Ivan Quan

I took ownership of the backtesting platform testing process by designing automated unit tests that simulated over 1,000 trading days of tick-level data. Reduced testing time by 40% and helped identify several edge-case calculation bugs.

Built a prototype using time-slicing modeling to predict short-term data distribution shifts in high-frequency trading environments. Although still exploratory, the model achieved a 12% improvement in predictive accuracy compared to the baseline.

Drafted and maintained a 30-page internal product manual documenting software usage, input/output formats, and debugging procedures, which was later used as onboarding material for new interns.

Learned to bridge finance and programming by working closely with both quantitative analysts and software engineers, strengthening my understanding of data pipelines, backtesting logic, and performance metrics in real trading systems.

Assisted a senior analyst in screening over 40 early-stage AI startups across the healthcare, education, and companionship sectors. I built a comparison sheet evaluating each company’s technology maturity, funding stage, and potential market size.

I took the initiative to lead a three-person intern team in researching the North American AI companion market. I designed a framework combining user demographic data, market adoption rates, and cost structures. Our analysis showed a 23% annual growth rate, and helped the team shortlist five high-potential startups.

Participated in post-investment tracking for a portfolio company by monitoring monthly KPI reports. When the startup faced a delay in product delivery, I summarized key risk factors and proposed mitigation suggestions, which were included in the team’s internal review memo.

Through this experience, I learned to think critically under data uncertainty, and gained first-hand exposure to how investment decisions are made—from preliminary screening to follow-up evaluation.