Consolidate ratings¶

Oftentimes, an individual security/entity has ratings from several rating agencies. For example, a security might have a BBB- rating from S&P, a BBB rating from Fitch, and a Ba1 rating from Moody's.
Now, let's imagine your investment guidelines only allow investments in securities that have an investment grade rating, i.e. Baa3/BBB- at a minimum. You might wonder which of the attached ratings is relevant? Is this particular security regarded as investment grade (as S&P and Fitch are indicating) or is it already a junk bond (as implied by Moody's)? — Well, there's no definitive answer, it simply depends.

There are several ways to answer this question. pyratings offers the following three functions in order to consolidate multiple ratings into a final rating:

1. get_best_ratings Choose the best rating among all rating agencies. That's the aggressive way.
2. get_second_best_ratings Choose the second-best rating among all rating agencies. That's a less aggressive way.
3. get_worst_ratings Choose the worst rating among all rating agencies. That's the conservative way.

In [1]:

import pandas as pd
import numpy as np

import pyratings as rtg

import pandas as pd import numpy as np import pyratings as rtg

In [2]:

ratings_df = pd.DataFrame(
    data=(
        {
            "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
            "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
            "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
        }
    ),
    index=[f'security_{i}' for i in range(5)]
)
ratings_df

ratings_df = pd.DataFrame( data=( { "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'], "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'], "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'], } ), index=[f'security_{i}' for i in range(5)] ) ratings_df

Out[2]:

	rating_S&P	rating_Moody's	rating_Fitch
security_0	AAA	Aa1	AA-
security_1	AA-	Aa3	AA-
security_2	AA+	Aa2	AA-
security_3	BB-	Ba3	B+
security_4	C	Ca	C

In [3]:

best_ratings = rtg.get_best_ratings(
    ratings=ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
)

second_best_ratings = rtg.get_second_best_ratings(
    ratings=ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
)
worst_ratings = rtg.get_worst_ratings(
    ratings=ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
)

pd.concat([best_ratings, second_best_ratings, worst_ratings], axis=1)

best_ratings = rtg.get_best_ratings( ratings=ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"] ) second_best_ratings = rtg.get_second_best_ratings( ratings=ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"] ) worst_ratings = rtg.get_worst_ratings( ratings=ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"] ) pd.concat([best_ratings, second_best_ratings, worst_ratings], axis=1)

Out[3]:

	best_rtg	second_best_rtg	worst_rtg
security_0	AAA	AA+	AA-
security_1	AA-	AA-	AA-
security_2	AA+	AA	AA-
security_3	BB-	BB-	B+
security_4	CC	C	C

Did you realize that the output dataframe only uses S&P/Fitch rating scales? If you prefer to get the results in a different ratings scale, you can specify the rating_provider_output argument. Also, if you don't specify rating_provider_input, pyratings tries to infer the correct rating provider from ratings_df column headers.

In [4]:

best_ratings = rtg.get_best_ratings(
    ratings=ratings_df, rating_provider_output="Moody",
)

second_best_ratings = rtg.get_second_best_ratings(
    ratings=ratings_df, rating_provider_output="Moody",
)
worst_ratings = rtg.get_worst_ratings(
    ratings=ratings_df, rating_provider_output="Moody"
)

pd.concat([best_ratings, second_best_ratings, worst_ratings], axis=1)

best_ratings = rtg.get_best_ratings( ratings=ratings_df, rating_provider_output="Moody", ) second_best_ratings = rtg.get_second_best_ratings( ratings=ratings_df, rating_provider_output="Moody", ) worst_ratings = rtg.get_worst_ratings( ratings=ratings_df, rating_provider_output="Moody" ) pd.concat([best_ratings, second_best_ratings, worst_ratings], axis=1)

Out[4]:

	best_rtg	second_best_rtg	worst_rtg
security_0	Aaa	Aa1	Aa3
security_1	Aa3	Aa3	Aa3
security_2	Aa1	Aa2	Aa3
security_3	Ba3	Ba3	B1
security_4	Ca	C	C