General resume template

Focused on stochastic calculus, optimization, derivatives pricing, statistical learning, and market microstructure research.

Completed advanced study in probability, algorithms, numerical methods, and statistical modeling.

• Developed 14 intraday equity alpha signals using alternative data, microstructure features, and regularized models, increasing live strategy Sharpe by 0.42.
• Built Python and C++ research pipelines processing 8TB of daily market data, reducing signal validation latency from hours to minutes.
• Designed transaction cost and capacity models that improved portfolio sizing decisions and reduced realized implementation shortfall by 17%.
• Partnered with traders and engineers to deploy three production strategies, contributing $6.4M in net annualized P&L during first year.

• Researched cross-sectional equity factors across 3,000 U.S. securities, improving monthly long-short information ratio from 0.61 to 0.88.
• Created robust backtesting workflows with survivorship-bias controls, point-in-time fundamentals, and risk-neutral portfolio construction across 15 years.
• Automated SQL-based data quality checks that identified vendor anomalies within one business day and reduced manual review time by 45%.
• Presented investment research to portfolio managers weekly, translating statistical findings into allocation recommendations for a $1.2B systematic sleeve.

• Researched sparse portfolio optimization methods under turnover constraints, improving simulated risk-adjusted returns by 18% versus mean-variance baselines.
• Implemented convex optimization experiments in Python and presented findings to a faculty committee and industry research mentors.

• Analyzed high-frequency price formation patterns using limit order book data and nonparametric estimators for short-horizon volatility clustering.
• Built reproducible data pipelines processing 400 million observations and documented methodology for subsequent student researchers.

• Built an event-driven Python backtester supporting transaction costs, slippage, borrow constraints, and walk-forward validation across 12 years of equity data.
• Reduced false-positive strategy selection by 31% by adding multiple-testing controls, regime filters, and out-of-sample performance diagnostics.
• Produced reusable research templates that shortened new signal evaluation cycles from five days to two days.

• Developed a gradient-boosted forecasting model using order book imbalance, realized volatility, and cross-asset features for intraday futures prediction.
• Improved one-hour directional accuracy from 53.1% to 56.8% and increased simulated Sharpe from 1.3 to 1.9 after costs.