redesign activation gate to expected value + make pipelines cron-configurable

Diagnosing "no qualified signals for 5 days": setups were generated but none
qualified. The gate required BOTH a high min_rr (2.0) AND a high
min_target_probability (60), which became contradictory after the Jun-15
probability recalibration — probability already embeds R:R via the 1/(rr+1) ruin
term, so high-R:R targets are inherently low-probability and nothing cleared both.

Gate is now expected value (R): p*rr - (1-p) from the primary target's
probability. R:R and confidence stay as floors; high-conviction / exclude-conflicts
/ min-target-probability become optional tighteners (default off). Defaults:
min_expected_value=0.15, min_rr=1.2, min_confidence=55. EV is only enforced when
computable. Migration 009 clears stored activation_* rows so the new defaults
apply. Backtest sweeps min_expected_value instead of target probability.

Scheduling: pipelines are now cron-configurable in Admin -> Jobs. daily_pipeline
(full, default 0 7 * * *) plus a new light intraday_pipeline (OHLCV + outcome eval,
default hourly US session) that keeps prices/live-R:R current without setup churn.
Fundamentals on its own early weekly cron. Timezone configurable (default
Europe/Berlin). Moving interval->CronTrigger also fixes the restart-deferral bug
where an interval job's countdown resets on every process restart.

319 backend unit tests pass; frontend tsc clean.

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

tests/unit/test_backtest_service.py

@@ -113,8 +113,8 @@ async def test_run_backtest_smoke(session):
     # the oscillating series should yield at least some resolved setups
     assert report["candidates"] >= 1
 
-    # sweep: lowering the threshold can only add qualifiers, never remove them
-    sweep = sorted(report["sweep"], key=lambda r: r["min_target_probability"], reverse=True)
+    # sweep: lowering the EV threshold can only add qualifiers, never remove them
+    sweep = sorted(report["sweep"], key=lambda r: r["min_expected_value"], reverse=True)
     counts = [r["total"] for r in sweep]
     assert counts == sorted(counts)  # ascending as threshold descends
     # every calibration row is internally consistent