swarms/fintech_tools/nation_collapse.py

import requests
import pandas as pd
from datetime import datetime, timedelta
import os
from typing import Dict, Tuple, Any
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# You'll need to set these environment variables with your actual API keys
ALPHA_VANTAGE_API_KEY = os.getenv('ALPHA_VANTAGE_API_KEY')
WORLD_BANK_API_KEY = os.getenv('WORLD_BANK_API_KEY')
FRED_API_KEY = os.getenv('FRED_API_KEY')

def fetch_real_economic_data(country: str, start_date: datetime, end_date: datetime) -> Tuple[str, Dict[str, Any]]:
    data = {}
    
    def get_alpha_vantage_data(indicator: str) -> pd.Series:
        try:
            url = f"https://www.alphavantage.co/query?function={indicator}&interval=monthly&apikey={ALPHA_VANTAGE_API_KEY}"
            response = requests.get(url)
            response.raise_for_status()
            df = pd.DataFrame(response.json()['Monthly Time Series']).T
            df.index = pd.to_datetime(df.index)
            df = df.sort_index()
            return df['4. close'].astype(float)
        except Exception as e:
            logger.error(f"Error fetching Alpha Vantage data: {str(e)}")
            return pd.Series()

    def get_world_bank_data(indicator: str) -> pd.Series:
        try:
            url = f"http://api.worldbank.org/v2/country/{country}/indicator/{indicator}?format=json&date={start_date.year}:{end_date.year}&per_page=1000"
            response = requests.get(url)
            response.raise_for_status()
            data = response.json()[1]
            df = pd.DataFrame(data)
            df['date'] = pd.to_datetime(df['date'], format='%Y')
            df = df.set_index('date').sort_index()
            return df['value'].astype(float)
        except Exception as e:
            logger.error(f"Error fetching World Bank data: {str(e)}")
            return pd.Series()

    def get_fred_data(series_id: str) -> pd.Series:
        try:
            url = f"https://api.stlouisfed.org/fred/series/observations?series_id={series_id}&api_key={FRED_API_KEY}&file_type=json"
            response = requests.get(url)
            response.raise_for_status()
            df = pd.DataFrame(response.json()['observations'])
            df['date'] = pd.to_datetime(df['date'])
            df = df.set_index('date').sort_index()
            return df['value'].astype(float)
        except Exception as e:
            logger.error(f"Error fetching FRED data: {str(e)}")
            return pd.Series()

    # Fetch data from different sources
    data['GDP_growth_rate'] = get_world_bank_data('NY.GDP.MKTP.KD.ZG')
    data['unemployment_rate'] = get_world_bank_data('SL.UEM.TOTL.ZS')
    data['inflation_rate'] = get_world_bank_data('FP.CPI.TOTL.ZG')
    data['debt_to_GDP_ratio'] = get_world_bank_data('GC.DOD.TOTL.GD.ZS')
    data['current_account_balance'] = get_world_bank_data('BN.CAB.XOKA.CD')
    data['yield_curve_slope'] = get_fred_data('T10Y2Y')
    data['stock_market_index'] = get_alpha_vantage_data('TIME_SERIES_MONTHLY')
    data['consumer_confidence_index'] = get_fred_data('CSCICP03USM665S')
    data['business_confidence_index'] = get_fred_data('BSCICP03USM665S')

    # Combine all data into a single DataFrame
    df = pd.DataFrame(data)
    df = df.loc[start_date:end_date]
    
    if df.empty:
        logger.warning("No data retrieved for the specified date range and country.")
        return "No data available", {"country": country, "real_time_data": {}, "historical_data": {}}

    # Prepare the dictionary output
    output_dict = {
        "country": country,
        "real_time_data": df.iloc[-1].to_dict(),
        "historical_data": df.to_dict()
    }

    # Create summary string
    summary = f"Economic Data Summary for {country} (as of {end_date.strftime('%Y-%m-%d')}):\n"
    for key, value in output_dict['real_time_data'].items():
        if pd.notna(value):
            summary += f"{key.replace('_', ' ').title()}: {value:.2f}\n"
        else:
            summary += f"{key.replace('_', ' ').title()}: Data not available\n"

    return summary, output_dict

# Example usage
if __name__ == "__main__":
    country = "US"  # ISO country code
    start_date = datetime(2020, 1, 1)
    end_date = datetime.now()

    summary, data = fetch_real_economic_data(country, start_date, end_date)
    print(summary)
    print("\nOutput Dictionary (truncated):")
    print(f"Country: {data['country']}")
    print("Real-time data:", data['real_time_data'])
    print("Historical data: {First day, Last day}")
    if data['historical_data']:
        first_day = min(next(iter(data['historical_data'].values())).keys())
        last_day = max(next(iter(data['historical_data'].values())).keys())
        print(f"  {first_day}:", {k: v[first_day] if first_day in v else 'N/A' for k, v in data['historical_data'].items()})
        print(f"  {last_day}:", {k: v[last_day] if last_day in v else 'N/A' for k, v in data['historical_data'].items()})
    else:
        print("  No historical data available.")