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201
stock_data_manager.py
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201
stock_data_manager.py
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import yfinance as yf
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import pandas as pd
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import sqlite3
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from datetime import datetime
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class StockDataManager:
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def __init__(self, db_name="stock_data.db"):
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self.db_name = db_name
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self.conn = None
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self.cursor = None
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self._connect_db()
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self._create_tables()
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def _connect_db(self):
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"""Stellt eine Verbindung zur SQLite-Datenbank her."""
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try:
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self.conn = sqlite3.connect(self.db_name)
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self.cursor = self.conn.cursor()
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print(f"Erfolgreich mit Datenbank '{self.db_name}' verbunden.")
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except sqlite3.Error as e:
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print(f"Fehler beim Verbinden mit der Datenbank: {e}")
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def _create_tables(self):
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"""Erstellt die Tabellen für Aktiendaten, falls sie noch nicht existieren."""
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if not self.conn:
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print("Keine Datenbankverbindung vorhanden.")
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return
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try:
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self.cursor.execute('''
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CREATE TABLE IF NOT EXISTS stocks (
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symbol TEXT PRIMARY KEY,
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company_name TEXT
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)
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''')
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self.cursor.execute('''
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CREATE TABLE IF NOT EXISTS daily_prices (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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symbol TEXT NOT NULL,
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date TEXT NOT NULL,
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open REAL,
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high REAL,
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low REAL,
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close REAL,
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adj_close REAL,
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volume INTEGER,
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FOREIGN KEY (symbol) REFERENCES stocks (symbol) ON DELETE CASCADE,
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UNIQUE (symbol, date)
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)
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''')
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self.conn.commit()
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print("Datenbanktabellen überprüft/erstellt.")
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except sqlite3.Error as e:
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print(f"Fehler beim Erstellen der Tabellen: {e}")
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def add_stock(self, symbol, company_name=""):
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"""Fügt ein Aktiensymbol zur 'stocks'-Tabelle hinzu."""
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if not self.conn: return
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try:
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self.cursor.execute("INSERT OR IGNORE INTO stocks (symbol, company_name) VALUES (?, ?)", (symbol.upper(), company_name))
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self.conn.commit()
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print(f"Aktie '{symbol.upper()}' zur Datenbank hinzugefügt (falls neu).")
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return True
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except sqlite3.Error as e:
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print(f"Fehler beim Hinzufügen der Aktie {symbol}: {e}")
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return False
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def get_all_symbols(self):
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"""Gibt eine Liste aller in der Datenbank gespeicherten Symbole zurück."""
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if not self.conn: return []
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self.cursor.execute("SELECT symbol FROM stocks")
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return [row[0] for row in self.cursor.fetchall()]
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def fetch_and_store_data(self, symbol, period="1y"):
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"""
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Holt historische Kursdaten für ein Symbol und speichert sie in der Datenbank.
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period: '1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max'
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"""
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symbol = symbol.upper()
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print(f"Hole Daten für {symbol}...")
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try:
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ticker = yf.Ticker(symbol)
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hist = ticker.history(period="1y", auto_adjust=False)
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if hist.empty:
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print(f"Keine Daten für {symbol} gefunden oder ungültiges Symbol.")
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return False
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# Füge das Symbol hinzu, falls es noch nicht existiert (z.B. wenn es direkt per API geholt wird)
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self.add_stock(symbol, ticker.info.get('longName', ''))
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data_to_insert = []
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for date, row in hist.iterrows():
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# Formatiere Datum als YYYY-MM-DD
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date_str = date.strftime('%Y-%m-%d')
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data_to_insert.append((
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symbol,
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date_str,
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row['Open'],
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row['High'],
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row['Low'],
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row['Close'],
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row['Adj Close'],
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row['Volume']
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))
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# Verwende INSERT OR IGNORE, um Duplikate zu vermeiden
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self.cursor.executemany("""
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INSERT OR IGNORE INTO daily_prices (symbol, date, open, high, low, close, adj_close, volume)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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""", data_to_insert)
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self.conn.commit()
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print(f"Daten für {symbol} erfolgreich gespeichert/aktualisiert.")
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return True
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except Exception as e:
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print(f"Fehler beim Holen/Speichern der Daten für {symbol}: {e}")
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return False
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def get_stock_data(self, symbol, start_date=None, end_date=None):
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"""
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Holt historische Kursdaten für ein Symbol aus der Datenbank als Pandas DataFrame.
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Optional: Filter nach Start- und Enddatum.
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"""
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symbol = symbol.upper()
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query = "SELECT date, open, high, low, close, adj_close, volume FROM daily_prices WHERE symbol = ?"
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params = [symbol]
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if start_date and end_date:
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query += " AND date BETWEEN ? AND ?"
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params.append(start_date)
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params.append(end_date)
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elif start_date:
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query += " AND date >= ?"
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params.append(start_date)
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elif end_date:
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query += " AND date <= ?"
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params.append(end_date)
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query += " ORDER BY date ASC"
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try:
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df = pd.read_sql(query, self.conn, params=params, parse_dates=['date'], index_col='date')
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if df.empty:
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print(f"Keine Daten für {symbol} im angegebenen Zeitraum in der Datenbank gefunden.")
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return df
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except sqlite3.Error as e:
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print(f"Fehler beim Abrufen der Daten für {symbol} aus der Datenbank: {e}")
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return pd.DataFrame()
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def close(self):
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"""Schließt die Datenbankverbindung."""
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if self.conn:
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self.conn.close()
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print("Datenbankverbindung geschlossen.")
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# Beispielnutzung (kann später entfernt werden, wenn GUI fertig ist)
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if __name__ == "__main__":
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manager = StockDataManager("my_stocks.db")
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# Symbole aus CSV hinzufügen (Beispiel)
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# Angenommen, du hast eine stocks.csv mit 'Symbol,CompanyName'
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# Beispiel-CSV-Inhalt:
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# Symbol,CompanyName
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# AAPL,Apple Inc.
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# MSFT,Microsoft Corp.
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# GOOGL,Alphabet Inc. (GOOGL)
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# AMZN,Amazon.com Inc.
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csv_file = "stocks.csv" # Erstelle diese Datei manuell für den Test
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try:
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df_symbols = pd.read_csv(csv_file)
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for index, row in df_symbols.iterrows():
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manager.add_stock(row['Symbol'], row.get('CompanyName', ''))
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except FileNotFoundError:
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print(f"'{csv_file}' nicht gefunden. Bitte erstellen Sie eine CSV-Datei mit 'Symbol,CompanyName'.")
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except KeyError:
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print(f"Fehler: '{csv_file}' muss Spalten 'Symbol' und optional 'CompanyName' enthalten.")
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symbols_to_fetch = manager.get_all_symbols()
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if not symbols_to_fetch:
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# Fallback: Wenn CSV leer ist oder nicht existiert, einige bekannte Symbole holen
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symbols_to_fetch = ["AAPL", "MSFT", "GOOGL"]
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for s in symbols_to_fetch:
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manager.add_stock(s)
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for symbol in symbols_to_fetch:
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manager.fetch_and_store_data(symbol, period="1y") # Holt Daten für 1 Jahr
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# Testen des Datenabrufs
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aapl_data = manager.get_stock_data("AAPL")
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if not aapl_data.empty:
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print("\nAAPL Daten (erste 5 Reihen):")
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print(aapl_data.head())
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msft_data = manager.get_stock_data("MSFT", start_date="2024-01-01")
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if not msft_data.empty:
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print("\nMSFT Daten seit 2024-01-01 (letzte 5 Reihen):")
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print(msft_data.tail())
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manager.close()
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