feat: Sprint-3 완결 + Sprint-4 착수 (WBS-3.2, 3.4, 5.2)

주요 변경:
- [WBS-3.2] 리밸런싱 V2 신호 가중 목표배분 (signal_weighted_ss001_v1)
  * equal_weight -> SS001_Norm_Score 비례 버킷내 배분
  * 하네스: 삼성(36.84%) > SK하이닉스(29.16%), Core=66.00% PASS
- [WBS-3.4] logDailyAssetHistory_ SpreadsheetApp.getActiveSpreadsheet() -> getSpreadsheet_() 수정
  * run_all 컨텍스트에서 null 반환 방지
- [WBS-5.2] deploy_gas.py 전면 재작성
  * src/gas_adapter_parts/ + src/gas/ 양쪽 소스 탐색
  * gdc_01+gdc_02 -> gas_data_collect.gs 번들링
  * dry-run PASS: 17개 파일 WARN 0건
- src/gas/ 디렉토리 신규 추가 (CLASP 조직화 구조)
- tools/automate_routine.py, download_trading_data.py 신규 추가
- .gitignore: .clasprc.json OAuth 토큰 제외 추가
- ROADMAP_WBS.md: Sprint-3 [x] 완료, Sprint-4 착수 목록 추가

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tools/build_rebalance_engine_v1.py

@@ -381,6 +381,9 @@ def main() -> int:
             continue
         seen_tickers.add(ticker)
         df_row = df_row_map.get(ticker, {})
+        # SS001_Norm_Score: 0~100 신호 강도. 0은 데이터 없음 → 최소 가중치 1.0으로 폴백
+        ss001_raw = _f(df_row.get("SS001_Norm_Score"), default=-1.0)
+        ss001_norm = max(1.0, ss001_raw) if ss001_raw > 0 else 1.0
         holdings.append({
             "ticker":       ticker,
             "name":         sp["name"] or _s(df_row.get("Name")),
@@ -392,6 +395,7 @@ def main() -> int:
             "final_action": _s(df_row.get("Final_Action")),
             "sell_reason":  _s(df_row.get("Sell_Reason")),
             "force_signal": _detect_force_signal(df_row),
+            "ss001_norm":   ss001_norm,
         })
 
     # data_feed 에만 있는 보유 종목 보완 (snap 에 없는 경우 — 비정상이지만 방어)
@@ -403,6 +407,8 @@ def main() -> int:
         acct_mv    = _f(row.get("Account_Market_Value"))
         if weight_pct <= 0 and acct_mv <= 0:
             continue
+        ss001_raw = _f(row.get("SS001_Norm_Score"), default=-1.0)
+        ss001_norm = max(1.0, ss001_raw) if ss001_raw > 0 else 1.0
         holdings.append({
             "ticker":       ticker,
             "name":         _s(row.get("Name")),
@@ -414,6 +420,7 @@ def main() -> int:
             "final_action": _s(row.get("Final_Action")),
             "sell_reason":  _s(row.get("Sell_Reason")),
             "force_signal": _detect_force_signal(row),
+            "ss001_norm":   ss001_norm,
         })
 
     # ── 3. 버킷별 현재 비중 집계 ─────────────────────────────────────────────
@@ -462,17 +469,22 @@ def main() -> int:
         })
 
     # ── 4. 종목별 분석 ───────────────────────────────────────────────────────
-    # 버킷 내 equal-weight target
-    bucket_ticker_count: dict[str, int] = {}
+    # [WBS-3.2] 신호 가중 목표배분 (SS001_Norm_Score 기반)
+    # ticker_target_pct = bucket_target × (ss001_norm / Σ ss001_norm in bucket)
+    # 폴백: ss001_norm 모두 1.0 → equal_weight 와 동일
+    bucket_ss001_total: dict[str, float] = {}
     for h in holdings:
-        bucket_ticker_count[h["bucket"]] = bucket_ticker_count.get(h["bucket"], 0) + 1
+        bucket_ss001_total[h["bucket"]] = (
+            bucket_ss001_total.get(h["bucket"], 0.0) + h.get("ss001_norm", 1.0)
+        )
 
     ticker_rows: list[dict] = []
     for h in holdings:
-        bname       = h["bucket"]
-        bcfg        = BUCKET_CONFIG.get(bname, BUCKET_CONFIG["Satellite"])
-        n_tickers   = bucket_ticker_count.get(bname, 1)
-        target_pct  = _round2(bcfg["target"] / n_tickers)
+        bname          = h["bucket"]
+        bcfg           = BUCKET_CONFIG.get(bname, BUCKET_CONFIG["Satellite"])
+        ss001_w        = h.get("ss001_norm", 1.0)
+        bucket_ss001   = max(bucket_ss001_total.get(bname, ss001_w), 0.01)
+        target_pct     = _round2(bcfg["target"] * ss001_w / bucket_ss001)
         current_pct = _round2(h["weight_pct"])
         drift       = _round2(current_pct - target_pct)
         band_min    = _round2(target_pct - band["contract"])
@@ -612,7 +624,7 @@ def main() -> int:
     out = {
         "formula_id": FORMULA_ID,
         "metadata": {
-            "per_ticker_target_method": "equal_weight_within_bucket",
+            "per_ticker_target_method": "signal_weighted_ss001_v1",
             "regime_source": "macro.REGIME_PRELIM > settings > harness_context > computed_harness",
         },
         "summary":      summary,