Discussing the article: "Recurrence Network Analysis (RNA) in MQL5: From Recurrence Matrices to Complex Networks"

[MetaQuotes]

Check out the new article: Recurrence Network Analysis (RNA) in MQL5: From Recurrence Matrices to Complex Networks.

The article extends the MQL5 recurrence library to Recurrence Network Analysis (RNA) by treating recurrence matrices as adjacency matrices of undirected graphs. It implements core network metrics—clustering, transitivity, average path length, betweenness, assortativity, and density—and applies them in rolling windows for single-series RNA and Joint RNA (JRNA). A modular metrics engine and two indicators visualize the evolving network structure on MetaTrader 5 charts for practical time-series analysis.

The image below shows the RNA indicator running on a live chart in MetaTrader 5. Five network metrics are plotted in a separate window: Average Clustering Coefficient (ACC), Transitivity, Normalized Average Path Length (NormAPL), Assortativity, and Average Betweenness Centrality. Each is computed from a rolling window of close prices, where each window's recurrence matrix is treated as a network.

Fig. 1. The RNA indicator on EURUSD H1, plotting five network metrics derived from rolling recurrence matrices

When ACC is high, recurrent states form tightly connected clusters: the market revisits groups of related states as a coherent unit. When Transitivity drops, those clusters fragment. When NormAPL is low, any two time points can be connected through a short chain of recurrences, indicating a "small world" structure in the dynamics. Assortativity measures whether highly connected nodes (frequently recurring states) tend to connect to each other. Average Betweenness identifies time points that act as bridges between otherwise separate clusters of recurrence.

Author: Hammad Dilber