Quant-in-training with a background in mathematics and philosophy. My work centres on financial modelling under uncertainty, ideally where fashionable methods fail and assumptions matter.
Currently completing an MSc in Mathematical Trading and Finance at Bayes Business School. President of the Quantitative Finance Society.
Current interests: regime-aware strategy design, GAN-based/Fractal Differentation-based time-series generation, and developing my entry for the Deutsche Bank Quant Challenge — replicate and present systematic trading strategies to the Quantitative Researchers at DB.
- Regime-aware systematic strategies across equity market-neutral, trend-following, global macro, and fixed income RV
- Quantitative model design and testing under structural constraints
- Cross-disciplinary methods for inference, robustness, and decision-making
| Project | Summary |
|---|---|
| Risk Analysis | Backtested six VaR models; proposed Expected Shortfall for tail-risk resilience. |
| Structured Bond Valuation | Priced and hedged a BNP Paribas structured product in QuantLib; neutralised DV01 using swaps and CDS. |
| Adjusted Momentum Strategy | Replicated Lou & Polk’s comomentum model; found no performance improvement over baseline. |
| Portfolio Forecasting | EGARCH-based allocation model; outperformed FTSE benchmark over ten-year backtest. |
| Film Clustering | PCA and clustering on IMDB dataset; benchmarked against LLM-generated pipeline. |
| Model Visualisation | Shiny app for exploring tree-based models; included SHAP visualisation and pruning. |
Python, MATLAB, QuantLib
Time-series modelling, backtesting, unsupervised learning, portfolio optimisation