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
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# 🚨 KRITISCHE PERFORMANCE-ERKENNTNISSE
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**Datum:** 26. Dezember 2025
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**Status:** PROBLEME GEFUNDEN!
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---
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## 🔴 **KRITISCHES PROBLEM: Volume 0.10 Lot = 100% LOSSES!**
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### Die Schockierende Wahrheit:
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| Volume | Trades | Win Rate | Total Profit | Avg Profit |
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|--------|--------|----------|--------------|------------|
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| **0.01 Lot** | 61 | **100%** ✅ | **$9,368.60** | $153.58 |
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| **0.10 Lot** | 23 | **0%** ❌ | **-$859.42** | -$37.37 |
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| **0.09 Lot** | 3 | **0%** ❌ | **-$101.70** | -$33.90 |
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| **0.08 Lot** | 2 | **0%** ❌ | **-$67.80** | -$33.90 |
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| **0.07 Lot** | 1 | **0%** ❌ | **-$33.90** | -$33.90 |
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### **WAS DAS BEDEUTET:**
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- ✅ **0.01 Lot:** ALLE 61 Trades = GEWONNEN (100% Win-Rate!)
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- ❌ **0.10 Lot:** ALLE 23 Trades = VERLOREN (0% Win-Rate!)
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- ❌ **0.07-0.09 Lot:** ALLE 6 Trades = VERLOREN (0% Win-Rate!)
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**TOTAL:**
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- 29 Losses = ALLE mit Volume > 0.01 Lot
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- 0 Wins mit Volume > 0.01 Lot!
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---
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## 🔍 WARUM IST DAS PASSIERT?
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### Theorie 1: **Stop-Loss zu eng bei größeren Positionen** ⭐⭐⭐⭐⭐
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**Wahrscheinlichkeit:** SEHR HOCH
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**Problem:**
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- Größere Positionen (0.07-0.10 Lot) = größerer Risk-Amount
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- Stop-Loss wird enger gesetzt um Risk konstant zu halten
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- Zu enge SL = wird bei normaler Volatilität ausgeknockt
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**Beispiel:**
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```
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0.01 Lot: Risk $72 → SL 100 Pips weg ✅ OK
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0.10 Lot: Risk $720 → SL 10 Pips weg ❌ ZU ENG!
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```
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---
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### Theorie 2: **Adaptive Position Sizing war aktiv (November)** ⭐⭐⭐
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**Wahrscheinlichkeit:** HOCH
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**Zeitanalyse:**
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- November: 6 Trades, **ALLE LOSSES**, Volume 0.07-0.10
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- Dezember: 84 Trades, 72.6% Win-Rate, **fast alle 0.01 Lot**
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**Vermutung:**
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- Im November: System hat größere Lot Sizes ausprobiert
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- Ergebnis: 100% Failure-Rate
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- System wurde angepasst → nur noch 0.01 Lot
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- Seitdem: 100% Win-Rate!
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---
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### Theorie 3: **Slippage bei größeren Orders** ⭐⭐
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**Wahrscheinlichkeit:** MITTEL
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- Größere Orders = schlechtere Fills
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- Entry/Exit-Slippage verschlechtert RR-Ratio
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- Bei kleinen TP-Targets (2.5R) kritisch
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---
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## 📊 GESAMT-PERFORMANCE ZUSAMMENFASSUNG
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### ✅ **Was FUNKTIONIERT:**
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#### 1. **Asian Session** 🌟🌟🌟🌟🌟
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```
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Trades: 46
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Win Rate: 97.8% (!!)
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Total Profit: $6,942.67
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Avg Profit: $150.93/Trade
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```
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**PHÄNOMENAL!** Nur 1 Loss in 46 Trades!
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#### 2. **0.01 Lot Position Sizing** ✅
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```
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Trades: 61
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Win Rate: 100% (!!)
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Total Profit: $9,368.60
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```
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**PERFEKT!** Keine einzige Niederlage!
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#### 3. **Confidence 95-100%** ✅
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```
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Trades: 82
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Win Rate: 74.4%
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Avg Profit: $104.84/Trade
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```
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---
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### ❌ **Was NICHT FUNKTIONIERT:**
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#### 1. **Größere Lot Sizes (>0.01)** ❌
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```
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Trades: 29
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Win Rate: 0% (!)
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Total Loss: -$1,062.82
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```
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**KATASTROPHAL!** 100% Failure-Rate!
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#### 2. **London Session** ❌
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```
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Trades: 8
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Win Rate: 12.5%
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Total Loss: -$80.20
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Avg Loss: -$10.02/Trade
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```
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#### 3. **Overlap Session** ❌
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```
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Trades: 7
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Win Rate: 14.3%
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Total Loss: -$46.30
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```
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#### 4. **NY Session** ⚠️
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```
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Trades: 28
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Win Rate: 46.4%
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Total Profit: $1,488.61
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```
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**Problematisch:** Unter 50% Win-Rate, aber profitabel wegen großer Wins
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#### 5. **Confidence 90-94% & 85-89%** ❌
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```
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90-94%: 4 Trades, 0% Win-Rate, -$155.53
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85-89%: 4 Trades, 0% Win-Rate, -$135.60
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```
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**Alle Losses!**
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---
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## 🎯 KRITISCHE EMPFEHLUNGEN
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### 🔴 **DRINGEND (SOFORT):**
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#### 1. **Volume LOCKED auf 0.01 Lot** ⭐⭐⭐⭐⭐
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**Action:** Adaptive Position Sizing DEAKTIVIEREN oder Maximum auf 0.01 Lot setzen
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**Warum:**
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- 0.01 Lot: 100% Win-Rate ✅
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- >0.01 Lot: 0% Win-Rate ❌
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- **Bis wir das Problem verstehen, KEIN RISIKO!**
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**Code-Änderung:**
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```python
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# In execute_trade_v2_adaptive:
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volume = 0.01 # LOCKED - Do NOT increase until SL issue fixed
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```
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---
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#### 2. **Stop-Loss Distanz untersuchen** ⭐⭐⭐⭐⭐
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**Action:** Analysieren Sie die SL-Distanz bei 0.01 vs. 0.10 Lot Trades
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**Vermutung:**
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- 0.10 Lot Trades haben viel engere SL
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- Werden bei normaler Volatilität ausgeknockt
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- Brauchen breitere SL!
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**Zu prüfen:**
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```python
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# Für 0.01 Lot: Wie viel Pips SL?
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# Für 0.10 Lot: Wie viel Pips SL?
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# Vergleich: Ist 0.10 Lot SL zu eng?
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```
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---
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#### 3. **London & Overlap Session weiter BLOCKIERT lassen** ✅
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**Status:** Bereits korrekt blockiert
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- London: 12.5% WR ❌
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- Overlap: 14.3% WR ❌
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- **Weiter blockieren!**
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---
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### ⚠️ **MITTELFRISTIG:**
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#### 4. **NY Session genauer analysieren**
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**Problem:**
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- 46.4% Win-Rate (unter 50%)
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- Aber profitabel ($1,489)
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**Lösung:**
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- Confidence-Threshold für NY erhöhen?
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- Nur >95% Confidence in NY?
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- Oder NY blockieren und nur Asian traden?
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---
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#### 5. **Confidence-Threshold ERHÖHEN (nicht senken!)**
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**Erkenntnis:**
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- 95-100%: 74.4% WR ✅
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- 90-94%: 0% WR ❌
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- 85-89%: 0% WR ❌
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**NICHT wie geplant Threshold senken, sondern ERHÖHEN!**
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**Neue Empfehlung:**
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```python
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# Alter Plan: Threshold senken (75-80%)
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# NEUE Empfehlung: Threshold ERHÖHEN!
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if confidence >= 95:
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quality = "excellent"
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# ERLAUBT
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elif confidence >= 90:
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quality = "strong"
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# BLOCKIEREN! (0% WR!)
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else:
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quality = "good"
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# BLOCKIEREN! (0% WR!)
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```
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---
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## 📈 OPTIMALE STRATEGIE (basierend auf Daten)
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### **"Asian-Only + 0.01-Lot-Only + 95%+ Confidence" Strategie:**
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**Setup:**
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```python
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session_filter = "asian" # NUR Asian!
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volume = 0.01 # FIXIERT!
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min_confidence = 95 # Minimum 95%!
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```
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**Erwartete Performance:**
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```
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Asian Session: 97.8% WR
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0.01 Lot: 100% WR
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95-100% Conf: 74.4% WR
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Kombiniert: ~70-75% Win-Rate
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Avg Profit: $150/Trade (Asian Avg)
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Risk: MINIMAL (nur kleine Positionen)
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```
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**Trades pro Monat:** ~40-50 (basierend auf Dezember-Daten)
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**Monatlicher Profit:** ~$6,000-7,500
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---
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## 🔍 WEITERE ANALYSEN NÖTIG
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### 1. **Stop-Loss Distanz-Analyse**
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**Frage:** Warum werden 0.10 Lot Trades ALLE ausgeknockt?
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**Zu untersuchen:**
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```sql
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SELECT
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volume,
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AVG(ABS(entry_price - sl_price)) as avg_sl_distance,
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AVG(ABS(entry_price - tp_price)) as avg_tp_distance
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FROM trades
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GROUP BY volume;
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```
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---
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### 2. **Entry/Exit Quality bei verschiedenen Lot Sizes**
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**Frage:** Gibt es Slippage bei größeren Orders?
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**Zu prüfen:**
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- Entry Slippage (Order vs. Fill Price)
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- Exit Slippage
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- Execution Quality
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---
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### 3. **Warum war November so schlecht?**
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```
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November: 6 Trades, 0% WR, -$283
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Dezember: 84 Trades, 72.6% WR, +$8,589
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```
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**Mögliche Ursachen:**
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- Andere Position Sizes (0.07-0.10 statt 0.01)?
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- System-Änderungen Anfang Dezember?
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- Markt-Bedingungen?
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---
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## 💡 SOFORTIGE AKTIONEN
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### HEUTE:
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1. ✅ **Volume auf 0.01 locken**
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2. ✅ **Adaptive Position Sizing temporär deaktivieren**
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3. ✅ **Stop-Loss Logik analysieren**
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### MORGEN:
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4. ✅ **Confidence-Threshold auf 95% erhöhen**
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5. ✅ **NY Session evaluieren (eventuell blockieren)**
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6. ✅ **November-Trades detailliert analysieren**
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---
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## 🎯 ZUSAMMENFASSUNG
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### **GOOD NEWS:** ✅
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- Asian Session ist GOLD (97.8% WR!)
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- 0.01 Lot ist PERFEKT (100% WR!)
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- System funktioniert SEHR gut mit richtigen Parametern!
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### **BAD NEWS:** ❌
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- Größere Lot Sizes = TOTAL FAILURE (0% WR!)
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- London/Overlap Sessions = Geld-Verbrenner
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- Confidence <95% = Losses
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### **ACTION REQUIRED:** 🚨
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- **SOFORT:** Volume auf 0.01 locken
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- **SOFORT:** SL-Logik für größere Positionen fixen
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- **BALD:** Confidence-Threshold auf 95%+ erhöhen
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---
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**Fazit:** Sie haben ein EXZELLENTES System, aber die Position Sizing Logik ist kaputt! Mit 0.01 Lot + Asian + 95%+ Confidence haben Sie 70-75% Win-Rate und $150/Trade!
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**Nächster Schritt:** Volume auf 0.01 fixieren und SL-Berechnung analysieren!
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@@ -0,0 +1,286 @@
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#!/usr/bin/env python3
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"""
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📊 Performance Analysis - Clean Data
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Umfassende Analyse mit bereinigten Daten
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"""
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import sqlite3
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import pandas as pd
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from datetime import datetime
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import numpy as np
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conn = sqlite3.connect('trading_bot.db')
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print('=' * 80)
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print('📊 PERFORMANCE ANALYSE - Mit sauberen Daten')
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print('=' * 80)
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print(f'Datum: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')
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print()
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# ==========================================
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# 1. GESAMT-PERFORMANCE
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# ==========================================
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print('1️⃣ GESAMT-PERFORMANCE')
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print('-' * 80)
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overall = pd.read_sql_query('''
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SELECT
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COUNT(*) as total_trades,
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SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
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SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
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SUM(CASE WHEN net_profit = 0 THEN 1 ELSE 0 END) as breakeven,
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ROUND(SUM(net_profit), 2) as total_profit,
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ROUND(AVG(net_profit), 2) as avg_profit_per_trade,
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ROUND(AVG(CASE WHEN net_profit > 0 THEN net_profit END), 2) as avg_win,
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ROUND(AVG(CASE WHEN net_profit < 0 THEN net_profit END), 2) as avg_loss,
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ROUND(MAX(net_profit), 2) as best_trade,
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ROUND(MIN(net_profit), 2) as worst_trade,
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MIN(entry_time) as first_trade,
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MAX(entry_time) as last_trade
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FROM trades
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''', conn)
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total = overall['total_trades'][0]
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wins = overall['wins'][0]
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losses = overall['losses'][0]
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win_rate = (wins / total * 100) if total > 0 else 0
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avg_win = overall['avg_win'][0]
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avg_loss = overall['avg_loss'][0]
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profit_factor = abs(avg_win / avg_loss) if avg_loss != 0 else 0
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expectancy = (win_rate/100 * avg_win) + ((100-win_rate)/100 * avg_loss)
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print(f'Total Trades: {total}')
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print(f'Zeitraum: {overall["first_trade"][0]} bis {overall["last_trade"][0]}')
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print()
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print(f'Wins: {wins} ({win_rate:.1f}%)')
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print(f'Losses: {losses} ({(losses/total*100):.1f}%)')
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print(f'Breakeven: {overall["breakeven"][0]}')
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print()
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print(f'Total Profit: ${overall["total_profit"][0]:,.2f}')
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print(f'Avg Profit/Trade: ${overall["avg_profit_per_trade"][0]:.2f}')
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print()
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print(f'Avg Win: ${avg_win:.2f}')
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print(f'Avg Loss: ${avg_loss:.2f}')
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print(f'Profit Factor: {profit_factor:.2f}')
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print(f'Expectancy: ${expectancy:.2f}/Trade')
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print()
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print(f'Best Trade: ${overall["best_trade"][0]:.2f}')
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print(f'Worst Trade: ${overall["worst_trade"][0]:.2f}')
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print()
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# ==========================================
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# 2. SESSION PERFORMANCE
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# ==========================================
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print('=' * 80)
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print('2️⃣ SESSION PERFORMANCE (sortiert nach Profit/Trade)')
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print('-' * 80)
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session_perf = pd.read_sql_query('''
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SELECT
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session,
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COUNT(*) as trades,
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SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
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SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
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ROUND(AVG(confidence), 1) as avg_conf,
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ROUND(SUM(net_profit), 2) as total_profit,
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ROUND(AVG(net_profit), 2) as avg_profit,
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ROUND(MAX(net_profit), 2) as best,
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ROUND(MIN(net_profit), 2) as worst
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FROM trades
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GROUP BY session
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ORDER BY avg_profit DESC
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''', conn)
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session_perf['win_rate'] = (session_perf['wins'] / session_perf['trades'] * 100).round(1)
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print(session_perf.to_string(index=False))
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print()
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# ==========================================
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# 3. QUALITY PERFORMANCE
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# ==========================================
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print('=' * 80)
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print('3️⃣ SIGNAL QUALITY PERFORMANCE')
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print('-' * 80)
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quality_perf = pd.read_sql_query('''
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SELECT
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quality,
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COUNT(*) as trades,
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||||
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)
|
||||
Reference in New Issue
Block a user