AgentSkillsCN

atft-research

驱动ATFT-GAT-FAN输出的定量分析、因子诊断及报告。

SKILL.md
--- frontmatter
name: atft-research
description: Drive quantitative analysis, factor diagnostics, and reporting for ATFT-GAT-FAN outputs.
proactive: true

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.