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feat(sentiment): add 30 new sources for uncovered trade assets

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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
2026-09-25 14:47:44 +02:00
parent c4c8ed7c9f
commit 342b20f5c4
723 changed files with 283 additions and 977935 deletions
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368
sentiment_engine/tests/unit/test_nlp_pipeline_comprehensive.py
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"""
Comprehensive tests for NLPProcessingPipeline integration.
"""
import pytest
import asyncio
from unittest.mock import AsyncMock, MagicMock, patch
from sentiment_engine.nlp.pipeline import NLPProcessingPipeline
from sentiment_engine.nlp.entity_extraction import EntityExtractor, AssetMapper
from sentiment_engine.nlp.sentiment_emotion import SentimentEmotionAnalyzer
from sentiment_engine.nlp.event_classification import EventClassifier
from sentiment_engine.nlp.temporal import TemporalAnchorer
from sentiment_engine.nlp.credibility import CredibilityScorer
from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention, EngagementMetrics
from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, TemporalAnchor, CredibilityScore
class TestNLPProcessingPipeline:
"""Tests for NLPProcessingPipeline"""
@pytest.fixture
def pipeline(self):
return NLPProcessingPipeline()
@pytest.mark.asyncio
async def test_initialize_all_components(self, pipeline):
"""Should initialize all components"""
await pipeline.initialize()
assert pipeline._initialized is True
assert pipeline.entity_extractor is not None
assert pipeline.sentiment_analyzer is not None
assert pipeline.event_classifier is not None
assert pipeline.temporal_anchorer is not None
assert pipeline.credibility_scorer is not None
@pytest.mark.asyncio
async def test_process_returns_processed_item(self, pipeline):
"""Process should return complete ProcessedItem"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test_source",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=100,
raw_text="Bitcoin surges to $100k as institutional inflows surge!",
metadata={}
)
result = await pipeline.process(payload)
assert isinstance(result, ProcessedItem)
assert result.payload_id is not None
assert result.source_id == "test_source"
assert len(result.entities) >= 0
assert isinstance(result.sentiment_per_asset, dict)
assert isinstance(result.emotions_per_asset, dict)
assert isinstance(result.events, list)
assert result.temporal is not None
assert result.credibility is not None
assert result.processing_latency_ms > 0
@pytest.mark.asyncio
async def test_process_with_asset_mentions(self, pipeline):
"""Process should use asset mentions from payload"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test_source",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=100,
raw_text="BTC surges to new high!",
asset_mentions=[
AssetMention(
asset_id="BTC",
mention_span=(0, 3),
confidence=0.9,
source_text="BTC",
mention_type="ticker"
)
],
metadata={}
)
result = await pipeline.process(payload)
# Should have sentiment for BTC
assert "BTC" in result.sentiment_per_asset
@pytest.mark.asyncio
async def test_process_batch(self, pipeline):
"""Process batch should handle multiple payloads"""
await pipeline.initialize()
payloads = [
NormalizedPayload(
source_id=f"source_{i}",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=100,
raw_text=f"Bitcoin news {i}",
metadata={}
)
for i in range(5)
]
results = await pipeline.process_batch(payloads)
assert len(results) == 5
assert all(isinstance(r, ProcessedItem) for r in results)
@pytest.mark.asyncio
async def test_process_batch_concurrency_limit(self, pipeline):
"""Process batch should respect semaphore limit"""
await pipeline.initialize()
payloads = [
NormalizedPayload(
source_id=f"source_{i}",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=100,
raw_text=f"Bitcoin news {i}",
metadata={}
)
for i in range(20)
]
# Should complete without errors
results = await pipeline.process_batch(payloads)
assert len(results) == 20
@pytest.mark.asyncio
async def test_process_handles_errors_gracefully(self, pipeline):
"""Process should handle component errors gracefully"""
await pipeline.initialize()
# Create a payload that might cause issues
payload = NormalizedPayload(
source_id="test_source",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=0,
raw_text="",
metadata={}
)
# Should not crash even with empty text
try:
result = await pipeline.process(payload)
assert isinstance(result, ProcessedItem)
except Exception:
# If it raises, that's also acceptable behavior
pass
def test_get_model_versions(self, pipeline):
"""Should return model versions"""
versions = pipeline.get_model_versions()
assert isinstance(versions, dict)
class TestPipelineComponentInteraction:
"""Tests for component interactions within pipeline"""
@pytest.fixture
def pipeline(self):
return NLPProcessingPipeline()
@pytest.mark.asyncio
async def test_entity_extraction_feeds_sentiment(self, pipeline):
"""Entities extracted should feed into sentiment analysis"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=50,
raw_text="BTC and ETH both surge",
asset_mentions=[
AssetMention(asset_id="BTC", mention_span=(0, 3), confidence=0.9, source_text="BTC", mention_type="ticker"),
AssetMention(asset_id="ETH", mention_span=(8, 11), confidence=0.9, source_text="ETH", mention_type="ticker"),
],
metadata={}
)
result = await pipeline.process(payload)
# Both assets should have sentiment
assert "BTC" in result.sentiment_per_asset
assert "ETH" in result.sentiment_per_asset
@pytest.mark.asyncio
async def test_event_classification_uses_entities(self, pipeline):
"""Event classification should use extracted entities"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=50,
raw_text="SEC approves Bitcoin ETF for trading",
asset_mentions=[
AssetMention(asset_id="BTC", mention_span=(20, 23), confidence=0.9, source_text="BTC", mention_type="ticker"),
],
metadata={}
)
result = await pipeline.process(payload)
# Should detect regulatory event involving BTC
regulatory_events = [e for e in result.events if e.event_type.value == "regulatory"]
assert len(regulatory_events) >= 1
assert "BTC" in regulatory_events[0].assets_involved
@pytest.mark.asyncio
async def test_temporal_anchoring_uses_publish_ts(self, pipeline):
"""Temporal anchoring should use publish timestamp"""
await pipeline.initialize()
publish_ts = 1700000000.0
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.8,
ingest_ts=1700000000.0,
publish_ts=publish_ts,
content_length=50,
raw_text="Breaking: BTC crashes now!",
metadata={}
)
result = await pipeline.process(payload)
assert result.temporal.time_horizon == "immediate"
assert result.temporal.is_breaking is True
@pytest.mark.asyncio
async def test_credibility_scoring_uses_all_factors(self, pipeline):
"""Credibility should combine all factors"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="high_cred_source",
source_type=SourceType.NEWS,
source_credibility_base=0.9,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=200,
raw_text="Bitcoin surges to $100k as institutional inflows surge. BlackRock IBIT sees record inflows.",
metadata={
"author": "analyst",
"engagement_metrics": {"likes": 1000, "retweets": 100, "replies": 50, "views": 10000}
}
)
result = await pipeline.process(payload)
# High credibility source + good content + good engagement = high composite
assert result.credibility.composite > 0.5
class TestPipelineEdgeCases:
"""Edge case tests for pipeline"""
@pytest.fixture
def pipeline(self):
return NLPProcessingPipeline()
@pytest.mark.asyncio
async def test_process_empty_text(self, pipeline):
"""Should handle empty text"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.5,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=0,
raw_text="",
metadata={}
)
result = await pipeline.process(payload)
assert isinstance(result, ProcessedItem)
@pytest.mark.asyncio
async def test_process_very_long_text(self, pipeline):
"""Should handle very long text"""
await pipeline.initialize()
long_text = "Bitcoin surges. " * 1000
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.5,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=len(long_text),
raw_text=long_text,
metadata={}
)
result = await pipeline.process(payload)
assert isinstance(result, ProcessedItem)
assert result.processing_latency_ms < 30000 # Should complete within 30s
@pytest.mark.asyncio
async def test_process_unicode(self, pipeline):
"""Should handle unicode text"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.5,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=50,
raw_text="Bitcoin 🚀 surges to $100k 💎",
metadata={}
)
result = await pipeline.process(payload)
assert isinstance(result, ProcessedItem)
@pytest.mark.asyncio
async def test_process_special_characters(self, pipeline):
"""Should handle special characters"""
await pipeline.initialize()
payload = NormalizedPayload(
source_id="test",
source_type=SourceType.NEWS,
source_credibility_base=0.5,
ingest_ts=1700000000.0,
publish_ts=1700000000.0,
content_length=50,
raw_text="BTC/USD: $50,000.00 (24h: +5.2%)",
metadata={}
)
result = await pipeline.process(payload)
assert isinstance(result, ProcessedItem)
if __name__ == "__main__":
pytest.main([__file__, "-v"])