在固定利率支出后，提高寻找投资组合终值的速度

Question

我有一个pd.DataFrame的回报系列对应多年，固定支出率为5％。我希望在每年支出后找到结束的投资组合价值。 val_after_spending年度t等于t val_before_spending年平均值t-1 val_after_spending乘以支出率。第一年，val_after_spending的t-1假定为1。

我现在有一个工作实现（下面），但它非常慢。有更快的方法来实现这个吗？

import pandas as pd
import numpy as np   
port_rets = pd.DataFrame({'port_ret': [.10,-.25,.15]})

spending_rate = .05

for index, row in port_rets.iterrows():
    if index != 0:
        port_rets.at[index, 'val_before_spending'] = port_rets['val_after_spending'][index - 1] * (1 + port_rets['port_ret'][index])
        port_rets.at[index, 'spending'] = np.mean([port_rets['val_after_spending'][index - 1], port_rets['val_before_spending'][index]]) * spending_rate 
    else:
        port_rets.at[index, 'val_before_spending'] = 1 * (1 + port_rets['port_ret'][index])
        port_rets.at[index, 'spending'] = np.mean([1, port_rets['val_before_spending'][index]]) * spending_rate

    port_rets.at[index, 'val_after_spending'] = port_rets['val_before_spending'][index] - port_rets['spending'][index]

#   port_ret    val_before_spending spending    val_after_spending
#0  0.100000    1.100000            0.052500    1.047500
#1  -0.250000   0.785625            0.045828    0.739797
#2  0.150000    0.850766            0.039764    0.811002

Answer 1

您在代码中与pandas非常密切地接触，就性能而言，这似乎是个坏主意。为了使其易于使用，大熊猫需要进行大量的簿记，这会导致性能降低。

我们在numpy中进行所有计算，然后获得所有构建块，最后构建数据帧。因此，代码转换为：

def get_vals(rates, spending_rate):
    n = len(rates)
    vals_after_spending = np.zeros((n+1, ))
    vals_before_spending = np.zeros((n+1, ))

    vals_after_spending[0] = 1.0

    for i in range(n):
        vals_before_spending[i+1] = vals_after_spending[i] * (1 + rates[i])

        spending = np.mean(np.array([vals_after_spending[i], vals_before_spending[i+1]])) * spending_rate
        vals_after_spending[i+1] = vals_before_spending[i+1] - spending

    return vals_before_spending[1:], vals_after_spending[1:]

rates = np.array(port_rets["port_ret"].tolist())
vals_before_spending, vals_after_spending = get_vals(rates, spending_rate)
port_rets = pd.DataFrame({'port_ret': rates, "val_before_spending": vals_before_spending, "val_after_spending": vals_after_spending})

我们可以通过JIT编译代码来进一步改进，因为python循环很慢。下面我用numba：

import numba as nb
@nb.njit(cache=True)  # as easy as putting this decorator
def get_vals(rates, spending_rate):
    n = len(rates)
    vals_after_spending = np.zeros((n+1, ))
    vals_before_spending = np.zeros((n+1, ))

    # ... code remains same, we are just compiling the function

如果我们考虑这样的随机费率列表：

port_rets = pd.DataFrame({'port_ret': np.random.uniform(low=-1.0, high=1.0, size=(100000,))})

我们得到了性能比较：

你的代码：15.758s

get_vals：1.407s

JITed get_vals：0.093s（第二次运行到折扣编译时）