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.

Reproducible pipeline for modeling financial markets with correlation-based networks and Graph Neural Networks (GNN). Builds financial asset graphs from historical data, computes network metrics, and trains GNNs to learn relational representations for market analysis.