Quantitative geopolitical risk dashboard tracking Iran-Israel conflict escalation via market signals, GDELT news analytics, and probabilistic portfolio regime guidance.

Risk attribution report for portfolio management, using MatLab and Excel

A market state factor based on graph automorphism group analysis. Detects market homogeneity through orbital structure of stock correlation networks, providing signals for risk management and portfolio construction.

Essential techniques to assess financial risks

Repository represents python usability of measuring and managing risks (practice tasks and real cases)

Quantitative analysis techniques with Python and Pandas to determine which portfolio is performing best across many areas: volatility, returns, risk, and Sharpe Ratios.

Quantitative risk infrastructure for Meridian Private Bank. Three Monte Carlo methods - LHS for venture valuation, scenario-based correlated simulation for macro stress testing, and historical simulation for LP reporting. Calibrated against Q1 2026 Iran war oil shock.

A Python-based portfolio optimization tool that implements Modern Portfolio Theory (MPT) to create efficient portfolios.

Calculate VaR of Tesla Equity share with Historical, Variance-Covariance and MonteCarlo simulations methods

SQL-backed volatility forecasting and market risk platform using Python, DuckDB and Streamlit, with realised volatility estimators, EWMA/GARCH/HAR forecasts, VaR/ES backtesting, regime detection, model validation reports and dashboard analytics.

Full-stack portfolio risk & stress-testing platform — historical VaR/CVaR, Fama–French factor models, and crisis/macro-shock scenarios. FastAPI · Next.js · PostgreSQL · Celery.

Mobile-first MRI-based market regime interpretation engine with risk-adjusted confidence modeling.

MATLAB multi-asset intelligent research dashboard with agentic AI analysis, portfolio risk, technical indicators, crypto data, and DeepSeek reports.

Python framework for multi-asset portfolio market risk analysis including rolling beta, VaR/CVaR, Monte Carlo simulation, stress testing, and risk decomposition.

Portfolio credit risk modeling project evolving from independent to multi-factor migration, with regime stress and tail diagnostics. Part of a broader transition toward quantitative finance and systematic risk modeling.

Autonomous options trading AI — signal engine, real-time Streamlit dashboard, paper trading via Alpaca, risk guardrails, congressional disclosure tracker, and live market news feed.

Static Parametric (Variance–Covariance) VaR model for equity portfolios using historical returns and normal distribution assumptions.

A data-driven risk analytics system for fund and benchmark analysis, translating NAV time series into actionable risk insights, drawdown signals, and structured reports to support investment decision-making.

Rolling parametric VaR model for an equity portfolio with backtesting, exception analysis, and diagnostic plots.

Derivatives pricing and risk engine: Black-Scholes, Greeks, Monte Carlo, implied vol, VaR/CVaR, real market data, and a 12-page Streamlit dashboard.