calc_top_down_cumulative_returns 错误

[MagicEthan]

def calc_top_down_cumulative_returns(self, period=None,
                                     demeaned=False, group_adjust=False):     
    if period is None:
        period = self._periods[0]
    period_col = convert_to_forward_returns_columns(period)
    mean_returns, _ = self.calc_mean_return_by_quantile(
        by_date=True, by_group=False,
        demeaned=demeaned, group_adjust=group_adjust,
    )
    mean_returns = mean_returns.apply(rate_of_return, axis=0) 
    #  period>1时，这里重复计算了。rate_of_return取了一次(1/N)次幂
    #  然后performance.cumulative_returns里又取了一次(1/N)次幂
    #  计算因子加权、分位数累计收益时，没有这个问题。只有top-down的多算了一次
    #  见图，纵坐标差太多

    upper_quant = mean_returns[period_col].xs(self._factor_quantile,
                                              level='factor_quantile')
    lower_quant = mean_returns[period_col].xs(1,
                                              level='factor_quantile')
    return pef.cumulative_returns(upper_quant - lower_quant, period=period)






def rate_of_return(period_ret):

period = int(period_ret.name.replace('period_', ''))
return period_ret.add(1).pow(1. / period).sub(1)



def cumulative_returns(returns, period):

returns = returns.fillna(0)

if period == 1:
    return returns.add(1).cumprod()
#
# 构建 N 个交错的投资组合
#

def split_portfolio(ret, period):
    return pd.DataFrame(np.diag(ret))

sub_portfolios = returns.groupby(
    np.arange(len(returns.index)) // period, axis=0
).apply(split_portfolio, period)
sub_portfolios.index = returns.index

#
# 将 N 期收益转换为 1 期收益, 方便计算累积收益
#

def rate_of_returns(ret, period):
    return ((np.nansum(ret) + 1)**(1. / period)) - 1

sub_portfolios = rolling_apply(
    sub_portfolios,
    window=period,
    func=rate_of_returns,
    min_periods=1,
    args=(period,)

)
sub_portfolios = sub_portfolios.add(1).cumprod()

#
# 求 N 个投资组合累积收益均值
#
return sub_portfolios.mean(axis=1)