Source code for uklib.products.with_profits.WP_UK_A.Data
# modelx: pseudo-python
# This file is part of a modelx model.
# It can be imported as a Python module, but functions defined herein
# are model formulas and may not be executable as standard Python.
"""Input data shared by every by-policy projection.
The three input CSVs are read here, **once per model**, and referenced from
:mod:`~.WP_UK_A.Projection` as ``data``. :mod:`~.WP_UK_A.Projection` is parameterized by
``point_id``, so each ``Projection[N]`` is a separate ItemSpace with its own cells
cache; if the readers lived there, every model point would re-read every file. Holding
them in an unparameterized Space reads each file once no matter how many policies are
projected.
Inputs are **external files**: plain CSVs in the model folder's parent directory,
``products/with_profits/``, rather than data stored inside the model. The model folder
therefore holds nothing but formulas — no ``_data/``, no IOSpec, no embedded values —
so a diff of the model shows logic changes only. This follows ``annuallife.TradLife_A``;
contrast ``basiclife.BasicTerm_S``, which keeps its inputs *inside* the model through
modelx's IOSpec machinery.
The consequence worth knowing: **the model is not portable on its own.** Copying the
``WP_UK_A`` folder without its parent's CSVs produces a model that reads and then fails
on first evaluation.
:func:`input_dir` resolves the directory from ``_model.path.parent`` at run time, so
the model works wherever the repository is checked out. Each table has a filename
Reference and a reader Cells:
====================== ========================== ==============================
Reference Cells File
====================== ========================== ==============================
model_point_file model_point_table() model_point_table.csv
mort_table_file mort_table() mort_table.csv
lapse_table_file lapse_table() lapse_table.csv
====================== ========================== ==============================
Note how little is in a file. The discretionary scale that actually drives this product
— the bonus rates, the smoothing cap, the target corridor, the guarantee-fill target,
the charge levels — lives in model point columns and ``Projection`` References rather
than in a rate table, and that is not an oversight: **none of it is published**. Firms'
principles and practices documents describe the discretion and withhold the numbers, so
every one of those values is a standardization, and putting them where a reader trips
over them is better than filing them in a table that looks like data.
"""
from modelx.serialize.jsonvalues import *
_formula = None
_bases = []
_allow_none = None
_spaces = []
# ---------------------------------------------------------------------------
# Cells
[docs]
def model_point_table():
"""The model point table, read from *model_point_table.csv*."""
return pd.read_csv( # noqa: F821
input_dir() / model_point_file, index_col="point_id") # noqa: F821
[docs]
def mort_table():
"""The base annual mortality rates by sex and age, from *mort_table.csv*.
A **[std]** proxy shaped like the ONS national life tables, which are the only
freely redistributable UK mortality source; CMI tables issued after March 2013 are
restricted to Authorised Users, so no current insured rate can be reproduced here.
``Projection.mort_be_factor`` carries the allowance for population mortality being
heavier than insured experience. Sorted on read, because ``Projection.mort_rate``
indexes into it.
"""
return pd.read_csv( # noqa: F821
input_dir() / mort_table_file, index_col=["sex", "age"]).sort_index()
[docs]
def lapse_table():
"""The base annual surrender rates by chassis and policy year, from *lapse_table.csv*.
Flat on the bond chassis and duration-declining on the endowment. Both are **[std]**
drafting constructions - no public UK with-profits lapse experience was retrieved -
and the dynamic multipliers layered on them in ``Projection.surr_rate`` matter more
than the levels do, because anti-selective exit when guarantees are in the money is
the dominant behavioural risk on this product.
"""
return pd.read_csv( # noqa: F821
input_dir() / lapse_table_file, # noqa: F821
index_col=["chassis", "policy_year"]).sort_index()
# ---------------------------------------------------------------------------
# References
model_point_file = "model_point_table.csv"
mort_table_file = "mort_table.csv"
lapse_table_file = "lapse_table.csv"
pd = ("Module", "pandas")
