Strong foundation in derivatives pricing, stochastic calculus, and market risk. Experienced in quantitative analysis and Python-based risk tools development within banking environments. Developed exposure to model risk, stress testing, and valuation controls, with a strong interest in Credit products. Analytical, detail-oriented, and comfortable interacting with risk and trading stakeholders in international settings.
- Current focus: Machine Learning, Deep Learning and CUDA
- Seeking for opportunities in: Quantitative Research · Data Science
- Open to relocation: France (Paris) · Switzerland (Zurich / Geneva)
| School | Degree | Period |
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Master in Economics and Financial Engineering (272) | Quantitative Track |
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Bachelor in Applied Economics | Economics and Financial Engineering Track |
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Programming Languages
Python Libraries
Areas of Expertise
| Area | Technologies |
|---|---|
| Derivatives Pricing | Trinomial Trees · Monte Carlo · Black-Scholes · Heston |
| Systematic Backtesting | Long/Short · Mean-Reversion · Pair Trading |
| Econometrics | ARIMA · GARCH · DCC · VAR · VECM · SETAR · MS Models |
| Machine Learning & Deep Learning | LSTM · Random Survival Forest · Gradient Boosting |
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WPF graphical application for multi-asset portfolio management. Markowitz optimization, multi-strategy backtesting, automated reporting. |
Replication of "Asymmetry in Stock Comovements: An Entropy Approach" (Jiang, Wu & Zhou): information-theoretic measures to quantify asymmetric dependencies in equity markets |
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Trinomial tree pricing engine for European and American options. Greeks computation and validation via convergence to Black-Scholes. |
Event-driven simulated trading platform in C++17. Full trade lifecycle execution featuring a limit order book, polymorphic strategy engine, and real-time portfolio risk management. |
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Leukemia survival prediction: model stacking (Cox PH, Random Survival Forest, Gradient Boosting), Bayesian optimization, evaluation via integrated Brier score. |
Systematic portfolio allocation framework implementing a Target Volatility Risk Parity strategy, featuring equal risk contribution weighting, dynamic covariance estimation, and leverage scaling to maintain a constant risk profile. |
| Hardware Enthusiast | Unix & Homelab | Gaming |
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| Lian Li O11 Vision Ryzen 7 7700 • RTX 5070 • 32GB |
Self-Hosting Ubuntu Server (Jellyfin, Ollama, n8n) |
Rocket League Grand Champ Player (Top <1%) Casual Valorant and Overwatch Player |
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