import os
import pandas as pd
import pandas_ta as ta
from dotenv import load_dotenv
from datetime import datetime, timedelta
from alpaca.data.historical import StockHistoricalDataClient
from alpaca.data.requests import StockBarsRequest
from alpaca.data.timeframe import TimeFrame

# --- SETUP ---
load_dotenv()
API_KEY = os.getenv('ALPACA_API_KEY')
SECRET_KEY = os.getenv('ALPACA_SECRET_KEY')
data_client = StockHistoricalDataClient(API_KEY, SECRET_KEY)

def scan_markets(tickers, rsi_period=25):
    results = []
    print(f"--- Scanne {len(tickers)} Aktien auf Signale ---")

    for symbol in tickers:
        try:
            # Daten für die letzten 30 Tage holen (reicht für RSI 14)
            start_date = datetime.now() - timedelta(days=50)
            request_params = StockBarsRequest(
                symbol_or_symbols=[symbol],
                timeframe=TimeFrame.Day,
                start=start_date
            )
            
            bars = data_client.get_stock_bars(request_params).df
            if bars.empty: continue
            
            df = bars.reset_index(level=0, drop=True)
            
            # RSI berechnen
            df['RSI'] = ta.rsi(df['close'], length=rsi_period)
            
            # Letzte Werte extrahieren
            current_price = df['close'].iloc[-1]
            current_rsi = df['RSI'].iloc[-1]
            
            # Signal-Logik
            signal = "NEUTRAL"
            if current_rsi < 30:
                signal = "KAUFEN (Überverkauft)"
            elif current_rsi > 70:
                signal = "VERKAUFEN (Überkauft)"
            
            results.append({
                'Symbol': symbol,
                'Preis': round(current_price, 2),
                'RSI': round(current_rsi, 2),
                'Signal': signal,
                'Zeitpunkt': datetime.now().strftime('%Y-%m-%d %H:%M')
            })
            print(f"{symbol}: RSI {current_rsi:.2f} -> {signal}")

        except Exception as e:
            print(f"Fehler beim Scannen von {symbol}: {e}")

    # Ergebnisse in DataFrame umwandeln
    scan_df = pd.DataFrame(results)
    
    # Als Excel speichern
    filename = f"markt_scan_{datetime.now().strftime('%Y%m%d')}.xlsx"
    scan_df.to_excel("output/"+filename, index=False)
    print(f"\n✅ Scan abgeschlossen. Ergebnisse gespeichert in: {filename}")
    
    return scan_df

if __name__ == "__main__":
    # Liste der zu scannenden Aktien
    my_watchlist = ["AAPL", "TSLA", "MSFT", "AMZN", "GOOGL", "NVDA", "AMD", "META"]
    
    scan_results = scan_markets(my_watchlist)
    
    # Optional: Nur die echten Signale anzeigen
    hits = scan_results[scan_results['Signal'] != "NEUTRAL"]
    if not hits.empty:
        print("\n🔥 AKTUELLE SIGNALE:")
        print(hits)
    else:
        print("\nKeine extremen RSI-Werte gefunden.")