Final-year computer science engineering student at ESPRIT Tunis, specialising in financial computing. I write Python tools for pricing and testing financial models. I'm looking for a 6-month end-of-studies project (PFE) starting February 2027, in quantitative development or ML in finance.

Prices European and American equity options from a ticker, using live option chains. Black-Scholes closed form, binomial tree and Monte Carlo, with Greeks by finite differences. Runs as a CLI, a FastAPI service or a PDF report.

• Variance reduction: at 100k paths, Monte Carlo standard error falls from 0.044 (plain) to 0.023 (antithetic) to 0.018 (control variate).
• Market data: Black-Scholes prices diverged from the market, so I added a market data layer and calibrated a volatility smile and surface on live chains.
• Testing: 184 offline tests, including hypothesis tests against the closed form. On live AAPL quotes, 70–79% of out-of-sample options priced inside the bid/ask, and the rate changes with each snapshot.

A backtesting engine in Python: immutable domain objects, Decimal-based portfolio valuation, a pluggable price-history provider, unit tests.

Python, SQL, Java, C++, R · pandas, NumPy, SciPy, scikit-learn, XGBoost · FastAPI, pytest, hypothesis · PostgreSQL, Docker, Git

Stochastic processes, Itô calculus, PDEs for finance, GARCH, and AWS Fundamentals (in preparation).

LinkedIn · Arabic, French, English