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

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.

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)

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.

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

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"

ASRI: Aggregated Systemic Risk Index for Cryptocurrency Markets — paper + real-time monitoring system | DAI-2509 | Dissensus AI Working Paper

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.

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

Global Systemic Fragility Observatory 2026 · Markov Model + Monte Carlo · Synthetic Bubble · 10 non-conventional systemic risk indicators · MCSH Framework · semanticfieldscience.org

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.

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

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).

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

Asymmetric liquidity flow dynamics in financial networks, interpreted via effective geometry and stability under the Victoria-Nash Asymmetric Equilibrium (VNAE).