Question

我有两个时间序列数据，A和B列。

我正在计算A列上不同持续时间的滚动移动平均值。例如（5,10,15,20）。

我想为每个平均列分配权重，以使权重和平均列的总和与B列具有最大的相关性。换句话说，如何在python中实现像优化一样的excel。

请查看示例代码，并提出前进的方向。

import pandas as pd
import numpy as np

dates = pd.date_range('20130101', periods=100)

df = pd.DataFrame(np.random.randn(100, 2), index=dates, columns=list('AB'))

df['sma_5']=df['A'].rolling(5).mean()

df['sma_10']=df['A'].rolling(10).mean()

df['sma_15']=df['A'].rolling(15).mean()

df['sma_20']=df['A'].rolling(20).mean()

w=[0.25,0.25,0.25,0.25]

df['B_friend'']= 
w[0]*df['sma_5']+w[1]*df['sma_10']+w[2]*df['sma_15']+w[3]*df['sma_20']

需要优化权重“ w”以最大化相关性。

df['B'].corr(df['B_friend'])

谢谢。

Answer 1

scipy.optimize.minimize函数看起来像您所需要的：https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize

代码看起来像这样：

import pandas as pd
import numpy as np
import scipy.optimize as opt

dates = pd.date_range('20130101', periods=100)
df = pd.DataFrame(np.random.randn(100, 2), index=dates, columns=list('AB'))
df['sma_5']=df['A'].rolling(5).mean()
df['sma_10']=df['A'].rolling(10).mean()
df['sma_15']=df['A'].rolling(15).mean()
df['sma_20']=df['A'].rolling(20).mean()

def fun(x):
    w = x
    B_friend=w[0]*df['sma_5']+w[1]*df['sma_10']+w[2]*df['sma_15']+w[3]*df['sma_20']
    # -np.abs(corr) instead of just corrr is used
    # in order to turn a maximization problem into a
    # minimization problem
    return -np.abs(df['B'].corr(B_friend))

w=[0.25,0.25,0.25,0.25]
opt.minimize(fun, w)

计算最佳权重以最大化相关性

1 个答案: