Financial Dataset Preprocessor

A Python package for preprocessing financial datasets from various sources. This package provides tools and utilities for cleaning, transforming, and preparing financial data for analysis.

Version Updates

v0.3.6 (2025-05-23)

• Added filtering functionality to Menu 2206 preprocessor
• Improved data quality by removing summary rows

v0.3.5 (2025-05-20)

• Enhanced Menu 2206 preprocessor with column renaming functionality
• Improved fund code handling in portfolio analysis

v0.3.4 (2025-05-20)

• Added Menu 2206 preprocessor module
• Added time series basis utilities
• Added Bloomberg time series preprocessor
• Enhanced integration with universal_timeseries_transformer

Features

• Menu 2205 Preprocessor
  • Corporation Name Finder
  • Domestic Beneficiary Certificates Processing
  • Domestic Bonds Analysis
  • Repo Agreement Processing
  • Borrowings Management
• Menu 2206 Preprocessor
  • Fund Portfolio Analysis
  • Investment Asset Classification
• Bloomberg Time Series Preprocessor
  • Index Data Processing
  • Currency Data Processing
• Time Series Utilities
  • Date Range Operations
  • Time Series Extension
• Additional preprocessors for other financial datasets (coming soon)

Installation

You can install the package using pip:

pip install financial_dataset_preprocessor

Requirements

• Python >= 3.11
• Dependencies are listed in requirements.txt

Usage Examples

1. Search for Funds with Bonds

from financial_dataset_preprocessor import (
    search_funds_having_domestic_bonds,
    get_domestic_bonds_by_fund
)

# Get all funds that have domestic bonds
fund_bonds = search_funds_having_domestic_bonds(date_ref='2025-02-21')

# Get bond details for a specific fund
fund_code = '100075'
bond_details = get_domestic_bonds_by_fund(fund_code=fund_code, date_ref='2025-02-21')

2. Analyze Fund Borrowings

from financial_dataset_preprocessor import (
    search_funds_having_borrowings,
    get_borriwings_by_fund
)

# Find funds with borrowings
funds_with_borrowings = search_funds_having_borrowings(date_ref='2025-02-21')

# Get borrowing details
fund_code = '100075'
borrowing_details = get_borriwings_by_fund(fund_code=fund_code, date_ref='2025-02-21')

3. Check Repo Agreements

from financial_dataset_preprocessor import (
    search_funds_having_repos,
    get_repos_by_fund
)

# Find funds with repos
funds_with_repos = search_funds_having_repos(date_ref='2025-02-21')

# Get repo details for a specific fund
fund_code = '100075'
repo_details = get_repos_by_fund(fund_code=fund_code, date_ref='2025-02-21')

Development

To set up the development environment:

1. Clone the repository
2. Create a virtual environment
3. Install dependencies:

pip install -r requirements.txt

License

This project is licensed under a proprietary license. All rights reserved.

Terms of Use

• Source code viewing and forking is allowed
• Commercial use is prohibited without explicit permission
• Redistribution or modification of the code is prohibited
• Academic and research use is allowed with proper attribution

Author

June Young Park
AI Management Development Team Lead & Quant Strategist at LIFE Asset Management

LIFE Asset Management is a hedge fund management firm that integrates value investing and engagement strategies with quantitative approaches and financial technology, headquartered in Seoul, South Korea.

Contact

• Email: juneyoungpaak@gmail.com
• Location: TWO IFC, Yeouido, Seoul