fix: core module review fixes (signal scoring, ML, session filter, telegram, MT filter)

enhanced_signal_scoring.py:
- mt -> mt5, add logging module, replace print() with logger
- Remove no-op df['tick_volume'] = df['tick_volume'] line
- Fix RSI division-by-zero: loss.replace(0, nan) + fillna(100)

ml_signal_predictor.py:
- Remove global warnings.filterwarnings('ignore') suppression
- Fix bare except -> except Exception with logger.warning
- Add note: default data files excluded from git, need manual export
- Add pickle security warning comment

session_confidence_filter.py:
- Move imports to file top, add logging + functools.wraps
- Remove repeated AdaptiveRhythmManager() per-call instantiation
- Add functools.wraps to preserve wrapped function metadata
- Document that extended_top_down_v2_adaptive is notebook-only
- Replace print() with logger, pass **kwargs through wrapper

telegram_notifier.py:
- Remove network call from __init__ -> explicit test_connection() method
- Add logging module, replace all print() with logger calls
- Narrow exception type: Exception -> requests.RequestException
- Remove unused imports (timedelta)
- notify_trade_entry/exit now return bool from send_message

multi_timeframe_regime_filter.py:
- Change debug default from True to False in wrapper to avoid verbose production output

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

enhanced_signal_scoring.py

@@ -11,11 +11,14 @@ NEUE FEATURES:
 5. Weighted Scoring System
 """
 
-import MetaTrader5 as mt
+import MetaTrader5 as mt5
 import pandas as pd
 import numpy as np
 from typing import Dict, List, Tuple, Optional
 from dataclasses import dataclass
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -96,12 +99,11 @@ class EnhancedSignalScorer:
         """
         try:
             # Get OHLCV data
-            rates = mt.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
+            rates = mt5.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
             if rates is None or len(rates) < 20:
                 return 50.0  # Neutral if no data
 
             df = pd.DataFrame(rates)
-            df['tick_volume'] = df['tick_volume']  # MT5 provides tick volume
 
             # Calculate Volume MA
             df['volume_ma_20'] = df['tick_volume'].rolling(20).mean()
@@ -125,7 +127,7 @@ class EnhancedSignalScorer:
             return score
 
         except Exception as e:
-            print(f"⚠️ Volume calculation error: {e}")
+            logger.warning(f"Volume calculation error: {e}")
             return 50.0
 
     # ==========================================
@@ -148,7 +150,7 @@ class EnhancedSignalScorer:
             (momentum_score, details_dict)
         """
         try:
-            rates = mt.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
+            rates = mt5.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
             if rates is None or len(rates) < 30:
                 return 50.0, {}
 
@@ -198,7 +200,7 @@ class EnhancedSignalScorer:
             return momentum_score, details
 
         except Exception as e:
-            print(f"⚠️ Momentum calculation error: {e}")
+            logger.warning(f"Momentum calculation error: {e}")
             return 50.0, {}
 
     def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series:
@@ -206,9 +208,10 @@ class EnhancedSignalScorer:
         delta = prices.diff()
         gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
         loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
-        rs = gain / loss
+        # Avoid division by zero: when loss == 0 the RSI is 100
+        rs = gain / loss.replace(0, np.nan)
         rsi = 100 - (100 / (1 + rs))
-        return rsi
+        return rsi.fillna(100.0)
 
     def _calculate_macd(self,
                        prices: pd.Series,
@@ -249,7 +252,7 @@ class EnhancedSignalScorer:
             (sr_score, details_dict)
         """
         try:
-            rates = mt.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
+            rates = mt5.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
             if rates is None or len(rates) < 50:
                 return 50.0, {}
 
@@ -301,7 +304,7 @@ class EnhancedSignalScorer:
             return score, details
 
         except Exception as e:
-            print(f"⚠️ Support/Resistance calculation error: {e}")
+            logger.warning(f"Support/Resistance calculation error: {e}")
             return 50.0, {}
 
     def _find_peaks(self, data: np.ndarray, distance: int = 10) -> List[float]:
@@ -334,7 +337,7 @@ class EnhancedSignalScorer:
             (fib_score, details_dict)
         """
         try:
-            rates = mt.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
+            rates = mt5.copy_rates_from_pos(symbol, self._tf_to_mt5(timeframe), 0, lookback)
             if rates is None or len(rates) < 50:
                 return 50.0, {}
 
@@ -386,7 +389,7 @@ class EnhancedSignalScorer:
             return score, details
 
         except Exception as e:
-            print(f"⚠️ Fibonacci calculation error: {e}")
+            logger.warning(f"Fibonacci calculation error: {e}")
             return 50.0, {}
 
     # ==========================================
@@ -483,12 +486,12 @@ class EnhancedSignalScorer:
     def _tf_to_mt5(self, timeframe: str):
         """Convert string timeframe to MT5 constant"""
         tf_map = {
-            'M1': mt.TIMEFRAME_M1, 'M5': mt.TIMEFRAME_M5,
-            'M15': mt.TIMEFRAME_M15, 'M30': mt.TIMEFRAME_M30,
-            'H1': mt.TIMEFRAME_H1, 'H4': mt.TIMEFRAME_H4,
-            'D1': mt.TIMEFRAME_D1, 'W1': mt.TIMEFRAME_W1
+            'M1': mt5.TIMEFRAME_M1, 'M5': mt5.TIMEFRAME_M5,
+            'M15': mt5.TIMEFRAME_M15, 'M30': mt5.TIMEFRAME_M30,
+            'H1': mt5.TIMEFRAME_H1, 'H4': mt5.TIMEFRAME_H4,
+            'D1': mt5.TIMEFRAME_D1, 'W1': mt5.TIMEFRAME_W1
         }
-        return tf_map.get(timeframe.upper(), mt.TIMEFRAME_H1)
+        return tf_map.get(timeframe.upper(), mt5.TIMEFRAME_H1)
 
 
 # ==========================================