8db3d48ae2
P0-B (registry is the single source of truth for scoring): - FACTORS now carries center/span normalization spec; unused hand-written per-theme surprise blocks in dashboard.py replaced by one registry-driven compute_theme_surprises() (themes.py). - THEMES['banks'] adds bank_npl weight so NPL is genuinely blended. - factor_value/normalize hardened against NaN/inf (finite guards). - Board re-ranks (TRUE/GULF up, TOP->3) per registry weights; 3 new tests incl. 'changing a registry weight changes output'. P3 (point-in-time backtest): - run_backtest is now a real multi-rebalance engine (reallocates every window, reconciles holdings, marks to market) instead of allocate-once+break. - Added leakage_guard (False unless a PIT score_fn is supplied), planned vs actual rebalances, and momentum_at() true 12-1 (skips last month, PIT). P4 (factor-weight learning): - weight_learning.py: cross-sectional Spearman IC, forward-return builder, IC aggregation + t-stat, and apply_weight_update (new = clip(old*(1+shrink*IC))). - GET /api/v1/learning/momentum endpoint. Live result: momentum IC=0.012 t=0.132 over 22 periods -> momentum has no reliable predictive power here. Macro/demographic factors blocked (no historical factor vintages yet). Two independent review gates passed (deleg_fe6f45cd, deleg_718218f8): empty security/logic arrays; their non-blocking suggestions applied (finite guards, dedupe leakage_guard resolution). 226 tests pass; Vite build passes.
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
"""Tests for the point-in-time multi-rebalance backtest engine."""
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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 unittest.mock import patch
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from app import backtest
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def _fake_series() -> dict:
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"""Synthetic daily price series for A (up) and B (flat), 300+ days."""
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def bars(base, drift):
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out = []
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for i in range(400):
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d = (dt.date(2025, 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, 0.1)}, "B": {"bars": bars(20.0, 0.0)}}
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class RebalanceDatesTest(unittest.TestCase):
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def test_monthly(self):
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d = backtest._rebalance_dates("2026-01-01", "2026-04-01")
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self.assertEqual(d, ["2026-01-01", "2026-02-01", "2026-03-01", "2026-04-01"])
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def test_quarterly_boundaries(self):
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d = backtest._rebalance_dates("2026-01-01", "2027-04-01", "quarterly")
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self.assertEqual(d, ["2026-01-01", "2026-04-01", "2026-07-01",
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"2026-10-01", "2027-01-01", "2027-04-01"])
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def test_end_after_start_required(self):
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with self.assertRaises(backtest.BacktestError):
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backtest._rebalance_dates("2026-06-01", "2026-06-01")
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def test_bad_freq(self):
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with self.assertRaises(backtest.BacktestError):
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backtest._rebalance_dates("2026-01-01", "2026-06-01", "weekly")
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class MomentumAtTest(unittest.TestCase):
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def test_true_12_1_skips_last_month(self):
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series = _fake_series()
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d = dt.date(2026, 5, 1)
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m = backtest.momentum_at(series, "A", d)
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# A rises 0.1/day; momentum over 12m should be clearly positive.
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self.assertIsNotNone(m)
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self.assertTrue(m is not None and m > 0.0)
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class RunBacktestTest(unittest.TestCase):
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def _score_fn(self, syms, as_of):
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return {s: {"combined": 1.0 if s == "A" else 0.5,
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"is_dividend": True, "dividend_yield": 2.0} for s in syms}
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@patch("app.backtest.load_price_snapshot", return_value=_fake_series())
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def test_multi_rebalance_reuses_portfolio(self, _load):
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res = backtest.run_backtest(
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"2026-01-01", "2026-06-01", capital=1_000_000,
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rebalance_freq="monthly", score_fn=self._score_fn,
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symbols=["A", "B"],
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)
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self.assertEqual(res.planned_rebalances, 6)
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# Real re-allocations happened (price data exists for every window).
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self.assertEqual(res.rebalances, 6)
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self.assertGreater(res.trades, 0)
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# Supplying a score_fn -> leakage_guard True (PIT contract).
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self.assertTrue(res.leakage_guard)
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@patch("app.backtest.load_price_snapshot", return_value={})
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def test_no_price_snapshot_raises(self, _load):
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with self.assertRaises(backtest.BacktestError):
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backtest.run_backtest("2026-01-01", "2026-06-01")
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@patch("app.backtest.load_price_snapshot", return_value=_fake_series())
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def test_default_no_score_fn_non_pit(self, _load):
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# Without a score_fn, the engine uses the current board -> non-PIT.
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res = backtest.run_backtest(
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"2026-01-01", "2026-03-01", capital=100_000,
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rebalance_freq="monthly", symbols=["A", "B"],
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)
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self.assertFalse(res.leakage_guard)
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if __name__ == "__main__":
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unittest.main()
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