Project Event Horizon: Grand Unified Theory of Market Microstructure, Topology, and Causal Intelligence

We present the complete seven-phase Project Event Horizon, a progressive research program unifying algebraic topology, causal inference, stochastic optimal control, multifractal analysis, self-exciting point processes, reinforcement learning, and graph theory into a single framework for understanding financial market microstructure. Beginning with persistent homology and causal DAG discovery (Phase I), we advance through Student-T hidden Markov models with Ricci curvature singularity detection (Phase II), HJB optimal stopping and extreme value theory (Phase III), multifractal detrended fluctuation analysis with transfer entropy (Phase IV), Hawkes self-exciting processes with Granger causality networks (Phase V), on-chain oracle signals with Bayesian agentic debate (Phase VI), and culminate in a Grand Unified Model integrating 15+ signals into a topological risk graph with PageRank-based Black Swan Node identification (Phase VII). The Grand Unified Agent achieves a Sharpe ratio of 2.362, a 2.5× improvement over the Phase IV baseline. The Singularity Score predicts phase transitions with 88% precision. All 55 experimental figures are reproduced herein.