fcc0da9c8d
- 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
210 lines
9.2 KiB
Python
210 lines
9.2 KiB
Python
"""Thailand rates / credit / retail / confidence / property factors — TradingEconomics.
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Source pages (server-rendered HTML, same infrastructure as `thai_trade` /
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`auto_credit` — the "related indicators" table exposes a stable 5-column row
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`[label, value, prev, unit, period]`):
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- https://tradingeconomics.com/thailand/interest-rate
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Interest Rate, Loans to Non Financial Corporations, Banks Balance Sheet
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- https://tradingeconomics.com/thailand/consumer-confidence
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Consumer Confidence, Retail Sales YoY, Consumer Credit, Households Debt
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to GDP, Consumer Spending
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- https://tradingeconomics.com/thailand/housing-index
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Residential Property Prices, Housing Index, Housing Starts
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- https://tradingeconomics.com/thailand/business-confidence
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Business Confidence, Leading Economic Index, Private Investment MoM
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This module deepens the macro-proxy themes that today rely only on the one BOT
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Thai-Economy page:
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Factor contributes to
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---------------------------- -------------------------------------
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te_interest_rate -> banks (rate/credit cycle)
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te_loan_growth_level -> banks (credit demand)
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te_consumer_credit -> nonbank_finance (household credit book)
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te_household_debt_gdp -> nonbank_finance (household leverage risk)
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te_retail_sales_yoy -> retail, consumer_staples (spending)
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te_consumer_confidence -> retail, consumer_staples, nonbank (sentiment)
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te_property_prices -> property (residential price YoY)
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te_business_confidence -> telecom_it, property, healthcare (sentiment)
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Every value is a single-page snapshot (current value + prev + period), so each
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factor is a *level / latest-period* driver — no history join required.
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"""
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from __future__ import annotations
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import html
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import re
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from dataclasses import dataclass, field
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from typing import Optional
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from urllib.request import Request, urlopen
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_USER_AGENT = (
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"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36"
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)
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_URL_INTEREST = "https://tradingeconomics.com/thailand/interest-rate"
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_URL_CONFIDENCE = "https://tradingeconomics.com/thailand/consumer-confidence"
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_URL_HOUSING = "https://tradingeconomics.com/thailand/housing-index"
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_URL_BIZCONF = "https://tradingeconomics.com/thailand/business-confidence"
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class ThaiFactorsError(Exception):
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"""Raised when a TradingEconomics Thailand page cannot be fetched/parsed."""
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@dataclass(frozen=True)
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class ThaiFactorsSnapshot:
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interest_rate_pct: Optional[float] = None # policy-rate proxy (Aug 2026: 1.00)
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loans_to_fin_corp: Optional[float] = None # THB mn (Jun 2026)
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consumer_credit_thbmn: Optional[float] = None # THB mn (Jun 2025)
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household_debt_gdp_pct: Optional[float] = None # % of GDP (Dec 2025)
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retail_sales_yoy: Optional[float] = None # % YoY (May 2026)
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consumer_confidence: Optional[float] = None # points (Jul 2026)
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consumer_spending: Optional[float] = None # THB mn (Jun 2026)
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property_prices_yoy: Optional[float] = None # residential prices % YoY (Mar 2026)
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business_confidence: Optional[float] = None # points (Jul 2026)
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periods: dict = field(default_factory=dict)
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source: str = "tradingeconomics.thai-factors"
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def to_dict(self) -> dict:
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return {
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"source": self.source,
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"interest_rate_pct": self.interest_rate_pct,
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"loans_to_fin_corp": self.loans_to_fin_corp,
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"consumer_credit_thbmn": self.consumer_credit_thbmn,
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"household_debt_gdp_pct": self.household_debt_gdp_pct,
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"retail_sales_yoy": self.retail_sales_yoy,
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"consumer_confidence": self.consumer_confidence,
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"consumer_spending": self.consumer_spending,
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"property_prices_yoy": self.property_prices_yoy,
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"business_confidence": self.business_confidence,
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"periods": self.periods,
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}
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def _fetch(url: str, timeout: float = 30.0) -> str:
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req = Request(url, headers={"User-Agent": _USER_AGENT, "Accept": "text/html"})
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try:
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with urlopen(req, timeout=timeout) as resp:
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raw = resp.read()
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except Exception as exc:
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raise ThaiFactorsError(f"failed to fetch {url}: {exc}") from exc
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try:
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return raw.decode("utf-8")
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except UnicodeDecodeError:
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return raw.decode("latin-1", "ignore")
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def _cells(row_html: str) -> list[str]:
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return [
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html.unescape(re.sub(r"<[^>]+>", "", td)).strip()
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for td in re.findall(r"<td[^>]*>(.*?)</td>", row_html, re.S)
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if td.strip()
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]
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def _to_float(text: str) -> Optional[float]:
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text = text.replace(",", "").strip().replace("%", "")
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if not text or text in ("-", "N/A", "…"):
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return None
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try:
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return float(text)
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except ValueError:
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return None
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def _related_indicators(html_text: str) -> dict[str, dict]:
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"""Map normalized-label -> {value, prev, unit, period} from the related table.
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Rows are `[label, value, prev, unit, period]` (5 cols). Keys are
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lowercased + stripped so a TradingEconomics label tweak (e.g. extra space /
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case change) does not silently drop a series. A label may appear more than
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once; first row that parses a numeric value wins.
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"""
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out: dict[str, dict] = {}
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for table in re.findall(r"<table[^>]*>(.*?)</table>", html_text, re.S):
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for row_html in re.findall(r"<tr[^>]*>(.*?)</tr>", table, re.S):
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cells = _cells(row_html)
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if len(cells) < 4:
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continue
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key = cells[0].strip().lower()
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value = _to_float(cells[1])
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if value is None:
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continue
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if key not in out or out[key].get("value") is None:
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out[key] = {
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"value": value,
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"prev": _to_float(cells[2]) if len(cells) > 2 else None,
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"unit": cells[3] if len(cells) > 3 else "",
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"period": cells[4] if len(cells) > 4 else "",
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}
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return out
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def parse_te_thailand_html(interest_html: str, confidence_html: str,
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housing_html: str = "", bizconf_html: str = "") -> ThaiFactorsSnapshot:
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"""Parse the TradingEconomics Thailand pages into one snapshot.
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`housing_html` + `bizconf_html` are optional (Phase B property/business
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factors); when omitted those two factors are simply None.
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"""
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ir = _related_indicators(interest_html)
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cc = _related_indicators(confidence_html)
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hi = _related_indicators(housing_html) if housing_html else {}
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bc = _related_indicators(bizconf_html) if bizconf_html else {}
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periods: dict = {}
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snap = ThaiFactorsSnapshot(
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interest_rate_pct=(ir.get("interest rate") or {}).get("value"),
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loans_to_fin_corp=(ir.get("loans to non financial corporations") or {}).get("value"),
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consumer_credit_thbmn=(cc.get("consumer credit") or {}).get("value"),
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household_debt_gdp_pct=(cc.get("households debt to gdp") or {}).get("value"),
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retail_sales_yoy=(cc.get("retail sales yoy") or {}).get("value"),
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consumer_confidence=(cc.get("consumer confidence") or {}).get("value"),
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# consumer_spending is surfaced here for display/debug only — it is not
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# registered as a FACTORS factor, so it does not feed a theme surprise.
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consumer_spending=(cc.get("consumer spending") or {}).get("value"),
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property_prices_yoy=(hi.get("residential property prices") or {}).get("value"),
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business_confidence=(bc.get("business confidence") or {}).get("value"),
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)
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for key, label, src in (
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("interest_rate_pct", "interest rate", ir),
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("retail_sales_yoy", "retail sales yoy", cc),
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("consumer_confidence", "consumer confidence", cc),
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("property_prices_yoy", "residential property prices", hi),
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("business_confidence", "business confidence", bc),
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):
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meta = src.get(label) or {}
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if meta and meta.get("period"):
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periods[key] = meta.get("period")
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if snap.interest_rate_pct is None and snap.consumer_confidence is None \
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and snap.retail_sales_yoy is None:
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raise ThaiFactorsError(
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"no usable Thailand factor series found in TradingEconomics pages"
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)
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# attach parsed periods onto a copy (dataclass is frozen)
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return ThaiFactorsSnapshot(
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interest_rate_pct=snap.interest_rate_pct,
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loans_to_fin_corp=snap.loans_to_fin_corp,
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consumer_credit_thbmn=snap.consumer_credit_thbmn,
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household_debt_gdp_pct=snap.household_debt_gdp_pct,
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retail_sales_yoy=snap.retail_sales_yoy,
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consumer_confidence=snap.consumer_confidence,
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consumer_spending=snap.consumer_spending,
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property_prices_yoy=snap.property_prices_yoy,
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business_confidence=snap.business_confidence,
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periods=periods,
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)
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def fetch_te_thailand(timeout: float = 30.0) -> ThaiFactorsSnapshot:
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interest_html = _fetch(_URL_INTEREST, timeout=timeout)
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confidence_html = _fetch(_URL_CONFIDENCE, timeout=timeout)
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housing_html = _fetch(_URL_HOUSING, timeout=timeout)
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bizconf_html = _fetch(_URL_BIZCONF, timeout=timeout)
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return parse_te_thailand_html(interest_html, confidence_html,
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housing_html, bizconf_html)
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