FREDML / frontend /fred_api_client.py

Edwin Salguero

Initial commit after git-lfs re-init and bugfixes

099d8d9 over 1 year ago

20.9 kB

"""
FRED ML - Real FRED API Client
Fetches actual economic data from the Federal Reserve Economic Data API
"""

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import requests
import json
from typing import Dict, List, Optional, Any
import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor, as_completed
import time

class FREDAPIClient:
    """Real FRED API client for fetching economic data"""
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.stlouisfed.org/fred"
        
    def _parse_fred_value(self, value_str: str) -> float:
        """Parse FRED value string to float, handling commas and other formatting"""
        try:
            # Remove commas and convert to float
            cleaned_value = value_str.replace(',', '')
            return float(cleaned_value)
        except (ValueError, AttributeError):
            return 0.0
    
    def get_series_data(self, series_id: str, start_date: str = None, end_date: str = None, limit: int = None) -> Dict[str, Any]:
        """Fetch series data from FRED API"""
        try:
            url = f"{self.base_url}/series/observations"
            params = {
                'series_id': series_id,
                'api_key': self.api_key,
                'file_type': 'json',
                'sort_order': 'asc'
            }
            
            if start_date:
                params['observation_start'] = start_date
            if end_date:
                params['observation_end'] = end_date
            if limit:
                params['limit'] = limit
                
            response = requests.get(url, params=params)
            response.raise_for_status()
            
            data = response.json()
            return data
            
        except Exception as e:
            return {'error': f"Failed to fetch {series_id}: {str(e)}"}
    
    def get_series_info(self, series_id: str) -> Dict[str, Any]:
        """Fetch series information from FRED API"""
        try:
            url = f"{self.base_url}/series"
            params = {
                'series_id': series_id,
                'api_key': self.api_key,
                'file_type': 'json'
            }
            
            response = requests.get(url, params=params)
            response.raise_for_status()
            
            data = response.json()
            return data
            
        except Exception as e:
            return {'error': f"Failed to fetch series info for {series_id}: {str(e)}"}
    
    def get_economic_data(self, series_list: List[str], start_date: str = None, end_date: str = None) -> pd.DataFrame:
        """Fetch multiple economic series and combine into DataFrame"""
        all_data = {}
        
        for series_id in series_list:
            series_data = self.get_series_data(series_id, start_date, end_date)
            
            if 'error' not in series_data and 'observations' in series_data:
                # Convert to DataFrame
                df = pd.DataFrame(series_data['observations'])
                df['date'] = pd.to_datetime(df['date'])
                # Use the new parsing function
                df['value'] = df['value'].apply(self._parse_fred_value)
                df = df.set_index('date')[['value']].rename(columns={'value': series_id})
                
                all_data[series_id] = df
        
        if all_data:
            # Combine all series
            combined_df = pd.concat(all_data.values(), axis=1)
            return combined_df
        else:
            return pd.DataFrame()
    
    def get_latest_values(self, series_list: List[str]) -> Dict[str, Any]:
        """Get latest values for multiple series"""
        latest_values = {}
        
        for series_id in series_list:
            # Get last 5 observations to calculate growth rate and avoid timeout issues
            series_data = self.get_series_data(series_id, limit=5)
            
            if 'error' not in series_data and 'observations' in series_data:
                observations = series_data['observations']
                if len(observations) >= 2:
                    # Get the latest (most recent) observation using proper parsing
                    current_value = self._parse_fred_value(observations[-1]['value'])
                    previous_value = self._parse_fred_value(observations[-2]['value'])
                    
                    # Calculate growth rate
                    if previous_value != 0:
                        growth_rate = ((current_value - previous_value) / previous_value) * 100
                    else:
                        growth_rate = 0
                    
                    latest_values[series_id] = {
                        'current_value': current_value,
                        'previous_value': previous_value,
                        'growth_rate': growth_rate,
                        'date': observations[-1]['date']
                    }
                elif len(observations) == 1:
                    # Only one observation available
                    current_value = self._parse_fred_value(observations[0]['value'])
                    latest_values[series_id] = {
                        'current_value': current_value,
                        'previous_value': current_value,  # Same as current for single observation
                        'growth_rate': 0,
                        'date': observations[0]['date']
                    }
        
        return latest_values
    
    def get_latest_values_parallel(self, series_list: List[str]) -> Dict[str, Any]:
        """Get latest values for multiple series using parallel processing"""
        latest_values = {}
        
        # Set recent date range to ensure we get current data
        from datetime import datetime, timedelta
        end_date = datetime.now().strftime('%Y-%m-%d')
        start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
        
        def fetch_series_data(series_id):
            """Helper function to fetch data for a single series"""
            try:
                # Fetch recent data with proper date range
                series_data = self.get_series_data(series_id, start_date=start_date, end_date=end_date, limit=10)
                
                if 'error' not in series_data and 'observations' in series_data:
                    observations = series_data['observations']
                    if len(observations) >= 2:
                        current_value = self._parse_fred_value(observations[-1]['value'])
                        previous_value = self._parse_fred_value(observations[-2]['value'])
                        
                        # Validate data is recent (within last 6 months)
                        current_date = datetime.strptime(observations[-1]['date'], '%Y-%m-%d')
                        if (datetime.now() - current_date).days > 180:
                            print(f"Warning: {series_id} data is old ({observations[-1]['date']})")
                        
                        if previous_value != 0:
                            growth_rate = ((current_value - previous_value) / previous_value) * 100
                        else:
                            growth_rate = 0
                        
                        return series_id, {
                            'current_value': current_value,
                            'previous_value': previous_value,
                            'growth_rate': growth_rate,
                            'date': observations[-1]['date']
                        }
                    elif len(observations) == 1:
                        current_value = self._parse_fred_value(observations[0]['value'])
                        return series_id, {
                            'current_value': current_value,
                            'previous_value': current_value,
                            'growth_rate': 0,
                            'date': observations[0]['date']
                        }
            except Exception as e:
                print(f"Error fetching {series_id}: {str(e)}")
            
            return series_id, None
        
        # Use ThreadPoolExecutor for parallel processing
        with ThreadPoolExecutor(max_workers=min(len(series_list), 10)) as executor:
            # Submit all tasks
            future_to_series = {executor.submit(fetch_series_data, series_id): series_id 
                              for series_id in series_list}
            
            # Collect results as they complete
            for future in as_completed(future_to_series):
                series_id, result = future.result()
                if result is not None:
                    latest_values[series_id] = result
        
        return latest_values

def generate_real_insights(api_key: str) -> Dict[str, Any]:
    """Generate real insights based on actual FRED data"""
    
    client = FREDAPIClient(api_key)
    
    # Define series to fetch with proper metadata
    series_metadata = {
        'GDPC1': {
            'name': 'Real GDP',
            'unit': 'Billions of Chained 2012 Dollars',
            'expected_min': 20000,  # $20T minimum
            'expected_max': 25000,  # $25T maximum
            'format': 'currency_billions'
        },
        'INDPRO': {
            'name': 'Industrial Production',
            'unit': 'Index (2017=100)',
            'expected_min': 90,
            'expected_max': 110,
            'format': 'index'
        },
        'RSAFS': {
            'name': 'Retail Sales',
            'unit': 'Millions of Dollars',
            'expected_min': 500000,  # $500B minimum
            'expected_max': 800000,  # $800B maximum
            'format': 'currency_millions'
        },
        'CPIAUCSL': {
            'name': 'Consumer Price Index',
            'unit': 'Index (1982-84=100)',
            'expected_min': 300,
            'expected_max': 330,
            'format': 'index'
        },
        'FEDFUNDS': {
            'name': 'Federal Funds Rate',
            'unit': 'Percent',
            'expected_min': 0,
            'expected_max': 10,
            'format': 'percentage'
        },
        'DGS10': {
            'name': '10-Year Treasury',
            'unit': 'Percent',
            'expected_min': 1,
            'expected_max': 8,
            'format': 'percentage'
        },
        'UNRATE': {
            'name': 'Unemployment Rate',
            'unit': 'Percent',
            'expected_min': 3,
            'expected_max': 8,
            'format': 'percentage'
        },
        'PAYEMS': {
            'name': 'Nonfarm Payrolls',
            'unit': 'Thousands of Persons',
            'expected_min': 150000,  # 150M minimum
            'expected_max': 165000,  # 165M maximum
            'format': 'thousands'
        },
        'PCE': {
            'name': 'Personal Consumption',
            'unit': 'Billions of Dollars',
            'expected_min': 15000,  # $15T minimum
            'expected_max': 22000,  # $22T maximum
            'format': 'currency_billions'
        },
        'M2SL': {
            'name': 'M2 Money Stock',
            'unit': 'Billions of Dollars',
            'expected_min': 20000,  # $20T minimum
            'expected_max': 25000,  # $25T maximum
            'format': 'currency_billions'
        },
        'TCU': {
            'name': 'Capacity Utilization',
            'unit': 'Percent',
            'expected_min': 70,
            'expected_max': 85,
            'format': 'percentage'
        },
        'DEXUSEU': {
            'name': 'US/Euro Exchange Rate',
            'unit': 'US Dollars per Euro',
            'expected_min': 0.8,
            'expected_max': 1.3,
            'format': 'exchange_rate'
        }
    }
    
    series_list = list(series_metadata.keys())
    
    # Use parallel processing for better performance
    print("Fetching economic data in parallel...")
    start_time = time.time()
    latest_values = client.get_latest_values_parallel(series_list)
    end_time = time.time()
    print(f"Data fetching completed in {end_time - start_time:.2f} seconds")
    
    # Generate insights based on real data with validation
    insights = {}
    
    for series_id, data in latest_values.items():
        current_value = data['current_value']
        growth_rate = data['growth_rate']
        metadata = series_metadata.get(series_id, {})
        
        # Validate data is within expected ranges
        expected_min = metadata.get('expected_min', 0)
        expected_max = metadata.get('expected_max', float('inf'))
        
        if current_value < expected_min or current_value > expected_max:
            print(f"Warning: {series_id} value {current_value} outside expected range [{expected_min}, {expected_max}]")
        
        # Format values based on metadata
        format_type = metadata.get('format', 'raw')
        
        if format_type == 'currency_billions':
            formatted_value = f"${current_value:,.1f}B"
        elif format_type == 'currency_millions':
            formatted_value = f"${current_value:,.1f}M"
        elif format_type == 'percentage':
            formatted_value = f"{current_value:.2f}%"
        elif format_type == 'thousands':
            formatted_value = f"{current_value:,.0f}K"
        elif format_type == 'index':
            formatted_value = f"{current_value:.1f}"
        elif format_type == 'exchange_rate':
            formatted_value = f"{current_value:.3f}"
        else:
            formatted_value = f"{current_value:,.1f}"
        
        # Generate insights based on the series type and current values
        if series_id == 'GDPC1':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.1f}%',
                'trend': 'Strong growth' if growth_rate > 2 else 'Moderate growth' if growth_rate > 0 else 'Declining',
                'forecast': f'{growth_rate + 0.2:+.1f}% next quarter',
                'key_insight': f'Real GDP at {formatted_value} with {growth_rate:+.1f}% growth. Economic activity {"expanding" if growth_rate > 0 else "contracting"} despite monetary tightening.',
                'risk_factors': ['Inflation persistence', 'Geopolitical tensions', 'Supply chain disruptions'],
                'opportunities': ['Technology sector expansion', 'Infrastructure investment', 'Green energy transition']
            }
        
        elif series_id == 'INDPRO':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.1f}%',
                'trend': 'Recovery phase' if growth_rate > 0 else 'Declining',
                'forecast': f'{growth_rate + 0.1:+.1f}% next month',
                'key_insight': f'Industrial Production at {formatted_value} with {growth_rate:+.1f}% growth. Manufacturing sector {"leading recovery" if growth_rate > 0 else "showing weakness"}.',
                'risk_factors': ['Supply chain bottlenecks', 'Labor shortages', 'Energy price volatility'],
                'opportunities': ['Advanced manufacturing', 'Automation adoption', 'Reshoring initiatives']
            }
        
        elif series_id == 'RSAFS':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.1f}%',
                'trend': 'Strong consumer spending' if growth_rate > 2 else 'Moderate spending',
                'forecast': f'{growth_rate + 0.2:+.1f}% next month',
                'key_insight': f'Retail Sales at {formatted_value} with {growth_rate:+.1f}% growth. Consumer spending {"robust" if growth_rate > 2 else "moderate"} despite inflation.',
                'risk_factors': ['Inflation impact on purchasing power', 'Interest rate sensitivity', 'Supply chain issues'],
                'opportunities': ['Digital transformation', 'Omnichannel retail', 'Personalization']
            }
        
        elif series_id == 'CPIAUCSL':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.1f}%',
                'trend': 'Moderating inflation' if growth_rate < 4 else 'Elevated inflation',
                'forecast': f'{growth_rate - 0.1:+.1f}% next month',
                'key_insight': f'CPI at {formatted_value} with {growth_rate:+.1f}% growth. Inflation {"moderating" if growth_rate < 4 else "elevated"} from peak levels.',
                'risk_factors': ['Energy price volatility', 'Wage pressure', 'Supply chain costs'],
                'opportunities': ['Productivity improvements', 'Technology adoption', 'Supply chain optimization']
            }
        
        elif series_id == 'FEDFUNDS':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.2f}%',
                'trend': 'Stable policy rate' if abs(growth_rate) < 0.1 else 'Changing policy',
                'forecast': f'{current_value} next meeting',
                'key_insight': f'Federal Funds Rate at {formatted_value}. Policy rate {"stable" if abs(growth_rate) < 0.1 else "adjusting"} to combat inflation.',
                'risk_factors': ['Inflation persistence', 'Economic slowdown', 'Financial stability'],
                'opportunities': ['Policy normalization', 'Inflation targeting', 'Financial regulation']
            }
        
        elif series_id == 'DGS10':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.2f}%',
                'trend': 'Declining yields' if growth_rate < 0 else 'Rising yields',
                'forecast': f'{current_value + growth_rate * 0.1:.2f}% next week',
                'key_insight': f'10-Year Treasury at {formatted_value} with {growth_rate:+.2f}% change. Yields {"declining" if growth_rate < 0 else "rising"} on economic uncertainty.',
                'risk_factors': ['Economic recession', 'Inflation expectations', 'Geopolitical risks'],
                'opportunities': ['Bond market opportunities', 'Portfolio diversification', 'Interest rate hedging']
            }
        
        elif series_id == 'UNRATE':
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.1f}%',
                'trend': 'Stable employment' if abs(growth_rate) < 0.1 else 'Changing employment',
                'forecast': f'{current_value + growth_rate * 0.1:.1f}% next month',
                'key_insight': f'Unemployment Rate at {formatted_value} with {growth_rate:+.1f}% change. Labor market {"tight" if current_value < 4 else "loosening"}.',
                'risk_factors': ['Labor force participation', 'Skills mismatch', 'Economic slowdown'],
                'opportunities': ['Workforce development', 'Technology training', 'Remote work adoption']
            }
        
        else:
            # Generic insights for other series
            insights[series_id] = {
                'current_value': formatted_value,
                'growth_rate': f'{growth_rate:+.1f}%',
                'trend': 'Growing' if growth_rate > 0 else 'Declining',
                'forecast': f'{growth_rate + 0.1:+.1f}% next period',
                'key_insight': f'{metadata.get("name", series_id)} at {formatted_value} with {growth_rate:+.1f}% growth.',
                'risk_factors': ['Economic uncertainty', 'Policy changes', 'Market volatility'],
                'opportunities': ['Strategic positioning', 'Market opportunities', 'Risk management']
            }
    
    return insights

def get_real_economic_data(api_key: str, start_date: str = None, end_date: str = None) -> Dict[str, Any]:
    """Get real economic data from FRED API"""
    
    client = FREDAPIClient(api_key)
    
    # Define series to fetch
    series_list = [
        'GDPC1',    # Real GDP
        'INDPRO',   # Industrial Production
        'RSAFS',    # Retail Sales
        'CPIAUCSL', # Consumer Price Index
        'FEDFUNDS', # Federal Funds Rate
        'DGS10',    # 10-Year Treasury
        'UNRATE',   # Unemployment Rate
        'PAYEMS',   # Total Nonfarm Payrolls
        'PCE',      # Personal Consumption Expenditures
        'M2SL',     # M2 Money Stock
        'TCU',      # Capacity Utilization
        'DEXUSEU'   # US/Euro Exchange Rate
    ]
    
    # Get economic data
    economic_data = client.get_economic_data(series_list, start_date, end_date)
    
    # Get insights
    insights = generate_real_insights(api_key)
    
    return {
        'economic_data': economic_data,
        'insights': insights,
        'series_list': series_list
    } 

	"""
	FRED ML - Real FRED API Client
	Fetches actual economic data from the Federal Reserve Economic Data API
	"""

	import pandas as pd
	import numpy as np
	from datetime import datetime, timedelta
	import requests
	import json
	from typing import Dict, List, Optional, Any
	import asyncio
	import aiohttp
	from concurrent.futures import ThreadPoolExecutor, as_completed
	import time

	class FREDAPIClient:
	"""Real FRED API client for fetching economic data"""

	def __init__(self, api_key: str):
	self.api_key = api_key
	self.base_url = "https://api.stlouisfed.org/fred"

	def _parse_fred_value(self, value_str: str) -> float:
	"""Parse FRED value string to float, handling commas and other formatting"""
	try:
	# Remove commas and convert to float
	cleaned_value = value_str.replace(',', '')
	return float(cleaned_value)
	except (ValueError, AttributeError):
	return 0.0

	def get_series_data(self, series_id: str, start_date: str = None, end_date: str = None, limit: int = None) -> Dict[str, Any]:
	"""Fetch series data from FRED API"""
	try:
	url = f"{self.base_url}/series/observations"
	params = {
	'series_id': series_id,
	'api_key': self.api_key,
	'file_type': 'json',
	'sort_order': 'asc'
	}

	if start_date:
	params['observation_start'] = start_date
	if end_date:
	params['observation_end'] = end_date
	if limit:
	params['limit'] = limit

	response = requests.get(url, params=params)
	response.raise_for_status()

	data = response.json()
	return data

	except Exception as e:
	return {'error': f"Failed to fetch {series_id}: {str(e)}"}

	def get_series_info(self, series_id: str) -> Dict[str, Any]:
	"""Fetch series information from FRED API"""
	try:
	url = f"{self.base_url}/series"
	params = {
	'series_id': series_id,
	'api_key': self.api_key,
	'file_type': 'json'
	}

	response = requests.get(url, params=params)
	response.raise_for_status()

	data = response.json()
	return data

	except Exception as e:
	return {'error': f"Failed to fetch series info for {series_id}: {str(e)}"}

	def get_economic_data(self, series_list: List[str], start_date: str = None, end_date: str = None) -> pd.DataFrame:
	"""Fetch multiple economic series and combine into DataFrame"""
	all_data = {}

	for series_id in series_list:
	series_data = self.get_series_data(series_id, start_date, end_date)

	if 'error' not in series_data and 'observations' in series_data:
	# Convert to DataFrame
	df = pd.DataFrame(series_data['observations'])
	df['date'] = pd.to_datetime(df['date'])
	# Use the new parsing function
	df['value'] = df['value'].apply(self._parse_fred_value)
	df = df.set_index('date')[['value']].rename(columns={'value': series_id})

	all_data[series_id] = df

	if all_data:
	# Combine all series
	combined_df = pd.concat(all_data.values(), axis=1)
	return combined_df
	else:
	return pd.DataFrame()

	def get_latest_values(self, series_list: List[str]) -> Dict[str, Any]:
	"""Get latest values for multiple series"""
	latest_values = {}

	for series_id in series_list:
	# Get last 5 observations to calculate growth rate and avoid timeout issues
	series_data = self.get_series_data(series_id, limit=5)

	if 'error' not in series_data and 'observations' in series_data:
	observations = series_data['observations']
	if len(observations) >= 2:
	# Get the latest (most recent) observation using proper parsing
	current_value = self._parse_fred_value(observations[-1]['value'])
	previous_value = self._parse_fred_value(observations[-2]['value'])

	# Calculate growth rate
	if previous_value != 0:
	growth_rate = ((current_value - previous_value) / previous_value) * 100
	else:
	growth_rate = 0

	latest_values[series_id] = {
	'current_value': current_value,
	'previous_value': previous_value,
	'growth_rate': growth_rate,
	'date': observations[-1]['date']
	}
	elif len(observations) == 1:
	# Only one observation available
	current_value = self._parse_fred_value(observations[0]['value'])
	latest_values[series_id] = {
	'current_value': current_value,
	'previous_value': current_value, # Same as current for single observation
	'growth_rate': 0,
	'date': observations[0]['date']
	}

	return latest_values

	def get_latest_values_parallel(self, series_list: List[str]) -> Dict[str, Any]:
	"""Get latest values for multiple series using parallel processing"""
	latest_values = {}

	# Set recent date range to ensure we get current data
	from datetime import datetime, timedelta
	end_date = datetime.now().strftime('%Y-%m-%d')
	start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')

	def fetch_series_data(series_id):
	"""Helper function to fetch data for a single series"""
	try:
	# Fetch recent data with proper date range
	series_data = self.get_series_data(series_id, start_date=start_date, end_date=end_date, limit=10)

	if 'error' not in series_data and 'observations' in series_data:
	observations = series_data['observations']
	if len(observations) >= 2:
	current_value = self._parse_fred_value(observations[-1]['value'])
	previous_value = self._parse_fred_value(observations[-2]['value'])

	# Validate data is recent (within last 6 months)
	current_date = datetime.strptime(observations[-1]['date'], '%Y-%m-%d')
	if (datetime.now() - current_date).days > 180:
	print(f"Warning: {series_id} data is old ({observations[-1]['date']})")

	if previous_value != 0:
	growth_rate = ((current_value - previous_value) / previous_value) * 100
	else:
	growth_rate = 0

	return series_id, {
	'current_value': current_value,
	'previous_value': previous_value,
	'growth_rate': growth_rate,
	'date': observations[-1]['date']
	}
	elif len(observations) == 1:
	current_value = self._parse_fred_value(observations[0]['value'])
	return series_id, {
	'current_value': current_value,
	'previous_value': current_value,
	'growth_rate': 0,
	'date': observations[0]['date']
	}
	except Exception as e:
	print(f"Error fetching {series_id}: {str(e)}")

	return series_id, None

	# Use ThreadPoolExecutor for parallel processing
	with ThreadPoolExecutor(max_workers=min(len(series_list), 10)) as executor:
	# Submit all tasks
	future_to_series = {executor.submit(fetch_series_data, series_id): series_id
	for series_id in series_list}

	# Collect results as they complete
	for future in as_completed(future_to_series):
	series_id, result = future.result()
	if result is not None:
	latest_values[series_id] = result

	return latest_values

	def generate_real_insights(api_key: str) -> Dict[str, Any]:
	"""Generate real insights based on actual FRED data"""

	client = FREDAPIClient(api_key)

	# Define series to fetch with proper metadata
	series_metadata = {
	'GDPC1': {
	'name': 'Real GDP',
	'unit': 'Billions of Chained 2012 Dollars',
	'expected_min': 20000, # $20T minimum
	'expected_max': 25000, # $25T maximum
	'format': 'currency_billions'
	},
	'INDPRO': {
	'name': 'Industrial Production',
	'unit': 'Index (2017=100)',
	'expected_min': 90,
	'expected_max': 110,
	'format': 'index'
	},
	'RSAFS': {
	'name': 'Retail Sales',
	'unit': 'Millions of Dollars',
	'expected_min': 500000, # $500B minimum
	'expected_max': 800000, # $800B maximum
	'format': 'currency_millions'
	},
	'CPIAUCSL': {
	'name': 'Consumer Price Index',
	'unit': 'Index (1982-84=100)',
	'expected_min': 300,
	'expected_max': 330,
	'format': 'index'
	},
	'FEDFUNDS': {
	'name': 'Federal Funds Rate',
	'unit': 'Percent',
	'expected_min': 0,
	'expected_max': 10,
	'format': 'percentage'
	},
	'DGS10': {
	'name': '10-Year Treasury',
	'unit': 'Percent',
	'expected_min': 1,
	'expected_max': 8,
	'format': 'percentage'
	},
	'UNRATE': {
	'name': 'Unemployment Rate',
	'unit': 'Percent',
	'expected_min': 3,
	'expected_max': 8,
	'format': 'percentage'
	},
	'PAYEMS': {
	'name': 'Nonfarm Payrolls',
	'unit': 'Thousands of Persons',
	'expected_min': 150000, # 150M minimum
	'expected_max': 165000, # 165M maximum
	'format': 'thousands'
	},
	'PCE': {
	'name': 'Personal Consumption',
	'unit': 'Billions of Dollars',
	'expected_min': 15000, # $15T minimum
	'expected_max': 22000, # $22T maximum
	'format': 'currency_billions'
	},
	'M2SL': {
	'name': 'M2 Money Stock',
	'unit': 'Billions of Dollars',
	'expected_min': 20000, # $20T minimum
	'expected_max': 25000, # $25T maximum
	'format': 'currency_billions'
	},
	'TCU': {
	'name': 'Capacity Utilization',
	'unit': 'Percent',
	'expected_min': 70,
	'expected_max': 85,
	'format': 'percentage'
	},
	'DEXUSEU': {
	'name': 'US/Euro Exchange Rate',
	'unit': 'US Dollars per Euro',
	'expected_min': 0.8,
	'expected_max': 1.3,
	'format': 'exchange_rate'
	}
	}

	series_list = list(series_metadata.keys())

	# Use parallel processing for better performance
	print("Fetching economic data in parallel...")
	start_time = time.time()
	latest_values = client.get_latest_values_parallel(series_list)
	end_time = time.time()
	print(f"Data fetching completed in {end_time - start_time:.2f} seconds")

	# Generate insights based on real data with validation
	insights = {}

	for series_id, data in latest_values.items():
	current_value = data['current_value']
	growth_rate = data['growth_rate']
	metadata = series_metadata.get(series_id, {})

	# Validate data is within expected ranges
	expected_min = metadata.get('expected_min', 0)
	expected_max = metadata.get('expected_max', float('inf'))

	if current_value < expected_min or current_value > expected_max:
	print(f"Warning: {series_id} value {current_value} outside expected range [{expected_min}, {expected_max}]")

	# Format values based on metadata
	format_type = metadata.get('format', 'raw')

	if format_type == 'currency_billions':
	formatted_value = f"${current_value:,.1f}B"
	elif format_type == 'currency_millions':
	formatted_value = f"${current_value:,.1f}M"
	elif format_type == 'percentage':
	formatted_value = f"{current_value:.2f}%"
	elif format_type == 'thousands':
	formatted_value = f"{current_value:,.0f}K"
	elif format_type == 'index':
	formatted_value = f"{current_value:.1f}"
	elif format_type == 'exchange_rate':
	formatted_value = f"{current_value:.3f}"
	else:
	formatted_value = f"{current_value:,.1f}"

	# Generate insights based on the series type and current values
	if series_id == 'GDPC1':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.1f}%',
	'trend': 'Strong growth' if growth_rate > 2 else 'Moderate growth' if growth_rate > 0 else 'Declining',
	'forecast': f'{growth_rate + 0.2:+.1f}% next quarter',
	'key_insight': f'Real GDP at {formatted_value} with {growth_rate:+.1f}% growth. Economic activity {"expanding" if growth_rate > 0 else "contracting"} despite monetary tightening.',
	'risk_factors': ['Inflation persistence', 'Geopolitical tensions', 'Supply chain disruptions'],
	'opportunities': ['Technology sector expansion', 'Infrastructure investment', 'Green energy transition']
	}

	elif series_id == 'INDPRO':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.1f}%',
	'trend': 'Recovery phase' if growth_rate > 0 else 'Declining',
	'forecast': f'{growth_rate + 0.1:+.1f}% next month',
	'key_insight': f'Industrial Production at {formatted_value} with {growth_rate:+.1f}% growth. Manufacturing sector {"leading recovery" if growth_rate > 0 else "showing weakness"}.',
	'risk_factors': ['Supply chain bottlenecks', 'Labor shortages', 'Energy price volatility'],
	'opportunities': ['Advanced manufacturing', 'Automation adoption', 'Reshoring initiatives']
	}

	elif series_id == 'RSAFS':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.1f}%',
	'trend': 'Strong consumer spending' if growth_rate > 2 else 'Moderate spending',
	'forecast': f'{growth_rate + 0.2:+.1f}% next month',
	'key_insight': f'Retail Sales at {formatted_value} with {growth_rate:+.1f}% growth. Consumer spending {"robust" if growth_rate > 2 else "moderate"} despite inflation.',
	'risk_factors': ['Inflation impact on purchasing power', 'Interest rate sensitivity', 'Supply chain issues'],
	'opportunities': ['Digital transformation', 'Omnichannel retail', 'Personalization']
	}

	elif series_id == 'CPIAUCSL':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.1f}%',
	'trend': 'Moderating inflation' if growth_rate < 4 else 'Elevated inflation',
	'forecast': f'{growth_rate - 0.1:+.1f}% next month',
	'key_insight': f'CPI at {formatted_value} with {growth_rate:+.1f}% growth. Inflation {"moderating" if growth_rate < 4 else "elevated"} from peak levels.',
	'risk_factors': ['Energy price volatility', 'Wage pressure', 'Supply chain costs'],
	'opportunities': ['Productivity improvements', 'Technology adoption', 'Supply chain optimization']
	}

	elif series_id == 'FEDFUNDS':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.2f}%',
	'trend': 'Stable policy rate' if abs(growth_rate) < 0.1 else 'Changing policy',
	'forecast': f'{current_value} next meeting',
	'key_insight': f'Federal Funds Rate at {formatted_value}. Policy rate {"stable" if abs(growth_rate) < 0.1 else "adjusting"} to combat inflation.',
	'risk_factors': ['Inflation persistence', 'Economic slowdown', 'Financial stability'],
	'opportunities': ['Policy normalization', 'Inflation targeting', 'Financial regulation']
	}

	elif series_id == 'DGS10':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.2f}%',
	'trend': 'Declining yields' if growth_rate < 0 else 'Rising yields',
	'forecast': f'{current_value + growth_rate * 0.1:.2f}% next week',
	'key_insight': f'10-Year Treasury at {formatted_value} with {growth_rate:+.2f}% change. Yields {"declining" if growth_rate < 0 else "rising"} on economic uncertainty.',
	'risk_factors': ['Economic recession', 'Inflation expectations', 'Geopolitical risks'],
	'opportunities': ['Bond market opportunities', 'Portfolio diversification', 'Interest rate hedging']
	}

	elif series_id == 'UNRATE':
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.1f}%',
	'trend': 'Stable employment' if abs(growth_rate) < 0.1 else 'Changing employment',
	'forecast': f'{current_value + growth_rate * 0.1:.1f}% next month',
	'key_insight': f'Unemployment Rate at {formatted_value} with {growth_rate:+.1f}% change. Labor market {"tight" if current_value < 4 else "loosening"}.',
	'risk_factors': ['Labor force participation', 'Skills mismatch', 'Economic slowdown'],
	'opportunities': ['Workforce development', 'Technology training', 'Remote work adoption']
	}

	else:
	# Generic insights for other series
	insights[series_id] = {
	'current_value': formatted_value,
	'growth_rate': f'{growth_rate:+.1f}%',
	'trend': 'Growing' if growth_rate > 0 else 'Declining',
	'forecast': f'{growth_rate + 0.1:+.1f}% next period',
	'key_insight': f'{metadata.get("name", series_id)} at {formatted_value} with {growth_rate:+.1f}% growth.',
	'risk_factors': ['Economic uncertainty', 'Policy changes', 'Market volatility'],
	'opportunities': ['Strategic positioning', 'Market opportunities', 'Risk management']
	}

	return insights

	def get_real_economic_data(api_key: str, start_date: str = None, end_date: str = None) -> Dict[str, Any]:
	"""Get real economic data from FRED API"""

	client = FREDAPIClient(api_key)

	# Define series to fetch
	series_list = [
	'GDPC1', # Real GDP
	'INDPRO', # Industrial Production
	'RSAFS', # Retail Sales
	'CPIAUCSL', # Consumer Price Index
	'FEDFUNDS', # Federal Funds Rate
	'DGS10', # 10-Year Treasury
	'UNRATE', # Unemployment Rate
	'PAYEMS', # Total Nonfarm Payrolls
	'PCE', # Personal Consumption Expenditures
	'M2SL', # M2 Money Stock
	'TCU', # Capacity Utilization
	'DEXUSEU' # US/Euro Exchange Rate
	]

	# Get economic data
	economic_data = client.get_economic_data(series_list, start_date, end_date)

	# Get insights
	insights = generate_real_insights(api_key)

	return {
	'economic_data': economic_data,
	'insights': insights,
	'series_list': series_list
	}