Y finance Date alignment

Question

This might be a relatively difficult question;

The scope of the code I want to write, is to automate the alignment of Dates that i pull from yfinance regarding BTC and S&P 500

since the S&P500 (SPY) is not traded on weekends, but BTC is, I want to automatically delete the columns of dates from BTC that fall on weekends (or days where the S&P isn't traded), to consistently align my 2 dataframes.

In this case I have 15 data rows in BTC, whereas I only have 10 in SPY

I only need data where the dates match

Does anyone have an idea how I could do that?

import yfinance as yf
import pandas as pd



BTC = pd.Dataframe = yf.download(tickers='BTC-USD', period = '2wk', interval = '1d')
SPY = yf.download('SPY', start='2022-03-07', end='2022-03-21')


print(BTC)

                    Open          High  ...     Adj Close       Volume
Date                                    ...                           
2022-03-07  38429.304688  39430.226562  ...  38062.039062  28546143503
2022-03-08  38059.902344  39304.441406  ...  38737.269531  25776583476
2022-03-09  38742.816406  42465.671875  ...  41982.925781  32284121034
2022-03-10  41974.070312  42004.726562  ...  39437.460938  31078064711
2022-03-11  39439.968750  40081.679688  ...  38794.972656  26364890465
2022-03-12  38794.464844  39308.597656  ...  38904.011719  14616450657
2022-03-13  38884.726562  39209.351562  ...  37849.664062  17300745310
2022-03-14  37846.316406  39742.500000  ...  39666.753906  24322159070
2022-03-15  39664.250000  39794.628906  ...  39338.785156  23934000868
2022-03-16  39335.570312  41465.453125  ...  41143.929688  39616916192
2022-03-17  41140.843750  41287.535156  ...  40951.378906  22009601093
2022-03-18  40944.839844  42195.746094  ...  41801.156250  34421564942
2022-03-19  41794.648438  42316.554688  ...  42190.652344  19664853187
2022-03-20  42191.406250  42241.164062  ...  41247.824219  20127946682
2022-03-21  41259.656250  41420.941406  ...  41400.390625  23117129728
[15 rows x 6 columns]



print(SPY)

                  Open        High  ...   Adj Close     Volume
Date                                ...                       
2022-03-07  431.549988  432.299988  ...  418.131012  137896600
2022-03-08  419.619995  427.209991  ...  414.960876  164772700
2022-03-09  425.140015  429.510010  ...  426.086304  116990800
2022-03-10  422.519989  426.429993  ...  424.162292   93972700
2022-03-11  428.119995  428.769989  ...  418.769043   95529600
2022-03-14  420.890015  424.549988  ...  415.708557   95729200
2022-03-15  419.769989  426.839996  ...  424.850159  106219100
2022-03-16  429.890015  435.679993  ...  434.270874  144954800
2022-03-17  433.589996  441.070007  ...  439.704010  102676900
2022-03-18  438.000000  444.859985  ...  444.519989  106250400
[10 rows x 6 columns]

user_11985088 · Answer

Here is a faster option for this purpose below:

f = pd.Series(BTC['Open'], index=SaP.index)

print(f)

user_13107804 · Answer

Given your understanding that what you want to achieve is to have the BTC-USD data frame on the same trading day as the SPY trading day, the easiest way to do this is to reassign the BTC data frame with the SPY index.

    BTC = BTC.reindex(SPY.index)

Full code:

import yfinance as yf
import pandas as pd

BTC = yf.download('BTC-USD', period = '2wk', interval = '1d')
SPY = yf.download('SPY', start='2022-03-07', end='2022-03-21')

BTC

    Open    High    Low     Close   Adj Close   Volume
Date                        
2022-03-07  38429.304688    39430.226562    37260.203125    38062.039062    38062.039062    28546143503
2022-03-08  38059.902344    39304.441406    37957.386719    38737.269531    38737.269531    25776583476
2022-03-09  38742.816406    42465.671875    38706.093750    41982.925781    41982.925781    32284121034
2022-03-10  41974.070312    42004.726562    38832.941406    39437.460938    39437.460938    31078064711
2022-03-11  39439.968750    40081.679688    38347.433594    38794.972656    38794.972656    26364890465
2022-03-12  38794.464844    39308.597656    38772.535156    38904.011719    38904.011719    14616450657
2022-03-13  38884.726562    39209.351562    37728.144531    37849.664062    37849.664062    17300745310
2022-03-14  37846.316406    39742.500000    37680.734375    39666.753906    39666.753906    24322159070
2022-03-15  39664.250000    39794.628906    38310.210938    39338.785156    39338.785156    23934000868
2022-03-16  39335.570312    41465.453125    39022.347656    41143.929688    41143.929688    39616916192
2022-03-17  41140.843750    41287.535156    40662.871094    40951.378906    40951.378906    22009601093
2022-03-18  40944.839844    42195.746094    40302.398438    41801.156250    41801.156250    34421564942
2022-03-19  41794.648438    42316.554688    41602.667969    42190.652344    42190.652344    19664853187
2022-03-20  42191.406250    42241.164062    41004.757812    41247.824219    41247.824219    20127946682
2022-03-21  41259.656250    41420.941406    40691.011719    41198.937500    41198.937500    22954921984

SPY

    Open    High    Low     Close   Adj Close   Volume
Date                        
2022-03-07  431.549988  432.299988  419.359985  419.429993  418.131012  137896600
2022-03-08  419.619995  427.209991  415.119995  416.250000  414.960876  164772700
2022-03-09  425.140015  429.510010  422.820007  427.410004  426.086304  116990800
2022-03-10  422.519989  426.429993  420.440002  425.480011  424.162292  93972700
2022-03-11  428.119995  428.769989  419.529999  420.070007  418.769043  95529600
2022-03-14  420.890015  424.549988  415.790009  417.000000  415.708557  95729200
2022-03-15  419.769989  426.839996  418.420013  426.170013  424.850159  106219100
2022-03-16  429.890015  435.679993  424.799988  435.619995  434.270874  144954800
2022-03-17  433.589996  441.070007  433.190002  441.070007  439.704010  102676900
2022-03-18  438.000000  444.859985  437.220001  444.519989  444.519989  106250400

BTC = BTC.reindex(SPY.index)

BTC

    Open    High    Low     Close   Adj Close   Volume
Date                        
2022-03-07  38429.304688    39430.226562    37260.203125    38062.039062    38062.039062    28546143503
2022-03-08  38059.902344    39304.441406    37957.386719    38737.269531    38737.269531    25776583476
2022-03-09  38742.816406    42465.671875    38706.093750    41982.925781    41982.925781    32284121034
2022-03-10  41974.070312    42004.726562    38832.941406    39437.460938    39437.460938    31078064711
2022-03-11  39439.968750    40081.679688    38347.433594    38794.972656    38794.972656    26364890465
2022-03-14  37846.316406    39742.500000    37680.734375    39666.753906    39666.753906    24322159070
2022-03-15  39664.250000    39794.628906    38310.210938    39338.785156    39338.785156    23934000868
2022-03-16  39335.570312    41465.453125    39022.347656    41143.929688    41143.929688    39616916192
2022-03-17  41140.843750    41287.535156    40662.871094    40951.378906    40951.378906    22009601093
2022-03-18  40944.839844    42195.746094    40302.398438    41801.156250    41801.156250    34421564942

Y finance Date alignment

2 Answers