Big refactoring

tests/unit/test_recommendation_service.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from app.services.recommendation_service import (
+    direction_analyzer,
+    probability_estimator,
+    signal_conflict_detector,
+    target_generator,
+)
+
+
+@dataclass
+class _SRLevelStub:
+    id: int
+    price_level: float
+    type: str
+    strength: int
+
+
+def test_high_confidence_long_example():
+    dimension_scores = {
+        "technical": 75.0,
+        "momentum": 68.0,
+        "fundamental": 55.0,
+    }
+
+    confidence = direction_analyzer.calculate_confidence(
+        direction="long",
+        dimension_scores=dimension_scores,
+        sentiment_classification="bullish",
+        conflicts=[],
+    )
+
+    assert confidence > 70.0
+
+
+def test_high_confidence_short_example():
+    dimension_scores = {
+        "technical": 30.0,
+        "momentum": 35.0,
+        "fundamental": 45.0,
+    }
+
+    confidence = direction_analyzer.calculate_confidence(
+        direction="short",
+        dimension_scores=dimension_scores,
+        sentiment_classification="bearish",
+        conflicts=[],
+    )
+
+    assert confidence > 70.0
+
+
+def test_detects_sentiment_technical_conflict():
+    conflicts = signal_conflict_detector.detect_conflicts(
+        dimension_scores={"technical": 72.0, "momentum": 55.0, "fundamental": 50.0},
+        sentiment_classification="bearish",
+    )
+
+    assert any("sentiment-technical" in conflict for conflict in conflicts)
+
+
+def test_generate_targets_respects_direction_and_order():
+    sr_levels = [
+        _SRLevelStub(id=1, price_level=110.0, type="resistance", strength=80),
+        _SRLevelStub(id=2, price_level=115.0, type="resistance", strength=70),
+        _SRLevelStub(id=3, price_level=120.0, type="resistance", strength=60),
+        _SRLevelStub(id=4, price_level=95.0, type="support", strength=75),
+    ]
+
+    targets = target_generator.generate_targets(
+        direction="long",
+        entry_price=100.0,
+        stop_loss=96.0,
+        sr_levels=sr_levels,  # type: ignore[arg-type]
+        atr_value=2.0,
+    )
+
+    assert len(targets) >= 1
+    assert all(target["price"] > 100.0 for target in targets)
+    distances = [target["distance_from_entry"] for target in targets]
+    assert distances == sorted(distances)
+
+
+def test_probability_ranges_by_classification():
+    config = {
+        "recommendation_signal_alignment_weight": 0.15,
+        "recommendation_sr_strength_weight": 0.20,
+        "recommendation_distance_penalty_factor": 0.10,
+    }
+    dimension_scores = {"technical": 70.0, "momentum": 70.0}
+
+    conservative = probability_estimator.estimate_probability(
+        {
+            "classification": "Conservative",
+            "sr_strength": 80,
+            "distance_atr_multiple": 1.5,
+        },
+        dimension_scores,
+        "bullish",
+        "long",
+        config,
+    )
+    moderate = probability_estimator.estimate_probability(
+        {
+            "classification": "Moderate",
+            "sr_strength": 60,
+            "distance_atr_multiple": 3.0,
+        },
+        dimension_scores,
+        "bullish",
+        "long",
+        config,
+    )
+    aggressive = probability_estimator.estimate_probability(
+        {
+            "classification": "Aggressive",
+            "sr_strength": 40,
+            "distance_atr_multiple": 6.0,
+        },
+        dimension_scores,
+        "bullish",
+        "long",
+        config,
+    )
+
+    assert conservative > 60
+    assert 40 <= moderate <= 70
+    assert aggressive < 50