add cross-sectional signal evaluation (factor rank-IC) to the backtest

The per-setup hit-rate report can't tell whether a signal predicts returns —
only how a target/stop structure built on one performs. This adds a
cross-sectional factor-IC pass: each week the universe is ranked by a price-only
signal and graded by its rank correlation (Spearman IC) and top-minus-bottom-
quintile spread against the forward 30-day return.

Candidate signals (point-in-time from price; sentiment/fundamentals have no
history in the replay): 12-1/6-1/3-1 month momentum, 1-month reversal,
price-vs-200d SMA, proximity to the 52-week high (George/Hwang), and 126-day
realized volatility (low-vol anomaly).

Reuses the existing per-ticker replay loop (no new data, no second DB pass);
results land in the cached backtest_report as `signal_eval` and render as a
"Signal edge" table in BacktestPanel beside the calibration curve.

330 backend tests pass (10 new in test_signal_eval); frontend build clean.

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

frontend/src/lib/types.ts

@@ -224,6 +224,16 @@ export interface BacktestSweepRow extends BacktestBucket {
   min_expected_value: number;
 }
 
+export interface BacktestSignalEvalRow {
+  signal: string;
+  weeks: number;
+  avg_cross_section: number | null;
+  mean_ic: number;
+  ic_t_stat: number | null;
+  ic_positive_pct: number;
+  mean_quintile_spread: number | null;
+}
+
 export interface BacktestReport {
   generated_at: string;
   tickers: number;
@@ -236,6 +246,8 @@ export interface BacktestReport {
   min_expected_value: number;
   sweep: BacktestSweepRow[];
   calibration: BacktestCalibrationRow[];
+  signal_eval?: BacktestSignalEvalRow[];
+  signal_eval_note?: string;
   note: string;
 }