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Ali H. Askar, CQF

Quantitative Algorithmic Trading Expert

Ali H. Askar, CQF is a prominent figure in the field of quantitative and algorithmic trading. He currently serves as the Head of Quantitative Trading at OTS Capital, where he leads a team in refining trading strategies and implementing advanced AI techniques.1

Professional Background

As a Chartered Quantitative Finance (CQF) professional, Askar possesses expertise in financial mathematics and quantitative analysis. His role at OTS Capital involves:

  • Leading a talented team
  • Refining trading strategies
  • Leveraging cutting-edge AI techniques in trading

Educational Pursuits

Askar is committed to continuous learning and professional development. In February 2024, he began pursuing a Master of Science in Applied Artificial Intelligence at Lebanese American University.2 This academic endeavor demonstrates his dedication to staying at the forefront of AI applications in finance.

Public Engagement

Askar is active in sharing his insights and expertise:

  • He has been interviewed by Bloomberg Al Sharq, discussing topics such as the FTX meltdown and its impact on cryptocurrency markets.3
  • He regularly posts on LinkedIn about various aspects of quantitative trading, market analysis, and algorithmic trading strategies.457

Areas of Expertise

Askar's LinkedIn posts reveal his deep knowledge in several areas:

  • Quantitative Trading: He discusses the challenges and dilemmas faced by algo traders, emphasizing the importance of skepticism in the field.7
  • Market Analysis: Askar shares insights on market behavior, including phenomena like overnight trading sessions.4
  • AI and Machine Learning: He explores the applications of technologies like ChatGPT and other large language models in trading.6

Ali H. Askar's combination of practical experience, academic pursuits, and public engagement positions him as a respected voice in the quantitative and algorithmic trading community.

Highlights

Jul 24 · linkedin

Is the Current Market Rally an AI Euphoria Bubble?

Jul 23 · linkedin

Despite widespread skepticism about the usefulness of technical analysis (TA) indicators in the machine learning (ML) world, I believe they are often applied incorrectly. Instead of predicting the next bar's returns, it might be more insightful to predict the average return over the next several bars.

In the attached plot, I am regressing the 14-period Relative Strength Index (RSI) against the exponentially weighted moving average (EWMA) of the next 3 bars' returns. The plot illustrates that significant changes in RSI often correspond to notable changes in returns, supporting the idea that TA indicators can provide valuable predictive insights when used with the right approach. By focusing on the average return over multiple bars, we capture more consistent and meaningful trends, potentially enhancing the predictive power of TA indicators in ML models, provided they are aligned correctly to avoid look-ahead bias.

Related Questions

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Ali H. Askar, CQF
Ali H. Askar, CQF, photo 1
Ali H. Askar, CQF, photo 2
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Experience

Head of Quantitative Algorithmic Trading at Yeyld (July 2023 - Present)
Senior Quant at RA Squared (November 2021 - July 2023); CTO - Quantitative Algo Trading at Confidential (March 2018 - November 2021); AWS Cloud Solutions Consultant at Zero & One (January 2017 - March 2018)

Education

Certified Quantitative Finance, Econometrics and Quantitative Economics from CQF Institute (2021 - 2022); Bachelor of Engineering (BE), Electrical, Electronics and Communications Engineering from Beir

Location

Dubai, United Arab Emirates