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abdel_oumarghad

Abdel Oumarghad

@abdel_oumarghad

Quantitative Analyst, Pine Script and TradingView Strategy Developer

França
Inglês, Francês, Árabe
Algumas informações são exibidas no idioma inglês.
Sobre mim
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.... Saiba mais

Habilidades

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abdel_oumarghad
Abdel Oumarghad
offline • 

Conheça meus serviços

Modelagem e Análise Estatística
I will do monte carlo simulation in python, r or excel
A partir deUS$ 15 /projetoMais detalhes
Redação de Roteiro
I will code any indicator or strategy in tradingview pinescript
A partir deUS$ 20 /projetoMais detalhes

Portfólio

Experiência profissional

AXA

Quantitative Analyst — Derivatives Pricing & Risk Modeling

AXA • Período integral

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.