ATFT Research Skill

ATFT Research Skill

Mission

• Quantify performance (Sharpe, RankIC, hit ratio) across horizons and cohorts.
• Inspect feature contributions, leakage risks, and stability of graph-based factors.
• Produce stakeholder-ready artifacts (reports, dashboards, notebooks).

Engagement Signals

• Requests to “analyze results”, “generate research report”, “compare to baseline”, “explain factor drift”.
• Need to validate new model output or dataset revisions before release.
• Desire for exploratory notebooks, plots, or KPI dashboards.

Baseline Workflow

1. Confirm availability of latest run: ls -lt runs | head.
2. Load metrics: python scripts/​research/​summarize_run.py --run runs/<timestamp>.
3. Compute comparison vs baseline:
   • make research-baseline RUN=runs/<timestamp> — compares to curated benchmark.
   • make research-plus RUN=runs/<timestamp> — full bundle (feature importance, turnover, drawdowns).
4. Plot diagnostics:
   • python scripts/​research/​plot_metrics.py --run runs/<timestamp> --horizons 1 5 10 20.
   • python scripts/​research/​graph_analytics.py --dataset output/​ml_dataset_latest_full.parquet.
5. Publish:
   • Output stored in reports/<timestamp>/.
   • Update docs/​research/​weekly_digest.md.

Specialized Analyses

Factor Stability / Drift

• python scripts/​research/​factor_drift.py --window 60 --features top50.
• python scripts/​research/​check_leakage.py --dataset output/​ml_dataset_latest_full.parquet.
• Alert when drift Z-score > 2.3 or leakage detection fails; escalate to pipeline skill to rebuild dataset.

Regime Segmentation

• python scripts/​research/​regime_detector.py --regimes 4 --method gaussian_hmm.
• python scripts/​research/​evaluate_by_regime.py --run runs/<timestamp> --regime-file output/​regimes/​latest.parquet.

Risk & Compliance

• python scripts/​research/​limit_checker.py --run runs/<timestamp> — verifies VAR, exposure, and shorting constraints.
• pytest tests/​research/​test_safety_constraints.py -k exposure if guard fails.

Visualization Arsenal

• make research-report FACTORS=returns_5d,ret_1d_vs_sec HORIZONS=1,5,10,20.
• python scripts/​research/​notebooks/​render.py docs/​notebooks/​performance_atlas.ipynb.
• python tools/​chart_creator.py --input reports/<timestamp>/​summary.json --output outputs/​figures/.

Data Sources

• Primary dataset: output/​ml_dataset_latest_full.parquet
• Model outputs: runs/<timestamp>/​predictions.parquet
• Feature metadata: dataset_features_detail.json
• Market benchmarks: data/​benchmarks/​nikkei225.parquet

Reporting Standards

• Include KPIs: Sharpe, RankIC, Top/​Bottom decile returns, MaxDD, Turnover.
• Break out metrics by sector (33 TSE industry codes) and market cap terciles.
• Document experiment context: dataset version hash, training config file, git SHA.
• Archive final report under docs/​research/​archive/<YYYY-MM-DD>_run_<timestamp>.md.

Codex Collaboration

• Engage ./​tools/​codex.sh "Generate new factor hypothesis from latest run" to synthesize research leads using Codex search + reasoning stack.
• Run codex exec --model gpt-5-codex "Summarize regime analysis findings in docs/​research/​weekly_digest.md" for automated reporting drafts.
• Feed Codex-generated notebooks or scripts back through this skill for validation before sharing with stakeholders.