ATFT Pipeline Skill

ATFT Pipeline Skill

Mission

• Provision fresh or historical parquet datasets for ATFT-GAT-FAN with GPU-accelerated ETL.
• Maintain deterministic feature graphs (approx. 395 engineered factors, 307 active).
• Guard J-Quants API quota, credential sanity, and cache health to prevent training stalls.

When To Engage

• Any request mentioning dataset builds, ETL, J-Quants, cache, RAPIDS/​cuDF, or feature graph refresh.
• Pre-training sanity checks (“ensure latest dataset”, “verify cache integrity”).
• Recovery tasks (“resume interrupted dataset job”, “clean corrupted cache shards”).

Preflight Checklist

• Confirm nvidia-smi reports at least one free A100 80GB GPU; fallback to CPU only if GPU unavailable.
• Validate credentials: .env contains JQUANTS_AUTH_EMAIL/​PASSWORD and JQUANTS_PLAN_TIER.
• Ensure python -m pip install -e . already executed (dependencies + entry points).
• Check latest health snapshot: tools/​project-health-check.sh --section dataset.
• Inspect existing dataset for reuse: ls -lh output/​ml_dataset_latest_full.parquet.

Core Playbooks

1. Background Five-Year Refresh (default)

1. make dataset-check-strict — GPU + secrets verification.
2. make dataset-bg START=<optional> END=<optional> — SSH-safe background run with logging in _logs/​dataset.
3. tail -f _logs/​dataset/​*.log — monitor progress (auto prints PID + PGID).
4. make cache-stats — ensure cache hit-rate & size in expected bounds (<2.5 TB).
5. python scripts/​pipelines/​run_full_dataset.py --dry-run — confirm metadata integrity without rebuild.

2. Hotfix / Forced Refresh

1. make dataset-gpu-refresh START=YYYY-MM-DD END=YYYY-MM-DD — bypasses cached parquet + API throttle aware.
2. make datasets-prune — keep latest dataset generation only.
3. make cache-prune CACHE_TTL_DAYS=90 — evict stale graph shards to recover disk.

3. Resource-Constrained Fallback

1. make dataset-check — relaxed diagnostics (CPU acceptable).
2. make dataset-cpu START=YYYY-MM-DD END=YYYY-MM-DD — chunked Pandas path.
3. make dataset-safe-resume — resume from last safe checkpoint if memory pressure triggered fallback.

4. Graph Feature Investigation

1. python scripts/​pipelines/​run_full_dataset.py --inspect-graph --start YYYY-MM-DD --end YYYY-MM-DD.
2. python -c "import polars as pl; df = pl.read_parquet('output/​ml_dataset_latest_full.parquet'); print(df.select(pl.all().is_null().sum()))" — null audit.
3. make cache-monitor — per-window edge density + overlap stats.

Observability Hooks

• _logs/​dataset/ for job logs, cache/​*.json metadata for cache.
• ml_dataset_latest_full_metadata.json for column coverage & horizon alignment.
• benchmark_output/​dataset_timestamps.json to confirm pipeline duration vs baseline (target: <42m GPU path).

Failure Triage

• Credential errors → run python scripts/​pipelines/​run_full_dataset.py --auth-test.
• CUDA OOM → rerun with make dataset-safe (40GB RMM pool pre-configured).
• API rate limits → throttle via make dataset-gpu REFRESH_THROTTLE=1.
• Corrupted parquet → make dataset-rebuild then python tools/​parquet_validator.py output/​ml_dataset_latest_full.parquet.

Codex Collaboration

• Escalate complex ETL debugging or architectural refactors via ./​tools/​codex.sh "Diagnose dataset pipeline bottleneck" (leverages OpenAI Codex deep reasoning).
• For long-running autonomous maintenance, schedule ./​tools/​codex.sh --max --exec "Perform full dataset pipeline audit" off-hours (uses .mcp.json from the Codex repo for filesystem/​git context).
• When Codex proposes changes, sync learnings back here and refresh dataset runbooks if any commands or defaults shift.

Handoff Notes

• Always update dataset_features_detail.json if schema changes.
• Announce new dataset snapshot in EXPERIMENT_STATUS.md with generation timestamp and settings.
• Surface anomalies (missing tickers, new features) via docs/​data_quality/ reports.