A framework for financial systemic risk valuation and analysis.

Financial research data services for academics.

SOAP - A Sockpuppet Auditing Tool for Very Large Online Platforms

This repository contains the codes for the paper "Machine-Learning-enhanced Systemic Risk Measure: A Two-Step Supervised Learning Approach" (by R. Liu and C.S. Pun)

Official implementation of "Predicting Systemic Risk in Financial Systems Using Deep Graph Learning"

A research-grade lab for stress-testing DeFi protocols using Solidity mini-systems, a Python simulation engine, and a Streamlit dashboard. Simulates price crashes, liquidity shifts, AMM behavior, lending liquidations, and systemic risk dynamics. Designed for DeFi engineers, auditors, and researchers.

A deep exploration of the economic physics governing DeFi crashes, AMM decay, liquidity spirals, and liquidation cascades. This article models decentralized finance as a nonlinear system driven by invariants, thresholds, and feedback loops, revealing why crashes follow predictable laws of motion.

Some codes used for the numerical examples proposed in https://arxiv.org/abs/1803.00445

An open-source platform for modeling systemic climate transition risks in financial systems. Developed by CFA Institute RPC & UK CGFI

Source code, data and plots for our paper "Analysis of Large Market Data Using Neural Networks: A Causal Approach"

Analysis of diversification breakdown during Bitcoin crash events. Found 89.5% compression in correlation gap between defensive and high-beta equities.

Python implementation of advanced financial network analysis toolkit for creating multi-layered Digital Twins of market dynamics. Implements information-theoretic Transfer Entropy and stochastic Kramers-Moyal methods to map non-linear, directed relationships between assets during normal and crisis periods.

Code repository for Restaking research, containing Python scripts, Dune SQL queries, and interactive data visualizations.

A quantitative research framework utilizing Linear Algebra (Spectral Decomposition) and Network Theory (PageRank) to decode systemic fragility in global markets.

End-to-End Python implementation of LPPLS (Log-Periodic Power Law Singularity) framework for detecting financial bubbles and critical transitions. Features Filimonov-Sornette calibration, Lagrange regularization, Lomb-Scargle spectral validation, and Monte Carlo significance testing. Complete computational replication of Hosseinzadeh (2025).

The Semantics of Collapse: Lawful Instability in Agentic Systems - A Safe-to-Exist Analysis of Optimization-Driven Systemic Risk

Algorithm for reconstructing topology of complex networks from a limited number of links (Bootstrapping method)

Implementation of the Self-Supervised Spatiotemporal GNN (ST-GNN) for detecting financial contagion and systemic risk using BIS banking data (1977–2023).

A kernel-based stochastic approximation (KBSA) framework for contextual optimization.

📊 Explore regime changes and real financial cycles through Minsky's hypothesis in a nonlinear framework, enhancing macroeconomic and financial analysis.