"""
Economic Data Fetcher
=====================
Downloads macroeconomic data from FRED and World Bank APIs.
Requires: fredapi, wbdata, pandas

Setup: Set FRED_API_KEY environment variable
Get a free key from: https://fred.stlouisfed.org/docs/api/api_key.html
"""

import os
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Optional, Dict

Parameters
----------
series_ids : list of str
    FRED series IDs (e.g., ['GDP', 'UNRATE', 'CPIAUCSL'])
start_date : str
    Start date in YYYY-MM-DD format
end_date : str, optional
    End date (defaults to today)
api_key : str, optional
    FRED API key (defaults to FRED_API_KEY env var)

Returns
-------
pd.DataFrame
    DataFrame with date index and series as columns

Example
-------
>>> df = fetch_fred_series(['GDP', 'UNRATE'], '2010-01-01')
"""
try:
    from fredapi import Fred
except ImportError:
    raise ImportError("Install fredapi: pip install fredapi")

# Get API key
api_key = api_key or os.environ.get('FRED_API_KEY')
if not api_key:
    raise ValueError(
        "FRED API key required. Set FRED_API_KEY environment variable "
        "or pass api_key parameter. Get a key at: "
        "https://fred.stlouisfed.org/docs/api/api_key.html"
    )

fred = Fred(api_key=api_key)
end_date = end_date or datetime.now().strftime('%Y-%m-%d')

# Fetch each series
data = {}
for series_id in series_ids:
    try:
        series = fred.get_series(
            series_id,
            observation_start=start_date,
            observation_end=end_date
        )
        data[series_id] = series
        print(f"✓ Downloaded {series_id}")
    except Exception as e:
        print(f"✗ Failed to download {series_id}: {e}")

# Combine into DataFrame
df = pd.DataFrame(data)
df.index.name = 'date'

return df

# Labor Market
'UNRATE': 'Unemployment Rate',
'PAYEMS': 'Total Nonfarm Payrolls',
'CIVPART': 'Labor Force Participation Rate',

# Prices
'CPIAUCSL': 'Consumer Price Index',
'PCEPI': 'PCE Price Index',
'CPILFESL': 'Core CPI',

# Interest Rates
'FEDFUNDS': 'Federal Funds Rate',
'DGS10': '10-Year Treasury Rate',
'T10Y2Y': '10Y-2Y Treasury Spread',

# Money and Credit
'M2SL': 'M2 Money Stock',
'TOTRESNS': 'Total Reserves',

Parameters
----------
indicators : dict
    Dict mapping indicator codes to names
    e.g., {'NY.GDP.PCAP.CD': 'gdp_per_capita'}
countries : list of str
    ISO 3-letter country codes
start_year : int
    Start year
end_year : int, optional
    End year (defaults to current year)

Returns
-------
pd.DataFrame
    Panel data with country and year

Example
-------
>>> indicators = {
...     'NY.GDP.PCAP.CD': 'gdp_per_capita',
...     'SP.POP.TOTL': 'population'
... }
>>> df = fetch_world_bank_data(indicators, ['USA', 'GBR'])
"""
try:
    import wbdata
except ImportError:
    raise ImportError("Install wbdata: pip install wbdata")

end_year = end_year or datetime.now().year

all_data = []

for indicator_code, indicator_name in indicators.items():
    try:
        # Fetch data
        data = wbdata.get_dataframe(
            {indicator_code: indicator_name},
            country=countries,
        )
        data = data.reset_index()
        all_data.append(data)
        print(f"✓ Downloaded {indicator_name}")
        
    except Exception as e:
        print(f"✗ Failed to download {indicator_name}: {e}")

# Merge all indicators
if all_data:
    df = all_data[0]
    for other_df in all_data[1:]:
        df = df.merge(other_df, on=['country', 'date'], how='outer')
    
    # Filter years
    df['year'] = pd.to_datetime(df['date']).dt.year
    df = df[(df['year'] >= start_year) & (df['year'] <= end_year)]
    
    return df

return pd.DataFrame()

# Population
'SP.POP.TOTL': 'Population, total',
'SP.URB.TOTL.IN.ZS': 'Urban population (%)',

# Trade
'NE.TRD.GNFS.ZS': 'Trade (% of GDP)',
'BX.KLT.DINV.WD.GD.ZS': 'FDI, net inflows (% of GDP)',

# Human Capital
'SE.XPD.TOTL.GD.ZS': 'Education expenditure (% of GDP)',
'SH.XPD.CHEX.GD.ZS': 'Health expenditure (% of GDP)',

# Inequality
'SI.POV.GINI': 'Gini index',
'SI.POV.DDAY': 'Poverty headcount ratio ($1.90/day)',

print("\nUS Macro Data (FRED):")
print(us_macro.tail())

# Save to CSV
us_macro.to_csv('data/us_macro_fred.csv')
print("\nSaved to data/us_macro_fred.csv")

# Example 2: Fetch cross-country data from World Bank
indicators = {
    'NY.GDP.PCAP.CD': 'gdp_per_capita',
    'SP.POP.TOTL': 'population',
    'NY.GDP.MKTP.KD.ZG': 'gdp_growth'
}

cross_country = fetch_world_bank_data(
    indicators=indicators,
    countries=['USA', 'GBR', 'DEU', 'FRA', 'JPN', 'CHN', 'IND', 'BRA'],
    start_year=2000
)

print("\nCross-Country Data (World Bank):")
print(cross_country.head(10))

# Save to CSV
cross_country.to_csv('data/cross_country_wb.csv', index=False)
print("\nSaved to data/cross_country_wb.csv")

undefined

pip install fredapi wbdata pandas

export FRED_API_KEY="your_key_here"