Add S/R v2 research and validation harness

tests/unit/test_indicator_service.py

@@ -164,6 +164,34 @@ class TestComputeVolumeProfile:
         with pytest.raises(ValidationError, match="Volume Profile requires"):
             compute_volume_profile(highs, lows, closes, volumes)
 
+    def test_close_bin_volume_no_double_count(self):
+        """Each bar's volume is counted once (close bin), not per span."""
+        # Wide bars that would span many bins under the old algorithm
+        n = 25
+        closes = [100.0 + (i % 5) for i in range(n)]
+        highs = [c + 20 for c in closes]  # wide range
+        lows = [c - 20 for c in closes]
+        volumes = [1000] * n
+        result = compute_volume_profile(highs, lows, closes, volumes, num_bins=20)
+        # Binned total equals true volume (close-bin assignment)
+        # We only expose poc/hvn; reconstruct by checking score fields exist
+        assert result["poc"] > 0
+        # With volume concentrated on a few close prices, HVNs should be few local peaks
+        assert len(result["hvn"]) < 20
+
+    def test_hvn_are_local_peaks_not_all_above_mean(self):
+        """HVN should be local histogram peaks, not every above-mean bin."""
+        # Two clusters of closes → two volume peaks
+        closes = [80.0] * 10 + [120.0] * 10 + [100.0] * 5
+        highs = [c + 1 for c in closes]
+        lows = [c - 1 for c in closes]
+        volumes = [1000] * len(closes)
+        result = compute_volume_profile(highs, lows, closes, volumes, num_bins=20)
+        # At most a handful of local peaks (not ~half of 20 bins)
+        assert len(result["hvn"]) <= 6
+        # POC should land near one of the high-volume clusters
+        assert result["poc"] < 95 or result["poc"] > 105
+
 
 # ---------------------------------------------------------------------------
 # Pivot Points
@@ -184,6 +212,26 @@ class TestComputePivotPoints:
         with pytest.raises(ValidationError, match="Pivot Points requires"):
             compute_pivot_points([1, 2], [0, 1], [0.5, 1.5])
 
+    def test_prominence_filters_tiny_swings(self):
+        # Mix of a large swing (depth ~10) and tiny fractal noise (depth ~1)
+        closes = [
+            10, 10.2, 10.5, 10.2, 10,   # tiny high around idx 2
+            10, 15, 20, 15, 10,         # large high around idx 7
+            10, 10.3, 10.6, 10.3, 10,   # tiny high around idx 12
+        ]
+        highs = list(closes)
+        lows = [c - 0.5 for c in closes]
+        highs[2] = 10.8
+        highs[7] = 20.5
+        highs[12] = 10.9
+        lows[7] = 10.0  # large window range at major swing
+        unfiltered = compute_pivot_points(highs, lows, closes, min_prominence=None)
+        filtered = compute_pivot_points(highs, lows, closes, min_prominence=5.0)
+        assert unfiltered["pivot_count"] > 0
+        assert filtered["pivot_count"] < unfiltered["pivot_count"]
+        # Major swing high should survive
+        assert any(h >= 20.0 for h in filtered["swing_highs"])
+
 
 # ---------------------------------------------------------------------------
 # EMA Cross