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1# Replication guide (WP46)23## 0. Environment4```bash5cd finance/wp46_garch_benchmark6uv venv -p 3.12 .venv && uv pip install -p .venv/bin/python -e ".[dev]" # arch==8.0.0 pinned7.venv/bin/python -m pytest -q tests/unit tests/integration # ~1 min, no data needed8.venv/bin/python -m pytest -q tests/simulation -m slow # Monte Carlo recovery, ~5 min9Rscript -e 'install.packages(c("rugarch","jsonlite"))' && .venv/bin/python -m pytest -q tests/replication -m r # rugarch cross-check10```11Without a FinData OS credential, stop here: the synthetic fixture `data/synthetic/` (see `scripts/make_synthetic.py`) exercises the12whole pipeline (panel → forecasts → losses → analysis) on simulated data.1314## 1. Data (requires the owner's FinData OS key; FirstRate Data private licence)15```bash16export FINDATA_API_KEY_FILE=~/findata-secrets/api-key-wp46.txt # or FINDATA_API_KEY17.venv/bin/python scripts/audit_findata.py # docs/findata_audit.md + results/manifests/findata_audit.json18.venv/bin/python scripts/ingest_bars.py --workers 3 # 1-min + daily bars -> ~/wp46-cache/bars (≈ 230 M rows, ≈ 25 min on the data node)19.venv/bin/python scripts/build_panels.py --workers 10 # daily panels -> ~/wp46-cache/panels, results/manifests/panels_manifest.json20.venv/bin/python scripts/build_rcov.py # Tier C realized covariances -> ~/wp46-cache/rcov21```22Dataset versions and SHA-256 of every exported file are in `results/manifests/ingest_manifest.json`; panel hashes in `panels_manifest.json`.2324## 2. Pilot and protocol25```bash26.venv/bin/python scripts/run_pilot.py --symbols SPY,AAPL,CL,EURUSD # validation period only -> results/metrics/pilot/27git tag prereg-frozen # the protocol was frozen at 3590bd6 before any test-period loss28```2930## 3. Benchmark (3,276 jobs; < 1 h on six Apple-silicon nodes, ≈ 30 CPU-hours)31```bash32.venv/bin/python scripts/make_jobs.py --name main33.venv/bin/python scripts/dispatch_nodes.py sync-panels --name main34.venv/bin/python scripts/dispatch_nodes.py launch --jobs results/manifests/jobs_main.json --name main35.venv/bin/python scripts/dispatch_nodes.py status --name main36.venv/bin/python scripts/dispatch_nodes.py collect --name main # -> results/forecasts/main/<symbol>/<spec_id>.parquet (+ .fits.parquet, _runs.jsonl)37# single machine alternative:38.venv/bin/python scripts/run_benchmark.py --jobs results/manifests/jobs_main.json --workers 8 --out results/forecasts/main39.venv/bin/python scripts/run_tier_c.py # Tier C (data node)40```4142## 4. Evaluation and exhibits43```bash44.venv/bin/python scripts/compute_losses.py --run main --workers 6 # results/metrics/losses_main/45.venv/bin/python scripts/analyze.py --run main --period test # results/metrics/analysis_main_test/, results/tables/pooled_main_test.csv46.venv/bin/python scripts/analyze.py --run main --period validation # exploratory, reported as such47.venv/bin/python scripts/analyze_tier_c.py test48.venv/bin/python scripts/build_paper.py --run main --period test # paper/numbers.tex, paper/tables/*.tex, paper/figures/*.pdf49cd paper && make # latexmk -> main.pdf50```5152## 5. Provenance53`results/forecasts/<run>/_runs.jsonl` records, per job, git SHA, host, Python/arch/numpy/scipy/pandas versions, CPU count, wall and54CPU time, peak RSS and status counts. `results/manifests/figure_audit.csv` lists the sources of every figure.55