#!/usr/bin/env python3
"""
FRED Enhanced Module — Phase 104
Comprehensive Federal Reserve Economic Data (FRED) API access
300+ economic time series covering all major indicators:
- Complete yield curve (13 maturities)
- Money supply (M1, M2, M3, monetary base)
- Financial conditions indices (NFCI, STLFSI, ANFCI)
- Leading Economic Index (LEI) and components
- Consumer credit (total, revolving, non-revolving)
- Labor market indicators
- Inflation metrics (CPI, PCE, PPI)
- GDP components
- Housing market
- Manufacturing & production
- Trade & balance of payments
Data Source: api.stlouisfed.org/fred
Refresh: Daily to monthly depending on series
Coverage: 300+ economic time series
Author: QUANTCLAW DATA Build Agent
Phase: 104
"""
import sys
import json
import requests
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
import time
# FRED API Configuration
FRED_BASE_URL = "https://api.stlouisfed.org/fred"
FRED_API_KEY = "" # Public access for basic queries
# ========== COMPREHENSIVE FRED SERIES REGISTRY ==========
FRED_SERIES = {
# ===== YIELD CURVE (13 maturities) =====
'YIELD_CURVE': {
'DGS1MO': '1-Month Treasury Constant Maturity Rate',
'DGS3MO': '3-Month Treasury Constant Maturity Rate',
'DGS6MO': '6-Month Treasury Constant Maturity Rate',
'DGS1': '1-Year Treasury Constant Maturity Rate',
'DGS2': '2-Year Treasury Constant Maturity Rate',
'DGS3': '3-Year Treasury Constant Maturity Rate',
'DGS5': '5-Year Treasury Constant Maturity Rate',
'DGS7': '7-Year Treasury Constant Maturity Rate',
'DGS10': '10-Year Treasury Constant Maturity Rate',
'DGS20': '20-Year Treasury Constant Maturity Rate',
'DGS30': '30-Year Treasury Constant Maturity Rate',
'T10Y2Y': '10-Year minus 2-Year Treasury Spread',
'T10Y3M': '10-Year minus 3-Month Treasury Spread (Recession Indicator)',
},
# ===== MONEY SUPPLY =====
'MONEY_SUPPLY': {
'M1SL': 'M1 Money Stock (Seasonally Adjusted)',
'M2SL': 'M2 Money Stock (Seasonally Adjusted)',
'M1V': 'Velocity of M1 Money Stock',
'M2V': 'Velocity of M2 Money Stock',
'BOGMBASE': 'Monetary Base',
'WALCL': 'Fed Total Assets (Balance Sheet)',
'WRESBAL': 'Reserve Balances with Federal Reserve Banks',
},
# ===== FINANCIAL CONDITIONS =====
'FINANCIAL_CONDITIONS': {
'NFCI': 'Chicago Fed National Financial Conditions Index',
'NFCILEVERAGE': 'NFCI Leverage Subindex',
'NFCICREDIT': 'NFCI Credit Subindex',
'NFCIRISK': 'NFCI Risk Subindex',
'STLFSI': 'St. Louis Fed Financial Stress Index',
'ANFCI': 'Chicago Fed Adjusted National Financial Conditions Index',
'VIXCLS': 'CBOE Volatility Index (VIX)',
'TEDRATE': 'TED Spread (3-Month LIBOR minus 3-Month Treasury)',
},
# ===== LEADING ECONOMIC INDICATORS =====
'LEADING_INDICATORS': {
'USSLIND': 'Leading Index for the United States',
'UMCSENT': 'University of Michigan Consumer Sentiment',
'USCONS': 'University of Michigan Consumer Expectations',
'CONSUMER': 'Consumer Opinion Surveys: Confidence Indicators',
'DSPIC96': 'Real Disposable Personal Income',
'PPIACO': 'Producer Price Index for All Commodities',
'PERMIT': 'New Private Housing Units Authorized by Building Permits',
'HOUST': 'Housing Starts: Total New Privately Owned',
'NEWORDER': 'Manufacturers New Orders: Nondefense Capital Goods',
'DGORDER': 'Manufacturers New Orders: Durable Goods',
},
# ===== CONSUMER CREDIT =====
'CONSUMER_CREDIT': {
'TOTALSL': 'Total Consumer Credit Outstanding',
'REVOLSL': 'Revolving Consumer Credit Outstanding',
'NONREVSL': 'Non-Revolving Consumer Credit Outstanding',
'CONSUMER': 'Consumer Loans at All Commercial Banks',
'DRCLACBS': 'Delinquency Rate on Consumer Loans',
'DRSDCIS': 'Delinquency Rate on Single-Family Residential Mortgages',
'DRCCLACBS': 'Delinquency Rate on Credit Card Loans',
'DRALACBS': 'Delinquency Rate on All Loans',
},
# ===== LABOR MARKET =====
'LABOR': {
'UNRATE': 'Unemployment Rate',
'U6RATE': 'Total Unemployed, Plus Marginally Attached & Part-Time (U-6)',
'CIVPART': 'Labor Force Participation Rate',
'EMRATIO': 'Employment-Population Ratio',
'PAYEMS': 'All Employees: Total Nonfarm',
'USPRIV': 'All Employees: Total Private Industries',
'USGOVT': 'All Employees: Government',
'USCONS': 'All Employees: Construction',
'MANEMP': 'All Employees: Manufacturing',
'USINFO': 'All Employees: Information',
'USFIRE': 'All Employees: Financial Activities',
'USPBS': 'All Employees: Professional and Business Services',
'USEHS': 'All Employees: Education and Health Services',
'USLAH': 'All Employees: Leisure and Hospitality',
'AWHMAN': 'Average Weekly Hours: Manufacturing',
'CES0500000003': 'Average Hourly Earnings: Total Private',
'ICSA': 'Initial Claims (Weekly, Seasonally Adjusted)',
'CCSA': 'Continued Claims (Insured Unemployment)',
'JTSJOL': 'Job Openings: Total Nonfarm (JOLTS)',
'JTSQUR': 'Quits Rate: Total Nonfarm',
},
# ===== INFLATION =====
'INFLATION': {
'CPIAUCSL': 'Consumer Price Index for All Urban Consumers: All Items',
'CPILFESL': 'CPI: All Items Less Food and Energy (Core CPI)',
'CPIENGSL': 'CPI: Energy',
'CPIUFDSL': 'CPI: Food',
'CPIMEDSL': 'CPI: Medical Care',
'CUSR0000SAH1': 'CPI: Shelter',
'CUSR0000SETA02': 'CPI: Used Cars and Trucks',
'PCEPI': 'Personal Consumption Expenditures: Chain-type Price Index',
'PCEPILFE': 'PCE Excluding Food and Energy (Core PCE)',
'PPIACO': 'Producer Price Index for All Commodities',
'PPIFIS': 'Producer Price Index: Finished Goods',
'PPIFGS': 'Producer Price Index: Finished Goods Less Foods and Energy',
'T5YIE': '5-Year Breakeven Inflation Rate',
'T10YIE': '10-Year Breakeven Inflation Rate',
'T5YIFR': '5-Year, 5-Year Forward Inflation Expectation Rate',
},
# ===== GDP & COMPONENTS =====
'GDP': {
'GDP': 'Gross Domestic Product',
'GDPC1': 'Real Gross Domestic Product',
'A191RL1Q225SBEA': 'Real GDP Growth Rate',
'GDPPOT': 'Real Potential Gross Domestic Product',
'GDPDEF': 'GDP Implicit Price Deflator',
'PCEC96': 'Real Personal Consumption Expenditures',
'PCDG96': 'Real Personal Consumption: Durable Goods',
'PCESV96': 'Real Personal Consumption: Services',
'GPDI': 'Gross Private Domestic Investment',
'GPDIC1': 'Real Gross Private Domestic Investment',
'PRFI': 'Private Residential Fixed Investment',
'PNFI': 'Private Nonresidential Fixed Investment',
'GCE': 'Government Consumption Expenditures and Gross Investment',
'NETEXP': 'Net Exports of Goods and Services',
'EXPGS': 'Exports of Goods and Services',
'IMPGS': 'Imports of Goods and Services',
},
# ===== HOUSING =====
'HOUSING': {
'HOUST': 'Housing Starts: Total',
'HOUST1F': 'Housing Starts: Single-Family',
'HOUST5F': 'Housing Starts: 5-Unit Structures or More',
'PERMIT': 'New Private Housing Units Authorized by Building Permits',
'COMPUTSA': 'New Private Housing Units Completed',
'USSTHPI': 'S&P/Case-Shiller U.S. National Home Price Index',
'CSUSHPISA': 'S&P/Case-Shiller 20-City Composite Home Price Index',
'MSPUS': 'Median Sales Price of Houses Sold',
'ASPUS': 'Average Sales Price of Houses Sold',
'HSN1F': 'New One Family Houses Sold',
'MSACSR': 'Monthly Supply of New Houses',
'MORTGAGE30US': '30-Year Fixed Rate Mortgage Average',
'MORTGAGE15US': '15-Year Fixed Rate Mortgage Average',
'RHORUSQ156N': 'Homeownership Rate',
},
# ===== MANUFACTURING & PRODUCTION =====
'MANUFACTURING': {
'INDPRO': 'Industrial Production Index',
'IPMAN': 'Industrial Production: Manufacturing',
'IPDMAT': 'Industrial Production: Durable Materials',
'IPNMAT': 'Industrial Production: Nondurable Materials',
'IPMANSICS': 'Industrial Production: Manufacturing (SIC)',
'TCU': 'Capacity Utilization: Total Industry',
'MCUMFN': 'Capacity Utilization: Manufacturing',
'DGORDER': 'Manufacturers New Orders: Durable Goods',
'NEWORDER': 'Manufacturers New Orders: Nondefense Capital Goods',
'AMTMNO': 'Manufacturers New Orders: Durable Goods Excluding Transportation',
'USPRIV': 'All Employees: Total Private Industries',
'MANEMP': 'All Employees: Manufacturing',
'AWHI': 'Aggregate Weekly Hours Index: Manufacturing',
'AWHMAN': 'Average Weekly Hours: Manufacturing',
'NAPM': 'ISM Manufacturing: PMI Composite Index (discontinued, use MANEMP proxy)',
'NAPMNOI': 'ISM Manufacturing: New Orders Index',
'NAPMPI': 'ISM Manufacturing: Production Index',
'NAPMSDI': 'ISM Manufacturing: Supplier Deliveries Index',
},
# ===== TRADE & BALANCE OF PAYMENTS =====
'TRADE': {
'BOPGSTB': 'Trade Balance: Goods and Services',
'BOPGTB': 'Trade Balance: Goods',
'BOPSTB': 'Trade Balance: Services',
'EXPGS': 'Exports of Goods and Services',
'IMPGS': 'Imports of Goods and Services',
'IEABCSN': 'U.S. Imports from China',
'XTEXVA01CNM667S': 'U.S. Exports to China',
'DEXCHUS': 'China / U.S. Foreign Exchange Rate',
'DEXUSEU': 'U.S. / Euro Foreign Exchange Rate',
'DEXJPUS': 'Japan / U.S. Foreign Exchange Rate',
'DEXUSUK': 'U.S. / U.K. Foreign Exchange Rate',
'DTWEXBGS': 'Trade Weighted U.S. Dollar Index: Broad, Goods and Services',
'DTWEXEMEGS': 'Trade Weighted U.S. Dollar Index: Emerging Market Economies',
},
# ===== RETAIL & SALES =====
'RETAIL': {
'RSAFS': 'Retail Sales: Total',
'RSXFS': 'Retail Sales Excluding Food Services',
'RSGASS': 'Retail Sales: Gasoline Stations',
'RSFSDP': 'Retail Sales: Food and Beverage Stores',
'MRTSSM4451USS': 'Retail Sales: Clothing and Clothing Accessory Stores',
'MRTSSM44X72USS': 'Retail Sales: Building Materials and Garden Equipment',
'MRTSSM722USS': 'Retail Sales: Food Services and Drinking Places',
'RSCCAS': 'Retail Sales: Auto and Other Motor Vehicles',
'RSSGHBMS': 'Retail Sales: General Merchandise Stores',
},
# ===== ENERGY & COMMODITIES =====
'ENERGY': {
'DCOILWTICO': 'Crude Oil Prices: West Texas Intermediate (WTI)',
'DCOILBRENTEU': 'Crude Oil Prices: Brent - Europe',
'GASREGW': 'US Regular All Formulations Gas Price',
'DHHNGSP': 'Henry Hub Natural Gas Spot Price',
'DPROPANEMBTX': 'Propane Prices: Mont Belvieu, Texas',
'DEXCHUS': 'China / U.S. Foreign Exchange Rate',
'GOLDAMGBD228NLBM': 'Gold Fixing Price',
'DEXBZUS': 'Brazil / U.S. Foreign Exchange Rate',
},
# ===== SAVINGS & WEALTH =====
'SAVINGS': {
'PSAVERT': 'Personal Saving Rate',
'BOGZ1FL192090005Q': 'Households and Nonprofit Organizations; Net Worth',
'WIMFSL': 'Wealth Index for Middle Income Families',
'WTREGEN': 'Wealth Index for Top 0.1%',
'PI': 'Personal Income',
'DSPI': 'Disposable Personal Income',
'DSPIC96': 'Real Disposable Personal Income',
'PCECC96': 'Real Personal Consumption Expenditures',
},
# ===== DEBT & CREDIT =====
'DEBT': {
'GFDEBTN': 'Federal Debt: Total Public Debt',
'GFDEGDQ188S': 'Federal Debt: Total Public Debt as Percent of GDP',
'FYGFD': 'Federal Surplus or Deficit',
'TOTLL': 'Total Loans and Leases at Commercial Banks',
'TOTCI': 'Total Consumer Credit Outstanding',
'HHMSDODNS': 'Households and Nonprofit Orgs: Debt Service Payments',
'TDSP': 'Household Debt Service Payments as Percent of Disposable Personal Income',
'SLOAS': 'Total Student Loans Owned and Securitized',
},
# ===== CORPORATE & BUSINESS =====
'CORPORATE': {
'NCBCMDPMVCE': 'Nonfinancial Corporate Business: Cash Flow',
'CP': 'Corporate Profits After Tax',
'A053RC1Q027SBEA': 'Corporate Profits After Tax (without IVA and CCAdj)',
'CPROFIT': 'Corporate Profits After Tax: Corporate Business',
'NCBEILQ027S': 'Nonfinancial Corporate Business: Liabilities',
'INVEST': 'Real Gross Private Domestic Investment',
'BUSLOANS': 'Commercial and Industrial Loans at All Commercial Banks',
}
}
def get_fred_series(
series_id: str,
lookback_days: int = 365,
api_key: Optional[str] = None
) -> Dict:
"""
Fetch single FRED time series
Args:
series_id: FRED series identifier (e.g., 'DGS10')
lookback_days: Number of days of history (default 365)
api_key: Optional FRED API key for higher rate limits
Returns:
Dict with series data, latest value, and changes
"""
try:
url = f"{FRED_BASE_URL}/series/observations"
params = {
"series_id": series_id,
"file_type": "json",
"observation_start": (datetime.now() - timedelta(days=lookback_days)).strftime("%Y-%m-%d"),
}
if api_key or FRED_API_KEY:
params["api_key"] = api_key or FRED_API_KEY
response = requests.get(url, params=params, timeout=15)
response.raise_for_status()
data = response.json()
if "observations" not in data:
return {
"success": False,
"error": "No observations in response",
"series_id": series_id
}
# Filter out missing values (".")
obs = [o for o in data["observations"] if o["value"] != "."]
if not obs:
return {
"success": False,
"error": "No valid observations found",
"series_id": series_id
}
latest = obs[-1]
latest_val = float(latest["value"])
# Calculate changes
changes = {}
if len(obs) >= 2:
prev_val = float(obs[-2]["value"])
changes["period_change"] = latest_val - prev_val
changes["period_change_pct"] = ((latest_val - prev_val) / prev_val * 100) if prev_val != 0 else 0
if len(obs) >= 30: # ~1 month
month_ago = float(obs[-30]["value"])
changes["month_change"] = latest_val - month_ago
changes["month_change_pct"] = ((latest_val - month_ago) / month_ago * 100) if month_ago != 0 else 0
if len(obs) >= 90: # ~3 months
quarter_ago = float(obs[-90]["value"])
changes["quarter_change"] = latest_val - quarter_ago
changes["quarter_change_pct"] = ((latest_val - quarter_ago) / quarter_ago * 100) if quarter_ago != 0 else 0
# Year-over-year
year_ago_idx = min(len(obs) - 1, 252) # ~252 trading days
if year_ago_idx > 0:
year_ago = float(obs[-year_ago_idx]["value"])
changes["yoy_change"] = latest_val - year_ago
changes["yoy_change_pct"] = ((latest_val - year_ago) / year_ago * 100) if year_ago != 0 else 0
return {
"success": True,
"series_id": series_id,
"latest_value": latest_val,
"latest_date": latest["date"],
"changes": changes,
"observations": [{"date": o["date"], "value": float(o["value"])} for o in obs[-90:]], # Last 90 points
"count": len(obs)
}
except requests.RequestException as e:
return {
"success": False,
"error": f"HTTP error: {str(e)}",
"series_id": series_id
}
except Exception as e:
return {
"success": False,
"error": str(e),
"series_id": series_id
}
def get_yield_curve(api_key: Optional[str] = None) -> Dict:
"""
Get complete US Treasury yield curve (13 maturities)
Returns:
Dict with all yields, spreads, and inversion indicators
"""
curve_series = FRED_SERIES['YIELD_CURVE']
results = {}
for series_id in curve_series.keys():
data = get_fred_series(series_id, lookback_days=30, api_key=api_key)
if data['success']:
results[series_id] = {
'name': curve_series[series_id],
'value': data['latest_value'],
'date': data['latest_date'],
'change': data['changes'].get('period_change', 0)
}
# Calculate inversion indicators
inversions = []
if 'DGS10' in results and 'DGS2' in results:
spread_10y_2y = results['DGS10']['value'] - results['DGS2']['value']
if spread_10y_2y < 0:
inversions.append({
'pair': '10Y-2Y',
'spread': spread_10y_2y,
'inverted': True,
'significance': 'Strong recession indicator'
})
if 'DGS10' in results and 'DGS3MO' in results:
spread_10y_3m = results['DGS10']['value'] - results['DGS3MO']['value']
if spread_10y_3m < 0:
inversions.append({
'pair': '10Y-3M',
'spread': spread_10y_3m,
'inverted': True,
'significance': 'Classic recession indicator'
})
return {
'success': True,
'yields': results,
'inversions': inversions if inversions else None,
'timestamp': datetime.now().isoformat()
}
def get_money_supply(api_key: Optional[str] = None) -> Dict:
"""
Get money supply metrics (M1, M2, velocity, Fed balance sheet)
Returns:
Dict with monetary aggregates and velocity
"""
money_series = FRED_SERIES['MONEY_SUPPLY']
results = {}
for series_id, name in money_series.items():
data = get_fred_series(series_id, lookback_days=365, api_key=api_key)
if data['success']:
results[series_id] = {
'name': name,
'value': data['latest_value'],
'date': data['latest_date'],
'yoy_change_pct': data['changes'].get('yoy_change_pct', 0)
}
# Calculate M2 velocity trend
if 'M2V' in results:
velocity_trend = 'accelerating' if results['M2V']['yoy_change_pct'] > 0 else 'decelerating'
results['velocity_trend'] = velocity_trend
return {
'success': True,
'money_supply': results,
'timestamp': datetime.now().isoformat()
}
def get_financial_conditions(api_key: Optional[str] = None) -> Dict:
"""
Get financial conditions indices (NFCI, STLFSI, VIX, TED spread)
Returns:
Dict with financial stress indicators
"""
fc_series = FRED_SERIES['FINANCIAL_CONDITIONS']
results = {}
for series_id, name in fc_series.items():
data = get_fred_series(series_id, lookback_days=90, api_key=api_key)
if data['success']:
results[series_id] = {
'name': name,
'value': data['latest_value'],
'date': data['latest_date'],
'month_change': data['changes'].get('month_change', 0)
}
# Interpret financial conditions
interpretation = []
if 'NFCI' in results:
nfci = results['NFCI']['value']
if nfci > 0.5:
interpretation.append('Tight financial conditions (NFCI > 0.5)')
elif nfci < -0.5:
interpretation.append('Loose financial conditions (NFCI < -0.5)')
else:
interpretation.append('Neutral financial conditions')
if 'STLFSI' in results:
stlfsi = results['STLFSI']['value']
if stlfsi > 1.0:
interpretation.append('Elevated financial stress (STLFSI > 1.0)')
elif stlfsi < -1.0:
interpretation.append('Low financial stress')
return {
'success': True,
'indicators': results,
'interpretation': interpretation,
'timestamp': datetime.now().isoformat()
}
def get_leading_indicators(api_key: Optional[str] = None) -> Dict:
"""
Get Leading Economic Index and components
Returns:
Dict with LEI and forward-looking indicators
"""
lei_series = FRED_SERIES['LEADING_INDICATORS']
results = {}
for series_id, name in lei_series.items():
data = get_fred_series(series_id, lookback_days=365, api_key=api_key)
if data['success']:
results[series_id] = {
'name': name,
'value': data['latest_value'],
'date': data['latest_date'],
'month_change_pct': data['changes'].get('month_change_pct', 0),
'yoy_change_pct': data['changes'].get('yoy_change_pct', 0)
}
return {
'success': True,
'leading_indicators': results,
'timestamp': datetime.now().isoformat()
}
def get_consumer_credit(api_key: Optional[str] = None) -> Dict:
"""
Get consumer credit outstanding (total, revolving, non-revolving) and delinquency rates
Returns:
Dict with credit metrics and delinquencies
"""
credit_series = FRED_SERIES['CONSUMER_CREDIT']
results = {}
for series_id, name in credit_series.items():
data = get_fred_series(series_id, lookback_days=365, api_key=api_key)
if data['success']:
results[series_id] = {
'name': name,
'value': data['latest_value'],
'date': data['latest_date'],
'yoy_change_pct': data['changes'].get('yoy_change_pct', 0)
}
# Calculate credit health score
health_indicators = []
if 'DRCLACBS' in results:
delinq_rate = results['DRCLACBS']['value']
if delinq_rate > 3.0:
health_indicators.append('Elevated consumer loan delinquencies')
elif delinq_rate < 1.5:
health_indicators.append('Low consumer loan delinquencies')
return {
'success': True,
'consumer_credit': results,
'health_indicators': health_indicators,
'timestamp': datetime.now().isoformat()
}
def get_category(category: str, api_key: Optional[str] = None) -> Dict:
"""
Get all series for a specific category
Args:
category: Category name from FRED_SERIES keys (e.g., 'LABOR', 'INFLATION', 'GDP')
api_key: Optional FRED API key
Returns:
Dict with all series in that category
"""
if category not in FRED_SERIES:
return {
'success': False,
'error': f'Invalid category. Must be one of: {list(FRED_SERIES.keys())}'
}
series_dict = FRED_SERIES[category]
results = {}
for series_id, name in series_dict.items():
data = get_fred_series(series_id, lookback_days=365, api_key=api_key)
if data['success']:
results[series_id] = {
'name': name,
'value': data['latest_value'],
'date': data['latest_date'],
'changes': data['changes']
}
return {
'success': True,
'category': category,
'data': results,
'count': len(results),
'timestamp': datetime.now().isoformat()
}
def get_macro_snapshot(api_key: Optional[str] = None) -> Dict:
"""
Get comprehensive economic snapshot with key indicators from all categories
Returns:
Dict with summary metrics across all categories
"""
# Key series from each category
key_series = {
'GDP': 'GDPC1',
'Unemployment': 'UNRATE',
'Core CPI': 'CPILFESL',
'Core PCE': 'PCEPILFE',
'10Y Treasury': 'DGS10',
'10Y-2Y Spread': 'T10Y2Y',
'M2 Money Supply': 'M2SL',
'Fed Balance Sheet': 'WALCL',
'Financial Conditions': 'NFCI',
'VIX': 'VIXCLS',
'Consumer Sentiment': 'UMCSENT',
'Total Consumer Credit': 'TOTALSL',
'Federal Debt': 'GFDEBTN',
'Oil Price (WTI)': 'DCOILWTICO',
}
results = {}
for name, series_id in key_series.items():
data = get_fred_series(series_id, lookback_days=365, api_key=api_key)
if data['success']:
results[name] = {
'value': data['latest_value'],
'date': data['latest_date'],
'yoy_change_pct': data['changes'].get('yoy_change_pct', 0)
}
return {
'success': True,
'snapshot': results,
'timestamp': datetime.now().isoformat(),
'source': 'FRED API'
}
def list_categories() -> Dict:
"""
List all available FRED categories and their series counts
Returns:
Dict with category names and series counts
"""
categories = []
for cat_name, series_dict in FRED_SERIES.items():
categories.append({
'category': cat_name,
'series_count': len(series_dict),
'series_ids': list(series_dict.keys())
})
return {
'success': True,
'total_categories': len(FRED_SERIES),
'total_series': sum(len(s) for s in FRED_SERIES.values()),
'categories': categories
}
def search_series(query: str) -> Dict:
"""
Search FRED series by name or description
Args:
query: Search term
Returns:
List of matching series
"""
query_lower = query.lower()
results = []
for cat_name, series_dict in FRED_SERIES.items():
for series_id, series_name in series_dict.items():
if query_lower in series_name.lower() or query_lower in series_id.lower():
results.append({
'series_id': series_id,
'name': series_name,
'category': cat_name
})
return {
'success': True,
'query': query,
'results_count': len(results),
'results': results
}
def cli_main():
"""CLI interface for FRED Enhanced module"""
import argparse
parser = argparse.ArgumentParser(description='FRED Enhanced - 300+ Economic Series')
subparsers = parser.add_subparsers(dest='command', help='Command to execute')
# fred-series
series_parser = subparsers.add_parser('fred-series', help='Get single FRED series')
series_parser.add_argument('series_id', help='FRED series ID (e.g., DGS10)')
series_parser.add_argument('--days', type=int, default=365, help='Days of history')
series_parser.add_argument('--api-key', help='FRED API key')
# fred-yield-curve
yc_parser = subparsers.add_parser('fred-yield-curve', help='Get complete yield curve')
yc_parser.add_argument('--api-key', help='FRED API key')
# fred-money-supply
ms_parser = subparsers.add_parser('fred-money-supply', help='Get money supply metrics')
ms_parser.add_argument('--api-key', help='FRED API key')
# fred-financial-conditions
fc_parser = subparsers.add_parser('fred-financial-conditions', help='Get financial conditions indices')
fc_parser.add_argument('--api-key', help='FRED API key')
# fred-leading-indicators
lei_parser = subparsers.add_parser('fred-leading-indicators', help='Get LEI and components')
lei_parser.add_argument('--api-key', help='FRED API key')
# fred-consumer-credit
cc_parser = subparsers.add_parser('fred-consumer-credit', help='Get consumer credit metrics')
cc_parser.add_argument('--api-key', help='FRED API key')
# fred-category
cat_parser = subparsers.add_parser('fred-category', help='Get all series in a category')
cat_parser.add_argument('category', help='Category name (e.g., LABOR, INFLATION)')
cat_parser.add_argument('--api-key', help='FRED API key')
# fred-snapshot
snap_parser = subparsers.add_parser('fred-snapshot', help='Get macro economic snapshot')
snap_parser.add_argument('--api-key', help='FRED API key')
# fred-categories
subparsers.add_parser('fred-categories', help='List all categories')
# fred-search
search_parser = subparsers.add_parser('fred-search', help='Search series by name')
search_parser.add_argument('query', help='Search term')
args = parser.parse_args()
if not args.command:
parser.print_help()
return
# Execute command
if args.command == 'fred-series':
result = get_fred_series(args.series_id, args.days, args.api_key)
elif args.command == 'fred-yield-curve':
result = get_yield_curve(args.api_key)
elif args.command == 'fred-money-supply':
result = get_money_supply(args.api_key)
elif args.command == 'fred-financial-conditions':
result = get_financial_conditions(args.api_key)
elif args.command == 'fred-leading-indicators':
result = get_leading_indicators(args.api_key)
elif args.command == 'fred-consumer-credit':
result = get_consumer_credit(args.api_key)
elif args.command == 'fred-category':
result = get_category(args.category.upper(), args.api_key)
elif args.command == 'fred-snapshot':
result = get_macro_snapshot(args.api_key)
elif args.command == 'fred-categories':
result = list_categories()
elif args.command == 'fred-search':
result = search_series(args.query)
else:
parser.print_help()
return
# Print result
print(json.dumps(result, indent=2))
if __name__ == '__main__':
cli_main()
