Atft Research
by wer-inc
Drive quantitative analysis, factor diagnostics, and reporting for ATFT-GAT-FAN outputs.
Skill Details
Repository Files
1 file in this skill directory
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
- Confirm availability of latest run:
ls -lt runs | head. - Load metrics:
python scripts/research/summarize_run.py --run runs/<timestamp>. - 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).
- 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.
- Publish:
- Output stored in
reports/<timestamp>/. - Update
docs/research/weekly_digest.md.
- Output stored in
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 exposureif 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.
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