01c19662cb
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
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Prompt: Definitive Convex Hazard Validation for Micro-Entropy (v50) You are to rigorously test whether extreme micro-entropy (v50_lambda_max_velocity) represents a true convex hazard zone that justifies nonlinear leverage tapering. The goal is not correlation. The goal is economic exploitability. Follow these steps precisely. 1️⃣ Data Requirements Use: Entire available historical dataset, use the fast vbt backtesting engine. Same production trading engine Same 6.0x leverage ceiling No modification to signal logic T-1 precursor alignment only (strict shift) Define: Daily return Tail event = bottom 10% of daily returns (fixed percentile, global) 2️⃣ Core Conditional Hazard Curve Compute: Baseline: Copy code
P(Tail) Then for v50 (T-1): For thresholds: 75th percentile 85th percentile 90th percentile 95th percentile 97.5th percentile 99th percentile Compute: Copy code
P(Tail | v50 > threshold) Also record: Number of days above threshold Number of tail days inside threshold 95% confidence interval (Wilson or exact binomial) Output full hazard curve. We are looking for nonlinear convex acceleration, not linear drift. 3️⃣ Economic Viability Test (CRITICAL) For each threshold: Compute: Mean return on spike days Mean return on non-spike days Median return Standard deviation Contribution of spike days to total CAGR Then simulate: Scenario A: Static 6.0x (baseline) Scenario B: 6.0x with taper when v50 > threshold (e.g., reduce leverage to 5.0x or apply 0.8 multiplier) Run: Median CAGR 5th percentile CAGR P(>40% DD) Median max DD Terminal wealth distribution (Monte Carlo, 1,000+ paths) If tapering: Reduces DD materially Does not reduce median CAGR significantly Improves 5th percentile CAGR → Hazard is economically real. If CAGR drops more than DD improves, → It is volatility clustering, not exploitable convexity. 4️⃣ Stability / Overfit Check Split data: First 50% Second 50% Compute hazard curve independently. If convexity disappears out-of-sample, discard hypothesis. Then run rolling 60-day window hazard estimation. Check consistency of lift. 5️⃣ Interaction Test Test whether hazard strengthens when: Copy code
v50 > 95th AND cross_corr > 95th Compute: P(Tail | joint condition) If joint hazard > 50% with low frequency, this may justify stronger taper. If not, keep taper mild. 6️⃣ Randomization Sanity Check Shuffle daily returns (destroy temporal relation). Recompute hazard curve. If similar convexity appears in shuffled data, your signal is statistical artifact. 7️⃣ Decision Criteria Micro-entropy qualifies as a true convex hazard zone only if: P(Tail | >95th) ≥ 2.5× baseline Convex acceleration visible between 90 → 95 → 97.5 Spike frequency ≤ 8% of days Taper improves 5th percentile CAGR Out-of-sample lift persists If any of these fail, reject hypothesis. 8️⃣ Final Output Produce: Hazard curve table Economic impact table Out-of-sample comparison Monte Carlo comparison Final verdict: True convex hazard Weak clustering Statistical artifact No narrative. Only statistical and economic evidence.