d87a1ada39
A. Cross-theme comparability: - compute_theme_surprises now weight-normalizes by total |weight| (weighted average), so every theme surprise on same [-1,1] scale regardless of factor count/weight (retail 0.189->0.145; auto_credit 1.0->0.64). B. Historical factor store (enables learning macro/demographic factors): - New factor_history.py: append-only per-factor JSONL, dedupes unchanged values, rejects non-finite, records every FACTORS value each scheduler run. - scheduler.py: jobs carry fetch_module; refresh_all records factor history (non-fatal); added bank_npl job. - GET /api/v1/learning/factors?min_points= reports n_points/learnable per factor so users see when P4 learning unlocks (validated query parsing). - weight_learning: generic learn_factor_series() aggregator (momentum reuses). Independent review deleg_5dd358e3 passed=true (empty security/logic arrays); its two robustness suggestions applied (finite guard in record(), clean 400 on bad min_points). 234 tests pass; Vite build passes.
79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
"""Tests for the historical factor store (P4 enabler)."""
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from __future__ import annotations
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import json
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import tempfile
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import unittest
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from pathlib import Path
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from app.factor_history import FactorHistory, FactorHistoryError
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class FactorHistoryTest(unittest.TestCase):
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def setUp(self):
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self._tmp = tempfile.TemporaryDirectory()
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self.dir = Path(self._tmp.name)
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self.fh = FactorHistory(self.dir)
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def tearDown(self):
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self._tmp.cleanup()
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def test_record_and_series_roundtrip(self):
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self.assertTrue(self.fh.record("macro_consumption", 4.9, ts="2026-01-01T00:00:00+00:00"))
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self.assertTrue(self.fh.record("macro_consumption", 5.2, ts="2026-02-01T00:00:00+00:00"))
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series = self.fh.series("macro_consumption")
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self.assertEqual(len(series), 2)
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self.assertEqual(series[0]["value"], 4.9)
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self.assertEqual(series[1]["value"], 5.2)
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def test_duplicate_value_not_rewritten(self):
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self.assertTrue(self.fh.record("f", 1.0, ts="2026-01-01T00:00:00+00:00"))
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self.assertFalse(self.fh.record("f", 1.0, ts="2026-02-01T00:00:00+00:00"))
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self.assertEqual(len(self.fh.series("f")), 1)
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def test_none_value_not_recorded(self):
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self.assertFalse(self.fh.record("f", None))
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self.assertEqual(len(self.fh.series("f")), 0)
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def test_non_finite_not_recorded(self):
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self.assertFalse(self.fh.record("f", float("nan")))
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self.assertFalse(self.fh.record("f", float("inf")))
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self.assertEqual(len(self.fh.series("f")), 0)
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def test_last_value(self):
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self.fh.record("f", 3.0)
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self.fh.record("f", 4.0)
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self.assertEqual(self.fh.last_value("f"), 4.0)
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self.assertIsNone(self.fh.last_value("missing"))
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class RecordAllTest(unittest.TestCase):
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def test_record_all_uses_fetched_dicts(self):
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import tempfile
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from pathlib import Path
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with tempfile.TemporaryDirectory() as td:
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fh = FactorHistory(Path(td))
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fetched = {
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"macro_thai": {
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"private_consumption_yoy": 4.9,
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"private_investment_yoy": 18.1,
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"headline_inflation_yoy": 1.95,
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"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,
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"vehicle_production": 117383.0, "auto_exports": 81526.0},
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"auto_npl": {"pct_of_npls": 3.95},
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"bank_npl": {"pct_of_npls": 1.07},
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}
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writes = fh.record_all(fetched)
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# Every registered factor with a fetch module present gets a write.
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self.assertGreaterEqual(len([w for w in writes.values() if w]), 5)
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# macro_consumption should have a recorded value 4.9 norm path.
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self.assertEqual(fh.last_value("macro_consumption"), 4.9)
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if __name__ == "__main__":
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unittest.main()
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