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.

nautilus_dolphin/mc/__init__.py

@@ -0,0 +1,85 @@
+"""
+Monte Carlo System Envelope Mapping for DOLPHIN NG
+==================================================
+
+Full-system operational envelope simulation and ML forewarning integration.
+
+This package implements the Monte Carlo System Envelope Specification for
+the Nautilus-Dolphin trading system. It provides:
+
+1. Parameter space sampling (Latin Hypercube Sampling)
+2. Internal consistency validation (V1-V4 constraint groups)
+3. Trial execution harness (backtest runner)
+4. Metric extraction (48 metrics, 10 classification labels)
+5. Result persistence (Parquet + SQLite index)
+6. ML envelope learning (One-Class SVM, XGBoost)
+7. Live forewarning API (risk assessment for configurations)
+
+Usage:
+    from nautilus_dolphin.mc import MCSampler, MCValidator, MCExecutor
+    
+    # Run envelope testing
+    python run_mc_envelope.py --mode run --stage 1 --n-samples 500
+    
+    # Train ML models on results
+    python run_mc_envelope.py --mode train --output-dir mc_results/
+    
+    # Assess a live configuration
+    python run_mc_envelope.py --mode assess --assess my_config.json
+
+Reference:
+    MONTE_CARLO_SYSTEM_ENVELOPE_SPEC.md - Complete specification document
+"""
+
+__version__ = "1.0.0"
+__author__ = "DOLPHIN NG Team"
+
+# Core modules (lazy import to avoid heavy dependencies on import)
+def __getattr__(name):
+    if name == "MCSampler":
+        from .mc_sampler import MCSampler
+        return MCSampler
+    elif name == "MCValidator":
+        from .mc_validator import MCValidator
+        return MCValidator
+    elif name == "MCExecutor":
+        from .mc_executor import MCExecutor
+        return MCExecutor
+    elif name == "MCMetrics":
+        from .mc_metrics import MCMetrics
+        return MCMetrics
+    elif name == "MCStore":
+        from .mc_store import MCStore
+        return MCStore
+    elif name == "MCRunner":
+        from .mc_runner import MCRunner
+        return MCRunner
+    elif name == "MCML":
+        from .mc_ml import MCML
+        return MCML
+    elif name == "DolphinForewarner":
+        from .mc_ml import DolphinForewarner
+        return DolphinForewarner
+    elif name == "MCTrialConfig":
+        from .mc_sampler import MCTrialConfig
+        return MCTrialConfig
+    elif name == "MCTrialResult":
+        from .mc_metrics import MCTrialResult
+        return MCTrialResult
+    raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
+
+__all__ = [
+    # Core classes
+    "MCSampler",
+    "MCValidator", 
+    "MCExecutor",
+    "MCMetrics",
+    "MCStore",
+    "MCRunner",
+    "MCML",
+    "DolphinForewarner",
+    "MCTrialConfig",
+    "MCTrialResult",
+    # Version
+    "__version__",
+]