Ismail Mohamed Anwar

Ismail Mohamed Anwar

القاهرة مصر
٨٩٤ متابع أكثر من 500 زميل

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نبذة عني

Data Force Multiplier
https://0x0049.com/

النشاط

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الخبرة

  • رسم بياني Deloitte

    Deloitte

    Deloitte Innovation Hub - Cairo

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    Cairo, Egypt

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    Cairo, Egypt

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    Medway, Kent

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    Cairo, Egypt

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    New Cairo, Cairo, Egypt

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    Cairo, Egypt

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    Cairo, Egypt

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    Cairo

التعليم

  • رسم بياني University of Kent

    University of Kent

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    Using Particle Swarm Optimization (PSO) to Compose Strategies for Market Timing.

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    Thesis topic: Using swarm intelligence techniques in the reduction of data for the purposes of machine learning.

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    My graduation project was on the use of finite state automatons and piecewise linear regression to detect charting patterns in historical prices for the purpose of trading.

التراخيص والشهادات

  • رسم بياني IBM Cognos BI PRofessional

    IBM Cognos BI PRofessional

    IBM

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  • رسم بياني Microsoft Certified Technology Specialist (SQL Server 2008, Business Intelligence Development and Maintenance)

    Microsoft Certified Technology Specialist (SQL Server 2008, Business Intelligence Development and Maintenance)

    Microsoft

    تم الإصدار في ⁦
    معرف الشهادة A922-0531
  • رسم بياني Microsoft Certified Solution Developer

    Microsoft Certified Solution Developer

    Microsoft

    تم الإصدار في ⁦
    معرف الشهادة A922-0515

المنشورات

  • A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing

    IEEE Cifer 2022

    Market timing is the issue of deciding when to buy or sell a given asset on a financial market. As one of the core issues of algorithmic trading systems, designers of such systems have turned to computational intelligence methods to aid them in this task. In our previous work, we introduced a number of Particle Swarm Optimization (PSO) algorithms to compose strategies for market timing using a novel training and testing methodology that reduced the likelihood of overfitting and tackled market…

    Market timing is the issue of deciding when to buy or sell a given asset on a financial market. As one of the core issues of algorithmic trading systems, designers of such systems have turned to computational intelligence methods to aid them in this task. In our previous work, we introduced a number of Particle Swarm Optimization (PSO) algorithms to compose strategies for market timing using a novel training and testing methodology that reduced the likelihood of overfitting and tackled market timing as a multiobjective optimization problem. In this paper, we provide a detailed analysis of these multiobjective PSO algorithms and address two limitations in the results presented previously. The first limitation is that the PSO algorithms have not been compared to well-known algorithms or market timing techniques. This is addressed by comparing the results obtained against NSGA-II and MACD, a technique commonly used in market timing strategies. The second limitation is that we have no insight regarding diversity of the Pareto sets returned by the algorithms. We address this by using RadViz to visualize the Pareto sets returned by all the algorithms, including NSGA-II and MACD. The results show that the multiobjective PSO algorithms return statistically significantly better results than NSGA-II and MACD. We also observe that the multiobjective PSOSP algorithm consistently displayed the best spread in its returned Pareto sets despite not having any explicit diversity promoting measures.

    مؤلفون آخرون
    • Fernando Otero
    عرض المنشور

  • A multiobjective optimization approach for market timing

    GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference

    مؤلفون آخرون
    • Fernando E.B. Otero
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  • Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing

    SciTePress

    مؤلفون آخرون
    • Fernando E.B. Otero

  • Using Particle Swarms to Build Strategies for Market Timing: A Comparative Study

    Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings

    مؤلفون آخرون
    • Fernando E.B. Otero

  • ADR-Miner: An Ant-based Data Reduction Algorithm for Classification

    Proceedings IEEE Congress on Evolutionary Computation, 2015

    مؤلفون آخرون

  • Data Reduction for Classification with Ant Colony Optimization

    Journal of Intelligent Data Analysis

    مؤلفون آخرون

  • Instance Selection with Ant Colony Optimization

    Proceedings INNS Big Data 2015 Conference

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اللغات

  • English

    إجادة اللغة الأم أو إجادة لغتين إجادة تامة

  • Arabic

    إجادة اللغة الأم أو إجادة لغتين إجادة تامة

المزيد من أنشطة Ismail

عرض ملف Ismail الشخصي الكامل

  • مشاهدة الأشخاص المشتركين الذين تعرفهم
  • تقديم تعارف
  • تواصل مع Ismail مباشرة
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