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>

tests/unit/test_backtest_service.py

@@ -108,10 +108,15 @@ async def test_run_backtest_smoke(session):
     # well-formed report
     assert report["tickers"] == 1
     assert isinstance(report["candidates"], int)
-    for key in ("overall_qualified", "overall_all", "by_direction", "calibration"):
+    for key in ("overall_qualified", "overall_all", "by_direction", "calibration", "sweep"):
         assert key in report
     # the oscillating series should yield at least some resolved setups
     assert report["candidates"] >= 1
+
+    # sweep: lowering the threshold can only add qualifiers, never remove them
+    sweep = sorted(report["sweep"], key=lambda r: r["min_target_probability"], reverse=True)
+    counts = [r["total"] for r in sweep]
+    assert counts == sorted(counts)  # ascending as threshold descends
     # every calibration row is internally consistent
     for row in report["calibration"]:
         assert 0 <= row["realized_hit_rate"] <= 100