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?
JavaScript
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import yfinance as yf2
import pandas as pd3
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BTC = pd.Dataframe = yf.download(tickers='BTC-USD', period = '2wk', interval = '1d')7
SPY = yf.download('SPY', start='2022-03-07', end='2022-03-21')8
9
10
print(BTC)11
12
Open High Adj Close Volume13
Date 14
2022-03-07 38429.304688 39430.226562 38062.039062 2854614350315
2022-03-08 38059.902344 39304.441406 38737.269531 2577658347616
2022-03-09 38742.816406 42465.671875 41982.925781 3228412103417
2022-03-10 41974.070312 42004.726562 39437.460938 3107806471118
2022-03-11 39439.968750 40081.679688 38794.972656 2636489046519
2022-03-12 38794.464844 39308.597656 38904.011719 1461645065720
2022-03-13 38884.726562 39209.351562 37849.664062 1730074531021
2022-03-14 37846.316406 39742.500000 39666.753906 2432215907022
2022-03-15 39664.250000 39794.628906 39338.785156 2393400086823
2022-03-16 39335.570312 41465.453125 41143.929688 3961691619224
2022-03-17 41140.843750 41287.535156 40951.378906 2200960109325
2022-03-18 40944.839844 42195.746094 41801.156250 3442156494226
2022-03-19 41794.648438 42316.554688 42190.652344 1966485318727
2022-03-20 42191.406250 42241.164062 41247.824219 2012794668228
2022-03-21 41259.656250 41420.941406 41400.390625 2311712972829
[15 rows x 6 columns]30
31
32
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print(SPY)34
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Open High Adj Close Volume36
Date 37
2022-03-07 431.549988 432.299988 418.131012 13789660038
2022-03-08 419.619995 427.209991 414.960876 16477270039
2022-03-09 425.140015 429.510010 426.086304 11699080040
2022-03-10 422.519989 426.429993 424.162292 9397270041
2022-03-11 428.119995 428.769989 418.769043 9552960042
2022-03-14 420.890015 424.549988 415.708557 9572920043
2022-03-15 419.769989 426.839996 424.850159 10621910044
2022-03-16 429.890015 435.679993 434.270874 14495480045
2022-03-17 433.589996 441.070007 439.704010 10267690046
2022-03-18 438.000000 444.859985 444.519989 10625040047
[10 rows x 6 columns]48
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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.
JavaScript
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BTC = BTC.reindex(SPY.index)2
Full code:
JavaScript
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import yfinance as yf2
import pandas as pd3
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BTC = yf.download('BTC-USD', period = '2wk', interval = '1d')5
SPY = yf.download('SPY', start='2022-03-07', end='2022-03-21')6
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BTC8
9
Open High Low Close Adj Close Volume10
Date 11
2022-03-07 38429.304688 39430.226562 37260.203125 38062.039062 38062.039062 2854614350312
2022-03-08 38059.902344 39304.441406 37957.386719 38737.269531 38737.269531 2577658347613
2022-03-09 38742.816406 42465.671875 38706.093750 41982.925781 41982.925781 3228412103414
2022-03-10 41974.070312 42004.726562 38832.941406 39437.460938 39437.460938 3107806471115
2022-03-11 39439.968750 40081.679688 38347.433594 38794.972656 38794.972656 2636489046516
2022-03-12 38794.464844 39308.597656 38772.535156 38904.011719 38904.011719 1461645065717
2022-03-13 38884.726562 39209.351562 37728.144531 37849.664062 37849.664062 1730074531018
2022-03-14 37846.316406 39742.500000 37680.734375 39666.753906 39666.753906 2432215907019
2022-03-15 39664.250000 39794.628906 38310.210938 39338.785156 39338.785156 2393400086820
2022-03-16 39335.570312 41465.453125 39022.347656 41143.929688 41143.929688 3961691619221
2022-03-17 41140.843750 41287.535156 40662.871094 40951.378906 40951.378906 2200960109322
2022-03-18 40944.839844 42195.746094 40302.398438 41801.156250 41801.156250 3442156494223
2022-03-19 41794.648438 42316.554688 41602.667969 42190.652344 42190.652344 1966485318724
2022-03-20 42191.406250 42241.164062 41004.757812 41247.824219 41247.824219 2012794668225
2022-03-21 41259.656250 41420.941406 40691.011719 41198.937500 41198.937500 2295492198426
27
SPY28
29
Open High Low Close Adj Close Volume30
Date 31
2022-03-07 431.549988 432.299988 419.359985 419.429993 418.131012 13789660032
2022-03-08 419.619995 427.209991 415.119995 416.250000 414.960876 16477270033
2022-03-09 425.140015 429.510010 422.820007 427.410004 426.086304 11699080034
2022-03-10 422.519989 426.429993 420.440002 425.480011 424.162292 9397270035
2022-03-11 428.119995 428.769989 419.529999 420.070007 418.769043 9552960036
2022-03-14 420.890015 424.549988 415.790009 417.000000 415.708557 9572920037
2022-03-15 419.769989 426.839996 418.420013 426.170013 424.850159 10621910038
2022-03-16 429.890015 435.679993 424.799988 435.619995 434.270874 14495480039
2022-03-17 433.589996 441.070007 433.190002 441.070007 439.704010 10267690040
2022-03-18 438.000000 444.859985 437.220001 444.519989 444.519989 10625040041
42
BTC = BTC.reindex(SPY.index)43
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BTC45
46
Open High Low Close Adj Close Volume47
Date 48
2022-03-07 38429.304688 39430.226562 37260.203125 38062.039062 38062.039062 2854614350349
2022-03-08 38059.902344 39304.441406 37957.386719 38737.269531 38737.269531 2577658347650
2022-03-09 38742.816406 42465.671875 38706.093750 41982.925781 41982.925781 3228412103451
2022-03-10 41974.070312 42004.726562 38832.941406 39437.460938 39437.460938 3107806471152
2022-03-11 39439.968750 40081.679688 38347.433594 38794.972656 38794.972656 2636489046553
2022-03-14 37846.316406 39742.500000 37680.734375 39666.753906 39666.753906 2432215907054
2022-03-15 39664.250000 39794.628906 38310.210938 39338.785156 39338.785156 2393400086855
2022-03-16 39335.570312 41465.453125 39022.347656 41143.929688 41143.929688 3961691619256
2022-03-17 41140.843750 41287.535156 40662.871094 40951.378906 40951.378906 2200960109357
2022-03-18 40944.839844 42195.746094 40302.398438 41801.156250 41801.156250 3442156494258