Are there any simple portfolio rebalancing strategies for reference?
tommark last edited by
Paska Houso last edited by 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.criteriadictionary which will probably be custom indicators which decide what the most attractive asset is.