Celes Renata
249 lines
9.3 KiB
Python
249 lines
9.3 KiB
Python
"""Tests for aggregation scoring — recency decay, source credibility weighting,
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and market context integration."""
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from datetime import datetime, timedelta, timezone
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from services.aggregation.scoring import (
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DEFAULT_CONFIG,
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ScoringConfig,
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WeightedSignal,
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compute_signal_weight,
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credibility_weight,
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market_context_multiplier,
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recency_weight,
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sentiment_to_numeric,
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weighted_sentiment_average,
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)
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from services.shared.schemas import MarketContext
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# ---------------------------------------------------------------------------
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# recency_weight
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# ---------------------------------------------------------------------------
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def test_recency_weight_at_zero_age():
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"""A document published exactly at reference time gets weight 1.0."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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assert recency_weight(now, now, "7d") == 1.0
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def test_recency_weight_future_document():
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"""A document published after reference time is clamped to 1.0."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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future = now + timedelta(hours=1)
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assert recency_weight(future, now, "7d") == 1.0
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def test_recency_weight_at_one_half_life():
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"""After exactly one half-life the weight should be ~0.5."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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half_life_7d = DEFAULT_CONFIG.half_life_hours["7d"] # 72 hours
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published = now - timedelta(hours=half_life_7d)
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w = recency_weight(published, now, "7d")
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assert abs(w - 0.5) < 1e-9
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def test_recency_weight_very_old_clamps_to_min():
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"""A very old document should not go below min_recency_weight."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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ancient = now - timedelta(days=365)
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w = recency_weight(ancient, now, "7d")
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assert w == DEFAULT_CONFIG.min_recency_weight
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def test_recency_weight_different_windows():
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"""Shorter windows decay faster than longer ones."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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published = now - timedelta(hours=24)
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w_intraday = recency_weight(published, now, "intraday")
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w_90d = recency_weight(published, now, "90d")
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assert w_intraday < w_90d
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def test_recency_weight_naive_datetimes():
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"""Naive datetimes are treated as UTC."""
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now = datetime(2026, 4, 11, 12, 0, 0)
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published = now - timedelta(hours=72)
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w = recency_weight(published, now, "7d")
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assert abs(w - 0.5) < 1e-9
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# ---------------------------------------------------------------------------
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# credibility_weight
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# ---------------------------------------------------------------------------
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def test_credibility_weight_high():
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"""High credibility source gets weight close to 1.0."""
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assert abs(credibility_weight(1.0) - 1.0) < 1e-9
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def test_credibility_weight_low_clamped():
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"""Credibility below floor is clamped to floor."""
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w = credibility_weight(0.0)
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assert abs(w - DEFAULT_CONFIG.credibility_floor) < 1e-9
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def test_credibility_weight_mid():
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"""Mid-range credibility passes through with exponent=1."""
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assert abs(credibility_weight(0.5) - 0.5) < 1e-9
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def test_credibility_weight_custom_exponent():
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"""Custom exponent penalises low credibility more."""
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cfg = ScoringConfig(credibility_exponent=2.0)
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w = credibility_weight(0.5, config=cfg)
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assert abs(w - 0.25) < 1e-9
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# ---------------------------------------------------------------------------
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# compute_signal_weight
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# ---------------------------------------------------------------------------
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def test_signal_weight_gates_low_confidence():
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"""Documents below confidence floor get zero combined weight."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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sw = compute_signal_weight(
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published_at=now,
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reference_time=now,
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window="7d",
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source_credibility=0.8,
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extraction_confidence=0.1, # below default 0.2 floor
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)
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assert sw.combined == 0.0
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assert sw.confidence_gate == 0.0
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def test_signal_weight_fresh_high_credibility():
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"""Fresh doc with high credibility and default novelty gets a strong weight."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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sw = compute_signal_weight(
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published_at=now,
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reference_time=now,
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window="7d",
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source_credibility=1.0,
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novelty_score=0.5,
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extraction_confidence=0.8,
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)
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# recency=1.0, credibility=1.0, bonus=0.125, gate=1.0
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expected = 1.0 * 1.0 * (1.0 + 0.125)
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assert abs(sw.combined - expected) < 1e-9
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def test_signal_weight_novelty_bonus():
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"""Higher novelty gives a proportionally higher combined weight."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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sw_low = compute_signal_weight(now, now, "7d", 0.8, novelty_score=0.0, extraction_confidence=0.8)
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sw_high = compute_signal_weight(now, now, "7d", 0.8, novelty_score=1.0, extraction_confidence=0.8)
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assert sw_high.combined > sw_low.combined
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# ---------------------------------------------------------------------------
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# sentiment helpers
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# ---------------------------------------------------------------------------
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def test_sentiment_to_numeric():
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assert sentiment_to_numeric("positive") == 1.0
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assert sentiment_to_numeric("negative") == -1.0
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assert sentiment_to_numeric("neutral") == 0.0
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assert sentiment_to_numeric("mixed") == 0.0
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assert sentiment_to_numeric("unknown") == 0.0
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def test_weighted_sentiment_average_empty():
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assert weighted_sentiment_average([]) == 0.0
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def test_weighted_sentiment_average_single():
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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sw = compute_signal_weight(now, now, "7d", 0.8, extraction_confidence=0.8)
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signals = [WeightedSignal("doc1", sw, sentiment_value=1.0, impact_score=0.7)]
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avg = weighted_sentiment_average(signals)
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assert abs(avg - 1.0) < 1e-9 # single positive signal → 1.0
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def test_weighted_sentiment_average_opposing():
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"""Equal-weight opposing signals should cancel to ~0."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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sw = compute_signal_weight(now, now, "7d", 0.8, extraction_confidence=0.8)
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signals = [
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WeightedSignal("doc1", sw, sentiment_value=1.0, impact_score=0.5),
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WeightedSignal("doc2", sw, sentiment_value=-1.0, impact_score=0.5),
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]
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avg = weighted_sentiment_average(signals)
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assert abs(avg) < 1e-9
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# ---------------------------------------------------------------------------
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# market_context_multiplier
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# ---------------------------------------------------------------------------
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def test_market_context_multiplier_none():
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"""No market context returns 1.0 (no adjustment)."""
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assert market_context_multiplier(None) == 1.0
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def test_market_context_multiplier_no_data():
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"""MarketContext with no bars returns 1.0."""
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ctx = MarketContext(ticker="AAPL", bars_available=0)
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assert market_context_multiplier(ctx) == 1.0
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def test_market_context_multiplier_low_volatility():
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"""Below-threshold volatility produces no boost."""
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ctx = MarketContext(ticker="AAPL", volatility=0.5, volume_change_pct=10.0, bars_available=5)
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assert market_context_multiplier(ctx) == 1.0
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def test_market_context_multiplier_high_volatility():
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"""Above-threshold volatility produces a boost > 1.0."""
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ctx = MarketContext(ticker="AAPL", volatility=3.0, volume_change_pct=10.0, bars_available=5)
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m = market_context_multiplier(ctx)
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assert m > 1.0
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assert m <= 1.0 + DEFAULT_CONFIG.volatility_recency_boost_max + DEFAULT_CONFIG.volume_surge_boost
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def test_market_context_multiplier_volume_surge():
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"""Volume surge above threshold adds a boost."""
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ctx = MarketContext(ticker="AAPL", volatility=0.5, volume_change_pct=80.0, bars_available=5)
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m = market_context_multiplier(ctx)
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assert abs(m - (1.0 + DEFAULT_CONFIG.volume_surge_boost)) < 1e-9
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def test_market_context_multiplier_both_triggers():
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"""Both volatility and volume surge stack."""
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ctx = MarketContext(ticker="AAPL", volatility=3.0, volume_change_pct=80.0, bars_available=5)
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m = market_context_multiplier(ctx)
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# Should be > 1.0 + volume_surge_boost alone
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assert m > 1.0 + DEFAULT_CONFIG.volume_surge_boost
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# ---------------------------------------------------------------------------
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# compute_signal_weight with market context
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# ---------------------------------------------------------------------------
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def test_signal_weight_with_market_context_boost():
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"""Market context with high volatility should increase combined weight."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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ctx = MarketContext(ticker="AAPL", volatility=3.0, volume_change_pct=80.0, bars_available=10)
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sw_no_ctx = compute_signal_weight(now, now, "7d", 0.8, extraction_confidence=0.8)
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sw_with_ctx = compute_signal_weight(now, now, "7d", 0.8, extraction_confidence=0.8, market_ctx=ctx)
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assert sw_with_ctx.combined > sw_no_ctx.combined
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assert sw_with_ctx.market_ctx_multiplier > 1.0
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assert sw_no_ctx.market_ctx_multiplier == 1.0
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def test_signal_weight_market_context_gated_still_zero():
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"""Low confidence docs stay at zero even with market context boost."""
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now = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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ctx = MarketContext(ticker="AAPL", volatility=5.0, volume_change_pct=100.0, bars_available=10)
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sw = compute_signal_weight(now, now, "7d", 0.8, extraction_confidence=0.1, market_ctx=ctx)
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assert sw.combined == 0.0
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