Merge pull request 'fix: portfolio_sector_exposure_summary renderer (SECTION_ERROR -> errors=0)' (#57) from feature/fix-sector-exposure-renderer into main

fix: portfolio_sector_exposure_summary renderer

tools/render_operational_report.py

@@ -361,6 +361,19 @@ def _portfolio_performance_summary(data_root: dict, hctx: dict, se: list) -> str
     return md
 
 
+def _portfolio_sector_exposure_summary(data_root: dict, hctx: dict, se: list) -> str:
+    raw = hctx.get("sector_concentration_json", [])
+    sectors = _sj(raw) if isinstance(raw, str) else raw
+    if not isinstance(sectors, list) or not sectors:
+        return "## 포트폴리오 섹터 노출\n\n_섹터 노출 데이터 없음_"
+    conc_gate = str(hctx.get("sector_concentration_gate") or "")
+    md = "## 포트폴리오 섹터 노출\n\n"
+    md += _kv([("섹터 집중 게이트", conc_gate)])
+    md += "\n\n"
+    md += _tbl(sectors, ["sector", "weight_pct", "gate"], max_rows=20)
+    return md
+
+
 def _sector_trend_analysis_v1(data_root: dict, hctx: dict, se: list) -> str:
     inner_data = data_root.get("data", {}) if isinstance(data_root.get("data"), dict) else {}
     payload = {"data": inner_data, "data_root": data_root, "_harness_context": hctx}
@@ -1152,6 +1165,7 @@ def main() -> int:
         "immediate_execution_playbook":           lambda: _immediate_execution_playbook(hctx, se),
         "market_context_learning_note":           lambda: _market_context_learning_note(hctx, se),
         "portfolio_performance_summary":          lambda: _portfolio_performance_summary(data_root, hctx, se),
+        "portfolio_sector_exposure_summary":      lambda: _portfolio_sector_exposure_summary(data_root, hctx, se),
         "sector_trend_analysis_v1":               lambda: _sector_trend_analysis_v1(data_root, hctx, se),
         "investment_quality_headline":            lambda: _investment_quality_headline(hctx, se),
         "operational_truth_score":                lambda: _operational_truth_score(hctx, se),