Fix scoring/recommendation correctness and calibration

Triggered by CNC showing "LONG (High Confidence)" with SHORT reasoning
and no long setup.

- A: recommendation action + reasoning are ticker-level and identical
  on both setups; reasoning always matches the shown action
- B: recommended_action only picks a direction with a tradeable setup;
  strong bias with no setup (e.g. price at ATH) → NEUTRAL with an
  explanatory reason instead of a fake LONG_HIGH
- C: confidence is a directional-agreement model — opposing signals push
  it below 50 (SHORT on a 92-technical/99-momentum stock ~0%, not 55%)
- D: fundamental score requires >=2 real metrics (market-cap-only no
  longer yields a high score)
- E: RSI score peaks at healthy momentum (~60) and penalizes
  overbought/oversold extremes instead of treating RSI 90 as maximal
- F: fundamentals chain merges fields across providers (FMP market cap
  + Finnhub P/E) instead of stopping at the first with any field
- NEUTRAL label: "No Clear Setup" (covers untradeable-bias case)

Scores recompute on next scan/scoring run; C and E shift score
distributions intentionally.

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

app/services/indicator_service.py

@@ -156,11 +156,27 @@ def compute_ema(
     }
 
 
+def _rsi_to_score(rsi: float) -> float:
+    """Map RSI to a 'healthy momentum' score, penalizing extremes.
+
+    Raw RSI as a score treats 90 as maximally bullish, but RSI 90 is extreme
+    overbought (exhaustion/reversal risk), not a green light. This peaks around
+    RSI 60 (healthy uptrend) and falls off toward both ends, with the overbought
+    side penalized harder than oversold (oversold can mean-revert upward).
+    """
+    peak = 60.0
+    if rsi <= peak:
+        score = 90.0 - (peak - rsi) * 0.9   # 60→90, 30→63, 0→36
+    else:
+        score = 90.0 - (rsi - peak) * 1.6   # 60→90, 80→58, 90→42, 100→26
+    return max(0.0, min(100.0, score))
+
+
 def compute_rsi(
     closes: list[float],
     period: int = 14,
 ) -> dict[str, Any]:
-    """Compute RSI. Score = RSI value (already 0-100)."""
+    """Compute RSI. Score is a peaked mapping (see _rsi_to_score), not raw RSI."""
     n = len(closes)
     if n < period + 1:
         raise ValidationError(
@@ -184,12 +200,10 @@ def compute_rsi(
         rs = avg_gain / avg_loss
         rsi = 100.0 - 100.0 / (1.0 + rs)
 
-    score = max(0.0, min(100.0, rsi))
-
     return {
         "rsi": round(rsi, 4),
         "period": period,
-        "score": round(score, 4),
+        "score": round(_rsi_to_score(rsi), 4),
     }