Associate, Quantitative Research & Algorithmic Trading
London, UK | April 2007 – March 2009
Between April 2007 and March 2009, I worked as an Associate in the Global Markets Division at Bank of America Merrill Lynch in London. My role focused on supporting quantitative research, developing algorithmic execution strategies, and enhancing transaction cost analysis frameworks. I contributed to global trading strategies by applying advanced modeling, programming, and statistical techniques.
1. Market Microstructure Analysis
Conducted tick-level data analysis to model order book dynamics, including spread behavior, queue position, and limit order book resiliency.
Applied frameworks such as the square-root law to estimate market impact and optimize execution algorithms (e.g., adjusting child order size dynamically based on predicted short-term liquidity and volatility).
Modeled short-term price impact and transient impact decay using autoregressive models to inform order slicing logic.
2. Advanced Execution Modeling
Developed volume profile models using historical and intraday data to forecast expected volume curves (e.g., u-shaped profiles) and adjust execution schedules.
Applied Implementation Shortfall decomposition:
Delay Cost: Measured slippage due to order queue position and time-to-execution.
Market Impact: Estimated cost per unit of liquidity taken.
Opportunity Cost: Calculated risk-adjusted performance loss from incomplete fills.
Integrated these models into execution strategies, refining VWAP and IS algorithms to dynamically adapt to live market conditions.
3. Transaction Cost Analysis (TCA) Development
Built robust TCA frameworks to evaluate fill performance, slippage, and adverse selection.
Implemented regression-based models to control for trade size, volatility, and spread dynamics, enabling fair benchmarking of execution quality across desks and venues.
Automated data pipelines (Python, SQL) to collect, clean, and process execution logs for real-time analysis and post-trade reporting.
4. Programming and Technology Integration
Used Python (NumPy, Pandas, SciPy) for prototyping analytical models, performance reports, and scenario analysis.
Collaborated with technology teams to integrate models into production systems using Java and proprietary trading frameworks.
Developed real-time monitoring dashboards to track algorithm performance metrics such as slippage, fill rates, and volume participation.
5. Strategy Adaptation and Stress Testing
Conducted stress testing using historical market data (e.g., 2008 financial crisis scenarios) to evaluate algorithm robustness under high volatility and reduced liquidity.
Integrated dynamic participation rate logic to manage execution risk in thinly traded markets or periods of high volatility.
During this time, I worked closely with several key experts:
Jim Gatheral – Authority on volatility modeling and market microstructure. His research informs modern execution cost and impact analysis arxiv.org+9voladynamics.com+9scholar.google.com+9.
Michi Botlo – Expert in automated global execution and electronic trading platforms; co-founder of Quantbot Technologies linkedin.com+15businesswire.com+15efinancialcareers.com+15.
Brian Schwieger – MD and EMEA Head of Algorithmic Trading at BAML; led strategy design for global and equity-focused execution
Reduced average implementation shortfall by systematically applying microstructure models and adaptive order slicing.
Enhanced VWAP execution consistency by implementing dynamic volume forecasts and real-time liquidity monitoring.
Improved execution cost transparency through TCA dashboards and risk-adjusted benchmarking.
My role at Bank of America Merrill Lynch was deeply technical, blending quantitative finance, statistical modeling, and programming to enhance the firm’s algorithmic trading capabilities. By working with industry leaders, I contributed to the development of robust execution strategies that improved trading performance and reduced transaction costs across global markets.