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/models/trade_setup.py

@@ -1,8 +1,8 @@
-from datetime import datetime
+from datetime import date, datetime
 
 import json
 
-from sqlalchemy import DateTime, Float, ForeignKey, String, Text
+from sqlalchemy import Date, DateTime, Float, ForeignKey, String, Text
 from sqlalchemy.orm import Mapped, mapped_column, relationship
 
 from app.database import Base
@@ -32,6 +32,10 @@ class TradeSetup(Base):
     reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
     risk_level: Mapped[str | None] = mapped_column(String(10), nullable=True)
     actual_outcome: Mapped[str | None] = mapped_column(String(20), nullable=True)
+    evaluated_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    outcome_date: Mapped[date | None] = mapped_column(Date, nullable=True)
 
     ticker = relationship("Ticker", back_populates="trade_setups")