cbazzaandClaude Sonnet 4.5
fcafa8b8c0
- Fixed default data-dir in auto_update_and_learn.py (was pointing to old iCloud path) - Updated run_update_and_learn.sh to explicitly pass correct data-dir - Now saves data to /Projekte/Lotto/data instead of iCloud This ensures automatic updates save to the correct location. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
475 lines
16 KiB
Python
Executable File
475 lines
16 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Automatischer Lotto 6aus49 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 * * 3,0 cd /path/to/Lotto && 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 LottoAPIUpdater
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from scripts.generators.ultimate_ai_ml_hybrid_generator import UltimateAIMLHybridGenerator
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from scripts.utils.notifier import LottoNotifier
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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 Lotto/data folder
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# data_dir = .../Lotto/data
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# We need .../Lotto/data/AlleLottozahlen.csv
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self.data_file = os.path.join(data_dir, "AlleLottozahlen.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 = LottoNotifier()
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print("🤖 AUTOMATISCHES UPDATE & LEARNING SYSTEM - LOTTO 6AUS49")
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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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# Sortiere nach Datum absteigend
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df = df.sort_values('datum', ascending=False)
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latest_draw = df.iloc[0] # Neueste Ziehung
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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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'Z6': int(latest_draw['Z6']),
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'SZ': int(latest_draw['SZ']) if pd.notna(latest_draw['SZ']) else None
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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 = LottoAPIUpdater(self.data_file)
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success = updater.update(api_name='github', 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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except Exception as e:
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print(f" ❌ Fehler beim Update: {e}")
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return False
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def evaluate_tips(self, new_draw: dict) -> dict:
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"""
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Evaluiert letzte generierte Tipps gegen neue Ziehung.
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Args:
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new_draw: Dict mit neuer Ziehung
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Returns:
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Evaluierungs-Ergebnisse
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"""
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print("\n🎯 EVALUIERE LETZTE TIPPS")
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print("=" * 70)
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# Finde Tipps-Datei die VOR der Ziehung generiert wurde
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if not os.path.exists(self.tips_dir):
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print(" ⚠️ Keine Tipps zum Evaluieren")
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return {}
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tip_files = sorted(
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[f for f in os.listdir(self.tips_dir) if f.endswith('.csv')],
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reverse=True
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)
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if not tip_files:
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print(" ⚠️ Keine Tipps-Dateien gefunden")
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return {}
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# Finde Tip-Datei die vor der Ziehung erstellt wurde
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draw_date = new_draw['date']
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selected_tip_file = None
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for tip_file in tip_files:
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# Parse Timestamp aus Dateiname: weekly_lotto_tips_YYYYMMDD_HHMMSS.csv
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try:
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parts = tip_file.replace('.csv', '').split('_')
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tip_date_str = parts[-2] # YYYYMMDD
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tip_date = pd.to_datetime(tip_date_str, format='%Y%m%d')
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# Nehme erste Datei die vor der Ziehung war
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if tip_date < draw_date:
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selected_tip_file = tip_file
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break
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except:
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continue
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if not selected_tip_file:
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# Fallback: nehme älteste Datei
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selected_tip_file = tip_files[-1]
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latest_tips_file = os.path.join(self.tips_dir, selected_tip_file)
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print(f" 📁 Evaluiere: {selected_tip_file}")
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try:
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tips_df = pd.read_csv(latest_tips_file)
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# Extrahiere gezogene Zahlen
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drawn_main = [new_draw[f'Z{i}'] for i in range(1, 7)]
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drawn_sz = new_draw.get('SZ')
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print(f" 🎲 Gezogene Zahlen: {drawn_main} + SZ: {drawn_sz}")
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print()
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results = []
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best_matches = {'main': 0, 'sz': False, 'tip': None}
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for _, tip in tips_df.iterrows():
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# Parse Hauptzahlen (Column name is 'Numbers' in Lotto)
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main_str = tip['Numbers'] if 'Numbers' in tip else tip.get('Main_Numbers', '')
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# Handle both formats: "[1, 2, 3]" and "1-2-3"
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if main_str.startswith('['):
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# Parse Python list format
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import ast
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tip_main = ast.literal_eval(main_str)
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else:
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# Parse dash-separated format
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tip_main = [int(n) for n in main_str.split('-')]
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# Parse Superzahl
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tip_sz = int(tip['Superzahl'])
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# Zähle Treffer
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main_matches = len(set(tip_main) & set(drawn_main))
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sz_match = (tip_sz == drawn_sz) if drawn_sz is not None else False
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results.append({
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'tip_number': tip['Tip_Number'],
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'strategy': tip['Strategy'],
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'main_matches': main_matches,
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'sz_match': sz_match,
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'total_score': main_matches + (1 if sz_match else 0)
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})
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# Track best
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total_score = main_matches + (1 if sz_match else 0)
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best_total = best_matches['main'] + (1 if best_matches['sz'] else 0)
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if total_score > best_total:
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best_matches = {
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'main': main_matches,
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'sz': sz_match,
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'tip': tip['Tip_Number']
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}
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# Ausgabe
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print(f" {'Tip':<5} {'Strategie':<15} {'Main':<6} {'SZ':<5} {'Score':<7} {'Bewertung'}")
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print(" " + "-" * 60)
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for r in results:
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rating = self._get_match_rating(r['main_matches'], r['sz_match'])
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sz_indicator = "✅" if r['sz_match'] else "⚪"
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print(f" #{r['tip_number']:<4} {r['strategy']:<15} "
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f"{r['main_matches']:<6} {sz_indicator:<5} "
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f"{r['total_score']:<7} {rating}")
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print()
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print(f" 🏆 Bester Tipp: #{best_matches['tip']} "
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f"({best_matches['main']} Main" +
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(f" + SZ" if best_matches['sz'] else "") + ")")
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return {
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'file': tip_files[0],
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'results': results,
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'best': best_matches,
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'avg_main_matches': sum(r['main_matches'] for r in results) / len(results),
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'sz_match_rate': sum(1 for r in results if r['sz_match']) / len(results)
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}
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except Exception as e:
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print(f" ❌ Fehler bei Evaluation: {e}")
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import traceback
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traceback.print_exc()
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return {}
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def _get_match_rating(self, main: int, sz_match: bool) -> str:
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"""Bewertung der Treffer."""
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if main == 6 and sz_match:
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return "🏆 JACKPOT!"
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elif main == 6:
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return "💰 Klasse 2"
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elif main == 5 and sz_match:
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return "💰 Klasse 3"
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elif main == 5:
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return "💰 Klasse 4"
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elif main == 4 and sz_match:
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return "💵 Klasse 5"
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elif main == 4:
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return "💵 Klasse 6"
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elif main == 3 and sz_match:
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return "✅ Klasse 7"
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elif main == 3:
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return "✅ Klasse 8"
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elif main == 2 and sz_match:
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return "👍 Klasse 9"
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elif main >= 2:
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return "👍 OK"
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else:
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return "⚪ Niedrig"
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def perform_learning_update(self, new_draw: dict) -> bool:
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"""
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Führt Real-Time Learning Update durch.
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Args:
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new_draw: Dict mit neuer Ziehung
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Returns:
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True bei Erfolg
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"""
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print("\n🧠 REAL-TIME LEARNING UPDATE")
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print("=" * 70)
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try:
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# Initialisiere Generator
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generator = UltimateAIMLHybridGenerator(
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self.data_file,
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fast_mode=True
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)
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# Learning Update (falls vorhanden)
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if hasattr(generator, 'real_time_learner'):
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generator.real_time_learner.learn_from_result(new_draw)
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print(" ✅ Learning Update durchgeführt")
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# Performance Tracking (falls vorhanden)
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if hasattr(generator, 'performance_tracker'):
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generator.performance_tracker.evaluate_predictions(new_draw)
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print(" 📊 Performance getrackt")
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return True
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except Exception as e:
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print(f" ⚠️ Learning Update nicht verfügbar: {e}")
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print(" ℹ️ Generator funktioniert weiterhin normal")
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return True
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def generate_report(self, new_draw: dict, evaluation: dict):
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"""Generiert Performance-Report."""
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print("\n📊 PERFORMANCE-REPORT")
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print("=" * 70)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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report_file = os.path.join(
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self.reports_dir,
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f"report_{timestamp}.txt"
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)
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report = []
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report.append("=" * 70)
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report.append("LOTTO 6AUS49 PERFORMANCE REPORT")
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report.append("=" * 70)
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report.append(f"Erstellt: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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report.append("")
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report.append(f"ZIEHUNG VOM {new_draw['date'].strftime('%Y-%m-%d')}")
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report.append("-" * 70)
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report.append(f"Hauptzahlen: {[new_draw[f'Z{i}'] for i in range(1, 7)]}")
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report.append(f"Superzahl: {new_draw.get('SZ', 'N/A')}")
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report.append("")
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if evaluation:
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report.append("EVALUATION DER TIPPS")
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report.append("-" * 70)
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report.append(f"Datei: {evaluation['file']}")
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report.append(f"Tipps evaluiert: {len(evaluation['results'])}")
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report.append(f"Avg Main Matches: {evaluation['avg_main_matches']:.2f}")
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report.append(f"SZ Match Rate: {evaluation['sz_match_rate']*100:.1f}%")
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report.append(f"Bester Tipp: #{evaluation['best']['tip']}")
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report.append(f" - Main Treffer: {evaluation['best']['main']}")
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report.append(f" - SZ Match: {'Ja' if evaluation['best']['sz'] else 'Nein'}")
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report.append("")
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report.append("=" * 70)
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# Speichern
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with open(report_file, 'w') as f:
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f.write('\n'.join(report))
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# Ausgabe
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for line in report:
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print(line)
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print(f"\n📁 Report gespeichert: {os.path.basename(report_file)}")
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def run(self):
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"""Führt kompletten Workflow aus."""
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print()
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print("=" * 70)
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print("START: AUTOMATISCHER UPDATE & LEARNING WORKFLOW")
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print("=" * 70)
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# 1. Update Daten
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print("\n[SCHRITT 1/4] Daten aktualisieren")
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self.update_data()
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# 2. Prüfe auf neue Ziehung
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print("\n[SCHRITT 2/4] Neue Ziehung prüfen")
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new_draw = self.check_for_new_draw()
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if not new_draw:
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print("\n⏭️ Keine neue Ziehung - Workflow beendet")
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return True
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# 3. Evaluiere Tipps
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print("\n[SCHRITT 3/4] Tipps evaluieren")
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evaluation = self.evaluate_tips(new_draw)
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# 4. Learning Update
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print("\n[SCHRITT 4/4] Learning Update")
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self.perform_learning_update(new_draw)
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# Report
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self.generate_report(new_draw, evaluation)
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# Log Update
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log = self.load_learning_log()
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log["updates"].append({
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"timestamp": datetime.now().isoformat(),
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"draw_date": new_draw['date'].strftime('%Y-%m-%d'),
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"evaluation": evaluation.get('best', {}),
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"avg_matches": {
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'main': evaluation.get('avg_main_matches', 0),
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'sz_rate': evaluation.get('sz_match_rate', 0)
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}
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})
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self.save_learning_log(log)
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# Sende Benachrichtigung
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try:
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best_match = {
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'main_matches': evaluation.get('best', {}).get('main', 0),
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'sz_match': evaluation.get('best', {}).get('sz', False)
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}
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evaluation_summary = {
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'avg_main': evaluation.get('avg_main_matches', 0),
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'sz_rate': evaluation.get('sz_match_rate', 0)
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}
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self.notifier.send_draw_results(new_draw, evaluation_summary, best_match)
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except Exception as e:
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print(f"⚠️ Benachrichtigung fehlgeschlagen: {e}")
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print("\n" + "=" * 70)
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print("✅ WORKFLOW ERFOLGREICH ABGESCHLOSSEN")
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print("=" * 70)
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return True
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def main():
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"""Hauptfunktion."""
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import argparse
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parser = argparse.ArgumentParser(
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description="Automatisches Update & Learning nach Ziehung"
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)
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parser.add_argument(
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'--data-dir',
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type=str,
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default="/Users/sebastianfrohlich/Projekte/Lotto/data",
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help='Daten-Verzeichnis'
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)
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args = parser.parse_args()
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# Run Workflow
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workflow = AutoUpdateAndLearn(args.data_dir)
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success = workflow.run()
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sys.exit(0 if success else 1)
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if __name__ == "__main__":
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main()
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