a
abdel_oumarghad

Abdel Oumarghad

@abdel_oumarghad

Quantitative Analyst, Pine Script and TradingView Strategy Developer

France
Anglais, Français, Arabe
Certaines informations sont présentées en anglais.
À propos de moi
Quantitative analyst turning trading ideas into clean, tested code. What I deliver: - Pine Script v5/v6 indicators, strategies and screeners for TradingView: alerts, backtests, clear inputs - Quant finance: option pricing (Black-Scholes, Monte Carlo), stochastic models, VaR and risk metrics - Python/R: backtesting engines, data pipelines, statistical modeling I document every line I write and never deliver a strategy I haven't stress-tested. Tell me your idea and I'll tell you honestly if it can be coded, and how.... Plus d’infos

Compétences

a
abdel_oumarghad
Abdel Oumarghad
hors ligne • 

Voir mes services

Modélisation et analyse statistique
I will do monte carlo simulation in python, r or excel
De15 $US / projetPlus de détails
Rédaction de script
I will code any indicator or strategy in tradingview pinescript
De20 $US / projetPlus de détails

Portfolio

Expérience professionnelle

AXA

Quantitative Analyst — Derivatives Pricing & Risk Modeling

AXA • Temps plein

Aug 2025 - Present • 1 yr 2 mos

Applied quantitative finance work carried out within the M2 Statistics & Risk (ISEFAR) program. Derivatives pricing and model validation — Built and automated a pricing engine covering Black-Scholes, CRR binomial trees and Monte Carlo (100,000 paths) in Python, VBA and Excel. Convergence validated under 1% vs. analytical benchmark; Greeks (Delta, Gamma, Vega) computed by finite differences to within 0.01%. Output consistency checks across a full payoff grid. Volatility modeling and VaR backtesting — GARCH, EGARCH and GJR-GARCH fitted on S&P 500 and CAC 40 returns (Python, arch library). VaR backtesting and stress scenarios including 3x volatility shocks and drift jumps, with automated reporting dashboards. Model uncertainty and variance reduction — Monte Carlo uncertainty quantification via batch method, 95% confidence intervals, importance sampling. Comparison of Exponential, Log-Normal and Pareto loss distributions with documented sensitivity analysis. Interest rate models — Vasicek and CIR short-rate models: calibration, bond pricing, valuation of swaps, caps and floors. Statistical pricing on real data — Claim severity (OLS, 2SLS with endogeneity correction) and frequency (Poisson, Negative Binomial) modeled on 5,352 policyholders; Gamma GLM on censored data (Tobit) and Cox survival analysis.