Projects

Selected work, with the problems, trade-offs, and implementation details behind it.

Implied Willow Tree for Derivatives

Quantitative research capstoneSeptember 2025 – December 2025

Python, numerical optimization, Treasury futures options

A study of risk-neutral densities from 10Y Treasury futures options, with martingale and no-arbitrage constraints for a new objective function.

  • Extends the willow-tree idea from equity options toward a liquid interest-rate derivatives market.
  • Uses capital-markets information and discretized risk-neutral nodes to study path-dependent pricing.

High Frequency Trading Competition

University competitionMarch 2025 – May 2025

Python, VWAP, SHIFT, FIX protocol, GitHub

A second-place market-making strategy that used VWAP to predict trends and adapt order size.

  • Led the team, delegated backtesting and research, and kept the group aligned through meeting notes.
  • Built backtesting and algorithm components and acted as the point of contact for SHIFT and FIX issues.

Hornet Trading Contest

Class projectFebruary 2025 – May 2025

VBA, Bloomberg, bond and FX derivatives

A risk-engineering trading contest covering zero-coupon bonds, forwards, calls, and puts on USDMXN.

  • Wrote VBA to calculate Delta, Gamma, USD and MXN Rho, and Vega for traded instruments.
  • Used current-events information from Bloomberg to anticipate a falling dollar and adjust delta exposure.

NBA Defensive Impact Analysis

Personal projectJuly 2024 – August 2024

Python, APIs, Pandas, SQL, MSSQL, Azure SQL, Power BI

A data pipeline that cleaned gigabytes of API data, cached rate-limited requests, and moved analysis into SQL for Power BI.

  • Removed unnecessary columns and changed data types to improve memory use.
  • Added backoff and caching for repeated API collection, then synchronized SQL tables to Azure SQL.

NBA Reddit AI Chatbot

Personal projectDecember 2023 – January 2024

Python, LangChain, retrieval-augmented generation

A chatbot over daily NBA subreddit posts and comments, tuned to understand community slang and nested discussion structure.

  • Built a LangChain retrieval pipeline over the collected post and comment corpus.
  • Used prompt engineering to make answers preserve the source community's terminology and context.