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/test_mini_5y.py

@@ -0,0 +1,67 @@
+"""Mini 5Y test - first 10 dates only"""
+import sys, time
+sys.stdout.reconfigure(encoding='utf-8', errors='replace')
+print('MINI 5Y BACKTEST (first 10 dates only)')
+print('='*50)
+
+t0 = time.time()
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
+from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
+
+VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines")
+META_COLS = {'timestamp', 'scan_number', 'v50_lambda_max_velocity', 'v150_lambda_max_velocity',
+             'v300_lambda_max_velocity', 'v750_lambda_max_velocity', 'vel_div',
+             'instability_50', 'instability_150'}
+
+parquet_files = sorted(VBT_DIR.glob("*.parquet"))[:10]  # Only 10 files
+print(f'Processing {len(parquet_files)} files...')
+
+# ACB
+acb = AdaptiveCircuitBreaker()
+date_strings = [pf.stem for pf in parquet_files]
+acb.preload_w750(date_strings)
+print(f'ACB w750 threshold: {acb._w750_threshold:.6f}')
+
+# Engine
+engine = NDAlphaEngine(
+    initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05,
+    min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0,
+    fraction=0.20, fixed_tp_pct=0.0095, stop_pct=1.0, max_hold_bars=120,
+    use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
+    dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
+    use_asset_selection=True, min_irp_alignment=0.45,
+    use_sp_fees=True, use_sp_slippage=True,
+    sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50,
+    use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
+    lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
+)
+engine.set_acb(acb)
+engine.set_esoteric_hazard_multiplier(0.0)
+
+# Process
+for i, pf in enumerate(parquet_files):
+    ds = pf.stem
+    df = pd.read_parquet(pf)
+    acols = [c for c in df.columns if c not in META_COLS]
+    
+    # Compute vol regime
+    bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
+    dvol = np.full(len(df), np.nan)
+    if bp is not None:
+        for j in range(50, len(bp)):
+            seg = bp[max(0,j-50):j]
+            if len(seg)<10: continue
+            dvol[j] = float(np.std(np.diff(seg)/seg[:-1]))
+    vol_p60 = 0.001  # Fixed for mini test
+    vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False)
+    
+    stats = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok)
+    print(f"  [{i+1}] {ds}: trades={stats['trades']} P&L=${stats['pnl']:+.0f} cap=${engine.capital:,.0f}")
+
+print('='*50)
+print(f'DONE in {time.time()-t0:.1f}s')
+print(f'Final capital: ${engine.capital:,.2f}')
+print(f'Total trades: {len(engine.trade_history)}')