signal_log: weight-research data points, scan_outcomes forward labels, watch provenance

New additive columns on every logged scan: bar_ts (primary closed-bar label, indexed with
timeframe), engine_fingerprint (hash of the scoring config incl. the regime weight table in
force - SignalAggregator.engine_config_fingerprint, twin of the gate fingerprint), code_version
(short git SHA), penalty_tags_json / penalty_total / momentum_fading / exhaustion_count, and
post-hoc watch_level / watch_kinds / watch_line written by violent_watch.attach_watch via
signal_log.update_watch. Each indicators_json entry now also carries eff_weight (base weight x
regime multiplier, the weight the composite actually used) and wscore.

engine: IndicatorSignal.effective_weight / weighted_score are stamped inside _compute_composite
(record-keeping only); regime_weight_table(config) is the single source of truth for the
multiplier table (override honoured). Composite output byte-identical (120-report hash proof).

New table scan_outcomes: model-free forward moves per scan (fwd_1h/4h/24h in $ and ATRs,
mfe/mae over 24h, burst_1h, bars_used, complete) measured on the stored candle stream with the
advisory model's continuity rule; labelled opportunistically at the end of log() (time-boxed,
throttled, idempotent) - no daemon; scripts/label_scan_outcomes.py drives the backlog
(--dry-run is read-only). analyze_signal_log --scan-outcomes: forward move + hit rate by
TF x signal/grade with a NEUTRAL baseline and burst rate by watch level. Readers dedup by the
first row per (timeframe, bar_ts).

Module 31 (10 tests). Suite: 463 passed. Backtester untouched (test-locked).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RWV2yLt3YXHHGVYHSQU2EE

engine.py

@@ -13,7 +13,7 @@ Design philosophy:
 
 import logging
 from dataclasses import dataclass, field
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
@@ -32,6 +32,13 @@ class IndicatorSignal:
     confidence: float  # 0-100
     description: str
     weight: float = 1.0
+    # RECORD-KEEPING ONLY (stamped by _compute_composite; nothing reads
+    # them back). effective_weight = weight x regime multiplier actually
+    # used, weighted_score = its contribution to the composite. Both stay
+    # None for indicators the composite skipped (weight 0, `_`-prefixed
+    # private series) and for analyses that never reached _compute_composite.
+    effective_weight: Optional[float] = None
+    weighted_score: Optional[float] = None
 
 
 @dataclass
@@ -50,6 +57,46 @@ class TechnicalAnalysis:
     pattern_signals: List[str] = field(default_factory=list)
 
 
+# NOTE: ROC is directionally redundant with MACD/Keltner/EMA
+# (0.8-0.94 correlation), but zeroing its weight was backtested
+# 2026-07 and LOST 13R — the redundant vote effectively raises
+# the composite bar and the thresholds are calibrated with it.
+# Don't remove without retuning score thresholds.
+_TRENDING_WEIGHTS = {
+    "ema_trend": 2.0, "rsi": 1.0, "macd": 2.0,
+    "bollinger": 0.5, "keltner": 1.5, "roc": 1.5,
+    "candlestick": 0.8, "divergence": 1.0, "adx": 0.8,
+}
+_DEFAULT_REGIME_WEIGHTS = {
+    "trending_up": _TRENDING_WEIGHTS,
+    "trending_down": _TRENDING_WEIGHTS,
+    "trending": _TRENDING_WEIGHTS,
+    "ranging": {
+        "ema_trend": 0.3, "rsi": 1.5, "macd": 1.5,
+        "bollinger": 1.5, "keltner": 2.0, "roc": 0.5,
+        "candlestick": 1.0, "divergence": 2.5, "adx": 0.5,
+    },
+    "transitioning": {
+        "ema_trend": 1.5, "rsi": 1.0, "macd": 2.5,
+        "bollinger": 1.0, "keltner": 2.5, "roc": 2.0,
+        "candlestick": 1.0, "divergence": 2.0, "adx": 1.0,
+    },
+}
+
+
+def regime_weight_table(config: TechnicalConfig) -> Dict[str, Dict[str, float]]:
+    """The regime -> indicator weight multipliers _compute_composite runs
+    with (``regime_weights_override`` wins, else the gold-optimized
+    defaults above).
+
+    Pure and side-effect free: ONE source of truth shared by the composite
+    and by signal_aggregator.engine_config_fingerprint. Returns a copy, so
+    a caller can never mutate the shipped table."""
+    override = getattr(config, "regime_weights_override", None)
+    table = override if override else _DEFAULT_REGIME_WEIGHTS
+    return {regime: dict(weights) for regime, weights in table.items()}
+
+
 class TechnicalEngine:
     """
     Core technical analysis engine.
@@ -1081,35 +1128,9 @@ class TechnicalEngine:
         total_weight = 0.0
         total_confidence = 0.0
 
-        # Use override weights if provided, else gold-optimized defaults
-        if self.config.regime_weights_override:
-            regime_weights = self.config.regime_weights_override
-        else:
-            # NOTE: ROC is directionally redundant with MACD/Keltner/EMA
-            # (0.8-0.94 correlation), but zeroing its weight was backtested
-            # 2026-07 and LOST 13R — the redundant vote effectively raises
-            # the composite bar and the thresholds are calibrated with it.
-            # Don't remove without retuning score thresholds.
-            _trending = {
-                "ema_trend": 2.0, "rsi": 1.0, "macd": 2.0,
-                "bollinger": 0.5, "keltner": 1.5, "roc": 1.5,
-                "candlestick": 0.8, "divergence": 1.0, "adx": 0.8,
-            }
-            regime_weights = {
-                "trending_up": _trending,
-                "trending_down": _trending,
-                "trending": _trending,
-                "ranging": {
-                    "ema_trend": 0.3, "rsi": 1.5, "macd": 1.5,
-                    "bollinger": 1.5, "keltner": 2.0, "roc": 0.5,
-                    "candlestick": 1.0, "divergence": 2.5, "adx": 0.5,
-                },
-                "transitioning": {
-                    "ema_trend": 1.5, "rsi": 1.0, "macd": 2.5,
-                    "bollinger": 1.0, "keltner": 2.5, "roc": 2.0,
-                    "candlestick": 1.0, "divergence": 2.0, "adx": 1.0,
-                },
-            }
+        # Override weights if provided, else gold-optimized defaults
+        # (regime_weight_table is the single source of truth).
+        regime_weights = regime_weight_table(self.config)
 
         active_regime_weights = regime_weights.get(result.market_regime, {})
 
@@ -1123,6 +1144,9 @@ class TechnicalEngine:
 
             signal_score = ind.signal.value
             weighted_score = signal_score * weight * (ind.confidence / 100.0)
+            # Record-keeping stamp (no maths depends on these two lines).
+            ind.effective_weight = float(weight)
+            ind.weighted_score = float(weighted_score)
 
             total_score += weighted_score
             total_weight += weight