data-split

Name: data-split
Availability: InStock
Author: adennng

Generate training, validation, and walk-forward datasets for financial models.

33|2|Updated May 13, 2026

Search Tags:#financial modeling #time series #backtesting #quantitative finance #data splitting

Ready to run

npx skills add https://github.com/adennng/stock_strategy_lab --skill data-split

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-split
Source: https://github.com/adennng/stock_strategy_lab/tree/main/src/strategy_lab/skills/signal_agent/data-split
Command: npx skills add https://github.com/adennng/stock_strategy_lab --skill data-split

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the creation of time series datasets for financial model training and validation, eliminating the need for manual data splitting.

Core Features & Use Cases

Automated Data Splitting: Generates training, validation, and walk-forward validation datasets.
Flexible Parameters: Customizable split ratios and fold counts for different validation strategies.
Use Case: For SignalAgent, it provides pre-splitted datasets for parameter tuning and robustness testing of trading strategies.

Quick Start

Run the data-split skill to create datasets for your SignalAgent model by providing the run state path.

Frequently Asked Questions about data-split

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I split time series data for financial model backtesting?▼

Splitting time series data for backtesting requires generating distinct training and validation datasets to prevent look-ahead bias. This Skill automates that division, creating segmented datasets necessary for robust quantitative finance model evaluation.

Can I use walk-forward validation for quantitative finance parameter tuning?▼

Walk-forward validation is fully supported for quantitative finance parameter tuning. You can customize fold counts and split ratios to generate sequential datasets, enabling continuous robustness testing and parameter optimization across evolving market conditions.

What is the best way to prepare datasets for a SignalAgent trading strategy?▼

Preparing datasets for a SignalAgent trading strategy involves providing the model run state path. This Skill reads the path and automatically outputs pre-split training and validation datasets tailored for parameter tuning and robustness testing.

Do I need manual data manipulation to create training and validation splits?▼

Manual data manipulation is not needed to create training and validation splits. This Skill eliminates manual effort by automating the generation of time series datasets, provided you supply the required run state path for data access.

Are flexible split ratios supported for custom financial modeling datasets?▼

Flexible split ratios are supported for custom financial modeling datasets. You can define specific ratios and fold counts to match different validation strategies, ensuring the generated time series segments align with your backtesting requirements.

When should I automate time series data splitting instead of manual segmentation?▼

Automating time series data splitting is ideal when running frequent backtests or parameter optimizations. It ensures consistent dataset generation for walk-forward validation, reducing manual errors and accelerating quantitative finance model iteration.