Change Log¶
0.3.1¶
Changes:¶
Update dependencies to recent versions
Update code base to handle Pandas 2.0
Update URLs in Documentation
Add stub for payment to a schedule in payment rules
Fixed:¶
Fixed bug in setup.py that resulted in omission of Jinja template files
0.3.0¶
Changes:¶
Version 0.3.0 introduces a new Terms Sheet format that is NOT backwards-compatible with prior versions of PyMBS. This new format stores tabular data as lists of dictionaries. All numerical values that are inputs to the model are stored as String values and converted into Python Decimal objects when the JSON is parsed.
See https://docs.python.org/3/library/decimal.html for more information on Python’s decimal module for fixed point and floating point arithmetic.
0.2.1¶
Changes:¶
Revised the way that we deal with some exceptions so that they are handled gracefully across various IPython kernels, including the Jupyter Notebook. Exceptions encountered when used outside of IPython/Jupyter are handled better as well.
0.2.0¶
Changes:¶
Changed all references to group_id to string values
0.1.5¶
Changes:¶
Fixed an issue with missing package config/data
0.1.0¶
Changes:¶
Significant update to Configuration management. Created new Config object to hold configuration settings. Provide ability to set settings via YAML file and/or environment variables.
Added some basic logging, especially in the Config object.
Significant updates to documentation.
0.0.2¶
Changes:¶
Significant update to intital architecture, but still in alpa stage.
Run cash flows for Assumed Collateral replines only.
- Implement payment functionality for the following payment rule types:
Calculate (Basic functioanlity for calulating buckets of cash)
Pay Accrue, for Z bond accrual
Pay Pro Rata
Pay Sequential
Generates cash flows for any waterfall that makes use of the above rules.
Introduces the Tranche object, to enable flexibilty when making payments to tranches from the waterfall. This appears to be a much better approach than trying to use Pandas DataFrames for this task. Once the cash flows have been generated for the tranche, they are converted to a DataFrame for display and use in a Jupyter Notebook.
Calculate Weighted Average Lives (WALs) for all tranches in the model that are paid using one of the pay rules described above.
0.0.1¶
Changes:¶
Initial version.