import backtrader as bt
import numpy as np
import pandas as pd

class Spillover(bt.Strategy):
    params = (
        ('lookback_period', 3*52),  # 3 years weekly lookback, adjust to your data frequency
        ('rebalance_interval', 1*5),  # weekly rebalance, adjust to your data frequency
    )

    def __init__(self):
        self.lookback_period = self.params.lookback_period
        self.rebalance_interval = self.params.rebalance_interval
        self.inds = {}

    def next(self):
        if len(self.datas) % self.rebalance_interval == 0:
            self.rebalance_portfolio()

    def rebalance_portfolio(self):
        # Here you should calculate the signals and determine portfolio weights
        # This is a complex operation that will depend on the exact specifications of your strategy
        # For simplicity, we will assume a function 'calculate_weights' that does this and returns a dictionary of weights
        weights = self.calculate_weights()

        for d, weight in weights.items():
            self.order_target_percent(d, target=weight)

    def calculate_weights(self):
        # Placeholder for weight calculation
        # This function should calculate and return the portfolio weights based on the strategy rules
        # You will need to replace this with your own implementation
        weights = {}
        for d in self.datas:
            weights[d] = 1.0 / len(self.datas)  # Equal weights for now
        return weights

if __name__ == '__main__':
    cerebro = bt.Cerebro()
    
    # Add data feeds for currency forwards, equity indices, and index futures
    # ...

    cerebro.addstrategy(Spillover)
    cerebro.run()