dennisthiessen
ef523474ad
The 5-year backtest confirmed the EV gate adds negative value (high threshold = worst expectancy) and that 12-1 month momentum is the one price signal with a plausible, right-signed cross-sectional IC (~0.05). So "qualified" now means: clears the R:R + confidence floors AND the ticker ranks in the top `min_momentum_percentile` of the universe by 12-1 momentum that week. - qualification.py: drop expected_value_r / the EV gate; add a momentum-percentile gate (duck-typed `momentum_percentile`, only enforced when attached + threshold set, else defers to floors). Mirrored in frontend qualification.ts. - activation config/schema: min_expected_value -> min_momentum_percentile (default 80 = top quintile). ActivationSettings, DashboardPage (ranks/【shows】 momentum instead of EV), and the BacktestPanel sweep follow. - backtest: rank each ISO week's universe by 12-1 momentum, assign a percentile, and qualify the top slice; the sweep now sweeps the percentile cutoff. Also offload the backtest's per-ticker compute to a worker thread so the heavy ~5y run no longer blocks the API event loop (the "backend offline" flicker). Production setups don't carry momentum_percentile yet — wiring the scanner to attach it (a universe momentum-rank step) is the next step; until then the live gate defers to floors while the backtest measures the momentum selection. 330 backend tests pass; frontend build clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
"""Unit tests for the activation qualification predicate (momentum-based gate)."""
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from __future__ import annotations
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from types import SimpleNamespace
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from app.services.qualification import best_target_probability, setup_qualifies
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# Default gate: floors only; the momentum selection is off (0). Conviction /
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# conflict / target-probability are optional tighteners, off here.
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DEFAULT_GATE = {
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"min_momentum_percentile": 0.0,
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"min_rr": 1.2,
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"min_confidence": 55.0,
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"min_target_probability": 0.0,
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"require_high_conviction": False,
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"exclude_conflicts": False,
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}
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# Gate with the cross-sectional momentum selection on (top quintile).
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MOMENTUM_GATE = {**DEFAULT_GATE, "min_momentum_percentile": 80.0}
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# Strict gate: every optional tightener turned on.
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STRICT_GATE = {
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"min_momentum_percentile": 0.0,
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"min_rr": 2.0,
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"min_confidence": 70.0,
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"min_target_probability": 60.0,
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"require_high_conviction": True,
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"exclude_conflicts": True,
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}
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def _setup(**kwargs):
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base = dict(
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rr_ratio=3.0,
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confidence_score=80.0,
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recommended_action="LONG_HIGH",
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risk_level="Low",
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targets=[{"probability": 50.0, "is_primary": True}],
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)
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base.update(kwargs)
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return SimpleNamespace(**base)
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class TestFloors:
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def test_passes_floors(self):
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assert setup_qualifies(_setup(), DEFAULT_GATE) is True
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def test_low_rr_floor_fails(self):
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assert setup_qualifies(_setup(rr_ratio=1.0), DEFAULT_GATE) is False
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def test_low_confidence_fails(self):
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assert setup_qualifies(_setup(confidence_score=40.0), DEFAULT_GATE) is False
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def test_conviction_and_conflict_ignored_by_default(self):
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s = _setup(recommended_action="LONG_MODERATE", risk_level="Medium")
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assert setup_qualifies(s, DEFAULT_GATE) is True
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def test_over_progressed_setup_fails_on_live_rr(self):
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s = _setup(direction="long", target=120.0, stop_loss=95.0, current_price=117.0)
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assert setup_qualifies(s, DEFAULT_GATE) is False
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def test_fresh_setup_passes_live_rr(self):
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s = _setup(direction="long", target=120.0, stop_loss=95.0, current_price=101.0)
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assert setup_qualifies(s, DEFAULT_GATE) is True
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def test_past_stop_fails_live_rr(self):
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s = _setup(direction="long", target=120.0, stop_loss=95.0, current_price=94.0)
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assert setup_qualifies(s, DEFAULT_GATE) is False
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class TestMomentumGate:
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def test_top_momentum_passes(self):
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assert setup_qualifies(_setup(momentum_percentile=92.0), MOMENTUM_GATE) is True
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def test_below_threshold_fails(self):
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assert setup_qualifies(_setup(momentum_percentile=50.0), MOMENTUM_GATE) is False
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def test_missing_percentile_defers_to_floors(self):
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# No percentile attached (e.g. production not yet wired) → the momentum
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# gate is skipped and the setup still clears on the floors.
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assert setup_qualifies(_setup(), MOMENTUM_GATE) is True
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def test_threshold_zero_disables_gate(self):
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# min_momentum_percentile 0 → a low-momentum name still passes.
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assert setup_qualifies(_setup(momentum_percentile=10.0), DEFAULT_GATE) is True
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def test_missing_key_defaults_off(self):
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legacy = {k: v for k, v in DEFAULT_GATE.items() if k != "min_momentum_percentile"}
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assert setup_qualifies(_setup(momentum_percentile=10.0), legacy) is True
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class TestStrictTighteners:
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def test_clean_high_conviction_passes(self):
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assert setup_qualifies(_setup(targets=[{"probability": 65.0, "is_primary": True}]), STRICT_GATE) is True
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def test_moderate_action_fails(self):
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s = _setup(recommended_action="LONG_MODERATE", targets=[{"probability": 65.0, "is_primary": True}])
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assert setup_qualifies(s, STRICT_GATE) is False
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def test_non_low_risk_fails(self):
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s = _setup(risk_level="Medium", targets=[{"probability": 65.0, "is_primary": True}])
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assert setup_qualifies(s, STRICT_GATE) is False
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def test_low_target_probability_fails(self):
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assert setup_qualifies(_setup(targets=[{"probability": 40.0, "is_primary": True}]), STRICT_GATE) is False
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class TestBestTargetProbability:
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def test_returns_max(self):
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s = _setup(targets=[{"probability": 40.0}, {"probability": 72.0}, {"probability": 55.0}])
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assert best_target_probability(s) == 72.0
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def test_empty_is_zero(self):
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assert best_target_probability(_setup(targets=[])) == 0.0
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