import backtrader as bt
import pandas as pd

class DemographicStrategy(bt.Strategy):
    def __init__(self):
        self.country_data = pd.DataFrame({
            'country': ['Australia', 'Austria', 'Belgium', 'Canada', 'Denmark', 'France', 'Germany', 'Italy', 'Japan', 'Netherlands', 'Spain', 'Sweden', 'Switzerland', 'UK', 'US'],
            'percent_over_65': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  # Fill in actual data
        })

    def next(self):
        if self.datetime.date().month != 12:
            return
        mean_over_65 = self.country_data['percent_over_65'].mean()
        std_over_65 = self.country_data['percent_over_65'].std()
        long_countries = self.country_data[self.country_data['percent_over_65'] < mean_over_65 - std_over_65]
        short_countries = self.country_data[self.country_data['percent_over_65'] > mean_over_65 + std_over_65]
        # Allocate equal weights to long and short positions
        long_weight = 1.0 / len(long_countries)
        short_weight = -1.0 / len(short_countries)
        # Adjust positions
        for d in self.getdatanames():
            country = d.split('.')[0]
            if country in long_countries['country'].values:
                self.order_target_percent(data=d, target=long_weight)
            elif country in short_countries['country'].values:
                self.order_target_percent(data=d, target=short_weight)
            else:
                self.order_target_percent(data=d, target=0.0)

cerebro = bt.Cerebro()
# Add data feeds for each country's ETF
# Data should be in a format that includes the country name in the dataname field
# Example: cerebro.adddata(bt.feeds.YahooFinanceData(dataname='Australia.ETF', ...))
# ...
cerebro.addstrategy(DemographicStrategy)
cerebro.run()