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%+
This commit is contained in:
Codex
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"""Tests for signal processing module"""
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import pytest
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import time
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from sentiment_engine.signal.velocity import VelocityComputer
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from sentiment_engine.signal.decay import TemporalDecay
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from sentiment_engine.signal.fusion import MultiSourceFusion
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from sentiment_engine.schemas.output import AssetSentiment, VelocityMetrics, PumpDumpScore, EventFlag
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class TestVelocityComputer:
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"""Tests for VelocityComputer"""
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@pytest.fixture
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def computer(self):
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return VelocityComputer()
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def test_insufficient_data(self, computer):
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"""Test with insufficient history"""
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velocity = computer.get_asset_velocity("BTC")
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assert velocity is None
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def test_velocity_direction_accelerating(self, computer):
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"""Test accelerating direction detection"""
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now = time.time()
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# Use compute method to properly initialize the window
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from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, TemporalAnchor, CredibilityScore, EventType
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for i in range(10):
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item = ProcessedItem(
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payload_id="test",
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source_id="test",
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source_type="news",
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ingest_ts=time.time() - 600 + i * 60,
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publish_ts=time.time() - 600 + i * 60,
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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=0.1 + i * 0.08)},
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events=[],
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temporal=TemporalAnchor(time_horizon="immediate", is_breaking=False, 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=time.time() - 600 + i * 60,
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processing_latency_ms=45.2,
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model_versions={}
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)
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computer.compute("BTC", item, 0.2, 0.8)
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velocity = computer.get_asset_velocity("BTC")
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assert velocity is not None
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assert velocity.velocity_direction == "accelerating"
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def test_velocity_direction_decelerating(self, computer):
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"""Test decelerating direction detection"""
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now = time.time()
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from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, TemporalAnchor, CredibilityScore, EventType
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for i in range(10):
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item = ProcessedItem(
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payload_id="test",
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source_id="test",
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source_type="news",
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ingest_ts=time.time() - 600 + i * 60,
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publish_ts=time.time() - 600 + i * 60,
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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=0.9 - i * 0.08)},
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events=[],
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temporal=TemporalAnchor(time_horizon="immediate", is_breaking=False, 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=time.time() - 600 + i * 60,
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processing_latency_ms=45.2,
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model_versions={}
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)
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computer.compute("BTC", item, 0.2, 0.8)
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velocity = computer.get_asset_velocity("BTC")
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assert velocity is not None
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assert velocity.velocity_direction == "decelerating"
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def test_pub_velocity(self, computer):
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"""Test publication velocity calculation"""
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now = time.time()
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from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, TemporalAnchor, CredibilityScore, EventType
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for i in range(5):
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item = ProcessedItem(
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payload_id="test",
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source_id=f"source_{i}",
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source_type="news",
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ingest_ts=time.time() - 300 + i * 60,
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publish_ts=time.time() - 300 + i * 60,
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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=0.5)},
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events=[],
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temporal=TemporalAnchor(time_horizon="immediate", is_breaking=False, 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=time.time() - 300 + i * 60,
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processing_latency_ms=45.2,
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model_versions={}
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)
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computer.compute("BTC", item, 0.2, 0.8)
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velocity = computer.get_asset_velocity("BTC")
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assert velocity is not None
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assert velocity.pub_velocity > 0
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assert velocity.source_count == 5
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class TestTemporalDecay:
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"""Tests for TemporalDecay"""
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@pytest.fixture
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def decay(self):
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return TemporalDecay()
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def test_decay_recent(self, decay):
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"""Test decay for recent timestamp"""
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now = time.time()
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factor = decay.compute(now)
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assert abs(factor - 1.0) < 1e-8
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def test_decay_half_life(self, decay):
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"""Test decay at half-life"""
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half_life = 180 * 60 # 180 minutes in seconds
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past = time.time() - half_life
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factor = decay.compute(past, halflife_minutes=180)
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assert 0.45 < factor < 0.55
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def test_decay_old(self, decay):
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"""Test decay for old timestamp"""
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past = time.time() - 24 * 3600 # 24 hours ago
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factor = decay.compute(past, halflife_minutes=180)
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assert factor < 0.01
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def test_apply_to_signal(self, decay):
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"""Test applying decay to a signal"""
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signal = 100.0
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past = time.time() - 180 * 60 # 1 half-life ago
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decayed = decay.apply_to_signal(signal, past, 180)
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assert 45 < decayed < 55
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def test_apply_to_asset_sentiment(self, decay):
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"""Test applying decay to AssetSentiment"""
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from sentiment_engine.schemas.output import AssetSentiment, PumpDumpScore, VelocityMetrics
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asset = AssetSentiment(
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asset_id="BTC",
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fear_state=50.0,
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greed_state=50.0,
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sentiment_polarity=0.0,
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pump_dump=PumpDumpScore(asset_id="BTC", pump_score=50.0, dump_score=50.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=time.time() - 180 * 60),
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velocity=VelocityMetrics(hype_velocity=0.5, pub_velocity=0.5, velocity_direction="neutral", window_minutes=15, source_count=1, unique_assets=1),
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last_update_ts=time.time() - 180 * 60, # 1 half-life ago
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contributing_sources=1,
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decay_factor=1.0
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)
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decay.apply_to_asset_sentiment(asset, {
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"fear_state": 180,
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"greed_state": 180,
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"pump_score": 180,
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"dump_score": 180,
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"hype_velocity": 60,
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"pub_velocity": 120,
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"event_flags": 480
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})
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assert abs(asset.fear_state - 25.0) < 0.001 # 50 * 0.5
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assert abs(asset.greed_state - 25.0) < 0.001
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assert abs(asset.pump_dump.pump_score - 25.0) < 0.02
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assert abs(asset.pump_dump.dump_score - 25.0) < 0.02
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class TestMultiSourceFusion:
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"""Tests for MultiSourceFusion"""
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@pytest.fixture
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def fusion(self):
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return MultiSourceFusion()
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def test_single_signal_no_fusion(self, fusion):
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"""Test single signal returns as-is"""
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now = time.time()
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signal = 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=now),
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last_update_ts=now,
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decay_factor=1.0
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)
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result = fusion.add_signal(signal)
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assert result is signal # Same object returned
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def test_two_signals_fusion(self, fusion):
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"""Test fusion of two signals"""
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now = time.time()
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signal1 = AssetSentiment(
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asset_id="BTC",
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fear_state=30.0,
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greed_state=70.0,
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sentiment_polarity=40.0,
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pump_dump=PumpDumpScore(asset_id="BTC", pump_score=60.0, dump_score=20.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=now),
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last_update_ts=now,
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decay_factor=1.0,
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contributing_sources=1
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)
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signal2 = AssetSentiment(
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asset_id="BTC",
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fear_state=10.0,
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greed_state=90.0,
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sentiment_polarity=80.0,
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pump_dump=PumpDumpScore(asset_id="BTC", pump_score=90.0, dump_score=10.0, pump_confidence=0.9, dump_confidence=0.6, last_update_ts=now),
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last_update_ts=now,
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decay_factor=1.0,
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contributing_sources=1
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)
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# Add first signal
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fusion.add_signal(signal1)
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# Add second signal - should trigger fusion
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result = fusion.add_signal(signal2)
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assert result is not signal1 and result is not signal2
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assert result.contributing_sources == 2
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# Fused values should be weighted average
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assert 10.0 < result.fear_state < 30.0
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assert 70.0 < result.greed_state < 90.0
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assert 60.0 < result.pump_dump.pump_score < 90.0
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def test_force_fuse_all(self, fusion):
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"""Test force fusion of all pending signals"""
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now = time.time()
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for i in range(3):
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signal = AssetSentiment(
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asset_id=f"ASSET{i}",
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fear_state=20.0 + i * 10,
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greed_state=80.0 - i * 10,
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sentiment_polarity=60.0 - i * 20,
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pump_dump=PumpDumpScore(asset_id=f"ASSET{i}", pump_score=50.0 + i * 10, dump_score=20.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=now),
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last_update_ts=now,
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decay_factor=1.0,
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contributing_sources=1
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)
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fusion.add_signal(signal)
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results = fusion.force_fuse_all()
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assert len(results) == 3
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for asset_id, signal in results.items():
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assert signal.contributing_sources == 1 # Each was single source
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