This monograph presents the complete record of Project Event Horizon, a seven-phase experimental research program that develops a unified framework for detecting, measuring, and trading financial market phase transitions. The program draws on techniques from algebraic topology, causal inference, stochastic optimal control, multifractal analysis, self-exciting point processes, deep reinforcement learning, and network graph theory. Beginning with persistent homology and causal DAG discovery in Phase I, the program advances through Student-T hidden Markov models and Ricci curvature-based singularity detection (Phase II), Hamilton-Jacobi-Bellman optimal stopping combined with extreme value theory (Phase III), multifractal detrended fluctuation analysis paired with cross-layer transfer entropy (Phase IV), Hawkes self-exciting processes and Granger causality network collapse (Phase V), on-chain DeFi oracle signals with Bayesian agentic debate (Phase VI), and culminates in a Grand Unified Model that integrates fifteen distinct signals into a topological risk graph with PageRank-based Black Swan Node identification (Phase VII). The Grand Unified Agent achieves an annualized Sharpe ratio of 2.362, representing a 2.5x improvement over the Phase IV baseline. The Singularity Score, a composite measure derived from Hawkes intensity and Ricci curvature, predicts market phase transitions with 88 percent precision at a 20-bar horizon. All code is Python-native. All 80 experimental figures are reproduced in this volume. The program was conducted entirely on synthetic market data calibrated to realistic microstructure dynamics and is presented as a proof-of-concept for topology-aware, multi-domain, agentic market intelligence.
Matthew Busel (2026) studied this question.