DUCKQUANT / THE QUANT & AI DIRECTORY
139 resources, ranked by in-directory PageRank
Enhanced drop-in replacement for QuantStats — portfolio analytics for quants
Rebalancing a portfolio with optimal buy/sell decisions using Metaheuristics
Market risk engine: VaR/ES (historical, parametric, Cornish-Fisher, Monte Carlo), Kupiec/Christoffersen/Basel backtests, stress tests, ex-ante tracking error
One-day VaR & ES for a cross-asset ETF portfolio via historical simulation, GJR-GARCH-t, and filtered historical simulation, with Kupiec, Christoffersen, & Acerbi–Székely backtesting.
Market risk analysis using historical, parametric and monte carlo VaR
Multi-asset portfolio risk model implementing VaR, Expected Shortfall, stress testing, model calibration, and risk decomposition
Market risk modeling using Value at Risk (VaR) and stress-conditioned probability of default with public financial data.
Eight VaR/ES models for a $10m multi-asset book, validated over 4,611 trading days with Kupiec, Christoffersen, Basel traffic-light and Acerbi-Szekely tests.
Rolling Var and expected shortfall using python
Public repository by ATHARV-IIT-BOMBAY. Primary language: Jupyter Notebook.
Market risk model implementing Historical, Parametric & Monte Carlo VaR + stress testing across crisis scenarios. Basel III framework in Python.
Public repository by AlexisAHG. Primary language: Jupyter Notebook.
Public repository by geheres-dotcom. Primary language: Python.
Market risk analysis using VaR and CVaR on a multi-asset ETF portfolio
VaR and Expected Shortfall with GARCH volatility forecasting and backtesting (Kupiec, Christoffersen, Basel)
Public repository by Parvmehra8755. Primary language: Python.
Public repository by SaahitKar. Primary language: Jupyter Notebook.
Market risk modeling using Value at Risk (VaR), Expected Shortfall and stress testing.
Public repository by Ruojieli. Primary language: Python.
Public repository by PhilippovRoman. Primary language: Python.
Market Risk Analysis using Historical, Parametric and Monte Carlo Value at Risk (VaR) with Python.
Python implementation of Value at Risk - Historical, Parametric & Monte Carlo simulation for NSE portfolio
Market risk(VaR), derivative pricing, CVA model in python and C++
Value at Risk (Normal, Student-t, Historical, Monte Carlo, GARCH) and volatility modeling on GGAL through Argentina's major market crises, with backtesting and macro/political event…
Rebalancing frequency, drift, and transaction cost analysis for global equity index portfolios
VaR framework with GARCH, backtesting, and stress testing
Public repository by selimabll1. Primary language: Python.
