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/shared/redis_keys.py

@@ -70,6 +70,7 @@ QUEUE_BROKER = "broker_orders"
 QUEUE_MACRO_CLASSIFICATION = "macro_classification"
 QUEUE_REPORT_GENERATION = "report_generation"
 QUEUE_REPORT_GENERATION = "report_generation"
+QUEUE_SIGNAL_ENGINE = "signal_engine"
 
 # --- Trading engine ---
 QUEUE_TRADING_DECISIONS = "trading_decisions"

services/signal_engine/__init__.py

@@ -0,0 +1 @@
+# Signal Engine - dual-pipeline signal evaluation (heuristic + probabilistic)

services/signal_engine/config.py

@@ -0,0 +1,355 @@
+"""Signal engine configuration loaded from risk_configs + environment.
+
+Defines ``SignalEngineConfig`` (the top-level dataclass) and four derived
+sub-configs — ``HardFilterConfig``, ``HeuristicConfig``,
+``ProbabilisticConfig``, ``ExitConfig`` — that expose relevant subsets for
+cleaner function signatures.
+
+``load_config()`` reads from the ``risk_configs`` table's JSONB ``config``
+column and falls back to safe defaults on any error.  Environment variables
+with the ``SIGNAL_ENGINE_`` prefix override database values.
+
+Requirements: 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from dataclasses import dataclass, field
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Sub-configs — thin wrappers over relevant subsets of SignalEngineConfig
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class HardFilterConfig:
+    """Thresholds for the pre-pipeline hard filter engine."""
+
+    valuation_min: float = 0.3
+    earnings_days: int = 5
+    macro_bias_skip: float = -1.0
+
+
+@dataclass
+class HeuristicConfig:
+    """Thresholds for the heuristic (deterministic) pipeline verdict."""
+
+    buy_confidence: float = 0.70
+    buy_s_total: float = 1.2
+    buy_valuation_min: float = 0.5
+    watch_confidence: float = 0.55
+    macro_bias_threshold: float = 0.0  # macro_bias must be > this for BUY
+    earnings_days_threshold: int = 5  # earnings_proximity must be > this for BUY
+
+
+@dataclass
+class ProbabilisticConfig:
+    """Thresholds for the probabilistic (Bayesian) pipeline verdict."""
+
+    buy_p_up: float = 0.60
+    buy_entropy_max: float = 0.90
+    buy_ev_r_min: float = 1.5
+    buy_valuation_min: float = 0.5
+    watch_p_up: float = 0.55
+    watch_entropy_max: float = 0.95
+    entropy_skip: float = 0.95
+
+    # Regime priors
+    regime_prior_bull: float = 0.58
+    regime_prior_range: float = 0.50
+    regime_prior_bear: float = 0.42
+
+    # Fundamental gates (same semantics as heuristic)
+    macro_bias_threshold: float = 0.0
+    earnings_days_threshold: int = 5
+
+
+@dataclass
+class ExitConfig:
+    """Configuration for the exit engine."""
+
+    trailing_stop_atr_multiplier: float = 2.0
+
+
+# ---------------------------------------------------------------------------
+# Top-level config
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class SignalEngineConfig:
+    """Configuration loaded from risk_configs + environment.
+
+    All fields carry safe defaults so that a fresh deployment works without
+    any database rows or environment variables.
+    """
+
+    dual_pipeline_enabled: bool = False
+    heuristic_pipeline_enabled: bool = True
+    probabilistic_pipeline_enabled: bool = True
+    shadow_mode: bool = False
+
+    # Timeframe weights
+    timeframe_weights: dict[str, float] = field(
+        default_factory=lambda: {
+            "M30": 0.03,
+            "H1": 0.07,
+            "H4": 0.15,
+            "D": 0.30,
+            "W": 0.30,
+            "M": 0.15,
+        }
+    )
+
+    # Hard filter thresholds
+    hard_filter_valuation_min: float = 0.3
+    hard_filter_earnings_days: int = 5
+    hard_filter_macro_bias_skip: float = -1.0
+
+    # Heuristic verdict thresholds
+    heuristic_buy_confidence: float = 0.70
+    heuristic_buy_s_total: float = 1.2
+    heuristic_buy_valuation_min: float = 0.5
+    heuristic_watch_confidence: float = 0.55
+
+    # Probabilistic verdict thresholds
+    prob_buy_p_up: float = 0.60
+    prob_buy_entropy_max: float = 0.90
+    prob_buy_ev_r_min: float = 1.5
+    prob_buy_valuation_min: float = 0.5
+    prob_watch_p_up: float = 0.55
+    prob_watch_entropy_max: float = 0.95
+    prob_entropy_skip: float = 0.95
+
+    # Regime priors
+    regime_prior_bull: float = 0.58
+    regime_prior_range: float = 0.50
+    regime_prior_bear: float = 0.42
+
+    # Exit engine
+    trailing_stop_atr_multiplier: float = 2.0
+
+    # Polling
+    polling_interval_seconds: int = 30
+
+    # -- Derived sub-configs ------------------------------------------------
+
+    @property
+    def hard_filter_config(self) -> HardFilterConfig:
+        return HardFilterConfig(
+            valuation_min=self.hard_filter_valuation_min,
+            earnings_days=self.hard_filter_earnings_days,
+            macro_bias_skip=self.hard_filter_macro_bias_skip,
+        )
+
+    @property
+    def heuristic_config(self) -> HeuristicConfig:
+        return HeuristicConfig(
+            buy_confidence=self.heuristic_buy_confidence,
+            buy_s_total=self.heuristic_buy_s_total,
+            buy_valuation_min=self.heuristic_buy_valuation_min,
+            watch_confidence=self.heuristic_watch_confidence,
+            macro_bias_threshold=0.0,
+            earnings_days_threshold=self.hard_filter_earnings_days,
+        )
+
+    @property
+    def probabilistic_config(self) -> ProbabilisticConfig:
+        return ProbabilisticConfig(
+            buy_p_up=self.prob_buy_p_up,
+            buy_entropy_max=self.prob_buy_entropy_max,
+            buy_ev_r_min=self.prob_buy_ev_r_min,
+            buy_valuation_min=self.prob_buy_valuation_min,
+            watch_p_up=self.prob_watch_p_up,
+            watch_entropy_max=self.prob_watch_entropy_max,
+            entropy_skip=self.prob_entropy_skip,
+            regime_prior_bull=self.regime_prior_bull,
+            regime_prior_range=self.regime_prior_range,
+            regime_prior_bear=self.regime_prior_bear,
+            macro_bias_threshold=0.0,
+            earnings_days_threshold=self.hard_filter_earnings_days,
+        )
+
+    @property
+    def exit_config(self) -> ExitConfig:
+        return ExitConfig(
+            trailing_stop_atr_multiplier=self.trailing_stop_atr_multiplier,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Config loading helpers
+# ---------------------------------------------------------------------------
+
+# SQL to fetch all signal_engine_* keys from the active risk_configs row's
+# JSONB config column.  The query extracts each top-level key/value pair and
+# filters to those prefixed with ``signal_engine_``.
+_CONFIG_QUERY = """
+SELECT key, value
+FROM (
+    SELECT key, value
+    FROM risk_configs,
+         jsonb_each_text(config)
+    WHERE active = TRUE
+    ORDER BY updated_at DESC
+    LIMIT 1
+) sub
+WHERE key LIKE 'signal_engine_%'
+"""
+
+# Mapping from risk_configs JSON key → SignalEngineConfig field name.
+# Keys in the DB are prefixed ``signal_engine_`` which is stripped to match
+# the dataclass field names.
+_FIELD_TYPES: dict[str, type] = {
+    "dual_pipeline_enabled": bool,
+    "heuristic_pipeline_enabled": bool,
+    "probabilistic_pipeline_enabled": bool,
+    "shadow_mode": bool,
+    "timeframe_weights": dict,
+    "hard_filter_valuation_min": float,
+    "hard_filter_earnings_days": int,
+    "hard_filter_macro_bias_skip": float,
+    "heuristic_buy_confidence": float,
+    "heuristic_buy_s_total": float,
+    "heuristic_buy_valuation_min": float,
+    "heuristic_watch_confidence": float,
+    "prob_buy_p_up": float,
+    "prob_buy_entropy_max": float,
+    "prob_buy_ev_r_min": float,
+    "prob_buy_valuation_min": float,
+    "prob_watch_p_up": float,
+    "prob_watch_entropy_max": float,
+    "prob_entropy_skip": float,
+    "regime_prior_bull": float,
+    "regime_prior_range": float,
+    "regime_prior_bear": float,
+    "trailing_stop_atr_multiplier": float,
+    "polling_interval_seconds": int,
+}
+
+
+def _parse_value(raw: str, target_type: type) -> Any:
+    """Coerce a raw string value from the DB/env into *target_type*.
+
+    Booleans accept ``true``/``false`` (case-insensitive).
+    Dicts are parsed as JSON.
+    """
+    if target_type is bool:
+        return raw.lower() in ("true", "1", "yes")
+    if target_type is dict:
+        return json.loads(raw)
+    if target_type is int:
+        return int(raw)
+    if target_type is float:
+        return float(raw)
+    return raw
+
+
+def _apply_db_rows(
+    config: SignalEngineConfig,
+    rows: list[tuple[str, str]],
+) -> None:
+    """Mutate *config* in-place from ``(key, value)`` DB rows.
+
+    Keys are expected to be prefixed ``signal_engine_`` — the prefix is
+    stripped before matching against dataclass fields.
+    """
+    for key, value in rows:
+        field_name = key.removeprefix("signal_engine_")
+        target_type = _FIELD_TYPES.get(field_name)
+        if target_type is None:
+            logger.debug("Ignoring unknown signal_engine config key: %s", key)
+            continue
+        try:
+            parsed = _parse_value(value, target_type)
+            setattr(config, field_name, parsed)
+        except (ValueError, TypeError, json.JSONDecodeError):
+            logger.warning(
+                "Invalid value for signal_engine config key %s: %r — keeping default",
+                key,
+                value,
+            )
+
+
+def _apply_env_overrides(config: SignalEngineConfig) -> None:
+    """Override config fields from environment variables.
+
+    Environment variables use the ``SIGNAL_ENGINE_`` prefix (upper-case).
+    For example ``SIGNAL_ENGINE_DUAL_PIPELINE_ENABLED=true`` overrides
+    ``dual_pipeline_enabled``.
+    """
+    prefix = "SIGNAL_ENGINE_"
+    for env_key, env_value in os.environ.items():
+        if not env_key.startswith(prefix):
+            continue
+        field_name = env_key[len(prefix):].lower()
+        target_type = _FIELD_TYPES.get(field_name)
+        if target_type is None:
+            continue
+        try:
+            parsed = _parse_value(env_value, target_type)
+            setattr(config, field_name, parsed)
+        except (ValueError, TypeError, json.JSONDecodeError):
+            logger.warning(
+                "Invalid env override %s=%r — keeping previous value",
+                env_key,
+                env_value,
+            )
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+async def load_config(pool: Any) -> SignalEngineConfig:
+    """Load signal engine configuration from the database and environment.
+
+    1. Start with safe defaults (``SignalEngineConfig()``).
+    2. Query ``risk_configs`` for keys prefixed ``signal_engine_``.
+    3. Apply matching values over the defaults.
+    4. Apply environment variable overrides (``SIGNAL_ENGINE_*``).
+    5. On any DB error, fall back to defaults with ``dual_pipeline_enabled=False``.
+
+    The *pool* argument is an ``asyncpg.Pool`` (typed as ``Any`` to avoid a
+    hard import dependency at module level).
+
+    Requirements: 13.1, 13.6, 13.7
+    """
+    config = SignalEngineConfig()
+
+    # Step 1 — read from risk_configs
+    try:
+        rows = await pool.fetch(_CONFIG_QUERY)
+        if rows:
+            _apply_db_rows(config, [(r["key"], r["value"]) for r in rows])
+    except Exception:
+        logger.warning(
+            "Failed to load signal engine config from risk_configs — "
+            "defaulting to disabled (fail-safe)",
+            exc_info=True,
+        )
+        # Ensure fail-safe: dual pipeline stays off
+        config.dual_pipeline_enabled = False
+
+    # Step 2 — environment overrides (always applied, even after DB failure)
+    _apply_env_overrides(config)
+
+    logger.info(
+        "Signal engine config loaded: dual_pipeline_enabled=%s, "
+        "heuristic=%s, probabilistic=%s, shadow_mode=%s, "
+        "polling_interval=%ds",
+        config.dual_pipeline_enabled,
+        config.heuristic_pipeline_enabled,
+        config.probabilistic_pipeline_enabled,
+        config.shadow_mode,
+        config.polling_interval_seconds,
+    )
+
+    return config

services/signal_engine/confluence.py

@@ -0,0 +1,136 @@
+"""Multi-Timeframe Confluence Engine.
+
+Evaluates signals across multiple timeframes and computes weighted confluence
+scores.  Signals must trigger on at least 2 timeframes **and** include at
+least one higher-timeframe anchor (D, W, or M) to pass the confluence filter.
+
+The weighted confluence score is:
+
+    C_confluence = Σ(w_tf · s_tf)
+
+where ``w_tf`` is the timeframe weight and ``s_tf`` is the signal strength on
+that timeframe (only summed over timeframes where the signal triggered).
+
+Requirements: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6
+"""
+
+from __future__ import annotations
+
+import logging
+from collections import Counter
+
+from services.signal_engine.models import (
+    ConfluenceSignal,
+    SignalDirection,
+    SignalResult,
+)
+
+logger = logging.getLogger(__name__)
+
+# Higher-timeframe anchors — at least one must be present for a signal to pass.
+HIGHER_TIMEFRAME_ANCHORS: frozenset[str] = frozenset({"D", "W", "M"})
+
+# Minimum number of timeframes a signal must trigger on.
+MIN_TIMEFRAME_COUNT: int = 2
+
+
+def _dominant_direction(results: dict[str, SignalResult]) -> SignalDirection:
+    """Determine the dominant direction from a set of per-timeframe results.
+
+    Counts bullish vs bearish votes across active timeframes.  Ties resolve
+    to NEUTRAL.
+    """
+    counts: Counter[SignalDirection] = Counter()
+    for sr in results.values():
+        counts[sr.direction] += 1
+
+    bullish = counts.get(SignalDirection.BULLISH, 0)
+    bearish = counts.get(SignalDirection.BEARISH, 0)
+
+    if bullish > bearish:
+        return SignalDirection.BULLISH
+    if bearish > bullish:
+        return SignalDirection.BEARISH
+    return SignalDirection.NEUTRAL
+
+
+def compute_confluence(
+    signal_results: dict[str, dict[str, SignalResult]],
+    weights: dict[str, float],
+) -> list[ConfluenceSignal]:
+    """Compute weighted confluence scores across timeframes.
+
+    Args:
+        signal_results: ``{signal_type: {timeframe: SignalResult}}``.
+            Each inner dict maps timeframe labels (e.g. ``"D"``, ``"H4"``)
+            to the :class:`SignalResult` produced by the signal evaluator on
+            that timeframe.
+        weights: ``{timeframe: weight}`` e.g.
+            ``{"M30": 0.03, "H1": 0.07, "H4": 0.15, "D": 0.30, "W": 0.30, "M": 0.15}``.
+
+    Returns:
+        List of :class:`ConfluenceSignal` objects that pass **both** filters:
+
+        1. **Minimum confluence threshold** — the signal must trigger on at
+           least :data:`MIN_TIMEFRAME_COUNT` (2) timeframes.
+        2. **Higher-timeframe anchor** — at least one of D, W, or M must be
+           among the active timeframes.
+
+    Requirements: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6
+    """
+    confluence_signals: list[ConfluenceSignal] = []
+
+    for signal_type, tf_results in signal_results.items():
+        active_timeframes = list(tf_results.keys())
+
+        # 3.3 — Minimum confluence threshold: discard if < 2 timeframes
+        if len(active_timeframes) < MIN_TIMEFRAME_COUNT:
+            logger.debug(
+                "Signal %s discarded: only %d timeframe(s) triggered (need >= %d)",
+                signal_type,
+                len(active_timeframes),
+                MIN_TIMEFRAME_COUNT,
+            )
+            continue
+
+        # 3.4 — Higher-timeframe anchor: discard if none of D, W, M present
+        if not HIGHER_TIMEFRAME_ANCHORS.intersection(active_timeframes):
+            logger.debug(
+                "Signal %s discarded: no higher-timeframe anchor (D/W/M) "
+                "among active timeframes %s",
+                signal_type,
+                active_timeframes,
+            )
+            continue
+
+        # 3.2 — Compute weighted confluence score
+        per_timeframe: dict[str, float] = {}
+        confluence_score = 0.0
+        for tf, sr in tf_results.items():
+            w = weights.get(tf, 0.0)
+            per_timeframe[tf] = sr.strength
+            confluence_score += w * sr.strength
+
+        # Determine dominant direction across active timeframes
+        direction = _dominant_direction(tf_results)
+
+        confluence_signals.append(
+            ConfluenceSignal(
+                signal_type=signal_type,
+                direction=direction,
+                confluence_score=confluence_score,
+                active_timeframes=active_timeframes,
+                per_timeframe=per_timeframe,
+            )
+        )
+
+        logger.debug(
+            "Signal %s passed confluence: score=%.4f, direction=%s, "
+            "timeframes=%s",
+            signal_type,
+            confluence_score,
+            direction.value,
+            active_timeframes,
+        )
+
+    return confluence_signals

services/signal_engine/correlation.py

@@ -0,0 +1,137 @@
+"""Signal cluster classification and within-cluster correlation penalty.
+
+Groups signals into four clusters — momentum, structure, volatility,
+fundamentals — and applies exponential decay within each cluster to prevent
+likelihood ratio stacking inflation in the Bayesian pipeline.
+
+Within a cluster the strongest signal (by ``|log_lr|``) contributes at full
+weight; subsequent signals contribute at ``0.5^(n-1)`` decay.  Signals in
+different clusters are treated as independent (no penalty).  Single-signal
+clusters receive no penalty.
+
+Requirements: 7.1, 7.2, 7.3, 7.4
+"""
+
+from __future__ import annotations
+
+import logging
+from collections import defaultdict
+from enum import Enum
+
+from services.signal_engine.models import LikelihoodRatio
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Signal cluster enum
+# ---------------------------------------------------------------------------
+
+
+class SignalCluster(str, Enum):
+    """Correlation cluster for grouping related signals."""
+
+    MOMENTUM = "momentum"  # MA stack, RSI
+    STRUCTURE = "structure"  # Fibonacci, Elliott Wave, Cup & Handle
+    VOLATILITY = "volatility"  # ATR-based, Bollinger-derived
+    FUNDAMENTALS = "fundamentals"  # valuation, earnings, macro
+
+
+# ---------------------------------------------------------------------------
+# Signal type → cluster mapping
+# ---------------------------------------------------------------------------
+
+_SIGNAL_CLUSTER_MAP: dict[str, SignalCluster] = {
+    # Momentum
+    "ma_stack": SignalCluster.MOMENTUM,
+    "rsi": SignalCluster.MOMENTUM,
+    # Structure
+    "fibonacci": SignalCluster.STRUCTURE,
+    "elliott_wave": SignalCluster.STRUCTURE,
+    "cup_handle": SignalCluster.STRUCTURE,
+    # Volatility
+    "atr": SignalCluster.VOLATILITY,
+    "bollinger": SignalCluster.VOLATILITY,
+    # Fundamentals
+    "valuation": SignalCluster.FUNDAMENTALS,
+    "earnings": SignalCluster.FUNDAMENTALS,
+    "macro": SignalCluster.FUNDAMENTALS,
+}
+
+# Decay factor applied to successive signals within the same cluster.
+_WITHIN_CLUSTER_DECAY = 0.5
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+def classify_signal(signal_type: str) -> SignalCluster:
+    """Map a signal type string to its correlation cluster.
+
+    Falls back to :pyattr:`SignalCluster.FUNDAMENTALS` for unknown signal
+    types so that unrecognised signals still participate in the penalty
+    system rather than silently bypassing it.
+    """
+    cluster = _SIGNAL_CLUSTER_MAP.get(signal_type)
+    if cluster is None:
+        logger.warning(
+            "Unknown signal type %r — defaulting to FUNDAMENTALS cluster",
+            signal_type,
+        )
+        return SignalCluster.FUNDAMENTALS
+    return cluster
+
+
+def apply_correlation_penalty(
+    likelihood_ratios: list[LikelihoodRatio],
+) -> list[LikelihoodRatio]:
+    """Apply within-cluster decay penalty to correlated signals.
+
+    Algorithm:
+    1. Group LRs by cluster.
+    2. Within each cluster, sort by ``abs(log_lr)`` descending (strongest
+       first).
+    3. The strongest signal keeps its full ``log_lr`` as
+       ``penalized_log_lr``.
+    4. The *n*-th signal (1-indexed) receives
+       ``penalized_log_lr = log_lr * 0.5^(n-1)``.
+    5. Single-signal clusters are untouched (``penalized_log_lr = log_lr``).
+    6. Cross-cluster signals are independent — no penalty applied across
+       clusters.
+
+    Returns a **new** list of :class:`LikelihoodRatio` instances with
+    updated ``penalized_log_lr`` values.  The original objects are not
+    mutated.
+    """
+    if not likelihood_ratios:
+        return []
+
+    # Group by cluster
+    clusters: dict[str, list[tuple[int, LikelihoodRatio]]] = defaultdict(list)
+    for idx, lr in enumerate(likelihood_ratios):
+        clusters[lr.cluster].append((idx, lr))
+
+    # Build result list preserving original order
+    result: list[LikelihoodRatio | None] = [None] * len(likelihood_ratios)
+
+    for cluster_name, members in clusters.items():
+        # Sort by abs(log_lr) descending — strongest first
+        sorted_members = sorted(members, key=lambda t: abs(t[1].log_lr), reverse=True)
+
+        for rank, (orig_idx, lr) in enumerate(sorted_members):
+            decay = _WITHIN_CLUSTER_DECAY ** rank  # 0.5^0=1, 0.5^1=0.5, ...
+            penalized = lr.log_lr * decay
+
+            result[orig_idx] = LikelihoodRatio(
+                signal_type=lr.signal_type,
+                cluster=lr.cluster,
+                lr=lr.lr,
+                log_lr=lr.log_lr,
+                penalized_log_lr=penalized,
+                hit_rate=lr.hit_rate,
+                strength=lr.strength,
+            )
+
+    # Safety: should never happen, but guard against it
+    return [r for r in result if r is not None]

services/signal_engine/delta.py

@@ -0,0 +1,139 @@
+"""Delta Analyzer — compares heuristic and probabilistic pipeline verdicts.
+
+Computes agreement flags, confidence deltas, disagreement reasons, and
+tracks a rolling 100-evaluation agreement rate per ticker in Redis.
+
+Requirements: 9.1, 9.2, 9.3, 9.4, 9.5, 9.6
+"""
+
+from __future__ import annotations
+
+import logging
+
+import redis.asyncio
+
+from services.signal_engine.models import (
+    DeltaResult,
+    HeuristicResult,
+    ProbabilisticResult,
+    Verdict,
+)
+
+logger = logging.getLogger(__name__)
+
+# Redis key pattern for rolling agreement tracking
+_AGREEMENT_KEY_PREFIX = "stonks:signal_engine:agreement"
+
+# Maximum number of evaluations to track for rolling agreement rate
+_ROLLING_WINDOW = 100
+
+# Agreement rate threshold below which a warning is logged
+_AGREEMENT_WARNING_THRESHOLD = 0.50
+
+
+def _compute_disagreement_reasons(
+    heuristic: HeuristicResult,
+    probabilistic: ProbabilisticResult,
+) -> list[str]:
+    """Identify reasons for pipeline disagreement.
+
+    Compares which conditions each pipeline met or failed to produce
+    human-readable disagreement reasons for training signal generation.
+    """
+    reasons: list[str] = []
+
+    if heuristic.verdict == probabilistic.verdict:
+        return reasons
+
+    # Heuristic-side reasons
+    if heuristic.confidence < 0.70:
+        reasons.append("heuristic_confidence_below_threshold")
+    if heuristic.s_total < 1.2:
+        reasons.append("heuristic_s_total_below_threshold")
+
+    # Probabilistic-side reasons
+    if probabilistic.p_up < 0.60:
+        reasons.append("probabilistic_p_up_below_threshold")
+    if probabilistic.entropy > 0.90:
+        reasons.append("probabilistic_entropy_too_high")
+    if probabilistic.ev_r < 1.5:
+        reasons.append("EV_R_below_threshold")
+
+    # Verdict-specific context
+    if heuristic.verdict == Verdict.BUY and probabilistic.verdict != Verdict.BUY:
+        reasons.append("heuristic_buy_probabilistic_disagrees")
+    elif probabilistic.verdict == Verdict.BUY and heuristic.verdict != Verdict.BUY:
+        reasons.append("probabilistic_buy_heuristic_disagrees")
+
+    return reasons
+
+
+async def analyze_delta(
+    heuristic: HeuristicResult,
+    probabilistic: ProbabilisticResult,
+    redis_client: redis.asyncio.Redis,
+    ticker: str,
+) -> DeltaResult:
+    """Compare pipeline verdicts and track agreement metrics.
+
+    1. Compute agreement flag (both verdicts identical).
+    2. Compute confidence delta: ``|heuristic_confidence - probabilistic_P_up|``.
+    3. Record disagreement reasons when verdicts differ.
+    4. Track rolling 100-evaluation agreement rate in Redis.
+    5. Log warning when agreement rate drops below 0.50.
+
+    Returns a ``DeltaResult`` with all computed fields.
+    """
+    # Step 1: Agreement flag
+    agreement = heuristic.verdict == probabilistic.verdict
+
+    # Step 2: Confidence delta
+    confidence_delta = abs(heuristic.confidence - probabilistic.p_up)
+
+    # Step 3: Disagreement reasons
+    disagreement_reasons = _compute_disagreement_reasons(heuristic, probabilistic)
+
+    # Step 4: Rolling agreement rate in Redis
+    rolling_agreement_rate: float | None = None
+    agreement_key = f"{_AGREEMENT_KEY_PREFIX}:{ticker}"
+
+    try:
+        # Push the agreement result (1 for agree, 0 for disagree)
+        await redis_client.lpush(agreement_key, "1" if agreement else "0")
+        # Trim to the last _ROLLING_WINDOW evaluations
+        await redis_client.ltrim(agreement_key, 0, _ROLLING_WINDOW - 1)
+        # Compute the rolling agreement rate
+        values = await redis_client.lrange(agreement_key, 0, _ROLLING_WINDOW - 1)
+        if values:
+            agree_count = sum(1 for v in values if v == b"1" or v == "1")
+            rolling_agreement_rate = agree_count / len(values)
+    except Exception:
+        logger.warning(
+            "Failed to update rolling agreement rate in Redis for %s",
+            ticker,
+            exc_info=True,
+        )
+
+    # Step 5: Log warning when agreement rate drops below threshold
+    if (
+        rolling_agreement_rate is not None
+        and rolling_agreement_rate < _AGREEMENT_WARNING_THRESHOLD
+    ):
+        logger.warning(
+            "Persistent pipeline disagreement for %s: rolling agreement rate %.2f "
+            "(below %.2f threshold over last %d evaluations)",
+            ticker,
+            rolling_agreement_rate,
+            _AGREEMENT_WARNING_THRESHOLD,
+            _ROLLING_WINDOW,
+        )
+
+    # Step 6: Return DeltaResult
+    return DeltaResult(
+        agreement=agreement,
+        confidence_delta=round(confidence_delta, 6),
+        heuristic_verdict=heuristic.verdict.value,
+        probabilistic_verdict=probabilistic.verdict.value,
+        disagreement_reasons=disagreement_reasons,
+        rolling_agreement_rate=rolling_agreement_rate,
+    )

services/signal_engine/exit_engine.py

@@ -0,0 +1,154 @@
+"""Exit engine — position-level exit management.
+
+Evaluates stop-loss hits, take-profit targets, and trailing ATR-based stops
+for open positions.  Called once per evaluation tick *before* the signal
+pipelines run so that exit signals take priority over new entry signals.
+
+Priority order (first match wins per position):
+    1. stop_loss hit   → EXIT_FULL, reason ``"stop_hit"``
+    2. target_2 hit    → EXIT_FULL, reason ``"target_2_hit"``
+    3. trailing stop   → EXIT_FULL, reason ``"trailing_stop_hit"``
+    4. target_1 hit    → EXIT_HALF, reason ``"target_1_hit"``
+
+Requirements: 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7
+"""
+
+from __future__ import annotations
+
+import logging
+
+from services.signal_engine.config import ExitConfig
+from services.signal_engine.models import (
+    ExitSignal,
+    ExitType,
+    OpenPositionState,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def evaluate_exits(
+    positions: list[OpenPositionState],
+    current_prices: dict[str, float],
+    config: ExitConfig,
+) -> list[ExitSignal]:
+    """Evaluate exit conditions for all open positions.
+
+    For each position the current price is looked up in *current_prices*
+    (keyed by ticker).  If the ticker is absent the position's own
+    ``current_price`` field is used as a fallback.
+
+    Checks are applied in priority order — only the **first** matching
+    condition per position emits an ``ExitSignal``.
+
+    Parameters
+    ----------
+    positions:
+        Snapshots of open positions to evaluate.
+    current_prices:
+        Latest prices keyed by ticker symbol.
+    config:
+        Exit engine configuration (trailing stop ATR multiplier, etc.).
+
+    Returns
+    -------
+    list[ExitSignal]
+        One signal per position that triggered an exit condition.
+        Positions with no exit condition produce no signal.
+    """
+    signals: list[ExitSignal] = []
+
+    for pos in positions:
+        price = current_prices.get(pos.ticker, pos.current_price)
+
+        signal = _evaluate_single_position(pos, price, config)
+        if signal is not None:
+            signals.append(signal)
+
+    return signals
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _evaluate_single_position(
+    pos: OpenPositionState,
+    price: float,
+    config: ExitConfig,
+) -> ExitSignal | None:
+    """Check exit conditions for a single position in priority order.
+
+    Priority: stop_loss > target_2 > trailing_stop > target_1.
+    """
+
+    # 1. Stop-loss hit (highest priority)
+    if price <= pos.stop_loss:
+        return ExitSignal(
+            position_id=pos.position_id,
+            ticker=pos.ticker,
+            exit_type=ExitType.EXIT_FULL,
+            reason="stop_hit",
+            price=price,
+        )
+
+    # 2. Target 2 hit → full exit
+    if price >= pos.target_2:
+        return ExitSignal(
+            position_id=pos.position_id,
+            ticker=pos.ticker,
+            exit_type=ExitType.EXIT_FULL,
+            reason="target_2_hit",
+            price=price,
+        )
+
+    # 3. Trailing stop (only active after partial exit)
+    if pos.partial_exit_done:
+        trailing_stop = _compute_trailing_stop(pos, price, config)
+        if price <= trailing_stop:
+            return ExitSignal(
+                position_id=pos.position_id,
+                ticker=pos.ticker,
+                exit_type=ExitType.EXIT_FULL,
+                reason="trailing_stop_hit",
+                price=price,
+            )
+
+    # 4. Target 1 hit → partial exit (only if not already done)
+    if not pos.partial_exit_done and price >= pos.target_1:
+        return ExitSignal(
+            position_id=pos.position_id,
+            ticker=pos.ticker,
+            exit_type=ExitType.EXIT_HALF,
+            reason="target_1_hit",
+            price=price,
+        )
+
+    return None
+
+
+def _compute_trailing_stop(
+    pos: OpenPositionState,
+    price: float,
+    config: ExitConfig,
+) -> float:
+    """Compute the effective trailing stop level.
+
+    The trailing stop is ``price - ATR * multiplier``, but it only
+    ratchets **upward** — if the position already has a higher trailing
+    stop recorded, that value is kept.
+
+    When ATR is unavailable (``None``), the existing ``trailing_stop``
+    on the position is returned as-is.  If neither is set, returns 0.0
+    (effectively no trailing stop).
+    """
+    existing = pos.trailing_stop if pos.trailing_stop is not None else 0.0
+
+    if pos.atr is None:
+        return existing
+
+    new_level = price - pos.atr * config.trailing_stop_atr_multiplier
+
+    # Ratchet upward only
+    return max(existing, new_level)

services/signal_engine/formatter.py

@@ -0,0 +1,233 @@
+"""Output Formatter — assembles the structured SignalOutput contract.
+
+Populates trade plans based on verdict combinations and maps
+``SignalOutput`` to the existing ``Recommendation`` schema for
+trading engine compatibility.
+
+Requirements: 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 12.1, 12.2, 12.3, 12.4, 12.5
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from services.shared.schemas import (
+    ActionType,
+    PositionSizing,
+    Recommendation,
+    RecommendationMode,
+)
+from services.signal_engine.config import SignalEngineConfig
+from services.signal_engine.models import (
+    DeltaResult,
+    ExitSignal,
+    HeuristicResult,
+    ProbabilisticResult,
+    SignalOutput,
+    TradePlan,
+    Verdict,
+)
+
+# ---------------------------------------------------------------------------
+# Position sizing constants
+# ---------------------------------------------------------------------------
+
+# Full position sizing (heuristic-only or dual confirmed)
+_FULL_POSITION_SIZE_PCT = 0.02
+_FULL_MAX_LOSS_PCT = 0.005
+
+# Reduced position sizing for probabilistic-only BUY (50% of standard)
+_REDUCED_POSITION_SIZE_PCT = 0.01
+
+# Trade plan price levels (relative to entry)
+_STOP_LOSS_FACTOR = 0.95
+_TARGET_1_FACTOR = 1.05
+_TARGET_2_FACTOR = 1.10
+
+
+def _build_trade_plan(
+    price: float,
+    *,
+    dual_confirmed: bool = False,
+    probabilistic_only: bool = False,
+) -> TradePlan:
+    """Build a trade plan with position sizing based on confirmation mode.
+
+    - dual_confirmed: full position sizing with dual_confirmed flag
+    - probabilistic_only: 50% position sizing with probabilistic_only flag
+    - heuristic-only (neither flag): standard full position sizing
+    """
+    if dual_confirmed:
+        position_size_pct = _FULL_POSITION_SIZE_PCT
+        max_loss_pct = _FULL_MAX_LOSS_PCT
+    elif probabilistic_only:
+        position_size_pct = _REDUCED_POSITION_SIZE_PCT
+        max_loss_pct = _FULL_MAX_LOSS_PCT
+    else:
+        # Heuristic-only BUY
+        position_size_pct = _FULL_POSITION_SIZE_PCT
+        max_loss_pct = _FULL_MAX_LOSS_PCT
+
+    return TradePlan(
+        entry_price=price,
+        stop_loss=round(price * _STOP_LOSS_FACTOR, 6),
+        target_1=round(price * _TARGET_1_FACTOR, 6),
+        target_2=round(price * _TARGET_2_FACTOR, 6),
+        position_size_pct=position_size_pct,
+        max_loss_pct=max_loss_pct,
+        dual_confirmed=dual_confirmed,
+        probabilistic_only=probabilistic_only,
+    )
+
+
+def format_output(
+    ticker: str,
+    price: float,
+    heuristic: HeuristicResult,
+    probabilistic: ProbabilisticResult,
+    delta: DeltaResult,
+    exit_signals: list[ExitSignal],
+    config: SignalEngineConfig,
+) -> SignalOutput:
+    """Assemble the structured ``SignalOutput`` contract.
+
+    Trade plan logic:
+    - Both BUY → ``dual_confirmed``, full position sizing
+    - Probabilistic-only BUY → ``probabilistic_only``, 50% position sizing
+    - Heuristic-only BUY → standard position sizing
+    - No BUY → no trade_plan (WATCH/SKIP persisted for analysis)
+    """
+    heuristic_buy = heuristic.verdict == Verdict.BUY
+    probabilistic_buy = probabilistic.verdict == Verdict.BUY
+
+    trade_plan: TradePlan | None = None
+
+    if heuristic_buy and probabilistic_buy:
+        # Both pipelines agree on BUY → dual confirmed
+        trade_plan = _build_trade_plan(
+            price, dual_confirmed=True, probabilistic_only=False
+        )
+    elif probabilistic_buy and not heuristic_buy:
+        # Probabilistic-only BUY → reduced position sizing
+        trade_plan = _build_trade_plan(
+            price, dual_confirmed=False, probabilistic_only=True
+        )
+    elif heuristic_buy and not probabilistic_buy:
+        # Heuristic-only BUY → standard position sizing
+        trade_plan = _build_trade_plan(
+            price, dual_confirmed=False, probabilistic_only=False
+        )
+    # else: No BUY → no trade_plan
+
+    return SignalOutput(
+        ticker=ticker,
+        timestamp=datetime.now(tz=timezone.utc),
+        price=price,
+        # Heuristic pipeline section
+        heuristic_verdict=heuristic.verdict.value,
+        heuristic_confidence=heuristic.confidence,
+        heuristic_s_total=heuristic.s_total,
+        # Probabilistic pipeline section
+        probabilistic_verdict=probabilistic.verdict.value,
+        probabilistic_p_up=probabilistic.p_up,
+        probabilistic_entropy=probabilistic.entropy,
+        probabilistic_ev_r=probabilistic.ev_r,
+        # Delta analysis section
+        delta_agreement=delta.agreement,
+        delta_confidence_delta=delta.confidence_delta,
+        delta_reasons=delta.disagreement_reasons,
+        # Trade plan and exit signals
+        trade_plan=trade_plan,
+        exit_signals=exit_signals,
+        # Detail payloads for audit
+        heuristic_detail=heuristic.model_dump(),
+        probabilistic_detail=probabilistic.model_dump(),
+        # Pipeline mode metadata
+        pipeline_mode="dual_pipeline",
+        shadow_mode=config.shadow_mode,
+    )
+
+
+def signal_output_to_recommendation(output: SignalOutput) -> Recommendation:
+    """Map a ``SignalOutput`` to the existing ``Recommendation`` schema.
+
+    Enables the trading engine to consume dual-pipeline outputs without
+    modification to its core ``evaluate_recommendation`` logic.
+
+    Confidence mapping:
+    - Dual confirmed: ``max(heuristic_confidence, probabilistic_P_up)``
+    - Probabilistic only: ``probabilistic_P_up * 0.8`` (20% haircut)
+    - Heuristic only: ``heuristic_confidence``
+    - No BUY: ``max(heuristic_confidence, probabilistic_P_up)``
+
+    Action mapping:
+    - BUY (either pipeline) → ``ActionType.BUY``
+    - WATCH → ``ActionType.WATCH``
+    - SKIP → ``ActionType.HOLD``
+
+    Mode: always ``RecommendationMode.PAPER_ELIGIBLE``
+    """
+    trade_plan = output.trade_plan
+
+    # Determine confidence based on confirmation mode
+    if trade_plan is not None and trade_plan.dual_confirmed:
+        confidence = max(output.heuristic_confidence, output.probabilistic_p_up)
+    elif trade_plan is not None and trade_plan.probabilistic_only:
+        confidence = output.probabilistic_p_up * 0.8
+    elif trade_plan is not None:
+        # Heuristic-only BUY
+        confidence = output.heuristic_confidence
+    else:
+        # No trade plan — use the best available confidence
+        confidence = max(output.heuristic_confidence, output.probabilistic_p_up)
+
+    # Clamp confidence to [0, 1]
+    confidence = max(0.0, min(1.0, confidence))
+
+    # Determine action from verdicts
+    h_verdict = output.heuristic_verdict
+    p_verdict = output.probabilistic_verdict
+
+    if h_verdict == Verdict.BUY.value or p_verdict == Verdict.BUY.value:
+        action = ActionType.BUY
+    elif h_verdict == Verdict.WATCH.value or p_verdict == Verdict.WATCH.value:
+        action = ActionType.WATCH
+    else:
+        action = ActionType.HOLD
+
+    # Build position sizing from trade plan if available
+    position_sizing = PositionSizing()
+    if trade_plan is not None:
+        position_sizing = PositionSizing(
+            portfolio_pct=trade_plan.position_size_pct,
+            max_loss_pct=trade_plan.max_loss_pct,
+        )
+
+    # Build thesis from delta analysis
+    thesis_parts: list[str] = []
+    if trade_plan is not None and trade_plan.dual_confirmed:
+        thesis_parts.append("Dual-pipeline confirmed BUY signal")
+    elif trade_plan is not None and trade_plan.probabilistic_only:
+        thesis_parts.append("Probabilistic-only BUY signal (reduced sizing)")
+    elif trade_plan is not None:
+        thesis_parts.append("Heuristic-only BUY signal")
+    else:
+        thesis_parts.append(f"No BUY signal (H={h_verdict}, P={p_verdict})")
+
+    if output.delta_reasons:
+        thesis_parts.append(f"Delta reasons: {', '.join(output.delta_reasons)}")
+
+    return Recommendation(
+        recommendation_id=output.output_id,
+        ticker=output.ticker,
+        action=action,
+        mode=RecommendationMode.PAPER_ELIGIBLE,
+        confidence=confidence,
+        time_horizon="signal_engine",
+        thesis="; ".join(thesis_parts),
+        position_sizing=position_sizing,
+        pipeline_mode="dual_pipeline",
+        p_bull=output.probabilistic_p_up,
+        expected_value=output.probabilistic_ev_r,
+        generated_at=output.timestamp,
+    )

services/signal_engine/hard_filter.py

@@ -0,0 +1,80 @@
+"""Hard Filter Engine — pre-pipeline gating for the dual-pipeline signal engine.
+
+Evaluates macro bias, valuation score, and earnings proximity to short-circuit
+both pipelines before evaluation.  All conditions are checked and all triggered
+reasons are collected (no short-circuit on first match).
+
+Requirements: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+
+from services.signal_engine.config import HardFilterConfig
+from services.signal_engine.models import NormalizedInput
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HardFilterResult:
+    """Outcome of the hard filter evaluation.
+
+    ``filtered=True`` means the ticker should be **skipped** — both pipelines
+    are short-circuited.  ``reasons`` lists every filter that triggered.
+    """
+
+    filtered: bool = False
+    reasons: list[str] = field(default_factory=list)
+
+
+def evaluate_hard_filters(
+    normalized: NormalizedInput,
+    config: HardFilterConfig,
+) -> HardFilterResult:
+    """Evaluate pre-pipeline hard filters.
+
+    Checks (all evaluated, not short-circuited):
+    - ``macro_bias == config.macro_bias_skip`` → reason ``"macro_bias_negative"``
+    - ``valuation_score < config.valuation_min`` → reason ``"valuation_below_threshold"``
+    - ``earnings_proximity_days <= config.earnings_days`` → reason ``"earnings_block"``
+
+    Missing optional fields (``valuation_score is None``,
+    ``earnings_proximity_days is None``) do **not** trigger a filter — missing
+    data should not produce a false-positive SKIP.
+
+    Returns a :class:`HardFilterResult` with ``filtered=True`` when at least
+    one reason was recorded.
+    """
+    reasons: list[str] = []
+
+    # 4.1 — macro_bias exact equality with configured skip value
+    if normalized.macro_bias == config.macro_bias_skip:
+        reasons.append("macro_bias_negative")
+
+    # 4.2 — valuation score below minimum threshold
+    if (
+        normalized.valuation_score is not None
+        and normalized.valuation_score < config.valuation_min
+    ):
+        reasons.append("valuation_below_threshold")
+
+    # 4.3 — earnings proximity within block window
+    if (
+        normalized.earnings_proximity_days is not None
+        and normalized.earnings_proximity_days <= config.earnings_days
+    ):
+        reasons.append("earnings_block")
+
+    filtered = len(reasons) > 0
+
+    if filtered:
+        logger.info(
+            "Hard filter triggered for %s: %s",
+            normalized.ticker,
+            ", ".join(reasons),
+        )
+
+    return HardFilterResult(filtered=filtered, reasons=reasons)

services/signal_engine/heuristic.py

@@ -0,0 +1,299 @@
+"""Heuristic Pipeline (Pipeline A) — Deterministic scoring and verdict.
+
+Computes ``S_total = S_company + S_macro + S_competitive`` from confluence-
+filtered signals and produces a confidence-gated BUY / WATCH / SKIP verdict.
+
+The pipeline reuses the existing ``compute_signal_weight`` infrastructure
+from ``services.aggregation.scoring`` for signal weighting and follows the
+three-layer signal aggregation model (company, macro, competitive).
+
+Requirements: 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7
+"""
+
+from __future__ import annotations
+
+import logging
+
+from services.signal_engine.config import HeuristicConfig
+from services.signal_engine.models import (
+    ConfluenceSignal,
+    HeuristicResult,
+    NormalizedInput,
+    SignalDirection,
+    Verdict,
+)
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Signal classification — which confluence signals belong to which layer
+# ---------------------------------------------------------------------------
+
+# Company-level technical signals (Layer 1)
+COMPANY_SIGNAL_TYPES: frozenset[str] = frozenset({
+    "fibonacci",
+    "ma_stack",
+    "rsi",
+    "cup_handle",
+    "elliott_wave",
+})
+
+# Competitive signals (Layer 3) — future expansion
+COMPETITIVE_SIGNAL_TYPES: frozenset[str] = frozenset()
+
+# Macro weight applied to macro_bias to produce S_macro
+_MACRO_WEIGHT: float = 0.5
+
+
+# ---------------------------------------------------------------------------
+# Score computation helpers
+# ---------------------------------------------------------------------------
+
+
+def _compute_s_company(confluence_signals: list[ConfluenceSignal]) -> tuple[float, list[dict]]:
+    """Sum confluence scores for company-level signals.
+
+    Returns the total S_company score and a list of per-signal weight
+    breakdowns for audit.
+    """
+    s_company = 0.0
+    weights: list[dict] = []
+
+    for sig in confluence_signals:
+        if sig.signal_type in COMPANY_SIGNAL_TYPES:
+            # Direction-aware: bullish contributes positively, bearish negatively
+            direction_sign = _direction_sign(sig.direction)
+            contribution = sig.confluence_score * direction_sign
+            s_company += contribution
+            weights.append({
+                "signal_type": sig.signal_type,
+                "layer": "company",
+                "confluence_score": sig.confluence_score,
+                "direction": sig.direction.value,
+                "contribution": contribution,
+                "active_timeframes": sig.active_timeframes,
+            })
+
+    return s_company, weights
+
+
+def _compute_s_macro(normalized: NormalizedInput) -> float:
+    """Compute macro score from macro_bias.
+
+    S_macro = macro_bias * weight, where macro_bias is in [-1.0, 1.0].
+    A positive macro_bias contributes positively; negative contributes
+    negatively.
+    """
+    return normalized.macro_bias * _MACRO_WEIGHT
+
+
+def _compute_s_competitive(confluence_signals: list[ConfluenceSignal]) -> float:
+    """Sum confluence scores for competitive-layer signals.
+
+    Currently returns 0.0 as no competitive signal types are defined in
+    the signal library.  This is a placeholder for future expansion.
+    """
+    s_competitive = 0.0
+    for sig in confluence_signals:
+        if sig.signal_type in COMPETITIVE_SIGNAL_TYPES:
+            direction_sign = _direction_sign(sig.direction)
+            s_competitive += sig.confluence_score * direction_sign
+    return s_competitive
+
+
+def _direction_sign(direction: SignalDirection) -> float:
+    """Map signal direction to a numeric sign."""
+    if direction == SignalDirection.BULLISH:
+        return 1.0
+    if direction == SignalDirection.BEARISH:
+        return -1.0
+    return 0.0
+
+
+# ---------------------------------------------------------------------------
+# Confidence computation
+# ---------------------------------------------------------------------------
+
+
+def _compute_confidence(
+    confluence_signals: list[ConfluenceSignal],
+) -> float:
+    """Compute pipeline confidence from confluence signals.
+
+    Confidence is derived from:
+    1. **Base confidence** — average signal strength across all confluence
+       signals (mean of confluence_score values).
+    2. **Source count boost** — more active signals increase confidence
+       (diminishing returns, capped contribution).
+    3. **Signal agreement boost** — if all signals point in the same
+       direction, confidence is boosted.
+    4. **Contradiction penalty** — if signals disagree on direction,
+       confidence is penalised.
+
+    Returns a value clamped to [0.0, 1.0].
+    """
+    if not confluence_signals:
+        return 0.0
+
+    # 1. Base confidence: average confluence score (already weighted by
+    #    timeframe importance)
+    total_score = sum(s.confluence_score for s in confluence_signals)
+    base_confidence = total_score / len(confluence_signals)
+
+    # 2. Source count factor: more signals → higher confidence, with
+    #    diminishing returns.  1 signal → 0.6, 2 → 0.75, 3 → 0.85,
+    #    4 → 0.90, 5+ → 0.95 (asymptotic).
+    n = len(confluence_signals)
+    source_factor = 1.0 - (0.4 / n)  # approaches 1.0 as n grows
+
+    # 3. Signal agreement / contradiction
+    directions = [s.direction for s in confluence_signals]
+    bullish_count = sum(1 for d in directions if d == SignalDirection.BULLISH)
+    bearish_count = sum(1 for d in directions if d == SignalDirection.BEARISH)
+
+    if n == 1:
+        agreement_factor = 1.0
+    elif bullish_count == n or bearish_count == n:
+        # Perfect agreement — boost
+        agreement_factor = 1.15
+    elif bullish_count > 0 and bearish_count > 0:
+        # Contradiction — penalty proportional to minority fraction
+        minority = min(bullish_count, bearish_count)
+        contradiction_ratio = minority / n
+        agreement_factor = 1.0 - (0.3 * contradiction_ratio)
+    else:
+        # Mix of directional and neutral — mild boost
+        agreement_factor = 1.05
+
+    confidence = base_confidence * source_factor * agreement_factor
+    return max(0.0, min(confidence, 1.0))
+
+
+# ---------------------------------------------------------------------------
+# Verdict logic
+# ---------------------------------------------------------------------------
+
+
+def _determine_verdict(
+    confidence: float,
+    s_total: float,
+    normalized: NormalizedInput,
+    config: HeuristicConfig,
+) -> tuple[Verdict, list[str]]:
+    """Apply threshold logic to determine BUY / WATCH / SKIP verdict.
+
+    Returns the verdict and a list of reasoning strings explaining the
+    decision.
+    """
+    reasoning: list[str] = []
+
+    valuation_score = normalized.valuation_score if normalized.valuation_score is not None else 0.0
+    earnings_days = normalized.earnings_proximity_days if normalized.earnings_proximity_days is not None else 0
+
+    # --- Check BUY conditions ---
+    buy_conditions = {
+        "confidence": confidence >= config.buy_confidence,
+        "s_total": s_total >= config.buy_s_total,
+        "valuation": valuation_score >= config.buy_valuation_min,
+        "macro_bias": normalized.macro_bias > config.macro_bias_threshold,
+        "earnings_proximity": earnings_days > config.earnings_days_threshold,
+    }
+
+    all_buy_met = all(buy_conditions.values())
+
+    if all_buy_met:
+        reasoning.append(
+            f"BUY: all conditions met — confidence={confidence:.3f} "
+            f"(>= {config.buy_confidence}), S_total={s_total:.3f} "
+            f"(>= {config.buy_s_total}), valuation={valuation_score:.2f} "
+            f"(>= {config.buy_valuation_min}), macro_bias={normalized.macro_bias:.2f} "
+            f"(> {config.macro_bias_threshold}), earnings_days={earnings_days} "
+            f"(> {config.earnings_days_threshold})"
+        )
+        return Verdict.BUY, reasoning
+
+    # --- Check WATCH conditions ---
+    if confidence >= config.watch_confidence:
+        # WATCH: confidence is sufficient but not all BUY conditions met
+        failed_conditions = [k for k, v in buy_conditions.items() if not v]
+        reasoning.append(
+            f"WATCH: confidence={confidence:.3f} (>= {config.watch_confidence}) "
+            f"but BUY conditions not fully met — failed: {', '.join(failed_conditions)}"
+        )
+        for cond_name, met in buy_conditions.items():
+            if not met:
+                reasoning.append(f"  - {cond_name} not met")
+        return Verdict.WATCH, reasoning
+
+    # --- SKIP ---
+    reasoning.append(
+        f"SKIP: confidence={confidence:.3f} < {config.watch_confidence} "
+        f"(watch threshold)"
+    )
+    return Verdict.SKIP, reasoning
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+def run_heuristic_pipeline(
+    normalized: NormalizedInput,
+    confluence_signals: list[ConfluenceSignal],
+    config: HeuristicConfig,
+) -> HeuristicResult:
+    """Run the deterministic heuristic pipeline.
+
+    Computes ``S_total = S_company + S_macro + S_competitive`` using the
+    existing three-layer signal aggregation model and produces a
+    confidence-gated BUY / WATCH / SKIP verdict.
+
+    Args:
+        normalized: The unified input structure for this evaluation tick.
+        confluence_signals: Signals that passed multi-timeframe confluence
+            filtering.
+        config: Heuristic pipeline thresholds.
+
+    Returns:
+        A :class:`HeuristicResult` with verdict, scores, weights, and
+        reasoning.
+
+    Requirements: 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7
+    """
+    # 1. Compute three-layer scores
+    s_company, signal_weights = _compute_s_company(confluence_signals)
+    s_macro = _compute_s_macro(normalized)
+    s_competitive = _compute_s_competitive(confluence_signals)
+    s_total = s_company + s_macro + s_competitive
+
+    # 2. Compute confidence
+    confidence = _compute_confidence(confluence_signals)
+
+    # 3. Determine verdict
+    verdict, reasoning = _determine_verdict(confidence, s_total, normalized, config)
+
+    logger.info(
+        "Heuristic pipeline [%s]: verdict=%s confidence=%.3f "
+        "S_total=%.3f (company=%.3f macro=%.3f competitive=%.3f) "
+        "signals=%d",
+        normalized.ticker,
+        verdict.value,
+        confidence,
+        s_total,
+        s_company,
+        s_macro,
+        s_competitive,
+        len(confluence_signals),
+    )
+
+    return HeuristicResult(
+        verdict=verdict,
+        confidence=confidence,
+        s_total=s_total,
+        s_company=s_company,
+        s_macro=s_macro,
+        s_competitive=s_competitive,
+        signal_weights=signal_weights,
+        reasoning=reasoning,
+    )

services/signal_engine/main.py

@@ -0,0 +1,180 @@
+"""Signal engine entry point — asyncio event loop and queue polling.
+
+Connects to PostgreSQL and Redis, loads configuration from ``risk_configs``,
+and polls the ``stonks:queue:signal_engine`` queue indefinitely.  Each
+queue message triggers a full evaluation tick via ``evaluate_tick()``.
+
+When ``dual_pipeline_enabled`` is ``False`` the worker sleeps and retries
+(fail-safe: the existing pipeline continues unchanged).
+
+Requirements: 13.1, 13.6, 13.7, 16.1, 16.6
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import sys
+
+import asyncpg
+import redis.asyncio
+
+from services.shared.config import load_config as load_app_config
+from services.shared.redis_keys import QUEUE_SIGNAL_ENGINE, queue_key
+from services.signal_engine.config import load_config as load_signal_config
+from services.signal_engine.worker import evaluate_tick
+
+logger = logging.getLogger(__name__)
+
+# BLPOP timeout in seconds — how long to wait for a queue message before
+# looping back to check the enabled flag.
+_BLPOP_TIMEOUT = 5
+
+
+async def main() -> None:
+    """Start the signal engine worker loop.
+
+    1. Connect to PostgreSQL (asyncpg pool) using env vars from
+       ``services.shared.config``.
+    2. Connect to Redis (redis.asyncio) using env vars.
+    3. Load signal engine config via ``load_config(pool)``.
+    4. Log active configuration at startup.
+    5. Poll ``stonks:queue:signal_engine`` queue indefinitely (BLPOP).
+    6. Check ``dual_pipeline_enabled`` flag; if disabled, sleep and retry.
+    7. On config read failure, default to disabled (fail-safe).
+    8. Parse queue message as JSON: ``{"ticker": "AAPL", "triggered_at": "..."}``.
+    9. Call ``evaluate_tick(pool, redis, ticker, config)`` for each message.
+
+    Requirements: 13.1, 13.6, 13.7, 16.1, 16.6
+    """
+    # --- Setup logging ---
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(name)s %(levelname)s %(message)s",
+        stream=sys.stdout,
+    )
+
+    logger.info("Signal engine starting up")
+
+    # --- Load shared app config for connection details ---
+    app_config = load_app_config()
+
+    # --- Connect to PostgreSQL ---
+    pool = await asyncpg.create_pool(
+        dsn=app_config.postgres.dsn,
+        min_size=2,
+        max_size=10,
+    )
+    logger.info("Connected to PostgreSQL at %s", app_config.postgres.host)
+
+    # --- Connect to Redis ---
+    redis_client = redis.asyncio.from_url(
+        app_config.redis.url,
+        decode_responses=True,
+    )
+    logger.info("Connected to Redis at %s", app_config.redis.host)
+
+    # --- Load signal engine config ---
+    try:
+        config = await load_signal_config(pool)
+    except Exception:
+        logger.warning(
+            "Failed to load signal engine config at startup — "
+            "defaulting to disabled (fail-safe)",
+            exc_info=True,
+        )
+        from services.signal_engine.config import SignalEngineConfig
+        config = SignalEngineConfig()  # dual_pipeline_enabled=False
+
+    logger.info(
+        "Signal engine config: dual_pipeline_enabled=%s, "
+        "heuristic=%s, probabilistic=%s, shadow_mode=%s, "
+        "polling_interval=%ds",
+        config.dual_pipeline_enabled,
+        config.heuristic_pipeline_enabled,
+        config.probabilistic_pipeline_enabled,
+        config.shadow_mode,
+        config.polling_interval_seconds,
+    )
+
+    # --- Queue key ---
+    signal_queue = queue_key(QUEUE_SIGNAL_ENGINE)
+    logger.info("Polling queue: %s", signal_queue)
+
+    # --- Main loop ---
+    try:
+        while True:
+            # Check if dual pipeline is enabled
+            if not config.dual_pipeline_enabled:
+                logger.debug(
+                    "Dual pipeline disabled — sleeping %ds before retry",
+                    config.polling_interval_seconds,
+                )
+                await asyncio.sleep(config.polling_interval_seconds)
+
+                # Reload config to pick up flag changes
+                try:
+                    config = await load_signal_config(pool)
+                except Exception:
+                    logger.warning(
+                        "Failed to reload signal engine config — "
+                        "keeping disabled (fail-safe)",
+                        exc_info=True,
+                    )
+                continue
+
+            # BLPOP: blocking pop from the signal engine queue
+            try:
+                result = await redis_client.blpop(
+                    signal_queue,
+                    timeout=_BLPOP_TIMEOUT,
+                )
+            except Exception:
+                logger.warning(
+                    "Redis BLPOP failed — sleeping before retry",
+                    exc_info=True,
+                )
+                await asyncio.sleep(5)
+                continue
+
+            if result is None:
+                # Timeout — no message, loop back
+                continue
+
+            # result is (queue_name, message)
+            _, raw_message = result
+
+            # Parse the queue message
+            try:
+                message = json.loads(raw_message)
+                ticker = message["ticker"]
+            except (json.JSONDecodeError, KeyError, TypeError):
+                logger.warning(
+                    "Invalid queue message — skipping: %s",
+                    raw_message,
+                )
+                continue
+
+            logger.info("Processing evaluation tick for %s", ticker)
+
+            # Run the evaluation tick
+            try:
+                await evaluate_tick(pool, redis_client, ticker, config)
+            except Exception:
+                logger.error(
+                    "Unhandled error in evaluate_tick for %s",
+                    ticker,
+                    exc_info=True,
+                )
+
+    except KeyboardInterrupt:
+        logger.info("Signal engine shutting down (KeyboardInterrupt)")
+    finally:
+        await pool.close()
+        await redis_client.aclose()
+        logger.info("Signal engine shut down")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

services/signal_engine/models.py

@@ -0,0 +1,271 @@
+"""Pydantic data models for the dual-pipeline signal engine.
+
+Defines all input, intermediate, and output models consumed by the heuristic
+pipeline, probabilistic pipeline, delta analyzer, exit engine, and output
+formatter.  Every model is a Pydantic ``BaseModel`` subclass with field-level
+constraints where applicable.
+"""
+
+from __future__ import annotations
+
+import uuid
+from datetime import datetime
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+# ---------------------------------------------------------------------------
+# Market data
+# ---------------------------------------------------------------------------
+
+
+class OHLCVBar(BaseModel):
+    """Single OHLCV bar for a timeframe."""
+
+    timestamp: datetime
+    open: float
+    high: float
+    low: float
+    close: float
+    volume: float
+
+
+# ---------------------------------------------------------------------------
+# Position state (for exit engine)
+# ---------------------------------------------------------------------------
+
+
+class OpenPositionState(BaseModel):
+    """Snapshot of an open position for exit evaluation."""
+
+    position_id: str
+    ticker: str
+    entry_price: float
+    current_price: float
+    stop_loss: float
+    target_1: float
+    target_2: float
+    trailing_stop: float | None = None
+    partial_exit_done: bool = False
+    atr: float | None = None
+
+
+# ---------------------------------------------------------------------------
+# Normalized input consumed by both pipelines
+# ---------------------------------------------------------------------------
+
+
+class NormalizedInput(BaseModel):
+    """Unified input structure consumed by both pipelines."""
+
+    ticker: str
+    evaluated_at: datetime
+
+    # Multi-timeframe OHLCV bars keyed by timeframe label
+    bars: dict[str, list[OHLCVBar]]  # {"M30": [...], "H1": [...], ...}
+
+    # Fundamental / macro context
+    valuation_score: float | None = None  # [0.0, 1.0]
+    earnings_proximity_days: int | None = None
+    macro_bias: float = 0.0  # [-1.0, 1.0]
+
+    # Open positions for exit evaluation
+    open_positions: list[OpenPositionState] = Field(default_factory=list)
+
+    # Price series helpers (used by probabilistic pipeline)
+    closing_prices: list[float] = Field(default_factory=list)
+    returns: list[float] = Field(default_factory=list)
+    current_price: float | None = None
+
+
+# ---------------------------------------------------------------------------
+# Signal evaluation primitives
+# ---------------------------------------------------------------------------
+
+
+class SignalDirection(str, Enum):
+    BULLISH = "bullish"
+    BEARISH = "bearish"
+    NEUTRAL = "neutral"
+
+
+class SignalResult(BaseModel):
+    """Output from a single signal evaluator on a single timeframe."""
+
+    signal_type: str
+    timeframe: str
+    strength: float = Field(ge=0.0, le=1.0)
+    direction: SignalDirection
+    confidence: float = Field(ge=0.0, le=1.0)
+    metadata: dict = Field(default_factory=dict)
+
+
+# ---------------------------------------------------------------------------
+# Multi-timeframe confluence
+# ---------------------------------------------------------------------------
+
+
+class ConfluenceSignal(BaseModel):
+    """A signal that passed multi-timeframe confluence filtering."""
+
+    signal_type: str
+    direction: SignalDirection
+    confluence_score: float
+    active_timeframes: list[str]
+    per_timeframe: dict[str, float]
+
+
+# ---------------------------------------------------------------------------
+# Pipeline verdicts
+# ---------------------------------------------------------------------------
+
+
+class Verdict(str, Enum):
+    BUY = "BUY"
+    WATCH = "WATCH"
+    SKIP = "SKIP"
+
+
+# ---------------------------------------------------------------------------
+# Heuristic pipeline output
+# ---------------------------------------------------------------------------
+
+
+class HeuristicResult(BaseModel):
+    """Output from the heuristic (deterministic) pipeline."""
+
+    verdict: Verdict
+    confidence: float = Field(ge=0.0, le=1.0)
+    s_total: float
+    s_company: float
+    s_macro: float
+    s_competitive: float
+    signal_weights: list[dict] = Field(default_factory=list)
+    reasoning: list[str] = Field(default_factory=list)
+
+
+# ---------------------------------------------------------------------------
+# Probabilistic pipeline output
+# ---------------------------------------------------------------------------
+
+
+class LikelihoodRatio(BaseModel):
+    """A single signal's likelihood ratio for Bayesian updating."""
+
+    signal_type: str
+    cluster: str
+    lr: float
+    log_lr: float
+    penalized_log_lr: float
+    hit_rate: float
+    strength: float
+
+
+class ProbabilisticResult(BaseModel):
+    """Output from the probabilistic (Bayesian) pipeline."""
+
+    verdict: Verdict
+    p_up: float = Field(ge=0.0, le=1.0)
+    entropy: float = Field(ge=0.0, le=1.0)
+    ev_r: float
+    prior: float
+    posterior: float
+    likelihood_ratios: list[LikelihoodRatio] = Field(default_factory=list)
+    regime: str
+    reasoning: list[str] = Field(default_factory=list)
+
+
+# ---------------------------------------------------------------------------
+# Delta analyzer output
+# ---------------------------------------------------------------------------
+
+
+class DeltaResult(BaseModel):
+    """Output from the delta analyzer comparing both pipelines."""
+
+    agreement: bool
+    confidence_delta: float
+    heuristic_verdict: str
+    probabilistic_verdict: str
+    disagreement_reasons: list[str] = Field(default_factory=list)
+    rolling_agreement_rate: float | None = None
+
+
+# ---------------------------------------------------------------------------
+# Exit engine
+# ---------------------------------------------------------------------------
+
+
+class ExitType(str, Enum):
+    EXIT_HALF = "EXIT_HALF"
+    EXIT_FULL = "EXIT_FULL"
+
+
+class ExitSignal(BaseModel):
+    """An exit signal for an open position."""
+
+    position_id: str
+    ticker: str
+    exit_type: ExitType
+    reason: str
+    price: float
+
+
+# ---------------------------------------------------------------------------
+# Trade plan
+# ---------------------------------------------------------------------------
+
+
+class TradePlan(BaseModel):
+    """Optional trade plan attached to a BUY signal."""
+
+    entry_price: float
+    stop_loss: float
+    target_1: float
+    target_2: float
+    position_size_pct: float = Field(ge=0.0, le=1.0)
+    max_loss_pct: float = Field(ge=0.0, le=1.0)
+    dual_confirmed: bool = False
+    probabilistic_only: bool = False
+
+
+# ---------------------------------------------------------------------------
+# Structured output contract
+# ---------------------------------------------------------------------------
+
+
+class SignalOutput(BaseModel):
+    """The structured output contract consumed by the trading engine and audit systems."""
+
+    output_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
+    ticker: str
+    timestamp: datetime
+    price: float
+
+    # Heuristic pipeline section
+    heuristic_verdict: str
+    heuristic_confidence: float
+    heuristic_s_total: float
+
+    # Probabilistic pipeline section
+    probabilistic_verdict: str
+    probabilistic_p_up: float
+    probabilistic_entropy: float
+    probabilistic_ev_r: float
+
+    # Delta analysis section
+    delta_agreement: bool
+    delta_confidence_delta: float
+    delta_reasons: list[str] = Field(default_factory=list)
+
+    # Optional trade plan and exit signals
+    trade_plan: TradePlan | None = None
+    exit_signals: list[ExitSignal] = Field(default_factory=list)
+
+    # Detail payloads for audit / dashboard
+    heuristic_detail: dict = Field(default_factory=dict)
+    probabilistic_detail: dict = Field(default_factory=dict)
+
+    # Pipeline mode metadata
+    pipeline_mode: str = "dual_pipeline"
+    shadow_mode: bool = False

services/signal_engine/normalizer.py

@@ -0,0 +1,459 @@
+"""Input Normalizer — fetches and assembles NormalizedInput for a single tick.
+
+Queries multiple data sources (market snapshots, trend windows, earnings
+calendar, macro impact records, position stop levels) and assembles them
+into a single ``NormalizedInput`` consumed by both pipelines.
+
+Missing data sources produce sentinel values (``None`` / empty list) with a
+logged warning — the normalizer never crashes on unavailable data.
+
+Requirements: 1.1, 1.2, 1.3, 1.4, 1.5
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from datetime import datetime, timezone
+
+import asyncpg
+
+from .config import SignalEngineConfig
+from .models import NormalizedInput, OHLCVBar, OpenPositionState
+
+logger = logging.getLogger(__name__)
+
+# Timeframes the signal engine evaluates, ordered shortest → longest.
+TIMEFRAMES = ("M30", "H1", "H4", "D", "W", "M")
+
+# ---------------------------------------------------------------------------
+# Direction → numeric bias mapping (same semantics as aggregation worker)
+# ---------------------------------------------------------------------------
+_DIRECTION_TO_BIAS: dict[str, float] = {
+    "positive": 1.0,
+    "negative": -1.0,
+    "mixed": 0.0,
+    "neutral": 0.0,
+}
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _validate_monotonic_timestamps(
+    bars: list[OHLCVBar],
+    timeframe: str,
+    ticker: str,
+) -> list[OHLCVBar]:
+    """Return *bars* sorted by timestamp, warning on non-monotonic input.
+
+    If timestamps are already strictly increasing the list is returned
+    unchanged.  Otherwise the bars are sorted and a warning is logged.
+    """
+    if len(bars) <= 1:
+        return bars
+
+    is_monotonic = all(
+        bars[i].timestamp < bars[i + 1].timestamp for i in range(len(bars) - 1)
+    )
+    if is_monotonic:
+        return bars
+
+    logger.warning(
+        "%s/%s: OHLCV timestamps not monotonically increasing — sorting",
+        ticker,
+        timeframe,
+    )
+    return sorted(bars, key=lambda b: b.timestamp)
+
+
+def _polygon_bar_to_ohlcv(row: asyncpg.Record) -> OHLCVBar | None:
+    """Convert a market_snapshots row (JSONB data column) to an OHLCVBar.
+
+    Polygon bar format stored in ``data``:
+        t  — timestamp in epoch milliseconds
+        o  — open
+        h  — high
+        l  — low
+        c  — close
+        v  — volume
+
+    Returns ``None`` if the row cannot be parsed.
+    """
+    data = row["data"]
+    if not isinstance(data, dict):
+        return None
+    try:
+        ts_ms = data.get("t")
+        if ts_ms is None:
+            return None
+        return OHLCVBar(
+            timestamp=datetime.fromtimestamp(int(ts_ms) / 1000, tz=timezone.utc),
+            open=float(data.get("o", 0)),
+            high=float(data.get("h", 0)),
+            low=float(data.get("l", 0)),
+            close=float(data.get("c", 0)),
+            volume=float(data.get("v", 0)),
+        )
+    except (TypeError, ValueError, OverflowError):
+        return None
+
+
+# ---------------------------------------------------------------------------
+# Data-source fetchers
+# ---------------------------------------------------------------------------
+
+
+async def _fetch_bars(
+    pool: asyncpg.Pool,
+    ticker: str,
+) -> dict[str, list[OHLCVBar]]:
+    """Fetch OHLCV bars from ``market_snapshots`` for all timeframes.
+
+    The current database stores daily bars (``snapshot_type = 'bar'``) from
+    Polygon.  Intraday bars are stored with ``snapshot_type = 'intraday_bar'``
+    when available.
+
+    For timeframes that have no dedicated data yet (H4, W, M) we derive them
+    from daily bars where possible:
+    - **W** (weekly): group daily bars by ISO week.
+    - **M** (monthly): group daily bars by calendar month.
+    - **H4 / H1 / M30**: sourced from intraday snapshots when present;
+      otherwise left empty.
+
+    Returns a dict keyed by timeframe label with validated bar lists.
+    """
+    bars: dict[str, list[OHLCVBar]] = {tf: [] for tf in TIMEFRAMES}
+
+    # --- Daily bars --------------------------------------------------------
+    try:
+        rows = await pool.fetch(
+            "SELECT data FROM market_snapshots "
+            "WHERE ticker = $1 AND snapshot_type = 'bar' "
+            "ORDER BY captured_at ASC",
+            ticker,
+        )
+        daily: list[OHLCVBar] = []
+        for row in rows:
+            bar = _polygon_bar_to_ohlcv(row)
+            if bar is not None:
+                daily.append(bar)
+        bars["D"] = daily
+    except Exception:
+        logger.warning("%s: failed to fetch daily bars", ticker, exc_info=True)
+
+    # --- Intraday bars (M30, H1) ------------------------------------------
+    try:
+        intraday_rows = await pool.fetch(
+            "SELECT data FROM market_snapshots "
+            "WHERE ticker = $1 AND snapshot_type = 'intraday_bar' "
+            "ORDER BY captured_at ASC",
+            ticker,
+        )
+        intraday: list[OHLCVBar] = []
+        for row in intraday_rows:
+            bar = _polygon_bar_to_ohlcv(row)
+            if bar is not None:
+                intraday.append(bar)
+
+        # Assign intraday bars to M30 and H1 buckets.
+        # The actual timespan depends on the source config; we store them
+        # under M30 (shortest) and duplicate to H1 for now.  When dedicated
+        # H1 bars are ingested they will replace this.
+        if intraday:
+            bars["M30"] = intraday
+            bars["H1"] = intraday
+    except Exception:
+        logger.warning("%s: failed to fetch intraday bars", ticker, exc_info=True)
+
+    # --- Derive H4 from intraday (4-hour grouping) ------------------------
+    # Left empty when no intraday data — sentinel value per Req 1.3.
+
+    # --- Derive weekly bars from daily ------------------------------------
+    if bars["D"]:
+        bars["W"] = _aggregate_bars_by_period(bars["D"], period="week")
+
+    # --- Derive monthly bars from daily -----------------------------------
+    if bars["D"]:
+        bars["M"] = _aggregate_bars_by_period(bars["D"], period="month")
+
+    return bars
+
+
+def _aggregate_bars_by_period(
+    daily_bars: list[OHLCVBar],
+    period: str,
+) -> list[OHLCVBar]:
+    """Aggregate daily bars into weekly or monthly bars.
+
+    Groups by ISO week (period="week") or calendar month (period="month"),
+    then computes OHLCV aggregates per group.
+    """
+    from collections import OrderedDict
+
+    groups: OrderedDict[tuple[int, int], list[OHLCVBar]] = OrderedDict()
+    for bar in daily_bars:
+        if period == "week":
+            iso = bar.timestamp.isocalendar()
+            key = (iso[0], iso[1])  # (year, week)
+        else:
+            key = (bar.timestamp.year, bar.timestamp.month)
+        groups.setdefault(key, []).append(bar)
+
+    result: list[OHLCVBar] = []
+    for group_bars in groups.values():
+        if not group_bars:
+            continue
+        result.append(
+            OHLCVBar(
+                timestamp=group_bars[0].timestamp,  # period open timestamp
+                open=group_bars[0].open,
+                high=max(b.high for b in group_bars),
+                low=min(b.low for b in group_bars),
+                close=group_bars[-1].close,
+                volume=sum(b.volume for b in group_bars),
+            )
+        )
+    return result
+
+
+async def _fetch_fundamentals(
+    pool: asyncpg.Pool,
+    ticker: str,
+) -> tuple[float | None, int | None]:
+    """Fetch valuation_score and earnings_proximity_days.
+
+    - **valuation_score**: derived from the latest ``trend_windows`` confidence
+      for the ticker (entity_type='company', entity_id=ticker).
+    - **earnings_proximity_days**: days until the next earnings date from
+      ``earnings_calendar``.
+
+    Returns ``(valuation_score, earnings_proximity_days)`` with ``None``
+    sentinels for unavailable data.
+    """
+    valuation_score: float | None = None
+    earnings_proximity_days: int | None = None
+
+    # --- Valuation score from trend_windows --------------------------------
+    try:
+        row = await pool.fetchrow(
+            "SELECT confidence FROM trend_windows "
+            "WHERE entity_type = 'company' AND entity_id = $1 "
+            "ORDER BY generated_at DESC LIMIT 1",
+            ticker,
+        )
+        if row is not None:
+            valuation_score = float(row["confidence"])
+        else:
+            logger.warning("%s: no trend_windows data — valuation_score=None", ticker)
+    except Exception:
+        logger.warning(
+            "%s: failed to fetch valuation_score", ticker, exc_info=True
+        )
+
+    # --- Earnings proximity from earnings_calendar -------------------------
+    try:
+        row = await pool.fetchrow(
+            "SELECT earnings_date FROM earnings_calendar "
+            "WHERE ticker = $1 AND earnings_date >= CURRENT_DATE "
+            "ORDER BY earnings_date ASC LIMIT 1",
+            ticker,
+        )
+        if row is not None:
+            delta = row["earnings_date"] - datetime.now(timezone.utc).date()
+            earnings_proximity_days = delta.days
+        else:
+            logger.warning(
+                "%s: no upcoming earnings in calendar — earnings_proximity_days=None",
+                ticker,
+            )
+    except Exception:
+        logger.warning(
+            "%s: failed to fetch earnings_proximity_days", ticker, exc_info=True
+        )
+
+    return valuation_score, earnings_proximity_days
+
+
+async def _fetch_macro_bias(
+    pool: asyncpg.Pool,
+    ticker: str,
+) -> float:
+    """Compute macro_bias for *ticker* from recent ``macro_impact_records``.
+
+    Averages the numeric bias of the most recent impact records (up to 10)
+    weighted by their confidence.  The direction string is mapped to a float
+    via ``_DIRECTION_TO_BIAS``.
+
+    Returns 0.0 (neutral) when no records are found or on error.
+    """
+    try:
+        rows = await pool.fetch(
+            "SELECT impact_direction, macro_impact_score, confidence "
+            "FROM macro_impact_records "
+            "WHERE ticker = $1 "
+            "ORDER BY computed_at DESC LIMIT 10",
+            ticker,
+        )
+        if not rows:
+            logger.warning("%s: no macro_impact_records — macro_bias=0.0", ticker)
+            return 0.0
+
+        weighted_sum = 0.0
+        weight_total = 0.0
+        for row in rows:
+            direction = row["impact_direction"] or "neutral"
+            bias = _DIRECTION_TO_BIAS.get(direction, 0.0)
+            score = float(row["macro_impact_score"] or 0.0)
+            conf = float(row["confidence"] or 0.5)
+            w = score * conf
+            weighted_sum += bias * w
+            weight_total += w
+
+        if weight_total == 0.0:
+            return 0.0
+
+        # Clamp to [-1.0, 1.0]
+        raw = weighted_sum / weight_total
+        return max(-1.0, min(1.0, raw))
+    except Exception:
+        logger.warning("%s: failed to fetch macro_bias", ticker, exc_info=True)
+        return 0.0
+
+
+async def _fetch_open_positions(
+    pool: asyncpg.Pool,
+    ticker: str,
+) -> list[OpenPositionState]:
+    """Fetch open positions for *ticker* from ``position_stop_levels``.
+
+    Joins with ``positions`` for current_price when available.
+    Returns an empty list on error or when no positions exist.
+    """
+    try:
+        rows = await pool.fetch(
+            "SELECT psl.id, psl.ticker, psl.entry_price, "
+            "       psl.stop_loss_price, psl.take_profit_price, "
+            "       psl.trailing_stop_active, psl.atr_value, "
+            "       psl.atr_multiplier, psl.reward_risk_ratio, "
+            "       COALESCE(p.current_price, psl.entry_price) AS current_price "
+            "FROM position_stop_levels psl "
+            "LEFT JOIN positions p ON p.ticker = psl.ticker "
+            "WHERE psl.ticker = $1 AND psl.active = TRUE",
+            ticker,
+        )
+        positions: list[OpenPositionState] = []
+        for row in rows:
+            entry = float(row["entry_price"])
+            current = float(row["current_price"])
+            stop = float(row["stop_loss_price"])
+            tp = float(row["take_profit_price"])
+            atr = float(row["atr_value"]) if row["atr_value"] else None
+            rr = float(row["reward_risk_ratio"]) if row["reward_risk_ratio"] else 2.0
+
+            # Derive target_2 from reward-risk ratio if only one TP level
+            target_1 = tp
+            target_2 = entry + (tp - entry) * rr if rr > 1.0 else tp
+
+            positions.append(
+                OpenPositionState(
+                    position_id=str(row["id"]),
+                    ticker=row["ticker"],
+                    entry_price=entry,
+                    current_price=current,
+                    stop_loss=stop,
+                    target_1=target_1,
+                    target_2=target_2,
+                    trailing_stop=None,  # computed by exit engine at runtime
+                    partial_exit_done=bool(row["trailing_stop_active"]),
+                    atr=atr,
+                )
+            )
+        return positions
+    except Exception:
+        logger.warning(
+            "%s: failed to fetch open positions", ticker, exc_info=True
+        )
+        return []
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+async def normalize_input(
+    pool: asyncpg.Pool,
+    ticker: str,
+    config: SignalEngineConfig,
+) -> NormalizedInput:
+    """Fetch and assemble all data needed for a single evaluation tick.
+
+    Sources:
+    - OHLCV bars from ``market_snapshots`` (M30, H1, H4, D, W, M)
+    - Fundamental metrics from ``trend_windows`` + ``earnings_calendar``
+    - Macro context from ``macro_impact_records``
+    - Open position state from ``position_stop_levels`` + ``positions``
+
+    Missing data sources produce sentinel values (``None`` / empty list)
+    with a logged warning.  The function never raises — it always returns
+    a valid ``NormalizedInput``.
+
+    Requirements: 1.1, 1.2, 1.3, 1.4, 1.5
+    """
+    now = datetime.now(timezone.utc)
+
+    # Fetch all data sources concurrently for efficiency.
+    # Each fetcher handles its own errors and returns sentinels on failure.
+
+    bars_task = asyncio.create_task(_fetch_bars(pool, ticker))
+    fundamentals_task = asyncio.create_task(_fetch_fundamentals(pool, ticker))
+    macro_task = asyncio.create_task(_fetch_macro_bias(pool, ticker))
+    positions_task = asyncio.create_task(_fetch_open_positions(pool, ticker))
+
+    bars = await bars_task
+    valuation_score, earnings_proximity_days = await fundamentals_task
+    macro_bias = await macro_task
+    open_positions = await positions_task
+
+    # Validate monotonic timestamps within each timeframe (Req 1.4)
+    for tf in TIMEFRAMES:
+        bars[tf] = _validate_monotonic_timestamps(bars[tf], tf, ticker)
+
+    # Compute closing_prices and returns from daily bars for regime
+    # classification (used by the probabilistic pipeline).
+    closing_prices: list[float] = []
+    returns: list[float] = []
+    daily = bars.get("D", [])
+    if daily:
+        closing_prices = [bar.close for bar in daily]
+        if len(closing_prices) >= 2:
+            returns = [
+                (closing_prices[i] - closing_prices[i - 1]) / closing_prices[i - 1]
+                if closing_prices[i - 1] != 0
+                else 0.0
+                for i in range(1, len(closing_prices))
+            ]
+
+    # Determine current_price from the latest close of the shortest
+    # available timeframe.
+    current_price: float | None = None
+    for tf in TIMEFRAMES:  # shortest first
+        if bars[tf]:
+            current_price = bars[tf][-1].close
+            break
+
+    return NormalizedInput(
+        ticker=ticker,
+        evaluated_at=now,
+        bars=bars,
+        valuation_score=valuation_score,
+        earnings_proximity_days=earnings_proximity_days,
+        macro_bias=macro_bias,
+        open_positions=open_positions,
+        closing_prices=closing_prices,
+        returns=returns,
+        current_price=current_price,
+    )

services/signal_engine/persistence.py

@@ -0,0 +1,107 @@
+"""Database persistence for signal engine outputs.
+
+Persists ``SignalOutput`` instances to the ``signal_engine_outputs`` table.
+Persistence failures are logged and swallowed — they never block signal
+emission to the trading queue.
+
+Requirements: 15.1, 15.4
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+import asyncpg
+
+from services.signal_engine.models import SignalOutput
+
+logger = logging.getLogger(__name__)
+
+# INSERT statement for the signal_engine_outputs table.
+_INSERT_SQL = """
+INSERT INTO signal_engine_outputs (
+    id,
+    ticker,
+    evaluated_at,
+    price,
+    heuristic_verdict,
+    heuristic_confidence,
+    heuristic_s_total,
+    probabilistic_verdict,
+    probabilistic_p_up,
+    probabilistic_entropy,
+    probabilistic_ev_r,
+    delta_agreement,
+    delta_confidence_delta,
+    delta_reasons,
+    trade_plan,
+    full_output,
+    exit_signals,
+    pipeline_mode,
+    shadow_mode
+) VALUES (
+    $1, $2, $3, $4, $5, $6, $7, $8, $9, $10,
+    $11, $12, $13, $14, $15, $16, $17, $18, $19
+)
+"""
+
+
+async def persist_signal_output(
+    pool: asyncpg.Pool,
+    output: SignalOutput,
+) -> None:
+    """Persist a SignalOutput to the signal_engine_outputs table.
+
+    Logs and continues on database errors (non-blocking).
+
+    Requirements: 15.1, 15.4
+    """
+    try:
+        trade_plan_json: str | None = None
+        if output.trade_plan is not None:
+            trade_plan_json = json.dumps(output.trade_plan.model_dump())
+
+        exit_signals_json = json.dumps(
+            [e.model_dump() for e in output.exit_signals]
+        )
+
+        delta_reasons_json = json.dumps(output.delta_reasons)
+
+        full_output_json = output.model_dump_json()
+
+        await pool.execute(
+            _INSERT_SQL,
+            output.output_id,                   # $1  id
+            output.ticker,                      # $2  ticker
+            output.timestamp,                   # $3  evaluated_at
+            output.price,                       # $4  price
+            output.heuristic_verdict,           # $5  heuristic_verdict
+            output.heuristic_confidence,        # $6  heuristic_confidence
+            output.heuristic_s_total,           # $7  heuristic_s_total
+            output.probabilistic_verdict,       # $8  probabilistic_verdict
+            output.probabilistic_p_up,          # $9  probabilistic_p_up
+            output.probabilistic_entropy,       # $10 probabilistic_entropy
+            output.probabilistic_ev_r,          # $11 probabilistic_ev_r
+            output.delta_agreement,             # $12 delta_agreement
+            output.delta_confidence_delta,      # $13 delta_confidence_delta
+            delta_reasons_json,                 # $14 delta_reasons (JSONB)
+            trade_plan_json,                    # $15 trade_plan (JSONB, nullable)
+            full_output_json,                   # $16 full_output (JSONB)
+            exit_signals_json,                  # $17 exit_signals (JSONB)
+            output.pipeline_mode,               # $18 pipeline_mode
+            output.shadow_mode,                 # $19 shadow_mode
+        )
+
+        logger.debug(
+            "Persisted signal output %s for %s",
+            output.output_id,
+            output.ticker,
+        )
+    except Exception:
+        logger.error(
+            "Failed to persist signal output %s for %s — continuing",
+            output.output_id,
+            output.ticker,
+            exc_info=True,
+        )

services/signal_engine/probabilistic.py

@@ -0,0 +1,380 @@
+"""Probabilistic Pipeline (Pipeline B) — Bayesian inference and verdict.
+
+Computes a posterior probability via regime-based priors, likelihood ratio
+accumulation with correlation penalty, entropy gating, and expected value
+calculation.  Produces a BUY / WATCH / SKIP verdict.
+
+The pipeline reuses the existing ``classify_regime`` infrastructure from
+``services.aggregation.regime`` for regime classification and wraps the
+Bayesian math with signal-cluster correlation penalties from
+``services.signal_engine.correlation``.
+
+Requirements: 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9,
+              14.1, 14.2, 14.3, 14.4, 14.5
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+
+from services.aggregation.regime import MarketRegime, RegimeClassification
+from services.signal_engine.config import ProbabilisticConfig
+from services.signal_engine.correlation import (
+    apply_correlation_penalty,
+    classify_signal,
+)
+from services.signal_engine.models import (
+    ConfluenceSignal,
+    LikelihoodRatio,
+    NormalizedInput,
+    ProbabilisticResult,
+    SignalDirection,
+    Verdict,
+)
+
+logger = logging.getLogger(__name__)
+
+# Default hit rate used when no historical hit rate is available.
+_DEFAULT_HIT_RATE: float = 0.6
+
+
+# ---------------------------------------------------------------------------
+# Regime → prior mapping
+# ---------------------------------------------------------------------------
+
+
+def _regime_to_prior(
+    regime: RegimeClassification,
+    config: ProbabilisticConfig,
+) -> float:
+    """Map a regime classification to a prior probability.
+
+    Mapping (Req 14.2):
+    - TREND_FOLLOWING with positive trend_indicator → bull prior (0.58)
+    - TREND_FOLLOWING with negative trend_indicator → bear prior (0.42)
+    - MEAN_REVERSION → range prior (0.50)
+    - PANIC → bear prior (0.42)
+    - UNCERTAINTY → range prior (0.50)
+    """
+    if regime.regime == MarketRegime.TREND_FOLLOWING:
+        if regime.trend_indicator > 0:
+            return config.regime_prior_bull
+        return config.regime_prior_bear
+    if regime.regime == MarketRegime.MEAN_REVERSION:
+        return config.regime_prior_range
+    if regime.regime == MarketRegime.PANIC:
+        return config.regime_prior_bear
+    # UNCERTAINTY or any unknown → range prior
+    return config.regime_prior_range
+
+
+# ---------------------------------------------------------------------------
+# Likelihood ratio computation
+# ---------------------------------------------------------------------------
+
+
+def _compute_likelihood_ratios(
+    confluence_signals: list[ConfluenceSignal],
+) -> list[LikelihoodRatio]:
+    """Compute raw likelihood ratios for each confluence signal.
+
+    For each signal:
+    - h = hit rate (use confidence as proxy, default 0.6)
+    - s = signal strength (confluence_score)
+    - P(sig|up) = h * s + (1 - h) * (1 - s) * 0.5
+    - P(sig|down) = 1 - P(sig|up)
+    - LR = P(sig|up) / P(sig|down)
+
+    Direction-aware: bearish signals invert the LR (use 1/LR) so that
+    bearish evidence reduces P_up.
+
+    Requirements: 6.2
+    """
+    ratios: list[LikelihoodRatio] = []
+
+    for sig in confluence_signals:
+        h = _DEFAULT_HIT_RATE
+        s = sig.confluence_score
+
+        # Clamp inputs to valid ranges to avoid numerical issues
+        h = max(0.01, min(h, 0.99))
+        s = max(0.01, min(s, 0.99))
+
+        p_sig_up = h * s + (1.0 - h) * (1.0 - s) * 0.5
+        p_sig_down = 1.0 - p_sig_up
+
+        # Guard against division by zero / near-zero
+        if p_sig_down < 1e-10:
+            p_sig_down = 1e-10
+
+        lr = p_sig_up / p_sig_down
+
+        # Bearish signals: invert the LR so it reduces P_up
+        if sig.direction == SignalDirection.BEARISH:
+            lr = 1.0 / lr if lr > 1e-10 else 1e10
+
+        log_lr = math.log(lr) if lr > 0 else 0.0
+
+        cluster = classify_signal(sig.signal_type)
+
+        ratios.append(
+            LikelihoodRatio(
+                signal_type=sig.signal_type,
+                cluster=cluster.value,
+                lr=lr,
+                log_lr=log_lr,
+                penalized_log_lr=log_lr,  # will be updated by penalty
+                hit_rate=h,
+                strength=s,
+            )
+        )
+
+    return ratios
+
+
+# ---------------------------------------------------------------------------
+# Log-odds / sigmoid helpers
+# ---------------------------------------------------------------------------
+
+
+def _logit(p: float) -> float:
+    """Compute logit(p) = log(p / (1 - p)).
+
+    Clamps p to (1e-10, 1 - 1e-10) to avoid infinities.
+    """
+    p = max(1e-10, min(p, 1.0 - 1e-10))
+    return math.log(p / (1.0 - p))
+
+
+def _sigmoid(x: float) -> float:
+    """Compute sigmoid(x) = 1 / (1 + exp(-x)).
+
+    Clamps the exponent to avoid overflow.
+    """
+    if x > 500:
+        return 1.0
+    if x < -500:
+        return 0.0
+    return 1.0 / (1.0 + math.exp(-x))
+
+
+# ---------------------------------------------------------------------------
+# Shannon entropy
+# ---------------------------------------------------------------------------
+
+
+def _shannon_entropy(p: float) -> float:
+    """Compute Shannon entropy H = -p·log₂(p) - (1-p)·log₂(1-p).
+
+    Returns 0.0 at the boundaries (p = 0 or p = 1).
+    Result is in [0, 1] for binary entropy.
+    """
+    if p <= 0.0 or p >= 1.0:
+        return 0.0
+    return -(p * math.log2(p) + (1.0 - p) * math.log2(1.0 - p))
+
+
+# ---------------------------------------------------------------------------
+# EV_R computation
+# ---------------------------------------------------------------------------
+
+
+def _compute_ev_r(
+    p_up: float,
+    confluence_signals: list[ConfluenceSignal],
+) -> float:
+    """Compute expected value per unit risk.
+
+    EV_R = P_up · E[win_R] - (1 - P_up) · 1.0
+
+    E[win_R] is estimated as the average confluence_score × 2.0
+    (heuristic for expected win in R-units).  Falls back to 1.0 if
+    no signals are available.
+    """
+    if confluence_signals:
+        avg_score = sum(s.confluence_score for s in confluence_signals) / len(
+            confluence_signals
+        )
+        e_win_r = avg_score * 2.0
+    else:
+        e_win_r = 1.0
+
+    return p_up * e_win_r - (1.0 - p_up) * 1.0
+
+
+# ---------------------------------------------------------------------------
+# Verdict logic
+# ---------------------------------------------------------------------------
+
+
+def _determine_verdict(
+    p_up: float,
+    entropy: float,
+    ev_r: float,
+    normalized: NormalizedInput,
+    config: ProbabilisticConfig,
+) -> tuple[Verdict, list[str]]:
+    """Apply threshold logic to determine BUY / WATCH / SKIP verdict.
+
+    Returns the verdict and a list of reasoning strings.
+
+    Requirements: 6.6, 6.7, 6.8
+    """
+    reasoning: list[str] = []
+
+    valuation_score = (
+        normalized.valuation_score if normalized.valuation_score is not None else 0.0
+    )
+
+    # --- Entropy gating (Req 6.4) ---
+    if entropy > config.entropy_skip:
+        reasoning.append(
+            f"SKIP: entropy={entropy:.4f} > {config.entropy_skip} (high_entropy)"
+        )
+        return Verdict.SKIP, reasoning
+
+    # --- Check BUY conditions (Req 6.6) ---
+    buy_conditions = {
+        "p_up": p_up >= config.buy_p_up,
+        "entropy": entropy <= config.buy_entropy_max,
+        "ev_r": ev_r >= config.buy_ev_r_min,
+        "macro_bias": normalized.macro_bias > config.macro_bias_threshold,
+        "valuation": valuation_score >= config.buy_valuation_min,
+    }
+
+    all_buy_met = all(buy_conditions.values())
+
+    if all_buy_met:
+        reasoning.append(
+            f"BUY: all conditions met — P_up={p_up:.4f} "
+            f"(>= {config.buy_p_up}), entropy={entropy:.4f} "
+            f"(<= {config.buy_entropy_max}), EV_R={ev_r:.4f} "
+            f"(>= {config.buy_ev_r_min}), macro_bias={normalized.macro_bias:.2f} "
+            f"(> {config.macro_bias_threshold}), valuation={valuation_score:.2f} "
+            f"(>= {config.buy_valuation_min})"
+        )
+        return Verdict.BUY, reasoning
+
+    # --- Check WATCH conditions (Req 6.7) ---
+    watch_conditions = {
+        "p_up": p_up >= config.watch_p_up,
+        "entropy": entropy <= config.watch_entropy_max,
+    }
+
+    if all(watch_conditions.values()):
+        failed_buy = [k for k, v in buy_conditions.items() if not v]
+        reasoning.append(
+            f"WATCH: P_up={p_up:.4f} (>= {config.watch_p_up}), "
+            f"entropy={entropy:.4f} (<= {config.watch_entropy_max}) "
+            f"but BUY conditions not fully met — failed: {', '.join(failed_buy)}"
+        )
+        return Verdict.WATCH, reasoning
+
+    # --- SKIP (Req 6.8) ---
+    reasoning.append(
+        f"SKIP: P_up={p_up:.4f}, entropy={entropy:.4f}, EV_R={ev_r:.4f} "
+        f"— does not meet WATCH or BUY thresholds"
+    )
+    return Verdict.SKIP, reasoning
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+def run_probabilistic_pipeline(
+    normalized: NormalizedInput,
+    confluence_signals: list[ConfluenceSignal],
+    regime: RegimeClassification,
+    config: ProbabilisticConfig,
+) -> ProbabilisticResult:
+    """Run the Bayesian probabilistic pipeline.
+
+    Steps:
+    1. Initialize regime-based prior (bull=0.58, range=0.50, bear=0.42)
+    2. Compute likelihood ratios per signal
+    3. Apply correlation penalty via ``apply_correlation_penalty()``
+    4. Accumulate via log-odds: logit(P_post) = logit(P_prior) + Σ log(LR_i)
+    5. Compute Shannon entropy and apply entropy gating
+    6. Compute EV_R = P_up · E[win_R] - (1 - P_up) · 1.0
+    7. Produce BUY / WATCH / SKIP verdict
+
+    Args:
+        normalized: The unified input structure for this evaluation tick.
+        confluence_signals: Signals that passed multi-timeframe confluence
+            filtering.
+        regime: The current market regime classification.
+        config: Probabilistic pipeline thresholds.
+
+    Returns:
+        A :class:`ProbabilisticResult` with verdict, posterior, entropy,
+        EV_R, likelihood ratios, and reasoning.
+
+    Requirements: 6.1–6.9, 14.1–14.5
+    """
+    reasoning: list[str] = []
+
+    # 1. Regime-based prior (Req 6.1, 14.2)
+    prior = _regime_to_prior(regime, config)
+    reasoning.append(
+        f"Regime={regime.regime.value}, trend_indicator={regime.trend_indicator:.1f} "
+        f"→ prior={prior:.2f}"
+    )
+
+    # 2. Compute likelihood ratios (Req 6.2)
+    raw_lrs = _compute_likelihood_ratios(confluence_signals)
+
+    # 3. Apply correlation penalty (Req 7.1–7.4)
+    penalized_lrs = apply_correlation_penalty(raw_lrs)
+
+    # 4. Accumulate via log-odds (Req 6.3, 14.3)
+    logit_prior = _logit(prior)
+    sum_penalized_log_lr = sum(lr.penalized_log_lr for lr in penalized_lrs)
+    logit_posterior = logit_prior + sum_penalized_log_lr
+    p_up = _sigmoid(logit_posterior)
+
+    reasoning.append(
+        f"logit(prior)={logit_prior:.4f} + Σ penalized_log_lr={sum_penalized_log_lr:.4f} "
+        f"= logit(posterior)={logit_posterior:.4f} → P_up={p_up:.4f}"
+    )
+
+    # 5. Shannon entropy (Req 6.4)
+    entropy = _shannon_entropy(p_up)
+    reasoning.append(f"Shannon entropy H={entropy:.4f}")
+
+    # 6. EV_R (Req 6.5)
+    ev_r = _compute_ev_r(p_up, confluence_signals)
+    reasoning.append(f"EV_R={ev_r:.4f}")
+
+    # 7. Verdict (Req 6.6, 6.7, 6.8)
+    verdict, verdict_reasoning = _determine_verdict(
+        p_up, entropy, ev_r, normalized, config
+    )
+    reasoning.extend(verdict_reasoning)
+
+    logger.info(
+        "Probabilistic pipeline [%s]: verdict=%s P_up=%.4f "
+        "entropy=%.4f EV_R=%.4f prior=%.2f regime=%s signals=%d",
+        normalized.ticker,
+        verdict.value,
+        p_up,
+        entropy,
+        ev_r,
+        prior,
+        regime.regime.value,
+        len(confluence_signals),
+    )
+
+    return ProbabilisticResult(
+        verdict=verdict,
+        p_up=p_up,
+        entropy=entropy,
+        ev_r=ev_r,
+        prior=prior,
+        posterior=p_up,
+        likelihood_ratios=penalized_lrs,
+        regime=regime.regime.value,
+        reasoning=reasoning,
+    )

services/signal_engine/signals/__init__.py

@@ -0,0 +1 @@
+# Signal Library - technical signal evaluators (Fibonacci, MA Stack, RSI, Cup & Handle, Elliott Wave)

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

services/signal_engine/signals/cup_handle.py

@@ -0,0 +1,206 @@
+"""Cup & Handle pattern signal evaluator.
+
+Detects the Cup & Handle chart pattern — a bullish continuation pattern
+consisting of a U-shaped price recovery (the cup) followed by a small
+consolidation pullback (the handle).
+
+Pattern detection algorithm:
+1. Find the left rim (local high in the first third of bars).
+2. Find the cup bottom (lowest low between left rim and right rim area).
+3. Find the right rim (local high in the last third of bars, near left rim price).
+4. Identify the handle as a small pullback after the right rim (last few bars).
+
+Pattern completeness scoring:
+- Cup depth: ``(left_rim - bottom) / left_rim`` — valid range 12–33%.
+- Symmetry: how close left_rim and right_rim prices are (within 5% = perfect).
+- Handle: small pullback (< 50% of cup depth) after right rim.
+
+The signal is always BULLISH (cup & handle is a bullish continuation pattern).
+"""
+
+from __future__ import annotations
+
+from services.signal_engine.models import OHLCVBar, SignalDirection, SignalResult
+from services.signal_engine.signals.base import validate_lookback
+
+# Default minimum number of bars required for cup & handle detection
+DEFAULT_MIN_BARS: int = 30
+
+# Cup depth valid range (as fraction of left rim price)
+_CUP_DEPTH_MIN: float = 0.12  # 12%
+_CUP_DEPTH_MAX: float = 0.33  # 33%
+
+# Symmetry: maximum allowed difference between left and right rim prices
+# as a fraction of left rim price for "perfect" symmetry
+_SYMMETRY_PERFECT_PCT: float = 0.05  # 5%
+
+# Handle: maximum pullback as fraction of cup depth
+_HANDLE_MAX_RETRACE: float = 0.50  # 50% of cup depth
+
+# Handle lookback: number of bars at the end to check for handle
+_HANDLE_LOOKBACK_FRACTION: float = 0.15  # last 15% of bars
+
+# Confidence multiplier
+_CONFIDENCE_MULTIPLIER: float = 0.90
+
+
+class CupHandleEvaluator:
+    """Cup & Handle pattern signal evaluator.
+
+    Satisfies the :class:`~services.signal_engine.signals.base.SignalEvaluator`
+    protocol.
+
+    Parameters
+    ----------
+    min_bars:
+        Minimum number of OHLCV bars required before the evaluator will
+        produce a signal.  Defaults to ``30``.
+    """
+
+    def __init__(self, min_bars: int = DEFAULT_MIN_BARS) -> None:
+        self.min_bars = min_bars
+
+    # ------------------------------------------------------------------
+    # Public API (SignalEvaluator protocol)
+    # ------------------------------------------------------------------
+
+    def evaluate(
+        self,
+        bars: list[OHLCVBar],
+        timeframe: str,
+    ) -> SignalResult | None:
+        """Evaluate Cup & Handle pattern on *bars* for *timeframe*.
+
+        Returns ``None`` when there are fewer than :pyattr:`min_bars` bars,
+        or when no valid cup & handle pattern is detected.
+        """
+        if not validate_lookback(bars, self.min_bars):
+            return None
+
+        n = len(bars)
+
+        # --- Step 1: Find the left rim (highest high in first third) ---
+        first_third_end = n // 3
+        if first_third_end < 1:
+            return None
+
+        left_rim_idx = 0
+        left_rim_price = bars[0].high
+        for i in range(1, first_third_end):
+            if bars[i].high > left_rim_price:
+                left_rim_idx = i
+                left_rim_price = bars[i].high
+
+        if left_rim_price <= 0:
+            return None
+
+        # --- Step 2: Find the right rim (highest high in last third) ---
+        last_third_start = n - (n // 3)
+        if last_third_start >= n:
+            return None
+
+        right_rim_idx = last_third_start
+        right_rim_price = bars[last_third_start].high
+        for i in range(last_third_start + 1, n):
+            if bars[i].high > right_rim_price:
+                right_rim_idx = i
+                right_rim_price = bars[i].high
+
+        # --- Step 3: Find the cup bottom (lowest low between rims) ---
+        search_start = left_rim_idx + 1
+        search_end = right_rim_idx
+        if search_start >= search_end:
+            return None
+
+        bottom_idx = search_start
+        bottom_price = bars[search_start].low
+        for i in range(search_start + 1, search_end):
+            if bars[i].low < bottom_price:
+                bottom_idx = i
+                bottom_price = bars[i].low
+
+        # --- Step 4: Validate cup depth ---
+        cup_depth = left_rim_price - bottom_price
+        if cup_depth <= 0:
+            return None
+
+        cup_depth_pct = cup_depth / left_rim_price
+        if cup_depth_pct < _CUP_DEPTH_MIN or cup_depth_pct > _CUP_DEPTH_MAX:
+            return None
+
+        # --- Step 5: Score symmetry (left rim vs right rim) ---
+        rim_diff_pct = abs(left_rim_price - right_rim_price) / left_rim_price
+        if rim_diff_pct <= _SYMMETRY_PERFECT_PCT:
+            symmetry_score = 1.0
+        else:
+            # Linear decay from 1.0 at 5% to 0.0 at 20%
+            max_diff = 0.20
+            symmetry_score = max(0.0, 1.0 - (rim_diff_pct - _SYMMETRY_PERFECT_PCT) / (max_diff - _SYMMETRY_PERFECT_PCT))
+
+        # Right rim must be at least close to left rim (within 20%)
+        if symmetry_score <= 0.0:
+            return None
+
+        # --- Step 6: Detect and score the handle ---
+        handle_lookback = max(2, int(n * _HANDLE_LOOKBACK_FRACTION))
+        handle_bars = bars[-handle_lookback:]
+
+        # Handle is a small pullback from the right rim
+        handle_low = min(b.low for b in handle_bars)
+        handle_depth = right_rim_price - handle_low
+
+        if cup_depth <= 0:
+            return None
+
+        handle_retrace = handle_depth / cup_depth
+
+        if handle_retrace > _HANDLE_MAX_RETRACE:
+            # Handle is too deep — not a valid cup & handle
+            return None
+
+        # Handle score: 1.0 when handle is very shallow, decreasing as it deepens
+        if handle_retrace <= 0:
+            handle_score = 1.0
+        else:
+            handle_score = 1.0 - (handle_retrace / _HANDLE_MAX_RETRACE)
+
+        # --- Step 7: Cup depth quality score ---
+        # Ideal cup depth is around 20-25% — score peaks in the middle of valid range
+        ideal_depth = (_CUP_DEPTH_MIN + _CUP_DEPTH_MAX) / 2.0  # 0.225
+        depth_deviation = abs(cup_depth_pct - ideal_depth) / ((_CUP_DEPTH_MAX - _CUP_DEPTH_MIN) / 2.0)
+        depth_score = max(0.0, 1.0 - depth_deviation)
+
+        # --- Step 8: Compute overall completeness ---
+        completeness = (
+            0.35 * symmetry_score
+            + 0.35 * depth_score
+            + 0.30 * handle_score
+        )
+        completeness = max(0.0, min(1.0, completeness))
+
+        # --- Step 9: Build signal result ---
+        strength = completeness
+        confidence = completeness * _CONFIDENCE_MULTIPLIER
+
+        return SignalResult(
+            signal_type="cup_handle",
+            timeframe=timeframe,
+            strength=strength,
+            direction=SignalDirection.BULLISH,
+            confidence=confidence,
+            metadata={
+                "left_rim": left_rim_price,
+                "left_rim_idx": left_rim_idx,
+                "right_rim": right_rim_price,
+                "right_rim_idx": right_rim_idx,
+                "bottom": bottom_price,
+                "bottom_idx": bottom_idx,
+                "cup_depth_pct": round(cup_depth_pct, 4),
+                "handle_depth": round(handle_depth, 4),
+                "handle_retrace_pct": round(handle_retrace, 4),
+                "symmetry_score": round(symmetry_score, 4),
+                "depth_score": round(depth_score, 4),
+                "handle_score": round(handle_score, 4),
+                "completeness": round(completeness, 4),
+            },
+        )

services/signal_engine/signals/elliott_wave.py

@@ -0,0 +1,499 @@
+"""Elliott Wave signal evaluator.
+
+Detects Elliott Wave patterns — impulse waves (5-wave structure) and
+corrective waves (3-wave structure) — using a simplified zigzag pivot
+filter.  Produces a signal with the current wave position and projected
+direction.
+
+Wave detection algorithm (simplified):
+1. Find significant pivot points (local highs and lows) using a zigzag
+   filter that identifies reversals of at least X% of the price range.
+2. Count alternating pivots to identify wave structure.
+3. Five alternating pivots = impulse wave (bullish if trending up,
+   bearish if trending down).
+4. Three alternating pivots after an impulse = corrective wave.
+
+Signal logic:
+- Impulse wave 3 or 5: strong signal in the trend direction.
+- Corrective wave (A, B, C): signal in the opposite direction
+  (anticipating next impulse).
+- Ambiguous wave count: return ``None``.
+"""
+
+from __future__ import annotations
+
+from services.signal_engine.models import OHLCVBar, SignalDirection, SignalResult
+from services.signal_engine.signals.base import validate_lookback
+
+# Default minimum number of bars required for evaluation
+DEFAULT_MIN_BARS: int = 30
+
+# Minimum zigzag reversal threshold as a fraction of the price range
+_DEFAULT_ZIGZAG_PCT: float = 0.05  # 5%
+
+# Wave type labels
+WAVE_TYPE_IMPULSE: str = "impulse"
+WAVE_TYPE_CORRECTIVE: str = "corrective"
+
+# Impulse wave positions (1-indexed)
+_IMPULSE_WAVE_COUNT: int = 5
+# Corrective wave positions
+_CORRECTIVE_WAVE_COUNT: int = 3
+
+# Confidence multiplier for wave clarity
+_CONFIDENCE_MULTIPLIER: float = 0.85
+
+
+class ElliottWaveEvaluator:
+    """Elliott Wave pattern signal evaluator.
+
+    Satisfies the :class:`~services.signal_engine.signals.base.SignalEvaluator`
+    protocol.
+
+    Parameters
+    ----------
+    min_bars:
+        Minimum number of OHLCV bars required before the evaluator will
+        produce a signal.  Defaults to ``30``.
+    zigzag_pct:
+        Minimum reversal threshold as a fraction of the overall price
+        range for the zigzag filter.  Defaults to ``0.05`` (5%).
+    """
+
+    def __init__(
+        self,
+        min_bars: int = DEFAULT_MIN_BARS,
+        zigzag_pct: float = _DEFAULT_ZIGZAG_PCT,
+    ) -> None:
+        self.min_bars = min_bars
+        self.zigzag_pct = zigzag_pct
+
+    # ------------------------------------------------------------------
+    # Public API (SignalEvaluator protocol)
+    # ------------------------------------------------------------------
+
+    def evaluate(
+        self,
+        bars: list[OHLCVBar],
+        timeframe: str,
+    ) -> SignalResult | None:
+        """Evaluate Elliott Wave pattern on *bars* for *timeframe*.
+
+        Returns ``None`` when there are fewer than :pyattr:`min_bars` bars,
+        when the market is flat (no price range), or when the wave count
+        is ambiguous.
+        """
+        if not validate_lookback(bars, self.min_bars):
+            return None
+
+        # Compute overall price range for the zigzag threshold
+        overall_high = max(b.high for b in bars)
+        overall_low = min(b.low for b in bars)
+        price_range = overall_high - overall_low
+
+        if price_range <= 0:
+            return None  # flat market
+
+        zigzag_threshold = price_range * self.zigzag_pct
+
+        # Find zigzag pivots
+        pivots = _find_zigzag_pivots(bars, zigzag_threshold)
+
+        if len(pivots) < _CORRECTIVE_WAVE_COUNT:
+            return None  # not enough pivots for any wave structure
+
+        # Try to identify wave structure from the pivots
+        wave_info = _classify_waves(pivots, price_range)
+
+        if wave_info is None:
+            return None  # ambiguous wave count
+
+        wave_type = wave_info["wave_type"]
+        current_position = wave_info["current_position"]
+        trend_up = wave_info["trend_up"]
+        clarity = wave_info["clarity"]
+
+        # Determine direction and strength based on wave type and position
+        direction: SignalDirection
+        strength: float
+
+        if wave_type == WAVE_TYPE_IMPULSE:
+            # Impulse wave: signal in the trend direction
+            direction = SignalDirection.BULLISH if trend_up else SignalDirection.BEARISH
+            # Waves 3 and 5 are the strongest signal points
+            if current_position in (3, 5):
+                strength = min(1.0, clarity * 1.0)
+            else:
+                strength = min(1.0, clarity * 0.6)
+        else:
+            # Corrective wave: signal opposite to the correction
+            # (anticipating next impulse in the original trend direction)
+            direction = SignalDirection.BULLISH if trend_up else SignalDirection.BEARISH
+            strength = min(1.0, clarity * 0.7)
+
+        confidence = min(1.0, clarity * _CONFIDENCE_MULTIPLIER)
+
+        # Build pivot list for metadata (index, price, type)
+        pivot_meta = [
+            {"index": p["index"], "price": p["price"], "type": p["type"]}
+            for p in pivots
+        ]
+
+        return SignalResult(
+            signal_type="elliott_wave",
+            timeframe=timeframe,
+            strength=strength,
+            direction=direction,
+            confidence=confidence,
+            metadata={
+                "wave_count": len(pivots),
+                "wave_type": wave_type,
+                "current_wave_position": current_position,
+                "trend_up": trend_up,
+                "clarity": round(clarity, 4),
+                "pivots": pivot_meta,
+            },
+        )
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _find_zigzag_pivots(
+    bars: list[OHLCVBar],
+    threshold: float,
+) -> list[dict]:
+    """Find significant pivot points using a zigzag filter.
+
+    A pivot is a local high or low where the price reverses by at least
+    *threshold* from the last confirmed pivot.
+
+    Returns a list of dicts with keys: ``index``, ``price``, ``type``
+    (``"high"`` or ``"low"``).
+    """
+    if len(bars) < 2:
+        return []
+
+    pivots: list[dict] = []
+
+    # Seed with the first bar's high and low as candidates
+    last_high_idx = 0
+    last_high = bars[0].high
+    last_low_idx = 0
+    last_low = bars[0].low
+
+    # Direction: 1 = looking for a high (trending up), -1 = looking for a low
+    # Start by determining initial direction from first two bars
+    if bars[1].close >= bars[0].close:
+        direction = 1  # trending up, looking for a high
+    else:
+        direction = -1  # trending down, looking for a low
+
+    for i in range(1, len(bars)):
+        bar = bars[i]
+
+        if direction == 1:
+            # Trending up — track the highest high
+            if bar.high >= last_high:
+                last_high = bar.high
+                last_high_idx = i
+            # Check for reversal: price dropped by threshold from the high
+            if last_high - bar.low >= threshold:
+                # Confirm the high as a pivot
+                pivots.append({
+                    "index": last_high_idx,
+                    "price": last_high,
+                    "type": "high",
+                })
+                # Switch direction: now looking for a low
+                direction = -1
+                last_low = bar.low
+                last_low_idx = i
+        else:
+            # Trending down — track the lowest low
+            if bar.low <= last_low:
+                last_low = bar.low
+                last_low_idx = i
+            # Check for reversal: price rose by threshold from the low
+            if bar.high - last_low >= threshold:
+                # Confirm the low as a pivot
+                pivots.append({
+                    "index": last_low_idx,
+                    "price": last_low,
+                    "type": "low",
+                })
+                # Switch direction: now looking for a high
+                direction = 1
+                last_high = bar.high
+                last_high_idx = i
+
+    # Add the final unconfirmed pivot (the current trend endpoint)
+    if direction == 1 and (not pivots or pivots[-1]["type"] != "high"):
+        pivots.append({
+            "index": last_high_idx,
+            "price": last_high,
+            "type": "high",
+        })
+    elif direction == -1 and (not pivots or pivots[-1]["type"] != "low"):
+        pivots.append({
+            "index": last_low_idx,
+            "price": last_low,
+            "type": "low",
+        })
+
+    return pivots
+
+
+def _classify_waves(
+    pivots: list[dict],
+    price_range: float,
+) -> dict | None:
+    """Classify the pivot sequence as impulse or corrective waves.
+
+    Returns a dict with ``wave_type``, ``current_position``, ``trend_up``,
+    and ``clarity``, or ``None`` if the wave count is ambiguous.
+    """
+    n = len(pivots)
+
+    if n < _CORRECTIVE_WAVE_COUNT:
+        return None
+
+    # Determine overall trend from first to last pivot
+    first_price = pivots[0]["price"]
+    last_price = pivots[-1]["price"]
+    trend_up = last_price > first_price
+
+    # Try impulse wave (5 pivots) first, then corrective (3 pivots)
+    if n >= _IMPULSE_WAVE_COUNT:
+        # Use the last 5 pivots for impulse wave detection
+        impulse_pivots = pivots[-_IMPULSE_WAVE_COUNT:]
+        impulse_result = _check_impulse(impulse_pivots, trend_up, price_range)
+        if impulse_result is not None:
+            return impulse_result
+
+        # Check if there's a corrective wave after an impulse
+        # (need at least 5 + 3 = 8 pivots for impulse + corrective)
+        if n >= _IMPULSE_WAVE_COUNT + _CORRECTIVE_WAVE_COUNT:
+            # Check if the first 5 pivots form an impulse
+            early_impulse = pivots[:_IMPULSE_WAVE_COUNT]
+            early_result = _check_impulse(early_impulse, trend_up, price_range)
+            if early_result is not None:
+                # The remaining pivots may form a corrective wave
+                corrective_pivots = pivots[_IMPULSE_WAVE_COUNT:_IMPULSE_WAVE_COUNT + _CORRECTIVE_WAVE_COUNT]
+                corrective_result = _check_corrective(
+                    corrective_pivots, trend_up, price_range,
+                )
+                if corrective_result is not None:
+                    return corrective_result
+
+    # Try corrective wave (3 pivots) from the tail
+    if n >= _CORRECTIVE_WAVE_COUNT:
+        corrective_pivots = pivots[-_CORRECTIVE_WAVE_COUNT:]
+        corrective_result = _check_corrective(
+            corrective_pivots, trend_up, price_range,
+        )
+        if corrective_result is not None:
+            return corrective_result
+
+    return None  # ambiguous
+
+
+def _check_impulse(
+    pivots: list[dict],
+    trend_up: bool,
+    price_range: float,
+) -> dict | None:
+    """Check if 5 pivots form a valid impulse wave.
+
+    For a bullish impulse (trend_up=True):
+    - Wave 1 (low→high): price rises
+    - Wave 2 (high→low): price falls but stays above wave 1 start
+    - Wave 3 (low→high): price rises above wave 1 high (wave 3 is longest)
+    - Wave 4 (high→low): price falls but stays above wave 1 high
+    - Wave 5 (low→high): price rises to new high
+
+    For bearish impulse, the pattern is inverted.
+    """
+    if len(pivots) != _IMPULSE_WAVE_COUNT:
+        return None
+
+    prices = [p["price"] for p in pivots]
+
+    if trend_up:
+        # Bullish impulse: alternating low-high-low-high-low or high-low-high-low-high
+        # Check for generally ascending pattern with higher highs
+        valid = _validate_bullish_impulse(prices)
+    else:
+        # Bearish impulse: generally descending pattern with lower lows
+        valid = _validate_bearish_impulse(prices)
+
+    if not valid:
+        return None
+
+    # Compute clarity: how clean the wave structure is
+    clarity = _compute_impulse_clarity(prices, trend_up, price_range)
+
+    # Current position is wave 5 (the last wave in the impulse)
+    return {
+        "wave_type": WAVE_TYPE_IMPULSE,
+        "current_position": 5,
+        "trend_up": trend_up,
+        "clarity": clarity,
+    }
+
+
+def _validate_bullish_impulse(prices: list[float]) -> bool:
+    """Validate a 5-pivot sequence as a bullish impulse.
+
+    Simplified rules:
+    - The overall trend is up (last > first).
+    - Wave 3 (pivot 2 to pivot 3) should be the largest move or
+      at least not the shortest.
+    - Wave 2 should not retrace below wave 1 start.
+    - Wave 4 should not overlap wave 1 end.
+    """
+    if len(prices) != 5:
+        return False
+
+    # Overall upward trend
+    if prices[-1] <= prices[0]:
+        return False
+
+    # Compute wave magnitudes
+    waves = [abs(prices[i + 1] - prices[i]) for i in range(4)]
+
+    # Wave 3 (index 2) should not be the shortest impulse wave
+    # Impulse waves are waves 0, 2, 4 (odd-indexed moves in 0-based)
+    impulse_waves = [waves[0], waves[2]]
+    if len(waves) > 3:
+        impulse_waves.append(waves[3])
+
+    # Wave 3 (waves[2]) should be significant
+    if waves[2] < min(waves[0], waves[2]) * 0.5:
+        return False
+
+    # The pattern should show alternating direction
+    # Check that consecutive pivots alternate in direction
+    for i in range(3):
+        move_a = prices[i + 1] - prices[i]
+        move_b = prices[i + 2] - prices[i + 1]
+        # Consecutive moves should be in opposite directions
+        if move_a * move_b >= 0:
+            return False
+
+    return True
+
+
+def _validate_bearish_impulse(prices: list[float]) -> bool:
+    """Validate a 5-pivot sequence as a bearish impulse.
+
+    Mirror of bullish validation with inverted price direction.
+    """
+    if len(prices) != 5:
+        return False
+
+    # Overall downward trend
+    if prices[-1] >= prices[0]:
+        return False
+
+    # Compute wave magnitudes
+    waves = [abs(prices[i + 1] - prices[i]) for i in range(4)]
+
+    # Wave 3 (waves[2]) should be significant
+    if waves[2] < min(waves[0], waves[2]) * 0.5:
+        return False
+
+    # Check alternating direction
+    for i in range(3):
+        move_a = prices[i + 1] - prices[i]
+        move_b = prices[i + 2] - prices[i + 1]
+        if move_a * move_b >= 0:
+            return False
+
+    return True
+
+
+def _compute_impulse_clarity(
+    prices: list[float],
+    trend_up: bool,
+    price_range: float,
+) -> float:
+    """Compute wave clarity for an impulse wave.
+
+    Clarity is based on:
+    - How well the pivots alternate (already validated).
+    - How proportional the wave magnitudes are.
+    - How significant the waves are relative to the price range.
+    """
+    if price_range <= 0:
+        return 0.0
+
+    waves = [abs(prices[i + 1] - prices[i]) for i in range(4)]
+    total_movement = sum(waves)
+
+    # Significance: total wave movement relative to price range
+    significance = min(1.0, total_movement / (price_range * 2.0))
+
+    # Proportionality: wave 3 should be the largest or close to it
+    max_wave = max(waves)
+    if max_wave <= 0:
+        return 0.0
+
+    wave3_ratio = waves[2] / max_wave  # 1.0 if wave 3 is the largest
+
+    # Overall clarity
+    clarity = 0.5 * significance + 0.5 * wave3_ratio
+    return max(0.0, min(1.0, clarity))
+
+
+def _check_corrective(
+    pivots: list[dict],
+    trend_up: bool,
+    price_range: float,
+) -> dict | None:
+    """Check if 3 pivots form a valid corrective wave (A-B-C).
+
+    A corrective wave moves against the main trend:
+    - For a bullish main trend: corrective wave moves down (A down, B up, C down).
+    - For a bearish main trend: corrective wave moves up (A up, B down, C up).
+    """
+    if len(pivots) != _CORRECTIVE_WAVE_COUNT:
+        return None
+
+    prices = [p["price"] for p in pivots]
+
+    # Check alternating direction
+    move_a = prices[1] - prices[0]
+    move_b = prices[2] - prices[1]
+
+    # Moves must be in opposite directions
+    if move_a * move_b >= 0:
+        return None
+
+    # For a bullish main trend, the corrective wave should move down overall
+    if trend_up:
+        if prices[2] >= prices[0]:
+            return None  # not a downward correction
+    else:
+        if prices[2] <= prices[0]:
+            return None  # not an upward correction
+
+    # Compute clarity
+    waves = [abs(prices[1] - prices[0]), abs(prices[2] - prices[1])]
+    total_movement = sum(waves)
+
+    if price_range <= 0:
+        return 0.0
+
+    significance = min(1.0, total_movement / price_range)
+    clarity = significance * 0.8  # corrective waves are inherently less clear
+
+    # Current position is wave C (the last wave in the correction)
+    return {
+        "wave_type": WAVE_TYPE_CORRECTIVE,
+        "current_position": 3,  # wave C
+        "trend_up": trend_up,
+        "clarity": max(0.0, min(1.0, clarity)),
+    }

services/signal_engine/signals/fibonacci.py

@@ -0,0 +1,127 @@
+"""Fibonacci retracement signal evaluator.
+
+Computes retracement levels using ``L(r) = SH - r * (SH - SL)`` for the
+standard ratios [0.236, 0.382, 0.5, 0.618, 0.786] and produces a signal
+based on the proximity of the current price to the nearest level.
+"""
+
+from __future__ import annotations
+
+from services.signal_engine.models import OHLCVBar, SignalDirection, SignalResult
+from services.signal_engine.signals.base import (
+    find_swing_high,
+    find_swing_low,
+    validate_lookback,
+)
+
+# Standard Fibonacci retracement ratios
+RETRACEMENT_RATIOS: list[float] = [0.236, 0.382, 0.5, 0.618, 0.786]
+
+# Ratios considered "key" levels — proximity to these yields higher confidence
+_KEY_RATIOS: set[float] = {0.5, 0.618}
+
+# Default minimum number of bars required for evaluation
+DEFAULT_MIN_BARS: int = 20
+
+
+class FibonacciEvaluator:
+    """Fibonacci retracement signal evaluator.
+
+    Satisfies the :class:`~services.signal_engine.signals.base.SignalEvaluator`
+    protocol.
+
+    Parameters
+    ----------
+    min_bars:
+        Minimum number of OHLCV bars required before the evaluator will
+        produce a signal.  Defaults to ``20``.
+    """
+
+    def __init__(self, min_bars: int = DEFAULT_MIN_BARS) -> None:
+        self.min_bars = min_bars
+
+    # ------------------------------------------------------------------
+    # Public API (SignalEvaluator protocol)
+    # ------------------------------------------------------------------
+
+    def evaluate(
+        self,
+        bars: list[OHLCVBar],
+        timeframe: str,
+    ) -> SignalResult | None:
+        """Evaluate Fibonacci retracement on *bars* for *timeframe*.
+
+        Returns ``None`` when there are fewer than :pyattr:`min_bars` bars,
+        or when the swing high equals the swing low (flat market — no valid
+        retracement).
+        """
+        if not validate_lookback(bars, self.min_bars):
+            return None
+
+        # Detect swing high / swing low within the evaluation window
+        sh_result = find_swing_high(bars, self.min_bars)
+        sl_result = find_swing_low(bars, self.min_bars)
+
+        if sh_result is None or sl_result is None:
+            return None
+
+        _sh_idx, sh_price = sh_result
+        _sl_idx, sl_price = sl_result
+
+        # SH must be strictly greater than SL for a valid retracement range
+        if sh_price <= sl_price:
+            return None
+
+        price_range = sh_price - sl_price
+        current_price = bars[-1].close
+
+        # Compute retracement levels: L(r) = SH - r * (SH - SL)
+        levels: dict[float, float] = {
+            r: sh_price - r * price_range for r in RETRACEMENT_RATIOS
+        }
+
+        # Find the nearest retracement level to the current price
+        nearest_ratio: float = RETRACEMENT_RATIOS[0]
+        nearest_level: float = levels[nearest_ratio]
+        min_distance: float = abs(current_price - nearest_level)
+
+        for ratio in RETRACEMENT_RATIOS[1:]:
+            distance = abs(current_price - levels[ratio])
+            if distance < min_distance:
+                min_distance = distance
+                nearest_ratio = ratio
+                nearest_level = levels[ratio]
+
+        # Signal strength: 1.0 - (distance / range), clamped to [0, 1]
+        raw_strength = 1.0 - (min_distance / price_range)
+        strength = max(0.0, min(1.0, raw_strength))
+
+        # Direction: BULLISH if price is near a retracement level and above SL
+        # (potential bounce off support).  Otherwise BEARISH.
+        if current_price >= sl_price:
+            direction = SignalDirection.BULLISH
+        else:
+            direction = SignalDirection.BEARISH
+
+        # Confidence: higher when the nearest level is a key ratio (0.618, 0.5)
+        if nearest_ratio in _KEY_RATIOS:
+            confidence = min(1.0, strength * 1.2)
+        else:
+            confidence = strength * 0.8
+
+        return SignalResult(
+            signal_type="fibonacci",
+            timeframe=timeframe,
+            strength=strength,
+            direction=direction,
+            confidence=confidence,
+            metadata={
+                "swing_high": sh_price,
+                "swing_low": sl_price,
+                "retracement_levels": levels,
+                "nearest_ratio": nearest_ratio,
+                "nearest_level": nearest_level,
+                "distance_to_nearest": min_distance,
+                "current_price": current_price,
+            },
+        )

services/signal_engine/signals/ma_stack.py

@@ -0,0 +1,182 @@
+"""Moving average stack signal evaluator.
+
+Detects bullish alignment (MA_10 > MA_20 > MA_50 > MA_200) and bearish
+alignment (MA_10 < MA_20 < MA_50 < MA_200), producing a signal strength
+proportional to the degree of alignment.
+
+Full alignment (4/4 MAs in order) yields strength 1.0, partial alignment
+(3/4) yields 0.6, and no alignment returns ``None`` (no signal).
+"""
+
+from __future__ import annotations
+
+from services.signal_engine.models import OHLCVBar, SignalDirection, SignalResult
+from services.signal_engine.signals.base import compute_sma, validate_lookback
+
+# MA periods used for stack evaluation
+MA_PERIODS: list[int] = [10, 20, 50, 200]
+
+# Minimum number of bars required (longest MA period)
+MIN_BARS: int = 200
+
+# Strength values
+_FULL_ALIGNMENT_STRENGTH: float = 1.0
+_PARTIAL_ALIGNMENT_STRENGTH: float = 0.6
+
+# Confidence multiplier (high confidence for clear alignment patterns)
+_CONFIDENCE_MULTIPLIER: float = 0.9
+
+
+class MAStackEvaluator:
+    """Moving average stack signal evaluator.
+
+    Satisfies the :class:`~services.signal_engine.signals.base.SignalEvaluator`
+    protocol.
+
+    Computes MA_10, MA_20, MA_50, and MA_200 and checks whether they are
+    in bullish or bearish order.  Full alignment (all four in strict order)
+    produces strength 1.0; partial alignment (any three consecutive in order)
+    produces strength 0.6.  When no alignment is detected the evaluator
+    returns ``None``.
+    """
+
+    # ------------------------------------------------------------------
+    # Public API (SignalEvaluator protocol)
+    # ------------------------------------------------------------------
+
+    def evaluate(
+        self,
+        bars: list[OHLCVBar],
+        timeframe: str,
+    ) -> SignalResult | None:
+        """Evaluate moving average stack alignment on *bars*.
+
+        Returns ``None`` when there are fewer than 200 bars (insufficient
+        data for MA_200) or when no alignment is detected.
+        """
+        if not validate_lookback(bars, MIN_BARS):
+            return None
+
+        # Compute all four moving averages
+        ma_10 = compute_sma(bars, 10)
+        ma_20 = compute_sma(bars, 20)
+        ma_50 = compute_sma(bars, 50)
+        ma_200 = compute_sma(bars, 200)
+
+        # Safety check — compute_sma returns None on insufficient data
+        if ma_10 is None or ma_20 is None or ma_50 is None or ma_200 is None:
+            return None
+
+        ma_values = [ma_10, ma_20, ma_50, ma_200]
+
+        # Check full bullish alignment: MA_10 > MA_20 > MA_50 > MA_200
+        full_bullish = ma_10 > ma_20 > ma_50 > ma_200
+
+        # Check full bearish alignment: MA_10 < MA_20 < MA_50 < MA_200
+        full_bearish = ma_10 < ma_20 < ma_50 < ma_200
+
+        if full_bullish:
+            return self._build_result(
+                direction=SignalDirection.BULLISH,
+                strength=_FULL_ALIGNMENT_STRENGTH,
+                alignment="full_bullish",
+                timeframe=timeframe,
+                ma_values=ma_values,
+            )
+
+        if full_bearish:
+            return self._build_result(
+                direction=SignalDirection.BEARISH,
+                strength=_FULL_ALIGNMENT_STRENGTH,
+                alignment="full_bearish",
+                timeframe=timeframe,
+                ma_values=ma_values,
+            )
+
+        # Check partial alignment (3 out of 4 consecutive MAs in order)
+        partial_bullish = self._check_partial_bullish(ma_values)
+        partial_bearish = self._check_partial_bearish(ma_values)
+
+        if partial_bullish:
+            return self._build_result(
+                direction=SignalDirection.BULLISH,
+                strength=_PARTIAL_ALIGNMENT_STRENGTH,
+                alignment="partial_bullish",
+                timeframe=timeframe,
+                ma_values=ma_values,
+            )
+
+        if partial_bearish:
+            return self._build_result(
+                direction=SignalDirection.BEARISH,
+                strength=_PARTIAL_ALIGNMENT_STRENGTH,
+                alignment="partial_bearish",
+                timeframe=timeframe,
+                ma_values=ma_values,
+            )
+
+        # No alignment detected — no signal
+        return None
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _check_partial_bullish(ma_values: list[float]) -> bool:
+        """Return ``True`` if any 3 consecutive MAs are in bullish order.
+
+        Checks windows [0:3] and [1:4] of the ordered MA list
+        (MA_10, MA_20, MA_50, MA_200) for strictly descending values
+        (higher MA value = bullish when shorter period > longer period).
+        """
+        # Window 1: MA_10 > MA_20 > MA_50
+        if ma_values[0] > ma_values[1] > ma_values[2]:
+            return True
+        # Window 2: MA_20 > MA_50 > MA_200
+        if ma_values[1] > ma_values[2] > ma_values[3]:
+            return True
+        return False
+
+    @staticmethod
+    def _check_partial_bearish(ma_values: list[float]) -> bool:
+        """Return ``True`` if any 3 consecutive MAs are in bearish order.
+
+        Checks windows [0:3] and [1:4] of the ordered MA list
+        for strictly ascending values (lower MA value = bearish when
+        shorter period < longer period).
+        """
+        # Window 1: MA_10 < MA_20 < MA_50
+        if ma_values[0] < ma_values[1] < ma_values[2]:
+            return True
+        # Window 2: MA_20 < MA_50 < MA_200
+        if ma_values[1] < ma_values[2] < ma_values[3]:
+            return True
+        return False
+
+    @staticmethod
+    def _build_result(
+        *,
+        direction: SignalDirection,
+        strength: float,
+        alignment: str,
+        timeframe: str,
+        ma_values: list[float],
+    ) -> SignalResult:
+        """Construct a ``SignalResult`` for the MA stack signal."""
+        confidence = strength * _CONFIDENCE_MULTIPLIER
+
+        return SignalResult(
+            signal_type="ma_stack",
+            timeframe=timeframe,
+            strength=strength,
+            direction=direction,
+            confidence=confidence,
+            metadata={
+                "ma_10": ma_values[0],
+                "ma_20": ma_values[1],
+                "ma_50": ma_values[2],
+                "ma_200": ma_values[3],
+                "alignment": alignment,
+            },
+        )

services/signal_engine/signals/rsi.py

@@ -0,0 +1,149 @@
+"""RSI (Relative Strength Index) signal evaluator.
+
+Computes the standard 14-period RSI using Wilder's smoothing method and
+produces overbought (RSI > 70 → BEARISH) or oversold (RSI < 30 → BULLISH)
+signals with strength scaled by distance from the threshold.
+
+When RSI is between 30 and 70 (neutral zone), no signal is produced.
+"""
+
+from __future__ import annotations
+
+from services.signal_engine.models import OHLCVBar, SignalDirection, SignalResult
+from services.signal_engine.signals.base import validate_lookback
+
+# Default RSI period (standard Wilder 14-period)
+DEFAULT_RSI_PERIOD: int = 14
+
+# Minimum bars required: period + 1 (for initial price change calculation)
+DEFAULT_MIN_BARS: int = DEFAULT_RSI_PERIOD + 1  # 15
+
+# Overbought / oversold thresholds
+OVERBOUGHT_THRESHOLD: float = 70.0
+OVERSOLD_THRESHOLD: float = 30.0
+
+# Maximum possible distance from threshold (used for strength scaling)
+_MAX_DISTANCE_OVERBOUGHT: float = 100.0 - OVERBOUGHT_THRESHOLD  # 30
+_MAX_DISTANCE_OVERSOLD: float = OVERSOLD_THRESHOLD - 0.0  # 30
+
+# Confidence multiplier
+_CONFIDENCE_MULTIPLIER: float = 0.85
+
+
+def compute_rsi(bars: list[OHLCVBar], period: int = DEFAULT_RSI_PERIOD) -> float | None:
+    """Compute RSI using Wilder's smoothing method.
+
+    Args:
+        bars: OHLCV bar series (oldest-first).
+        period: RSI period (default 14).
+
+    Returns:
+        RSI value in [0, 100], or ``None`` if insufficient data.
+    """
+    min_bars = period + 1
+    if len(bars) < min_bars:
+        return None
+
+    closes = [bar.close for bar in bars]
+
+    # Calculate price changes
+    changes = [closes[i] - closes[i - 1] for i in range(1, len(closes))]
+
+    # Separate gains and losses for the first `period` changes
+    first_gains = [max(0.0, c) for c in changes[:period]]
+    first_losses = [max(0.0, -c) for c in changes[:period]]
+
+    avg_gain = sum(first_gains) / period
+    avg_loss = sum(first_losses) / period
+
+    # Apply Wilder smoothing for subsequent changes
+    for c in changes[period:]:
+        gain = max(0.0, c)
+        loss = max(0.0, -c)
+        avg_gain = (avg_gain * (period - 1) + gain) / period
+        avg_loss = (avg_loss * (period - 1) + loss) / period
+
+    # Avoid division by zero: if avg_loss is 0, RSI is 100
+    if avg_loss == 0.0:
+        return 100.0
+
+    rs = avg_gain / avg_loss
+    rsi = 100.0 - (100.0 / (1.0 + rs))
+    return rsi
+
+
+class RSIEvaluator:
+    """RSI signal evaluator.
+
+    Satisfies the :class:`~services.signal_engine.signals.base.SignalEvaluator`
+    protocol.
+
+    Parameters
+    ----------
+    period:
+        RSI calculation period.  Defaults to ``14``.
+    """
+
+    def __init__(self, period: int = DEFAULT_RSI_PERIOD) -> None:
+        self.period = period
+        self.min_bars = period + 1
+
+    # ------------------------------------------------------------------
+    # Public API (SignalEvaluator protocol)
+    # ------------------------------------------------------------------
+
+    def evaluate(
+        self,
+        bars: list[OHLCVBar],
+        timeframe: str,
+    ) -> SignalResult | None:
+        """Evaluate RSI on *bars* for *timeframe*.
+
+        Returns ``None`` when there are fewer than ``period + 1`` bars
+        or when RSI is in the neutral zone (30–70).
+        """
+        if not validate_lookback(bars, self.min_bars):
+            return None
+
+        rsi = compute_rsi(bars, self.period)
+        if rsi is None:
+            return None
+
+        # Overbought: RSI > 70 → BEARISH (potential reversal down)
+        if rsi > OVERBOUGHT_THRESHOLD:
+            distance = rsi - OVERBOUGHT_THRESHOLD
+            strength = min(1.0, max(0.0, distance / _MAX_DISTANCE_OVERBOUGHT))
+            confidence = strength * _CONFIDENCE_MULTIPLIER
+            return SignalResult(
+                signal_type="rsi",
+                timeframe=timeframe,
+                strength=strength,
+                direction=SignalDirection.BEARISH,
+                confidence=confidence,
+                metadata={
+                    "rsi": rsi,
+                    "period": self.period,
+                    "zone": "overbought",
+                },
+            )
+
+        # Oversold: RSI < 30 → BULLISH (potential reversal up)
+        if rsi < OVERSOLD_THRESHOLD:
+            distance = OVERSOLD_THRESHOLD - rsi
+            strength = min(1.0, max(0.0, distance / _MAX_DISTANCE_OVERSOLD))
+            confidence = strength * _CONFIDENCE_MULTIPLIER
+            return SignalResult(
+                signal_type="rsi",
+                timeframe=timeframe,
+                strength=strength,
+                direction=SignalDirection.BULLISH,
+                confidence=confidence,
+                metadata={
+                    "rsi": rsi,
+                    "period": self.period,
+                    "zone": "oversold",
+                },
+            )
+
+        # Neutral zone (30 ≤ RSI ≤ 70): no signal
+        return None

services/signal_engine/worker.py

@@ -0,0 +1,300 @@
+"""Top-level orchestrator for a single evaluation tick.
+
+Coordinates input normalization, exit evaluation, hard filters, signal
+evaluation, both pipelines (concurrent), delta analysis, output formatting,
+persistence, and Redis queue publication.
+
+Requirements: 11.1, 11.2, 11.3, 11.4, 11.5, 11.6
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import time
+
+import asyncpg
+import redis.asyncio
+
+from services.aggregation.regime import classify_regime
+from services.signal_engine.config import SignalEngineConfig
+from services.signal_engine.confluence import compute_confluence
+from services.signal_engine.delta import analyze_delta
+from services.signal_engine.exit_engine import evaluate_exits
+from services.signal_engine.formatter import (
+    format_output,
+    signal_output_to_recommendation,
+)
+from services.signal_engine.hard_filter import evaluate_hard_filters
+from services.signal_engine.heuristic import run_heuristic_pipeline
+from services.signal_engine.models import (
+    HeuristicResult,
+    NormalizedInput,
+    ProbabilisticResult,
+    SignalOutput,
+    SignalResult,
+    Verdict,
+)
+from services.signal_engine.normalizer import normalize_input
+from services.signal_engine.persistence import persist_signal_output
+from services.signal_engine.probabilistic import run_probabilistic_pipeline
+from services.signal_engine.signals.cup_handle import CupHandleEvaluator
+from services.signal_engine.signals.elliott_wave import ElliottWaveEvaluator
+from services.signal_engine.signals.fibonacci import FibonacciEvaluator
+from services.signal_engine.signals.ma_stack import MAStackEvaluator
+from services.signal_engine.signals.rsi import RSIEvaluator
+
+logger = logging.getLogger(__name__)
+
+# Redis queue for trading decisions
+_TRADING_QUEUE = "stonks:queue:trading_decisions"
+
+# All signal evaluators
+_EVALUATORS = [
+    FibonacciEvaluator(),
+    MAStackEvaluator(),
+    RSIEvaluator(),
+    CupHandleEvaluator(),
+    ElliottWaveEvaluator(),
+]
+
+# Default SKIP results used when a pipeline fails
+_SKIP_HEURISTIC = HeuristicResult(
+    verdict=Verdict.SKIP,
+    confidence=0.0,
+    s_total=0.0,
+    s_company=0.0,
+    s_macro=0.0,
+    s_competitive=0.0,
+    signal_weights=[],
+    reasoning=["pipeline_error: heuristic pipeline raised an exception"],
+)
+
+_SKIP_PROBABILISTIC = ProbabilisticResult(
+    verdict=Verdict.SKIP,
+    p_up=0.5,
+    entropy=1.0,
+    ev_r=0.0,
+    prior=0.5,
+    posterior=0.5,
+    likelihood_ratios=[],
+    regime="uncertainty",
+    reasoning=["pipeline_error: probabilistic pipeline raised an exception"],
+)
+
+
+def _evaluate_signals(
+    normalized: NormalizedInput,
+) -> dict[str, dict[str, SignalResult]]:
+    """Run all signal evaluators across all timeframes.
+
+    Returns ``{signal_type: {timeframe: SignalResult}}`` for signals that
+    fired.  Signals that returned ``None`` (insufficient data or no trigger)
+    are omitted.
+    """
+    from services.signal_engine.normalizer import TIMEFRAMES
+
+    results: dict[str, dict[str, SignalResult]] = {}
+
+    for evaluator in _EVALUATORS:
+        for tf in TIMEFRAMES:
+            bars = normalized.bars.get(tf, [])
+            if not bars:
+                continue
+
+            try:
+                result = evaluator.evaluate(bars, tf)
+            except Exception:
+                logger.warning(
+                    "Signal evaluator %s failed on %s/%s",
+                    type(evaluator).__name__,
+                    normalized.ticker,
+                    tf,
+                    exc_info=True,
+                )
+                continue
+
+            if result is not None:
+                results.setdefault(result.signal_type, {})[tf] = result
+
+    return results
+
+
+async def evaluate_tick(
+    pool: asyncpg.Pool,
+    redis_client: redis.asyncio.Redis,
+    ticker: str,
+    config: SignalEngineConfig,
+) -> SignalOutput | None:
+    """Run a full evaluation tick for a single ticker.
+
+    Steps:
+    1. Normalize inputs (single fetch, shared reference)
+    2. Evaluate exit conditions for open positions
+    3. Run hard filters (short-circuit if filtered)
+    4. Evaluate signals across timeframes via Signal Library
+    5. Compute confluence
+    6. Classify regime via existing ``classify_regime()``
+    7. Run both pipelines concurrently via ``asyncio.gather``
+    8. Compute delta analysis
+    9. Format output
+    10. Persist to database and publish to Redis queue
+
+    Returns ``None`` if the ticker is hard-filtered or both pipelines fail.
+
+    Requirements: 11.1, 11.2, 11.3, 11.4, 11.5, 11.6
+    """
+    tick_start = time.monotonic()
+
+    # Step 1: Normalize inputs
+    normalized = await normalize_input(pool, ticker, config)
+
+    # Step 2: Evaluate exit conditions (before pipelines — Req 8.6)
+    current_price = normalized.current_price or 0.0
+    exit_signals = evaluate_exits(
+        normalized.open_positions,
+        {ticker: current_price},
+        config.exit_config,
+    )
+
+    # Step 3: Hard filters
+    filter_result = evaluate_hard_filters(normalized, config.hard_filter_config)
+    if filter_result.filtered:
+        logger.info(
+            "Ticker %s hard-filtered: %s",
+            ticker,
+            ", ".join(filter_result.reasons),
+        )
+        return None
+
+    # Step 4: Evaluate signals across timeframes
+    signal_results = _evaluate_signals(normalized)
+
+    # Step 5: Compute confluence
+    confluence_signals = compute_confluence(signal_results, config.timeframe_weights)
+
+    # Step 6: Classify regime
+    regime = classify_regime(normalized.closing_prices, normalized.returns)
+
+    # Step 7: Run both pipelines concurrently
+    heuristic_start = time.monotonic()
+
+    async def _run_heuristic() -> HeuristicResult:
+        return run_heuristic_pipeline(
+            normalized, confluence_signals, config.heuristic_config
+        )
+
+    async def _run_probabilistic() -> ProbabilisticResult:
+        return run_probabilistic_pipeline(
+            normalized, confluence_signals, regime, config.probabilistic_config
+        )
+
+    results = await asyncio.gather(
+        _run_heuristic(),
+        _run_probabilistic(),
+        return_exceptions=True,
+    )
+
+    pipeline_elapsed = time.monotonic() - heuristic_start
+
+    # Handle pipeline exceptions — SKIP verdict for failed pipeline
+    heuristic_result: HeuristicResult
+    probabilistic_result: ProbabilisticResult
+
+    if isinstance(results[0], BaseException):
+        logger.error(
+            "Heuristic pipeline failed for %s: %s",
+            ticker,
+            results[0],
+            exc_info=results[0],
+        )
+        heuristic_result = _SKIP_HEURISTIC
+    else:
+        heuristic_result = results[0]
+
+    if isinstance(results[1], BaseException):
+        logger.error(
+            "Probabilistic pipeline failed for %s: %s",
+            ticker,
+            results[1],
+            exc_info=results[1],
+        )
+        probabilistic_result = _SKIP_PROBABILISTIC
+    else:
+        probabilistic_result = results[1]
+
+    # If both pipelines failed, return None
+    if isinstance(results[0], BaseException) and isinstance(results[1], BaseException):
+        logger.error(
+            "Both pipelines failed for %s — skipping tick",
+            ticker,
+        )
+        return None
+
+    logger.info(
+        "Pipelines completed for %s in %.3fs — heuristic=%s, probabilistic=%s",
+        ticker,
+        pipeline_elapsed,
+        heuristic_result.verdict.value,
+        probabilistic_result.verdict.value,
+    )
+
+    # Step 8: Delta analysis
+    delta = await analyze_delta(
+        heuristic_result, probabilistic_result, redis_client, ticker
+    )
+
+    # Step 9: Format output
+    price = normalized.current_price or 0.0
+    output = format_output(
+        ticker,
+        price,
+        heuristic_result,
+        probabilistic_result,
+        delta,
+        exit_signals,
+        config,
+    )
+
+    # Step 10: Persist to database
+    await persist_signal_output(pool, output)
+
+    # Step 11: Publish to trading queue (only if at least one BUY and not shadow_mode)
+    has_buy = (
+        heuristic_result.verdict == Verdict.BUY
+        or probabilistic_result.verdict == Verdict.BUY
+    )
+
+    if has_buy and not config.shadow_mode:
+        try:
+            recommendation = signal_output_to_recommendation(output)
+            await redis_client.rpush(
+                _TRADING_QUEUE,
+                recommendation.model_dump_json(),
+            )
+            logger.info(
+                "Published trading recommendation for %s to %s",
+                ticker,
+                _TRADING_QUEUE,
+            )
+        except Exception:
+            logger.error(
+                "Failed to publish trading recommendation for %s",
+                ticker,
+                exc_info=True,
+            )
+    elif has_buy and config.shadow_mode:
+        logger.info(
+            "Shadow mode: BUY signal for %s persisted but not published to trading queue",
+            ticker,
+        )
+
+    # Log wall-clock execution time
+    tick_elapsed = time.monotonic() - tick_start
+    logger.info(
+        "Evaluation tick for %s completed in %.3fs",
+        ticker,
+        tick_elapsed,
+    )
+
+    return output