GT MTF 프로필·캘리브레이션과 04 매칭/시뮬/실거래 파이프라인을 추가한다.

3분~일봉 GT 타점 분석(03c), leg 체결 순서 수정, 총자산 90% 검증 루프,
walk-forward Go/No-Go 시뮬, monitor·live_trader 및 reference 문서를 포함한다.

Co-authored-by: Cursor <cursoragent@cursor.com>

deepcoin/analysis/general_analysis_snapshot.py

@@ -5,6 +5,8 @@ general_analysis ground truth 타점 MTF 스냅샷 생성.
 from __future__ import annotations
 
 import json
+import sys
+import time
 from pathlib import Path
 from typing import Any
 
@@ -40,15 +42,31 @@ def build_trade_mtf_snapshots(
     Returns:
         wide DataFrame (1 row per trade).
     """
+    n_trades = len(trades)
     enriched: dict[int, pd.DataFrame] = {}
-    for iv in GENERAL_ANALYSIS_INTERVALS:
+    t0 = time.time()
+    print(f"[03b] Phase A: 8TF enrich (1분봉 제외, 전 기법) — {len(GENERAL_ANALYSIS_INTERVALS)}개 간격")
+    sys.stdout.flush()
+    for step, iv in enumerate(GENERAL_ANALYSIS_INTERVALS, start=1):
         raw = frames.get(iv)
         if raw is None or raw.empty:
+            print(f"  [{step}/8] {interval_tf_prefix(iv)} SKIP (데이터 없음)")
+            sys.stdout.flush()
             continue
-        print(f"  [GA] {interval_tf_prefix(iv)} 봉 지표 계산 ({len(raw)}봉)...")
+        label = interval_tf_prefix(iv)
+        print(f"  [{step}/8] {label} enrich 시작 ({len(raw):,}봉)...")
+        sys.stdout.flush()
+        t_iv = time.time()
         enriched[iv] = general_analysis_enrich_bars(raw, iv, full_context=True)
+        print(f"  [{step}/8] {label} 완료 — {len(enriched[iv].columns)}열, {time.time() - t_iv:.0f}초")
+        sys.stdout.flush()
+    print(f"[03b] Phase A 완료 (누적 {time.time() - t0:.0f}초)")
+    sys.stdout.flush()
 
+    print(f"[03b] Phase B: GT 타점 스냅샷 {n_trades}건")
+    sys.stdout.flush()
     rows: list[dict[str, Any]] = []
+    t_b = time.time()
     for i, t in enumerate(sorted(trades, key=lambda x: x["dt"])):
         ts = pd.Timestamp(t["dt"])
         row: dict[str, Any] = {
@@ -70,12 +88,62 @@ def build_trade_mtf_snapshots(
         row.update(flat)
         row.update(general_analysis_mtf_scores(flat))
         rows.append(row)
-        if (i + 1) % 50 == 0:
-            print(f"  타점 스냅샷 {i + 1}/{len(trades)}")
+        done = i + 1
+        if done == 1 or done % 25 == 0 or done == n_trades:
+            elapsed = time.time() - t_b
+            rate = done / elapsed if elapsed > 0 else 0
+            eta = (n_trades - done) / rate if rate > 0 else 0
+            print(
+                f"  타점 {done}/{n_trades} "
+                f"({elapsed:.0f}초 경과, ETA 약 {eta:.0f}초)"
+            )
+            sys.stdout.flush()
 
+    print(f"[03b] Phase B 완료 ({time.time() - t_b:.0f}초)")
+    sys.stdout.flush()
     return pd.DataFrame(rows)
 
 
+def append_missing_gt_snapshots(
+    frames: dict[int, pd.DataFrame],
+    trades_path: Path | str = DEFAULT_TRADES_FILE,
+    output_csv: Path | str = DEFAULT_OUTPUT_CSV,
+) -> int:
+    """
+    CSV에 없는 GT 타점만 MTF 스냅샷 추가.
+
+    Args:
+        frames: interval → OHLCV.
+        trades_path: ground_truth JSON.
+        output_csv: 03b CSV.
+
+    Returns:
+        추가된 행 수.
+    """
+    out = Path(output_csv)
+    if not out.is_file():
+        return 0
+    data = load_ground_truth(Path(trades_path))
+    if not data:
+        return 0
+    trades = data.get("trades") or []
+    existing = pd.read_csv(out)
+    have = set(zip(existing["dt"].astype(str), existing["action"].astype(str)))
+    missing = [
+        t
+        for t in trades
+        if (str(t["dt"]), str(t["action"])) not in have
+    ]
+    if not missing:
+        return 0
+    print(f"[03b] 누락 GT 타점 {len(missing)}건 스냅샷 추가")
+    add_df = build_trade_mtf_snapshots(frames, missing)
+    merged = pd.concat([existing, add_df], ignore_index=True)
+    merged.to_csv(out, index=False, encoding="utf-8-sig")
+    print(f"[03b] CSV 갱신: {out} ({len(merged)}행)")
+    return len(missing)
+
+
 def export_trade_snapshots(
     frames: dict[int, pd.DataFrame],
     trades_path: Path | str = DEFAULT_TRADES_FILE,