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>

app/services/backtest_service.py

@@ -36,7 +36,11 @@ from app.services.outcome_service import (
     evaluate_setup_against_bars,
 )
 from app.services.price_service import query_ohlcv
-from app.services.qualification import best_target_probability, setup_qualifies
+from app.services.qualification import (
+    best_target_probability,
+    expected_value_r,
+    setup_qualifies,
+)
 from app.services.recommendation_service import (
     _choose_recommended_action,
     _classify_by_probability,
@@ -131,6 +135,10 @@ def _window_setups(
         primary = _select_primary_target(targets)
         if primary is None:
             continue
+        # Flag the primary so qualification's EV uses the primary target's
+        # probability (matching production's enhance_trade_setup).
+        for t in targets:
+            t["is_primary"] = t is primary
         per_dir[direction] = {"stop": stop, "targets": targets, "primary": primary}
 
     available = set(per_dir.keys())
@@ -160,12 +168,13 @@ def _window_setups(
             stop_loss=stop,
             entry_price=entry,
         )
-        # meets_core = clears every gate EXCEPT target probability, so the report
-        # can sweep the min_target_probability threshold without re-replaying.
-        core_config = {**activation, "min_target_probability": 0.0}
+        # meets_core = clears every gate EXCEPT the expected-value floor, so the
+        # report can sweep the min_expected_value threshold without re-replaying.
+        core_config = {**activation, "min_expected_value": float("-inf")}
         meets_core = setup_qualifies(setup_ns, core_config)
+        ev = expected_value_r(setup_ns)
         best_prob = best_target_probability(setup_ns)
-        min_tp = float(activation.get("min_target_probability", 0.0))
+        min_ev = float(activation.get("min_expected_value", 0.0))
         out.append({
             "direction": direction,
             "entry": entry,
@@ -175,10 +184,11 @@ def _window_setups(
             "confidence": confidences[direction],
             "primary_prob": float(primary["probability"]),
             "best_prob": best_prob,
+            "ev": ev,
             "meets_core": meets_core,
             "action": action,
             "risk_level": risk_level,
-            "qualified": meets_core and best_prob >= min_tp,
+            "qualified": meets_core and ev is not None and ev >= min_ev,
         })
     return out
 
@@ -216,6 +226,7 @@ def _replay_ticker(symbol: str, records: list, config: dict, activation: dict) -
                 "confidence": s["confidence"],
                 "primary_prob": s["primary_prob"],
                 "best_prob": s["best_prob"],
+                "ev": s["ev"],
                 "meets_core": s["meets_core"],
                 "qualified": s["qualified"],
                 "outcome": outcome,
@@ -288,14 +299,17 @@ async def run_backtest(
     longs = [c for c in qualified if c["direction"] == "long"]
     shorts = [c for c in qualified if c["direction"] == "short"]
 
-    # Threshold sweep: re-apply the gate at several min_target_probability values
+    # Threshold sweep: re-apply the gate at several min_expected_value values
     # (holding the other conditions fixed) so the trade-off between how many
     # setups qualify and their expectancy is visible without re-replaying.
-    current_min_tp = float(activation.get("min_target_probability", 60.0))
+    current_min_ev = float(activation.get("min_expected_value", 0.15))
     sweep = []
-    for threshold in (60, 55, 50, 45, 40, 35, 30):
-        cands = [c for c in candidates if c["meets_core"] and c["best_prob"] >= threshold]
-        sweep.append({"min_target_probability": threshold, **_bucket_stats(cands)})
+    for threshold in (0.4, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0.0):
+        cands = [
+            c for c in candidates
+            if c["meets_core"] and c["ev"] is not None and c["ev"] >= threshold
+        ]
+        sweep.append({"min_expected_value": threshold, **_bucket_stats(cands)})
 
     return {
         "generated_at": datetime.now(timezone.utc).isoformat(),
@@ -310,7 +324,7 @@ async def run_backtest(
             "long": _bucket_stats(longs),
             "short": _bucket_stats(shorts),
         },
-        "min_target_probability": current_min_tp,
+        "min_expected_value": current_min_ev,
         "sweep": sweep,
         "calibration": _calibration(candidates),
         "note": (