Add Bitcoin Trading Signal System with MACD and News Sentiment Analysis
- Implement comprehensive Bitcoin trading signal system - Add MACD (Moving Average Convergence Divergence) indicator - Integrate news sentiment analysis from CryptoCompare - Combine technical analysis with market sentiment - Generate trading recommendations (Strong Buy, Buy, Hold, Sell, Strong Sell) Features: - Real-time Bitcoin price data from Binance and CoinGecko APIs - Historical data analysis with MACD indicator - News sentiment analysis with keyword-based scoring - Weighted signal combination (60% MACD, 40% Sentiment) - Confidence scoring for each recommendation - Detailed reasoning for trading signals - CLI interface with verbose and quick modes Components: - data_fetcher.py: Bitcoin price and market data retrieval - macd_indicator.py: MACD calculation and signal generation - news_sentiment.py: News analysis and sentiment scoring - signal_generator.py: Combined signal generation - bitcoin_trader.py: Main CLI application Usage: python bitcoin_trading/bitcoin_trader.py [--verbose] [--days N] [--quick] Documentation in bitcoin_trading/README.md
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"""
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MACD (Moving Average Convergence Divergence) Indicator
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Berechnet MACD-Signale für Bitcoin Trading
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, Tuple, Optional
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from enum import Enum
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class MACDSignal(Enum):
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"""MACD Signal-Typen"""
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STRONG_BUY = "STARKER KAUF"
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BUY = "KAUF"
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NEUTRAL = "NEUTRAL"
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SELL = "VERKAUF"
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STRONG_SELL = "STARKER VERKAUF"
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class MACDIndicator:
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"""
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MACD-Indikator für technische Analyse
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Standard-Parameter:
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- Fast EMA: 12 Perioden
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- Slow EMA: 26 Perioden
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- Signal: 9 Perioden
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"""
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def __init__(self, fast_period: int = 12, slow_period: int = 26, signal_period: int = 9):
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"""
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Args:
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fast_period: Schnelle EMA-Periode (Standard: 12)
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slow_period: Langsame EMA-Periode (Standard: 26)
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signal_period: Signal-Linie-Periode (Standard: 9)
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"""
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self.fast_period = fast_period
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self.slow_period = slow_period
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self.signal_period = signal_period
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def calculate_ema(self, data: pd.Series, period: int) -> pd.Series:
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"""
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Berechnet Exponential Moving Average (EMA)
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Args:
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data: Preisdaten
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period: EMA-Periode
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Returns:
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EMA-Serie
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"""
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return data.ewm(span=period, adjust=False).mean()
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def calculate_macd(self, prices: pd.Series) -> pd.DataFrame:
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"""
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Berechnet MACD-Indikator
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Args:
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prices: Preis-Serie (normalerweise Close-Preise)
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Returns:
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DataFrame mit MACD, Signal und Histogram
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"""
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# Berechne EMAs
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ema_fast = self.calculate_ema(prices, self.fast_period)
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ema_slow = self.calculate_ema(prices, self.slow_period)
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# MACD-Linie
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macd_line = ema_fast - ema_slow
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# Signal-Linie (9-Tage EMA der MACD-Linie)
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signal_line = self.calculate_ema(macd_line, self.signal_period)
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# MACD-Histogramm (Differenz zwischen MACD und Signal)
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histogram = macd_line - signal_line
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# Erstelle DataFrame
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df = pd.DataFrame({
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'macd': macd_line,
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'signal': signal_line,
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'histogram': histogram
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})
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return df
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def get_signal(self, df: pd.DataFrame) -> Tuple[MACDSignal, Dict]:
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"""
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Generiert Trading-Signal basierend auf MACD
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Args:
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df: DataFrame mit price-Spalte
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Returns:
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Tuple aus (Signal, Details-Dict)
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"""
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if df.empty or len(df) < self.slow_period + self.signal_period:
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return MACDSignal.NEUTRAL, {
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'reason': 'Nicht genug Daten für MACD-Berechnung',
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'confidence': 0
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}
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# Berechne MACD
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macd_df = self.calculate_macd(df['price'])
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# Hole aktuelle und vorherige Werte
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current_macd = macd_df['macd'].iloc[-1]
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current_signal = macd_df['signal'].iloc[-1]
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current_histogram = macd_df['histogram'].iloc[-1]
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prev_macd = macd_df['macd'].iloc[-2]
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prev_signal = macd_df['signal'].iloc[-2]
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prev_histogram = macd_df['histogram'].iloc[-2]
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# Bestimme Signal-Typ
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signal = MACDSignal.NEUTRAL
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confidence = 0
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reasons = []
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# 1. Bullish Crossover (MACD kreuzt Signal von unten)
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if prev_macd <= prev_signal and current_macd > current_signal:
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signal = MACDSignal.BUY
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confidence = 70
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reasons.append("Bullish Crossover: MACD kreuzt Signal-Linie von unten")
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# Starkes Kaufsignal, wenn zusätzlich im negativen Bereich
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if current_macd < 0:
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signal = MACDSignal.STRONG_BUY
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confidence = 85
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reasons.append("MACD im negativen Bereich → Überkauft")
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# 2. Bearish Crossover (MACD kreuzt Signal von oben)
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elif prev_macd >= prev_signal and current_macd < current_signal:
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signal = MACDSignal.SELL
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confidence = 70
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reasons.append("Bearish Crossover: MACD kreuzt Signal-Linie von oben")
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# Starkes Verkaufssignal, wenn zusätzlich im positiven Bereich
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if current_macd > 0:
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signal = MACDSignal.STRONG_SELL
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confidence = 85
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reasons.append("MACD im positiven Bereich → Überverkauft")
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# 3. Divergenz-Analyse (Histogramm)
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else:
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histogram_trend = current_histogram - prev_histogram
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if histogram_trend > 0 and current_histogram > 0:
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signal = MACDSignal.BUY
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confidence = 55
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reasons.append("Positives Momentum: Histogramm steigt")
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elif histogram_trend < 0 and current_histogram < 0:
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signal = MACDSignal.SELL
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confidence = 55
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reasons.append("Negatives Momentum: Histogramm fällt")
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else:
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signal = MACDSignal.NEUTRAL
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confidence = 30
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reasons.append("Kein klarer Trend erkennbar")
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# Zusätzliche Analyse: Preis-Trend
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recent_prices = df['price'].tail(10)
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price_change = ((recent_prices.iloc[-1] - recent_prices.iloc[0]) / recent_prices.iloc[0]) * 100
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if abs(price_change) > 5:
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if price_change > 0:
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reasons.append(f"Preis-Trend: +{price_change:.2f}% (aufwärts)")
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else:
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reasons.append(f"Preis-Trend: {price_change:.2f}% (abwärts)")
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details = {
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'macd': float(current_macd),
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'signal': float(current_signal),
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'histogram': float(current_histogram),
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'prev_histogram': float(prev_histogram),
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'crossover': current_macd > current_signal,
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'confidence': confidence,
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'reasons': reasons,
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'price_change_10d': float(price_change)
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}
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return signal, details
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def analyze_trend(self, df: pd.DataFrame, periods: int = 20) -> str:
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"""
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Analysiert den Trend der letzten Perioden
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Args:
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df: DataFrame mit MACD-Daten
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periods: Anzahl Perioden zur Analyse
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Returns:
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Trend-Beschreibung
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"""
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macd_df = self.calculate_macd(df['price'])
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if len(macd_df) < periods:
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return "Nicht genug Daten"
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recent_histogram = macd_df['histogram'].tail(periods)
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# Zähle positive/negative Balken
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positive_bars = (recent_histogram > 0).sum()
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negative_bars = (recent_histogram < 0).sum()
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if positive_bars > negative_bars * 1.5:
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return "Starker Aufwärtstrend"
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elif positive_bars > negative_bars:
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return "Leichter Aufwärtstrend"
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elif negative_bars > positive_bars * 1.5:
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return "Starker Abwärtstrend"
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elif negative_bars > positive_bars:
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return "Leichter Abwärtstrend"
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else:
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return "Seitwärtstrend"
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if __name__ == "__main__":
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# Test mit simulierten Daten
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print("=== MACD Indicator Test ===\n")
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# Erstelle Test-Daten
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dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
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prices = 40000 + np.cumsum(np.random.randn(100) * 500) # Random Walk
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df = pd.DataFrame({
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'timestamp': dates,
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'price': prices
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})
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# Initialisiere MACD
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macd = MACDIndicator()
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# Berechne MACD
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macd_df = macd.calculate_macd(df['price'])
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print("Letzte 5 MACD-Werte:")
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print(macd_df.tail())
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print("\n=== Trading Signal ===")
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signal, details = macd.get_signal(df)
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print(f"Signal: {signal.value}")
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print(f"Konfidenz: {details['confidence']}%")
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print(f"\nMACD: {details['macd']:.2f}")
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print(f"Signal: {details['signal']:.2f}")
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print(f"Histogram: {details['histogram']:.2f}")
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print(f"\nGründe:")
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for reason in details['reasons']:
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print(f" - {reason}")
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print(f"\nTrend-Analyse: {macd.analyze_trend(df)}")
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