160 lines
6.1 KiB
Python
160 lines
6.1 KiB
Python
"""Tests for schema validation"""
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import pytest
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from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention, EngagementMetrics
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from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, EventType
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from sentiment_engine.schemas.output import AssetSentiment, MarketSentiment, PumpDumpScore, VelocityMetrics, EventFlag
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class TestPayloadSchemas:
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"""Test payload schema validation"""
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def test_normalized_payload_valid(self):
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payload = NormalizedPayload(
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source_id="test",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1724262300.0,
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raw_text="BTC surges to new highs",
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asset_mentions=[AssetMention(asset_id="BTC", mention_span=(0, 3), confidence=0.9, source_text="BTC", mention_type="ticker")],
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content_length=25,
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language="en"
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)
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assert payload.source_id == "test"
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assert payload.has_assets is True
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assert payload.get_assets() == ["BTC"]
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def test_normalized_payload_empty_text_raises(self):
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with pytest.raises(ValueError, match="raw_text cannot be empty"):
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NormalizedPayload(
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source_id="test",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1724262300.0,
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raw_text="",
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content_length=0,
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language="en"
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)
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def test_engagement_metrics_total(self):
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metrics = EngagementMetrics(retweets=10, likes=50, replies=5, upvotes=100, comments=20)
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assert metrics.total_engagement() == 185
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class TestProcessedSchemas:
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"""Test processed item schemas"""
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def test_sentiment_scores_bounds(self):
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scores = SentimentScores(
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polarity=0.5,
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confidence=0.8,
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positive_prob=0.7,
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negative_prob=0.1,
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neutral_prob=0.2
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)
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assert -1.0 <= scores.polarity <= 1.0
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assert 0.0 <= scores.confidence <= 1.0
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def test_emotion_scores_bounds(self):
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emotions = EmotionScores(
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joy=0.8, fear=0.1, anger=0.05, greed=0.7, sadness=0.05, intensity=0.75
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)
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for val in [emotions.joy, emotions.fear, emotions.anger, emotions.greed, emotions.sadness, emotions.intensity]:
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assert 0.0 <= val <= 1.0
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def test_event_classification(self):
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event = EventClassification(
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event_type=EventType.LISTING,
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confidence=0.8,
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assets_involved=["BTC"],
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key_details={"exchange": "Binance"},
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severity=0.7
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)
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assert event.event_type == EventType.LISTING
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assert 0.0 <= event.severity <= 1.0
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class TestOutputSchemas:
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"""Test output schemas"""
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def test_asset_sentiment_acb_signals(self):
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asset = AssetSentiment(
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asset_id="BTC",
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fear_state=20.0,
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greed_state=80.0,
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sentiment_polarity=60.0,
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pump_dump=PumpDumpScore(asset_id="BTC", pump_score=75.0, dump_score=15.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=1724262305.0),
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last_update_ts=1724262305.0
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)
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# Test ACB signal extraction
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market = MarketSentiment(
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fear_state=25.0,
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greed_state=75.0,
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sentiment_index=50.0,
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hype_velocity=65.0,
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pub_velocity=55.0,
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aggregate_pump_risk=75.0,
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aggregate_dump_risk=20.0,
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last_update_ts=1724262305.0
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)
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from sentiment_engine.schemas.output import SentimentOutput
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output = SentimentOutput(timestamp=1724262305.0, market=market, assets={"BTC": asset})
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acb = output.get_acb_signals()
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assert "market_sentiment_state" in acb
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assert "aggregate_pump_risk" in acb
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assert -1.0 <= acb["market_sentiment_state"] <= 1.0
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assert 0.0 <= acb["aggregate_pump_risk"] <= 1.0
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def test_book_health_veto(self):
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asset = AssetSentiment(
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asset_id="BTC",
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fear_state=20.0,
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greed_state=80.0,
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sentiment_polarity=60.0,
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pump_dump=PumpDumpScore(asset_id="BTC", pump_score=80.0, dump_score=15.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=1724262305.0),
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last_update_ts=1724262305.0
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)
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from sentiment_engine.schemas.output import SentimentOutput, MarketSentiment
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market = MarketSentiment(
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fear_state=25.0, greed_state=75.0, sentiment_index=50.0,
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hype_velocity=65.0, pub_velocity=55.0,
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aggregate_pump_risk=75.0, aggregate_dump_risk=20.0,
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last_update_ts=1724262305.0
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)
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output = SentimentOutput(timestamp=1724262305.0, market=market, assets={"BTC": asset})
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veto = output.get_book_health_veto(threshold=75.0)
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assert "BTC" in veto
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veto_low = output.get_book_health_veto(threshold=85.0)
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assert "BTC" not in veto_low
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def test_exit_context(self):
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asset = AssetSentiment(
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asset_id="BTC",
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fear_state=85.0,
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greed_state=15.0,
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sentiment_polarity=-70.0,
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pump_dump=PumpDumpScore(asset_id="BTC", pump_score=10.0, dump_score=80.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=1724262305.0),
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last_update_ts=1724262305.0
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)
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from sentiment_engine.schemas.output import SentimentOutput, MarketSentiment
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market = MarketSentiment(
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fear_state=80.0, greed_state=20.0, sentiment_index=-60.0,
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hype_velocity=30.0, pub_velocity=40.0,
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aggregate_pump_risk=15.0, aggregate_dump_risk=80.0,
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last_update_ts=1724262305.0
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)
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output = SentimentOutput(timestamp=1724262305.0, market=market, assets={"BTC": asset})
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ctx = output.get_exit_context(dump_threshold=70.0, fear_threshold=80.0)
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assert "BTC" in ctx["high_dump_assets"]
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assert "BTC" in ctx["high_fear_assets"]
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assert ctx["market_dump_risk"] == 80.0
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assert ctx["market_fear"] == 80.0
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