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