Fix portfolio and benchmark length matching

[northern-64bit]

Description

Closes #4201 by fixing the length matching performed in the "bench" command.

How has this been tested?

Running the command and making sure the results stay the same

• Make sure affected commands still run in terminal
• Ensure the SDK still works
• Check any related reports

Checklist:

• I have adhered to the GitFlow naming convention and my branch name is in the format of feature/feature-name or hotfix/hotfix-name.
• Update our documentation following these guidelines. Update any user guides that are affected by the changes.
• Update our tests following these guidelines.
• Make sure you are following our CONTRIBUTING guidelines.
• If a feature was added make sure to add it to the corresponding integration test script.

Others

• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.

[montezdesousa]

IIUC we are assuming that if DataFrames share the same length the dates match, this might not be always the case, right?

Or even if they have different length is it guaranteed that the smallest is a subset of the largest?

I tried this example and the resulting DataFrames don't share the same length

from openbb_terminal.portfolio.portfolio_helper import make_equal_length
import pandas as pd
df0 = pd.DataFrame({'returns': [0.4, 0.6, 0.8]}, index=[1, 3, 4])
df1 = pd.DataFrame({'returns': [0.3, 0.5]}, index=[2, 3])
df0_new, df1_new = make_equal_length(df0, df1)
df0_new, df1_new

If the case above is an issue we might use pandas inner merge and then provide each DataFrame individually.

[jmaslek]

IIUC we are assuming that if DataFrames share the same length the dates match, this might not be always the case, right?

Or even if they have different length is it guaranteed that the smallest is a subset of the largest?

I tried this example and the resulting DataFrames don't share the same length

from openbb_terminal.portfolio.portfolio_helper import make_equal_length
import pandas as pd
df0 = pd.DataFrame({'returns': [0.4, 0.6, 0.8]}, index=[1, 3, 4])
df1 = pd.DataFrame({'returns': [0.3, 0.5]}, index=[2, 3])
df0_new, df1_new = make_equal_length(df0, df1)
df0_new, df1_new

If the case above is an issue we might use pandas inner merge and then provide each DataFrame individually.

So I think the tricky part is holidays on one exchange. I am not sure what the correct way to report that would be. My gut says to do an outer join and then just backfill (and just assume that a couple days of 0 returns doesnt mess with statistics or correlations)

[montezdesousa]

IIUC we are assuming that if DataFrames share the same length the dates match, this might not be always the case, right?
Or even if they have different length is it guaranteed that the smallest is a subset of the largest?
I tried this example and the resulting DataFrames don't share the same length

from openbb_terminal.portfolio.portfolio_helper import make_equal_length
import pandas as pd
df0 = pd.DataFrame({'returns': [0.4, 0.6, 0.8]}, index=[1, 3, 4])
df1 = pd.DataFrame({'returns': [0.3, 0.5]}, index=[2, 3])
df0_new, df1_new = make_equal_length(df0, df1)
df0_new, df1_new

If the case above is an issue we might use pandas inner merge and then provide each DataFrame individually.

So I think the tricky part is holidays on one exchange. I am not sure what the correct way to report that would be. My gut says to do an outer join and then just backfill (and just assume that a couple days of 0 returns doesnt mess with statistics or correlations)

sounds good

[northern-64bit]

IIUC we are assuming that if DataFrames share the same length the dates match, this might not be always the case, right?
Or even if they have different length is it guaranteed that the smallest is a subset of the largest?
I tried this example and the resulting DataFrames don't share the same length

from openbb_terminal.portfolio.portfolio_helper import make_equal_length
import pandas as pd
df0 = pd.DataFrame({'returns': [0.4, 0.6, 0.8]}, index=[1, 3, 4])
df1 = pd.DataFrame({'returns': [0.3, 0.5]}, index=[2, 3])
df0_new, df1_new = make_equal_length(df0, df1)
df0_new, df1_new

If the case above is an issue we might use pandas inner merge and then provide each DataFrame individually.

So I think the tricky part is holidays on one exchange. I am not sure what the correct way to report that would be. My gut says to do an outer join and then just backfill (and just assume that a couple days of 0 returns doesnt mess with statistics or correlations)

sounds good

It has now been fixed by applying outer join and just replacing all NaNs with zeros.

[jmaslek]

Loading the example portfolio is now invalid:

2023 Feb 19, 20:09 (🦋) /portfolio/ $ load --example

Loading an example, please type `about` to learn how to create your own Portfolio Excel sheet.

         Loading benchmark:  50%|████████████████████████████████████████████████████████████████████▌                                                                    | 2/4 [00:00<00:00, 10.45it/s]Error: 'Series' object has no attribute 'columns'

                                                                                                                                                                                                        
Portfolio: OpenBB Example Portfolio
Risk Free Rate: 0.00%
Benchmark: SPDR S&P 500 ETF Trust (SPY)

[northern-64bit]

Loading the example portfolio is now invalid:

2023 Feb 19, 20:09 (🦋) /portfolio/ $ load --example

Loading an example, please type `about` to learn how to create your own Portfolio Excel sheet.

         Loading benchmark:  50%|████████████████████████████████████████████████████████████████████▌                                                                    | 2/4 [00:00<00:00, 10.45it/s]Error: 'Series' object has no attribute 'columns'

                                                                                                                                                                                                        
Portfolio: OpenBB Example Portfolio
Risk Free Rate: 0.00%
Benchmark: SPDR S&P 500 ETF Trust (SPY)

Fixed :)

[montezdesousa]

There are commands breaking for some reason, like summary, some on metrics, yret

Run python terminal.py -t 33 -v to check. alloc is broken, but apparently it's on develop branch already

[northern-64bit]

Hello @montezdesousa! Can you please review the PR so that we can merge it. Thanks!

[montezdesousa]

Sorry must have missed this one, tks for reminding