refactor(cleanup): remove dead code + no-op calibration dimension (P1.4, P1.6)

src/agent_loop/confidence_calibrator.py

@@ -58,21 +58,18 @@ def calibrate(
 
         Adjustments applied in order:
         1. Historical accuracy-based adjustment (per-action)
-        2. Per-sector accuracy adjustment
-        3. Regime-based adjustment
+        2. Regime-based adjustment
+
+        ``sector`` is accepted but not yet used — per-sector calibration needs
+        sector metadata on the decisions table, which does not exist yet.
         """
         adjustment = 0.0
 
         # 1. Action-level calibration
         action_adj = self._action_calibration(action)
         adjustment += action_adj
 
-        # 2. Sector-level calibration
-        if sector:
-            sector_adj = self._sector_calibration(sector)
-            adjustment += sector_adj
-
-        # 3. Regime adjustment
+        # 2. Regime adjustment
         regime_adj = self._regime_adjustment(regime, action)
         adjustment += regime_adj
 
@@ -83,13 +80,12 @@ def calibrate(
 
         if abs(adjustment) > 0.01:
             logger.debug(
-                "Calibration %s %s: %.2f → %.2f (action=%.3f sector=%.3f regime=%.3f)",
+                "Calibration %s %s: %.2f → %.2f (action=%.3f regime=%.3f)",
                 action,
                 symbol,
                 raw_confidence,
                 calibrated,
                 action_adj,
-                sector_adj if sector else 0.0,
                 regime_adj,
             )
 
@@ -263,12 +259,6 @@ def _action_calibration(self, action: str) -> float:
         finally:
             conn.close()
 
-    def _sector_calibration(self, sector: str) -> float:
-        """Compute confidence adjustment based on sector-level accuracy."""
-        # Sector tracking would require joining decisions with thesis/symbol metadata.
-        # For now, return 0 — will be enhanced when we add sector to decisions table.
-        return 0.0
-
     def _regime_adjustment(self, regime: str, action: str) -> float:
         """Apply regime-based confidence adjustment."""
         adjustments = self._regime_adjustments.get(regime, {})

src/prediction/analysis_frameworks.py

@@ -15,56 +15,6 @@
 import math
 from typing import Any
 
-# ---------------------------------------------------------------------------
-# Legacy frameworks — DEPRECATED
-# Use SEVEN_DIMENSION_FRAMEWORK (full analysis) or QUICK_DIMENSION_FRAMEWORK
-# (quick insights) instead.  Kept only for backward compatibility.
-# ---------------------------------------------------------------------------
-
-PROFESSIONAL_ANALYSIS_FRAMEWORK = """\
-You are a professional A-share analyst combining multi-school investment methodologies. \
-Follow this framework strictly when analyzing.
-Write all output text in Chinese.
-
-## Analysis Methodology
-
-### 1. Quantitative Factor Analysis (AQR / Two Sigma style)
-- **Momentum factor**: Recent trend direction, MA alignment (bullish 多头 / bearish 空头 / converging 粘合)
-- **Volatility factor**: Recent amplitude changes, Bollinger Band width
-- **Volume factor**: Volume ratio changes, price-volume correlation
-
-### 2. Value Investing Check (Buffett framework)
-- **Margin of safety**: Is the current valuation level reasonable?
-- **Trend confirmation**: Does the medium-to-long term trend support the current judgment?
-
-### 3. Contrarian Thinking Check (Munger framework)
-- **Inversion analysis**: What scenarios would invalidate the current judgment?
-- **Psychological bias check**: Recency bias (recent moves distorting objectivity), anchoring (fixation on historical highs/lows), herding (is the sector overheated?)
-- **Multi-factor cross-validation**: Are technicals, capital flow, and news aligned?
-
-### 4. A-share Specific Dimensions
-- **Policy sensitivity**: Impact of policy direction on the industry/stock
-- **Capital flow**: Direction and magnitude of institutional money flow (super-large + large order net inflow)
-- **Sector linkage**: Overall sector performance, concept rotation position
-- **Price limit mechanism**: Consider the stock's daily price limit when analyzing
-
-## Data Quality Rules
-- If data is labeled "non-realtime" or "historical", state this clearly in the analysis
-- Do not misattribute other sectors' market conditions to the current stock
-- Quantitative strategy signals serve only as one reference dimension — never let them alone determine the conclusion
-- Bayesian probabilities provide historical statistical support, but consider whether the current market environment is comparable to historical patterns
-"""
-
-# DEPRECATED — use QUICK_DIMENSION_FRAMEWORK instead
-QUICK_ANALYSIS_FRAMEWORK = """\
-You are a professional A-share analyst. Key analysis points:
-Write all output text in Chinese.
-1. Synthesize quantitative signals (strategy consensus, Bayesian probability) with technicals to form a judgment
-2. Account for the stock's daily price limit and sector classification
-3. If data is labeled non-realtime, state this clearly
-4. Consider reversal risk — never blindly follow a single signal
-"""
-
 # ═══════════════════════════════════════════════════════════════════════════
 # v7.0 Seven-Dimension Framework  (FR-PR001)
 # ═══════════════════════════════════════════════════════════════════════════