Spectral Graph Topology with Technical Indicators for Stock Market Regime Detection
Keywords:
Technical indicators; Bollinger Bands; RSI; MACD; support and resistance; spectral graph theory; graph Laplacian; market regime detection; Gaussian kernel; NIFTY 50; sliding-window clustering.Abstract
The financial markets generate an abundance of technical signals, such as price, momentum, volatility, volume and S/R levels, that are used to capture the activity of traders across time. Although spectral graph clustering of sliding-window feature vectors has been shown to be better at detecting market regime than traditional baselines, existing methods limit feature space to mean return and realised volatility and neglect much of the information contained in popular technical indicators. This paper considers the Indicator-Weighted Similarity Graph (IWSG) a principled extension of the Temporal Similarity Graph (TSG) where each 8-day window is represented by a 14-dimensional feature vector containing: Bollinger Band Width, %B, Relative Strength Index (RSI), MACD histogram, Stochastic %K/%D, Commodity Channel Index (CCI), Average True Range (ATR), On-Balance Volume (OBV), and fractal support/resistance proximity score. A feature-adaptive Gaussian kernel on this space produces the IWSG adjacency matrix whose symmetric normalised Laplacian is proved positive semi-definite by means of an extended Dirichlet form argument, which provides richer spectral embedding. In experiments using ten NIFTY 50 constituents (January 2019–December 2023), the IWSG performs better than the baseline TSG model in clustering by silhouette, Davies–Bouldin, time consistency, and Calinski–Harabasz.
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