Algorithmic Trader - Systematic Desk
Quantitative Research · London · Full-time · On-site
Annual base salary: £195,000 – £230,000 GBP
Design and run high-frequency and mid-frequency automated trading strategies across digital asset venues.
Role description
Westren Capital is a London-based proprietary trading firm specialising in digital asset markets. Our systematic desk designs and runs high-frequency and mid-frequency automated strategies across centralised and decentralised venues, competing directly on execution speed, signal quality, and infrastructure.
Traders on this desk work across the full stack of a strategy, from signal to execution to live risk management, rather than handing pieces off and hoping they reassemble correctly downstream.
Responsibilities
Design, test, and deploy systematic trading strategies across digital asset venues, with a focus on strategies that hold up outside the backtest
Monitor live strategy performance continuously, distinguishing genuine signal decay from noise, and normal drawdown from something actually broken
Work directly with engineers to improve execution logic, reduce latency, and close the gap between theoretical and realised fills
Manage position sizing, exposure limits, and risk controls across a live, automated book
Investigate anomalies in fills, slippage, or venue behaviour as they happen, not after the postmortem
Contribute new strategy ideas backed by evidence rather than conviction alone
Requirements
Requirements:
Demonstrated experience building or trading systematic strategies, ideally in HFT or other latency-sensitive environments
Strong programming skills in Python and/or C++, able to move from research code to something that survives live markets
Solid understanding of market microstructure, order book dynamics, and execution mechanics
Comfortable owning a strategy end-to-end, including the unglamorous parts that break first if ignored
Experience in crypto or other 24/7 markets is an advantage given the operational demands of always-on trading
Calm, methodical approach to debugging live issues under time pressure