I am a final-year Actuarial and Financial Studies student at UCD, on track for First Class Honours, and I am very keen to work in quantitative trading. Most of what I build is for financial markets, tools to trade, price and model them, and I put as much work into testing them as I do into building them.
A systematic trading system for Polymarket prediction markets. It began as an open-source framework I built with a friend (Polymarket_trader). Since then I have taken it private and built it out end to end, largely on my own:
- a live data layer streaming Polymarket order books and Binance prices, with around 40 scheduled services recording market data around the clock (150+ GB so far)
- pricing for short-dated crypto binaries as digital options under a Merton jump-diffusion, with volatility and jump intensity estimated live
- fractional-Kelly sizing with correlation haircuts, slippage-aware caps and drawdown circuit breakers
- a wall-clock backtester that runs the exact same strategy code as the live loop
- a statistical promotion gate (cluster-bootstrap confidence intervals, calibration checks, latency replay) that any strategy has to pass before it gets near real money
- a FastAPI dashboard over all of it
Roughly 140,000 lines of Python behind 3,800+ tests, paper trading unattended 24/7. It stays private because the execution stack and the strategies live there.
- options-toolkit: options analytics with the checks attached. JAX Black-Scholes and CRR American pricing, arbitrage-free SSVI vol surfaces fitted to bid-ask bands (butterfly and calendar conditions verified numerically, never assumed), a delta-hedged market-making simulator with GLFT quoting and adverse-selection experiments, a no-arbitrage scanner, and a daily option-chain capture feeding surface-dynamics studies. 132 offline tests.
- market-regime-detection: Markov-switching volatility regimes on the S&P 500, built to be look-ahead-free and tested for it. Filtered vs smoothed vs walk-forward probabilities, Student-t emissions from scratch, financial turbulence, the Kritzman absorption ratio with its false-alarm rate measured, point-in-time macro data (ALFRED first releases) and a costed regime-based allocation backtest.
- equity-forecasting: ARIMA (mean) and GJR-GARCH (volatility) forecasting with a walk-forward out-of-sample backtest, scored with QLIKE against EWMA and rolling baselines, with Mincer-Zarnowitz and Diebold-Mariano tests. The GJR-GARCH volatility forecasts beat both baselines on QLIKE for all three tickers.
- pairs-trading-toolkit: Engle-Granger cointegration screening, mean-reversion spread backtesting with carry costs and quarterly recalibration, paired block bootstrap and portfolio optimisation, with causality tests that corrupt future prices and check no earlier signal changes.
- Polymarket_trader: the open-source framework layer of the system above. CLOB execution, wall-clock backtester, pre-trade slippage gate, probability-fed fractional-Kelly sizing, FastAPI dashboard, 816 tests.
Python (NumPy · pandas · SciPy · statsmodels · JAX · pytest) · R · SQL · Git · options pricing · time-series · Kelly sizing
Co-president of one of Ireland's largest college poker societies. Competed in RITC x Dublin (the Rotman International Trading Competition's Dublin event, hosted at Trinity College Dublin), live and in person, 6th of 100 teams. Actuarial internships at Aviva (two summers, group-protection pricing) and Grant Thornton (seconded to the BMA Regulator Data Analytics & AI team).