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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Claude
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"""
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Bitcoin Price Data Fetcher
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Ruft aktuelle und historische Bitcoin-Preisdaten von verschiedenen APIs ab
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"""
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import requests
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from datetime import datetime, timedelta
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import pandas as pd
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from typing import Dict, List, Optional
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import time
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class BitcoinDataFetcher:
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"""Fetches Bitcoin price data from various sources"""
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def __init__(self):
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self.base_url_coingecko = "https://api.coingecko.com/api/v3"
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self.base_url_binance = "https://api.binance.com/api/v3"
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def get_current_price(self) -> Optional[float]:
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"""
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Holt den aktuellen Bitcoin-Preis in USD
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Returns:
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float: Aktueller BTC/USD Preis oder None bei Fehler
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"""
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try:
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# Versuche zuerst Binance (schneller und zuverlässiger)
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url = f"{self.base_url_binance}/ticker/price"
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params = {"symbol": "BTCUSDT"}
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response = requests.get(url, params=params, timeout=10)
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if response.status_code == 200:
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data = response.json()
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return float(data['price'])
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except Exception as e:
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print(f"Fehler beim Abrufen von Binance: {e}")
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try:
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# Fallback zu CoinGecko
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url = f"{self.base_url_coingecko}/simple/price"
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params = {
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"ids": "bitcoin",
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"vs_currencies": "usd"
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}
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response = requests.get(url, params=params, timeout=10)
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if response.status_code == 200:
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data = response.json()
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return float(data['bitcoin']['usd'])
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except Exception as e:
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print(f"Fehler beim Abrufen von CoinGecko: {e}")
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return None
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def get_historical_data(self, days: int = 30) -> pd.DataFrame:
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"""
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Holt historische Bitcoin-Preisdaten
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Args:
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days: Anzahl der Tage zurück
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Returns:
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DataFrame mit Spalten: timestamp, price, volume
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"""
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try:
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# Binance Klines (Candlestick-Daten)
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url = f"{self.base_url_binance}/klines"
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# Berechne Zeitstempel
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end_time = int(datetime.now().timestamp() * 1000)
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start_time = int((datetime.now() - timedelta(days=days)).timestamp() * 1000)
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params = {
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"symbol": "BTCUSDT",
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"interval": "1h", # Stündliche Daten
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"startTime": start_time,
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"endTime": end_time,
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"limit": 1000
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}
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response = requests.get(url, params=params, timeout=30)
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if response.status_code == 200:
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data = response.json()
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# Konvertiere zu DataFrame
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df = pd.DataFrame(data, columns=[
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'timestamp', 'open', 'high', 'low', 'close',
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'volume', 'close_time', 'quote_volume', 'trades',
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'taker_buy_base', 'taker_buy_quote', 'ignore'
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])
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# Behalte nur relevante Spalten
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df = df[['timestamp', 'close', 'volume']]
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df.columns = ['timestamp', 'price', 'volume']
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# Konvertiere Datentypen
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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df['price'] = df['price'].astype(float)
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df['volume'] = df['volume'].astype(float)
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return df
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except Exception as e:
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print(f"Fehler beim Abrufen historischer Daten: {e}")
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# Fallback: Leeres DataFrame
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return pd.DataFrame(columns=['timestamp', 'price', 'volume'])
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def get_market_data(self) -> Dict:
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"""
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Holt erweiterte Marktdaten für Bitcoin
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Returns:
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Dict mit Marktdaten (Volumen, Marktkapitalisierung, etc.)
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"""
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try:
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url = f"{self.base_url_coingecko}/coins/bitcoin"
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params = {
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"localization": "false",
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"tickers": "false",
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"market_data": "true",
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"community_data": "false",
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"developer_data": "false"
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}
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response = requests.get(url, params=params, timeout=15)
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if response.status_code == 200:
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data = response.json()
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market_data = data.get('market_data', {})
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return {
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'current_price': market_data.get('current_price', {}).get('usd'),
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'market_cap': market_data.get('market_cap', {}).get('usd'),
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'total_volume': market_data.get('total_volume', {}).get('usd'),
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'price_change_24h': market_data.get('price_change_percentage_24h'),
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'price_change_7d': market_data.get('price_change_percentage_7d'),
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'price_change_30d': market_data.get('price_change_percentage_30d'),
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'high_24h': market_data.get('high_24h', {}).get('usd'),
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'low_24h': market_data.get('low_24h', {}).get('usd'),
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}
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except Exception as e:
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print(f"Fehler beim Abrufen von Marktdaten: {e}")
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return {}
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if __name__ == "__main__":
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# Test
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fetcher = BitcoinDataFetcher()
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print("=== Bitcoin Preis ===")
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price = fetcher.get_current_price()
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if price:
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print(f"Aktueller BTC/USD Preis: ${price:,.2f}")
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print("\n=== Marktdaten ===")
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market = fetcher.get_market_data()
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for key, value in market.items():
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if value is not None:
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if 'price' in key or 'cap' in key or 'volume' in key or 'high' in key or 'low' in key:
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print(f"{key}: ${value:,.2f}")
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else:
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print(f"{key}: {value:.2f}%")
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print("\n=== Historische Daten (letzte 7 Tage) ===")
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df = fetcher.get_historical_data(days=7)
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if not df.empty:
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print(f"Anzahl Datenpunkte: {len(df)}")
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print(df.tail())
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