Add trade setup outcome tracking and performance stats

Closes the feedback loop on R:R scanner signals:

- Nightly outcome_evaluator job replays unresolved setups against daily
  OHLCV bars: target_hit / stop_hit / ambiguous (same-bar, counted as
  loss) / expired after OUTCOME_EVALUATION_MAX_BARS (default 30)
- Migration 004: evaluated_at + outcome_date on trade_setups
- GET /trades/performance: hit rate, expectancy (avg R), total R with
  breakdowns by direction, recommended action, and confidence bucket
- New Performance page (stat cards, breakdown tables, Evaluate Now,
  methodology disclosure) wired into sidebar and mobile nav
- 17 new unit tests for evaluation logic and stats aggregation

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

app/scheduler.py

@@ -34,6 +34,7 @@ from app.providers.fundamentals_chain import build_fundamental_provider_chain
 from app.providers.openai_sentiment import OpenAISentimentProvider
 from app.providers.protocol import SentimentData
 from app.services import fundamental_service, ingestion_service, sentiment_service
+from app.services.outcome_service import evaluate_pending_setups
 from app.services.rr_scanner_service import scan_all_tickers
 from app.services.ticker_universe_service import bootstrap_universe
 
@@ -676,6 +677,52 @@ async def scan_rr() -> None:
         _runtime_finish(job_name, "error", processed=processed, total=total, message=str(exc))
 
 
+# ---------------------------------------------------------------------------
+# Job: Outcome Evaluator
+# ---------------------------------------------------------------------------
+
+
+async def evaluate_outcomes() -> None:
+    """Evaluate unresolved trade setups against OHLCV data collected since.
+
+    Writes actual_outcome / outcome_date / evaluated_at on each decided setup.
+    Undecided setups stay pending and are re-checked on the next run.
+    """
+    job_name = "outcome_evaluator"
+    logger.info(json.dumps({"event": "job_start", "job": job_name}))
+    _runtime_start(job_name, total=1)
+
+    try:
+        async with async_session_factory() as db:
+            if not await _is_job_enabled(db, job_name):
+                logger.info(json.dumps({"event": "job_skipped", "job": job_name, "reason": "disabled"}))
+                _runtime_finish(job_name, "skipped", processed=0, total=1, message="Disabled")
+                return
+
+            summary = await evaluate_pending_setups(
+                db, max_bars=settings.outcome_evaluation_max_bars
+            )
+
+        _runtime_progress(job_name, processed=1, total=1)
+        _runtime_finish(
+            job_name, "completed", processed=1, total=1,
+            message=f"Evaluated {summary['evaluated']}, pending {summary['still_pending']}",
+        )
+        logger.info(json.dumps({
+            "event": "job_complete",
+            "job": job_name,
+            "summary": summary,
+        }))
+    except Exception as exc:
+        _runtime_finish(job_name, "error", processed=0, total=1, message=str(exc))
+        logger.error(json.dumps({
+            "event": "job_error",
+            "job": job_name,
+            "error_type": type(exc).__name__,
+            "message": str(exc),
+        }))
+
+
 # ---------------------------------------------------------------------------
 # Job: Ticker Universe Sync
 # ---------------------------------------------------------------------------
@@ -804,6 +851,16 @@ def configure_scheduler() -> None:
         replace_existing=True,
     )
 
+    # Outcome Evaluator — nightly, after fresh OHLCV has been collected
+    scheduler.add_job(
+        evaluate_outcomes,
+        "interval",
+        hours=24,
+        id="outcome_evaluator",
+        name="Outcome Evaluator",
+        replace_existing=True,
+    )
+
     logger.info(
         json.dumps({
             "event": "scheduler_configured",