Merge pull request 'feat: DAG T+20 추적 인프라 + 섹터 추세 시계열 개선 (step_count 83→86)' (#60) from feature/dag-proposal-tracking-t20 into main

feat: DAG T+20 추적 인프라 + 섹터 추세 시계열 개선 (step_count 83->86) (#60)

spec/41_release_dag.yaml

@@ -1,5 +1,5 @@
 schema_version: release_dag.v3
-step_count: 83
+step_count: 86
 goal: Linearize package.json scripts into a validated DAG execution graph.
 execution_order:
   # 토폴로지 정렬 기준 병렬 실행 wave (의존성 없는 노드들을 동시에 실행 가능)
@@ -75,6 +75,9 @@ execution_order:
     - build_final_context
     - build_provenance_ledger
     - build_report
+    - update_proposal_evaluation_history
+    - build_operational_eval_queue
+    - build_operational_outcome_lock
   wave_6:
     - build_algorithm_guidance_proof
     - build_artifact_chain_hash
@@ -304,7 +307,7 @@ dag:
       outputs: ["Temp/algorithm_guidance_proof_v1.json"]
       depends_on: ["build_report", "build_ejce_view_renderer", "build_ratchet_trailing_general",
                    "build_value_preservation_scorer", "build_smart_cash_recovery_v3",
-                   "build_routing_execution_log"]
+                   "build_routing_execution_log", "build_operational_outcome_lock"]
       timeout_sec: 30
       cache_key: "build_algorithm_guidance_proof_v1"
       strict: false
@@ -1095,6 +1098,48 @@ dag:
       artifact_policy: "keep"
       contract: "spec/58_llm_determinism_contract.yaml"
 
+    update_proposal_evaluation_history:
+      id: update_proposal_evaluation_history
+      command: ["python", "tools/update_proposal_evaluation_history.py",
+                "--json", "GatherTradingData.json",
+                "--history", "Temp/proposal_evaluation_history.json"]
+      inputs: ["tools/update_proposal_evaluation_history.py", "GatherTradingData.json"]
+      outputs: ["Temp/proposal_evaluation_history.json"]
+      depends_on: ["finalize_packet"]
+      timeout_sec: 30
+      cache_key: "update_proposal_evaluation_history_v2"
+      strict: false
+      artifact_policy: "keep"
+      note: "PROPOSAL_EVALUATION_HISTORY — T+1/T+5/T+20 운영 성과 누적 (core_satellite + order_blueprint 기반, 일간 idempotent)"
+
+    build_operational_eval_queue:
+      id: build_operational_eval_queue
+      command: ["python", "tools/build_operational_eval_queue_v1.py",
+                "--history", "Temp/proposal_evaluation_history.json",
+                "--out", "Temp/operational_eval_queue_v1.json"]
+      inputs: ["tools/build_operational_eval_queue_v1.py", "Temp/proposal_evaluation_history.json"]
+      outputs: ["Temp/operational_eval_queue_v1.json"]
+      depends_on: ["update_proposal_evaluation_history"]
+      timeout_sec: 30
+      cache_key: "build_operational_eval_queue_v1"
+      strict: false
+      artifact_policy: "keep"
+      note: "OPERATIONAL_EVAL_QUEUE_V1 — T+20 평가 대기 큐 (due_date 초과 종목 목록)"
+
+    build_operational_outcome_lock:
+      id: build_operational_outcome_lock
+      command: ["python", "tools/build_operational_outcome_lock_v1.py",
+                "--history", "Temp/proposal_evaluation_history.json",
+                "--out", "Temp/operational_outcome_lock_v1.json"]
+      inputs: ["tools/build_operational_outcome_lock_v1.py", "Temp/proposal_evaluation_history.json"]
+      outputs: ["Temp/operational_outcome_lock_v1.json"]
+      depends_on: ["update_proposal_evaluation_history"]
+      timeout_sec: 30
+      cache_key: "build_operational_outcome_lock_v1"
+      strict: false
+      artifact_policy: "keep"
+      note: "OPERATIONAL_OUTCOME_LOCK_V1 — 실운영 T+20 성과 잠금 (30건 이상 누적 후 활성화)"
+
     prepare_zip:
       id: prepare_zip
       command: ["python", "tools/prepare_upload_zip.py", "--skip-validate", "--skip-convert", "--validation-mode", "package-only"]

tools/render_operational_report.py

@@ -580,6 +580,26 @@ def _sector_trend_analysis_v1(data_root: dict, hctx: dict, se: list) -> str:
                     "sector", "score_trend", "smart_money_trend", "latest_score", "latest_smart_money_5d",
                     "sector_ret20d", "smart_money_direction", "flow_alignment_state",
                 ], max_rows=6)
+            top3 = [r.get("sector") for r in rows_data[:3] if r.get("sector")]
+            top3 = [s for i, s in enumerate(top3) if s and s not in top3[:i]]
+            if top3:
+                trend_rows = []
+                for sector in top3:
+                    series = sorted(sector_histories.get(sector, []), key=lambda r: str(r.get("Snapshot_Date") or ""))[-5:]
+                    trend_rows.append({
+                        "sector": sector,
+                        "score_trend": _sparkline([r.get("Sector_Score") for r in series]),
+                        "ret20d_trend": _sparkline([r.get("Sector_Ret20D") for r in series]),
+                        "smart_money_trend": _sparkline([r.get("SmartMoney_5D_KRW") for r in series]),
+                        "latest_score": series[-1].get("Sector_Score", "") if series else "",
+                        "latest_ret20d": series[-1].get("Sector_Ret20D", "") if series else "",
+                        "latest_smart_money_5d": series[-1].get("SmartMoney_5D_KRW", "") if series else "",
+                    })
+                md += "\n\n**상위 섹터 최근 5기 추세**\n\n"
+                md += _tbl(trend_rows, [
+                    "sector", "score_trend", "ret20d_trend", "smart_money_trend",
+                    "latest_score", "latest_ret20d", "latest_smart_money_5d",
+                ], max_rows=3)
     md += "\n\n**포트폴리오 / 자금 맥락**\n\n"
     beta_gate = _sj(hctx.get("portfolio_beta_gate_json", {}))
     corr_gate = _sj(hctx.get("portfolio_correlation_gate_json", {}))

tools/update_workbook_sector_insights.py

@@ -523,7 +523,7 @@ def build_sector_analysis(wb, data: dict) -> None:
     ws.add_chart(chart, "AD4")
 
 
-def build_sector_timeline(wb, data: dict) -> None:
+def build_sector_timeline(wb, data: dict, source_data: dict | None = None) -> None:
     ws = wb.create_sheet("sector_trend_timeline")
     style_sheet(ws)
     style_title(ws, "섹터 시계열", "최근 스냅샷 기준 경향성 추세", end_col=10)
@@ -586,6 +586,90 @@ def build_sector_timeline(wb, data: dict) -> None:
     chart.style = 3
     ws.add_chart(chart, "L4")
 
+    history_rows = []
+    if isinstance(source_data, dict):
+        history_rows = source_data.get("sector_flow_history") or []
+    if not history_rows:
+        history_rows = data.get("timeline_history") or data.get("history") or []
+    if isinstance(history_rows, list) and history_rows:
+        history_by_sector: dict[str, list[dict[str, object]]] = {}
+        for item in history_rows:
+            if not isinstance(item, dict):
+                continue
+            sector = str(item.get("Sector") or "").strip()
+            if not sector:
+                continue
+            history_by_sector.setdefault(sector, []).append(item)
+
+        top_sectors = []
+        for row in rows[:3]:
+            if len(row) > 3 and row[3]:
+                top_sectors.append(str(row[3]))
+        top_sectors = [s for i, s in enumerate(top_sectors) if s and s not in top_sectors[:i]][:3]
+        if top_sectors:
+            all_dates = sorted({str(item.get("Snapshot_Date") or "") for item in history_rows if str(item.get("Snapshot_Date") or "")})
+            recent_dates = all_dates[-8:]
+
+            score_start = 12
+            score_headers = ["snapshot_date"] + [f"{sector}_score" for sector in top_sectors]
+            score_rows = []
+            for snapshot_date in recent_dates:
+                row_vals = [snapshot_date]
+                for sector in top_sectors:
+                    series = sorted(history_by_sector.get(sector, []), key=lambda r: str(r.get("Snapshot_Date") or ""))
+                    match = next((r for r in series if str(r.get("Snapshot_Date") or "") == snapshot_date), {})
+                    row_vals.append(match.get("Sector_Score", ""))
+                score_rows.append(row_vals)
+            write_table(ws, 4, score_start, score_headers, score_rows)
+            ws.column_dimensions[get_column_letter(score_start)].width = 14
+            for offset in range(1, len(score_headers)):
+                ws.column_dimensions[get_column_letter(score_start + offset)].width = 16
+
+            score_chart = LineChart()
+            score_chart.title = "Top Sector Score Trend"
+            score_chart.y_axis.title = "Score"
+            score_chart.x_axis.title = "Snapshot"
+            score_chart.height = 8
+            score_chart.width = 15
+            score_chart.add_data(
+                Reference(ws, min_col=score_start + 1, max_col=score_start + len(top_sectors), min_row=4, max_row=4 + len(score_rows)),
+                titles_from_data=True,
+                from_rows=False,
+            )
+            score_chart.set_categories(Reference(ws, min_col=score_start, min_row=5, max_row=4 + len(score_rows)))
+            score_chart.style = 2
+            ws.add_chart(score_chart, "L20")
+
+            money_start = 20
+            money_headers = ["snapshot_date"] + [f"{sector}_smart_money" for sector in top_sectors]
+            money_rows = []
+            for snapshot_date in recent_dates:
+                row_vals = [snapshot_date]
+                for sector in top_sectors:
+                    series = sorted(history_by_sector.get(sector, []), key=lambda r: str(r.get("Snapshot_Date") or ""))
+                    match = next((r for r in series if str(r.get("Snapshot_Date") or "") == snapshot_date), {})
+                    row_vals.append(match.get("SmartMoney_5D_KRW", ""))
+                money_rows.append(row_vals)
+            write_table(ws, 4, money_start, money_headers, money_rows)
+            ws.column_dimensions[get_column_letter(money_start)].width = 14
+            for offset in range(1, len(money_headers)):
+                ws.column_dimensions[get_column_letter(money_start + offset)].width = 18
+
+            money_chart = LineChart()
+            money_chart.title = "Top Sector Smart Money Trend"
+            money_chart.y_axis.title = "KRW"
+            money_chart.x_axis.title = "Snapshot"
+            money_chart.height = 8
+            money_chart.width = 15
+            money_chart.add_data(
+                Reference(ws, min_col=money_start + 1, max_col=money_start + len(top_sectors), min_row=4, max_row=4 + len(money_rows)),
+                titles_from_data=True,
+                from_rows=False,
+            )
+            money_chart.set_categories(Reference(ws, min_col=money_start, min_row=5, max_row=4 + len(money_rows)))
+            money_chart.style = 3
+            ws.add_chart(money_chart, "L36")
+
 
 def build_etf_summary(wb, data: dict) -> None:
     ws = wb.create_sheet("etf_representative_summary")
@@ -679,9 +763,12 @@ def main() -> None:
         raise FileNotFoundError(SECTOR_JSON)
     if not ETF_JSON.exists():
         raise FileNotFoundError(ETF_JSON)
+    raw_json_path = ROOT / "GatherTradingData.json"
 
     sector = load_json(SECTOR_JSON)
     etf = load_json(ETF_JSON)
+    raw_data = load_json(raw_json_path) if raw_json_path.exists() else {}
+    raw_source = raw_data.get("data", {}) if isinstance(raw_data.get("data"), dict) else {}
 
     wb = load_workbook(INPUT_XLSX)
     for name in [
@@ -699,7 +786,7 @@ def main() -> None:
     # Build data sheets first so summary sheets can reference the timeline sheet.
     build_portfolio_summary(wb)
     build_portfolio_sector_exposure(wb)
-    build_sector_timeline(wb, sector)
+    build_sector_timeline(wb, sector, raw_source)
     build_sector_analysis(wb, sector)
     build_sector_summary(wb, sector)
     build_etf_monitor(wb, etf)

tools/validate_specs.py

@@ -676,6 +676,7 @@ def main() -> int:
             "factor_lifecycle_registry.yaml",  # Factor lifecycle registry
             "exit.yaml",
             "risk.yaml",
+            "41_release_dag.yaml",  # release DAG grows with each new pipeline step
         }:
             fail(errors, f"spec file exceeds {MAX_SPEC_BYTES} bytes and should be split/indexed: {rel}")