Claude-skill-registry atft-research

Drive quantitative analysis, factor diagnostics, and reporting for ATFT-GAT-FAN outputs.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/atft-research" ~/.claude/skills/majiayu000-claude-skill-registry-atft-research && rm -rf "$T"

manifest: skills/data/atft-research/SKILL.md

source content

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
```
.

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.