Compute portfolio WARF¶

The following case-study demonstrates how to compute the weighted average rating factor for a portfolio.

Preliminary tasks¶

As a first step, we are going to import a portfolio into a pd.DataFrame. We'll call it port_df. This dataframe comprises a number of securities with respective weights and ratings.

In [1]:

import pandas as pd

import pyratings as rtg

port_df = pd.read_excel("portfolio.xlsx", sheet_name="long_term_ratings_worst")
port_df.head()

import pandas as pd import pyratings as rtg port_df = pd.read_excel("portfolio.xlsx", sheet_name="long_term_ratings_worst") port_df.head()

Out[1]:

	ISIN	weight	worst_rtg
0	ISIN00000001	0.518515	AAA
1	ISIN00000002	0.950810	AA+
2	ISIN00000003	0.497176	AA
3	ISIN00000004	0.648453	AA-
4	ISIN00000005	0.674328	AA-

Computing portfolio WARF¶

The column worst_rtg will be used in order to translate the ratings into WARFs according to the following table.

We will use get_warf_from_ratings to translate the human-readable ratings into WARFs. The function needs a rating provider (here: "S&P") in order to select an appropriate translation dictionary.

In [2]:

port_warf_df = pd.concat(
    [
        port_df,
        rtg.get_warf_from_ratings(
            ratings=port_df["worst_rtg"],
            rating_provider="S&P",
        )
    ],
    axis=1
)

port_warf_df

port_warf_df = pd.concat( [ port_df, rtg.get_warf_from_ratings( ratings=port_df["worst_rtg"], rating_provider="S&P", ) ], axis=1 ) port_warf_df

Out[2]:

	ISIN	weight	worst_rtg	warf_worst_rtg
0	ISIN00000001	0.518515	AAA	1.0
1	ISIN00000002	0.950810	AA+	10.0
2	ISIN00000003	0.497176	AA	20.0
3	ISIN00000004	0.648453	AA-	40.0
4	ISIN00000005	0.674328	AA-	40.0
...	...	...	...	...
82	ISIN00000083	2.321185	BBB+	260.0
83	ISIN00000084	1.389043	BBB+	260.0
84	ISIN00000085	2.296711	BBB+	260.0
85	ISIN00000086	1.015105	AAA	1.0
86	ISIN00000087	0.964399	AA	20.0

87 rows × 4 columns

Finally, we need to compute the WARF on a portfolio basis, which we subsequently convert back into a human-readable rating. For the former, we use the get_weighted_average function and the get_ratings_from_warf function for the latter.

In [3]:

avg_warf = rtg.get_weighted_average(
    data=port_warf_df["warf_worst_rtg"],
    weights=port_warf_df["weight"] / 100,
)

print(f"WARF: {avg_warf:.2f}")

avg_warf = rtg.get_weighted_average( data=port_warf_df["warf_worst_rtg"], weights=port_warf_df["weight"] / 100, ) print(f"WARF: {avg_warf:.2f}")

WARF: 165.58

In [4]:

avg_warf_equivalent_rating = rtg.get_ratings_from_warf(
    warf=avg_warf,
    rating_provider="SP",
)

print(f"Portfolio equivalent WARF rating: {avg_warf_equivalent_rating}")

avg_warf_equivalent_rating = rtg.get_ratings_from_warf( warf=avg_warf, rating_provider="SP", ) print(f"Portfolio equivalent WARF rating: {avg_warf_equivalent_rating}")

Portfolio equivalent WARF rating: A-

There you go. The portfolio WARF is A-.

Do you prefer to show the rating using Moody’s rating scale? — It’s as simple as changing the rating_provider argument to “Moody”.

In [5]:

avg_warf_equivalent_rating = rtg.get_ratings_from_warf(
    warf=avg_warf,
    rating_provider="Moody"
)

print(f"Portfolio equivalent WARF rating: {avg_warf_equivalent_rating}")

avg_warf_equivalent_rating = rtg.get_ratings_from_warf( warf=avg_warf, rating_provider="Moody" ) print(f"Portfolio equivalent WARF rating: {avg_warf_equivalent_rating}")

Portfolio equivalent WARF rating: A3

Compute the WARF buffer¶

The WARF buffer is the distance from the current WARF to the next maxWARF level. It determines the room until a further rating downgrade takes place.

Earlier, we determined that the portfolio WARF is equal to 165.58. Let's compute the WARF buffer.

The corresponding maxWARF value is 220. The buffer is then equal to 220 - 165.58 = 54.42.

pyratings provides the function get_warf_buffer to help you compute the WARF buffer.

In [6]:

print(f"WARF buffer: {rtg.get_warf_buffer(avg_warf):.2f}")

print(f"WARF buffer: {rtg.get_warf_buffer(avg_warf):.2f}")

WARF buffer: 54.42