model_clo_insight.engines.pool_builders module

Module for the methods which build hypo pools

build_pool(ob_max, ind_max, riskscore_max, ob_ex, ind_ex, risk_score_tbl, tables, force_high, force_low, is_simple_industry, n_simple_industry)

Creates a hypothetical pool

General Algorithm

  1. Number of obligors is determined based on concentration limitations. Start with exceptions, then use base

  2. Rating mixture of the pool is determined based on max DBRSM Risk Score. First, impose credit dispersion via the force_low and force_high parameters. Then solve for rating mixture on the remaining pool such that the total pool risk score is approximately the target riskscore_max

  3. Apply industry exceptions starting with largest obligors

  4. Apply industries to the remaining obligors using the base max industry limit

Parameters

  • ob_max (float) – base max obligor concentration limitation

  • ind_max (float) – base max industry concentration limitation

  • riskscore_max (float) – max DBRSM risk score of the pool

  • ob_ex (list[objects.ConcLimitException]) – obligor concentration limitations

  • ind_ex (list[objects.ConcLimitException]) – industry concentration limitations

  • risk_score_tb (dict[int, float]) – lookup table for DBRSM Risk Score

  • tables (dict) – lookup tables. Must contain keys: ind_codes, dscore, dbrs_rtg

  • force_high (tuple) – (rating_number, pct) for higher rtg prior to solving for risk score, force this on pool

  • force_low (tuple) – (rating_number, pct) for lower rtg prior to solving for risk score, force this on pool

  • is_simple_industry (bool) – if true, industry allocation is done on a repeating sequence. Hypo only iterates on obligors in this case.

  • n_simple_industry (int) – if is_simple_industry is True, this is the number of industries to repeat in sequence

Returns

pool of Obligors. Assumes one dummy security per obligor, these are setup later.

Return type

OrderedDict[objects.Obligor]

Notes

This method does not constrain on pool-wide diversity metrics

build_pool_ind_mult_of_ob(ob_max, ind_mult, ind_floor, riskscore_max, ob_ex, ind_ex, risk_score_tbl, tables, force_high, force_low, is_simple_industry, n_simple_industry)

Used When Building a Pool Where Base Industry is a Function of base ob and ind floor

Parameters

  • ob_max (float) – base max obligor concentration limitation

  • riskscore_max (float) – maximum DBRSM risk score constraint

  • ob_ex (list[clo_internal_objects.ConcLimitException]) – obligor concentration limitations

  • ind_ex (list[clo_internal_objects.ConcLimitException]) – industry concentration limitations

  • tables (dict[str, np.ndarray]) – Lookup tables for diversity score and industry codes

  • ind_mult (float) – base industry concentration is a function of this multiple * obligor base conc limit

  • ind_floor (float) – lowest level that the base industry concentration limitation is allowed to spin down to

Returns

pool of Obligors. Assumes one dummy security per obligor

Return type

OrderedDict[objects.Obligor]

build_rating_mixture(riskscore_max, hypo_pool, risk_score_tbl, force_high, force_low)
Parameters

  • riskscore_max (float) – maximum DBRSM risk score constraint

  • hypo_pool (list[clo_internal_objects.Obligor]) – pool of obligors to calculate the metric on

  • risk_score_tbl (dict[int, float]) – lookup table for DBRSM Risk Score

  • force_high (tuple) – (rating_number, pct) for higher rtg prior to solving for risk score, force this on pool

  • force_low (tuple) – (rating_number, pct) for lower rtg prior to solving for risk score, force this on pool

Returns

length of hypo pool, array of ratings after applying force high, force low ratings

Return type

numpy.ndarray