"Developing An Intelligent Trading Model For The Ethiopia Commodity Exchange (ECX) Using Deep Reinforcement Learning Algorithms" - Information and Links:

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"Developing An Intelligent Trading Model For The Ethiopia Commodity Exchange (ECX) Using Deep Reinforcement Learning Algorithms" and the language of the book is English.


“Developing An Intelligent Trading Model For The Ethiopia Commodity Exchange (ECX) Using Deep Reinforcement Learning Algorithms” Metadata:

  • Title: ➤  Developing An Intelligent Trading Model For The Ethiopia Commodity Exchange (ECX) Using Deep Reinforcement Learning Algorithms
  • Author:
  • Language: English

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  • Internet Archive ID: 93743

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"Developing An Intelligent Trading Model For The Ethiopia Commodity Exchange (ECX) Using Deep Reinforcement Learning Algorithms" Description:

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<div> <table cellspacing="0" cellpadding="0" align="left"> <tbody><tr> <td valign="top" align="left" style="padding-top:0cm;padding-right:9.35pt;padding-bottom:0cm;padding-left:9.35pt;"> <p class="MsoNormal" style="margin-top:6pt;margin-right:0cm;margin-bottom:6pt;margin-left:0cm;text-align:justify;line-height:10pt;"><span lang="en-us" style="font-size:8.5pt;" xml:lang="en-us">The Ethiopian Commodity Exchange (ECX) faces significant challenges, including manual trading processes, market inefficiencies, and data fragmentation, which hinder its ability to operate effectively in a volatile and dynamic environment. This research develops an intelligent trading model leveraging Deep Reinforcement Learning (DRL) algorithms, specifically Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Advantage Actor-Critic (A2C), to address these issues.</span></p><p></p> <p class="MsoNormal" style="margin-top:6pt;margin-right:0cm;margin-bottom:6pt;margin-left:0cm;text-align:justify;line-height:10pt;"><span lang="en-us" style="font-size:8.5pt;" xml:lang="en-us">The proposed framework utilizes DRL to enable agents to learn optimal trading policies through interactions with simulated ECX market environments. The model employs historical market data, representing state features such as price trends, trading volumes, and external economic indicators. Actions are defined as buy, sell, or hold decisions, while reward structures are designed to incentivize profit and penalize excessive risk. The research integrates techniques such as experience replay and target networks in DQN, action evaluation in DDQN, and advantage functions in A2C to enhance model performance and stability.</span></p><p></p> <p class="MsoNormal" style="margin-top:6pt;margin-right:0cm;margin-bottom:6pt;margin-left:0cm;text-align:justify;line-height:10pt;"><span lang="en-us" style="font-size:8.5pt;" xml:lang="en-us">Experimental results demonstrate that the DRL models significantly improve trading efficiency and decision-making accuracy compared to manual processes. DDQN outperforms DQN in managing noisy and volatile market conditions, while A2C excels in handling continuous decision variables, such as dynamic trade volumes. The results highlight the scalability and adaptability of the proposed system in addressing ECX-specific challenges, including risk management and market transparency.</span></p><p></p> <p class="MsoNormal" style="margin-top:6pt;margin-right:0cm;margin-bottom:6pt;margin-left:0cm;text-align:justify;line-height:10pt;"><span lang="en-us" style="font-size:8.5pt;" xml:lang="en-us">The study concludes that the DRL-based trading model offers transformative potential for the ECX by automating decision-making, optimizing trade execution, and promoting equitable participation among stakeholders. This research provides a foundation for integrating advanced machine learning techniques into emerging commodity markets, ensuring their efficiency and competitiveness in a global context.</span></p><p></p> </td> </tr> </tbody></table> </div>

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