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_arrow_adapter_quick.py

@@ -0,0 +1,53 @@
+"""Quick smoke test: Arrow adapter + Arrow->Parquet converter."""
+import sys, logging
+sys.path.insert(0, r'.')
+logging.basicConfig(level=logging.INFO, format='%(levelname)s %(message)s')
+
+from datetime import datetime
+from nautilus_dolphin.nautilus.arrow_data_adapter import (
+    ArrowEigenvalueDataAdapter,
+    ArrowToParquetBatchConverter,
+)
+import pandas as pd
+
+ARROW_DIR  = r'c:\Users\Lenovo\Documents\- Dolphin NG5\correlation_arb512\arrow_scans'
+PARQUET_OUT = r'vbt_cache_ng5'
+DATE = '2026-02-25'
+
+# ── 1: Arrow adapter ──────────────────────────────────────────────────────────
+print("\n=== ARROW ADAPTER ===")
+adapter = ArrowEigenvalueDataAdapter(ARROW_DIR, venue='BINANCE_FUTURES')
+files = adapter.load_date_range(datetime(2026,2,25), datetime(2026,2,25))
+print(f"Files found: {len(files)}")
+assert len(files) > 0, "No files found!"
+
+ok = 0
+for f in files[:5]:
+    scan = adapter.load_scan_file(f)
+    if scan is None:
+        print(f"  SKIP: {f.name}")
+        continue
+    w50  = scan['windows'].get('50',  {}).get('tracking_data', {})
+    w150 = scan['windows'].get('150', {}).get('tracking_data', {})
+    vd   = w50.get('lambda_max_velocity', 0) - w150.get('lambda_max_velocity', 0)
+    prices = scan.get('pricing_data', {}).get('current_prices', {})
+    print(f"  {f.name}: vel_div={vd:.6f}  prices_count={len(prices)}")
+    ok += 1
+
+assert ok > 0, "No scans could be read!"
+print(f"[PASS] Arrow adapter: {ok}/5 scans read")
+
+# ── 2: Arrow -> Parquet ───────────────────────────────────────────────────────
+print("\n=== ARROW → PARQUET ===")
+conv = ArrowToParquetBatchConverter(ARROW_DIR, PARQUET_OUT)
+out  = conv.convert_date(DATE)
+assert out is not None, "Parquet conversion returned None!"
+df = pd.read_parquet(out)
+print(f"Parquet rows: {len(df)}")
+print(f"Columns: {list(df.columns[:10])}")
+assert len(df) > 0, "Empty Parquet!"
+assert 'v50_lambda_max_velocity' in df.columns, "Missing velocity column!"
+print(df[['timestamp','scan_number','v50_lambda_max_velocity','vel_div']].head(3).to_string())
+print(f"[PASS] Arrow→Parquet conversion: {len(df)} rows, {len(df.columns)} columns")
+
+print("\n===== ALL TESTS PASSED =====")