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>

tests/unit/test_scheduler.py

@@ -83,6 +83,7 @@ class TestConfigureScheduler:
             "outcome_evaluator",
             "alerts",
             "market_regime",
+            "backtest",
         }
 
     def test_configure_is_idempotent(self):
@@ -93,6 +94,7 @@ class TestConfigureScheduler:
         # Each ID should appear exactly once
         assert sorted(job_ids) == sorted([
             "alerts",
+            "backtest",
             "data_collector",
             "fundamental_collector",
             "market_regime",