Quantitative Analyst
E-Trading Quantitative Researcher - eRates
Overview
This team explores advanced quantitative approaches to electronic trading, with a focus on market-making and algorithmic strategy enhancement. Its goal is to leverage data-driven insights and sophisticated modelling to improve trading performance and uncover opportunities in complex markets.
Core Activities
- Strategy Exploration: Develop and refine market-making and trading strategies, balancing pricing, liquidity, and execution in dynamic environments.
- Algorithm Development: Improve existing models and implement innovative quantitative techniques to enhance efficiency, risk management, and overall performance.
- Research & Signal Discovery: Analyse historical and real-time market data to identify patterns, new trading opportunities, and potential alpha signals.
- Quantitative Analysis: Apply statistical methods, machine learning, and time-series analysis to generate actionable insights and support data-driven decision-making.
- Collaboration: Work closely with other researchers, technologists, and trading professionals to translate findings into practical tools and strategies.
- Innovation & Continuous Improvement: Explore novel approaches, methodologies, and technologies to maintain a competitive edge in quantitative trading.
Typical Background & Skills
- Experience: Prior exposure to quantitative research or electronic trading, ideally including market microstructure and trading strategies.
- Technical Proficiency: Strong coding skills in Python or Java; familiarity with KDB+ (Q) is advantageous. Experience with large-scale market data and statistical modelling is key.
- Quantitative Expertise: Knowledge of statistical methods, machine learning, model calibration, optimisation, and risk management techniques.
- Education: Advanced degrees (Master's or PhD) in Mathematics, Statistics, Physics, Computer Science, Engineering, or related quantitative disciplines.
- Collaboration & Communication: Ability to work effectively in a team, conveying complex insights clearly, and contributing to a research-driven environment.