Our client is a top tier investment bank in HK looking for someone who can define and drive the evolution of the D1 desk, contribute to existing agendas (iMM/CRB and basis trading) and tackle the technological/data/infrastructure challenges faced by the desk in their effort to transform, automate and optimize the trading operations and defining and implementing cutting-edge next-generation analytics to support this business transformation.
This team covers all equities businesses, works closely with traders to develop data-driven solutions and combines them into automated trading processes or trading algorithms. You will play a key role in the development of systematic strategies (high to low frequency), trading signals, risk models, portfolio optimization, flow categorization and clustering.
- Build trading analytics and algorithmic trading strategies such as portfolio optimization, index arbitrage, statistical arbitrage and market-making strategies (on Equity cash, Futures, ETFs) for the Delta One and Cash trading desks.
- Translate high-level vision of the evolution of the D1 desk into an actionable plan, including building and cleaning step by step the data/risk/operational infrastructure
- Identify business opportunities and contribute to the entire lifecycle from idea generation to production: perform research, design prototype, implement analytics and strategies, monitor daily usage and analyze performance.
- Support trading activity by investigating model and algorithm behaviour (scenarios and post trade analysis, historical behaviour).
- Devise hedging and trading strategies and build execution logic.
- PhD or Master's Degree in a quantitative discipline from a top-tier institution.
- At least five years of relevant quant experience in systematic quantitative trading in Equity or related asset classes.
- Strong written and verbal communication skills, ability to convey the ideas behind complex research in a clear and precise manner.
- A thorough understanding of algorithmic trading, market-making, index and statistical arbitrage.
- Good expertise in statistical modelling & optimization, including standard models, linear, convex & conic optimization).
- A strong coding background with proficiency in C++, Python and relevant quantitative packages (numpy, pandas). Ability to manipulate and analyse complex, large scale, high-dimensionality data from multiple sources. Knowledge of KDB/Q expected.
- Knowledge of derivatives pricing and risk management theory (vanilla options and volatility products) is a plus.