Loomis Sayles is currently looking to hire a candidate into its Multi-Asset Risk Premia Strategies (MARP) group. This person will work on research related to enhancing our quantitative framework and trading process.
As a primary function, the analyst will utilize advanced optimization techniques and machine learning approaches to examine trading data in an effort to increase profitability, decrease risk, and reduce transaction costs in addition to conceiving new trading ideas and devising the simulations needed to test them. The investment objective of the MARP group is to deliver a steady, low volatility, positive return stream with limited draw-downs in stand-alone and multi-asset class products. The group seeks to achieve this objective by developing and running a variety of quantitative, systematic investment strategies by applying a rigorous scientific approach to design, develop, implement and manage strategies particularly in the futures, index, derivatives, cash and ETF (Equities, Credit, Rates, Volatility, Commodities and FX) markets and liaison with other teams at the firm to build multi-asset portfolios.Primary Job Responsibilities Include Design and testing of methodology to calculate transaction costs while accounting for different trading protocols, market data availability and data quality.
Analyze pre-trade cost estimate and post trade analytics, support the development and improvement of the overall transaction cost analysis infrastructure. Incorporate alpha decay research at strategy level into portfolio rebalancing. Build fractional trading models to execute trades over multiple days. Optimize trading size based on daily volumes, trade timing and strategy rebalancing frequency Automate and manage orders for daily account maintenance, fractional rebalancing including investing cash and withdrawals Experience generating high or medium frequency signals for alpha generationand assist team with other modeling tasks
The ideal candidate will have at least 3 and up to 10 years experience in the financial industry. High frequency or statistical arbitrage background is preferred but not required.
A demonstrated history of developing successful quantitative models, preferably involving transaction cost analysis, algorithmic and/or high-frequency trading.The candidate will have an MS or PhD degree in Physics, Computer Science, Mathematics, Statistics, Operations Research or related quantitative field. Proficiency in analytical programming (with a preference for MATLAB and PYTHON skills)Highly motivated, detail-oriented, and able to effectively communicate across different audiences
Prior experience in a data driven analytical research environment
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