Manuel Touyaa's porfotlio of Python projects/assignments for Finance Market Risk.
This is about calculating Market Risk capital charge under Standardised Approach(SBM) in FRTB
Advanced Approach under FRTB(IMA) requires institutions to meet quantitative tests namely Backtesting and PnL attribution..
FRTB IMA-compliant daily VaR/Expected Shortfall risk monitor: stress calibration, liquidity-horizon scaling, NMRF checks, Acerbi-Szekely/Kupiec/Christoffersen backtesting, a Plotly Dash dashboard, and Claude-generated narratives.
FRTB-aligned VaR & Expected Shortfall risk engine with GARCH volatility modeling, regulatory backtesting, and Streamlit dashboard — built for Swedish equities.
FRTB IMA engine: VaR, ES, full backtesting suite, stressed calibration, and PLAT test in Python.
Multi-asset market risk framework: VaR, Expected Shortfall, stress testing, and backtesting across equity, IG/HY credit, and US Treasury instruments.
Institutional-grade portfolio risk management platform: 12 Kotlin services + Python quant engine + React dashboard. VaR/ES/Greeks, FRTB SA, hash-chained audit, six-level pre-trade limits, regime-adaptive risk.
Add a description, image, and links to the frtb topic page so that developers can more easily learn about it.
To associate your repository with the frtb topic, visit your repo's landing page and select "manage topics."