initial: import DOLPHIN baseline 2026-04-21 from dolphinng5_predict working tree

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.
2026-04-21 16:58:38 +02:00
commit 01c19662cb
643 changed files with 260241 additions and 0 deletions
--- a/nautilus_dolphin/test_forewarner_gamut.py
+++ b/nautilus_dolphin/test_forewarner_gamut.py
@@ -0,0 +1,108 @@
+import os
+import sys
+import numpy as np
+import pandas as pd
+from collections import Counter
+from pathlib import Path
+
+# Adjust paths
+PROJECT_ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from mc.mc_ml import DolphinForewarner
+from mc.mc_sampler import MCSampler
+
+MODELS_DIR = PROJECT_ROOT / "mc_results" / "models"
+
+def determine_risk_level(report):
+    env = report.envelope_score
+    cat = report.catastrophic_probability
+    champ = report.champion_probability
+
+    if cat > 0.25 or env < -1.0:
+        return "RED"
+    elif env < 0 or cat > 0.10:
+        return "ORANGE"
+    elif env > 0 and champ > 0.4:
+        return "AMBER"
+    elif env > 0.5 and champ > 0.6:
+        return "GREEN"
+    else:
+        return "AMBER"
+
+def run_gamut_test():
+    print("======================================================================")
+    print("MONTE CARLO FOREWARNER GAMUT TEST ('Montecarlo the Montecarlo')")
+    print("======================================================================")
+    
+    if not MODELS_DIR.exists():
+        print(f"[ERROR] Models directory not found at {MODELS_DIR}.")
+        return
+
+    print("[1/3] Initializing ML Models...")
+    try:
+        forewarner = DolphinForewarner(models_dir=str(MODELS_DIR))
+    except Exception as e:
+        print(f"[ERROR] Failed to load ML models: {e}")
+        return
+        
+    print("[2/3] Generating 2,500 random parameter configurations across the manifold...")
+    sampler = MCSampler(base_seed=1337)
+    # Generate a large set of raw trials (they don't even need to be strictly 'valid' 
+    # for the ML model to assess them, but we'll use the sampler to get structurally sound vectors)
+    trials = sampler.generate_trials(n_samples_per_switch=10, max_trials=2500)
+    print(f"      Generated {len(trials)} unique parameter vectors.")
+    
+    print("[3/3] Feeding gamut into the Forewarning Alert System...")
+    
+    results = []
+    level_counts = Counter()
+    
+    for i, trial in enumerate(trials):
+        config_dict = trial.to_dict()
+        report = forewarner.assess_config_dict(config_dict)
+        level = determine_risk_level(report)
+        
+        level_counts[level] += 1
+        results.append({
+            'trial_id': i,
+            'level': level,
+            'env_score': report.envelope_score,
+            'cat_prob': report.catastrophic_probability,
+            'champ_prob': report.champion_probability,
+            'predicted_roi': report.predicted_roi,
+            'config': config_dict
+        })
+        
+        if (i + 1) % 500 == 0:
+            print(f"      Assessed {i + 1} / {len(trials)} configs...")
+
+    print("\n======================================================================")
+    print("FOREWARNER RESPONSE DISTRIBUTION")
+    print("======================================================================")
+    
+    total = len(trials)
+    print(f"GREEN  (Safe / Optimal):      {level_counts['GREEN']:>4} ({level_counts['GREEN']/total*100:.1f}%)")
+    print(f"AMBER  (Transitional):        {level_counts['AMBER']:>4} ({level_counts['AMBER']/total*100:.1f}%)")
+    print(f"ORANGE (High Risk Warning):   {level_counts['ORANGE']:>4} ({level_counts['ORANGE']/total*100:.1f}%)")
+    print(f"RED    (Catastrophic Alert):  {level_counts['RED']:>4} ({level_counts['RED']/total*100:.1f}%)")
+    
+    print("\n======================================================================")
+    print("SAMPLE EXTREMUM PROFILES")
+    print("======================================================================")
+    
+    # Sort by envelope score
+    results_sorted = sorted(results, key=lambda x: x['env_score'])
+    
+    print("\\n--- WORST 'RED' CONFIGURATION DETECTED ---")
+    worst = results_sorted[0]
+    print(f"Envelope Score: {worst['env_score']:.3f} | Catastrophic Prob: {worst['cat_prob']:.1%} | Predicted ROI: {worst['predicted_roi']:.1f}%")
+    print(f"Key Triggers: max_leverage={worst['config']['max_leverage']:.2f}, fraction={worst['config']['fraction']:.2f}, vel_div_extreme={worst['config']['vel_div_extreme']:.4f}")
+    
+    print("\n--- BEST 'GREEN' CONFIGURATION DETECTED ---")
+    best = results_sorted[-1]
+    print(f"Envelope Score: {best['env_score']:.3f} | Champion Prob: {best['champ_prob']:.1%} | Predicted ROI: {best['predicted_roi']:.1f}%")
+    print(f"Key Triggers: max_leverage={best['config']['max_leverage']:.2f}, fraction={best['config']['fraction']:.2f}, vel_div_extreme={best['config']['vel_div_extreme']:.4f}")
+
+if __name__ == "__main__":
+    run_gamut_test()