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.
113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
"""Tests for the factor-weight learning loop (P4)."""
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from __future__ import annotations
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import datetime as dt
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import unittest
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from app import weight_learning as wl
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class SpearmanICTest(unittest.TestCase):
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def test_perfect_positive(self):
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# Factor and forward returns perfectly rank-aligned -> IC = 1.
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fv = {"A": 1.0, "B": 2.0, "C": 3.0, "D": 4.0}
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fr = {"A": 0.1, "B": 0.2, "C": 0.3, "D": 0.4}
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ic = wl.spearman_ic(fv, fr)
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self.assertIsNotNone(ic)
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self.assertTrue(ic is not None and abs(ic - 1.0) < 1e-5)
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def test_inverse_is_negative_one(self):
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fv = {"A": 1.0, "B": 2.0, "C": 3.0, "D": 4.0}
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fr = {"A": 0.4, "B": 0.3, "C": 0.2, "D": 0.1}
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ic = wl.spearman_ic(fv, fr)
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self.assertIsNotNone(ic)
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self.assertTrue(ic is not None and abs(ic + 1.0) < 1e-5)
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def test_too_few_symbols_returns_none(self):
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self.assertIsNone(wl.spearman_ic({"A": 1.0}, {"A": 0.1}))
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def test_invalid_symbols_excluded(self):
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fv = {"A": 1.0, "B": 2.0, "C": 3.0, "D": 4.0, "E": float("nan")}
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fr = {"A": 0.1, "B": 0.2, "C": 0.3, "D": 0.4, "E": 0.5}
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ic = wl.spearman_ic(fv, fr)
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self.assertIsNotNone(ic)
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self.assertTrue(ic is not None and abs(ic - 1.0) < 1e-5)
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class WeightUpdateTest(unittest.TestCase):
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def test_positive_ic_raises_weight(self):
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l = wl.FactorLearning("f", n_periods=12, ic_mean=0.3,
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old_weight=1.0)
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wl.apply_weight_update(l, shrink=0.5)
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self.assertIsNotNone(l.new_weight)
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self.assertTrue(l.new_weight is not None and l.new_weight > 1.0)
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def test_negative_ic_lowers_weight(self):
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l = wl.FactorLearning("f", n_periods=12, ic_mean=-0.4,
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old_weight=1.0)
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wl.apply_weight_update(l, shrink=0.5)
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self.assertIsNotNone(l.new_weight)
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self.assertTrue(l.new_weight is not None and l.new_weight < 1.0)
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def test_clamped_to_bounds(self):
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l = wl.FactorLearning("f", n_periods=12, ic_mean=10.0, old_weight=1.0)
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wl.apply_weight_update(l, shrink=1.0, max_w=3.0)
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self.assertEqual(l.new_weight, 3.0)
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def test_blocked_keeps_weight(self):
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l = wl.FactorLearning("f", n_periods=0, ic_mean=None,
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old_weight=1.0, blocked=True)
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wl.apply_weight_update(l)
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self.assertEqual(l.new_weight, 1.0)
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def test_no_old_weight_returns(self):
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l = wl.FactorLearning("f", n_periods=12, ic_mean=0.2, old_weight=None)
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wl.apply_weight_update(l)
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self.assertIsNone(l.new_weight)
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class MomentumLearningTest(unittest.TestCase):
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@staticmethod
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def _series():
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# A trends up strongly (positive momentum), B flat.
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def bars(base, drift):
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out = []
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for i in range(500):
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d = (dt.date(2024, 1, 1) + dt.timedelta(days=i)).isoformat()
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out.append({"date": d, "adjusted_close": base + drift * i})
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return out
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return {"A": {"bars": bars(10.0, 0.05)}, "B": {"bars": bars(20.0, 0.0)}}
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def test_learn_runs_without_error(self):
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res = wl.learn_momentum(self._series(), ["A", "B"],
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"2025-06-01", "2026-06-01")
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self.assertIsInstance(res, wl.FactorLearning)
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self.assertGreaterEqual(res.n_periods, 0)
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class AddMonthsTest(unittest.TestCase):
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def test_clamps_day_to_end_of_month(self):
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# Jan 31 + 1 month must clamp to Feb 28/29, not raise.
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self.assertEqual(wl._add_months(dt.date(2026, 1, 31), 1), dt.date(2026, 2, 28))
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self.assertEqual(wl._add_months(dt.date(2026, 1, 31), 2), dt.date(2026, 3, 31))
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self.assertEqual(wl._add_months(dt.date(2024, 1, 31), 1), dt.date(2024, 2, 29)) # leap
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class LearnFactorSeriesTest(unittest.TestCase):
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def test_aggregates_ics(self):
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res = wl.learn_factor_series([0.1, 0.2, 0.3, 0.4])
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self.assertEqual(res.n_periods, 4)
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self.assertIsNotNone(res.ic_mean)
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self.assertTrue(res.ic_mean is not None and abs(res.ic_mean - 0.25) < 1e-6)
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self.assertIsNotNone(res.ic_tstat)
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def test_empty_is_blocked_like(self):
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res = wl.learn_factor_series([])
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self.assertEqual(res.n_periods, 0)
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self.assertIsNone(res.ic_mean)
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if __name__ == "__main__":
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unittest.main()
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