feat: add NewsItem, sentiment scoring, and stock selection models

Add NewsCategory enum and NewsItem model to shared/models.py.
Create sentiment_models.py with SymbolScore, MarketSentiment, SelectedStock, Candidate.

1 files changed, 114 insertions, 0 deletions

diff --git a/shared/tests/test_sentiment_models.py b/shared/tests/test_sentiment_models.py
new file mode 100644
index 0000000..74f1acd
--- /dev/null
+++ b/shared/tests/test_sentiment_models.py
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+"""Tests for news and sentiment models."""
+
+import pytest
+from datetime import datetime, timezone
+
+from shared.models import NewsCategory, NewsItem, OrderSide
+from shared.sentiment_models import SymbolScore, MarketSentiment, SelectedStock, Candidate
+
+
+def test_news_item_defaults():
+    item = NewsItem(
+        source="finnhub",
+        headline="Test headline",
+        published_at=datetime(2026, 4, 2, tzinfo=timezone.utc),
+        sentiment=0.5,
+        category=NewsCategory.MACRO,
+    )
+    assert item.id
+    assert item.symbols == []
+    assert item.summary is None
+    assert item.raw_data == {}
+    assert item.created_at is not None
+
+
+def test_news_item_with_symbols():
+    item = NewsItem(
+        source="rss",
+        headline="AAPL earnings beat",
+        published_at=datetime(2026, 4, 2, tzinfo=timezone.utc),
+        sentiment=0.8,
+        category=NewsCategory.EARNINGS,
+        symbols=["AAPL"],
+    )
+    assert item.symbols == ["AAPL"]
+    assert item.category == NewsCategory.EARNINGS
+
+
+def test_news_category_values():
+    assert NewsCategory.POLICY == "policy"
+    assert NewsCategory.EARNINGS == "earnings"
+    assert NewsCategory.MACRO == "macro"
+    assert NewsCategory.SOCIAL == "social"
+    assert NewsCategory.FILING == "filing"
+    assert NewsCategory.FED == "fed"
+
+
+def test_symbol_score():
+    score = SymbolScore(
+        symbol="AAPL",
+        news_score=0.5,
+        news_count=10,
+        social_score=0.3,
+        policy_score=0.0,
+        filing_score=0.2,
+        composite=0.3,
+        updated_at=datetime(2026, 4, 2, tzinfo=timezone.utc),
+    )
+    assert score.symbol == "AAPL"
+    assert score.composite == 0.3
+
+
+def test_market_sentiment():
+    ms = MarketSentiment(
+        fear_greed=25,
+        fear_greed_label="Extreme Fear",
+        vix=32.5,
+        fed_stance="hawkish",
+        market_regime="risk_off",
+        updated_at=datetime(2026, 4, 2, tzinfo=timezone.utc),
+    )
+    assert ms.market_regime == "risk_off"
+    assert ms.vix == 32.5
+
+
+def test_market_sentiment_no_vix():
+    ms = MarketSentiment(
+        fear_greed=50,
+        fear_greed_label="Neutral",
+        fed_stance="neutral",
+        market_regime="neutral",
+        updated_at=datetime(2026, 4, 2, tzinfo=timezone.utc),
+    )
+    assert ms.vix is None
+
+
+def test_selected_stock():
+    ss = SelectedStock(
+        symbol="NVDA",
+        side=OrderSide.BUY,
+        conviction=0.85,
+        reason="CHIPS Act expansion",
+        key_news=["Trump signs CHIPS Act expansion"],
+    )
+    assert ss.conviction == 0.85
+    assert len(ss.key_news) == 1
+
+
+def test_candidate():
+    c = Candidate(
+        symbol="TSLA",
+        source="sentiment",
+        direction=OrderSide.BUY,
+        score=0.75,
+        reason="High social buzz",
+    )
+    assert c.direction == OrderSide.BUY
+
+    c2 = Candidate(
+        symbol="XOM",
+        source="llm",
+        score=0.6,
+        reason="Oil price surge",
+    )
+    assert c2.direction is None