Noosphere Prime Intelligence Engine · 8-Layer Architecture
Technical documentation for SIGMA — the multi-layer scoring engine that quantifies systemic fragility and regime transition probability in financial markets.
SIGMA (Systemic Intelligence & Global Market Analysis) integrates eight classes of heterogeneous signals — energy, maritime, behavioral, textual, network, temporal, and technical — into a composite score normalized on the interval [0, 100]. Aggregation uses an exponential asymptotic function that ensures extreme scores require simultaneous deterioration across multiple layers, more faithfully modeling the real dynamics of systemic crises. SIGMA is not a stock screener. It is a financial intelligence engine that applies open-source analysis methodology to capital markets.
Models the economy as an organism with metabolic rate, immunological capacity, and collapse thresholds. β = response speed to external shocks. immuneResponse = system capacity to absorb stress without regime transition. Output: metabolicScore ∈ [0,1].
Calculates potential energy accumulated in the system, analogous to a physical system near a critical point. Detects Critical Slowing Down through increasing variance and decreasing recovery rate from perturbations. Minsky Moment: boolean trigger when fragility exceeds the structural stability threshold.
Analyzes text from GDELT sources and reports via NLP. Detects: linguistic hedging anomalies, divergences between official statements and market behavior, signs of capitulation or narrative euphoria. Language precedes action.
Models the financial system as a graph of interconnected nodes. Financial R₀ = the average number of entities affected if a central node fails. Calculated via percolation models on scale-free networks. R₀ > 1 indicates conditions of potential systemic contagion.
Identifies causal chains in textual corpus: cause → mechanism → effect → probability of impact. Detects absence-as-signal — when sources that normally speak go silent. Narrative/structural divergence is often the earliest regime signal.
Three complementary models: Hawkes Process for self-excitation of volatility events, Hidden Markov Model for detecting transitions between latent regimes, Hurst Exponent H for characterizing time-series memory (H<0.5 mean-reverting, H>0.5 trending). CSD Score integrates all indicators of proximity to bifurcation.
SIGMA is not static. Layer 7 recalibrates the weights of the other layers based on historical performance verified at T+30, T+60 and T+90. If Layer 2 had lower accuracy in the last 90 days on the energy sector, its weight automatically decreases for that sector.
The classical layer acts as a validator and counterbalance for layers 1-7. It is not the primary element of the final score, but severe technical anomalies amplify signals from the upper layers. Integration is bidirectional: structural SIGMA informs the interpretation of technical signals.
Noosphere Score and regime classifications are data analysis tools for informational purposes. They do not constitute investment advice or financial recommendation. Historical accuracy does not guarantee future performance. The Prediction Ledger verifies predictions against real market data at T+30, T+60, T+90 — the complete verification methodology is available at /predictions.