add backtest harness (Phase 1): historical replay + hit-rate & calibration reports

Replays the price-derived engine over stored OHLCV: at each weekly as-of date,
rebuild the setup from bars <= D (no lookahead) and walk the actual forward bars
for the realized outcome. Reports realized hit-rate/expectancy of qualified
setups (and all setups, by direction) plus a probability calibration curve
(predicted target prob vs realized hit rate).

Reuses pure functions throughout; extracted compute_technical_from_arrays /
compute_momentum_from_closes from scoring_service so live and backtest stay in
sync. Runs as a weekly/triggerable 'backtest' job caching the report in a
SystemSetting; GET /backtest/report serves it. Sentiment/fundamentals held
neutral (no point-in-time history) — calibrates the price/S-R/probability machinery.

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

app/services/admin_service.py

@@ -484,6 +484,7 @@ VALID_JOB_NAMES = {
     "outcome_evaluator",
     "alerts",
     "market_regime",
+    "backtest",
 }
 
 JOB_LABELS = {
@@ -495,6 +496,7 @@ JOB_LABELS = {
     "outcome_evaluator": "Outcome Evaluator",
     "alerts": "Alerts Dispatcher",
     "market_regime": "Market Regime",
+    "backtest": "Backtest",
 }