2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study
| Format: | General Document |
|---|
| _version_ | 1860798361799491584 |
|---|---|
| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection1147208 |
| copyright | Copyright©PWB2026 |
| country | Malaysia |
| date | 2025-08-10 |
| format | General Document |
| id | 17463 |
| institution | UniSZA |
| originalfilename | DESIGNING AN EXPERT ADVISOR SYSTEM FOR FOREX TRADING USING LWMA- BASED TECHNICAL INDICATOR MALAYSIA CASE STUDY (PHD_2025).pdf |
| person | Zarith Sofia Zulkifli |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=17463 |
| sourcemedia | Server storage Scanned document |
| spelling | 17463 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=17463 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection1147208 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.5 Microsoft® Word 2016 Public Access Server storage Scanned document Universiti Sultan Zainal Abidin Universiti Sultan Zainal Abidin 338 Dissertations, Academic Copyright©PWB2026 Thesis Decision Support Systems 2025-08-10 Zarith Sofia Zulkifli Automated Trading (AT) Algorithmic Trading Expert Advisor (EA) Foreign Exchange (Forex) Trading Linear Weighted Moving Average (LWMA) Technical Indicators Trading Performance Quantitative Trading Malaysian Forex Traders Financial Market Technology Foreign Exchange Market Investment Analysis Financial Engineering Electronic Trading of Securities 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study The rapid evolution of Automated or Algorithmic Trading (AT) has significantly transformed trading approaches in the Foreign Exchange (Forex) market, introducing new complexities and increasing the demand for proficiency in technological tools. However, Malaysian traders face challenges, such as a lack of skills and delays in adopting advanced technologies, due to limited personalized solutions that cater to their specific trading preferences and market conditions. This research aims to bridge these gaps by examining Malaysian traders' expertise, preferences, and technical requirements for AT and developing a specialized Expert Advisor (EA) to enhance their trading performance. The objectives are to assess the preferences, skills, and knowledge of Malaysian traders in AT using technical indicators, design a tailored EA system aligned with their identified needs, and evaluate the EA's effectiveness in improving trading performance. This study employs a quantitative research design comprising surveys and experimental validation. A total of 252 Malaysian Forex traders participated in the survey, providing insights into their trading preferences, market behavior, and the effectiveness of existing AT systems. Data collection was facilitated through structured questionnaires and experimental testing of the EA, with the survey providing demographic insights and gauging traders' knowledge levels. The survey data were analyzed using statistical software (SPSS), and statistical performance metrics were used to evaluate the EA’s performance. Based on the survey findings, a custom EA was designed using the Linear Weighted Moving Average (LWMA) indicator to enhance trade accuracy and decision-making. To evaluate the EA's performance and effectiveness, a series of controlled experiments were conducted over a ten-year period (2014–2023) for EUR/USD and Gold trading and five years (2019–2023) for Bitcoin trading, ensuring robustness across different market conditions. Comparative analysis was performed against existing trading strategies and traditional Expert Advisors. Rigorous statistical tests, including return on investment (ROI) analysis, profit factor assessment, and maximum drawdown evaluation, were used to measure the EA's effectiveness. Results indicate that the newly developed EA outperformed conventional strategies with an 18% increase in accuracy, 25% faster execution, and 15% higher profitability. Further sensitivity analysis of LWMA parameters demonstrated the EA’s ability to adjust effectively to fluctuating market conditions, ensuring stable performance across various trading environments. Additionally, the EA’s performance was benchmarked against other common Moving Averages (MA), such as Simple Moving Average (SMA) and Exponential Moving Average (EMA), confirming its superiority in different trading scenarios. The developed Expert Advisor (EA) also demonstrated strong performance in trading commodities (Gold) and cryptocurrencies (Bitcoin), as evidenced by its high accuracy, faster execution, and increased profitability. However, the effectiveness of the EA for other asset classes, such as stocks and other financial instruments, remains unexplored because these markets have different price dynamics, volatility patterns, and regulatory constraints. This research contributes to the practical application of AT in Malaysia by providing a new personalized EA model that meets local traders' needs. The importance of this research lies in its ability to offer a practical solution that bridges the gap between modern trading technology and the specific needs of Malaysian Forex traders. Future work will focus on improving the EA's predictive capabilities by incorporating more Machine Learning (ML) techniques and exploring its use across various financial asset classes. This study offers practical implications for traders by providing a model for creating EAs that align with user preferences and enhance trading efficiency. It also paves the way for future AT research, particularly in underdeveloped countries with low adoption of such technology, encourages greater use of AT strategies in the region, and contributes to the global conversation on the future of trading technology. uuid:304a2abf-f692-4984-99bb-94d626f4828c DESIGNING AN EXPERT ADVISOR SYSTEM FOR FOREX TRADING USING LWMA- BASED TECHNICAL INDICATOR MALAYSIA CASE STUDY (PHD_2025).pdf |
| spellingShingle | 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study |
| state | Terengganu |
| subject | Dissertations, Academic Decision Support Systems Foreign Exchange Market Investment Analysis Financial Engineering Electronic Trading of Securities |
| summary | The rapid evolution of Automated or Algorithmic Trading (AT) has significantly transformed trading approaches in the Foreign Exchange (Forex) market, introducing new complexities and increasing the demand for proficiency in technological tools. However, Malaysian traders face challenges, such as a lack of skills and delays in adopting advanced technologies, due to limited personalized solutions that cater to their specific trading preferences and market conditions. This research aims to bridge these gaps by examining Malaysian traders' expertise, preferences, and technical requirements for AT and developing a specialized Expert Advisor (EA) to enhance their trading performance. The objectives are to assess the preferences, skills, and knowledge of Malaysian traders in AT using technical indicators, design a tailored EA system aligned with their identified needs, and evaluate the EA's effectiveness in improving trading performance. This study employs a quantitative research design comprising surveys and experimental validation. A total of 252 Malaysian Forex traders participated in the survey, providing insights into their trading preferences, market behavior, and the effectiveness of existing AT systems. Data collection was facilitated through structured questionnaires and experimental testing of the EA, with the survey providing demographic insights and gauging traders' knowledge levels. The survey data were analyzed using statistical software (SPSS), and statistical performance metrics were used to evaluate the EA’s performance. Based on the survey findings, a custom EA was designed using the Linear Weighted Moving Average (LWMA) indicator to enhance trade accuracy and decision-making. To evaluate the EA's performance and effectiveness, a series of controlled experiments were conducted over a ten-year period (2014–2023) for EUR/USD and Gold trading and five years (2019–2023) for Bitcoin trading, ensuring robustness across different market conditions. Comparative analysis was performed against existing trading strategies and traditional Expert Advisors. Rigorous statistical tests, including return on investment (ROI) analysis, profit factor assessment, and maximum drawdown evaluation, were used to measure the EA's effectiveness. Results indicate that the newly developed EA outperformed conventional strategies with an 18% increase in accuracy, 25% faster execution, and 15% higher profitability. Further sensitivity analysis of LWMA parameters demonstrated the EA’s ability to adjust effectively to fluctuating market conditions, ensuring stable performance across various trading environments. Additionally, the EA’s performance was benchmarked against other common Moving Averages (MA), such as Simple Moving Average (SMA) and Exponential Moving Average (EMA), confirming its superiority in different trading scenarios. The developed Expert Advisor (EA) also demonstrated strong performance in trading commodities (Gold) and cryptocurrencies (Bitcoin), as evidenced by its high accuracy, faster execution, and increased profitability. However, the effectiveness of the EA for other asset classes, such as stocks and other financial instruments, remains unexplored because these markets have different price dynamics, volatility patterns, and regulatory constraints. This research contributes to the practical application of AT in Malaysia by providing a new personalized EA model that meets local traders' needs. The importance of this research lies in its ability to offer a practical solution that bridges the gap between modern trading technology and the specific needs of Malaysian Forex traders. Future work will focus on improving the EA's predictive capabilities by incorporating more Machine Learning (ML) techniques and exploring its use across various financial asset classes. This study offers practical implications for traders by providing a model for creating EAs that align with user preferences and enhance trading efficiency. It also paves the way for future AT research, particularly in underdeveloped countries with low adoption of such technology, encourages greater use of AT strategies in the region, and contributes to the global conversation on the future of trading technology. |
| title | 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study |
| title_full | 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study |
| title_fullStr | 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study |
| title_full_unstemmed | 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study |
| title_short | 2026_Designing an Expert Advisor System for Forex Trading Using Lwma- Based Technical Indicator: Malaysia Case Study |
| title_sort | 2026_designing an expert advisor system for forex trading using lwma- based technical indicator: malaysia case study |