backtest: add min target-probability sweep

Re-applies the activation gate at several min_target_probability thresholds
(60→30, other conditions fixed) over the already-replayed candidates, so the
trade-off between how many setups qualify and their expectancy is visible in one
table — the cheap "optimize" half of Phase 2. Candidates now carry meets_core +
best_prob so the sweep needs no re-replay. New sweep table in BacktestPanel with
the current threshold starred.

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

app/services/backtest_service.py

@@ -160,6 +160,12 @@ def _window_setups(
             stop_loss=stop,
             entry_price=entry,
         )
+        # meets_core = clears every gate EXCEPT target probability, so the report
+        # can sweep the min_target_probability threshold without re-replaying.
+        core_config = {**activation, "min_target_probability": 0.0}
+        meets_core = setup_qualifies(setup_ns, core_config)
+        best_prob = best_target_probability(setup_ns)
+        min_tp = float(activation.get("min_target_probability", 0.0))
         out.append({
             "direction": direction,
             "entry": entry,
@@ -168,10 +174,11 @@ def _window_setups(
             "rr": rr,
             "confidence": confidences[direction],
             "primary_prob": float(primary["probability"]),
-            "best_prob": best_target_probability(setup_ns),
+            "best_prob": best_prob,
+            "meets_core": meets_core,
             "action": action,
             "risk_level": risk_level,
-            "qualified": setup_qualifies(setup_ns, activation),
+            "qualified": meets_core and best_prob >= min_tp,
         })
     return out
 
@@ -208,6 +215,8 @@ def _replay_ticker(symbol: str, records: list, config: dict, activation: dict) -
                 "rr": s["rr"],
                 "confidence": s["confidence"],
                 "primary_prob": s["primary_prob"],
+                "best_prob": s["best_prob"],
+                "meets_core": s["meets_core"],
                 "qualified": s["qualified"],
                 "outcome": outcome,
                 "target_hit": target_hit,
@@ -279,6 +288,15 @@ async def run_backtest(
     longs = [c for c in qualified if c["direction"] == "long"]
     shorts = [c for c in qualified if c["direction"] == "short"]
 
+    # Threshold sweep: re-apply the gate at several min_target_probability values
+    # (holding the other conditions fixed) so the trade-off between how many
+    # setups qualify and their expectancy is visible without re-replaying.
+    current_min_tp = float(activation.get("min_target_probability", 60.0))
+    sweep = []
+    for threshold in (60, 55, 50, 45, 40, 35, 30):
+        cands = [c for c in candidates if c["meets_core"] and c["best_prob"] >= threshold]
+        sweep.append({"min_target_probability": threshold, **_bucket_stats(cands)})
+
     return {
         "generated_at": datetime.now(timezone.utc).isoformat(),
         "tickers": total,
@@ -292,6 +310,8 @@ async def run_backtest(
             "long": _bucket_stats(longs),
             "short": _bucket_stats(shorts),
         },
+        "min_target_probability": current_min_tp,
+        "sweep": sweep,
         "calibration": _calibration(candidates),
         "note": (
             "Sentiment & fundamentals held neutral (no point-in-time history). "