Are there any simple portfolio rebalancing strategies for reference?

[tommark]

per title

[Paska Houso]

Don't know but it should be fairly easy (a couple of free hours during the Christmas break ... )

def next(self):
    rebalanced_datas = sorted(self.criteria.items(), lambda data, criterion: criterion)

    l = len(rebalanced_datas)
    upper = 90
    lower = 10
    # simple weight allocation
    percents = [(upper - x * (upper - lower) / (l - 1)) / 100.0 for x in range(l)]

    # normalize the percentages
    sperc = sum(percents)
    percents = [p / sperc for p in percents]

    for data, percent in zip(rebalanced_datas, percents):
        self.order_target_percent(data, target=percent)

The sauce is obviously in the values of the self.criteria dictionary which will probably be custom indicators which decide what the most attractive asset is.