WizardQuant
Quantitative Developers work closely with Quantitative Researchers and other software engineering teams to develop, evolve, and maintain critical components and systems within the quantitative research and production life cycles. They build components such as optimizers, research tools, computation frameworks (including simulators and backtesting platforms), and business-level systems to support model execution and portfolio construction and management based on mathematical and computer science methods. They also contribute to processes such as accounting, risk management, and integration with execution platforms.
Job Description
Develop research tools and platforms to support quantitative research in both interactive and batch-processing modes. Utilize or develop high-performance computing and parallel computing frameworks or libraries to optimize the performance of core components in research and production trading environments. Develop quantitative systems that process massive amounts of time-series financial data, such as simulation and backtesting systems powered by distributed computing infrastructure. Research and implement quantitative algorithms and procedures such as portfolio optimization, based on a deep understanding of systematic trading business logic and engineering expertise. Contribute to emerging business areas, such as new investment horizons, frequencies, asset classes, and overseas markets. Qualifications
Bachelor’s degree or above, majoring in Computer Science, Software Engineering, Mathematics, or related STEM fields. Solid foundation in algorithms, data structures, and proficiency in software development within Linux environments. Experience in quantitative software engineering, with proven experience in designing and developing production-ready quantitative systems. Hands-on professional experience with modern C++ or Python, including concurrent and parallel programming. Preferred Qualifications
Experience with overseas futures and equities markets and relevant quantitative trading systems. Deep understanding of software systems supporting quantitative research and production trading. Experience applying high-performance computing techniques to process large-scale time-series financial datasets. Experience with linear algebra, numerical computations, and numerical optimization. Experience with GPU computing and distributed computing. Solid understanding of computer science fundamentals such as architecture, OS and compilers. Experience with machine learning frameworks such as PyTorch. Seniority level
Entry level Employment type
Full-time
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Develop research tools and platforms to support quantitative research in both interactive and batch-processing modes. Utilize or develop high-performance computing and parallel computing frameworks or libraries to optimize the performance of core components in research and production trading environments. Develop quantitative systems that process massive amounts of time-series financial data, such as simulation and backtesting systems powered by distributed computing infrastructure. Research and implement quantitative algorithms and procedures such as portfolio optimization, based on a deep understanding of systematic trading business logic and engineering expertise. Contribute to emerging business areas, such as new investment horizons, frequencies, asset classes, and overseas markets. Qualifications
Bachelor’s degree or above, majoring in Computer Science, Software Engineering, Mathematics, or related STEM fields. Solid foundation in algorithms, data structures, and proficiency in software development within Linux environments. Experience in quantitative software engineering, with proven experience in designing and developing production-ready quantitative systems. Hands-on professional experience with modern C++ or Python, including concurrent and parallel programming. Preferred Qualifications
Experience with overseas futures and equities markets and relevant quantitative trading systems. Deep understanding of software systems supporting quantitative research and production trading. Experience applying high-performance computing techniques to process large-scale time-series financial datasets. Experience with linear algebra, numerical computations, and numerical optimization. Experience with GPU computing and distributed computing. Solid understanding of computer science fundamentals such as architecture, OS and compilers. Experience with machine learning frameworks such as PyTorch. Seniority level
Entry level Employment type
Full-time
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