import backtrader as btclass CountryMomentum(bt.Strategy):
    params = (
        ('lookback_period', 11),  # X-month momentum (11 months for 12-month lookback, including the current month)
        ('top_n', 5),            # Top 5 countries
    )

    def __init__(self):
        self.inds = {}
        for d in self.datas:
            self.inds[d] = {}
            self.inds[d]['momentum'] = bt.indicators.Momentum(d.close, period=self.params.lookback_period)

    def next(self):
        country_momentums = []
        for d in self.datas:
            country_momentums.append((d, self.inds[d]['momentum'][0]))
        # Sort countries by momentum and select top N countries
        top_countries = sorted(country_momentums, key=lambda x: x[1], reverse=True)[:self.params.top_n]
        # Calculate equal weights for the selected countries
        weight = 1 / len(top_countries)
        # Rebalance the portfolio
        for d in self.datas:
            if d in [country_data for country_data, _ in top_countries]:
                self.order_target_percent(d, target=weight)
            else:
                self.order_target_percent(d, target=0)

# Create a Cerebro engine
cerebro = bt.Cerebro()
# Add the data feeds to Cerebro
# Add each country-specific ETF as a data feed here
# Add the strategy to Cerebro
cerebro.addstrategy(CountryMomentum)
# Run Cerebro engine
results = cerebro.run()
# Plot the results
cerebro.plot()