International Journal of Mathematics And Computer Research

Abstract

This paper introduces a novel graph-theoretic framework for modeling smart money flows within decentralized and traditional financial systems, integrating Fibonacci-based structural dynamics and wave pattern analysis into the topology of liquidity networks. By representing capital movements as directed weighted graphs, where nodes correspond to liquidity pools, institutional wallets, or strategic trading clusters, and edges capture transaction velocity and directionality, we uncover emergent beheavour that mirror fractal wave formations seen in market cycles. The incorporation of Fibonacci nodes—points of harmonic capital convergence derived from proportional flow ratios—enables quantification of recursive liquidity feedback loops. Furthermore, the system’s wave patterns as eigenmodes of graph Laplacians, revealing cyclical market phases corresponding to expansion and contraction of liquidity. Empirical simulations demonstrate that the proposed model effectively identifies accumulation–distribution structures and anticipates liquidity pivots preceding price reversals. This interdisciplinary approach bridges graph theory, fractal market hypothesis, and network liquidity analytics, offering a mathematically grounded tool for decoding intelligent capital movements and predicting systemic transitions in financial ecosystems.