evtpooling 0.1.4

evtpooling contains the framework needed to improve tail risk forecasts

evtpooling contains the framework needed to improve tail risk forecasts through robust data cleaning, transformation, and loss return calculations. It provides flexible ETL utilities for handling time series stock data, validating completeness, transforming data, and calculating daily and weekly loss returns.

Features

• Full ETL pipeline for financial time series data

• Data validation with dtype checking

• Missing data imputation by group means

• Categorical cleaning and fuzzy matching for string variables

• Daily percentage loss return calculations

• Weekly loss return calculations with anchor logic

• Flexible pivoting to generate wide-format datasets for downstream modeling

• Clean architecture with separate transform and test modules

Installation

You can install the released version from PyPI using:

pip install evtpooling

Or install directly from the source (development version):

git clone https://github.com/JTKimQF/evtpooling.git
cd evtpooling
pip install -e .

Usage Example

Example ETL usage:

from evtpooling import (
    extract_file,
    transform_data,
    load_file,
    etl_pipeline
)

# filepath = 'path/to/your/data.csv'

clean_df = etl_pipeline(filepath)

Documentation

Full documentation and function reference is available inside the code base (src/evtpooling/etl/transform.py).

License

MIT License

Copyright (c) 2025 J.T. Kim

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