Job Summary
We are seeking a motivated and detail-oriented Quantitative Analyst to run back-testing and optimize trading strategies. In this role, you will play a pivotal part in driving the development, evaluation, and continual refinement of quantitative trading strategies. Leveraging your expertise in back-testing frameworks and quantitative analysis, you’ll rigorously test and optimize systematic models using historical and simulated market data. You will be collaborating with the CIO, researchers, and traders to interpret back-testing results, uncover performance drivers, and enhance strategy robustness. Your ability to dissect complex data, automate analytical processes, and communicate actionable insights will directly shape trading decisions and the evolution of our investment strategies. By maintaining rigorous documentation and adapting models to market dynamics, you’ll ensure our strategies remain cutting-edge, transparent, and compliant within a fast-paced, data-driven environment.
Responsibilities:
- Design, develop, and implement back-testing frameworks to rigorously evaluate trading strategies using historical and simulated market data.
- Analyze and optimize quantitative strategies by identifying inefficiencies, refining parameters, and maximizing performance metrics (e.g., Sharpe ratio, drawdown).
- Collaborate closely with the CIO, traders, and researchers to interpret back-test results and translate findings into actionable improvements.
- Continuously monitor and refine models based on changing market conditions, feedback from live trading, and performance diagnostics.
- Document research methodologies, results, and assumptions thoroughly for transparency and regulatory compliance.
- Automate data pipelines to support high-throughput back-tests and large-scale parameter sweeps.
- Present analysis and optimization outcomes to both technical and non-technical stakeholders with clear visualizations and explanations.
Requirements:
- Advanced degree (Master’s or PhD preferred) in Mathematics, Statistics, Engineering, Computer Science, Finance, or related field.
- Strong proficiency in Python, R, MATLAB, or C++; demonstrated experience with back-testing libraries and platforms (e.g., QuantLib, Zipline, Backtrader).
- Deep understanding of statistical analysis, probability theory, and time-series analysis relevant to financial markets.
- Hands-on experience designing, back-testing, and optimizing trading models (preferably in equities, FX, commodities, or derivatives).
- Ability to assess model performance using relevant quantitative metrics; skilled in scenario analysis and parameter optimization.
- Familiarity with a variety of trading strategies (e.g., mean-reversion, momentum, arbitrage) and their risk exposures.
- Effective at documenting and presenting complex analytical work to diverse audiences.
- Exposure to high-frequency trading environments, data management tools, and cloud-based compute resources.
