Initial commit: Eurojackpot analysis and prediction system
This repository contains a comprehensive Eurojackpot lottery analysis and prediction system including: - Historical data analysis and processing - ML-based prediction models - Automated weekly tip generation - Position and range analysis tools - Notification system for results 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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# Eurojackpot Automation System
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Vollautomatisches System für wöchentliche Tipp-Generierung und Learning nach Ziehungen.
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## 📋 Überblick
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Das System besteht aus 2 Hauptkomponenten:
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1. **Weekly Tip Generator** - Generiert automatisch Tipps vor Ziehungen
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2. **Auto Update & Learn** - Aktualisiert Daten und lernt nach Ziehungen
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## 🚀 Installation
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### 1. Virtuelle Umgebung aktivieren
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```bash
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cd "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot"
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source venv/bin/activate
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```
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### 2. Scripts ausführbar machen
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```bash
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chmod +x scripts/automation/*.py
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```
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## 🎯 Manuelle Nutzung
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### Tipps generieren
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```bash
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# Normale Generierung (prüft ob nötig)
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python scripts/automation/weekly_tip_generator.py
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# Erzwinge Generierung
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python scripts/automation/weekly_tip_generator.py --force
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# Generiere 20 Tipps statt 10
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python scripts/automation/weekly_tip_generator.py --num-tips 20
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# Zeige nur Historie
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python scripts/automation/weekly_tip_generator.py --history
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```
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### Nach Ziehung aktualisieren
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```bash
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# Kompletter Workflow: Update + Evaluation + Learning
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python scripts/automation/auto_update_and_learn.py
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```
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## ⏰ Automatisierung mit Cron
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### Setup
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1. Öffne Crontab:
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```bash
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crontab -e
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```
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2. Füge folgende Zeilen hinzu:
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```bash
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# Pfad zur virtuellen Umgebung
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EUROJACKPOT_PATH="/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot"
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# Montag & Donnerstag 09:00 Uhr: Tipps generieren (vor Ziehung)
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0 9 * * 1,4 cd "$EUROJACKPOT_PATH" && source venv/bin/activate && python scripts/automation/weekly_tip_generator.py >> logs/tips_generation.log 2>&1
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# Dienstag & Freitag 21:00 Uhr: Update nach Ziehung
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0 21 * * 2,5 cd "$EUROJACKPOT_PATH" && source venv/bin/activate && python scripts/automation/auto_update_and_learn.py >> logs/auto_update.log 2>&1
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```
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3. Speichern (ESC, :wq in vim)
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### Cron Zeitplan
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```
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Montag 09:00 → Tipps für Dienstag-Ziehung generieren
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Dienstag 20:00 → Ziehung findet statt
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Dienstag 21:00 → Auto-Update & Learning
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Donnerstag 09:00 → Tipps für Freitag-Ziehung generieren
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Freitag 20:00 → Ziehung findet statt
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Freitag 21:00 → Auto-Update & Learning
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```
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### Logs prüfen
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```bash
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# Erstelle Log-Verzeichnis
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mkdir -p logs
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# Zeige letzte Tipps-Generierung
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tail -50 logs/tips_generation.log
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# Zeige letzte Updates
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tail -50 logs/auto_update.log
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```
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## 📊 Dateien & Verzeichnisse
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```
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data/
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├── generated_tips/ # Generierte Tipps
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│ ├── weekly_tips_*.csv # Tipp-Dateien
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│ └── generation_history.json # Historie
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│
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├── performance_reports/ # Performance-Reports
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│ └── report_*.txt # Reports nach Ziehungen
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│
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├── learning_log.json # Learning-Historie
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└── backups/ # Automatische Backups
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logs/
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├── tips_generation.log # Cron Log für Generierung
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└── auto_update.log # Cron Log für Updates
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```
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## 🔍 Features im Detail
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### Weekly Tip Generator
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**Intelligente Prüfung:**
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- Generiert nur wenn nötig (> 3 Tage seit letzter Generierung)
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- Berücksichtigt Wochentage (Montag/Donnerstag vor Ziehungen)
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- Vermeidet unnötige Generierungen
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**Historie-Tracking:**
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- Speichert alle Generierungen
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- Trackt Confidence & Quality Scores
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- Ermöglicht Performance-Analyse über Zeit
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**Ausgabe:**
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```
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📊 GENERIERUNGS-HISTORIE
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======================================================================
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Nr Datum Tips Confidence Quality Datei
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----------------------------------------------------------------------
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1 2025-01-20 09:00 10 0.5823 0.6734 weekly_tips_20250120_090000.csv
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2 2025-01-16 09:00 10 0.5891 0.6812 weekly_tips_20250116_090000.csv
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```
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### Auto Update & Learn
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**4-Schritt-Workflow:**
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1. **Daten aktualisieren**
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- Lädt neueste Ziehung von API
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- Erstellt automatisches Backup
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- Validiert Daten
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2. **Neue Ziehung prüfen**
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- Erkennt automatisch neue Ziehungen
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- Verhindert Doppel-Verarbeitung
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- Extrahiert Ziehungsdaten
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3. **Tipps evaluieren**
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- Vergleicht letzte Tipps mit Ziehung
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- Zählt Treffer (Main + Euro)
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- Ermittelt beste Strategien
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4. **Learning Update**
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- Passt ML-Modelle an
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- Aktualisiert Predictions
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- Speichert Performance-Metriken
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**Evaluation Output:**
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```
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🎯 EVALUIERE LETZTE TIPPS
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======================================================================
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Tip Strategie Main Euro Gesamt Bewertung
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------------------------------------------------------------
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#1 PURE-AI 2 1 3 ✅ Gut
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#2 HYBRID-OPT 3 0 3 ✅ Gut
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#3 PURE-PATTERN 1 1 2 👍 OK
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...
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🏆 Bester Tipp: #2 (3 Main + 0 Euro)
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```
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## 📈 Performance-Reports
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Reports werden automatisch nach jeder Ziehung erstellt:
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```
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======================================================================
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EUROJACKPOT PERFORMANCE REPORT
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======================================================================
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Erstellt: 2025-01-17 21:05:23
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ZIEHUNG VOM 2025-01-17
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----------------------------------------------------------------------
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Hauptzahlen: [3, 9, 12, 24, 39]
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Eurozahlen: [5, 10]
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EVALUATION DER TIPPS
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----------------------------------------------------------------------
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Datei: weekly_tips_20250115_090000.csv
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Tipps evaluiert: 10
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Avg Main Matches: 1.80
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Avg Euro Matches: 0.60
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Bester Tipp: #5
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- Main Treffer: 3
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- Euro Treffer: 1
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======================================================================
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```
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## 🛠️ Troubleshooting
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### "No module named 'requests'"
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```bash
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cd "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot"
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source venv/bin/activate
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pip install requests beautifulsoup4 pandas numpy scikit-learn
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```
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### "Permission denied"
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```bash
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chmod +x scripts/automation/*.py
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```
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### Cron läuft nicht
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```bash
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# Prüfe Cron-Service
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launchctl list | grep cron
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# Prüfe System-Logs
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tail -f /var/log/system.log | grep cron
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# Test manuell
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cd "/path/to/eurojackpot"
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source venv/bin/activate
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python scripts/automation/weekly_tip_generator.py
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```
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### Logs sind leer
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Stelle sicher dass das logs-Verzeichnis existiert:
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```bash
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mkdir -p logs
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```
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## 🎯 Best Practices
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### 1. Teste manuell vor Cron
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```bash
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# Test 1: Tipps generieren
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python scripts/automation/weekly_tip_generator.py --force
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# Test 2: Update & Learn
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python scripts/automation/auto_update_and_learn.py
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```
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### 2. Überwache Logs regelmäßig
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```bash
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# Wöchentlich prüfen
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tail -100 logs/tips_generation.log
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tail -100 logs/auto_update.log
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```
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### 3. Backup-Strategie
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Backups werden automatisch erstellt, aber zusätzlich:
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```bash
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# Manuelles Backup
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cp AlleEurojackpotzahlen.csv "AlleEurojackpotzahlen_$(date +%Y%m%d).csv"
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```
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### 4. Performance-Analyse
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```bash
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# Zeige Generierungs-Historie
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python scripts/automation/weekly_tip_generator.py --history
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# Analysiere Reports
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cat data/performance_reports/report_*.txt | grep "Avg Main Matches"
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```
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## 📱 Benachrichtigungen (Optional)
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Füge Benachrichtigungen zu Cron hinzu:
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### macOS Notification
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```bash
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# In Cron-Zeile hinzufügen:
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&& osascript -e 'display notification "Eurojackpot Tipps generiert" with title "Automation"'
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```
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### Email (wenn konfiguriert)
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```bash
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# In Cron-Zeile:
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&& echo "Tipps generiert" | mail -s "Eurojackpot Update" your@email.com
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```
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## 🔄 Workflow-Diagramm
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```
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WOCHE
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│
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├─ Montag 09:00
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│ └─ 🎲 Generiere Tipps für Dienstag
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│ └─ weekly_tips_TIMESTAMP.csv
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│
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├─ Dienstag 20:00
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│ └─ 🎰 Ziehung findet statt
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│
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├─ Dienstag 21:00
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│ └─ 🔄 Auto-Update & Learning
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│ ├─ Daten aktualisieren
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│ ├─ Tipps evaluieren
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│ ├─ Learning Update
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│ └─ Report erstellen
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│
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├─ Donnerstag 09:00
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│ └─ 🎲 Generiere Tipps für Freitag
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│ └─ weekly_tips_TIMESTAMP.csv
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│
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├─ Freitag 20:00
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│ └─ 🎰 Ziehung findet statt
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│
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└─ Freitag 21:00
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└─ 🔄 Auto-Update & Learning
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├─ Daten aktualisieren
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├─ Tipps evaluieren
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├─ Learning Update
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└─ Report erstellen
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```
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## 💡 Tipps
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1. **Erste Woche:** Nutze `--force` um initiale Tipps zu generieren
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2. **Strategie-Mix:** Das System nutzt automatisch den optimalen Mix
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3. **Learning:** Nach 4-5 Ziehungen beginnt das System besser zu werden
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4. **Reports:** Analysiere Reports monatlich für Trend-Analyse
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5. **Backup:** Die automatischen Backups werden im backups/ Ordner gespeichert
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## 🆘 Support
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Bei Problemen:
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1. Prüfe Logs: `tail -100 logs/*.log`
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2. Teste manuell: `python script.py`
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3. Prüfe venv: `source venv/bin/activate && pip list`
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4. Checke Cronjobs: `crontab -l`
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---
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**Version:** 1.0
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**Erstellt:** 2025-01-25
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**Kompatibel mit:** Ultimate AI-ML Generator V2.1
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#!/usr/bin/env python3
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"""
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Automatischer Eurojackpot Update & Learning Workflow
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Nach jeder Ziehung:
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1. Aktualisiert Daten von API
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2. Evaluiert letzte generierte Tipps
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3. Real-Time Learning Update
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4. Performance-Report
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Verwendung:
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python auto_update_and_learn.py
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Oder als Cronjob (nach Ziehung):
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0 21 * * 2,5 cd /path/to/eurojackpot && source venv/bin/activate && python scripts/automation/auto_update_and_learn.py
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"""
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import sys
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import os
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from datetime import datetime, timedelta
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import json
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import pandas as pd
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# Path Setup
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script_dir = os.path.dirname(os.path.abspath(__file__))
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project_dir = os.path.dirname(os.path.dirname(script_dir))
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sys.path.insert(0, project_dir)
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from scripts.utils.update_from_api import EurojackpotAPIUpdater
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from scripts.generators.ultimate_ai_ml_eurojackpot_generator import UltimateAIMLEurojackpotGenerator
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from scripts.utils.notifier import EurojackpotNotifier
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class AutoUpdateAndLearn:
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"""Automatisches Update und Learning System."""
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def __init__(self, data_dir: str):
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self.data_dir = data_dir
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# CSV file is in Eurojackpot/data folder
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# data_dir = .../Eurojackpot/data
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# We need .../Eurojackpot/data/AlleEurojackpotzahlen.csv
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self.data_file = os.path.join(data_dir, "AlleEurojackpotzahlen.csv")
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self.tips_dir = os.path.join(data_dir, "generated_tips")
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self.reports_dir = os.path.join(data_dir, "performance_reports")
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self.learning_log = os.path.join(data_dir, "learning_log.json")
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os.makedirs(self.reports_dir, exist_ok=True)
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# Initialisiere Notifier
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self.notifier = EurojackpotNotifier()
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print("🤖 AUTOMATISCHES UPDATE & LEARNING SYSTEM")
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print("=" * 70)
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def load_learning_log(self) -> dict:
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"""Lädt Learning Log."""
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if os.path.exists(self.learning_log):
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with open(self.learning_log, 'r') as f:
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return json.load(f)
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return {"updates": []}
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def save_learning_log(self, log: dict):
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"""Speichert Learning Log."""
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with open(self.learning_log, 'w') as f:
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json.dump(log, f, indent=2)
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def check_for_new_draw(self) -> dict:
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"""
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Prüft ob neue Ziehung verfügbar ist.
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Returns:
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Dict mit neuer Ziehung oder None
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"""
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print("\n🔍 PRÜFE AUF NEUE ZIEHUNG")
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print("=" * 70)
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log = self.load_learning_log()
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# Lade aktuelle Daten
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try:
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df = pd.read_csv(self.data_file, sep=';')
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df['datum'] = pd.to_datetime(df['datum'], format='%Y-%m-%d')
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latest_draw = df.iloc[0] # Neueste Ziehung (sortiert absteigend)
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latest_date = latest_draw['datum']
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print(f" 📅 Neueste Ziehung in Daten: {latest_date.strftime('%Y-%m-%d')}")
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# Prüfe ob schon verarbeitet
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if log["updates"]:
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last_processed = log["updates"][-1].get("draw_date")
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if last_processed == latest_date.strftime('%Y-%m-%d'):
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print(f" ⏭️ Bereits verarbeitet")
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return None
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print(f" ✅ Neue Ziehung gefunden!")
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return {
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'date': latest_date,
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'Z1': int(latest_draw['Z1']),
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'Z2': int(latest_draw['Z2']),
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'Z3': int(latest_draw['Z3']),
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'Z4': int(latest_draw['Z4']),
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'Z5': int(latest_draw['Z5']),
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'SZ1': int(latest_draw['SZ1']),
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'SZ2': int(latest_draw['SZ2'])
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}
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||||
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except Exception as e:
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print(f" ❌ Fehler beim Prüfen: {e}")
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return None
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def update_data(self) -> bool:
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"""
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||||
Aktualisiert Daten von API.
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Returns:
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True bei Erfolg
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"""
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print("\n📥 AKTUALISIERE DATEN VON API")
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print("=" * 70)
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try:
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updater = EurojackpotAPIUpdater(self.data_file)
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success = updater.update(api_name='all', create_backup=True)
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if success:
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print(" ✅ Daten erfolgreich aktualisiert")
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else:
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print(" ⚠️ Update ohne neue Daten")
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return success
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||||
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||||
except Exception as e:
|
||||
print(f" ❌ Fehler beim Update: {e}")
|
||||
return False
|
||||
|
||||
def evaluate_tips(self, new_draw: dict) -> dict:
|
||||
"""
|
||||
Evaluiert letzte generierte Tipps gegen neue Ziehung.
|
||||
|
||||
Args:
|
||||
new_draw: Dict mit neuer Ziehung
|
||||
|
||||
Returns:
|
||||
Evaluierungs-Ergebnisse
|
||||
"""
|
||||
print("\n🎯 EVALUIERE LETZTE TIPPS")
|
||||
print("=" * 70)
|
||||
|
||||
# Finde neueste Tipps-Datei
|
||||
if not os.path.exists(self.tips_dir):
|
||||
print(" ⚠️ Keine Tipps zum Evaluieren")
|
||||
return {}
|
||||
|
||||
tip_files = sorted(
|
||||
[f for f in os.listdir(self.tips_dir) if f.endswith('.csv')],
|
||||
reverse=True
|
||||
)
|
||||
|
||||
if not tip_files:
|
||||
print(" ⚠️ Keine Tipps-Dateien gefunden")
|
||||
return {}
|
||||
|
||||
latest_tips_file = os.path.join(self.tips_dir, tip_files[0])
|
||||
print(f" 📁 Evaluiere: {tip_files[0]}")
|
||||
|
||||
try:
|
||||
tips_df = pd.read_csv(latest_tips_file)
|
||||
|
||||
# Extrahiere gezogene Zahlen
|
||||
drawn_main = [new_draw[f'Z{i}'] for i in range(1, 6)]
|
||||
drawn_euro = [new_draw['SZ1'], new_draw['SZ2']]
|
||||
|
||||
print(f" 🎲 Gezogene Zahlen: {drawn_main} + Euro {drawn_euro}")
|
||||
print()
|
||||
|
||||
results = []
|
||||
best_matches = {'main': 0, 'euro': 0, 'tip': None}
|
||||
|
||||
for _, tip in tips_df.iterrows():
|
||||
# Parse Hauptzahlen
|
||||
main_str = tip['Main_Numbers']
|
||||
|
||||
# Handle both formats: "[1, 2, 3]" and "1-2-3"
|
||||
if main_str.startswith('['):
|
||||
# Parse Python list format
|
||||
import ast
|
||||
tip_main = ast.literal_eval(main_str)
|
||||
else:
|
||||
# Parse dash-separated format
|
||||
tip_main = [int(n) for n in main_str.split('-')]
|
||||
|
||||
# Parse Eurozahlen
|
||||
euro_str = tip['Euro_Numbers']
|
||||
if euro_str.startswith('['):
|
||||
import ast
|
||||
tip_euro = ast.literal_eval(euro_str)
|
||||
else:
|
||||
tip_euro = [int(n) for n in euro_str.split('-')]
|
||||
|
||||
# Zähle Treffer
|
||||
main_matches = len(set(tip_main) & set(drawn_main))
|
||||
euro_matches = len(set(tip_euro) & set(drawn_euro))
|
||||
|
||||
total_matches = main_matches + euro_matches
|
||||
|
||||
results.append({
|
||||
'tip_number': tip['Tip_Number'],
|
||||
'strategy': tip['Strategy'],
|
||||
'main_matches': main_matches,
|
||||
'euro_matches': euro_matches,
|
||||
'total_matches': total_matches
|
||||
})
|
||||
|
||||
# Track best
|
||||
if total_matches > (best_matches['main'] + best_matches['euro']):
|
||||
best_matches = {
|
||||
'main': main_matches,
|
||||
'euro': euro_matches,
|
||||
'tip': tip['Tip_Number']
|
||||
}
|
||||
|
||||
# Ausgabe
|
||||
print(f" {'Tip':<5} {'Strategie':<15} {'Main':<6} {'Euro':<6} {'Gesamt':<8} {'Bewertung'}")
|
||||
print(" " + "-" * 60)
|
||||
|
||||
for r in results:
|
||||
rating = self._get_match_rating(r['main_matches'], r['euro_matches'])
|
||||
print(f" #{r['tip_number']:<4} {r['strategy']:<15} "
|
||||
f"{r['main_matches']:<6} {r['euro_matches']:<6} "
|
||||
f"{r['total_matches']:<8} {rating}")
|
||||
|
||||
print()
|
||||
print(f" 🏆 Bester Tipp: #{best_matches['tip']} "
|
||||
f"({best_matches['main']} Main + {best_matches['euro']} Euro)")
|
||||
|
||||
return {
|
||||
'file': tip_files[0],
|
||||
'results': results,
|
||||
'best': best_matches,
|
||||
'avg_main_matches': sum(r['main_matches'] for r in results) / len(results),
|
||||
'avg_euro_matches': sum(r['euro_matches'] for r in results) / len(results)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Fehler bei Evaluation: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return {}
|
||||
|
||||
def _get_match_rating(self, main: int, euro: int) -> str:
|
||||
"""Bewertung der Treffer."""
|
||||
total = main + euro
|
||||
|
||||
if main == 5 and euro == 2:
|
||||
return "🏆 JACKPOT!"
|
||||
elif main == 5 and euro == 1:
|
||||
return "💰 Klasse 2"
|
||||
elif main == 5 and euro == 0:
|
||||
return "💰 Klasse 3"
|
||||
elif main == 4 and euro == 2:
|
||||
return "💰 Klasse 4"
|
||||
elif main == 4 and euro == 1:
|
||||
return "💵 Klasse 5"
|
||||
elif total >= 3:
|
||||
return "✅ Gut"
|
||||
elif total >= 2:
|
||||
return "👍 OK"
|
||||
else:
|
||||
return "⚪ Niedrig"
|
||||
|
||||
def perform_learning_update(self, new_draw: dict) -> bool:
|
||||
"""
|
||||
Führt Real-Time Learning Update durch.
|
||||
|
||||
Args:
|
||||
new_draw: Dict mit neuer Ziehung
|
||||
|
||||
Returns:
|
||||
True bei Erfolg
|
||||
"""
|
||||
print("\n🧠 REAL-TIME LEARNING UPDATE")
|
||||
print("=" * 70)
|
||||
|
||||
try:
|
||||
# Initialisiere Generator
|
||||
generator = UltimateAIMLEurojackpotGenerator(
|
||||
self.data_file,
|
||||
fast_mode=True
|
||||
)
|
||||
|
||||
# Learning Update
|
||||
generator.real_time_learner.learn_from_result(new_draw)
|
||||
|
||||
# Performance Tracking
|
||||
generator.performance_tracker.evaluate_predictions(new_draw)
|
||||
|
||||
print(" ✅ Learning Update durchgeführt")
|
||||
print(" 📊 Models wurden angepasst")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Fehler beim Learning: {e}")
|
||||
return False
|
||||
|
||||
def generate_report(self, new_draw: dict, evaluation: dict):
|
||||
"""Generiert Performance-Report."""
|
||||
print("\n📊 PERFORMANCE-REPORT")
|
||||
print("=" * 70)
|
||||
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
report_file = os.path.join(
|
||||
self.reports_dir,
|
||||
f"report_{timestamp}.txt"
|
||||
)
|
||||
|
||||
report = []
|
||||
report.append("=" * 70)
|
||||
report.append("EUROJACKPOT PERFORMANCE REPORT")
|
||||
report.append("=" * 70)
|
||||
report.append(f"Erstellt: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
report.append("")
|
||||
report.append(f"ZIEHUNG VOM {new_draw['date'].strftime('%Y-%m-%d')}")
|
||||
report.append("-" * 70)
|
||||
report.append(f"Hauptzahlen: {[new_draw[f'Z{i}'] for i in range(1, 6)]}")
|
||||
report.append(f"Eurozahlen: [{new_draw['SZ1']}, {new_draw['SZ2']}]")
|
||||
report.append("")
|
||||
|
||||
if evaluation:
|
||||
report.append("EVALUATION DER TIPPS")
|
||||
report.append("-" * 70)
|
||||
report.append(f"Datei: {evaluation['file']}")
|
||||
report.append(f"Tipps evaluiert: {len(evaluation['results'])}")
|
||||
report.append(f"Avg Main Matches: {evaluation['avg_main_matches']:.2f}")
|
||||
report.append(f"Avg Euro Matches: {evaluation['avg_euro_matches']:.2f}")
|
||||
report.append(f"Bester Tipp: #{evaluation['best']['tip']}")
|
||||
report.append(f" - Main Treffer: {evaluation['best']['main']}")
|
||||
report.append(f" - Euro Treffer: {evaluation['best']['euro']}")
|
||||
|
||||
report.append("")
|
||||
report.append("=" * 70)
|
||||
|
||||
# Speichern
|
||||
with open(report_file, 'w') as f:
|
||||
f.write('\n'.join(report))
|
||||
|
||||
# Ausgabe
|
||||
for line in report:
|
||||
print(line)
|
||||
|
||||
print(f"\n📁 Report gespeichert: {os.path.basename(report_file)}")
|
||||
|
||||
def run(self):
|
||||
"""Führt kompletten Workflow aus."""
|
||||
print()
|
||||
print("=" * 70)
|
||||
print("START: AUTOMATISCHER UPDATE & LEARNING WORKFLOW")
|
||||
print("=" * 70)
|
||||
|
||||
# 1. Update Daten
|
||||
print("\n[SCHRITT 1/4] Daten aktualisieren")
|
||||
self.update_data()
|
||||
|
||||
# 2. Prüfe auf neue Ziehung
|
||||
print("\n[SCHRITT 2/4] Neue Ziehung prüfen")
|
||||
new_draw = self.check_for_new_draw()
|
||||
|
||||
if not new_draw:
|
||||
print("\n⏭️ Keine neue Ziehung - Workflow beendet")
|
||||
return True
|
||||
|
||||
# 3. Evaluiere Tipps
|
||||
print("\n[SCHRITT 3/4] Tipps evaluieren")
|
||||
evaluation = self.evaluate_tips(new_draw)
|
||||
|
||||
# 4. Learning Update
|
||||
print("\n[SCHRITT 4/4] Learning Update")
|
||||
self.perform_learning_update(new_draw)
|
||||
|
||||
# Report
|
||||
self.generate_report(new_draw, evaluation)
|
||||
|
||||
# Log Update
|
||||
log = self.load_learning_log()
|
||||
log["updates"].append({
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"draw_date": new_draw['date'].strftime('%Y-%m-%d'),
|
||||
"evaluation": evaluation.get('best', {}),
|
||||
"avg_matches": {
|
||||
'main': evaluation.get('avg_main_matches', 0),
|
||||
'euro': evaluation.get('avg_euro_matches', 0)
|
||||
}
|
||||
})
|
||||
self.save_learning_log(log)
|
||||
|
||||
# Sende Benachrichtigung
|
||||
try:
|
||||
best_match = {
|
||||
'main_matches': evaluation.get('best', {}).get('main', 0),
|
||||
'euro_matches': evaluation.get('best', {}).get('euro', 0)
|
||||
}
|
||||
|
||||
evaluation_summary = {
|
||||
'avg_main': evaluation.get('avg_main_matches', 0),
|
||||
'avg_euro': evaluation.get('avg_euro_matches', 0)
|
||||
}
|
||||
|
||||
self.notifier.send_draw_results(new_draw, evaluation_summary, best_match)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Benachrichtigung fehlgeschlagen: {e}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("✅ WORKFLOW ERFOLGREICH ABGESCHLOSSEN")
|
||||
print("=" * 70)
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def main():
|
||||
"""Hauptfunktion."""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Automatisches Update & Learning nach Ziehung"
|
||||
)
|
||||
parser.add_argument(
|
||||
'--data-dir',
|
||||
type=str,
|
||||
default="/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot/data",
|
||||
help='Daten-Verzeichnis'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Run Workflow
|
||||
workflow = AutoUpdateAndLearn(args.data_dir)
|
||||
success = workflow.run()
|
||||
|
||||
sys.exit(0 if success else 1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,278 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Wöchentlicher Eurojackpot Tipp-Generator
|
||||
|
||||
Automatisiert:
|
||||
1. Prüft ob neue Tipps nötig sind (basierend auf letzter Generierung)
|
||||
2. Generiert 10 Ultimate Tipps
|
||||
3. Speichert mit Timestamp
|
||||
4. Trackt Generierungs-Historie
|
||||
|
||||
Verwendung:
|
||||
python weekly_tip_generator.py
|
||||
|
||||
Oder als Cronjob:
|
||||
0 9 * * 2,5 cd /path/to/eurojackpot && source venv/bin/activate && python scripts/automation/weekly_tip_generator.py
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
import json
|
||||
|
||||
# Füge Parent-Verzeichnisse zum Path hinzu
|
||||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
project_dir = os.path.dirname(os.path.dirname(script_dir))
|
||||
generators_dir = os.path.join(project_dir, 'scripts', 'generators')
|
||||
sys.path.insert(0, project_dir)
|
||||
sys.path.insert(0, generators_dir)
|
||||
|
||||
# Import des Ultimate Generators
|
||||
from scripts.generators.ultimate_ai_ml_eurojackpot_generator import UltimateAIMLEurojackpotGenerator
|
||||
from scripts.utils.notifier import EurojackpotNotifier
|
||||
|
||||
|
||||
class WeeklyTipGenerator:
|
||||
"""Automatischer wöchentlicher Tipp-Generator."""
|
||||
|
||||
def __init__(self, data_dir: str):
|
||||
self.data_dir = data_dir
|
||||
# CSV file is in Eurojackpot/data folder
|
||||
# data_dir = .../Eurojackpot/data
|
||||
# We need .../Eurojackpot/data/AlleEurojackpotzahlen.csv
|
||||
self.data_file = os.path.join(data_dir, "AlleEurojackpotzahlen.csv")
|
||||
self.tips_dir = os.path.join(data_dir, "generated_tips")
|
||||
self.history_file = os.path.join(self.tips_dir, "generation_history.json")
|
||||
|
||||
os.makedirs(self.tips_dir, exist_ok=True)
|
||||
|
||||
# Initialisiere Notifier
|
||||
self.notifier = EurojackpotNotifier()
|
||||
|
||||
print("🤖 AUTOMATISCHER WÖCHENTLICHER TIPP-GENERATOR")
|
||||
print("=" * 70)
|
||||
|
||||
def load_history(self) -> dict:
|
||||
"""Lädt Generierungs-Historie."""
|
||||
if os.path.exists(self.history_file):
|
||||
with open(self.history_file, 'r') as f:
|
||||
return json.load(f)
|
||||
return {"generations": []}
|
||||
|
||||
def save_history(self, history: dict):
|
||||
"""Speichert Historie."""
|
||||
with open(self.history_file, 'w') as f:
|
||||
json.dump(history, f, indent=2)
|
||||
|
||||
def needs_new_tips(self) -> bool:
|
||||
"""
|
||||
Prüft ob neue Tipps nötig sind.
|
||||
|
||||
Logik:
|
||||
- Eurojackpot: Dienstag & Freitag Ziehungen
|
||||
- Generiere Tipps wenn:
|
||||
a) Noch nie generiert
|
||||
b) Letzte Generierung > 3 Tage her
|
||||
c) Es ist Montag oder Donnerstag (vor Ziehung)
|
||||
"""
|
||||
history = self.load_history()
|
||||
|
||||
if not history["generations"]:
|
||||
print(" ℹ️ Noch nie Tipps generiert")
|
||||
return True
|
||||
|
||||
last_gen = history["generations"][-1]
|
||||
last_date = datetime.fromisoformat(last_gen["timestamp"])
|
||||
days_since = (datetime.now() - last_date).days
|
||||
|
||||
print(f" 📅 Letzte Generierung: {last_date.strftime('%Y-%m-%d %H:%M')}")
|
||||
print(f" ⏱️ Vor {days_since} Tagen")
|
||||
|
||||
# Wenn > 3 Tage her
|
||||
if days_since > 3:
|
||||
print(" ✅ Mehr als 3 Tage her - neue Tipps nötig")
|
||||
return True
|
||||
|
||||
# Prüfe Wochentag (0=Montag, 4=Freitag)
|
||||
today = datetime.now().weekday()
|
||||
|
||||
# Montag (vor Dienstag-Ziehung)
|
||||
if today == 0 and days_since >= 1:
|
||||
print(" ✅ Montag - generiere für Dienstag-Ziehung")
|
||||
return True
|
||||
|
||||
# Donnerstag (vor Freitag-Ziehung)
|
||||
if today == 3 and days_since >= 1:
|
||||
print(" ✅ Donnerstag - generiere für Freitag-Ziehung")
|
||||
return True
|
||||
|
||||
print(" ⏭️ Keine neuen Tipps nötig")
|
||||
return False
|
||||
|
||||
def generate_tips(self, num_tips: int = 10, force: bool = False) -> bool:
|
||||
"""
|
||||
Generiert neue Tipps.
|
||||
|
||||
Args:
|
||||
num_tips: Anzahl Tipps
|
||||
force: Ignoriere needs_new_tips Check
|
||||
|
||||
Returns:
|
||||
True bei Erfolg
|
||||
"""
|
||||
print("\n🎯 TIPP-GENERIERUNG")
|
||||
print("=" * 70)
|
||||
|
||||
# Check ob nötig
|
||||
if not force and not self.needs_new_tips():
|
||||
print("\n⏭️ Keine Generierung nötig")
|
||||
return True
|
||||
|
||||
print(f"\n🚀 Generiere {num_tips} Ultimate Tipps...")
|
||||
print("-" * 70)
|
||||
|
||||
try:
|
||||
# Initialisiere Generator
|
||||
generator = UltimateAIMLEurojackpotGenerator(
|
||||
self.data_file,
|
||||
fast_mode=True
|
||||
)
|
||||
|
||||
# Generiere Tipps
|
||||
tips = generator.generate_ultimate_tips(num_tips=num_tips)
|
||||
|
||||
if not tips:
|
||||
print("\n❌ Keine Tipps generiert")
|
||||
return False
|
||||
|
||||
# Speichere Tipps
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
output_file = os.path.join(
|
||||
self.tips_dir,
|
||||
f"weekly_tips_{timestamp}.csv"
|
||||
)
|
||||
|
||||
generator.export_tips_to_csv(tips, output_file)
|
||||
|
||||
# Update Historie
|
||||
history = self.load_history()
|
||||
history["generations"].append({
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"num_tips": len(tips),
|
||||
"file": os.path.basename(output_file),
|
||||
"avg_confidence": sum(t['confidence'] for t in tips) / len(tips),
|
||||
"avg_quality": sum(t['quality'] for t in tips) / len(tips)
|
||||
})
|
||||
self.save_history(history)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("✅ TIPPS ERFOLGREICH GENERIERT")
|
||||
print(f"📁 Datei: {os.path.basename(output_file)}")
|
||||
print(f"📊 Anzahl: {len(tips)}")
|
||||
print(f"🎯 Avg Confidence: {history['generations'][-1]['avg_confidence']:.4f}")
|
||||
print(f"💎 Avg Quality: {history['generations'][-1]['avg_quality']:.4f}")
|
||||
print("=" * 70)
|
||||
|
||||
# Sende Benachrichtigung
|
||||
try:
|
||||
# Finde besten Tipp (höchste Confidence)
|
||||
best_tip = max(tips, key=lambda t: t.get('confidence', 0))
|
||||
|
||||
# Formatiere für Notification
|
||||
best_tip_formatted = {
|
||||
'main_numbers': best_tip.get('main_numbers', '?'),
|
||||
'euro_numbers': best_tip.get('euro_numbers', '?'),
|
||||
'confidence': best_tip.get('confidence', 0),
|
||||
'strategy': best_tip.get('strategy', 'UNKNOWN')
|
||||
}
|
||||
|
||||
timestamp_formatted = datetime.now().strftime('%Y-%m-%d %H:%M')
|
||||
self.notifier.send_tips_generated(tips, timestamp_formatted, best_tip_formatted)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Benachrichtigung fehlgeschlagen: {e}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Fehler bei Generierung: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
def show_history(self):
|
||||
"""Zeigt Generierungs-Historie."""
|
||||
history = self.load_history()
|
||||
|
||||
if not history["generations"]:
|
||||
print("\n ℹ️ Noch keine Generierungen")
|
||||
return
|
||||
|
||||
print("\n📊 GENERIERUNGS-HISTORIE")
|
||||
print("=" * 70)
|
||||
print(f"{'Nr':<4} {'Datum':<20} {'Tips':<6} {'Confidence':<12} {'Quality':<12} {'Datei'}")
|
||||
print("-" * 70)
|
||||
|
||||
for i, gen in enumerate(reversed(history["generations"][-10:]), 1):
|
||||
timestamp = datetime.fromisoformat(gen["timestamp"])
|
||||
print(f"{i:<4} {timestamp.strftime('%Y-%m-%d %H:%M'):<20} "
|
||||
f"{gen['num_tips']:<6} {gen['avg_confidence']:<12.4f} "
|
||||
f"{gen['avg_quality']:<12.4f} {gen['file']}")
|
||||
|
||||
print("-" * 70)
|
||||
print(f"Total: {len(history['generations'])} Generierungen")
|
||||
|
||||
|
||||
def main():
|
||||
"""Hauptfunktion."""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Wöchentlicher Eurojackpot Tipp-Generator"
|
||||
)
|
||||
parser.add_argument(
|
||||
'--force',
|
||||
action='store_true',
|
||||
help='Generiere Tipps auch wenn nicht nötig'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--num-tips',
|
||||
type=int,
|
||||
default=10,
|
||||
help='Anzahl Tipps zu generieren (default: 10)'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--history',
|
||||
action='store_true',
|
||||
help='Zeige nur Historie'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--data-dir',
|
||||
type=str,
|
||||
default="/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot/data",
|
||||
help='Daten-Verzeichnis'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Initialisiere
|
||||
generator = WeeklyTipGenerator(args.data_dir)
|
||||
|
||||
# Zeige Historie wenn gewünscht
|
||||
if args.history:
|
||||
generator.show_history()
|
||||
return
|
||||
|
||||
# Generiere Tipps
|
||||
success = generator.generate_tips(
|
||||
num_tips=args.num_tips,
|
||||
force=args.force
|
||||
)
|
||||
|
||||
# Zeige Historie
|
||||
generator.show_history()
|
||||
|
||||
sys.exit(0 if success else 1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user