Equity Quantitative Trader
4 weeks ago
I’m working with a senior quant portfolio manager running a significant book (3/4 yard) of fully systematic equity strategies. They focus on stat-arb predominantly (Mid-high frequency), and made almost 100M in PnL last year. Off the back of this success, they are looking for a sub-PM to join and be allocated a proportion of the book.
We are looking for a sub-PM or senior quant researcher, able to create their own strategies. They need to have at least 3 years of alpha generation experience. They’re open to a profile from either a collaborative research environment, or a high performing pod.
It’s a great opportunity to get exposure to the entire investment process whilst establishing your own track record. You will be working closely with a more junior research profile to support you. The PM is based in London at a large hedge fund and is open to candidates working in London or Paris. Comp is very competitive and can be formulaic to PnL.
Responsibilities:
- Develop and implement mid-frequency systematic equity trading strategies in collaboration with our research and data analytics teams.
- Utilize advanced quantitative techniques and statistical models to analyse market data and identify potential investment opportunities.
- Manage and optimize existing systematic equity portfolios, ensuring alignment with investment objectives and risk parameters.
- Conduct rigorous performance attribution and risk analysis to assess portfolio performance and make data-driven adjustments as necessary.
- Stay informed about market trends, economic indicators, and industry developments to adapt strategies and exploit market inefficiencies.
- Collaborate with the PM and researchers to enhance the overall investment process and share insights across the pod.
Requirements:
- Bachelor's or Master's degree in Finance, Economics, Mathematics, Computer Science, or a related quantitative field.
- Proven experience building mid-frequency equity trading strategies with a Sharpe of at least 1.5.
- Strong proficiency in programming languages such as Python or R for data analysis and strategy development.
- Expertise in utilizing statistical tools and machine learning techniques to develop trading strategies and improve performance.
- Deep understanding of equity markets, factor models, and portfolio optimization techniques.
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