feat: implement 3-stage stock selector (sentiment → technical → LLM)

Adds SentimentCandidateSource (DB scores), LLMCandidateSource (Claude
news analysis), and StockSelector orchestrating candidate merge,
RSI/EMA20/volume technical filter, and LLM final 2-3 pick selection
with Redis publish and DB persistence.

1 files changed, 78 insertions, 0 deletions

diff --git a/services/strategy-engine/tests/test_stock_selector.py b/services/strategy-engine/tests/test_stock_selector.py
new file mode 100644
index 0000000..a2f5bca
--- /dev/null
+++ b/services/strategy-engine/tests/test_stock_selector.py
@@ -0,0 +1,78 @@
+"""Tests for stock selector engine."""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from datetime import datetime, timezone
+from decimal import Decimal
+
+from shared.models import OrderSide
+from shared.sentiment_models import SymbolScore, MarketSentiment, SelectedStock, Candidate
+
+from strategy_engine.stock_selector import (
+    SentimentCandidateSource,
+    StockSelector,
+    _parse_llm_selections,
+)
+
+
+async def test_sentiment_candidate_source():
+    mock_db = MagicMock()
+    mock_db.get_top_symbol_scores = AsyncMock(return_value=[
+        {"symbol": "AAPL", "composite": 0.8, "news_count": 5},
+        {"symbol": "NVDA", "composite": 0.6, "news_count": 3},
+    ])
+
+    source = SentimentCandidateSource(mock_db)
+    candidates = await source.get_candidates()
+
+    assert len(candidates) == 2
+    assert candidates[0].symbol == "AAPL"
+    assert candidates[0].source == "sentiment"
+
+
+def test_parse_llm_selections_valid():
+    llm_response = """
+    [
+        {"symbol": "NVDA", "side": "BUY", "conviction": 0.85, "reason": "AI demand", "key_news": ["NVDA beats earnings"]},
+        {"symbol": "XOM", "side": "BUY", "conviction": 0.72, "reason": "Oil surge", "key_news": ["Oil prices up"]}
+    ]
+    """
+    selections = _parse_llm_selections(llm_response)
+    assert len(selections) == 2
+    assert selections[0].symbol == "NVDA"
+    assert selections[0].conviction == 0.85
+
+
+def test_parse_llm_selections_invalid():
+    selections = _parse_llm_selections("not json")
+    assert selections == []
+
+
+def test_parse_llm_selections_with_markdown():
+    llm_response = """
+    Here are my picks:
+    ```json
+    [
+        {"symbol": "TSLA", "side": "BUY", "conviction": 0.7, "reason": "Momentum", "key_news": ["Tesla rally"]}
+    ]
+    ```
+    """
+    selections = _parse_llm_selections(llm_response)
+    assert len(selections) == 1
+    assert selections[0].symbol == "TSLA"
+
+
+async def test_selector_blocks_on_risk_off():
+    mock_db = MagicMock()
+    mock_db.get_latest_market_sentiment = AsyncMock(return_value={
+        "fear_greed": 15,
+        "fear_greed_label": "Extreme Fear",
+        "vix": 35.0,
+        "fed_stance": "neutral",
+        "market_regime": "risk_off",
+        "updated_at": datetime.now(timezone.utc),
+    })
+
+    selector = StockSelector(db=mock_db, broker=MagicMock(), alpaca=MagicMock(), anthropic_api_key="test")
+    result = await selector.select()
+    assert result == []