Pandas Resample 2M и 3M каждый месяц ⇐ Python

[Anonymous]

I have this code to calculate Returns:

import yfinance as yf import numpy as np import pandas as pd df = yf.download('SPY', '2023-01-01') df = df[['Close']] df['d_returns'] = np.log(df.div(df.shift(1))) df.dropna(inplace = True) df_1M = pd.DataFrame() df_2M = pd.DataFrame() df_3M = pd.DataFrame() df_1M['1M cummreturns'] = df.d_returns.cumsum().apply(np.exp) df_2M['2M cummreturns']= df.d_returns.cumsum().apply(np.exp) df_3M['3M cummreturns'] = df.d_returns.cumsum().apply(np.exp) df1 = df_1M[['1M cummreturns']].resample('1M').max() df2 = df_2M[['2M cummreturns']].resample('2M').max() df3 = df_3M[['3M cummreturns']].resample('3M').max() df1 = pd.concat([df1, df2, df3], axis=1) df1 This gives the following:

1M cummreturns 2M cummreturns 3M cummreturns Date 2023-01-31 1.067381 1.067381 1.067381 2023-02-28 1.094428 NaN NaN 2023-03-31 1.075022 1.094428 NaN 2023-04-30 1.092196 NaN 1.094428 2023-05-31 1.103356 1.103356 NaN 2023-06-30 1.164014 NaN NaN 2023-07-31 1.202116 1.202116 1.202116 2023-08-31 1.198677 NaN NaN 2023-09-30 1.184785 1.198677 NaN 2023-10-31 1.145738 NaN 1.198677 2023-11-30 1.198466 1.198466 NaN 2023-12-31 1.251746 NaN NaN 2024-01-31 1.290032 1.290032 1.290032 2024-02-29 1.334174 NaN NaN 2024-03-31 1.346699 1.346699 NaN 2024-04-30 NaN NaN 1.346699 How to get valid values in 2M cummreturns and 3M cummreturns columns for every row?

For instance, 2023-02-28 row represents Feb-2023 month. The columns 2M cummreturns and 3M cummreturns need to have max returns in the next 2 Months and 3 Months time respectively starting from Feb-2023 the same way 1M cummreturns gives max returns in the next 1 Month time.


Источник: https://stackoverflow.com/questions/780 ... very-month