signal_aggregator + chart_patterns: drop dead parameters and shadowed literals (no behaviour change)

- analyze_pair(context=...): no caller ever supplied it (main, telegram_bot,
  pair_signals, tests), so the value was always None; the parameter and the
  pass-through to analyze_from_dataframes go (analyze_from_dataframes keeps
  its own context parameter).
- The three specific "SELL FILTER: quality|volatility|regime" stale-prefix
  entries were shadowed by the general "SELL FILTER: " entry in the same
  startswith() tuple; every emitter carries the general prefix.
- chart_patterns: the detectors never read the Open series, so the `o`
  parameter is dropped from the three detectors, the PATTERN_DETECTORS
  lambdas and the dispatcher.
- Corrected the pb_funnel/cp_funnel comment: nothing reads those counters
  (the "read by backtest tooling" claim was stale); they stay as cheap
  diagnostics.

Parity: 12-month M5 and H1 books regenerated byte-identical
(md5 a28fed4becd056466c26d6a947e1a0e5 / d20405ef64f069770c258bb9bed1ac90).
Suite: 461 passed, 0 failed.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0156YhogLytzj9P2iH1skGbL

chart_patterns.py

@@ -97,7 +97,7 @@ def _first_close_beyond(closes: np.ndarray, start: int, level: float,
 
 # ── Double bottom / top ──────────────────────────────────────────────
 
-def _detect_double(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
+def _detect_double(h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
     w = cfg.cp_swing_window
     ext = l if bullish else h
     idx = _swing_indices(ext, w, find_high=not bullish)
@@ -147,7 +147,7 @@ def _detect_double(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
 
 # ── Head & shoulders (top and inverse) ───────────────────────────────
 
-def _detect_hs(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
+def _detect_hs(h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
     """bullish=True → inverse H&S (three swing LOWS, head deepest)."""
     w = cfg.cp_swing_window
     ext = l if bullish else h
@@ -210,7 +210,7 @@ def _detect_hs(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
 
 # ── Flags (bull and bear) ────────────────────────────────────────────
 
-def _detect_flag(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
+def _detect_flag(h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
     n = len(c)
     lo_r, hi_r = cfg.cp_flag_retrace
     if bullish:
@@ -257,12 +257,12 @@ def _detect_flag(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
 # Priority order: most structured / rarest first — if two patterns
 # complete on the same bar, the more specific one names the trade.
 PATTERN_DETECTORS = {
-    "inv_hs":        lambda o, h, l, c, atr, cfg: _detect_hs(o, h, l, c, atr, cfg, True),
-    "hs_top":        lambda o, h, l, c, atr, cfg: _detect_hs(o, h, l, c, atr, cfg, False),
-    "double_bottom": lambda o, h, l, c, atr, cfg: _detect_double(o, h, l, c, atr, cfg, True),
-    "double_top":    lambda o, h, l, c, atr, cfg: _detect_double(o, h, l, c, atr, cfg, False),
-    "bull_flag":     lambda o, h, l, c, atr, cfg: _detect_flag(o, h, l, c, atr, cfg, True),
-    "bear_flag":     lambda o, h, l, c, atr, cfg: _detect_flag(o, h, l, c, atr, cfg, False),
+    "inv_hs":        lambda h, l, c, atr, cfg: _detect_hs(h, l, c, atr, cfg, True),
+    "hs_top":        lambda h, l, c, atr, cfg: _detect_hs(h, l, c, atr, cfg, False),
+    "double_bottom": lambda h, l, c, atr, cfg: _detect_double(h, l, c, atr, cfg, True),
+    "double_top":    lambda h, l, c, atr, cfg: _detect_double(h, l, c, atr, cfg, False),
+    "bull_flag":     lambda h, l, c, atr, cfg: _detect_flag(h, l, c, atr, cfg, True),
+    "bear_flag":     lambda h, l, c, atr, cfg: _detect_flag(h, l, c, atr, cfg, False),
 }
 
 
@@ -276,7 +276,6 @@ def detect_chart_pattern(df: pd.DataFrame, atr: float, cfg) -> Optional[Dict]:
     if atr <= 0 or len(df) < cfg.cp_lookback + 1:
         return None
     tail = df.iloc[-(cfg.cp_lookback + 1):]
-    o = tail["Open"].to_numpy(dtype=np.float64)
     h = tail["High"].to_numpy(dtype=np.float64)
     l = tail["Low"].to_numpy(dtype=np.float64)
     c = tail["Close"].to_numpy(dtype=np.float64)
@@ -284,7 +283,7 @@ def detect_chart_pattern(df: pd.DataFrame, atr: float, cfg) -> Optional[Dict]:
     for name, det in PATTERN_DETECTORS.items():
         if name not in enabled:
             continue
-        hit = det(o, h, l, c, atr, cfg)
+        hit = det(h, l, c, atr, cfg)
         if hit is not None:
             return hit
     return None

signal_aggregator.py

@@ -150,8 +150,9 @@ class SignalReport:
 class SignalAggregator:
     """Master aggregator for live signal analysis."""
 
-    # Diagnostic funnel for the M5 pullback route (shared across
-    # instances; read by backtest tooling, harmless in live use).
+    # Diagnostic rejection counters for the M5 pullback route (class-level,
+    # shared across instances). Nothing in the repo reads them; they are
+    # cheap and kept for ad-hoc inspection from a REPL.
     pb_funnel: Dict[str, int] = {}
     # Same, for the chart-pattern route (XAU_CP).
     cp_funnel: Dict[str, int] = {}
@@ -172,7 +173,6 @@ class SignalAggregator:
     def analyze_pair(
         self,
         symbol: Optional[str] = None,
-        context: Optional[FundamentalContext] = None,
         log_source: Optional[str] = None,
     ) -> SignalReport:
         """Live-mode entry: fetch all needed timeframes then delegate.
@@ -244,7 +244,6 @@ class SignalAggregator:
         report = self.analyze_from_dataframes(
             symbol=symbol,
             dfs=dfs,
-            context=context,
             use_news=True,
         )
 
@@ -919,9 +918,6 @@ class SignalAggregator:
             "EXHAUSTION BLOCK",
             "EXHAUSTED TREND FILTER:",
             "MTF OPPOSITION:",
-            "SELL FILTER: quality",
-            "SELL FILTER: volatility",
-            "SELL FILTER: regime",
             "SELL FILTER: ",
             "M5 FILTER: |score|",
             "M5 FILTER: no MTF agreement",