import pandas as pd
import numpy as np
from backtesting import Backtest, Strategy
from backtesting.lib import crossover
from ta.momentum import rsi
from scipy.signal import savgol_filter

# Daten vorbereiten (df muss OHLCV-Daten enthalten: 'open', 'high', 'low', 'close', 'volume')
df = pd.read_csv('xauusd_5min.csv', parse_dates=['time'])
df.set_index('time', inplace=True)

# Indikatoren berechnen
def calculate_indicators(df):
    df['ema21'] = df['close'].ewm(span=21).mean()
    df['ema50'] = df['close'].ewm(span=50).mean()
    df['rsi9'] = rsi(df['close'], length=9)
    df['rsi14'] = rsi(df['close'], length=14)
    df['trend'] = savgol_filter(df['close'], window_length=25, polyorder=3)
    return df

df = calculate_indicators(df)

# Strategie definieren
class Gold5MinStrategy(Strategy):
    def init(self):
        # Indikatoren für den Plot
        self.add_indicator('EMA21', self.data.ema21)
        self.add_indicator('EMA50', self.data.ema50)
    
    def next(self):
        current_index = len(self.data.close) - 1
        
        # Long-Signal (Kauf)
        if (
            crossover(self.data.ema21, self.data.ema50)
            and self.data.rsi14[-1] < 65
            and self.data.rsi9[-1] > 50
            and self.data.trend[-1] > self.data.trend[-2]
            and not self.position.is_long
        ):
            self.buy(sl=self.data.low[-1] * 0.995, tp=self.data.close[-1] * 1.01)  # 0.5% SL, 1% TP
        
        # Short-Signal (Verkauf)
        elif (
            crossover(self.data.ema50, self.data.ema21)
            and self.data.rsi14[-1] > 35
            and self.data.rsi9[-1] < 50
            and self.data.trend[-1] < self.data.trend[-2]
            and not self.position.is_short
        ):
            self.sell(sl=self.data.high[-1] * 1.005, tp=self.data.close[-1] * 0.99)  # 0.5% SL, 1% TP

# Backtest ausführen
bt = Backtest(df, Gold5MinStrategy, commission=0.0002, margin=0.05)  # 0.02% Kommission, 5% Margin
stats = bt.run()
print(stats)

# Optimierung (optional)
# stats_opt = bt.optimize(
#     rsi_long_upper=[60, 65, 70],
#     rsi_short_lower=[30, 35, 40],
#     maximize='Return [%]'
# )

# Ergebnisse plotten
bt.plot()