# Reproducibility Audit

This directory documents the data provenance for every figure, table, and
quantitative claim in the paper. It is intended as a reviewer-facing
reproducibility manifest: any number, panel, or claim in the paper should
be traceable to a specific backtest run, CSV, or source document.

## Audit anchor

- **Commit:** `a4bf9e6` (`fix(figure): use 6M case-study runs for US sector matrix`)
- **Audit date:** 2026-04-28

The audit was performed after fixing a previously-discovered run-ID
mismatch in the U.S. sector matrix figure (the script wired the figure to
GPT-5.4 3M ablation runs but the paper text described it as a 6M case
study). All other figures, tables, and prose claims were re-verified
against actual run metrics during the same audit.

## Files in this directory

| File | Scope |
|---|---|
| `README.md` | This overview |
| `run_inventory.md` | All backtest runs referenced anywhere in the paper, indexed by experiment family |
| `figures.md` | Per-figure provenance (which script, which runs, which data file) |
| `tables.md` | Per-table provenance plus verified cell values |
| `claims.md` | Key quantitative claims in the prose with data sources |

## How to verify a number in the paper

1. Locate the figure/table/claim in question.
2. Look it up in `figures.md`, `tables.md`, or `claims.md`.
3. The audit doc names the source (run ID, CSV path, or source doc).
4. The actual run artifacts live in the parent repository under
   `reports/backtest/<run_id>/` (`metrics.json`, `trades.csv`,
   `equity_curve.csv`).

## Metric scope (v1)

Each `runs/**/metrics.json` ships the metric set actually used in the
paper plus the supporting fidelity diagnostics:

- **Performance:** `total_return`, `annualized_return`, `max_drawdown`,
  `max_drawdown_duration`, `volatility`, `sharpe_ratio`, `sortino_ratio`
- **Benchmark anchor:** `benchmark_total_return`,
  `benchmark_annualized_return`, `benchmark_source`
- **Fidelity:** `total_trades`, `trading_days`, `avg_position_days`,
  `avg_cash_ratio`, `avg_gross_exposure`, `win_rate`
- **Profile-specific:** e.g. `value_filter_pass_rate`,
  `value_consistency_score` for `Fundamental Value`

Benchmark-relative fields (`alpha`, `beta`, `tracking_error`,
`information_ratio`, `excess_return`, `calmar_ratio`,
`up_capture`, `down_capture`) are **intentionally not shipped in v1**.
They were computed per run during development but require a harmonized
benchmark-return series and a risk-free rate that are out of scope for
the matched-window protocol used here, and the implementation gave
different values across the U.S. and CN paths during validation. A
v1.1 release will reintroduce them after cross-substrate harmonization;
the v1 paper does not depend on these fields.

The same applies to `derived/all_metrics.csv` and
`derived/gpt54_robustness/gpt54_us_3m_summary.csv`: the v1 release ships
only the columns above. Bundle maintainers can rerun
`tools/strip_benchmark_relative_metrics.py` to enforce this scope on a
freshly built bundle.

## Data layout (parent repository)

```
<repo_root>/
├── latex/                          # Paper source (this is the Overleaf project)
│   ├── sec/                        # Main-text sections
│   ├── appendix/                   # Appendices
│   ├── figures/                    # Compiled figure PDFs/PNGs
│   ├── scripts/                    # Figure/table generation scripts
│   ├── data/artifact_ingest/          # Optional static input artifacts for local rebuilds
│   └── audit/                      # ← This directory
└── reports/
    ├── backtest/<run_id>/          # Per-run artifacts (metrics.json, trades.csv, ...)
    └── multi_personality/<bundle>/ # Cross-mandate comparison bundles
```