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)

services/signal_engine/signals/base.py

@@ -0,0 +1,127 @@
+"""Base protocol and common helpers for signal evaluators.
+
+Defines the ``SignalEvaluator`` protocol that every signal in the Signal
+Library must satisfy, plus shared utility functions for swing detection,
+lookback validation, and simple moving average computation.
+"""
+
+from __future__ import annotations
+
+from typing import Protocol
+
+from services.signal_engine.models import OHLCVBar, SignalResult
+
+# ---------------------------------------------------------------------------
+# Signal evaluator protocol
+# ---------------------------------------------------------------------------
+
+
+class SignalEvaluator(Protocol):
+    """Protocol for all signal evaluators in the Signal Library.
+
+    Each evaluator receives a list of OHLCV bars for a single timeframe
+    and returns a ``SignalResult`` when the signal triggers, or ``None``
+    when insufficient data is available or the signal does not fire.
+    """
+
+    def evaluate(
+        self,
+        bars: list[OHLCVBar],
+        timeframe: str,
+    ) -> SignalResult | None:
+        """Evaluate a signal on a single timeframe's bar data.
+
+        Returns ``None`` when insufficient data is available.
+        """
+        ...
+
+
+# ---------------------------------------------------------------------------
+# Common helper functions
+# ---------------------------------------------------------------------------
+
+
+def find_swing_high(
+    bars: list[OHLCVBar],
+    lookback: int,
+) -> tuple[int, float] | None:
+    """Find the highest high in the last *lookback* bars.
+
+    Args:
+        bars: OHLCV bar series (oldest-first).
+        lookback: Number of recent bars to search.
+
+    Returns:
+        ``(index, price)`` of the bar with the highest high within the
+        lookback window, or ``None`` if *bars* has fewer than *lookback*
+        entries.
+    """
+    if len(bars) < lookback or lookback <= 0:
+        return None
+
+    window = bars[-lookback:]
+    offset = len(bars) - lookback
+
+    best_idx = 0
+    best_price = window[0].high
+    for i, bar in enumerate(window):
+        if bar.high >= best_price:
+            best_idx = i
+            best_price = bar.high
+
+    return (offset + best_idx, best_price)
+
+
+def find_swing_low(
+    bars: list[OHLCVBar],
+    lookback: int,
+) -> tuple[int, float] | None:
+    """Find the lowest low in the last *lookback* bars.
+
+    Args:
+        bars: OHLCV bar series (oldest-first).
+        lookback: Number of recent bars to search.
+
+    Returns:
+        ``(index, price)`` of the bar with the lowest low within the
+        lookback window, or ``None`` if *bars* has fewer than *lookback*
+        entries.
+    """
+    if len(bars) < lookback or lookback <= 0:
+        return None
+
+    window = bars[-lookback:]
+    offset = len(bars) - lookback
+
+    best_idx = 0
+    best_price = window[0].low
+    for i, bar in enumerate(window):
+        if bar.low <= best_price:
+            best_idx = i
+            best_price = bar.low
+
+    return (offset + best_idx, best_price)
+
+
+def validate_lookback(bars: list[OHLCVBar], min_bars: int) -> bool:
+    """Return ``True`` if *bars* contains at least *min_bars* entries."""
+    return len(bars) >= min_bars
+
+
+def compute_sma(bars: list[OHLCVBar], period: int) -> float | None:
+    """Compute the simple moving average of close prices over the last *period* bars.
+
+    Args:
+        bars: OHLCV bar series (oldest-first).
+        period: Number of recent bars to average.
+
+    Returns:
+        The arithmetic mean of the last *period* close prices, or ``None``
+        if *bars* has fewer than *period* entries or *period* is not
+        positive.
+    """
+    if period <= 0 or len(bars) < period:
+        return None
+
+    total = sum(bar.close for bar in bars[-period:])
+    return total / period