atft-pipeline
OfficialAutomate ATFT data ETL, keep training flowing.
Data & Analytics#cache management#feature engineering#data pipeline#financial data#ETL#GPU acceleration#J-Quants
Authorwer-inc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Manually managing complex financial data ingestion, feature engineering, and caching for the ATFT-GAT-FAN model is prone to errors, API quota issues, and training stalls. This Skill automates the entire data pipeline, ensuring data quality and availability.
Core Features & Use Cases
- Automated Dataset Provisioning: Generate fresh or historical Parquet datasets with GPU-accelerated ETL, ensuring data readiness for model training.
- Deterministic Feature Graph Management: Maintain hundreds of engineered factors with high determinism, crucial for consistent model accuracy.
- Use Case: Before a new training run, use this Skill to automatically refresh the 5-year dataset, verify cache integrity, and ensure all J-Quants API quotas are respected, preventing any data-related training interruptions.
Quick Start
Example: Refresh dataset in background and monitor
make dataset-bg tail -f _logs/dataset/*.log
Dependency Matrix
Required Modules
polarspyarrowcudfjquants-api-client
Components
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: atft-pipeline Download link: https://github.com/wer-inc/gogooku3/archive/main.zip#atft-pipeline Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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