Quantitative Research for Systematic Trading – E-Markets

2 weeks ago

Hong Kong, Hong Kong SAR China JPMorgan Chase & Co. Full time

If you are passionate, and have strong desire to set and achieve challenging goals, you have found the right team.

As a Vice President in the Quantitative Research for Systematic Trading team, you will identify opportunities to transform, automate and optimize our trading operations, and you will design and implement next generation analytics and trading applications to support this business transformation. Our team covers all equities businesses and works closely with the various trading desks to develop data-driven solutions such as algorithmic strategies (high to low frequency), trading signals, risk models, portfolio optimization, flow categorization and clustering – and to combine them into automated trading processes or trading algorithms.

Communication skills and drive are critical for this role as we expect the candidate to actively engage with the business and act as a culture carrier for modern data-driven methods and business automation.

Job Responsibilities

Work closely with the Equity Volatility trading desks to build quantitative analytics and data-driven processes with the view of automating and optimizing the derivatives trading operations. This includes alpha research for volatility (. carry and dispersion signals), optimal hedging for option portfolios, listed option algorithmic execution, derivatives market making (. warrants and options), volatility analytics.  Build trading analytics and algorithmic execution strategies, in the context of systematic delta/gamma hedging for a volatility desk, leveraging techniques such as portfolio optimization, market impact models, index arbitrage, statistical arbitrage (for Equity Stocks, Index and Single Stock Futures, ADRs, GDRs, ETFs instruments) Identify business opportunities and contribute to the entire lifecycle from idea generation to production: perform and lead research, design prototype, implement analytics and trading strategies, monitor daily usage, and analyse performance. Design and build end-to-end applications and/or specialized components fully integrated within the existing ecosystems. This can range from the development of a scalable backtesting infrastructure to replicate a specific flow, to designing from scratch complex application components (. event based asynchronous microservices, complex data pipelines)

Required Qualifications, Capabilities and skills

PhD or Master’s Degree in a quantitative discipline from a top-tier institution Minimum 5 years of experience in quantitative research in Equity or related asset classes, with a practical knowledge of derivatives pricing and risk management theory (. vanilla options and other derivatives) and a thorough understanding of algorithmic trading (. execution or market making) and statistical arbitrage (low to mid frequency). Strong written and verbal communication skills, ability to convey the ideas behind complex research in a clear and precise manner. Advanced expertise in statistical modelling & optimization, including standard models, linear, convex & conic optimization) Strong full-stack application coding background (from frontend to backend) with proficiency in Python and relevant quantitative packages (numpy, pandas, scipy, scikit-learn…).  Ability to manipulate and analyse complex, large scale, high-dimensionality data from multiple sources. 

Preferred Qualifications, Capabilities and skills

Knowledge of advanced AI/ML techniques (Deep Neural Network, Reinforcement Learning) and associated libraries (Tensorflow, PyTorch) is a plus. Knowledge of KDB/Q and C/C++ is a strong plus.

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