fix: stage missing code review fixes (7 files)

Files were edited but not staged in earlier commits:
- adaptive_rhythm_manager.py: mt→mt5, pytz→timezone, get_volatility_level, shutdown()
- check_market_regime.py: ADX_THRESHOLD, Wilder EWM, try/finally, UTC timestamp, sys import
- check_system_status.py: remove duplicate cursor.execute
- drawdown_protection.py: float(inf), persist pause state, DB save_setting, Markdown fix
- performance_analysis.py: KeyError export fix, profit factor, drawdown positive, SQL filter
- performance_analysis_simple.py: fromisoformat, numeric bin sort, profit factor
- trading_dashboard.py: st.rerun(), session_state auto-refresh, pathlib DB path, errors=coerce

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

performance_analysis.py

@@ -16,7 +16,6 @@ import json
 def get_connection(db_path="trading_bot.db"):
     """Verbindung zur Datenbank"""
     conn = sqlite3.connect(db_path)
-    conn.row_factory = sqlite3.Row
     return conn
 
 # ==========================================
@@ -25,7 +24,12 @@ def get_connection(db_path="trading_bot.db"):
 
 def load_closed_trades(conn, exclude_historical=True):
     """Lade geschlossene Trades"""
-    query = """
+    # 'historical' is a distinct status for imported legacy trades.
+    # exclude_historical=True  → only live bot trades (status='closed')
+    # exclude_historical=False → live + historical imports
+    status_filter = "status = 'closed'" if exclude_historical else "status IN ('closed', 'historical')"
+
+    query = f"""
         SELECT
             ticket, symbol, type, volume,
             entry_price, exit_price,
@@ -38,20 +42,15 @@ def load_closed_trades(conn, exclude_historical=True):
             profit_pct, rr_ratio,
             exit_reason, status
         FROM trades
-        WHERE status = 'closed'
+        WHERE {status_filter}
+        ORDER BY exit_time DESC
     """
 
-    if exclude_historical:
-        query += " AND status != 'historical'"
-
-    query += " ORDER BY exit_time DESC"
-
     df = pd.read_sql_query(query, conn)
 
-    # Convert datetime columns
     if not df.empty:
-        df['entry_time'] = pd.to_datetime(df['entry_time'], format='mixed')
-        df['exit_time'] = pd.to_datetime(df['exit_time'], format='mixed')
+        df['entry_time'] = pd.to_datetime(df['entry_time'], errors='coerce')
+        df['exit_time'] = pd.to_datetime(df['exit_time'], errors='coerce')
         df['duration_hours'] = (df['exit_time'] - df['entry_time']).dt.total_seconds() / 3600
         df['win'] = df['net_profit'] > 0
 
@@ -78,7 +77,9 @@ def calculate_overall_metrics(df):
     avg_win = df[df['win']]['net_profit'].mean() if winning_trades > 0 else 0
     avg_loss = df[~df['win']]['net_profit'].mean() if losing_trades > 0 else 0
 
-    profit_factor = abs(avg_win / avg_loss) if avg_loss != 0 else 0
+    gross_profit = df[df['win']]['net_profit'].sum()
+    gross_loss = abs(df[~df['win']]['net_profit'].sum())
+    profit_factor = round(gross_profit / gross_loss, 2) if gross_loss > 0 else 0
 
     avg_duration = df['duration_hours'].mean()
 
@@ -87,7 +88,7 @@ def calculate_overall_metrics(df):
     df_sorted['cumulative'] = df_sorted['net_profit'].cumsum()
     df_sorted['running_max'] = df_sorted['cumulative'].cummax()
     df_sorted['drawdown'] = df_sorted['cumulative'] - df_sorted['running_max']
-    max_drawdown = df_sorted['drawdown'].min()
+    max_drawdown = abs(df_sorted['drawdown'].min())
 
     return {
         'total_trades': total_trades,
@@ -429,7 +430,7 @@ def run_performance_analysis(db_path="trading_bot.db", exclude_historical=True):
 
     conn.close()
 
-    return {
+    results = {
         'overall': overall,
         'by_session': session_df,
         'by_confidence': conf_df,
@@ -439,6 +440,8 @@ def run_performance_analysis(db_path="trading_bot.db", exclude_historical=True):
         'by_regime': regime_df
     }
 
+    return results
+
 # ==========================================
 # EXPORT TO JSON
 # ==========================================
@@ -457,7 +460,7 @@ def export_analysis_to_json(results, output_file="performance_analysis.json"):
         'by_regime': results['by_regime'].to_dict('records') if not results['by_regime'].empty else []
     }
 
-    with open(output_file, 'w') as f:
+    with open(output_file, 'w', encoding='utf-8') as f:
         json.dump(output, f, indent=2)
 
     print(f"\n✅ Analysis exported to: {output_file}")