feat: trailing-stop exit sweep in the backtest

Third exit model alongside target-vs-stop and the fixed take-profit. The TP sweep
showed the edge lives in the fat tail (avg R keeps rising as you let winners run),
but a fixed wide target is win-rate-brutal and gives everything back on a reversal.
A trailing stop harvests the tail while protecting gains.

Per setup the replay computes the realized R for several trail widths (3/5/7/10/
15/20%) in a single conservative pass — stop ratchets up via max(initial_stop,
peak*(1-trail)), exit on the pullback or at the horizon close, R vs the initial
risk. Aggregated into a trailing sweep (win rate = share closed in profit, avg R,
total R) over the qualified set and shown as a new table in the Backtest panel.

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

tests/unit/test_backtest_service.py

@@ -95,6 +95,43 @@ class TestTakeProfitBucket:
         assert b["avg_r"] is None
 
 
+class TestTrailingExits:
+    def test_locks_gain_on_pullback(self):
+        # Runs to 120, then a 10% trail (from peak 120 → 108) is pierced on the drop.
+        res = bt._trailing_exits("long", 100.0, 90.0, (0.10,), [_bar(120, 110, 118), _bar(130, 100, 105)], 30)
+        assert res[10] == pytest.approx(0.8)  # (108-100)/100 / 0.10 risk
+
+    def test_initial_stop_caps_loss(self):
+        # Trail (20%) is looser than the initial stop → initial stop governs = -1R.
+        res = bt._trailing_exits("long", 100.0, 90.0, (0.20,), [_bar(101, 89, 90)], 30)
+        assert res[20] == pytest.approx(-1.0)
+
+    def test_timeout_exits_at_close(self):
+        res = bt._trailing_exits("long", 100.0, 90.0, (0.20,), [_bar(105, 98, 104), _bar(106, 100, 105)], 30)
+        assert res[20] == pytest.approx(0.5)  # close 105 → +5% / 10% risk
+
+    def test_multiple_widths_one_pass(self):
+        # Tighter trail locks in more here (exit at 114 vs 108).
+        res = bt._trailing_exits("long", 100.0, 90.0, (0.10, 0.05), [_bar(120, 110, 118), _bar(130, 100, 105)], 30)
+        assert res[10] == pytest.approx(0.8)
+        assert res[5] == pytest.approx(1.4)
+
+
+class TestTrailingBucket:
+    def test_bucket(self):
+        cands = [
+            {"trail_r": {5: 1.4, 10: 0.8}},
+            {"trail_r": {5: -1.0, 10: -1.0}},
+            {"trail_r": {5: 0.5, 10: 0.5}},
+        ]
+        b = bt._trailing_bucket(cands, 5)
+        assert b["total"] == 3
+        assert b["wins"] == 2
+        assert b["win_rate"] == pytest.approx(66.7, abs=0.1)
+        assert b["total_r"] == pytest.approx(0.9, abs=0.01)
+        assert b["avg_r"] == pytest.approx(0.3, abs=0.01)
+
+
 def test_bucket_stats_counts_and_expectancy():
     cands = [
         _cand(70, OUTCOME_TARGET_HIT, 3.0),   # +3R win