Revert blue-sky projection; keep played-out setup UX

A local backtest (offline prod snapshot, 506 tickers) evaluated blue-sky
projected targets under the PRODUCTION exit (3x ATR trailing + 30d max hold,
paper_trade_service DEFAULT_EXIT_MODE="atr_trailing"). Blue-sky setups are
dilutive: the qualified book scored 328% return / Sharpe 1.84 / DD -21.0%
WITHOUT them vs 300% / 1.58 / -18.7% WITH them. They rank high on momentum by
construction, so they grab slots from S/R setups that catch bigger runs under
a trailing-stop exit (only ~2pp worse drawdown doesn't justify the lost return
and Sharpe).

Reverts the scanner/TargetGenerator measured-move projection, the stricter
projected activation gate, the frontend qualification mirror, the `projected`
type field, and the projected tests -- all backend files are now byte-identical
to the pre-blue-sky commit.

Keeps the played-out "No current setup" UX (RecommendationPanel): when price
has run past the target (played out) or through the stop (invalidated), the
panel shows a plain no-setup state instead of a stale actionable card. This is
frontend-only (reads last close + existing setup fields) and is what actually
fixes the reported stale-below-price bug -- no backend change or rescan needed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/services/rr_scanner_service.py

@@ -29,7 +29,6 @@ from app.models.trade_setup import TradeSetup
 from app.services.indicator_service import _extract_ohlcv, compute_atr
 from app.services.price_service import query_ohlcv
 from app.services.recommendation_service import (
-    PROJECTED_TARGET_ATR_MULTIPLE,
     _risk_level_from_conflicts,
     build_recommendation_snapshot,
     enhance_trade_setup,
@@ -68,16 +67,6 @@ def _compute_quality_score(
     return w_rr * norm_rr + w_strength * norm_strength + w_proximity * norm_proximity
 
 
-def _projected_target(direction: str, entry_price: float, atr_value: float) -> float:
-    """Measured-move target for a blue-sky direction (no overhead S/R).
-
-    Mirrors the projection in recommendation_service so the scanner's emission
-    decision and the enhanced target agree.
-    """
-    move = PROJECTED_TARGET_ATR_MULTIPLE * atr_value
-    return entry_price + move if direction == "long" else entry_price - move
-
-
 async def _get_dimension_scores(db: AsyncSession, ticker_id: int) -> dict[str, float]:
     result = await db.execute(
         select(DimensionScore).where(DimensionScore.ticker_id == ticker_id)
@@ -439,13 +428,13 @@ async def scan_ticker(
     now = datetime.now(timezone.utc)
     setups: list[TradeSetup] = []
 
-    stop = entry_price - (atr_value * atr_multiplier)
-    risk = entry_price - stop
-    if risk > 0:
-        best_candidate_rr = 0.0
-        best_candidate_target = 0.0
-        if levels_above:
+    if levels_above:
+        stop = entry_price - (atr_value * atr_multiplier)
+        risk = entry_price - stop
+        if risk > 0:
             best_quality = 0.0
+            best_candidate_rr = 0.0
+            best_candidate_target = 0.0
             for lv in levels_above:
                 reward = lv.price_level - entry_price
                 if reward <= 0:
@@ -459,29 +448,21 @@ async def scan_ticker(
                     best_quality = quality
                     best_candidate_rr = rr
                     best_candidate_target = lv.price_level
-        else:
-            # Blue-sky: no resistance overhead. Project a measured-move target so
-            # a breakout name still yields a setup (it faces a stricter gate).
-            projected = _projected_target("long", entry_price, atr_value)
-            projected_rr = (projected - entry_price) / risk
-            if projected_rr >= rr_threshold:
-                best_candidate_rr = projected_rr
-                best_candidate_target = projected
 
-        if best_candidate_rr > 0:
-            setups.append(TradeSetup(
-                ticker_id=ticker.id,
-                direction="long",
-                entry_price=round(entry_price, 4),
-                stop_loss=round(stop, 4),
-                target=round(best_candidate_target, 4),
-                rr_ratio=round(best_candidate_rr, 4),
-                composite_score=round(composite_score, 4),
-                detected_at=now,
-                momentum_percentile=momentum_percentile,
-                strategy_rank=strategy_rank,
-                volatility_percentile=volatility_percentile,
-            ))
+            if best_candidate_rr > 0:
+                setups.append(TradeSetup(
+                    ticker_id=ticker.id,
+                    direction="long",
+                    entry_price=round(entry_price, 4),
+                    stop_loss=round(stop, 4),
+                    target=round(best_candidate_target, 4),
+                    rr_ratio=round(best_candidate_rr, 4),
+                    composite_score=round(composite_score, 4),
+                    detected_at=now,
+                    momentum_percentile=momentum_percentile,
+                    strategy_rank=strategy_rank,
+                    volatility_percentile=volatility_percentile,
+                ))
 
     if levels_below:
         stop = entry_price + (atr_value * atr_multiplier)