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"""Tests for sentiment aggregator."""
from datetime import UTC, datetime, timedelta
import pytest
from shared.sentiment import SentimentAggregator
@pytest.fixture
def aggregator():
return SentimentAggregator()
def test_freshness_decay_recent():
a = SentimentAggregator()
now = datetime.now(UTC)
assert a._freshness_decay(now, now) == 1.0
def test_freshness_decay_3_hours():
a = SentimentAggregator()
now = datetime.now(UTC)
assert a._freshness_decay(now - timedelta(hours=3), now) == 0.7
def test_freshness_decay_12_hours():
a = SentimentAggregator()
now = datetime.now(UTC)
assert a._freshness_decay(now - timedelta(hours=12), now) == 0.3
def test_freshness_decay_old():
a = SentimentAggregator()
now = datetime.now(UTC)
assert a._freshness_decay(now - timedelta(days=2), now) == 0.0
def test_compute_composite():
a = SentimentAggregator()
composite = a._compute_composite(
news_score=0.5, social_score=0.3, policy_score=0.8, filing_score=0.2
)
expected = 0.5 * 0.3 + 0.3 * 0.2 + 0.8 * 0.3 + 0.2 * 0.2
assert abs(composite - expected) < 0.001
def test_aggregate_news_by_symbol(aggregator):
now = datetime.now(UTC)
news_items = [
{"symbols": ["AAPL"], "sentiment": 0.8, "category": "earnings", "published_at": now},
{
"symbols": ["AAPL"],
"sentiment": 0.3,
"category": "macro",
"published_at": now - timedelta(hours=2),
},
{"symbols": ["MSFT"], "sentiment": -0.5, "category": "policy", "published_at": now},
]
scores = aggregator.aggregate(news_items, now)
assert "AAPL" in scores
assert "MSFT" in scores
assert scores["AAPL"].news_count == 2
assert scores["AAPL"].news_score > 0
assert scores["MSFT"].policy_score < 0
def test_aggregate_empty(aggregator):
now = datetime.now(UTC)
assert aggregator.aggregate([], now) == {}
def test_determine_regime():
a = SentimentAggregator()
assert a.determine_regime(15, None) == "risk_off"
assert a.determine_regime(15, 35.0) == "risk_off"
assert a.determine_regime(50, 35.0) == "risk_off"
assert a.determine_regime(70, 15.0) == "risk_on"
assert a.determine_regime(50, 20.0) == "neutral"
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