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/scoring_service.py

@@ -482,13 +482,22 @@ async def _compute_fundamental_score(
             "reason": "Earnings surprise data not available",
         })
 
+    # Require at least two real metrics — a single available metric (e.g. only
+    # market cap is free on FMP) does not make a meaningful fundamental score.
+    MIN_METRICS = 2
+    if len(scores) < MIN_METRICS:
+        unavailable.append({
+            "name": "insufficient_metrics",
+            "reason": f"Only {len(scores)} fundamental metric(s) available; need {MIN_METRICS}+ to score.",
+        })
+
     breakdown: dict = {
         "sub_scores": sub_scores,
         "formula": formula,
         "unavailable": unavailable,
     }
 
-    if not scores:
+    if len(scores) < MIN_METRICS:
         return None, breakdown
 
     return sum(scores) / len(scores), breakdown