Quantitative Researcher (Monetisation)

Company Overview

Join a premier quantitative proprietary high-frequency trading firm in London that is expanding its focus toward mid-frequency trading strategies across global equities, futures, and derivatives markets.

Role Summary

The successful candidate will join a specialised team dedicated to alpha blending, monetisation, and optimisation. This team transforms raw alpha signals from the research group into active, risk-managed trading strategies, working across the entire process from signal integration to execution.

Key Responsibilities

• Integrate and assign weights to a broad variety of raw alpha signals to create consistent, tradable strategies, carefully managing signal correlations and overlaps.
• Develop and maintain the optimisation framework, covering portfolio construction, capital allocation, and sizing positions across different signals and markets.
• Quantify and reduce trading costs, including market impact, transaction expenses, and capacity limitations when converting signals to positions.
• Continuously evaluate live performance by monitoring profit and loss, diagnosing alpha degradation, adjusting signal weightings, and enhancing capital efficiency over time.
• Collaborate with infrastructure and execution teams to deploy strategies in live environments and refine them post-deployment.
• Oversee and rigorously assess the real-time risk profile of the combined portfolio, managing exposures effectively.

Candidate Requirements

• An advanced academic degree (PhD or MSc) in quantitative fields such as Mathematics, Physics, Statistics, Computer Science, or related disciplines.
• Extensive expertise in statistical modelling and machine learning techniques, especially in optimisation, ensemble methodologies, and portfolio construction approaches such as convex optimisation and mean-variance analysis.
• Proven experience in signal combination, alpha mixing, or building systematic portfolios, preferably within mid-frequency trading environments.
• Strong proficiency in Python programming; knowledge of C++ and high-performance computing is advantageous.
• Demonstrated success in transitioning research models to live trading environments and generating real-world profit and loss outcomes.
• Familiarity with financial time-series analytics, market microstructure concepts, or transaction cost modelling is desirable.