feat: OpenBB Investment Analysis API

REST API wrapping OpenBB SDK for stock data, sentiment analysis,
technical indicators, macro data, and rule-based portfolio analysis.

- Stock data via yfinance (quote, profile, metrics, financials, historical, news)
- News sentiment via Alpha Vantage (per-article, per-ticker scores)
- Analyst data via Finnhub (recommendations, insider trades, upgrades)
- Macro data via FRED (Fed rate, CPI, GDP, unemployment, treasury yields)
- Technical indicators via openbb-technical (RSI, MACD, SMA, EMA, Bollinger)
- Rule-based portfolio analysis engine (BUY_MORE/HOLD/SELL)
- Stock discovery (gainers, losers, active, undervalued, growth)
- 102 tests, all passing

tests/test_analysis_service.py

@@ -0,0 +1,132 @@
+from models import ActionEnum, ConfidenceEnum
+from analysis_service import (
+    compute_analysis,
+    _score_pnl,
+    _score_pe,
+    _score_revenue_growth,
+    _score_target_price,
+)
+
+
+class TestScorePnl:
+    def test_large_loss_suggests_buy(self):
+        score, reason = _score_pnl(-0.25)
+        assert score == 1
+        assert "averaging down" in reason
+
+    def test_large_profit_suggests_sell(self):
+        score, reason = _score_pnl(0.60)
+        assert score == -1
+        assert "taking profit" in reason
+
+    def test_moderate_suggests_hold(self):
+        score, reason = _score_pnl(0.10)
+        assert score == 0
+
+
+class TestScorePe:
+    def test_low_pe(self):
+        score, _ = _score_pe(10.0)
+        assert score == 1
+
+    def test_high_pe(self):
+        score, _ = _score_pe(50.0)
+        assert score == -1
+
+    def test_normal_pe(self):
+        score, _ = _score_pe(20.0)
+        assert score == 0
+
+    def test_negative_pe(self):
+        score, _ = _score_pe(-5.0)
+        assert score == -1
+
+    def test_none_pe(self):
+        score, reason = _score_pe(None)
+        assert score == 0
+        assert reason is None
+
+
+class TestScoreRevenueGrowth:
+    def test_strong_growth(self):
+        score, _ = _score_revenue_growth(0.20)
+        assert score == 1
+
+    def test_negative_growth(self):
+        score, _ = _score_revenue_growth(-0.05)
+        assert score == -1
+
+    def test_moderate_growth(self):
+        score, _ = _score_revenue_growth(0.05)
+        assert score == 0
+
+    def test_none(self):
+        score, reason = _score_revenue_growth(None)
+        assert score == 0
+
+
+class TestScoreTargetPrice:
+    def test_big_upside(self):
+        score, _ = _score_target_price(100.0, 120.0)
+        assert score == 1
+
+    def test_big_downside(self):
+        score, _ = _score_target_price(100.0, 85.0)
+        assert score == -1
+
+    def test_near_target(self):
+        score, _ = _score_target_price(100.0, 105.0)
+        assert score == 0
+
+    def test_none_price(self):
+        score, _ = _score_target_price(None, 120.0)
+        assert score == 0
+
+
+class TestComputeAnalysis:
+    def test_strong_buy_signals(self):
+        result = compute_analysis(
+            current_price=100.0,
+            buy_in_price=130.0,  # loss > 20%
+            target_price=120.0,  # upside > 15%
+            metrics={"pe_ratio": 10.0, "revenue_growth": 0.20},
+        )
+        assert result.action == ActionEnum.BUY_MORE
+        assert result.confidence == ConfidenceEnum.HIGH
+
+    def test_strong_sell_signals(self):
+        result = compute_analysis(
+            current_price=200.0,
+            buy_in_price=100.0,  # profit > 50%
+            target_price=170.0,  # downside
+            metrics={"pe_ratio": 50.0, "revenue_growth": -0.10},
+        )
+        assert result.action == ActionEnum.SELL
+
+    def test_mixed_signals_hold(self):
+        result = compute_analysis(
+            current_price=100.0,
+            buy_in_price=95.0,
+            target_price=105.0,
+            metrics={"pe_ratio": 20.0, "revenue_growth": 0.05},
+        )
+        assert result.action == ActionEnum.HOLD
+
+    def test_no_data(self):
+        result = compute_analysis(
+            current_price=None,
+            buy_in_price=100.0,
+            target_price=None,
+            metrics={},
+        )
+        assert result.action == ActionEnum.HOLD
+        assert result.confidence == ConfidenceEnum.LOW
+
+    def test_reasons_populated(self):
+        result = compute_analysis(
+            current_price=100.0,
+            buy_in_price=90.0,
+            target_price=110.0,
+            metrics={"pe_ratio": 25.0},
+        )
+        assert len(result.reasons) > 0