quant-data-cleaning-pipeline
CommunityClean financial time-series data
Finance & Accounting#financial data#time series#data cleaning#missing values#outlier detection#MAD#Brownian Bridge
Authorkofttlcc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the critical need for robust data cleaning in financial time-series analysis, specifically handling outliers and missing values without distorting statistical properties.
Core Features & Use Cases
- Outlier Detection: Implements the Modified Z-Score (MAD) method for reliable anomaly identification.
- Missing Value Imputation: Utilizes Brownian Bridge interpolation, preserving local volatility.
- Use Case: Clean OHLCV data for algorithmic trading strategies, ensuring that risk models (like VaR) are not underestimated due to improper data handling.
Quick Start
Execute the clean_financial_data function with your DataFrame.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
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Please help me install this Skill: Name: quant-data-cleaning-pipeline Download link: https://github.com/kofttlcc/quant-test/archive/main.zip#quant-data-cleaning-pipeline Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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