[verified] Cover full SET50 with 13 themes + per-theme score detail in symbol view

- THEME_SYMBOLS expanded: added banks, retail, telecom_it, property, healthcare, petrochem_materials, consumer_staples, utilities, nonbank_finance, exploration -> all 49 SET50 names now in a theme
- THEME_LABELS_TH Thai labels; THEME_FREQUENCY per theme
- symbol_breakdown now lists EVERY theme the symbol belongs to (label_th + surprise, or 'ยังไม่มีข้อมูล'), so theme_score is transparent per source
- frontend: theme column maps all 49 symbols (mirrors backend); modal shows per-theme score detail
- Fixed test for BANPU multi-theme; full suite 199 OK
- Verified: 49/49 rows have theme chip; AOT modal shows ท่องเที่ยว 0.57σ + full calc

backend/app/themes.py

@@ -29,18 +29,32 @@ from typing import Optional
 # the SET50 universe; symbols not listed here get no direct theme exposure for
 # that theme (theme_score contribution = those themes' factor value applied via
 # a generic market exposure fallback, see scoring).
+#
+# Expanded to cover the FULL SET50 universe (2026, 49 names): every symbol is
+# assigned to at least one industry/theme so the board + per-symbol detail can
+# always report a theme. Deterministic, curated by industry sector.
 THEME_SYMBOLS: dict[str, set[str]] = {
     "tourism": {
-        "AOT", "CENTEL", "MINT", "ERW", "DHOUSE", "AWC", "SNNP", "CPN", "CRC",
-        "MAJOR", "BEM", "BTS",
+        "AOT", "CENTEL", "MINT", "AWC", "CPN", "CRC", "BEM", "BTS", "ERW",
+        "DHOUSE", "MAJOR", "SNNP",
     },
     "auto_credit": {
-        "KKP", "TISCO", "TCAP", "THANI", "MTC", "SAWAD", "NSI", "GLAND",
-        "THG", "ASI", "TGPRO", "AEONTS", "TK",
+        "KKP", "TISCO", "TCAP", "THANI", "MTC", "SAWAD", "NSI", "AEONTS", "TK",
+        "THG", "GLAND", "ASI", "TGPRO",
     },
     "refining_energy": {
         "PTT", "PTTGC", "TOP", "IRPC", "SPRC", "BCP", "ESSO", "BANPU", "GPSC",
     },
+    "banks": {"BBL", "KBANK", "KTB", "SCB", "TTB"},
+    "retail": {"CPALL", "COM7", "GLOBAL", "HMPRO", "OSP", "OR", "CPN", "CRC"},
+    "telecom_it": {"ADVANC", "TRUE", "DELTA", "COM7"},
+    "property": {"LH", "AWC", "CPN", "CRC", "GLOBAL", "HMPRO"},
+    "healthcare": {"BDMS", "BH"},
+    "petrochem_materials": {"IVL", "SCC", "SCGP", "PTTGC", "BANPU"},
+    "consumer_staples": {"CPF", "TU", "OSP", "CBG"},
+    "utilities": {"BGRIM", "EGCO", "RATCH", "GPSC", "GULF", "BANPU", "EA"},
+    "nonbank_finance": {"JMT", "JMART", "KTC", "TIDLOR", "MTC", "SAWAD", "KTC", "AEONTS"},
+    "exploration": {"PTTEP", "PTT"},
 }
 
 # Theme frequency (data cadence of the underlying factor). Used to keep
@@ -49,6 +63,33 @@ THEME_FREQUENCY: dict[str, str] = {
     "tourism": "monthly",
     "auto_credit": "monthly",
     "refining_energy": "quarterly",
+    "banks": "quarterly",
+    "retail": "monthly",
+    "telecom_it": "quarterly",
+    "property": "quarterly",
+    "healthcare": "quarterly",
+    "petrochem_materials": "quarterly",
+    "consumer_staples": "quarterly",
+    "utilities": "quarterly",
+    "nonbank_finance": "quarterly",
+    "exploration": "quarterly",
+}
+
+# Thai labels for every theme (used in the board theme column + per-symbol view)
+THEME_LABELS_TH: dict[str, str] = {
+    "tourism": "ท่องเที่ยว",
+    "auto_credit": "รถยนต์/สินเชื่อ",
+    "refining_energy": "พลังงาน/โรงกลั่น",
+    "banks": "ธนาคาร",
+    "retail": "ค้าปลีก",
+    "telecom_it": "สื่อสาร/ไอที",
+    "property": "อสังหาริมทรัพย์",
+    "healthcare": "โรงพยาบาล",
+    "petrochem_materials": "ปิโตรเคมี/วัสดุ",
+    "consumer_staples": "อาหาร/อุปโภค",
+    "utilities": "สาธารณูปโภค",
+    "nonbank_finance": "การเงินนอกธนาคาร",
+    "exploration": "สำรวจ/ผลิตพลังงาน",
 }
 
 
@@ -192,7 +233,17 @@ def symbol_breakdown(
         s = theme_surprises.get(tid)
         if s is not None:
             theme_values.append(float(s))
-            theme_lines.append({"theme": tid, "surprise": round(float(s), 3)})
+            theme_lines.append({
+                "theme": tid,
+                "label_th": THEME_LABELS_TH.get(tid, tid),
+                "surprise": round(float(s), 3),
+            })
+        else:
+            theme_lines.append({
+                "theme": tid,
+                "label_th": THEME_LABELS_TH.get(tid, tid),
+                "surprise": None,
+            })
     theme_score = (sum(theme_values) / len(theme_values)) if theme_values else 0.0
 
     # find the factor row for this symbol

backend/tests/test_themes.py

@@ -101,5 +101,7 @@ class SymbolBreakdownTest(unittest.TestCase):
         from app import themes
         d = themes.symbol_breakdown("BANPU", factor_view=factor_view, theme_surprises={})
         self.assertEqual(d["theme_score"], 0.0)
-        self.assertEqual(d["themes"], ["refining_energy"])  # BANPU in energy map
-        self.assertEqual(d["theme_contributions"], [])     # but no surprise set -> 0
+        # BANPU is in energy/petrochem/utilities maps (full SET50 coverage)
+        self.assertTrue(set(d["themes"]) >= {"refining_energy", "petrochem_materials", "utilities"})
+        # no surprises set -> every contribution has surprise=None and theme_score 0
+        self.assertTrue(all(c["surprise"] is None for c in d["theme_contributions"]))