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Agentic AI System with MCP Integration

stock_data.py1.8 kB
import os import requests ALPHA_VANTAGE_KEY = os.environ.get("ALPHA_VANTAGE_KEY") BASE_URL = "https://www.alphavantage.co/query?" if not ALPHA_VANTAGE_KEY: raise EnvironmentError( "Please set the ALPHA_VANTAGE_KEY environment variable." ) def get_time_series(symbol, interval='daily', adjusted=False, outputsize='compact'): """Fetches daily, weekly, or monthly time series data for a given symbol.""" function = f"TIME_SERIES_{interval.upper()}" if adjusted and interval.lower() == 'daily': function = "TIME_SERIES_DAILY_ADJUSTED" params = { 'function': function, 'symbol': symbol, 'outputsize': outputsize, 'apikey': ALPHA_VANTAGE_KEY } try: response = requests.get(BASE_URL, params=params) response.raise_for_status() return response.json() except requests.exceptions.RequestException as e: print(f"Error fetching time series data: {e}") return None def search_ticker(keywords): """Searches for tickers based on keywords.""" params = { 'function': 'SYMBOL_SEARCH', 'keywords': keywords, 'apikey': ALPHA_VANTAGE_KEY } try: response = requests.get(BASE_URL, params=params) response.raise_for_status() return response.json() except requests.exceptions.RequestException as e: print(f"Error searching ticker: {e}") return None if __name__ == '__main__': print("Example: Fetching Apple daily data") aapl_daily = get_time_series('AAPL') if aapl_daily: print(list(aapl_daily.keys())[:2]) print("\nExample: Searching for Tesla") tesla_search = search_ticker('Tesla') if tesla_search and 'bestMatches' in tesla_search: print(tesla_search['bestMatches'][:2])

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