prediction models will be continued for

the next process.

The last step in KDD is the interpretation and evaluation of the prediction

models. There are five predictions models from the previous step, which are ARIMA,

LSTM, SARIMA, Holt Winter and Prophet models. These five models will undergo

an evaluation to analyse the performance of the prediction and choose the best model

among them. The evaluation method that will be used in this step is RMSE and MAE.

After the evaluation, the best model will be chosen to be implemented into the

application for stock price prediction purposes.

CHAPTER 4

53

PROJECT SPECIFICATION

4.1 Introduction

The specification of this project will be discussed in this chapter through the use case

diagram, use case description, functional requirements and non-functional

requirements.

4.2 Use Case Modelling

4.2.1 Use Case Diagram

The use case diagram below shows the relationship between use cases, system and

user.

Figure 4.1: Use Case Diagram

4.2.2 Use Case Description

54

Use case description describes the details and flow of all the use cases in the use case

diagram above.

4.2.2.1 Create Account

Table 4.1: Use case description for creating account

Use Case Name: Create Account Use Case ID: 1 Importance Level: High

Primary Actor: User Use Case Type: Detailed, Essential

Stakeholders and interests:

User – wants to create an account of the mobile application for stock price

prediction.

Brief Description: This use case description describes how user creates a user

account for the mobile application for stock price prediction.

Trigger: User downloads the application and wants to register an account for this

mobile application.

Relationship:

Association: User

Include: -

Extend: -

Generalization: -

Normal flow of events:

1. User clicks on “Sign Up” button on the first page of the interface of the

application.

2. The Sign-Up page will be navigated.

3. User enters username, email, password and confirm password to create the

account.

4. System creates an account that only belong to particular user.

Subflows:

None

Alternative/exceptional flows:

3.1 The username or email have been used.

3.1.1 User re-enter another username or email which is not stored in the

database.

3.1.2 Account created.

55

4.2.2.2 Login Account

Table 4.2: Use case description for login account

Use Case Name: Login Account Use Case ID: 2 Importance Level: High



User – wants to login into the registered account of the mobile application for stock

price prediction.

Brief Description: This use case description describes how user login into his/her

registered account of this mobile application for stock price prediction.

Trigger: User wants to login to their registered account of the mobile application for

stock price prediction.

Relationship:

Association: User

Include: -

Extend: -

Generalization: -


1. User input registered email or username and designated password to the “Log

In” page.

2. User clicks on the “Log In” button to verify the email and password in order

to login.

3. System starts to verify the validity of the email or username and password

entered.

4. User successfully login to the home page of the application.

Subflows:

None


4.1 The email or username and the password have been wrongly entered.

4.1.1 System pops out a warning message about the invalid email or

username or password entered.

4.1.2 User needs to re-enter the correct email or username and password to

login.

56

4.1.3 Email or username and password have been verified and user is

successfully login.

4.2.2.3 Manage Favourite Stocks

Table 4.3: Use case description for managing favourite stocks

Use Case Name: Manage Favourite

Stocks

Use Case ID: 3 Importance Level: High



User – wants to monitor some favourite stocks in a more convenient way.

Brief Description: This use case description describes how user manage their

favourite stocks by adding them to a wish list or removing them from the wish list.

Trigger: User wants to monitor the stocks in a list or user do not want to monitor the

stock in the list anymore.

Relationship:

Association: User

Include: -

Extend: -

Generalization: -


1. User wants to modify the stocks in the Favourite Stocks list.

2. User add or remove the stocks from the Favourite Stocks list.

If user wants to add stocks to Favourite Stocks list, 2.1: Add stocks to

Favourite Stocks list subflow is performed.

If user wants to remove stocks from Favourite Stock list, 2.2: Remove

stocks from Favourite Stocks list subflow is performed.

3. The Favourite Stocks list has been managed.

Subflows:

2.1 Add stocks to Favourite Stocks list

2.1.1 User wants to monitor and has interest on the stock.

2.1.2 User clicks on the heart button on that particular stock.

2.1.3 The stock has been stored in the Favourite Stocks list.

57

2.2 Remove stocks from Favourite Stocks list

2.2.1 User does not want to monitor the stock anymore.

2.2.2 User click on the heart icon on the menu bar to navigate to Favourite

Stocks list page.

2.2.3 User clicks on the heart button of the stock that wanted to be removed

from the list.

2.2.4 The stock has been removed from the Favourite Stocks list.


None

4.2.2.4 Search Stocks

Table 4.4: Use case description for searching stocks

Use Case Name: Search Stocks Use Case ID: 4 Importance Level: High



User – wants to find stocks that are not in the wish list.

Brief Description: This use case description describes how user search the stocks that

are not in the wish list.

Trigger: User wants to search the stocks which are new to them.

Relationship:

Association: User

Include: -

Extend: -

Generalization: -


1. User found some new stocks from other stock portal or news.

2. User click on the magnifying glass icon on the menu bar to navigate to search

page.

3. User enters the details of the stocks on the search bar.

4. User clicks on the “Search” button to search for the stocks.

5. The stocks which related to the stocks’ details entered will be shown on the

search page.

58

Subflows:

3.1 User enters the name of the stocks that wanted to be searched.

3.2 User enters the code of the stocks that wanted to be searched.


3.1 The name or code of the stocks that wanted to be searched could not be

found.

3.2 The use case ended.

4.2.2.5 Extract Historical Stock Prices from Another Stock Portal

Table 4.5: Use case description for extracting historical stock prices from another

stock portal

Use Case Name: Extract Historical

Stock Prices from Another Stock

Portal


Primary Actor: - Use Case Type: Detailed, Essential


System – get the historical stock data so as to use for predicting the future stock

prices.

User – get a more reliable results of prediction based on historical stock data.

Brief Description: This use case description describes how mobile application for

stock price prediction extract the historical stock prices form another stock portal.

Trigger: System wants to predict the future stock prices.

Relationship:

Association: User

Include: Predict Stock Prices via Time Series Algorithms

Extend: -

Generalization: -


1. System will use historical stock prices to predict the future stock prices.

2. System extracts the historical stock data from another stock portal.

3. System stores the historical stock data into the database of mobile application

for stock price prediction.

59

Subflows:

None


None

4.2.2.6 Predict Stock Prices via Time Series Algorithms

Table 4.6: Use case description for predicting stock prices via time series algorithms

Use Case Name: Predict Stock Prices

via Time Series Algorithms


Primary Actor: - Use Case Type: Detailed, Essential


User – able to know the future stock prices using time series algorithm

Brief Description: This use case description describes how the system predicts the

future stock prices using time series algorithm.

Trigger: User wants to get the predicted stock prices.

Relationship:

Association: User

Include: View Predicted Stock Prices

View Charts of Predicted Stock Prices

Extend: -

Generalization: -


1. A few time series algorithms will be used to test the best model for stock

price prediction.

2. Those time series algorithms will go through evaluation method to find the

best and most accurate model.

3. The model is then used to predict the future stock prices.

4. The input data will be the historical stock data retrieve from database.

5. The predicted stock prices will be sent to the front end of the mobile

application for stock price prediction.

Subflows:

None

60


None

4.2.2.7 View Predicted Stock Prices

Table 4.7: Use case description for viewing predicted stock prices

Use Case Name: View Predicted

Stock Prices




User – able to view the future stock prices for maximizing the profit.

Brief Description: This use case description describes how user view the predicted

stock prices.

Trigger: User wants to view the predicted stock prices.

Relationship:

Association: User

Include: -

Extend: -

Generalization: -


1. User wants to know the predicted stock prices of certain stocks.

2. User search the stocks from the system.

3. User click on the “Predict” button to predict the stock prices.

4. The predicted stock prices appear on the page for the user.

Subflows:

2.1 User search the stocks through search page.

2.2 User find the stocks from the Favourite Stocks list.


None

4.2.2.8 View Charts of Predicted Stock Prices

61

Table 4.8: Use case description for viewing charts of predicted stock prices

Use Case Name: View Charts of

Predicted Stock Prices




User – able to view the predicted stock prices in an easier way which is in charts

form.

Brief Description: This use case description describes how user view the predicted

stock prices in a chart view.

Trigger: User wants to view the charts of predicted stock prices.

Relationship:

Association: User

Include: -

Extend: -

Generalization: -


1. User wants to know the predicted stock prices of certain stocks in chart form.

2. User search the stocks from the system.

3. User click on the “Predict Chart” button to predict the stock prices.

4. The predicted stock prices in a chart form appear on the page for the user.

Subflows:

2.1 User search the stocks through search page.

2.2 User find the stocks from the Favourite Stocks list.


None

4.3 System Requirements

62

The system requirements, which consist of functional requirements and non-

functional requirements, will be discussed.

4.3.1 Functional Requirements

• The system shall allow users to search the blue-chip stocks in the Malaysian

stocks market (KLSE) by entering stock’s name and stock’s code.

• The system shall allow users to manage their favourite stocks list.

i. The system shall allow users to store favourite stocks into a list.

ii. The system shall allow users to remove stocks from the favourite

stock list.

• The system shall be able to extract historical stock prices for prediction

purposes from another stock portal.

• The system shall be able to predict future stocks prices by using time series

algorithms.

• The system shall be able to view the predicted stock prices to the users.

• The system shall be able to display the predicted stock prices charts to the

users.

4.3.2 Non-Functional Requirements

• The system shall allow users who do not have an account to create their

account for this application.

• The system shall allow users to login to their registered account of this

mobile application.

• The system shall restore all the favourite list of the users once they login to

their account although using other mobile devices.

• The system shall be able to provide instructions manual to the users so that

they can use it without any problem.

• The system shall be secure for the users to use.

CHAPTER 5

63

SYSTEM DESIGN

5.1 Introduction

In this chapter, the design of the system including the software architecture, database

and user interface design.

5.2 Software Architecture Design

The system architecture designed for the mobile application is a client-server

architecture. The client-side is the user interface that the users interact with while the

server-side is the combination of the database and the stock price prediction model.

The user interface was built by using react native framework while the

database was created in SQLite. The user interface consists of Log In, Sign Up,

Favourite, Search, Stocks List, Stock and Profile screens. All these screens will be

shown in the section below. The database is created to store the information of stocks,

the list of the users and the favourite stocks of each user. The details of the database

design will also be shown in the section below.

The performance of a few stock price prediction models was examined in

order to select the best performance model to predict the stock prices. All the

historical stock prices of the stocks were retrieved from the Investing.com website

through an API which retrieve the data from the website to python file. This can

ensure that the data can run automatically through the model without manually

download the csv files from the website. Other than that, the prediction algorithm

scripted in Python is hosted in a local server so that the mobile application can pass

the stock code to the algorithm and get the predicted stock prices from the algorithm.

This process needs a web framework called Fast API to allow the communication

between the prediction algorithm and mobile application.

64

Figure 5.1: Software Architecture Design

5.3 Database Design

5.3.1 Physical Entity Relationship Diagram

Figure 5.2: Physical entity relationship diagram

65

5.3.2 Logical Entity Relationship Diagram

Figure 5.3: Logical entity relationship diagram

5.3.3 Data Dictionary

Entity: Users

Table 5.1: Users’ entity data dictionary

Column Description Data

Type

Key FK

Referenced

Table

Null

user_id Unique identification for

all registered users

Integer PK - Not

Null

user_name Preferred name of the

registered users

Text - - Not

Null

user_email Email of the registered Text - - Not

66

users Null

user_password Password of the

registered users

Text - - Not

Null

Entity: Sectors

Table 5.2: Sectors’ entity data dictionary


Type

Key FK

Referenced

Table

Null

sector_id Unique identification for

all sectors

Integer PK - Not

Null

sector_name Name of the sector in

Bursa Malaysia market.

(Example: Technology)

Text - - Not

Null

Entity: Stocks

Table 5.3: Stocks’ entity data dictionary


Type

Key FK

Referenced

Table

Null

stock_id Unique identification

for all stocks

Integer PK - Not

Null

sector_id Unique identification

for all sectors

Integer FK Sectors Not

Null

stock_name Name of the stocks in

Bursa Malaysia

market. (Example:

MAYBANK)

Text - - Not

Null

stock_code Code of the stocks in

Bursa Malaysia

market. (Example

Text - - Not

Null

67

stock code for

MAYBANK: 1155)

stock_InvestingCode Code of the Bursa

Malaysia stocks in

Investing.com

website. (Example

code for

MAYBANK:

MBBM)

Text - - Not

Null

Entity: Favourites

Table 5.4: Favourites’ entity data dictionary


Type

Key FK

Referenced

Table

Null

id Unique identification for all

favourite stocks for each user

Integer PK - Not Null

user_id Unique identification for all

registered users

Integer FK Users Not Null

stock_id Unique identification for all

stocks

Integer FK Stocks Not Null

68

5.4 User Interface

5.4.1 Log In Page

Log In screen requires users to input email or username which are already registered

and the particular password to log in to the system. If visitors have not registered as

users, they need to click on the red “Sign Up” title to navigate to the sign up page.

Figure 5.4: User interface for log in page

Figure 5.5: User interface for log in page when user’s credentials are incorrect

69

5.4.2 Sign Up Page

Sign Up page let users input their personal information such as preferred username,

email and password to create an account for this application.

Figure 5.6: User interface for sign up page

Figure 5.7: User interface for sign up page when duplicate username or email entered

70

5.4.3 Favourite Page

Favourite page allows users to store the stocks they prefer so they can monitor these

stocks frequently in a list.

Figure 5.8: User interface for favourite page

5.4.4 Search Page

Search page allows users to type in certain keywords such as the name of the stocks

or the stock code of the stocks to search for a particular stock.

Figure 5.9: User interface for search page

71

Figure 5.10: User interface for search page when keyword is entered

5.4.5 Sector Page

Sector page shows all the sectors in the database to the users.

Figure 5.11: User interface for sector page

72

Figure 5.12: User interface for clicking into sector page

5.4.6 Individual Stock Page

Individual stock page enables users to favourite the stock, shows related information,

show historical chart and also predict the future stock prices of that particular stock.

Figure 5.13: User interface for individual stock page

73

Figure 5.14: User interface for individual stock page after Predict button is clicked

Figure 5.15: User interface for individual stock page after Historical Chart button is

clicked

74

5.4.7 Profile Page

Profile page allows users to edit their username, change password and log out.

Figure 5.16: User interface for profile page

Figure 5.17: User interface for profile edit username page

75

Figure 5.18: User interface for profile change password page

76

CHAPTER 6

IMPLEMENTATION

6.1 Introduction

The selection of time series algorithms, implementation of the prediction model and

API endpoints will be discussed in this chapter.

6.2 Selection of Time Series Algorithms

As discussed in Chapter 2, Long Short-Term Memory, Holt Winter, Auto-Regression

Integrated Moving Average, Seasonal Auto-Regression Integrated Moving Average

and Prophet were evaluated to find the best prediction model for implementing into

the mobile application.

6.2.1 Data Collection

First of all, the stock market focused on in this project is the Bursa Malaysia. Hence,

the data must retrieve from Bursa Malaysia stocks. The stocks that underwent all five

prediction algorithms were retrieved from six sectors of stocks: Construction,

Technology, Plantation, Real Estate Investment Trusts, Health Care and Utilities.

The total number of stocks from these six sectors is 216.

After finding the targeted stocks, the historical prices of these stocks were

retrieved from Investing.com. The historical prices have to be called and retrieved

automatically for the prediction algorithms. In this case, an API named investpy is

used along with the prediction algorithms to retrieve the historical prices from

Investing.com.

6.2.2 Data Pre-Processing

The period of historical prices was set to one year. The historical prices retrieved

may contain some null or missing values, so these values were cleaned to ensure

better accuracy on the prediction results. The one-year historical prices were split

into two sets of data: the train set and the test set. 70% of the trading days in a year

belonged to the train set, while the other 30% belonged to the test set. The train set

was used to train prediction algorithms while the test set was used to evaluate the

accuracy of prediction algorithms.

77

Other than that, the historical prices retrieved contain daily open prices, daily

high prices, daily low prices, daily close prices and daily volume. There was only

one type of prices taken to underwent prediction algorithms which was daily close

prices.

6.2.3 Evaluate Prediction Algorithms

After data collection and data pre-processing, the train set was passed through all

five prediction algorithms: LSTM, ARIMA, Holt Winter, SARIMA and Prophet to

train all these algorithms. The predicted stock prices of each prediction algorithm

were then compared with the test set using graph plotting and evaluation method:

Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). After

conducting all the training and predicting activities, the average of RMSE and MAE

from 216 stocks were being calculated. Parameters of prediction algorithms are

shown in the first table below as other tables are followed same parameters. The

results are shown in the tables below.

Table 6.1: Average RMSE and MAE for 5 prediction algorithms for all stocks

Algorithms RMSE MAE

ARIMA

Parameters:

order=(4,1,0)

0.026992 0.018855

LSTM

Parameters:

Dropout(0.2),

Dense(units = 1),

optimizer = 'RMSProp',

epochs = 100,

batch_size = 32

0.049386 0.038917

SARIMA

Parameters:

order=(2,1,4),

seasonal_order=(0,1,1,7)

0.141947 0.117713

Holt Winter 0.144702 0.119861

78

Parameters:

seasonal_periods=7,

trend='add',

seasonal='add',

Prophet 0.169365 0.144526

Table 6.2: Average RMSE and MAE for 5 prediction algorithms for Construction

sector

Algorithms RMSE MAE

ARIMA 0.016676418 0.011720139

LSTM 0.029513763 0.024086467

SARIMA 0.084710008 0.071691036

Holt Winter 0.096906925 0.082272729

Prophet 0.078496928 0.066122917

Table 6.3: Average RMSE and MAE for 5 prediction algorithms for Technology

sector

Algorithms RMSE MAE

ARIMA 0.021602484 0.015221912

LSTM 0.046597948 0.0387713

SARIMA 0.132089442 0.11457744

Holt Winter 0.13086616 0.113678954

Prophet 0.166523867 0.149357676

Table 6.4: Average RMSE and MAE for 5 prediction algorithms for Plantation sector

Algorithms RMSE MAE

ARIMA 0.048977215 0.033637076

LSTM 0.084139441 0.061222394

SARIMA 0.252713371 0.195633329

79

Holt Winter 0.252997757 0.194311614

Prophet 0.311426245 0.245653238

Table 6.5: Average RMSE and MAE for 5 prediction algorithms for Health Care

sector

Algorithms RMSE MAE

ARIMA 0.025698962 0.017260259

LSTM 0.04264388 0.033139367

SARIMA 0.104144608 0.086326631

Holt Winter 0.113768482 0.094152217

Prophet 0.097223454 0.084216448

Table 6.6: Average RMSE and MAE for 5 prediction algorithms for Real Estate

Investment Trusts sector

Algorithms RMSE MAE

ARIMA 0.013911074 0.009869636

LSTM 0.018993417 0.015720226

SARIMA 0.067014767 0.060490379

Holt Winter 0.064642397 0.058342467

Prophet 0.09504377 0.087113114

Table 6.7: Average RMSE and MAE for 5 prediction algorithms for Utilities sector

Algorithms RMSE MAE

ARIMA 0.048797025 0.035789725

LSTM 0.077359327 0.060349001

SARIMA 0.215506139 0.17899808

Holt Winter 0.211562492 0.176297634

Prophet 0.278322143 0.248588347

80

From the tables above, it is clearly seen that ARIMA has the lowest RMSE

and MAE, which means it has the least error in prediction. There are graphs

comparing the actual stock prices and the predicted stock prices which shown as

below.

Figure 6.1: Graph comparing the actual and predicted stock prices of ARIMA

Figure 6.2: Graph comparing the actual and predicted stock prices of ARIMA

81

Figure 6.3: Graph comparing the actual and predicted stock prices of SARIMA

Figure 6.4: Graph comparing the actual and predicted stock prices of Holt Winter

82

Figure 6.5: Graph comparing the actual and predicted stock prices of Prophet

From the graphs and table shown above, ARIMA is the best among all five

prediction algorithms because it has the least error and most accurate when

comparing to actual stock prices. Therefore, ARIMA was chosen as the stock price

prediction model for the mobile application of this project.

6.3 Implementation of Prediction Model

After the prediction model had been developed, it was implemented into the

mobile application and there is user interface to show the predicted prices to the

users. To run the prediction model in the mobile application, an API is needed to link

Python script, which consists of the prediction model, and the mobile application.

The API used in this project is FastAPI, which hosts the prediction model on

the local server, and let the mobile application call the prediction results from the

local server. A few functions which return the prediction results, history stock prices

and information of stocks were developed in the Python script and is hosted by

FastAPI. On the other hand, the backend of React Native, which scripted the mobile

application, needs to pass the specific stock codes and date through FastAPI to the

prediction model. This can ensure that the prediction model can get historical stock

prices of specific stocks and perform prediction.

83

The connection between the mobile application and the Python script needed

to be established. This is because the port of the mobile device and the local server is

different. In the FastAPI hosted Python script, Cross-Origin Resource Sharing

(CORS) was configured to establish the communication between React Native

backend and the Python script. In the mobile application, the port number of the local

server also needed to be acknowledged. From the code below, the 8081 is the port

number for the mobile device which runs the mobile application while 8000 is the

port number of the local server.

Figure 6.6: Code for CORS in Python script

Figure 6.7: Code for acknowledgement of local server port in mobile application

84

6.4 API Endpoints

There are in total 9 API endpoints hosted by FastAPI which return different

information of stocks.

Table 6.8: List of FastAPI endpoints

Method: Get

Path Description Parameters

/predict/{stock_code}/?toDate=

{toDate}&fromDate={fromDate}

Get the prediction results of

specific stock.

• stock_code

• toDate

• fromDate

/history/{stock_code}/?toDate=


Get one-year historical daily

close prices of specific stock.

• stock_code

• toDate

• fromDate

/yearHigh/{stock_code}/?toDate=


Get the highest reached stock

price in a year of specific

stock.

• stock_code

• toDate

• fromDate

/yearLow/{stock_code}/?toDate=


Get the lowest reached stock

price in a year of specific

stock.

• stock_code

• toDate

• fromDate

/open/{stock_code}/?toDate=


Get the current daily open

price of specific stock.

• stock_code

• toDate

• fromDate

/high/{stock_code}/?toDate=


Get current daily highest price

of specific stock.

• stock_code

• toDate

• fromDate

/low/{stock_code}/?toDate=


Get current daily lowest price

of specific stock.

• stock_code

• toDate

• fromDate

/close/{stock_code}/?toDate=


Get current daily close price

of specific stock.

• stock_code

• toDate

• fromDate

85

Table 6.9: List of FastAPI endpoints (continued)

Method: Get

Path Description Parameters

/volume/{stock_code}/?toDate=


Get current daily

transaction volume

of specific stock.

• stock_code

• toDate

• fromDate

86

CHAPTER 7

TESTING

7.1 Introduction

This chapter discusses the testing of this project, including unit testing, integration

testing, and system testing.

7.2 Unit Testing

Unit testing was conducted when the mobile application had been developed. Its

purpose is to perform testing on the functionality of the individual units or functions

of the mobile application. If errors or defects were found, corrective actions will be

taken to minimise the errors and defects before deployment.

7.2.1 Sign Up Module

Table 7.1: Unit Test Cases for Sign Up Module

Test Case Test Execution Steps Expected Results Results

(Pass/Fail)

Sign Up page

navigation

1. Users do not have any

account to login.

2. Users want to register an

account.

3. Click on the red “Sign

Up” quote to navigate to

Sign Up page.

The view navigates

from Log In page to

Sign Up page.

Pass

Field required

validation

1. Let username, email,

password or confirm

password empty.

2. Click on the “Sign Up”

button to register an account.

An alert window will

pop up to inform

users that the field is

required.

Pass

Email format

validation

1. Type an invalid email.

(example: 1234word)

2. Fill in other fields with

An alert window

which stated “Email

format is incorrect”

Pass

87

valid values.



will pop up.

Password

matching

validation

1. Fill in all fields with valid

values.

2. Fill in differently for

password field and confirm

password field.



An alert window

which stated

“Password did not

match” will pop up.

Pass

Same username

or email

validation

1. Fill in username or email

that already exists in the

system.

2. Fill in all other fields with

valid values.



An alert window

which stated

“Username or email

already exists” will

pop up.

Pass

Account

registration

1. Fill in all fields with valid

values.



An alert window

which stated “You

are Registered

Successfully” will

pop up and navigate

back to Log In page.

Pass

88

7.2.2 Log In Module

Table 7.2: Unit Test Cases for Log In Module


(Pass/Fail)

Log In page

initiation

1. Launch the mobile

application.

The view is Log In

page once launching

application.

Pass

Fields required

validation

1. Leave “Email or

Username” or “Password”

field empty.

2. Click “Log In” button to

login to the system.


pop up to inform

users that the field is

required.

Pass

Field validation 1. Fill in email, username or

password that are not

registered in the system.



An alert window

which stated

“Incorrect

Credentials!” will

pop up.

Pass

Users Log In 1. Fill in email or username

and password that are

registered in the system.



Users successful

logging in and

navigate to Favourite

page.

Pass

7.2.3 Favourite Module

Table 7.3: Unit Test Cases for Favourite Module


(Pass/Fail)

Favourite page

navigation after

logging in

1. Log in to the system. The view is

Favourite page once

logging in.

Pass

Favourite page

navigation in

1. Click on the bottom tab

menu which contains a heart

The view navigates

to Favourite page.

Pass

89

other page shape icon and stated

“Favourite”.

Show favourite

stocks list

1. Favourite a few stocks by

clicking the heart icon at

Individual Stock page.

2. Navigate back to Favourite

page.

Favourite page

contains that stocks

that have been

favourited.

Pass

Individual

Stocks page

navigation

1. Click on a specific stock

from the favourite stocks list.

The view navigates

from Favourite page

to Individual Stock

page.

Pass

7.2.4 Search Module

Table 7.4: Unit Test Cases for Favourite Module


(Pass/Fail)

Search page

navigation


menu which contains a

magnifying glass shape icon

and stated “Search”.

The view navigates

to Search page.

Pass

Search stock

without

keyword

1. Leave empty for the field.

2. Click “Search” button to

search the stocks.

All stocks shown in

the list following

alphabetical order.

Pass

Search stock

with stock

symbol (stock

name) in

capital letter.

1. Type in specific stock

name in capital letter.


search the stocks.

All stocks related to

the stock name

shown in the list

following

alphabetical order.

Pass

Search stock

with stock

symbol (stock

name) in small


name in small letter.


search the stocks.


the stock name

shown in the list

following

Pass

90

letter. alphabetical order.

Search stock

with stock code


code.


search the stocks.


the stock code shown

in the list following

alphabetical order.

Pass

Search stock

with stock

name or stock

code that is not

in the system.


name or stock code that is not

in the system.


search the stocks.

An alert window

which stated “No

stock found” will pop

up.

Pass

Individual

Stocks page

navigation

1. Click on a specific stock

from the stocks list.

The view navigates

from Search page to

Individual Stock

page.

Pass

7.2.5 Sectors Module

Table 7.5: Unit Test Cases for Sectors Module


(Pass/Fail)

Sectors page

navigation


menu which contains an

incline bars shape icon

and stated “Sectors”.

The view navigates to

Sectors page.

Pass

Show sector list 1. Navigate to Sectors

page.

All the sectors that

stored in the system will

be shown.

Pass

Stock List page

navigation

1. Click on a sector that

shown in the sector list.

All the stocks belong to

the sector will be shown.

Pass

Individual

Stocks page

navigation

1. Click on a specific

stock from the stocks list.

The view navigates from

Stock List page to


Pass

91

7.2.6 Individual Stock Module

Table 7.6: Unit Test Cases for Individual Stock Module


(Pass/Fail)

Show stock

information

1. Navigate to Individual

Stock page.

The information

which are high, low,

open, close, 52

weeks high and low

and volume of the

stock will be shown.

Pass

Favourite stock 1. Click on the heart shape

icon which is in grey colour.

The heart shape icon

will turn to red

colour once clicked.

Pass

Unfavourite

stock

1. Click on the heart shape

icon which is in red colour.

The heart shape icon

will turn to grey

colour once clicked.

Pass

Historical Chart

page navigation

1. Click on the “Historical

Chart” button on Individual

Stock page.

The view navigates

from Individual

Stock page to

Historical Chart

page.

Pass

Show one-year

historical chart

1. Click on the “Show 1 Year

Historical Chart” button on

Historical Chart page.

The page shows a

chart which has the

trend line of one-year

historical stock

prices of the stock.

Pass

Show predicted

stock price

1. Click on the “Predict”

button on Individual Stock

page.

The predicted stock

prices will be shown

in chart and price

table after a few

seconds.

Pass

92

7.2.7 Profile Module

Table 7.7: Unit Test Cases for Profile Module


(Pass/Fail)

Profile page

navigation


menu which contains a

profile shape icon and

stated “Prrofile”.

The view navigates to

Profile page.

Pass

Show username 1. Navigate to Profile page. The view shows a

quote which stated

“Welcome,

{username}” on top.

Pass

Edit Username

page navigation

1. Click on the “Edit

Username” button on

profile page.

The view navigates

from Profile page to

Edit Username page.

Pass

Show username

and email in

Edit Username

page.

1. Navigate to Edit

Username page.

The view shows

current username and

email of the user.

Pass

Change

username

validation

1. Leave all field as it

current state or fill in

username that already

registered.

2. Click “Confirm” button

to change username.

An alert window

stated “Username is

required” will pop up.

Pass

Change email

validation

1. Change the email in

Email field.

The Email field not

allow user to input

data.

Pass

Change

username

1. Insert a valid username in

Username field.

2. Click “Confirm” button

to change username.

An alert window

stated “Username

updated successfully

and navigate back to

Profile page with new

Pass

93

username welcome

banner.

Change

Password

navigation

1. Click on the “Change

Password” button on the

Profile page.

The view navigates

from Profile page to

Change Password

page.

Pass

Field required

validation

1. Leave “Old Password”,

“New Password” or

“Confirm Password” field

empty.

2. Click on “Change

Password” button on

Change Password page to

change the password of the

account.


pop up to inform users

that the field is

required.

Pass

Old Password

validation

1. Fill in an incorrect old

password.





account.

An alert window

which stated

“Incorrect old

password entered” will

pop up.

Pass

Password

matching

validation

1. Fill in correct old

password.

2. Fill in different password

for New Password field and

Confirm Password field.





account.

An alert window

which stated

“Password did not

match” will pop up.

Pass

Change 1. Fill in correct old An alert window Pass

94

password password.

2. Fill in new password.

3. Fill in confirm password

that match with new

password.





account.

which stated

“Password updated

successfully” will pop

up and navigate back

to Profile page.

Log Out 1. Click on “Log Out”

button on Profile page.

Application log out

and navigate to Log In

page.

Pass

7.3 Integration Testing

Integration testing is to test the functionality of two or more individual units when

integrated together. This can ensure that related functions of the application are

working well when they are combined.

7.3.1 Sign Up and Log In Module

Table 7.8: Integration Test Case for Sign Up & Log In

Test Case Test Execution Steps Expected

Results

Results

(Pass/Fail)

Sign Up

and Log In

with same

credentials

1. Click on “Sign Up” quote to navigate to

Sign Up page.

2. Fill in the field with valid values.

3. Click on the “Sign Up” button to

register an account.

4. Fill in the credentials that registered in

the Log In page.

5. Click on the “Log In” button to login.

Account

successfully

registered

and login

using the

same

credentials.

Pass

95

7.3.2 Edit Profile and Log In Module

Table 7.9: Integration Test Case for Edit Profile and Log In


Results

Results

(Pass/Fail)

Edit

username or

password

and login

successfully

1. Click on the bottom tab menu which

contains a profile shape icon and stated

“Profile”.

2.1. Click on “Edit Username” button on

Profile page.

2.2. Fill in username filed with valid

values.

2.3. Click on “Confirm” button to

change username.

OR

3.1. Click on “Change Password” button

on Profile page.

3.2. Fill in fields with valid values.

3.3. Click “Change Password” button to

change password.

4. Click “Log Out” button on Profile

page.

5. Fill in username and password that

have been changed in Log In page.

6. Click on the “Log In” button to login.

Account

successfully

login with the

changed

username and

password.

Pass

7.3.3 Store Favourite Stocks Module



Results

Results

(Pass/Fail)

Store

favourite

stocks in

1.1. Click on the bottom tab menu

which contains a magnifying glass

shape icon and stated “Search”.

The stock that

is favourited

will show in

Pass

96

Favourite

page

1.2. Search stocks with valid values.

1.3. Click on a particular row of stock

and navigate to Individual Stock page.

OR


which contains an incline bars shape

icon and stated “Sectors”.

2.2. Click on a sector that shown in the

sector list.

2.3. Click on a specific stock from the

stocks list to navigate to Individual

Stock page.

3. Click on the heart shape icon in grey

colour on Individual Stock page.


contains a heart shape icon and stated

“Favourite”.

the list in

Favourite

page.

7.3.4 Remove Favourite Stocks Module



Results

Results

(Pass/Fail)

Remove

favourite

stocks in

Favourite

page




1.2. Search stocks with valid values.

1.3. Click on a row of favourite stock

and navigate to Individual Stock page.

OR


which contains an incline bars shape

icon and stated “Sectors”.

The stock that

is

unfavourited

will remove

from the list in

Favourite

page.

Pass

97

2.2. Click on a sector that shown in the

sector list.

2.3. Click on a specific favourite stock

from the stocks list to navigate to


OR


which contains a heart shape icon and

stated “Favourite”.

3.2. Click on a specific stock from the

favourite stocks list to navigate to


4. Click on the heart shape icon in red

colour on Individual Stock page.


contains a heart shape icon and stated

“Favourite”.

7.4 System Testing

The purpose of system testing is to test on the system which has all units fully

integrated. System testing can verify the functionality and flow of the whole system.

Table 7.12: Test Case for System Testing


Results

Results

(Pass/Fail)

Login to

predict

stock

prices

1. Fill in the credentials in Log In

page.

2. Click on the “Log In” button on

Log In page.

3. Click on the bottom tab menu



Every account

will have

different

favourite stocks

list in Favourite

page. The

predicted prices

Pass

98

4. Search stocks with specific stock

name or stock code.

5. Click on the row of a stock in the

searched stocks list on Search page.

6. Click on the heart shape icon in

grey colour on Individual Stock page.


which contains a heart shape icon

and stated “Favourite”.

8. Click on the row of a stock in the

favourite stocks list on Favourite

page.

9. Click on the “Predict” button on

the Individual Stock page.


which contains a profile shape icon

and stated “Profile”.

11. Click “Log Out” button on

Profile page.

12. Repeat step 1 to step 11 with

another account.

of the stock will

be shown

successfully and

they are differed

for every stock.

99

CHAPTER 8

CONCLUSION AND RECOMMENDATIONS

8.1 Introduction

This chapter discusses the limitations of this project, recommendations and future

works and the conclusion of this project.

8.2 Limitations

Although all required features were developed in this mobile application, there are

still some limitations of this mobile application. One of the limitations is the

prediction is not accurate all the time. It might be inaccurate if there is some external

factor affecting the trend of the market. Another limitation is the period of the

prediction results is fixed to 65 trading days. Users cannot set the period that they

want to predict the stock prices. The last limitation is there is no any solution that

user can do when user forget their password to login to the system.

8.3 Recommendations and Future Works

One of the recommendations is to monitor the performance of the prediction model

from time to time. Some improvement can be taken if there are new solutions found

to increase the accuracy of the prediction model and decrease the time used to predict

future stock prices. Other than that, the mobile application can allow users to input

the period of prediction. In this case, users can specify a period of trading days and

the prediction results will show only the specific period. Lastly, the mobile

application can send an email notification to reset password for the users who forget

their password.

8.4 Conclusion

In conclusion, a mobile application for stock price prediction was developed to let

users have a better view on the stocks market trend. Five time series algorithms had

been evaluated to find out the best prediction models for the mobile application.

ARIMA has the best accuracy among all five time series algorithms and it had been

chosen as the prediction model for the mobile application for stock price prediction.

100

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APPENDICES

Appendix A: User Interface Design Iteration 1

a mobile application for stock price prediction

Documents