I don't understand why you're requesting review for this code. This feature branch is not yet ready to be merged down to main.

Please clean up the diagnostics, and re-submit as a new Question.

Pandas 2.2.2 output, under Python 3.12.4:

PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  .drop("index", axis=1)

FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df_corr_name.iloc[i][i + 1] = float(0)

It would be helpful if Review Context or # comments described jargon such as "tenor" and "tcc quality".

I don't understand why you're requesting review for this code. This feature branch is not yet ready to be merged down to main.

Please clean up the diagnostics, and re-submit as a new Question.

Pandas 2.2.2 output, under Python 3.12.4:

PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  .drop("index", axis=1)

FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df_corr_name.iloc[i][i + 1] = float(0)

It would be helpful if Review Context or # comments described jargon such as "tenor" and "tcc quality". And apparently "pkl" is unrelated to pickle.

If there is some author you relied on when developing this code, then it would be very helpful to include a citation.