Jupyter notebook examples using QuantLib.

💰AI-driven web interface integrates Ministry of Finance pension data, providing personalized fund recommendations and interactive analysis tools for informed retirement planning decisions.

Financial data analysis with TEFAS Fund Data

In this repository I use Beautiful Soup to automate the information retrieval from the website of the Peruvian financial regulator. The final goal is to calculate and compare the most recent returns and assets of the 4 pension funds that provide services in Peru.

an optimal fixed-income portfolio to immunize $9.3B in inflation-indexed pension liabilities over an 80-quarter horizon. Implemented Linear Programming in R to minimize cost while managing credit scores, inflation-linkage caps, and liquidity constraints

Binaries T4U with Unified DPM Database for Solvency II and Pension Funds Reporting

NuGet package tailored for fetching banks and pension fund data, prioritizing secure externalization and data access. Emphasizing best practices for app development, ensuring streamlined retrieval and usage of non-sensitive financial data.

Norwegian Pension Fund analysis: EDA (sector/regional allocation), preprocessing (ESG features), ML engagement prediction (XGBoost/SHAP), and voting patterns analysis (6,692 disagreements, 86.5% persistence rate, 2020-2025).

Lightweight portfolio optimization for pension funds

Aplicação desenvolvida como resultado do Trabalho de Conclusão do Curso em Ciências Atuariais pela Universidade Federal de Pernambuco. Seu objetivo consiste no cálculo de Provisões Matemáticas em planos de Benefício Definido para Entidades Fechadas de Previdência Complementar.

End-to-end Python framework for robust pension fund management under parameter uncertainty. Implements three Distributionally Robust (DRO) Asset-Liability Management (ALM) formulations (Mixture, Box, Wasserstein) with GBM scenario generation, convex optimization (LP/SOCP), and comprehensive backtesting. Based on 2026 research by Ghahtarani et al.