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