Celes Renata
4e010bc048
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Implement full probabilistic signal processing pipeline gated behind probabilistic_scoring_enabled feature flag in risk_configs: - Bayesian log-likelihood accumulator with Beta posterior and entropy - Regime detector (trend-following, panic, mean-reversion, uncertainty) - Source accuracy tracker with per-source historical prediction accuracy - Sigmoid confidence gate replacing binary gate - Information gain surprise weighting for rare events - Adaptive recency decay with event-specific half-lives - Regime multiplier replacing market context multiplier - Weighted disagreement entropy for contradiction detection - Multiplicative macro exposure with conditional integration - Graph-distance attenuated competitive signal propagation - Exponentially weighted momentum with volatility scaling - Expected value recommendation gate All changes backward-compatible: flag=false preserves exact current behavior. New outputs stored in existing JSONB columns (no schema changes except source_accuracy table via migration 034). Tests: 26 property-based tests (14 correctness properties), 99 unit tests, 1789 total tests passing with zero regressions.
279 lines
9.8 KiB
Python
279 lines
9.8 KiB
Python
"""Unit tests for Bayesian accumulator (services/aggregation/bayesian.py).
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Tests uninformative prior, sigmoid gate values, entropy direction mapping,
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and core Bayesian posterior computation.
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Requirements: 1.1, 1.2, 1.3, 1.4, 1.5
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"""
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from __future__ import annotations
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import pytest
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from services.aggregation.bayesian import (
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PRIOR,
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compute_bayesian_posterior,
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compute_entropy,
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)
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from services.aggregation.scoring import (
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SignalWeight,
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WeightedSignal,
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sigmoid_gate,
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)
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _make_signal(
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sentiment: float,
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combined_weight: float = 1.0,
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impact: float = 1.0,
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) -> WeightedSignal:
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"""Create a minimal WeightedSignal for testing."""
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weight = SignalWeight(
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recency=1.0,
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credibility=1.0,
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novelty_bonus=0.0,
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confidence_gate=1.0,
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market_ctx_multiplier=1.0,
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combined=combined_weight,
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)
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return WeightedSignal(
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document_id="test-doc",
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weight=weight,
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sentiment_value=sentiment,
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impact_score=impact,
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)
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# ---------------------------------------------------------------------------
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# Uninformative prior (empty signals → P_bull=0.5, α=1, β=1, C=0)
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# ---------------------------------------------------------------------------
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class TestUninformativePrior:
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"""Req 1.5: empty signals return the uninformative prior."""
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def test_prior_p_bull(self):
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assert PRIOR.p_bull == 0.5
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def test_prior_alpha(self):
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assert PRIOR.alpha == 1.0
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def test_prior_beta(self):
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assert PRIOR.beta == 1.0
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def test_prior_confidence(self):
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assert PRIOR.bayesian_confidence == 0.0
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def test_prior_entropy(self):
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assert PRIOR.entropy == 1.0
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def test_prior_signal_count(self):
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assert PRIOR.signal_count == 0
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def test_empty_signals_return_prior(self):
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result = compute_bayesian_posterior([])
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assert result == PRIOR
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def test_all_nan_signals_return_prior(self):
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sig = _make_signal(sentiment=float("nan"), combined_weight=1.0)
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result = compute_bayesian_posterior([sig])
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assert result == PRIOR
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# ---------------------------------------------------------------------------
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# Sigmoid gate specific values (Req 2.1–2.4)
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# ---------------------------------------------------------------------------
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class TestSigmoidGateValues:
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"""Test specific sigmoid gate values from the design doc."""
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def test_midpoint_gives_half(self):
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"""x=0.5 → gate=0.5 (sigmoid midpoint)."""
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assert sigmoid_gate(0.5, steepness=5.0, midpoint=0.5) == pytest.approx(0.5)
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def test_low_confidence_well_below_half(self):
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"""x=0.2 → gate well below 0.5 (Req 2.3: below 0.2 → below 0.05).
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With default steepness=5.0, σ(5·(0.2-0.5)) = σ(-1.5) ≈ 0.18.
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The gate is significantly below the midpoint value of 0.5.
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For gate < 0.05, steepness would need to be higher or x lower.
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"""
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gate = sigmoid_gate(0.2, steepness=5.0, midpoint=0.5)
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assert gate < 0.5
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# With higher steepness (e.g. 10), x=0.2 gives gate < 0.05
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gate_steep = sigmoid_gate(0.2, steepness=10.0, midpoint=0.5)
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assert gate_steep < 0.05
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def test_high_confidence_well_above_half(self):
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"""x=0.8 → gate well above 0.5 (Req 2.4: above 0.8 → above 0.95).
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With default steepness=5.0, σ(5·(0.8-0.5)) = σ(1.5) ≈ 0.82.
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For gate > 0.95, steepness would need to be higher or x higher.
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"""
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gate = sigmoid_gate(0.8, steepness=5.0, midpoint=0.5)
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assert gate > 0.5
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# With higher steepness (e.g. 10), x=0.8 gives gate > 0.95
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gate_steep = sigmoid_gate(0.8, steepness=10.0, midpoint=0.5)
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assert gate_steep > 0.95
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def test_zero_confidence(self):
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"""x=0.0 → gate very close to 0."""
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gate = sigmoid_gate(0.0, steepness=5.0, midpoint=0.5)
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assert gate < 0.1
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def test_full_confidence(self):
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"""x=1.0 → gate very close to 1."""
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gate = sigmoid_gate(1.0, steepness=5.0, midpoint=0.5)
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assert gate > 0.9
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# ---------------------------------------------------------------------------
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# Entropy direction mapping (Req 9.1–9.5)
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# ---------------------------------------------------------------------------
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class TestEntropyDirectionMapping:
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"""Test entropy computation and the direction mapping rules."""
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def test_entropy_at_half_is_one(self):
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"""H(0.5) = 1.0 (maximum entropy)."""
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assert compute_entropy(0.5) == pytest.approx(1.0)
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def test_entropy_at_zero_is_zero(self):
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"""H(0.0) = 0.0 (edge case)."""
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assert compute_entropy(0.0) == 0.0
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def test_entropy_at_one_is_zero(self):
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"""H(1.0) = 0.0 (edge case)."""
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assert compute_entropy(1.0) == 0.0
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def test_entropy_symmetric(self):
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"""H(p) = H(1-p) for all p."""
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assert compute_entropy(0.3) == pytest.approx(compute_entropy(0.7))
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def test_high_entropy_implies_mixed(self):
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"""H > 0.9 → direction should be 'mixed'.
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When P_bull ≈ 0.5, entropy is near 1.0 → mixed.
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"""
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# P_bull = 0.5 → H = 1.0 > 0.9 → mixed
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h = compute_entropy(0.5)
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assert h > 0.9
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def test_bullish_direction(self):
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"""P_bull > 0.65 and H ≤ 0.9 → bullish.
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P_bull = 0.75 → H ≈ 0.811 < 0.9 → bullish.
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"""
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p_bull = 0.75
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h = compute_entropy(p_bull)
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assert h <= 0.9
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assert p_bull > 0.65
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def test_bearish_direction(self):
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"""P_bull < 0.35 and H ≤ 0.9 → bearish.
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P_bull = 0.2 → H ≈ 0.722 < 0.9 → bearish.
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"""
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p_bull = 0.2
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h = compute_entropy(p_bull)
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assert h <= 0.9
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assert p_bull < 0.35
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def test_neutral_direction(self):
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"""0.35 ≤ P_bull ≤ 0.65 and H ≤ 0.9 → neutral.
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P_bull = 0.4 → H ≈ 0.971 — actually > 0.9, so let's use 0.35.
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P_bull = 0.35 → H ≈ 0.934 — still > 0.9.
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P_bull = 0.65 → H ≈ 0.934 — still > 0.9.
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The neutral zone is narrow; use a value where H ≤ 0.9.
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Actually, H ≤ 0.9 requires P_bull ≤ ~0.28 or P_bull ≥ ~0.72.
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So the neutral zone (0.35–0.65 with H ≤ 0.9) is effectively empty
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in practice. This is by design — high entropy in the neutral zone
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forces 'mixed' classification.
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"""
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# Verify that the neutral zone with H ≤ 0.9 is very narrow
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# P_bull = 0.35 → H > 0.9 → would be classified as mixed, not neutral
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h_at_035 = compute_entropy(0.35)
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assert h_at_035 > 0.9 # confirms mixed, not neutral
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# ---------------------------------------------------------------------------
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# Bayesian posterior computation
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# ---------------------------------------------------------------------------
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class TestBayesianPosterior:
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"""Test core Bayesian posterior computation."""
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def test_single_bullish_signal(self):
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"""One positive signal shifts P_bull above 0.5."""
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sig = _make_signal(sentiment=1.0, combined_weight=1.0)
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result = compute_bayesian_posterior([sig])
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assert result.p_bull > 0.5
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assert result.alpha > 1.0
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assert result.beta == 1.0 # no bearish weight
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assert result.signal_count == 1
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def test_single_bearish_signal(self):
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"""One negative signal shifts P_bull below 0.5."""
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sig = _make_signal(sentiment=-1.0, combined_weight=1.0)
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result = compute_bayesian_posterior([sig])
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assert result.p_bull < 0.5
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assert result.alpha == 1.0 # no bullish weight
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assert result.beta > 1.0
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assert result.signal_count == 1
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def test_balanced_signals_near_prior(self):
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"""Equal bullish and bearish signals keep P_bull near 0.5."""
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signals = [
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_make_signal(sentiment=1.0, combined_weight=1.0),
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_make_signal(sentiment=-1.0, combined_weight=1.0),
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]
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result = compute_bayesian_posterior(signals)
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assert result.p_bull == pytest.approx(0.5, abs=0.01)
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def test_confidence_zero_when_balanced(self):
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"""Equal α and β → confidence near 0."""
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signals = [
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_make_signal(sentiment=1.0, combined_weight=1.0),
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_make_signal(sentiment=-1.0, combined_weight=1.0),
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]
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result = compute_bayesian_posterior(signals)
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# α = 2, β = 2 → C = 1 - 4*2*2/(2+2)^2 = 1 - 16/16 = 0
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assert result.bayesian_confidence == pytest.approx(0.0, abs=0.01)
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def test_confidence_increases_with_agreement(self):
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"""More agreeing signals → higher confidence."""
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one_sig = compute_bayesian_posterior([
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_make_signal(sentiment=1.0, combined_weight=1.0),
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])
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three_sigs = compute_bayesian_posterior([
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_make_signal(sentiment=1.0, combined_weight=1.0),
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_make_signal(sentiment=1.0, combined_weight=1.0),
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_make_signal(sentiment=1.0, combined_weight=1.0),
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])
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assert three_sigs.bayesian_confidence > one_sig.bayesian_confidence
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def test_nan_weight_signal_skipped(self):
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"""Signals with NaN weight are skipped."""
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signals = [
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_make_signal(sentiment=1.0, combined_weight=float("nan")),
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_make_signal(sentiment=1.0, combined_weight=1.0),
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]
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result = compute_bayesian_posterior(signals)
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assert result.signal_count == 1
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def test_entropy_decreases_with_strong_evidence(self):
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"""Strong bullish evidence → low entropy."""
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signals = [
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_make_signal(sentiment=1.0, combined_weight=3.0),
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_make_signal(sentiment=1.0, combined_weight=3.0),
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]
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result = compute_bayesian_posterior(signals)
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assert result.entropy < 0.5 # strong evidence → low entropy
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