Interested in quantitative trading and research internships in 2026 and 2027, especially options, market making, and systematic macro.
I'm a Physics, Math, and CS student at Pitzer who spends a lot of time across the street at Harvey Mudd learning and playing poker.
Most of my work right now lives in an options volatility arbitrage platform: a C++ Heston pricer wrapped in a Python-based delta-neutural volatility arbitrage trading strategy, a FastAPI backend, and a React dashboard wired to SPY options data.
Some things that are really important to me are: clear baselines, careful experiments, and code that another person could clone and run without fighting it.
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Comfortable with stochastic-calculus-based economics (Black Scholes, Heston, Greeks)
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Heavy Python / C++ for numerics, profiling, and optimization
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Happy living in pandas, NumPy, and writing tests around experiments
Full stack options vol arb system: C++ Heston FFT pricer, Python backtest library, FastAPI API, and a React dashboard for experiments and monitoring.
- ~26k lines of code across Python, C++, TypeScript
- Sub‑millisecond pricing with C++ + pybind11 and an ~80% cache hit rate
- Event‑driven backtester with walk‑forward validation, bootstrap tests, and 190+ tests
- Built around SPY options data from 2019–2024
Collection of systematic trading experiments and prototypes.
- Focus on realistic assumptions: transaction costs, slippage, and out‑of‑sample evaluation
- Uses pandas / NumPy / scikit‑learn for signals and risk
- Sandbox for ideas that may graduate into the main vol arb system
Interactive explorer for core algorithms and data structures.
- Visualizes graphs, trees, and other DS & A staples
- Built with TypeScript and React as a data structures & algorithms final project
- Written to be readable for other students, not just a one‑off assignment
Simulation project for the 10,000 dice game looking at strategy and expected value.
- Compares brute‑force and heuristic strategies with Monte Carlo runs
- Small, focused example of how I approach modeling and experimentation
The best place to reach me is LinkedIn:
Or email: whammond@students.pitzer.edu