Integrate unused indicators into technical scoring; fix indicator dropdown

- Technical dimension now uses all directional indicators:
  0.30*ADX + 0.20*EMA + 0.20*RSI + 0.15*EMA_Cross (bullish=80 /
  neutral=50 / bearish=20) + 0.10*Volume_Profile (POC proximity)
  + 0.05*Pivot_Points (structure confluence); weights re-normalize
  when data is insufficient, as before
- ATR stays out of scoring (volatility input for scanner stops,
  not a directional signal)
- IndicatorSelector uses the shared Select so the option list is
  dark instead of the native white popup
- Update technical scoring tests for the six-component breakdown

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

app/services/scoring_service.py

@@ -88,7 +88,8 @@ async def _save_weights(db: AsyncSession, weights: dict[str, float]) -> None:
 async def _compute_technical_score(
     db: AsyncSession, symbol: str
 ) -> tuple[float | None, dict | None]:
-    """Compute technical dimension score from ADX, EMA, RSI.
+    """Compute technical dimension score from ADX, EMA, RSI, EMA Cross,
+    Volume Profile and Pivot Points.
 
     Returns (score, breakdown) where breakdown follows the ScoreBreakdown
     TypedDict shape: {sub_scores, formula, unavailable}.
@@ -96,7 +97,10 @@ async def _compute_technical_score(
     from app.services.indicator_service import (
         compute_adx,
         compute_ema,
+        compute_ema_cross,
+        compute_pivot_points,
         compute_rsi,
+        compute_volume_profile,
         _extract_ohlcv,
     )
     from app.services.price_service import query_ohlcv
@@ -105,27 +109,33 @@ async def _compute_technical_score(
     if not records:
         return None, None
 
-    _, highs, lows, closes, _ = _extract_ohlcv(records)
+    _, highs, lows, closes, volumes = _extract_ohlcv(records)
+
+    formula = (
+        "Weighted average: 0.30*ADX + 0.20*EMA + 0.20*RSI + 0.15*EMA_Cross "
+        "+ 0.10*Volume_Profile + 0.05*Pivot_Points, re-normalized if any "
+        "sub-score unavailable."
+    )
 
     scores: list[tuple[float, float]] = []  # (weight, score)
     sub_scores: list[dict] = []
     unavailable: list[dict[str, str]] = []
 
-    # ADX (weight 0.4) — needs 28+ bars
+    # ADX (weight 0.30) — needs 28+ bars
     try:
         adx_result = compute_adx(highs, lows, closes)
-        scores.append((0.4, adx_result["score"]))
+        scores.append((0.30, adx_result["score"]))
         sub_scores.append({
             "name": "ADX",
             "score": adx_result["score"],
-            "weight": 0.4,
+            "weight": 0.30,
             "raw_value": adx_result["adx"],
             "description": "ADX value (0-100). Higher = stronger trend.",
         })
     except Exception as exc:
         unavailable.append({"name": "ADX", "reason": str(exc) or "Insufficient data for ADX"})
 
-    # EMA (weight 0.3) — needs period+1 bars
+    # EMA (weight 0.20) — needs period+1 bars
     try:
         ema_result = compute_ema(closes)
         pct_diff = (
@@ -138,35 +148,79 @@ async def _compute_technical_score(
             if ema_result["ema"] != 0
             else 0.0
         )
-        scores.append((0.3, ema_result["score"]))
+        scores.append((0.20, ema_result["score"]))
         sub_scores.append({
             "name": "EMA",
             "score": ema_result["score"],
-            "weight": 0.3,
+            "weight": 0.20,
             "raw_value": pct_diff,
             "description": f"Price {pct_diff}% {'above' if pct_diff >= 0 else 'below'} EMA(20). Score: 50 + pct_diff * 10.",
         })
     except Exception as exc:
         unavailable.append({"name": "EMA", "reason": str(exc) or "Insufficient data for EMA"})
 
-    # RSI (weight 0.3) — needs 15+ bars
+    # RSI (weight 0.20) — needs 15+ bars
     try:
         rsi_result = compute_rsi(closes)
-        scores.append((0.3, rsi_result["score"]))
+        scores.append((0.20, rsi_result["score"]))
         sub_scores.append({
             "name": "RSI",
             "score": rsi_result["score"],
-            "weight": 0.3,
+            "weight": 0.20,
             "raw_value": rsi_result["rsi"],
             "description": "RSI(14) value. Score equals RSI.",
         })
     except Exception as exc:
         unavailable.append({"name": "RSI", "reason": str(exc) or "Insufficient data for RSI"})
 
+    # EMA Cross (weight 0.15) — needs 51+ bars. Directional trend signal.
+    try:
+        cross_result = compute_ema_cross(closes)
+        cross_score = {"bullish": 80.0, "neutral": 50.0, "bearish": 20.0}[cross_result["signal"]]
+        scores.append((0.15, cross_score))
+        sub_scores.append({
+            "name": "EMA_Cross",
+            "score": cross_score,
+            "weight": 0.15,
+            "raw_value": cross_result["signal"],
+            "description": "EMA(20) vs EMA(50): bullish=80, neutral=50, bearish=20.",
+        })
+    except Exception as exc:
+        unavailable.append({"name": "EMA_Cross", "reason": str(exc) or "Insufficient data for EMA Cross"})
+
+    # Volume Profile (weight 0.10) — needs 20+ bars. Price near POC = trading
+    # at accepted value; far from POC = extended.
+    try:
+        vp_result = compute_volume_profile(highs, lows, closes, volumes)
+        scores.append((0.10, vp_result["score"]))
+        sub_scores.append({
+            "name": "Volume_Profile",
+            "score": vp_result["score"],
+            "weight": 0.10,
+            "raw_value": vp_result["poc"],
+            "description": "Proximity of price to the Point of Control (volume-accepted value).",
+        })
+    except Exception as exc:
+        unavailable.append({"name": "Volume_Profile", "reason": str(exc) or "Insufficient data for Volume Profile"})
+
+    # Pivot Points (weight 0.05) — needs 5+ bars. Price near swing structure.
+    try:
+        pivot_result = compute_pivot_points(highs, lows, closes)
+        scores.append((0.05, pivot_result["score"]))
+        sub_scores.append({
+            "name": "Pivot_Points",
+            "score": pivot_result["score"],
+            "weight": 0.05,
+            "raw_value": pivot_result["pivot_count"],
+            "description": "Share of swing pivots within 2% of price (structure confluence).",
+        })
+    except Exception as exc:
+        unavailable.append({"name": "Pivot_Points", "reason": str(exc) or "Insufficient data for Pivot Points"})
+
     if not scores:
         breakdown: dict = {
             "sub_scores": [],
-            "formula": "Weighted average: 0.4*ADX + 0.3*EMA + 0.3*RSI, re-normalized if any sub-score unavailable.",
+            "formula": formula,
             "unavailable": unavailable,
         }
         return None, breakdown
@@ -179,7 +233,7 @@ async def _compute_technical_score(
 
     breakdown = {
         "sub_scores": sub_scores,
-        "formula": "Weighted average: 0.4*ADX + 0.3*EMA + 0.3*RSI, re-normalized if any sub-score unavailable.",
+        "formula": formula,
         "unavailable": unavailable,
     }
 

frontend/src/components/ticker/IndicatorSelector.tsx

@@ -1,6 +1,7 @@
 import { useState } from 'react';
 import { useQuery } from '@tanstack/react-query';
 import { getIndicator, getEMACross } from '../../api/indicators';
+import { Select } from '../ui/Field';
 import type { IndicatorResult, EMACrossResult } from '../../lib/types';
 
 const INDICATOR_TYPES = ['ADX', 'EMA', 'RSI', 'ATR', 'volume_profile', 'pivot_points'] as const;
@@ -85,16 +86,16 @@ export function IndicatorSelector({ symbol }: IndicatorSelectorProps) {
       <h3 className="mb-3 text-xs font-medium uppercase tracking-widest text-gray-500">Indicators</h3>
 
       <div className="mb-4">
-        <select
+        <Select
           value={selectedType}
           onChange={(e) => setSelectedType(e.target.value)}
-          className="w-full input-glass px-3 py-2.5 text-sm"
+          className="w-full !py-2.5"
         >
           <option value="">Select indicator…</option>
           {INDICATOR_TYPES.map((type) => (
             <option key={type} value={type}>{type}</option>
           ))}
-        </select>
+        </Select>
       </div>
 
       {selectedType && indicatorQuery.isLoading && (

tests/unit/test_scoring_service_technical.py

@@ -44,8 +44,9 @@ async def test_returns_none_tuple_when_no_records(db_session):
 
 @pytest.mark.asyncio
 async def test_returns_breakdown_with_all_sub_scores(db_session):
-    """With enough data, returns score and breakdown with ADX, EMA, RSI sub-scores."""
+    """With enough data, returns score and breakdown with all six sub-scores."""
-    records = _make_ohlcv_records(50)
+    # 60 bars: enough for every indicator incl. EMA Cross (needs 51)
+    records = _make_ohlcv_records(60)
 
     with patch(
         "app.services.price_service.query_ohlcv",
@@ -66,17 +67,27 @@ async def test_returns_breakdown_with_all_sub_scores(db_session):
     assert "ADX" in names
     assert "EMA" in names
     assert "RSI" in names
+    assert "EMA_Cross" in names
+    assert "Volume_Profile" in names
+    assert "Pivot_Points" in names
 
-    # Verify weights
+    # Verify weights sum to 1 and match the formula
     weight_map = {s["name"]: s["weight"] for s in breakdown["sub_scores"]}
-    assert weight_map["ADX"] == 0.4
+    assert weight_map["ADX"] == 0.30
-    assert weight_map["EMA"] == 0.3
+    assert weight_map["EMA"] == 0.20
-    assert weight_map["RSI"] == 0.3
+    assert weight_map["RSI"] == 0.20
+    assert weight_map["EMA_Cross"] == 0.15
+    assert weight_map["Volume_Profile"] == 0.10
+    assert weight_map["Pivot_Points"] == 0.05
+    assert sum(weight_map.values()) == pytest.approx(1.0)
 
-    # Verify raw_value is present and numeric
+    # Verify raw_value is present (numeric except EMA_Cross's signal string)
     for sub in breakdown["sub_scores"]:
         assert sub["raw_value"] is not None
-        assert isinstance(sub["raw_value"], (int, float))
+        if sub["name"] == "EMA_Cross":
+            assert sub["raw_value"] in ("bullish", "neutral", "bearish")
+        else:
+            assert isinstance(sub["raw_value"], (int, float))
         assert sub["description"]
 
     assert breakdown["unavailable"] == []
@@ -112,8 +123,8 @@ async def test_partial_sub_scores_with_insufficient_data(db_session):
 @pytest.mark.asyncio
 async def test_all_sub_scores_unavailable(db_session):
     """With very few bars (not enough for any indicator), returns None score with breakdown."""
-    # 5 bars: not enough for any indicator
+    # 4 bars: not enough for any indicator (Pivot Points needs 5+)
-    records = _make_ohlcv_records(5)
+    records = _make_ohlcv_records(4)
 
     with patch(
         "app.services.price_service.query_ohlcv",
@@ -125,12 +136,15 @@ async def test_all_sub_scores_unavailable(db_session):
     assert score is None
     assert breakdown is not None
     assert breakdown["sub_scores"] == []
-    assert len(breakdown["unavailable"]) == 3
+    assert len(breakdown["unavailable"]) == 6
 
     unavail_names = [u["name"] for u in breakdown["unavailable"]]
     assert "ADX" in unavail_names
     assert "EMA" in unavail_names
     assert "RSI" in unavail_names
+    assert "EMA_Cross" in unavail_names
+    assert "Volume_Profile" in unavail_names
+    assert "Pivot_Points" in unavail_names
 
 
 @pytest.mark.asyncio