Add comprehensive performance analysis with key insights

Performance Analysis Results (90 clean trades):

Overall Performance:
- Win Rate: 67.8% (61/90)
- Total Profit: $8,305.78
- Profit Factor: 4.19
- Avg Profit/Trade: $92.29
- Max Drawdown: -12.1%

KEY INSIGHT: Lot Size Reduction Success
- BEFORE (Nov 27 - Dec 4): 0.07-0.10 Lot → 0% WR, -$1,062 loss
- AFTER (Dec 10+): 0.01 Lot → 100% WR, +$9,368 profit
- Change was made Dec 4, results improved dramatically!

Session Performance:
- Asian: 97.8% WR, $150.93/trade (EXCELLENT!) 🌟
- NY: 46.4% WR, $53.16/trade (profitable but low WR)
- London: 12.5% WR (correctly blocked)
- Overlap: 14.3% WR (correctly blocked)

Confidence Analysis:
- 95-100%: 74.4% WR, 82 trades ✅
- 90-94%: 0% WR, 4 trades (all losses)
- 85-89%: 0% WR, 4 trades (all losses)
- Recommendation: Keep threshold at 95%+ (current excellent quality)

Monthly Trend:
- November: 6 trades, 0% WR, -$283 (testing phase)
- December: 84 trades, 72.6% WR, +$8,589 (optimized!)

Recommendations:
1. Keep current lot size (0.01) - working perfectly
2. Asian session is best performer (97.8% WR)
3. Current confidence threshold (95%+) is optimal
4. London/Overlap correctly blocked
5. System is well-optimized after December changes

performance_analysis_clean.py

@@ -0,0 +1,286 @@
+#!/usr/bin/env python3
+"""
+📊 Performance Analysis - Clean Data
+Umfassende Analyse mit bereinigten Daten
+"""
+
+import sqlite3
+import pandas as pd
+from datetime import datetime
+import numpy as np
+
+conn = sqlite3.connect('trading_bot.db')
+
+print('=' * 80)
+print('📊 PERFORMANCE ANALYSE - Mit sauberen Daten')
+print('=' * 80)
+print(f'Datum: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')
+print()
+
+# ==========================================
+# 1. GESAMT-PERFORMANCE
+# ==========================================
+print('1️⃣ GESAMT-PERFORMANCE')
+print('-' * 80)
+
+overall = pd.read_sql_query('''
+    SELECT
+        COUNT(*) as total_trades,
+        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
+        SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
+        SUM(CASE WHEN net_profit = 0 THEN 1 ELSE 0 END) as breakeven,
+        ROUND(SUM(net_profit), 2) as total_profit,
+        ROUND(AVG(net_profit), 2) as avg_profit_per_trade,
+        ROUND(AVG(CASE WHEN net_profit > 0 THEN net_profit END), 2) as avg_win,
+        ROUND(AVG(CASE WHEN net_profit < 0 THEN net_profit END), 2) as avg_loss,
+        ROUND(MAX(net_profit), 2) as best_trade,
+        ROUND(MIN(net_profit), 2) as worst_trade,
+        MIN(entry_time) as first_trade,
+        MAX(entry_time) as last_trade
+    FROM trades
+''', conn)
+
+total = overall['total_trades'][0]
+wins = overall['wins'][0]
+losses = overall['losses'][0]
+win_rate = (wins / total * 100) if total > 0 else 0
+avg_win = overall['avg_win'][0]
+avg_loss = overall['avg_loss'][0]
+profit_factor = abs(avg_win / avg_loss) if avg_loss != 0 else 0
+expectancy = (win_rate/100 * avg_win) + ((100-win_rate)/100 * avg_loss)
+
+print(f'Total Trades: {total}')
+print(f'Zeitraum: {overall["first_trade"][0]} bis {overall["last_trade"][0]}')
+print()
+print(f'Wins: {wins} ({win_rate:.1f}%)')
+print(f'Losses: {losses} ({(losses/total*100):.1f}%)')
+print(f'Breakeven: {overall["breakeven"][0]}')
+print()
+print(f'Total Profit: ${overall["total_profit"][0]:,.2f}')
+print(f'Avg Profit/Trade: ${overall["avg_profit_per_trade"][0]:.2f}')
+print()
+print(f'Avg Win: ${avg_win:.2f}')
+print(f'Avg Loss: ${avg_loss:.2f}')
+print(f'Profit Factor: {profit_factor:.2f}')
+print(f'Expectancy: ${expectancy:.2f}/Trade')
+print()
+print(f'Best Trade: ${overall["best_trade"][0]:.2f}')
+print(f'Worst Trade: ${overall["worst_trade"][0]:.2f}')
+print()
+
+# ==========================================
+# 2. SESSION PERFORMANCE
+# ==========================================
+print('=' * 80)
+print('2️⃣ SESSION PERFORMANCE (sortiert nach Profit/Trade)')
+print('-' * 80)
+
+session_perf = pd.read_sql_query('''
+    SELECT
+        session,
+        COUNT(*) as trades,
+        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
+        SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
+        ROUND(AVG(confidence), 1) as avg_conf,
+        ROUND(SUM(net_profit), 2) as total_profit,
+        ROUND(AVG(net_profit), 2) as avg_profit,
+        ROUND(MAX(net_profit), 2) as best,
+        ROUND(MIN(net_profit), 2) as worst
+    FROM trades
+    GROUP BY session
+    ORDER BY avg_profit DESC
+''', conn)
+
+session_perf['win_rate'] = (session_perf['wins'] / session_perf['trades'] * 100).round(1)
+
+print(session_perf.to_string(index=False))
+print()
+
+# ==========================================
+# 3. QUALITY PERFORMANCE
+# ==========================================
+print('=' * 80)
+print('3️⃣ SIGNAL QUALITY PERFORMANCE')
+print('-' * 80)
+
+quality_perf = pd.read_sql_query('''
+    SELECT
+        quality,
+        COUNT(*) as trades,
+        ROUND(MIN(confidence), 1) as min_conf,
+        ROUND(AVG(confidence), 1) as avg_conf,
+        ROUND(MAX(confidence), 1) as max_conf,
+        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
+        SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
+        ROUND(SUM(net_profit), 2) as total_profit,
+        ROUND(AVG(net_profit), 2) as avg_profit
+    FROM trades
+    WHERE quality IS NOT NULL
+    GROUP BY quality
+    ORDER BY avg_conf DESC
+''', conn)
+
+quality_perf['win_rate'] = (quality_perf['wins'] / quality_perf['trades'] * 100).round(1)
+
+print(quality_perf.to_string(index=False))
+print()
+
+# ==========================================
+# 4. CONFIDENCE BANDS ANALYSE
+# ==========================================
+print('=' * 80)
+print('4️⃣ CONFIDENCE BANDS ANALYSE (wichtig für Adaptive Sizing!)')
+print('-' * 80)
+
+confidence_bands = pd.read_sql_query('''
+    SELECT
+        CASE
+            WHEN confidence >= 95 THEN '95-100% (Excellent)'
+            WHEN confidence >= 90 THEN '90-94% (Very Strong)'
+            WHEN confidence >= 85 THEN '85-89% (Strong)'
+            WHEN confidence >= 80 THEN '80-84% (High)'
+            WHEN confidence >= 75 THEN '75-79% (Good)'
+            WHEN confidence >= 70 THEN '70-74% (Medium)'
+            ELSE '<70% (Low)'
+        END as conf_band,
+        COUNT(*) as trades,
+        ROUND(AVG(confidence), 1) as avg_conf,
+        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
+        ROUND(SUM(net_profit), 2) as total_profit,
+        ROUND(AVG(net_profit), 2) as avg_profit
+    FROM trades
+    WHERE confidence IS NOT NULL
+    GROUP BY conf_band
+    ORDER BY avg_conf DESC
+''', conn)
+
+confidence_bands['win_rate'] = (confidence_bands['wins'] / confidence_bands['trades'] * 100).round(1)
+
+print(confidence_bands.to_string(index=False))
+print()
+
+# ==========================================
+# 5. VOLUME ANALYSE
+# ==========================================
+print('=' * 80)
+print('5️⃣ VOLUME (LOT SIZE) ANALYSE')
+print('-' * 80)
+
+volume_stats = pd.read_sql_query('''
+    SELECT
+        volume,
+        COUNT(*) as trades,
+        ROUND(AVG(confidence), 1) as avg_conf,
+        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
+        ROUND(SUM(net_profit), 2) as total_profit,
+        ROUND(AVG(net_profit), 2) as avg_profit
+    FROM trades
+    GROUP BY volume
+    ORDER BY volume DESC
+''', conn)
+
+volume_stats['win_rate'] = (volume_stats['wins'] / volume_stats['trades'] * 100).round(1)
+
+print(volume_stats.to_string(index=False))
+print()
+
+# ==========================================
+# 6. MONATLICHE PERFORMANCE
+# ==========================================
+print('=' * 80)
+print('6️⃣ MONATLICHE PERFORMANCE')
+print('-' * 80)
+
+monthly = pd.read_sql_query('''
+    SELECT
+        strftime('%Y-%m', entry_time) as month,
+        COUNT(*) as trades,
+        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
+        ROUND(SUM(net_profit), 2) as profit,
+        ROUND(AVG(net_profit), 2) as avg_profit
+    FROM trades
+    GROUP BY month
+    ORDER BY month DESC
+''', conn)
+
+monthly['win_rate'] = (monthly['wins'] / monthly['trades'] * 100).round(1)
+
+print(monthly.to_string(index=False))
+print()
+
+# ==========================================
+# 7. DRAWDOWN ANALYSE
+# ==========================================
+print('=' * 80)
+print('7️⃣ DRAWDOWN & EQUITY CURVE')
+print('-' * 80)
+
+trades_timeline = pd.read_sql_query('''
+    SELECT
+        DATE(entry_time) as date,
+        net_profit
+    FROM trades
+    ORDER BY entry_time
+''', conn)
+
+# Calculate cumulative profit
+trades_timeline['cumulative_profit'] = trades_timeline['net_profit'].cumsum()
+trades_timeline['running_max'] = trades_timeline['cumulative_profit'].cummax()
+trades_timeline['drawdown'] = trades_timeline['cumulative_profit'] - trades_timeline['running_max']
+
+max_dd = trades_timeline['drawdown'].min()
+max_dd_pct = (max_dd / trades_timeline['running_max'].max() * 100) if trades_timeline['running_max'].max() > 0 else 0
+
+print(f'Max Drawdown: ${max_dd:.2f} ({max_dd_pct:.1f}%)')
+print(f'Current Equity: ${trades_timeline["cumulative_profit"].iloc[-1]:.2f}')
+print(f'Peak Equity: ${trades_timeline["running_max"].max():.2f}')
+print()
+
+# ==========================================
+# 8. TOP TRADES
+# ==========================================
+print('=' * 80)
+print('8️⃣ TOP 5 BEST TRADES')
+print('-' * 80)
+
+best_trades = pd.read_sql_query('''
+    SELECT
+        DATE(entry_time) as date,
+        session,
+        quality,
+        ROUND(confidence, 1) as conf,
+        volume,
+        ROUND(net_profit, 2) as profit
+    FROM trades
+    ORDER BY net_profit DESC
+    LIMIT 5
+''', conn)
+
+print(best_trades.to_string(index=False))
+print()
+
+print('=' * 80)
+print('9️⃣ TOP 5 WORST TRADES')
+print('-' * 80)
+
+worst_trades = pd.read_sql_query('''
+    SELECT
+        DATE(entry_time) as date,
+        session,
+        quality,
+        ROUND(confidence, 1) as conf,
+        volume,
+        ROUND(net_profit, 2) as profit
+    FROM trades
+    ORDER BY net_profit ASC
+    LIMIT 5
+''', conn)
+
+print(worst_trades.to_string(index=False))
+print()
+
+conn.close()
+
+print('=' * 80)
+print('✅ ANALYSE ABGESCHLOSSEN')
+print('=' * 80)