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.

3 files changed, 181 insertions, 0 deletions

diff --git a/shared/src/shared/models.py b/shared/src/shared/models.py
index 70820b5..a436c03 100644
--- a/shared/src/shared/models.py
+++ b/shared/src/shared/models.py
@@ -74,3 +74,26 @@ class Position(BaseModel):
     @property
     def unrealized_pnl(self) -> Decimal:
         return self.quantity * (self.current_price - self.avg_entry_price)
+
+
+class NewsCategory(str, Enum):
+    POLICY = "policy"
+    EARNINGS = "earnings"
+    MACRO = "macro"
+    SOCIAL = "social"
+    FILING = "filing"
+    FED = "fed"
+
+
+class NewsItem(BaseModel):
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
+    source: str
+    headline: str
+    summary: Optional[str] = None
+    url: Optional[str] = None
+    published_at: datetime
+    symbols: list[str] = []
+    sentiment: float
+    category: NewsCategory
+    raw_data: dict = {}
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
diff --git a/shared/src/shared/sentiment_models.py b/shared/src/shared/sentiment_models.py
new file mode 100644
index 0000000..a009601
--- /dev/null
+++ b/shared/src/shared/sentiment_models.py
@@ -0,0 +1,44 @@
+"""Sentiment scoring and stock selection models."""
+
+from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel
+
+from shared.models import OrderSide
+
+
+class SymbolScore(BaseModel):
+    symbol: str
+    news_score: float
+    news_count: int
+    social_score: float
+    policy_score: float
+    filing_score: float
+    composite: float
+    updated_at: datetime
+
+
+class MarketSentiment(BaseModel):
+    fear_greed: int
+    fear_greed_label: str
+    vix: Optional[float] = None
+    fed_stance: str
+    market_regime: str
+    updated_at: datetime
+
+
+class SelectedStock(BaseModel):
+    symbol: str
+    side: OrderSide
+    conviction: float
+    reason: str
+    key_news: list[str]
+
+
+class Candidate(BaseModel):
+    symbol: str
+    source: str
+    direction: Optional[OrderSide] = None
+    score: float
+    reason: str
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
@@ -0,0 +1,114 @@
+"""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