feat(sentiment): add 30 new sources for uncovered trade assets
Add 5 RSS feeds + 25 Telegram web_crawl channels for assets with ZERO coverage: - STX: BlockstackUpdate, StacksChat (missed +43% ONE, -5.65% STX) - FET: fetch_ai_announcements, fetch_ai (missed +22.68%) - XTZ: TezosAnnouncements, TezosPlatform (missed +3.85%) - ENJ: enjininsights, ejsnews (missed +5.13%) - ETC: etcnetwork, EtcHash + RSS (missed +8.52%) - TRX: tronnetworkEN, Tron_TRX_News (missed -0.44%) - ONG: ontologyannouncements, OntologyNetwork + RSS (missed +6.37%) - DASH: dashnewsbot, dash_chat + RSS (missed +6.45%) - LTC: litecoin_crypto, litecoin_fundamentals + RSS (missed +5.45%) - ZIL: zilliqann, zilliqachat, ZilliqaDevs + RSS (missed -2.88%, 9x SHORT loss) - NEAR: NearAnnouncements (missed +19.26%) - APT: AptosAnnouncements (missed +10.35%) - SUI: SuiAnnouncements (missed +10.87%) - ICP: dfinity (missed +10.86%) All sources verified: RSS feeds return valid XML, Telegram public preview URLs return HTML. Coverage for trade assets: 40% → ~95%+
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"""Pytest configuration and fixtures"""
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import asyncio
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import pytest
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from unittest.mock import AsyncMock, MagicMock
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
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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, TemporalAnchor, CredibilityScore, EventType
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from sentiment_engine.schemas.output import AssetSentiment, MarketSentiment, IndustrySentiment, SentimentOutput, PumpDumpScore, VelocityMetrics, EventFlag
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@pytest.fixture
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def event_loop():
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"""Create event loop for async tests"""
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loop = asyncio.new_event_loop()
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yield loop
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loop.close()
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@pytest.fixture
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def sample_payload():
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"""Sample normalized payload"""
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return NormalizedPayload(
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source_id="rss:coindesk.com",
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source_type=SourceType.NEWS,
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source_credibility_base=0.85,
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ingest_ts=1724262300.0,
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publish_ts=1724262200.0,
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asset_mentions=[
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AssetMention(asset_id="BTC", mention_span=(0, 3), confidence=0.9, source_text="BTC", mention_type="ticker"),
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AssetMention(asset_id="ETH", mention_span=(10, 13), confidence=0.9, source_text="ETH", mention_type="ticker"),
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],
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raw_text="BTC surges to $65K as ETH follows with strong momentum. Market sentiment turns bullish.",
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title="Bitcoin Surges to $65K",
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url="https://coindesk.com/btc-surges",
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author="John Doe",
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engagement_metrics=EngagementMetrics(retweets=100, likes=500, replies=50),
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content_length=200,
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language="en",
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metadata={"feed_url": "https://coindesk.com/feed"}
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)
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@pytest.fixture
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def sample_processed_item(sample_payload):
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"""Sample processed item"""
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return ProcessedItem(
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payload_id="test_payload_1",
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source_id=sample_payload.source_id,
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source_type=sample_payload.source_type.value,
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ingest_ts=sample_payload.ingest_ts,
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publish_ts=sample_payload.publish_ts,
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entities=[
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EntityExtraction(asset_id="BTC", mention_span=(0, 3), confidence=0.9, entity_type="ticker", canonical_name="BTC"),
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EntityExtraction(asset_id="ETH", mention_span=(10, 13), confidence=0.9, entity_type="ticker", canonical_name="ETH"),
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],
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sentiment_per_asset={
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"BTC": SentimentScores(polarity=0.7, confidence=0.85, positive_prob=0.8, negative_prob=0.1, neutral_prob=0.1),
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"ETH": SentimentScores(polarity=0.5, confidence=0.8, positive_prob=0.7, negative_prob=0.15, neutral_prob=0.15),
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},
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emotions_per_asset={
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"BTC": EmotionScores(joy=0.8, fear=0.1, anger=0.05, greed=0.7, sadness=0.05, intensity=0.75),
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"ETH": EmotionScores(joy=0.6, fear=0.15, anger=0.05, greed=0.5, sadness=0.05, intensity=0.6),
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},
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events=[
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EventClassification(event_type=EventType.LISTING, confidence=0.7, assets_involved=["BTC"], key_details={}, severity=0.5),
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],
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temporal=TemporalAnchor(event_time=None, time_horizon="immediate", is_breaking=True, is_scheduled=False),
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credibility=CredibilityScore(source_base=0.85, content_quality=0.8, engagement_authenticity=0.7, cross_source_corroboration=0.6, historical_accuracy=0.8, composite=0.78),
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processed_ts=1724262305.0,
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processing_latency_ms=45.2,
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model_versions={"finbert": "1.0", "gemma": "3-4b"}
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)
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@pytest.fixture
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def sample_asset_sentiment():
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"""Sample asset sentiment output"""
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return 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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emotion_profile={"joy": 0.8, "fear": 0.1, "anger": 0.05, "greed": 0.7, "sadness": 0.05, "intensity": 0.75},
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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, coordinating_sources=3, last_update_ts=1724262305.0),
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event_flags=[EventFlag(event_type="listing", asset_id="BTC", strength=60.0, confidence=0.7, first_seen_ts=1724262300.0, last_seen_ts=1724262305.0, source_count=2)],
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velocity=VelocityMetrics(hype_velocity=0.7, pub_velocity=0.5, velocity_direction="accelerating", window_minutes=15, source_count=3, unique_assets=1),
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last_update_ts=1724262305.0,
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contributing_sources=3,
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decay_factor=0.95
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)
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@pytest.fixture
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def sample_market_sentiment(sample_asset_sentiment):
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"""Sample market sentiment output"""
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return 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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top_pump_assets=["BTC", "ETH", "SOL"],
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top_dump_assets=[],
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dominant_events=[],
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industry_breakdown={},
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last_update_ts=1724262305.0,
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total_sources=10,
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total_assets=50
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)
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@pytest.fixture
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def sample_sentiment_output(sample_market_sentiment, sample_asset_sentiment):
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"""Complete sentiment output"""
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industry = IndustrySentiment(
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industry="Smart Contract Platform",
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assets=["BTC", "ETH"],
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fear_state=22.0,
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greed_state=78.0,
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avg_polarity=55.0,
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pump_risk=75.0,
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dump_risk=15.0,
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dominant_events=[],
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asset_count=2,
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last_update_ts=1724262305.0
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)
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return SentimentOutput(
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timestamp=1724262305.0,
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market=sample_market_sentiment,
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industries={"Smart Contract Platform": industry},
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assets={"BTC": sample_asset_sentiment, "ETH": sample_asset_sentiment}
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)
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