feat(factor): te_thailand rate/credit/retail/property/confidence + thai_trade external sector; fix sign inversion on bearish factors

- add te_thailand collector (TradingEconomics) -> 8 factors: interest rate,
  business loan growth, consumer credit, household debt/GDP, retail sales YoY,
  consumer confidence, residential property prices, business confidence;
  feed banks/retail/consumer_staples/nonbank_finance/property/telecom/healthcare
- add thai_trade collector (TradingEconomics external sector) -> exports/
  imports/current-account factors (concurrent in-tree work, verified green)
- fix sign inversion: theme weights were negative on sign:-1 factors (NPL,
  inflation, unemployment) so higher NPL/inflation RAISED scores; direction now
  lives only in factor sign, theme weights positive (regression-locked)
- tests: te_thailand parse+direction, value-key resolution contract, dashboard
  8-sources, scheduler vintage counts; suite 362 OK

backend/app/dashboard.py

@@ -75,7 +75,8 @@ class RealDashboard:
 
     def build(self) -> dict:
         # 1) live theme data (real, no fallback)
-        from . import auto_credit, auto_npl, bank_npl, energy_thai, bot_tourism
+        from . import (auto_credit, auto_npl, bank_npl, energy_thai, bot_tourism,
+                       macro_thai, te_thailand, thai_trade)
 
         tourism = None
         try:
@@ -94,11 +95,16 @@ class RealDashboard:
             self.cache, "energy_thai", lambda: energy_thai.fetch_energy_thai().to_dict(), "energy_thai")
         macro_d = _fetch_with_cache(
             self.cache, "macro_thai", lambda: macro_thai.fetch_macro_thai().to_dict(), "macro_thai")
+        trade_d = _fetch_with_cache(
+            self.cache, "thai_trade", lambda: thai_trade.fetch_thai_trade().to_dict(), "thai_trade")
+        te_d = _fetch_with_cache(
+            self.cache, "te_thailand", lambda: te_thailand.fetch_te_thailand().to_dict(), "te_thailand")
 
         # 2) per-theme surprise — registry-driven (THE single source of truth)
         fetched = {
             "macro_thai": macro_d, "auto_credit": auto_d,
             "auto_npl": npl_d, "energy_thai": en_d, "bank_npl": bnpl_d,
+            "thai_trade": trade_d, "te_thailand": te_d,
         }
         tourism_surprise = self._tourism_surprise()
         surprises = themes_mod.compute_theme_surprises(
@@ -114,27 +120,38 @@ class RealDashboard:
         ]
         # macro-proxy reads for the expanded SET50 themes (deterministic)
         proxy_reads = {
-            "banks": {"source": "BOT macro (invest) + NPL ภาคการเงิน",
+            "banks": {"source": "BOT macro + NPL + TE (rate/credit)",
                       "frequency": "monthly",
                       "invest_yoy": macro_d.get("private_investment_yoy"),
                       "inflation_yoy": macro_d.get("headline_inflation_yoy"),
-                      "bank_npl_pct": (bnpl_d or {}).get("pct_of_npls")},
+                      "bank_npl_pct": (bnpl_d or {}).get("pct_of_npls"),
-            "retail": {"source": "BOT macro (consumption)", "frequency": "monthly",
+                      "interest_rate_pct": (te_d or {}).get("interest_rate_pct"),
-                       "consumption_yoy": macro_d.get("private_consumption_yoy")},
+                      "loan_growth": (te_d or {}).get("loans_to_fin_corp")},
-            "consumer_staples": {"source": "BOT macro (consumption)", "frequency": "monthly",
+            "retail": {"source": "BOT macro + TE (retail/confidence)", "frequency": "monthly",
-                                 "consumption_yoy": macro_d.get("private_consumption_yoy")},
+                       "consumption_yoy": macro_d.get("private_consumption_yoy"),
+                       "retail_sales_yoy": (te_d or {}).get("retail_sales_yoy"),
+                       "consumer_confidence": (te_d or {}).get("consumer_confidence")},
+            "consumer_staples": {"source": "BOT macro + TE (retail/confidence)", "frequency": "monthly",
+                                 "consumption_yoy": macro_d.get("private_consumption_yoy"),
+                                 "retail_sales_yoy": (te_d or {}).get("retail_sales_yoy"),
+                                 "consumer_confidence": (te_d or {}).get("consumer_confidence")},
             "telecom_it": {"source": "BOT macro (consumption)", "frequency": "monthly",
                            "consumption_yoy": macro_d.get("private_consumption_yoy")},
-            "property": {"source": "BOT macro (invest)", "frequency": "monthly",
+            "property": {"source": "BOT macro + TE (property)", "frequency": "monthly",
-                         "invest_yoy": macro_d.get("private_investment_yoy")},
+                         "invest_yoy": macro_d.get("private_investment_yoy"),
+                         "property_prices_yoy": (te_d or {}).get("property_prices_yoy"),
+                         "business_confidence": (te_d or {}).get("business_confidence")},
             "utilities": {"source": "BOT macro (mfg)", "frequency": "monthly",
                           "mfg_yoy": macro_d.get("manufacturing_yoy")},
             "petrochem_materials": {"source": "BOT macro (mfg)", "frequency": "monthly",
                                     "mfg_yoy": macro_d.get("manufacturing_yoy")},
             "healthcare": {"source": "BOT macro (consumption)", "frequency": "monthly",
                            "consumption_yoy": macro_d.get("private_consumption_yoy")},
-            "nonbank_finance": {"source": "BOT macro (consumption)", "frequency": "monthly",
+            "nonbank_finance": {"source": "BOT macro + TE (credit/confidence)", "frequency": "monthly",
-                                "consumption_yoy": macro_d.get("private_consumption_yoy")},
+                                "consumption_yoy": macro_d.get("private_consumption_yoy"),
+                                "consumer_credit": (te_d or {}).get("consumer_credit_thbmn"),
+                                "household_debt_gdp": (te_d or {}).get("household_debt_gdp_pct"),
+                                "consumer_confidence": (te_d or {}).get("consumer_confidence")},
             "exploration": {"source": "TOP energy (refining)", "frequency": "quarterly",
                             "energy_proxy": surprises.get("refining_energy")},
         }
@@ -145,7 +162,7 @@ class RealDashboard:
         board = self._build_board(themes, macro_d)
 
         # 5) source provenance table
-        sources = self._build_sources(auto_d, npl_d, en_d, macro_d, tourism, bnpl_d)
+        sources = self._build_sources(auto_d, npl_d, en_d, macro_d, tourism, bnpl_d, trade_d, te_d)
 
         return {
             "themes": themes,
@@ -283,7 +300,7 @@ class RealDashboard:
         board.sort(key=lambda r: r["combined"], reverse=True)
         return board
 
-    def _build_sources(self, auto_d, npl_d, en_d, macro_d, tourism, bnpl_d=None) -> list:
+    def _build_sources(self, auto_d, npl_d, en_d, macro_d, tourism, bnpl_d=None, trade_d=None, te_d=None) -> list:
         """Sources derived from the FACTORS registry — adding a factor to
         factors.py auto-appends its source row here (no hardcoded list)."""
         import datetime as _dt
@@ -293,6 +310,7 @@ class RealDashboard:
         fetched = {
             "auto_credit": auto_d, "auto_npl": npl_d,
             "energy_thai": en_d, "macro_thai": macro_d, "bank_npl": bnpl_d,
+            "thai_trade": trade_d, "te_thailand": te_d,
         }
         # group FACTORS by fetch module -> one row per distinct source
         source_by_module: dict = {}
@@ -323,6 +341,8 @@ class RealDashboard:
                 "energy_thai": "Thai Oil investor",
                 "macro_thai": "BOT Thai Economy",
                 "bot_tourism": "BOT Tourism",
+                "thai_trade": "TradingEconomics",
+                "te_thailand": "TradingEconomics",
             }
             freq = meta["freq"]
             as_of = data.get("as_of") or data.get("period") or _period(data, meta["factors"])

backend/app/factors.py

@@ -25,6 +25,8 @@ _FETCH_MODULE: dict[str, str] = {
     "bank_npl": "bank_npl",
     "energy_thai": "energy_thai",
     "macro_thai": "macro_thai",
+    "thai_trade": "thai_trade",
+    "te_thailand": "te_thailand",
 }
 
 # Factor -> value key. Sign: +1 higher-is-bullish, -1 lower-is-bullish.
@@ -142,6 +144,144 @@ FACTORS: dict[str, dict[str, Any]] = {
         "weight": 0.5,
         "center": 2.0, "span": 10.0,  # ~2% target; higher is worse (sign -1)
     },
+    # ---- BOT Thai Economy fields that were already fetched but never used ----
+    # Wired into the registry so every scraped value actually feeds analysis
+    # (the user's rule: a fetched data point must be used, not just displayed).
+    "macro_core_inflation": {
+        "name_th": "เงินเฟ้อพื้นฐาน (Core)",
+        "source": "BOT",
+        "frequency": "monthly",
+        "fetch": "macro_thai",
+        "value_key": "core_inflation_yoy",
+        "sign": -1,
+        "weight": 0.4,
+        "center": 1.0, "span": 6.0,  # core ~1% target; higher is worse
+    },
+    "macro_unemployment": {
+        "name_th": "อัตราการว่างงาน",
+        "source": "BOT",
+        "frequency": "monthly",
+        "fetch": "macro_thai",
+        "value_key": "unemployment_pct",
+        "sign": -1,
+        "weight": 0.5,
+        "center": 1.0, "span": 3.0,  # ~1% Thailand; higher unemployment is worse
+    },
+    # ---- Thailand external sector (TradingEconomics, current through 2026) ----
+    "external_current_account": {
+        "name_th": "ดุลบัญชีเดินสะพัด (USD ล้าน)",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "thai_trade",
+        "value_key": "current_account_usdm",
+        "sign": 1,
+        "weight": 1.0,
+        "center": 0.0, "span": 3000.0,  # USD mn; surplus positive, deficit negative
+    },
+    "external_exports": {
+        "name_th": "มูลค่าส่งออก (USD ล้าน)",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "thai_trade",
+        "value_key": "exports_usdm",
+        "sign": 1,
+        "weight": 1.0,
+        "center": 30000.0, "span": 8000.0,  # ~$34k USD mn/month
+    },
+    "external_imports": {
+        "name_th": "มูลค่านำเข้า (USD ล้าน)",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "thai_trade",
+        "value_key": "imports_usdm",
+        "sign": 1,
+        "weight": 1.0,
+        "center": 34000.0, "span": 8000.0,  # ~$38k USD mn/month (domestic demand proxy)
+    },
+    # ---- Thailand rates / credit / retail / confidence (TradingEconomics) ----
+    # Single-page snapshot factors deepening the macro-proxy themes. Sign +1 =
+    # higher value is bullish/helpful; sign -1 = the opposite. Theme weights are
+    # always positive magnitude (direction lives in `sign`, per the sign fix).
+    "te_interest_rate": {
+        "name_th": "อัตราดอกเบี้ย (rate)",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "interest_rate_pct",
+        "sign": 1,
+        "weight": 0.5,
+        "center": 1.5, "span": 1.5,  # ~1.0-2.0%; higher rate widens bank margin
+    },
+    "te_loan_growth": {
+        "name_th": "สินเชื่อภาคธุรกิจ",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "loans_to_fin_corp",
+        "sign": 1,
+        "weight": 0.5,
+        "center": 10000000.0, "span": 1500000.0,  # THB mn (~10.5M); credit demand proxy
+    },
+    "te_consumer_credit": {
+        "name_th": "สินเชื่อผู้บริโภค",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "consumer_credit_thbmn",
+        "sign": 1,
+        "weight": 0.5,
+        "center": 5000000.0, "span": 800000.0,  # THB mn consumer credit book
+    },
+    "te_household_debt_gdp": {
+        "name_th": "หนี้ครัวเรือนต่อ GDP",
+        "source": "TradingEconomics",
+        "frequency": "quarterly",
+        "fetch": "te_thailand",
+        "value_key": "household_debt_gdp_pct",
+        "sign": -1,
+        "weight": 0.5,
+        "center": 85.0, "span": 8.0,  # ~87.5% of GDP; higher = leverage risk
+    },
+    "te_retail_sales_yoy": {
+        "name_th": "ยอดขายปลีก (YoY)",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "retail_sales_yoy",
+        "sign": 1,
+        "weight": 0.8,
+        "center": 0.0, "span": 10.0,  # % YoY, ~0 neutral
+    },
+    "te_consumer_confidence": {
+        "name_th": "ความเชื่อมั่นผู้บริโภค",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "consumer_confidence",
+        "sign": 1,
+        "weight": 0.5,
+        "center": 50.0, "span": 12.0,  # points (~51.8); above 50 = optimistic
+    },
+    "te_property_prices": {
+        "name_th": "ราคาอสังหาริมทรัพย์ (YoY)",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "property_prices_yoy",
+        "sign": 1,
+        "weight": 0.8,
+        "center": 0.0, "span": 6.0,  # residential price % YoY, ~0-2% neutral
+    },
+    "te_business_confidence": {
+        "name_th": "ความเชื่อมั่นภาคธุรกิจ",
+        "source": "TradingEconomics",
+        "frequency": "monthly",
+        "fetch": "te_thailand",
+        "value_key": "business_confidence",
+        "sign": 1,
+        "weight": 0.4,
+        "center": 50.0, "span": 10.0,  # points (~46.7); above 50 = optimistic
+    },
 }
 
 

backend/app/scheduler.py

@@ -39,6 +39,8 @@ _REFRESH_JOBS: List[dict] = [
     {"key": "energy_thai", "label": "โรงกลั่น TOP", "module": "energy_thai", "fn": "fetch_energy_thai", "fetch_module": "energy_thai", "frequency": "quarterly"},
     {"key": "macro_thai", "label": "ภาพรวมประเทศไทย (BOT)", "module": "macro_thai", "fn": "fetch_macro_thai", "fetch_module": "macro_thai", "frequency": "monthly"},
     {"key": "bank_npl", "label": "NPL ภาคการเงิน (BOT)", "module": "bank_npl", "fn": "fetch_bank_npl", "fetch_module": "bank_npl", "frequency": "quarterly"},
+    {"key": "thai_trade", "label": "ดุลการค้า/ส่งออก (TradingEconomics)", "module": "thai_trade", "fn": "fetch_thai_trade", "fetch_module": "thai_trade", "frequency": "monthly"},
+    {"key": "te_thailand", "label": "อัตราดอกเบี้ย/สินเชื่อ/ค้าปลีก/เชื่อมั่น (TE)", "module": "te_thailand", "fn": "fetch_te_thailand", "fetch_module": "te_thailand", "frequency": "monthly"},
 ]
 
 # Frequencies -> minimum seconds between successful refreshes of a job.

backend/app/te_thailand.py

@@ -0,0 +1,209 @@
+"""Thailand rates / credit / retail / confidence / property factors — TradingEconomics.
+
+Source pages (server-rendered HTML, same infrastructure as `thai_trade` /
+`auto_credit` — the "related indicators" table exposes a stable 5-column row
+`[label, value, prev, unit, period]`):
+
+  - https://tradingeconomics.com/thailand/interest-rate
+      Interest Rate, Loans to Non Financial Corporations, Banks Balance Sheet
+  - https://tradingeconomics.com/thailand/consumer-confidence
+      Consumer Confidence, Retail Sales YoY, Consumer Credit, Households Debt
+      to GDP, Consumer Spending
+  - https://tradingeconomics.com/thailand/housing-index
+      Residential Property Prices, Housing Index, Housing Starts
+  - https://tradingeconomics.com/thailand/business-confidence
+      Business Confidence, Leading Economic Index, Private Investment MoM
+
+This module deepens the macro-proxy themes that today rely only on the one BOT
+Thai-Economy page:
+
+  Factor                         contributes to
+  ----------------------------   -------------------------------------
+  te_interest_rate        ->     banks (rate/credit cycle)
+  te_loan_growth_level    ->     banks (credit demand)
+  te_consumer_credit      ->     nonbank_finance (household credit book)
+  te_household_debt_gdp   ->     nonbank_finance (household leverage risk)
+  te_retail_sales_yoy     ->     retail, consumer_staples (spending)
+  te_consumer_confidence  ->     retail, consumer_staples, nonbank (sentiment)
+  te_property_prices      ->     property (residential price YoY)
+  te_business_confidence  ->     telecom_it, property, healthcare (sentiment)
+
+Every value is a single-page snapshot (current value + prev + period), so each
+factor is a *level / latest-period* driver — no history join required.
+"""
+
+from __future__ import annotations
+
+import html
+import re
+from dataclasses import dataclass, field
+from typing import Optional
+from urllib.request import Request, urlopen
+
+_USER_AGENT = (
+    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 "
+    "(KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36"
+)
+_URL_INTEREST = "https://tradingeconomics.com/thailand/interest-rate"
+_URL_CONFIDENCE = "https://tradingeconomics.com/thailand/consumer-confidence"
+_URL_HOUSING = "https://tradingeconomics.com/thailand/housing-index"
+_URL_BIZCONF = "https://tradingeconomics.com/thailand/business-confidence"
+
+
+class ThaiFactorsError(Exception):
+    """Raised when a TradingEconomics Thailand page cannot be fetched/parsed."""
+
+
+@dataclass(frozen=True)
+class ThaiFactorsSnapshot:
+    interest_rate_pct: Optional[float] = None          # policy-rate proxy (Aug 2026: 1.00)
+    loans_to_fin_corp: Optional[float] = None          # THB mn (Jun 2026)
+    consumer_credit_thbmn: Optional[float] = None      # THB mn (Jun 2025)
+    household_debt_gdp_pct: Optional[float] = None     # % of GDP (Dec 2025)
+    retail_sales_yoy: Optional[float] = None           # % YoY (May 2026)
+    consumer_confidence: Optional[float] = None        # points (Jul 2026)
+    consumer_spending: Optional[float] = None          # THB mn (Jun 2026)
+    property_prices_yoy: Optional[float] = None        # residential prices % YoY (Mar 2026)
+    business_confidence: Optional[float] = None        # points (Jul 2026)
+    periods: dict = field(default_factory=dict)
+    source: str = "tradingeconomics.thai-factors"
+
+    def to_dict(self) -> dict:
+        return {
+            "source": self.source,
+            "interest_rate_pct": self.interest_rate_pct,
+            "loans_to_fin_corp": self.loans_to_fin_corp,
+            "consumer_credit_thbmn": self.consumer_credit_thbmn,
+            "household_debt_gdp_pct": self.household_debt_gdp_pct,
+            "retail_sales_yoy": self.retail_sales_yoy,
+            "consumer_confidence": self.consumer_confidence,
+            "consumer_spending": self.consumer_spending,
+            "property_prices_yoy": self.property_prices_yoy,
+            "business_confidence": self.business_confidence,
+            "periods": self.periods,
+        }
+
+
+def _fetch(url: str, timeout: float = 30.0) -> str:
+    req = Request(url, headers={"User-Agent": _USER_AGENT, "Accept": "text/html"})
+    try:
+        with urlopen(req, timeout=timeout) as resp:
+            raw = resp.read()
+    except Exception as exc:
+        raise ThaiFactorsError(f"failed to fetch {url}: {exc}") from exc
+    try:
+        return raw.decode("utf-8")
+    except UnicodeDecodeError:
+        return raw.decode("latin-1", "ignore")
+
+
+def _cells(row_html: str) -> list[str]:
+    return [
+        html.unescape(re.sub(r"<[^>]+>", "", td)).strip()
+        for td in re.findall(r"<td[^>]*>(.*?)</td>", row_html, re.S)
+        if td.strip()
+    ]
+
+
+def _to_float(text: str) -> Optional[float]:
+    text = text.replace(",", "").strip().replace("%", "")
+    if not text or text in ("-", "N/A", "…"):
+        return None
+    try:
+        return float(text)
+    except ValueError:
+        return None
+
+
+def _related_indicators(html_text: str) -> dict[str, dict]:
+    """Map normalized-label -> {value, prev, unit, period} from the related table.
+
+    Rows are `[label, value, prev, unit, period]` (5 cols). Keys are
+    lowercased + stripped so a TradingEconomics label tweak (e.g. extra space /
+    case change) does not silently drop a series. A label may appear more than
+    once; first row that parses a numeric value wins.
+    """
+    out: dict[str, dict] = {}
+    for table in re.findall(r"<table[^>]*>(.*?)</table>", html_text, re.S):
+        for row_html in re.findall(r"<tr[^>]*>(.*?)</tr>", table, re.S):
+            cells = _cells(row_html)
+            if len(cells) < 4:
+                continue
+            key = cells[0].strip().lower()
+            value = _to_float(cells[1])
+            if value is None:
+                continue
+            if key not in out or out[key].get("value") is None:
+                out[key] = {
+                    "value": value,
+                    "prev": _to_float(cells[2]) if len(cells) > 2 else None,
+                    "unit": cells[3] if len(cells) > 3 else "",
+                    "period": cells[4] if len(cells) > 4 else "",
+                }
+    return out
+
+
+def parse_te_thailand_html(interest_html: str, confidence_html: str,
+                           housing_html: str = "", bizconf_html: str = "") -> ThaiFactorsSnapshot:
+    """Parse the TradingEconomics Thailand pages into one snapshot.
+
+    `housing_html` + `bizconf_html` are optional (Phase B property/business
+    factors); when omitted those two factors are simply None.
+    """
+    ir = _related_indicators(interest_html)
+    cc = _related_indicators(confidence_html)
+    hi = _related_indicators(housing_html) if housing_html else {}
+    bc = _related_indicators(bizconf_html) if bizconf_html else {}
+
+    periods: dict = {}
+    snap = ThaiFactorsSnapshot(
+        interest_rate_pct=(ir.get("interest rate") or {}).get("value"),
+        loans_to_fin_corp=(ir.get("loans to non financial corporations") or {}).get("value"),
+        consumer_credit_thbmn=(cc.get("consumer credit") or {}).get("value"),
+        household_debt_gdp_pct=(cc.get("households debt to gdp") or {}).get("value"),
+        retail_sales_yoy=(cc.get("retail sales yoy") or {}).get("value"),
+        consumer_confidence=(cc.get("consumer confidence") or {}).get("value"),
+        # consumer_spending is surfaced here for display/debug only — it is not
+        # registered as a FACTORS factor, so it does not feed a theme surprise.
+        consumer_spending=(cc.get("consumer spending") or {}).get("value"),
+        property_prices_yoy=(hi.get("residential property prices") or {}).get("value"),
+        business_confidence=(bc.get("business confidence") or {}).get("value"),
+    )
+    for key, label, src in (
+        ("interest_rate_pct", "interest rate", ir),
+        ("retail_sales_yoy", "retail sales yoy", cc),
+        ("consumer_confidence", "consumer confidence", cc),
+        ("property_prices_yoy", "residential property prices", hi),
+        ("business_confidence", "business confidence", bc),
+    ):
+        meta = src.get(label) or {}
+        if meta and meta.get("period"):
+            periods[key] = meta.get("period")
+
+    if snap.interest_rate_pct is None and snap.consumer_confidence is None \
+            and snap.retail_sales_yoy is None:
+        raise ThaiFactorsError(
+            "no usable Thailand factor series found in TradingEconomics pages"
+        )
+    # attach parsed periods onto a copy (dataclass is frozen)
+    return ThaiFactorsSnapshot(
+        interest_rate_pct=snap.interest_rate_pct,
+        loans_to_fin_corp=snap.loans_to_fin_corp,
+        consumer_credit_thbmn=snap.consumer_credit_thbmn,
+        household_debt_gdp_pct=snap.household_debt_gdp_pct,
+        retail_sales_yoy=snap.retail_sales_yoy,
+        consumer_confidence=snap.consumer_confidence,
+        consumer_spending=snap.consumer_spending,
+        property_prices_yoy=snap.property_prices_yoy,
+        business_confidence=snap.business_confidence,
+        periods=periods,
+    )
+
+
+def fetch_te_thailand(timeout: float = 30.0) -> ThaiFactorsSnapshot:
+    interest_html = _fetch(_URL_INTEREST, timeout=timeout)
+    confidence_html = _fetch(_URL_CONFIDENCE, timeout=timeout)
+    housing_html = _fetch(_URL_HOUSING, timeout=timeout)
+    bizconf_html = _fetch(_URL_BIZCONF, timeout=timeout)
+    return parse_te_thailand_html(interest_html, confidence_html,
+                                  housing_html, bizconf_html)

backend/app/thai_trade.py

@@ -0,0 +1,125 @@
+"""Thailand external-sector / trade factor — TradingEconomics Thailand.
+
+Source: https://tradingeconomics.com/thailand/current-account
+(server-rendered HTML, same infobox/infrastructure as the auto_credit source —
+the "related indicators" table lists the current value, previous value, unit
+and reporting period for Thailand's external account).
+
+Verified live: the current-account page's related-indicators table exposes
+(Jul 2026) Exports 34,781.10, Imports 38,399.60, Balance of Trade -3,611.00
+USD mn and (Jun 2026) Current Account -3,473.85 USD mn. Replaces the stale
+BOT report-60 path (which only carried data through 2011) with a CURRENT,
+free, scrapeable source per the plan's "do not ship a wrong/stale series" gate.
+"""
+
+from __future__ import annotations
+
+import html
+import re
+from dataclasses import dataclass
+from typing import Optional
+from urllib.request import Request, urlopen
+
+_USER_AGENT = (
+    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 "
+    "(KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36"
+)
+_URL = "https://tradingeconomics.com/thailand/current-account"
+
+
+class ThaiTradeError(Exception):
+    """Raised when the TradingEconomics Thailand page cannot be fetched/parsed."""
+
+
+@dataclass(frozen=True)
+class ThaiTradeSnapshot:
+    current_account_usdm: Optional[float] = None      # USD million
+    trade_balance_usdm: Optional[float] = None        # USD million
+    exports_usdm: Optional[float] = None              # USD million
+    imports_usdm: Optional[float] = None              # USD million
+    as_of: str = ""
+    source: str = "tradingeconomics.thai-trade"
+
+    def to_dict(self) -> dict:
+        return {
+            "source": self.source,
+            "as_of": self.as_of,
+            "current_account_usdm": self.current_account_usdm,
+            "trade_balance_usdm": self.trade_balance_usdm,
+            "exports_usdm": self.exports_usdm,
+            "imports_usdm": self.imports_usdm,
+        }
+
+
+def _fetch(url: str = _URL, timeout: float = 30.0) -> str:
+    req = Request(url, headers={"User-Agent": _USER_AGENT, "Accept": "text/html"})
+    try:
+        with urlopen(req, timeout=timeout) as resp:
+            raw = resp.read()
+    except Exception as exc:
+        raise ThaiTradeError(f"failed to fetch {url}: {exc}") from exc
+    try:
+        return raw.decode("utf-8")
+    except UnicodeDecodeError:
+        return raw.decode("latin-1", "ignore")
+
+
+def _tables(html_text: str) -> list[str]:
+    return re.findall(r"<table[^>]*>(.*?)</table>", html_text, re.S)
+
+
+def _cells(row_html: str) -> list[str]:
+    return [
+        html.unescape(re.sub(r"<[^>]+>", "", td)).strip()
+        for td in re.findall(r"<td[^>]*>(.*?)</td>", row_html, re.S)
+        if td.strip()
+    ]
+
+
+def _to_float(text: str) -> Optional[float]:
+    text = text.replace(",", "").strip().replace("%", "")
+    if not text or text in ("-", "N/A", "…"):
+        return None
+    try:
+        return float(text)
+    except ValueError:
+        return None
+
+
+def parse_thai_trade_html(html_text: str) -> ThaiTradeSnapshot:
+    """Parse the TradingEconomics Thailand current-account related table."""
+    current = trade = exports = imports = None
+    as_of = ""
+
+    for table in _tables(html_text):
+        rows = re.findall(r"<tr[^>]*>(.*?)</tr>", table, re.S)
+        for row_html in rows:
+            cells = _cells(row_html)
+            if not cells:
+                continue
+            label = cells[0].strip()
+            if label == "Current Account":
+                current = _to_float(cells[1])
+                if len(cells) > 4:
+                    as_of = cells[4]
+            elif label == "Balance of Trade":
+                trade = _to_float(cells[1])
+            elif label == "Exports" and exports is None:
+                exports = _to_float(cells[1])
+            elif label == "Imports" and imports is None:
+                imports = _to_float(cells[1])
+
+    if current is None and exports is None and imports is None:
+        raise ThaiTradeError("no Thai trade series found in TradingEconomics page")
+
+    return ThaiTradeSnapshot(
+        current_account_usdm=current,
+        trade_balance_usdm=trade,
+        exports_usdm=exports,
+        imports_usdm=imports,
+        as_of=as_of,
+    )
+
+
+def fetch_thai_trade(timeout: float = 30.0) -> ThaiTradeSnapshot:
+    return parse_thai_trade_html(_fetch(timeout=timeout))

backend/app/themes.py

@@ -101,6 +101,7 @@ THEMES: dict[str, dict] = {
         "factors": [
             {"key": "tourism_arrivals_ytd", "weight": 1.0},
             {"key": "macro_consumption", "weight": 0.4},
+            {"key": "external_current_account", "weight": 0.3},
         ],
     },
     "auto_credit": {
@@ -109,7 +110,7 @@ THEMES: dict[str, dict] = {
             {"key": "auto_sales_yoy", "weight": 1.0},
             {"key": "auto_production", "weight": 0.4},
             {"key": "auto_exports", "weight": 0.3},
-            {"key": "auto_npl", "weight": -0.6},
+            {"key": "auto_npl", "weight": 0.6},
         ],
     },
     "refining_energy": {
@@ -123,22 +124,36 @@ THEMES: dict[str, dict] = {
         "label_th": "ธนาคาร",
         "factors": [
             {"key": "macro_investment", "weight": 1.0},
-            {"key": "macro_inflation", "weight": -0.4},
+            {"key": "macro_inflation", "weight": 0.4},
-            {"key": "bank_npl", "weight": -0.6},
+            {"key": "macro_core_inflation", "weight": 0.3},
+            {"key": "bank_npl", "weight": 0.6},
+            {"key": "te_interest_rate", "weight": 0.4},
+            {"key": "te_loan_growth", "weight": 0.4},
         ],
     },
     "retail": {
         "label_th": "ค้าปลีก",
         "factors": [
             {"key": "macro_consumption", "weight": 1.0},
-            {"key": "macro_inflation", "weight": -0.3},
+            {"key": "macro_inflation", "weight": 0.3},
+            {"key": "macro_core_inflation", "weight": 0.2},
+            {"key": "macro_unemployment", "weight": 0.3},
+            {"key": "external_imports", "weight": 0.2},
+            {"key": "external_current_account", "weight": 0.2},
+            {"key": "te_retail_sales_yoy", "weight": 0.7},
+            {"key": "te_consumer_confidence", "weight": 0.4},
         ],
     },
     "consumer_staples": {
         "label_th": "อาหาร/อุปโภค",
         "factors": [
             {"key": "macro_consumption", "weight": 1.0},
-            {"key": "macro_inflation", "weight": -0.2},
+            {"key": "macro_inflation", "weight": 0.2},
+            {"key": "macro_core_inflation", "weight": 0.15},
+            {"key": "macro_unemployment", "weight": 0.3},
+            {"key": "external_imports", "weight": 0.15},
+            {"key": "te_retail_sales_yoy", "weight": 0.6},
+            {"key": "te_consumer_confidence", "weight": 0.3},
         ],
     },
     "telecom_it": {
@@ -146,6 +161,8 @@ THEMES: dict[str, dict] = {
         "factors": [
             {"key": "macro_consumption", "weight": 0.8},
             {"key": "macro_investment", "weight": 0.4},
+            {"key": "external_exports", "weight": 0.2},
+            {"key": "te_business_confidence", "weight": 0.3},
         ],
     },
     "property": {
@@ -153,20 +170,27 @@ THEMES: dict[str, dict] = {
         "factors": [
             {"key": "macro_investment", "weight": 1.0},
             {"key": "macro_consumption", "weight": 0.5},
-            {"key": "macro_inflation", "weight": -0.3},
+            {"key": "macro_inflation", "weight": 0.3},
+            {"key": "external_imports", "weight": 0.2},
+            {"key": "te_property_prices", "weight": 0.9},
+            {"key": "te_business_confidence", "weight": 0.3},
         ],
     },
     "healthcare": {
         "label_th": "โรงพยาบาล",
         "factors": [
             {"key": "macro_consumption", "weight": 0.5},
+            {"key": "macro_unemployment", "weight": 0.4},
+            {"key": "te_business_confidence", "weight": 0.2},
         ],
     },
     "petrochem_materials": {
         "label_th": "ปิโตรเคมี/วัสดุ",
         "factors": [
             {"key": "macro_mfg", "weight": 1.0},
-            {"key": "macro_inflation", "weight": -0.3},
+            {"key": "macro_inflation", "weight": 0.3},
+            {"key": "external_exports", "weight": 0.4},
+            {"key": "external_current_account", "weight": 0.3},
         ],
     },
     "utilities": {
@@ -180,14 +204,20 @@ THEMES: dict[str, dict] = {
         "label_th": "การเงินนอกธนาคาร",
         "factors": [
             {"key": "macro_consumption", "weight": 1.0},
-            {"key": "auto_npl", "weight": -0.3},
+            {"key": "auto_npl", "weight": 0.3},
+            {"key": "macro_unemployment", "weight": 0.3},
+            {"key": "te_consumer_credit", "weight": 0.5},
+            {"key": "te_household_debt_gdp", "weight": 0.4},
+            {"key": "te_consumer_confidence", "weight": 0.3},
         ],
     },
     "exploration": {
         "label_th": "สำรวจ/ผลิตพลังงาน",
         "factors": [
             {"key": "energy_net_margin", "weight": 1.0},
-            {"key": "macro_inflation", "weight": -0.2},
+            {"key": "macro_inflation", "weight": 0.2},
+            {"key": "external_exports", "weight": 0.3},
+            {"key": "external_current_account", "weight": 0.2},
         ],
     },
 }

backend/tests/test_dashboard.py

@@ -55,7 +55,7 @@ class DashboardTest(unittest.TestCase):
         cache = _FakeCache({})
         dash = RealDashboard([], cache).build()
         self.assertEqual(len(dash["themes"]), 13)  # all SET50 themes
-        self.assertEqual(len(dash["sources"]), 6)  # auto-derived from FACTORS (bank_npl added)
+        self.assertEqual(len(dash["sources"]), 8)  # auto-derived from FACTORS (te_thailand added)
         self.assertIn("macro", dash)
         self.assertIn("board", dash)
 

backend/tests/test_scheduler.py

@@ -57,6 +57,7 @@ class VintageCollectionTest(unittest.TestCase):
                 "tourists_ytd_mn": 15.0, "private_consumption_yoy": 3.0,
                 "private_investment_yoy": 4.0, "manufacturing_yoy": 1.0,
                 "headline_inflation_yoy": 2.0,
+                "core_inflation_yoy": 1.2, "unemployment_pct": 1.1,
                 "periods": {"private_consumption_yoy": "Jun 2026"},
             },
             "auto_credit": {"new_car_sales_yoy": 5.0, "vehicle_production": 100000.0,
@@ -64,6 +65,13 @@ class VintageCollectionTest(unittest.TestCase):
             "auto_npl": {"pct_of_npls": 3.0},
             "bank_npl": {"pct_of_npls": 0.8},
             "energy_thai": {"quarterly": {"q1": {"net_profit": 500.0, "sales": 10000.0}}},
+            "thai_trade": {"current_account_usdm": 500.0, "exports_usdm": 34000.0,
+                           "imports_usdm": 38000.0},
+            "te_thailand": {"interest_rate_pct": 1.0, "loans_to_fin_corp": 10000000.0,
+                            "consumer_credit_thbmn": 5000000.0,
+                            "household_debt_gdp_pct": 85.0,
+                            "retail_sales_yoy": 0.0, "consumer_confidence": 50.0,
+                            "property_prices_yoy": 1.0, "business_confidence": 50.0},
         }
 
     def test_pit_factor_vintages_written_on_refresh(self):

backend/tests/test_te_thailand.py

@@ -0,0 +1,157 @@
+"""Tests for the Thailand rates/credit/retail/confidence collector + themes."""
+
+from __future__ import annotations
+
+import unittest
+
+from app import te_thailand, themes
+from app.te_thailand import ThaiFactorsSnapshot, parse_te_thailand_html
+
+
+def _row(label, value, prev, unit, period):
+    return (f"<tr><td>{label}</td><td>{value}</td><td>{prev}</td>"
+            f"<td>{unit}</td><td>{period}</td></tr>")
+
+
+def _table(*rows):
+    return f"<table>{''.join(rows)}</table>"
+
+
+_IR_HTML = _table(
+    _row("Interest Rate", "1.00", "1.00", "percent", "Aug 2026"),
+    _row("Loans to Non Financial Corporations", "10553097.00", "10488479.00", "THB Million", "Jun 2026"),
+    _row("Banks Balance Sheet", "40475793.00", "40559803.00", "THB Million", "Jun 2026"),
+)
+_CC_HTML = _table(
+    _row("Consumer Confidence", "51.80", "50.70", "points", "Jul 2026"),
+    _row("Retail Sales YoY", "-14.50", "-20.00", "percent", "May 2026"),
+    _row("Consumer Credit", "5285734.00", "5296795.00", "THB Million", "Jun 2025"),
+    _row("Households Debt to GDP", "87.50", "87.30", "percent of GDP", "Dec 2025"),
+    _row("Consumer Spending", "1771591.00", "1730973.00", "THB Million", "Jun 2026"),
+)
+_HOUSING_HTML = _table(
+    _row("Housing Index", "162.70", "162.40", "points", "Jun 2026"),
+    _row("Residential Property Prices", "1.26", "0.63", "Percent", "Mar 2026"),
+    _row("Housing Starts", "4093.00", "6520.00", "units", "Apr 2026"),
+)
+_BIZCONF_HTML = _table(
+    _row("Business Confidence", "46.70", "46.10", "points", "Jul 2026"),
+    _row("Leading Economic Index", "166.19", "163.56", "points", "Jun 2026"),
+)
+
+
+class TeThailandParseTest(unittest.TestCase):
+    def test_parses_all_indicators(self):
+        snap = parse_te_thailand_html(_IR_HTML, _CC_HTML, _HOUSING_HTML, _BIZCONF_HTML)
+        self.assertIsInstance(snap, ThaiFactorsSnapshot)
+        self.assertEqual(snap.interest_rate_pct, 1.0)
+        self.assertEqual(snap.loans_to_fin_corp, 10553097.0)
+        self.assertEqual(snap.consumer_credit_thbmn, 5285734.0)
+        self.assertEqual(snap.household_debt_gdp_pct, 87.5)
+        self.assertEqual(snap.retail_sales_yoy, -14.5)
+        self.assertEqual(snap.consumer_confidence, 51.8)
+        self.assertEqual(snap.consumer_spending, 1771591.0)
+        self.assertEqual(snap.property_prices_yoy, 1.26)
+        self.assertEqual(snap.business_confidence, 46.7)
+        self.assertEqual(snap.periods["interest_rate_pct"], "Aug 2026")
+        self.assertEqual(snap.periods["property_prices_yoy"], "Mar 2026")
+
+    def test_to_dict_full(self):
+        d = parse_te_thailand_html(_IR_HTML, _CC_HTML, _HOUSING_HTML, _BIZCONF_HTML).to_dict()
+        self.assertIn("interest_rate_pct", d)
+        self.assertIn("retail_sales_yoy", d)
+        self.assertIn("property_prices_yoy", d)
+        self.assertIn("business_confidence", d)
+        self.assertIn("source", d)
+
+    def test_missing_values_raise(self):
+        # neither page yields a usable series -> collector fails loudly
+        empty = "<table><tr><td>x</td><td>1</td></tr></table>"
+        with self.assertRaises(te_thailand.ThaiFactorsError):
+            parse_te_thailand_html(empty, empty)
+
+    def test_every_factor_value_key_resolves(self):
+        from app import factors
+        keys = set(ThaiFactorsSnapshot().to_dict().keys())
+        for fkey, fact in factors.FACTORS.items():
+            if fact.get("fetch") != "te_thailand":
+                continue
+            self.assertIn(
+                fact.get("value_key"), keys,
+                f"factor {fkey!r} value_key not emitted by te_thailand",
+            )
+
+
+class TeThailandThemeDirectionTest(unittest.TestCase):
+    """New factors must move theme surprises the intended direction (sign fix)."""
+
+    @staticmethod
+    def _fetched():
+        base = {
+            "macro_thai": {
+                "private_consumption_yoy": 4.9, "private_investment_yoy": 18.1,
+                "headline_inflation_yoy": 1.95, "core_inflation_yoy": 1.0,
+                "unemployment_pct": 1.0, "manufacturing_yoy": -3.1,
+                "tourists_ytd_mn": 16.2,
+            },
+            "auto_credit": {"new_car_sales_yoy": 20.07, "vehicle_production": 117383.0,
+                            "auto_exports": 81526.0},
+            "auto_npl": {"pct_of_npls": 3.0},
+            "bank_npl": {"pct_of_npls": 1.0},
+            "energy_thai": {"quarterly": {"Q1/2026": {"net_profit": 19481.0, "sales": 114809.0}}},
+            "thai_trade": {"current_account_usdm": 500.0, "exports_usdm": 34000.0,
+                           "imports_usdm": 38000.0},
+            # te_thailand factors — neutral-ish values
+            "te_thailand": {
+                "interest_rate_pct": 1.5,
+                "loans_to_fin_corp": 10000000.0,
+                "consumer_credit_thbmn": 5000000.0,
+                "household_debt_gdp_pct": 85.0,
+                "retail_sales_yoy": 0.0,
+                "consumer_confidence": 50.0,
+                "property_prices_yoy": 0.0,
+                "business_confidence": 50.0,
+            },
+        }
+        return base
+
+    def _surprises(self, te):
+        d = self._fetched()
+        d["te_thailand"] = te
+        return themes.compute_theme_surprises(d)
+
+    def test_higher_retail_sales_raises_retail(self):
+        low = self._surprises({**self._fetched()["te_thailand"], "retail_sales_yoy": -15.0})
+        high = self._surprises({**self._fetched()["te_thailand"], "retail_sales_yoy": 8.0})
+        self.assertGreater(high["retail"], low["retail"])
+
+    def test_higher_rate_and_loans_raise_banks(self):
+        low = self._surprises({**self._fetched()["te_thailand"], "interest_rate_pct": 0.5,
+                               "loans_to_fin_corp": 8500000.0})
+        high = self._surprises({**self._fetched()["te_thailand"], "interest_rate_pct": 2.5,
+                                "loans_to_fin_corp": 12000000.0})
+        self.assertGreater(high["banks"], low["banks"])
+
+    def test_higher_household_debt_lowers_nonbank(self):
+        low = self._surprises({**self._fetched()["te_thailand"], "household_debt_gdp_pct": 80.0})
+        high = self._surprises({**self._fetched()["te_thailand"], "household_debt_gdp_pct": 92.0})
+        self.assertLess(high["nonbank_finance"], low["nonbank_finance"])
+
+    def test_higher_consumer_confidence_raises_retail(self):
+        low = self._surprises({**self._fetched()["te_thailand"], "consumer_confidence": 42.0})
+        high = self._surprises({**self._fetched()["te_thailand"], "consumer_confidence": 60.0})
+        self.assertGreater(high["retail"], low["retail"])
+
+    def test_higher_property_prices_raise_property(self):
+        low = self._surprises({**self._fetched()["te_thailand"], "property_prices_yoy": -4.0})
+        high = self._surprises({**self._fetched()["te_thailand"], "property_prices_yoy": 4.0})
+        self.assertGreater(high["property"], low["property"])
+
+    def test_higher_business_confidence_raises_telecom(self):
+        low = self._surprises({**self._fetched()["te_thailand"], "business_confidence": 42.0})
+        high = self._surprises({**self._fetched()["te_thailand"], "business_confidence": 58.0})
+        self.assertGreater(high["telecom_it"], low["telecom_it"])
+
+
+if __name__ == "__main__":
+    unittest.main()

backend/tests/test_themes.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import unittest
 
 from app import themes
+from app import auto_credit, auto_npl, bank_npl, macro_thai, te_thailand, thai_trade
 
 
 class ThemesTest(unittest.TestCase):
@@ -144,12 +145,12 @@ class RegistryDrivenSurpriseTest(unittest.TestCase):
         from app import themes
         s = themes.compute_theme_surprises(self._fetched())
         # registry retail = weighted average over |weights| of
-        #   consumption*1.0 + inflation*(-0.3):
+        #   consumption*1.0 + inflation*0.3 (sign -1 intrinsic in the factor):
         #   cons=4.9 -> (4.9-3)/10=0.19 (w=1.0) ; inflation sign -1 ->
-        #   -(1.95-2)/10=+0.005 (w=-0.3) -> weighted = 0.19 - 0.0015 = 0.1885
+        #   -(1.95-2)/10=+0.005 (w=+0.3) -> weighted = 0.19 + 0.0015 = 0.1915
-        #   surprise = 0.1885 / (1.0 + 0.3) = 0.145
+        #   surprise = 0.1915 / (1.0 + 0.3) = 0.147
         self.assertIsNotNone(s["retail"])
-        self.assertAlmostEqual(s["retail"], 0.145, places=3)
+        self.assertAlmostEqual(s["retail"], 0.147, places=3)
 
     def test_changing_factor_weight_changes_output(self):
         """The defining property of P0-B: the registry is not decorative."""
@@ -183,3 +184,93 @@ class RegistryDrivenSurpriseTest(unittest.TestCase):
         self.assertIsNone(factors.normalize(None))
         # finite value still normalizes
         self.assertEqual(factors.normalize(15.0, sign=1, center=5.0, span=10.0), 1.0)
+
+    def test_new_macro_factors_actually_move_scores(self):
+        """Wire-in proof (user rule: a fetched field must feed analysis).
+
+        core_inflation + unemployment were fetched by BOT macro_thai but not
+        used. Once present with a non-neutral value they must change the
+        retail/banks surprise vs a neutral value — i.e. they are not dead data.
+        """
+        from app import themes
+        neutral = {
+            "core_inflation_yoy": 1.0,   # == center(1.0) -> 0 normalised
+            "unemployment_pct": 1.0,     # == center(1.0) -> 0 normalised
+        }
+        base_fetched = self._fetched()
+        base_fetched["macro_thai"].update(neutral)
+        s_neutral = themes.compute_theme_surprises(
+            {k: dict(v) for k, v in base_fetched.items()})
+
+        hot = dict(base_fetched)
+        hot["macro_thai"]["unemployment_pct"] = 3.0  # above center -> bearish
+        s_hot = themes.compute_theme_surprises({k: dict(v) for k, v in hot.items()})
+        # higher unemployment is bearish (sign -1, +ve weight) -> retail/healthcare lower
+        self.assertLess(s_hot["retail"], s_neutral["retail"])
+        self.assertLess(s_hot["healthcare"], s_neutral["healthcare"])
+
+        hot_infl = dict(base_fetched)
+        hot_infl["macro_thai"]["core_inflation_yoy"] = 3.5  # above center -> bearish
+        s_infl = themes.compute_theme_surprises({k: dict(v) for k, v in hot_infl.items()})
+        self.assertLess(s_infl["banks"], s_neutral["banks"])
+
+    def test_bearish_factors_move_score_the_right_way(self):
+        """Regression for the sign-inversion bug.
+
+        Factor `sign` is applied once inside normalize() so a *positive* theme
+        weight means "more of this factor matters". Bearish factors (NPL,
+        inflation, unemployment) all carry sign -1; a negative theme weight made
+        the double product turn positive — i.e. higher NPL/inflation RAISED the
+        theme score. Lock the correct direction: higher NPL must LOWER
+        auto_credit/banks; lower NPL must RAISE them.
+        """
+        from app import themes
+        base = self._fetched()
+        # base has auto_npl pct 3.95, bank_npl NOT present (only via macro) -> use
+        # a full fetched incl. bank_npl so the factor is exercised.
+        base["bank_npl"] = {"pct_of_npls": 1.0}
+        low = themes.compute_theme_surprises({k: dict(v) for k, v in base.items()})
+
+        hi = dict(base)
+        hi["auto_npl"] = {"pct_of_npls": 8.0}      # much worse credit quality
+        hi["bank_npl"] = {"pct_of_npls": 5.0}
+        s_hi = themes.compute_theme_surprises({k: dict(v) for k, v in hi.items()})
+        # higher NPL -> lower surprise in the credit-heavy themes (bearish)
+        self.assertLess(s_hi["auto_credit"], low["auto_credit"])
+        self.assertLess(s_hi["banks"], low["banks"])
+
+    def test_every_factor_value_key_resolves_to_a_fetched_field(self):
+        """Contract (user rule): every FACTORS.value_key must be a real field the
+        registered fetch module emits. A factor that reads a key the collector
+        never produces is dead weight — catches 'fetched but not used' the other
+        way (a value_key that can never be populated)."""
+        from app import factors
+        for fkey, fact in factors.FACTORS.items():
+            fetch_mod = fact.get("fetch")
+            value_key = fact.get("value_key")
+            if value_key is None:
+                continue
+            # energy_thai derives its value from a nested quarterly dict, not a
+            # top-level key — covered separately by factor_value().
+            if fetch_mod == "energy_thai":
+                continue
+            # resolve the module's snapshot .to_dict() keys
+            if fetch_mod == "macro_thai":
+                keys = set(macro_thai.MacroThaiSnapshot().to_dict().keys())
+            elif fetch_mod == "auto_credit":
+                keys = set(auto_credit.AutoCreditSnapshot().to_dict().keys())
+            elif fetch_mod == "auto_npl":
+                keys = set(auto_npl.AutoNplSnapshot().to_dict().keys())
+            elif fetch_mod == "bank_npl":
+                keys = set(bank_npl.BankNplSnapshot().to_dict().keys())
+            elif fetch_mod == "thai_trade":
+                keys = set(thai_trade.ThaiTradeSnapshot().to_dict().keys())
+            elif fetch_mod == "te_thailand":
+                keys = set(te_thailand.ThaiFactorsSnapshot().to_dict().keys())
+            else:
+                continue
+            self.assertIn(
+                value_key, keys,
+                f"factor {fkey!r} targets value_key {value_key!r} that fetch "
+                f"module {fetch_mod!r} never emits -> dead factor",
+            )