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389 lines
13 KiB
Python
389 lines
13 KiB
Python
"""Unit tests for libs.backtest.scoring."""
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from __future__ import annotations
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import pytest
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from libs.backtest.scoring import (
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_close_strength_score,
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_direction_clarity_score,
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_earnings_surprise_score,
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_event_quality_score,
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_gap_score,
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_parse_confidence_score,
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_reaction_score,
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_risk_penalty_score,
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_text_sentiment_score,
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_volume_score,
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compute_entry_score,
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)
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class TestReactionScore:
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"""Reaction day return scoring."""
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def test_sweet_spot_moderate_positive(self):
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"""Moderate positive return (0.5-3%) is ideal."""
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assert _reaction_score({"reaction_day_return": 0.01}) == 0.9
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assert _reaction_score({"reaction_day_return": 0.025}) == 0.9
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def test_flat(self):
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"""Flat to slightly positive (0-0.5%)."""
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assert _reaction_score({"reaction_day_return": 0.002}) == 0.65
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def test_large_positive_penalized(self):
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"""Large positive (3-8%) — getting priced in."""
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assert _reaction_score({"reaction_day_return": 0.05}) == 0.45
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def test_extreme_positive_penalized(self):
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"""Extreme positive (>8%) — mean reversion risk."""
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assert _reaction_score({"reaction_day_return": 0.15}) == 0.2
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def test_mild_negative(self):
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"""Small negative (-2% to 0%)."""
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assert _reaction_score({"reaction_day_return": -0.01}) == 0.4
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def test_moderate_negative(self):
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"""Moderate negative (-5% to -2%)."""
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assert _reaction_score({"reaction_day_return": -0.03}) == 0.25
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def test_strongly_bearish(self):
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"""Strongly bearish (<-5%)."""
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assert _reaction_score({"reaction_day_return": -0.08}) == 0.1
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def test_missing_returns_default(self):
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assert _reaction_score({}) == 0.5
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class TestCloseStrengthScore:
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"""Close location scoring."""
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def test_near_high(self):
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score = _close_strength_score({"close_location": 0.9})
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assert score == pytest.approx(0.91, abs=0.01)
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def test_near_low(self):
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score = _close_strength_score({"close_location": 0.1})
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assert score == pytest.approx(0.19, abs=0.01)
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def test_midpoint(self):
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score = _close_strength_score({"close_location": 0.5})
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assert score == pytest.approx(0.55, abs=0.01)
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def test_missing_returns_default(self):
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assert _close_strength_score({}) == 0.5
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def test_clamped_above_1(self):
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"""Values > 1.0 are clamped."""
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score = _close_strength_score({"close_location": 1.5})
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assert score == pytest.approx(1.0, abs=0.01)
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class TestVolumeScore:
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"""Volume ratio scoring."""
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def test_healthy_conviction(self):
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assert _volume_score({"volume_ratio_20d": 1.5}) == 0.8
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def test_normal(self):
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assert _volume_score({"volume_ratio_20d": 1.1}) == 0.6
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def test_high_possible_exhaustion(self):
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assert _volume_score({"volume_ratio_20d": 2.5}) == 0.55
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def test_extreme_exhaustion(self):
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assert _volume_score({"volume_ratio_20d": 4.0}) == 0.4
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def test_below_average(self):
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assert _volume_score({"volume_ratio_20d": 0.8}) == 0.3
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def test_missing_returns_default(self):
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assert _volume_score({}) == 0.5
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class TestGapScore:
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"""Gap size scoring."""
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def test_orderly_positive(self):
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assert _gap_score({"gap_size": 0.01}) == 0.8
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def test_neutral(self):
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assert _gap_score({"gap_size": 0.002}) == 0.6
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def test_extended(self):
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assert _gap_score({"gap_size": 0.03}) == 0.5
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def test_exhaustion_gap(self):
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assert _gap_score({"gap_size": 0.07}) == 0.3
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def test_mild_negative(self):
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assert _gap_score({"gap_size": -0.01}) == 0.4
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def test_bearish_gap(self):
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assert _gap_score({"gap_size": -0.03}) == 0.2
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def test_missing_returns_default(self):
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assert _gap_score({}) == 0.5
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class TestComputeEntryScore:
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"""Composite score tests."""
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def test_all_missing_returns_neutral(self):
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"""All features missing → all defaults at 0.5 → composite 0.5."""
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score = compute_entry_score({})
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assert score == pytest.approx(0.5, abs=0.02)
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def test_ideal_setup_scores_high(self):
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"""Moderate positive return + close near high + healthy volume.
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event=0.5*0.35 + reaction=0.9*0.25 + close=0.82*0.18 + volume=0.8*0.14 + gap=0.8*0.08 ≈ 0.72
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"""
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row = {
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"reaction_day_return": 0.01, # sweet spot → 0.9
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"close_location": 0.8, # near high → 0.82
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"volume_ratio_20d": 1.5, # conviction → 0.8
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"gap_size": 0.01, # orderly → 0.8
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}
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score = compute_entry_score(row)
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assert score > 0.65
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def test_bearish_setup_scores_low(self):
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"""Negative return + close near low + below avg volume."""
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row = {
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"reaction_day_return": -0.04, # moderate negative → 0.25
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"close_location": 0.1, # near low → 0.19
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"volume_ratio_20d": 0.8, # no conviction → 0.3
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"gap_size": -0.03, # bearish gap → 0.2
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}
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score = compute_entry_score(row)
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assert score < 0.38
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def test_extreme_positive_penalized(self):
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"""Very large positive reaction should be penalized."""
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extreme = compute_entry_score({
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"reaction_day_return": 0.15, # extreme → 0.2
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"close_location": 0.7,
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"volume_ratio_20d": 3.5, # extreme volume → 0.4
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"gap_size": 0.08, # exhaustion gap → 0.3
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})
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moderate = compute_entry_score({
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"reaction_day_return": 0.015, # sweet spot → 0.9
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"close_location": 0.7,
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"volume_ratio_20d": 1.5, # healthy → 0.8
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"gap_size": 0.01, # orderly → 0.8
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})
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assert moderate > extreme
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def test_score_bounded_0_to_1(self):
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"""Score is always in [0, 1]."""
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extremes = [
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{"reaction_day_return": -0.5, "close_location": 0.0,
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"volume_ratio_20d": 0.1, "gap_size": -0.1},
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{"reaction_day_return": 0.5, "close_location": 1.0,
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"volume_ratio_20d": 10.0, "gap_size": 0.2},
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]
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for row in extremes:
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score = compute_entry_score(row)
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assert 0.0 <= score <= 1.0
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def test_aapl_like_scores_highest(self):
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"""AAPL-like setup (moderate +, close near high) scores well."""
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aapl = compute_entry_score({
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"reaction_day_return": 0.007,
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"close_location": 0.74,
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"volume_ratio_20d": 1.45,
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"gap_size": 0.006,
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})
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# DDOG-like: extreme positive, high volume
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ddog = compute_entry_score({
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"reaction_day_return": 0.137,
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"close_location": 0.63,
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"volume_ratio_20d": 2.83,
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"gap_size": 0.089,
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})
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assert aapl > ddog, f"AAPL-like {aapl:.3f} should beat DDOG-like {ddog:.3f}"
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def test_real_data_scores(self):
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"""Verify scores for real data samples match expectations."""
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# TSLA 1/2: bearish (return -2.6%, close near low)
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tsla = compute_entry_score({
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"reaction_day_return": -0.026,
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"close_location": 0.12,
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"volume_ratio_20d": 1.13,
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"gap_size": 0.018,
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})
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# AAPL 1/29: bullish (moderate +, close near high)
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aapl = compute_entry_score({
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"reaction_day_return": 0.007,
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"close_location": 0.74,
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"volume_ratio_20d": 1.45,
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"gap_size": 0.006,
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})
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assert aapl > 0.65, f"AAPL should be above 0.65, got {aapl:.3f}"
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assert tsla < 0.46, f"TSLA should be below 0.46, got {tsla:.3f}"
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assert aapl > tsla
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def test_event_features_boost_score(self):
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"""Strong event features should boost overall score."""
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market_only = compute_entry_score({
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"reaction_day_return": 0.01,
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"close_location": 0.6,
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"volume_ratio_20d": 1.5,
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"gap_size": 0.01,
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})
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with_events = compute_entry_score({
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"reaction_day_return": 0.01,
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"close_location": 0.6,
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"volume_ratio_20d": 1.5,
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"gap_size": 0.01,
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"signal_strength_score": 0.8,
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"guidance_direction_score": 1.0,
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"document_quality_score": 0.7,
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"oneoff_penalty": 0.0,
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})
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assert with_events > market_only
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def test_removed_features_dont_affect_composite(self):
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"""Changing non-alpha features should not change composite score.
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eps_growth_qoq, oneoff_penalty, parse_confidence_overall,
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event_direction, and lm_net_sentiment are excluded from composite.
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"""
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base_row = {
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"reaction_day_return": 0.01,
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"close_location": 0.6,
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"volume_ratio_20d": 1.5,
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"gap_size": 0.01,
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}
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base_score = compute_entry_score(base_row)
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# Varying removed features should not change score
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for extra in [
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{"eps_growth_qoq": 0.50},
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{"oneoff_penalty": 1.0},
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{"parse_confidence_overall": 0.9},
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{"event_direction": "bullish"},
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{"lm_net_sentiment": 0.01},
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]:
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row = {**base_row, **extra}
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assert compute_entry_score(row) == pytest.approx(base_score), (
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f"Feature {list(extra.keys())[0]} should not affect composite"
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)
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class TestEarningsSurpriseScore:
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"""Earnings surprise (SUE) scoring."""
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def test_strong_beat(self):
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assert _earnings_surprise_score({"event_type": "earnings_release", "eps_growth_qoq": 0.30}) == 0.9
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def test_moderate_beat(self):
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assert _earnings_surprise_score({"event_type": "earnings_release", "eps_growth_qoq": 0.10}) == 0.75
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def test_inline(self):
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assert _earnings_surprise_score({"event_type": "earnings_release", "eps_growth_qoq": 0.0}) == 0.5
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def test_moderate_miss(self):
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assert _earnings_surprise_score({"event_type": "earnings_release", "eps_growth_qoq": -0.10}) == 0.25
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def test_severe_miss(self):
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assert _earnings_surprise_score({"event_type": "earnings_release", "eps_growth_qoq": -0.30}) == 0.1
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def test_non_earnings_returns_neutral(self):
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assert _earnings_surprise_score({"event_type": "guidance_update", "eps_growth_qoq": 0.30}) == 0.5
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def test_missing_returns_neutral(self):
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assert _earnings_surprise_score({"event_type": "earnings_release"}) == 0.5
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def test_no_event_type_returns_neutral(self):
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assert _earnings_surprise_score({}) == 0.5
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class TestEventQualityScore:
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"""Event quality scoring."""
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def test_strong_signals(self):
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score = _event_quality_score({
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"signal_strength_score": 0.8,
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"guidance_direction_score": 1.0,
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"document_quality_score": 0.7,
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})
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assert score > 0.7
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def test_weak_signals(self):
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score = _event_quality_score({
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"signal_strength_score": 0.0,
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"guidance_direction_score": 0.0,
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"document_quality_score": 0.3,
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})
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assert score < 0.15
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def test_missing_returns_neutral(self):
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assert _event_quality_score({}) == 0.5
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def test_partial_features(self):
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"""Works with only some event features present."""
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score = _event_quality_score({"signal_strength_score": 0.8})
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assert score == pytest.approx(0.8, abs=0.01)
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class TestRiskPenaltyScore:
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"""Risk penalty scoring."""
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def test_no_risk(self):
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assert _risk_penalty_score({"oneoff_penalty": 0.0}) == pytest.approx(0.9)
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def test_max_risk(self):
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assert _risk_penalty_score({"oneoff_penalty": 1.0}) == pytest.approx(0.2)
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def test_moderate_risk(self):
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score = _risk_penalty_score({"oneoff_penalty": 0.5})
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assert 0.4 < score < 0.7
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def test_missing_returns_neutral(self):
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assert _risk_penalty_score({}) == 0.5
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class TestParseConfidenceScore:
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"""Parse confidence scoring."""
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def test_high_confidence(self):
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assert _parse_confidence_score({"parse_confidence_overall": 0.9}) == 0.9
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def test_moderate_confidence(self):
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assert _parse_confidence_score({"parse_confidence_overall": 0.7}) == 0.7
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def test_borderline_confidence(self):
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assert _parse_confidence_score({"parse_confidence_overall": 0.55}) == 0.5
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def test_low_confidence(self):
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assert _parse_confidence_score({"parse_confidence_overall": 0.45}) == 0.3
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def test_very_low_confidence(self):
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assert _parse_confidence_score({"parse_confidence_overall": 0.3}) == 0.2
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def test_missing_returns_neutral(self):
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assert _parse_confidence_score({}) == 0.5
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class TestDirectionClarityScore:
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"""Direction clarity scoring."""
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def test_bullish(self):
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assert _direction_clarity_score({"event_direction": "bullish"}) == 0.9
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def test_mixed(self):
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assert _direction_clarity_score({"event_direction": "mixed"}) == 0.4
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def test_neutral(self):
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assert _direction_clarity_score({"event_direction": "neutral"}) == 0.3
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def test_bearish(self):
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assert _direction_clarity_score({"event_direction": "bearish"}) == 0.1
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def test_unknown(self):
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assert _direction_clarity_score({"event_direction": "unknown"}) == 0.2
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def test_missing_returns_neutral(self):
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assert _direction_clarity_score({}) == 0.5
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