feat: implement dual-pipeline signal engine service

New service at services/signal_engine/ implementing concurrent heuristic
(deterministic scoring) and probabilistic (Bayesian inference) pipelines
that evaluate technical signals across 6 timeframes (M30-M) and produce
independent BUY/WATCH/SKIP verdicts per ticker per evaluation tick.

Components:
- Input Normalizer: multi-source data assembly with sentinel fallbacks
- Signal Library: Fibonacci, MA Stack, RSI, Cup & Handle, Elliott Wave
- Multi-Timeframe Confluence Engine: weighted scoring with D/W/M anchors
- Hard Filter Engine: macro_bias, valuation, earnings proximity gating
- Heuristic Pipeline: S_total scoring with confidence-gated verdicts
- Probabilistic Pipeline: Bayesian log-odds with regime priors, entropy
  gating, EV_R calculation, and signal correlation penalty
- Exit Engine: stop-loss, targets, trailing ATR-based stops
- Delta Analyzer: pipeline agreement tracking with rolling Redis metrics
- Output Formatter: SignalOutput contract + Recommendation schema mapping
- Worker orchestrator: concurrent pipelines with failure isolation
- Main entry point: queue polling with fail-safe config loading

Infrastructure:
- Migration 039: signal_engine_outputs table with 3 indexes
- Helm chart: signalEngine service entry (processing tier)
- Redis key: QUEUE_SIGNAL_ENGINE constant

Tests: 390 tests (unit + property-based) covering all components
Config: dual_pipeline_enabled=false by default (safe rollout)

infra/helm/stonks-oracle/values.yaml

@@ -127,6 +127,17 @@ services:
       requests: { cpu: 50m, memory: 64Mi }
       limits: { cpu: 200m, memory: 128Mi }
 
+  signalEngine:
+    replicas: 1
+    pipeline: true
+    image: signal-engine
+    command: "python -m services.signal_engine.main"
+    tier: processing
+    secrets: [stonks-core-secrets, stonks-market-secrets]
+    resources:
+      requests: { cpu: 100m, memory: 128Mi }
+      limits: { cpu: 500m, memory: 256Mi }
+
   lakePublisher:
     replicas: 1
     pipeline: true