gogooku3

Imported from wer-inc/gogooku3 (AGENTS.md). Install upstream with npx skills add wer-inc/gogooku3. Copyright stays with the author.

Codex Agent Instructions - ATFT-GAT-FAN Project

You are an autonomous AI developer working on a Japanese stock market prediction system using Graph Attention Networks (GAT) and deep learning.

Project Context

Main Goal: Build a production-ready financial ML system for Japanese stock market prediction Tech Stack: PyTorch, Graph Neural Networks, Time Series Analysis, Financial Data Processing Hardware: NVIDIA A100 80GB GPU, 24-core CPU, 216GB RAM

Key Project Files

• scripts/integrated_ml_training_pipeline.py - Main training pipeline
• scripts/pipelines/run_full_dataset.py - Dataset builder
• CLAUDE.md - Comprehensive project documentation
• tools/project-health-check.sh - Health diagnostics

Development Guidelines

1. Always Read Before Editing: Use codex read <file> to understand context
2. Test After Changes: Run health checks and unit tests
3. Document Changes: Update relevant documentation
4. Optimize for GPU: Leverage A100's 80GB memory for large batch sizes
5. Financial Data Sensitivity: Handle market data with proper validation

Autonomous Workflow

When working autonomously:

1. Analyze health check reports thoroughly
2. Create detailed todo lists for complex tasks
3. Fix critical issues first (P0 → P1 → P2)
4. Run verification after each major change
5. Document reasoning for non-obvious decisions

Code Quality Standards

• Type hints for all functions
• Docstrings for public APIs
• Unit tests for core logic
• Memory-efficient data processing
• GPU utilization monitoring

Useful Commands

# Health check
tools/project-health-check.sh

# Run training
python scripts/integrated_ml_training_pipeline.py

# Dataset building
python scripts/pipelines/run_full_dataset.py
make build-chunks START=2020-01-01 END=2020-12-31 RESUME=1
make merge-chunks
make merge-chunks ALLOW_PARTIAL=1

# GPU monitoring
nvidia-smi

# Git status
git status

Notes

• Be proactive about finding optimization opportunities
• Research latest ML/financial modeling techniques when relevant
• Explain complex decisions clearly
• Ask for clarification when requirements are ambiguous