[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",
+        "AOT", "CENTEL", "MINT", "AWC", "CPN", "CRC", "BEM", "BTS", "ERW",
-        "MAJOR", "BEM", "BTS",
+        "DHOUSE", "MAJOR", "SNNP",
     },
     "auto_credit": {
-        "KKP", "TISCO", "TCAP", "THANI", "MTC", "SAWAD", "NSI", "GLAND",
+        "KKP", "TISCO", "TCAP", "THANI", "MTC", "SAWAD", "NSI", "AEONTS", "TK",
-        "THG", "ASI", "TGPRO", "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
+        # BANPU is in energy/petrochem/utilities maps (full SET50 coverage)
-        self.assertEqual(d["theme_contributions"], [])     # but no surprise set -> 0
+        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"]))

frontend/src/App.vue

@@ -58,21 +58,31 @@ const signalSummary = computed(() => {
   return { long: s?.long ?? 0, short: s?.short ?? 0, neutral: s?.neutral ?? 0, total: s?.total ?? 0 }
 })
 const freqLabel = (f) => ({ monthly: 'รายเดือน', quarterly: 'รายไตรมาส', annual: 'รายปี', daily: 'รายวัน' })[f] || f
-// theme id -> Thai label (for the theme column)
+// theme id -> Thai label (for the theme column). Mirrors backend THEME_LABELS_TH.
 const themeLabelById = {
-  tourism: 'ท่องเที่ยว', auto_credit: 'รถยนต์/สินเชื่อ', refining_energy: 'พลังงาน',
+  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',
+}
+// which themes a symbol belongs to (mirrors backend THEME_SYMBOLS for full SET50)
+const THEME_BY_SYMBOL = {
+  'AOT':'tourism','CENTEL':'tourism','MINT':'tourism','AWC':'tourism','CPN':'tourism','CRC':'tourism','BEM':'tourism','BTS':'tourism',
+  'MTC':'auto_credit','SAWAD':'auto_credit','TISCO':'auto_credit',
+  'BANPU':'utilities','GPSC':'utilities','PTT':'utilities','PTTGC':'refining_energy','TOP':'refining_energy','IVL':'petrochem_materials',
+  'BBL':'banks','KBANK':'banks','KTB':'banks','SCB':'banks','TTB':'banks',
+  'COM7':'retail','CPALL':'retail','GLOBAL':'retail','HMPRO':'retail','OR':'retail','OSP':'retail',
+  'ADVANC':'telecom_it','TRUE':'telecom_it','DELTA':'telecom_it',
+  'LH':'property','BDMS':'healthcare','BH':'healthcare','SCC':'petrochem_materials','SCGP':'petrochem_materials',
+  'CPF':'consumer_staples','TU':'consumer_staples','CBG':'consumer_staples',
+  'BGRIM':'utilities','EGCO':'utilities','RATCH':'utilities','GULF':'utilities','EA':'utilities',
+  'JMT':'nonbank_finance','JMART':'nonbank_finance','KTC':'nonbank_finance','TIDLOR':'nonbank_finance',
+  'PTTEP':'exploration',
 }
-// which themes a symbol belongs to (id + th label)
 function symbolThemes(symbol) {
-  const out = []
+  const id = THEME_BY_SYMBOL[symbol]
-  for (const t of dashboardThemes.value) {
+  if (!id) return []
-    if ((t.id === 'tourism' && ['AOT','CENTEL','MINT','AWC','ERW','CPN','CRC','BEM','BTS','SNNP','MAJOR','DHOUSE'].includes(symbol)) ||
+  return [themeLabelById[id] || id]
-        (t.id === 'auto_credit' && ['KKP','TISCO','TCAP','THANI','MTC','SAWAD','NSI','GLAND','THG','ASI','TGPRO','AEONTS','TK'].includes(symbol)) ||
-        (t.id === 'refining_energy' && ['PTT','PTTGC','TOP','IRPC','SPRC','BCP','ESSO','BANPU','GPSC'].includes(symbol))) {
-      out.push(themeLabelById[t.id])
-    }
-  }
-  return out
 }
 const factorAvailable = computed(() => factorData.value?.available ?? false)
 const dividendCount = computed(() => factorData.value?.dividend_count ?? 0)
@@ -655,16 +665,16 @@ onMounted(loadDashboard)
         <div v-else-if="symbolDetail?.error" class="state-card error-state">{{ symbolDetail.error }}</div>
         <div v-else-if="symbolDetail" class="modal-body">
           <div class="modal-section">
-            <div class="modal-section-title">ธีมที่เกี่ยวข้อง</div>
+            <div class="modal-section-title">ธีมที่เกี่ยวข้อง (คะแนนต่อธีม)</div>
             <div v-if="symbolDetail.themes?.length" class="modal-themes">
-              <span v-for="t in symbolDetail.themes" :key="t" class="theme-tag large">{{ themeLabelById[t] || t }}</span>
-              <span class="modal-sub">surprise ต่อธีม</span>
               <div v-for="c in symbolDetail.theme_contributions" :key="c.theme" class="contrib-line">
-                <span>{{ themeLabelById[c.theme] || c.theme }}</span>
+                <span class="contrib-name">{{ c.label_th || themeLabelById[c.theme] || c.theme }}</span>
-                <strong>{{ formatNumber(c.surprise) }}σ</strong>
+                <strong v-if="c.surprise != null">{{ formatNumber(c.surprise) }}σ</strong>
+                <strong v-else class="muted-cell">ยังไม่มีข้อมูล</strong>
               </div>
+              <div class="modal-sub">คะแนนธีม = ค่าเฉลี่ยของค่าเหล่านี้ ที่หุ้นนี้อยู่ใน (เฉพาะธีมที่มีข้อมูล)</div>
             </div>
-            <div v-else class="muted-cell">ไม่มีธีมที่ชัดเจน (หุ้นนอกแผนที่ธีม)</div>
+            <div v-else class="muted-cell">หุ้นนี้ยังไม่ได้จัดอยู่ในธีมใด (จะอัปเดตเมื่อเพิ่มธีม)</div>
           </div>
 
           <div class="modal-section">