sentiment-engine/sentiment_engine/tests/conftest.py

"""Pytest configuration and fixtures"""

import asyncio
import pytest
from unittest.mock import AsyncMock, MagicMock

import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))

from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention, EngagementMetrics
from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, TemporalAnchor, CredibilityScore, EventType
from sentiment_engine.schemas.output import AssetSentiment, MarketSentiment, IndustrySentiment, SentimentOutput, PumpDumpScore, VelocityMetrics, EventFlag


@pytest.fixture
def event_loop():
    """Create event loop for async tests"""
    loop = asyncio.new_event_loop()
    yield loop
    loop.close()


@pytest.fixture
def sample_payload():
    """Sample normalized payload"""
    return NormalizedPayload(
        source_id="rss:coindesk.com",
        source_type=SourceType.NEWS,
        source_credibility_base=0.85,
        ingest_ts=1724262300.0,
        publish_ts=1724262200.0,
        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=(10, 13), confidence=0.9, source_text="ETH", mention_type="ticker"),
        ],
        raw_text="BTC surges to $65K as ETH follows with strong momentum. Market sentiment turns bullish.",
        title="Bitcoin Surges to $65K",
        url="https://coindesk.com/btc-surges",
        author="John Doe",
        engagement_metrics=EngagementMetrics(retweets=100, likes=500, replies=50),
        content_length=200,
        language="en",
        metadata={"feed_url": "https://coindesk.com/feed"}
    )


@pytest.fixture
def sample_processed_item(sample_payload):
    """Sample processed item"""
    return ProcessedItem(
        payload_id="test_payload_1",
        source_id=sample_payload.source_id,
        source_type=sample_payload.source_type.value,
        ingest_ts=sample_payload.ingest_ts,
        publish_ts=sample_payload.publish_ts,
        entities=[
            EntityExtraction(asset_id="BTC", mention_span=(0, 3), confidence=0.9, entity_type="ticker", canonical_name="BTC"),
            EntityExtraction(asset_id="ETH", mention_span=(10, 13), confidence=0.9, entity_type="ticker", canonical_name="ETH"),
        ],
        sentiment_per_asset={
            "BTC": SentimentScores(polarity=0.7, confidence=0.85, positive_prob=0.8, negative_prob=0.1, neutral_prob=0.1),
            "ETH": SentimentScores(polarity=0.5, confidence=0.8, positive_prob=0.7, negative_prob=0.15, neutral_prob=0.15),
        },
        emotions_per_asset={
            "BTC": EmotionScores(joy=0.8, fear=0.1, anger=0.05, greed=0.7, sadness=0.05, intensity=0.75),
            "ETH": EmotionScores(joy=0.6, fear=0.15, anger=0.05, greed=0.5, sadness=0.05, intensity=0.6),
        },
        events=[
            EventClassification(event_type=EventType.LISTING, confidence=0.7, assets_involved=["BTC"], key_details={}, severity=0.5),
        ],
        temporal=TemporalAnchor(event_time=None, time_horizon="immediate", is_breaking=True, is_scheduled=False),
        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),
        processed_ts=1724262305.0,
        processing_latency_ms=45.2,
        model_versions={"finbert": "1.0", "gemma": "3-4b"}
    )


@pytest.fixture
def sample_asset_sentiment():
    """Sample asset sentiment output"""
    return AssetSentiment(
        asset_id="BTC",
        fear_state=20.0,
        greed_state=80.0,
        sentiment_polarity=60.0,
        emotion_profile={"joy": 0.8, "fear": 0.1, "anger": 0.05, "greed": 0.7, "sadness": 0.05, "intensity": 0.75},
        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),
        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)],
        velocity=VelocityMetrics(hype_velocity=0.7, pub_velocity=0.5, velocity_direction="accelerating", window_minutes=15, source_count=3, unique_assets=1),
        last_update_ts=1724262305.0,
        contributing_sources=3,
        decay_factor=0.95
    )


@pytest.fixture
def sample_market_sentiment(sample_asset_sentiment):
    """Sample market sentiment output"""
    return MarketSentiment(
        fear_state=25.0,
        greed_state=75.0,
        sentiment_index=50.0,
        hype_velocity=65.0,
        pub_velocity=55.0,
        aggregate_pump_risk=75.0,
        aggregate_dump_risk=20.0,
        top_pump_assets=["BTC", "ETH", "SOL"],
        top_dump_assets=[],
        dominant_events=[],
        industry_breakdown={},
        last_update_ts=1724262305.0,
        total_sources=10,
        total_assets=50
    )


@pytest.fixture
def sample_sentiment_output(sample_market_sentiment, sample_asset_sentiment):
    """Complete sentiment output"""
    industry = IndustrySentiment(
        industry="Smart Contract Platform",
        assets=["BTC", "ETH"],
        fear_state=22.0,
        greed_state=78.0,
        avg_polarity=55.0,
        pump_risk=75.0,
        dump_risk=15.0,
        dominant_events=[],
        asset_count=2,
        last_update_ts=1724262305.0
    )

    return SentimentOutput(
        timestamp=1724262305.0,
        market=sample_market_sentiment,
        industries={"Smart Contract Platform": industry},
        assets={"BTC": sample_asset_sentiment, "ETH": sample_asset_sentiment}
    )