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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Codex
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"""Tests for output sinks"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from sentiment_engine.output.hazelcast_sink import HazelcastSink
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from sentiment_engine.output.clickhouse_sink import ClickHouseSink
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from sentiment_engine.output.manager import OutputManager
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from sentiment_engine.schemas.output import SentimentOutput, AssetSentiment, MarketSentiment, IndustrySentiment, PumpDumpScore, VelocityMetrics, EventFlag
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class TestHazelcastSink:
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"""Tests for HazelcastSink"""
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@pytest.fixture
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def sink(self):
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return HazelcastSink()
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@pytest.mark.asyncio
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async def test_publish_scores(self, sink):
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"""Test publishing scores to Hazelcast"""
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# This would require a running Hazelcast instance
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# For now, just verify the sink can be instantiated
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assert sink is not None
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def test_prepare_exf_data(self, sink):
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"""Test preparing ExF data structure"""
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from sentiment_engine.schemas.output import SentimentOutput, MarketSentiment
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from datetime import datetime
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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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top_pump_assets=["BTC", "ETH"],
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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=1234567890.0,
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total_sources=10,
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total_assets=50
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)
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output = SentimentOutput(
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timestamp=1234567890.0,
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market=market,
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assets={}
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)
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# Verify ACB signals extraction
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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 "fear_state" in acb
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assert "greed_state" in acb
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assert "hype_velocity" in acb
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class TestClickHouseSink:
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"""Tests for ClickHouseSink"""
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@pytest.fixture
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def sink(self):
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return ClickHouseSink()
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def test_buffer_raw_item(self, sink):
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"""Test buffering raw item for batch insert"""
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from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention, EngagementMetrics
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from datetime import datetime
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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=1234567890.0,
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publish_ts=1234567880.0,
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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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raw_text="Test article",
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title="Test",
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url="https://test.com",
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author="Test",
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content_length=100,
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language="en",
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metadata={}
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)
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# This should not raise an error
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sink.buffer_raw_item(payload)
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assert len(sink._batch_buffer) == 1
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def test_buffer_processed_item(self, sink):
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"""Test buffering processed item for batch insert"""
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from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, EventType, TemporalAnchor, CredibilityScore
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from datetime import datetime
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item = ProcessedItem(
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payload_id="test_1",
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source_id="test",
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source_type="news",
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ingest_ts=1234567890.0,
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publish_ts=1234567880.0,
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entities=[EntityExtraction(asset_id="BTC", mention_span=(0, 3), confidence=0.9, entity_type="ticker", canonical_name="BTC")],
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sentiment_per_asset={"BTC": SentimentScores(polarity=0.7, confidence=0.85, positive_prob=0.8, negative_prob=0.1, neutral_prob=0.1)},
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emotions_per_asset={"BTC": EmotionScores(joy=0.8, fear=0.1, anger=0.05, greed=0.7, sadness=0.05, intensity=0.75)},
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events=[EventClassification(event_type=EventType.LISTING, confidence=0.7, assets_involved=["BTC"], key_details={}, severity=0.5)],
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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=0.8, 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=1234567895.0,
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processing_latency_ms=45.2,
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model_versions={}
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)
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sink.buffer_processed_item(item)
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assert len(sink._batch_buffer) == 1
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def test_buffer_score_output(self, sink):
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"""Test buffering scored output"""
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from sentiment_engine.schemas.output import SentimentOutput, MarketSentiment, AssetSentiment, PumpDumpScore, VelocityMetrics
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import time
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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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top_pump_assets=["BTC", "ETH"],
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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=time.time(),
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total_sources=10,
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total_assets=50
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)
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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=time.time()),
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last_update_ts=time.time()
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)
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output = SentimentOutput(
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timestamp=time.time(),
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market=market,
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assets={"BTC": asset}
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)
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sink.buffer_score_output(output)
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assert len(sink._batch_buffer) >= 2 # asset + market
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