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The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. 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Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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{"parentUuid":"12d91f7b-689b-4e5c-baea-3d66e3855183","isSidechain":false,"promptId":"5493f38d-8d8e-461a-af5f-6f1cc7bdb6dc","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018B3Nudkdi7m2C6cPt7fDbG","type":"tool_result","content":"# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth with standardised predictors\n\n## Scientific question\nDoes a fixed-penalty ridge regression on eight standardised monthly predictors forecast next month's US industrial production growth better than two simple baselines — the naive (last value) forecast and the expanding historical mean — over 60 rolling origins from 2021-09 to 2026-08? The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n  - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n  - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n  - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n  - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:52 .\ndrwxr-xr-x  35 simon-pierreboucher  staff  1120 oct.  30 00:11 ..\n-rw-r--r--   1 simon-pierreboucher  staff   517 oct.  29 23:52 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:46 data\n-rw-r--r--   1 simon-pierreboucher  staff  1862 oct.  29 23:47 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  5520 oct.  29 23:47 TASK.md\ndata:\nip_predictors.csv\nREADME.md","is_error":false}]},"uuid":"78762106-2724-4119-ad8c-9fd2e687f9b1","timestamp":"2026-10-30T04:11:16.691Z","toolUseResult":{"stdout":"# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth with standardised predictors\n\n## Scientific question\nDoes a fixed-penalty ridge regression on eight standardised monthly predictors forecast next month's US industrial production growth better than two simple baselines — the naive (last value) forecast and the expanding historical mean — over 60 rolling origins from 2021-09 to 2026-08? The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n  - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n  - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n  - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n  - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:52 .\ndrwxr-xr-x  35 simon-pierreboucher  staff  1120 oct.  30 00:11 ..\n-rw-r--r--   1 simon-pierreboucher  staff   517 oct.  29 23:52 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:46 data\n-rw-r--r--   1 simon-pierreboucher  staff  1862 oct.  29 23:47 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  5520 oct.  29 23:47 TASK.md\ndata:\nip_predictors.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"12d91f7b-689b-4e5c-baea-3d66e3855183","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"aa1616b0-d652-4dcd-83c3-94772678cef4","isSidechain":false,"promptId":"5493f38d-8d8e-461a-af5f-6f1cc7bdb6dc","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Djz4hb3fvbYBEyo9k29S8K","type":"tool_result","content":"# data/README.md — F3-conf-03\n\n## ip_predictors.csv\nOne row per month (`month`, YYYY-MM-01), 1992-01 to 2026-09 (417 data rows), built from six FRED series (Federal Reserve Bank of St. Louis), each retrieved with `https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>` on 2026-10-30 (UTC times below). SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). Results therefore measure the predictability of the revised series under a stylised timing assumption — not historical real-time performance.\n{\n  \"task_id\": \"F3-conf-03\",\n  \"family\": \"F3\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n  \"files\": {\n    \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n    \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n    \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n    \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n  }\n}{\n  \"task_id\": \"F3-conf-03\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"lambda\": {\"type\": \"number\", \"nullable\": false},\n    \"normalisation_window\": {\"type\": \"string\", \"nullable\": false},\n    \"n_features\": {\"type\": \"integer\", \"nullable\": false},\n    \"rmse_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ridge\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_ridge\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_vs_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_vs_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\"],\n  \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\nmonth,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400","is_error":false}]},"uuid":"056ab372-ff32-4ebb-9d4c-1148ba1a2b71","timestamp":"2026-10-30T04:11:18.355Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-03\n\n## ip_predictors.csv\nOne row per month (`month`, YYYY-MM-01), 1992-01 to 2026-09 (417 data rows), built from six FRED series (Federal Reserve Bank of St. Louis), each retrieved with `https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>` on 2026-10-30 (UTC times below). SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). 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{"parentUuid":"68601832-7bca-4a41-a465-e32ea24ba2c3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmWd16Chgbo241qVxBN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016tytuoG4H2KB71WdtbLK6f","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F3-conf-03\n**Question.** Does a fixed-penalty (lambda=50) ridge regression on 8 standardised predictors forecast next-month US IP growth (100*dln INDPRO) better than (a) naive last value and (b) expanding historical mean, over 60 rolling origins 2021-09-01..2026-08-01, h=1?\n**Estimands.** Out-of-sample RMSE and MAE over the 60 targets (2021-10..2026-09) for naive, mean, ridge; rel improvements 1 - RMSE_ridge/RMSE_baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), 60-point evaluation sample (small; no significance test required).\n**Ridge spec.** Training rows s in [1992-03, t-1]; target ip_{s+1}; z_s = (ip_s, ip_{s-1}, ip_{s-2}, oil_s, fedfunds_s, permit_s, neworder_s, tcu_s). Standardise with training-window mean / sd (ddof=1), centre y with training mean; b=(Z'Z+50I)^-1 Z'(y-ybar); forecast = ybar + ((z_t-mu)/sd)·b. Recomputed every origin.\n**Baselines.** naive = ip_t; mean = mean(ip over 1992-04..t).\n**Info rules.** Nothing dated > t used at origin t, including scaling stats.\n**Outputs.** out/forecasts.csv (180 rows: origin_date,target_date,horizon,forecast,actual,model), out/results.json (keys per TASK.md table), out/report.md (6 sections), out/analysis.py, out/claims.json; plus state files, exclusions.csv.\n**Invalidating conditions.** Missing values within needed window, missing target months, leakage of future data into scaling, non-contiguous months.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written. P1 started.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1107,"cache_read_input_tokens":8857,"output_tokens":1142,"output_tokens_details":{"thinking_tokens":187},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1107},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmWcHfsX6vVZqKqfR7L","requestRef":"ba2ea678-9f90-4a1e-8a30-b1b133841c84","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1bad528e-8afe-4148-9d8c-36f0510fbfac","timestamp":"2026-10-30T04:11:34.639Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"d97e850a-de1b-4a76-9ec4-ab71a6e486d7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmXPQZWii1qXA2giFrG","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (baselines + ridge, with exclusion ledger)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1157,"cache_read_input_tokens":9964,"output_tokens":3009,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1157},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmXP8gaU4nXncpUGvUB","requestRef":"18fc1848-1be6-418f-816e-2a1872c2bc12","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8039b492-daaf-437b-a7f1-efbf2257dfed","timestamp":"2026-10-30T04:11:35.665Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"8039b492-daaf-437b-a7f1-efbf2257dfed","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmXPQZWii1qXA2giFrG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XNRpLYvHXApKJc55zGYnLX","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nDATA = ROOT / \"data\" / \"ip_predictors.csv\"\nOUT = ROOT / \"out\"\nLAM = 50.0\nFIRST_ORIGIN, LAST_ORIGIN = pd.Timestamp(\"2021-09-01\"), pd.Timestamp(\"2026-08-01\")\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.sort_values(\"month\").set_index(\"month\")\nassert not d.index.duplicated().any()\nassert (d.index == pd.date_range(d.index[0], d.index[-1], freq=\"MS\")).all(), \"non-contiguous months\"\n\n# feature matrix z_s\nF = pd.DataFrame({\n    \"ip0\": d[\"ip_growth\"], \"ip1\": d[\"ip_growth\"].shift(1), \"ip2\": d[\"ip_growth\"].shift(2),\n    \"oil\": d[\"oil_chg\"], \"ff\": d[\"fedfunds\"], \"permit\": d[\"permit_chg\"],\n    \"neworder\": d[\"neworder_chg\"], \"tcu\": d[\"tcu\"]})\ny_next = d[\"ip_growth\"].shift(-1)  # target for row s is ip_{s+1}\nmonths = d.index\n\norigins = pd.date_range(FIRST_ORIGIN, LAST_ORIGIN, freq=\"MS\")\nrows = []\nn_train_last = None\nfor t in origins:\n    tgt = t + pd.offsets.MonthBegin(1)\n    actual = float(d.loc[tgt, \"ip_growth\"])\n    # naive\n    rows.append((t, tgt, d.loc[t, \"ip_growth\"], actual, \"naive\"))\n    # expanding mean 1992-04..t\n    rows.append((t, tgt, d.loc[MEAN_START:t, \"ip_growth\"].mean(), actual, \"mean\"))\n    # ridge: training rows s in [1992-03, t-1] (s+1 <= t)\n    s_last = t - pd.offsets.MonthBegin(1)\n    Xtr = F.loc[TRAIN_START:s_last].to_numpy(float)\n    ytr = y_next.loc[TRAIN_START:s_last].to_numpy(float)\n    assert np.isfinite(Xtr).all() and np.isfinite(ytr).all()\n    assert y_next.loc[TRAIN_START:s_last].index.max() + pd.offsets.MonthBegin(1) <= t\n    mu = Xtr.mean(0); sd = Xtr.std(0, ddof=1); ybar = ytr.mean()\n    Z = (Xtr - mu) / sd\n    b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (ytr - ybar))\n    zt = (F.loc[t].to_numpy(float) - mu) / sd\n    rows.append((t, tgt, float(ybar + zt @ b), actual, \"ridge\"))\n    n_train_last = len(ytr)\n\nfc = pd.DataFrame(rows, columns=[\"origin_date\", \"target_date\", \"forecast\", \"actual\", \"model\"])\nfc.insert(2, \"horizon\", 1)\nfc[\"origin_date\"] = fc[\"origin_date\"].dt.strftime(\"%Y-%m-%d\")\nfc[\"target_date\"] = fc[\"target_date\"].dt.strftime(\"%Y-%m-%d\")\nfc.to_csv(OUT / \"forecasts.csv\", index=False, float_format=\"%.8f\")\n\nres = {\"task_id\": \"F3-conf-03\", \"n_origins\": len(origins),\n       \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n       \"lambda\": LAM, \"normalisation_window\": \"expanding\", \"n_features\": F.shape[1]}\nerr = {}\nfor m in [\"naive\", \"mean\", \"ridge\"]:\n    e = (fc.loc[fc.model == m, \"actual\"] - fc.loc[fc.model == m, \"forecast\"]).to_numpy()\n    err[m] = e\n    res[f\"rmse_{m}\"] = float(np.sqrt(np.mean(e ** 2)))\n    res[f\"mae_{m}\"] = float(np.mean(np.abs(e)))\nres[\"rel_improvement_vs_naive\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_naive\"]\nres[\"rel_improvement_vs_mean\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_mean\"]\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"data_issues\"] = [\n    {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02 (NEWORDER starts 1992-02).\",\n     \"handling\": \"Outside every training window (training starts 1992-03 as specified); no imputation needed.\"},\n    {\"issue\": \"data/README.md says the first month (1992-01) of each change series is empty, but ip_growth, oil_chg and permit_chg have values in 1992-01.\",\n     \"handling\": \"Values used as given; only ip_growth 1992-01 enters (as lag ip_{s-2} for s=1992-03), as implied by the task's feature definition.\"},\n    {\"issue\": \"Current-vintage (revised) data; publication lags ignored; evaluation window includes no extreme shocks but training includes 2020 COVID swings.\",\n     \"handling\": \"No adjustment; results are not real-time and are interpreted accordingly.\"},\n]\nres[\"n_input_rows\"] = {\"ip_predictors.csv\": n_raw}\nres[\"n_analysed\"] = n_train_last\nres[\"random_seed\"] = None\n# supplementary (not required): Diebold-Mariano-type tests on squared-error loss, h=1, plain variance\nfrom scipy import stats\nsupp = {}\nfor base in [\"naive\", \"mean\"]:\n    dl = err[base] ** 2 - err[\"ridge\"] ** 2\n    dm = dl.mean() / np.sqrt(dl.var(ddof=1) / len(dl))\n    supp[f\"dm_stat_ridge_vs_{base}\"] = float(dm)\n    supp[f\"dm_pvalue_ridge_vs_{base}\"] = float(2 * (1 - stats.t.cdf(abs(dm), df=len(dl) - 1)))\nres[\"supplementary\"] = supp\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2))\n\n# exclusion ledger: raw rows -> rows used as ridge training rows at last origin\nlast = origins[-1]\nsteps = [(\"raw\", \"all rows read\", n_raw, n_raw)]\nn1 = int((months >= TRAIN_START).sum())\nsteps.append((\"1\", \"drop months before 1992-03 (no lags / neworder_chg missing)\", n_raw, n1))\nn2 = int(((months >= TRAIN_START) & (months <= last - pd.offsets.MonthBegin(1))).sum())\nsteps.append((\"2\", \"drop months s with s+1 > last origin 2026-08 (target not available at last origin)\", n1, n2))\nex = pd.DataFrame([(s, r, a, b, a - b) for s, r, a, b in steps],\n                  columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\"])\nex[\"rationale\"] = [\"input\", \"task-specified training start; features need ip lags and neworder_chg\",\n                   \"information rule: s+1 <= t at the last origin\"]\nex.to_csv(OUT / \"exclusions.csv\", index=False)\nassert n2 == n_train_last\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\"))}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | tail -12","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1157,"cache_read_input_tokens":9964,"output_tokens":3009,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1157},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmXP8gaU4nXncpUGvUB","requestRef":"18fc1848-1be6-418f-816e-2a1872c2bc12","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d9a06c22-f9f5-4b3f-984a-cd28f7ece47e","timestamp":"2026-10-30T04:11:57.747Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"d9a06c22-f9f5-4b3f-984a-cd28f7ece47e","isSidechain":false,"promptId":"5493f38d-8d8e-461a-af5f-6f1cc7bdb6dc","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XNRpLYvHXApKJc55zGYnLX","type":"tool_result","content":"{\n  \"rmse_naive\": 0.8830699166753823,\n  \"mae_naive\": 0.6989594000000001,\n  \"rmse_mean\": 0.5439355781141625,\n  \"mae_mean\": 0.42929777256194207,\n  \"rmse_ridge\": 0.5607989303521244,\n  \"mae_ridge\": 0.45141289074453916,\n  \"rel_improvement_vs_naive\": 0.3649439078805412,\n  \"rel_improvement_vs_mean\": -0.031002480654837017\n}\nstep,rule,n_before,n_after,n_removed,rationale\nraw,all rows read,417,417,0,input\n1,drop months before 1992-03 (no lags / neworder_chg missing),417,415,2,task-specified training start; features need ip lags and neworder_chg\n2,drop months s with s+1 > last origin 2026-08 (target not available at last origin),415,413,2,information rule: s+1 <= t at the last origin\n  \"n_input_rows\": {\n    \"ip_predictors.csv\": 417\n  },\n  \"n_analysed\": 413,\n  \"random_seed\": null,\n  \"supplementary\": {\n    \"dm_stat_ridge_vs_naive\": 3.364959492184776,\n    \"dm_pvalue_ridge_vs_naive\": 0.0013506687241571225,\n    \"dm_stat_ridge_vs_mean\": -0.5392198591267084,\n    \"dm_pvalue_ridge_vs_mean\": 0.5917642583421929\n  }\n}","is_error":false}]},"uuid":"ee0791f0-55fb-4b46-a57b-ce7c86380a50","timestamp":"2026-10-30T04:11:58.506Z","toolUseResult":{"stdout":"{\n  \"rmse_naive\": 0.8830699166753823,\n  \"mae_naive\": 0.6989594000000001,\n  \"rmse_mean\": 0.5439355781141625,\n  \"mae_mean\": 0.42929777256194207,\n  \"rmse_ridge\": 0.5607989303521244,\n  \"mae_ridge\": 0.45141289074453916,\n  \"rel_improvement_vs_naive\": 0.3649439078805412,\n  \"rel_improvement_vs_mean\": -0.031002480654837017\n}\nstep,rule,n_before,n_after,n_removed,rationale\nraw,all rows read,417,417,0,input\n1,drop months before 1992-03 (no lags / neworder_chg missing),417,415,2,task-specified training start; features need ip lags and neworder_chg\n2,drop months s with s+1 > last origin 2026-08 (target not available at last origin),415,413,2,information rule: s+1 <= t at the last origin\n  \"n_input_rows\": {\n    \"ip_predictors.csv\": 417\n  },\n  \"n_analysed\": 413,\n  \"random_seed\": null,\n  \"supplementary\": {\n    \"dm_stat_ridge_vs_naive\": 3.364959492184776,\n    \"dm_pvalue_ridge_vs_naive\": 0.0013506687241571225,\n    \"dm_stat_ridge_vs_mean\": -0.5392198591267084,\n    \"dm_pvalue_ridge_vs_mean\": 0.5917642583421929\n  }\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d9a06c22-f9f5-4b3f-984a-cd28f7ece47e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","hash":"2fdb7d111e932b35fe72d663d0d4324b1cd37786c6dbd2ba2488546833c68fe3","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (baselines + ridge, with exclusion ledger)."},{"type":"tool_use","id":"toolu_01XNRpLYvHXApKJc55zGYnLX","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nDATA = ROOT / \"data\" / \"ip_predictors.csv\"\nOUT = ROOT / \"out\"\nLAM = 50.0\nFIRST_ORIGIN, LAST_ORIGIN = pd.Timestamp(\"2021-09-01\"), pd.Timestamp(\"2026-08-01\")\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.sort_values(\"month\").set_index(\"month\")\nassert not d.index.duplicated().any()\nassert (d.index == pd.date_range(d.index[0], d.index[-1], freq=\"MS\")).all(), \"non-contiguous months\"\n\n# feature matrix z_s\nF = pd.DataFrame({\n    \"ip0\": d[\"ip_growth\"], \"ip1\": d[\"ip_growth\"].shift(1), \"ip2\": d[\"ip_growth\"].shift(2),\n    \"oil\": d[\"oil_chg\"], \"ff\": d[\"fedfunds\"], \"permit\": d[\"permit_chg\"],\n    \"neworder\": d[\"neworder_chg\"], \"tcu\": d[\"tcu\"]})\ny_next = d[\"ip_growth\"].shift(-1)  # target for row s is ip_{s+1}\nmonths = d.index\n\norigins = pd.date_range(FIRST_ORIGIN, LAST_ORIGIN, freq=\"MS\")\nrows = []\nn_train_last = None\nfor t in origins:\n    tgt = t + pd.offsets.MonthBegin(1)\n    actual = float(d.loc[tgt, \"ip_growth\"])\n    # naive\n    rows.append((t, tgt, d.loc[t, \"ip_growth\"], actual, \"naive\"))\n    # expanding mean 1992-04..t\n    rows.append((t, tgt, d.loc[MEAN_START:t, \"ip_growth\"].mean(), actual, \"mean\"))\n    # ridge: training rows s in [1992-03, t-1] (s+1 <= t)\n    s_last = t - pd.offsets.MonthBegin(1)\n    Xtr = F.loc[TRAIN_START:s_last].to_numpy(float)\n    ytr = y_next.loc[TRAIN_START:s_last].to_numpy(float)\n    assert np.isfinite(Xtr).all() and np.isfinite(ytr).all()\n    assert y_next.loc[TRAIN_START:s_last].index.max() + pd.offsets.MonthBegin(1) <= t\n    mu = Xtr.mean(0); sd = Xtr.std(0, ddof=1); ybar = ytr.mean()\n    Z = (Xtr - mu) / sd\n    b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (ytr - ybar))\n    zt = (F.loc[t].to_numpy(float) - mu) / sd\n    rows.append((t, tgt, float(ybar + zt @ b), actual, \"ridge\"))\n    n_train_last = len(ytr)\n\nfc = pd.DataFrame(rows, columns=[\"origin_date\", \"target_date\", \"forecast\", \"actual\", \"model\"])\nfc.insert(2, \"horizon\", 1)\nfc[\"origin_date\"] = fc[\"origin_date\"].dt.strftime(\"%Y-%m-%d\")\nfc[\"target_date\"] = fc[\"target_date\"].dt.strftime(\"%Y-%m-%d\")\nfc.to_csv(OUT / \"forecasts.csv\", index=False, float_format=\"%.8f\")\n\nres = {\"task_id\": \"F3-conf-03\", \"n_origins\": len(origins),\n       \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n       \"lambda\": LAM, \"normalisation_window\": \"expanding\", \"n_features\": F.shape[1]}\nerr = {}\nfor m in [\"naive\", \"mean\", \"ridge\"]:\n    e = (fc.loc[fc.model == m, \"actual\"] - fc.loc[fc.model == m, \"forecast\"]).to_numpy()\n    err[m] = e\n    res[f\"rmse_{m}\"] = float(np.sqrt(np.mean(e ** 2)))\n    res[f\"mae_{m}\"] = float(np.mean(np.abs(e)))\nres[\"rel_improvement_vs_naive\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_naive\"]\nres[\"rel_improvement_vs_mean\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_mean\"]\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"data_issues\"] = [\n    {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02 (NEWORDER starts 1992-02).\",\n     \"handling\": \"Outside every training window (training starts 1992-03 as specified); no imputation needed.\"},\n    {\"issue\": \"data/README.md says the first month (1992-01) of each change series is empty, but ip_growth, oil_chg and permit_chg have values in 1992-01.\",\n     \"handling\": \"Values used as given; only ip_growth 1992-01 enters (as lag ip_{s-2} for s=1992-03), as implied by the task's feature definition.\"},\n    {\"issue\": \"Current-vintage (revised) data; publication lags ignored; evaluation window includes no extreme shocks but training includes 2020 COVID swings.\",\n     \"handling\": \"No adjustment; results are not real-time and are interpreted accordingly.\"},\n]\nres[\"n_input_rows\"] = {\"ip_predictors.csv\": n_raw}\nres[\"n_analysed\"] = n_train_last\nres[\"random_seed\"] = None\n# supplementary (not required): Diebold-Mariano-type tests on squared-error loss, h=1, plain variance\nfrom scipy import stats\nsupp = {}\nfor base in [\"naive\", \"mean\"]:\n    dl = err[base] ** 2 - err[\"ridge\"] ** 2\n    dm = dl.mean() / np.sqrt(dl.var(ddof=1) / len(dl))\n    supp[f\"dm_stat_ridge_vs_{base}\"] = float(dm)\n    supp[f\"dm_pvalue_ridge_vs_{base}\"] = float(2 * (1 - stats.t.cdf(abs(dm), df=len(dl) - 1)))\nres[\"supplementary\"] = supp\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2))\n\n# exclusion ledger: raw rows -> rows used as ridge training rows at last origin\nlast = origins[-1]\nsteps = [(\"raw\", \"all rows read\", n_raw, n_raw)]\nn1 = int((months >= TRAIN_START).sum())\nsteps.append((\"1\", \"drop months before 1992-03 (no lags / neworder_chg missing)\", n_raw, n1))\nn2 = int(((months >= TRAIN_START) & (months <= last - pd.offsets.MonthBegin(1))).sum())\nsteps.append((\"2\", \"drop months s with s+1 > last origin 2026-08 (target not available at last origin)\", n1, n2))\nex = pd.DataFrame([(s, r, a, b, a - b) for s, r, a, b in steps],\n                  columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\"])\nex[\"rationale\"] = [\"input\", \"task-specified training start; features need ip lags and neworder_chg\",\n                   \"information rule: s+1 <= t at the last origin\"]\nex.to_csv(OUT / \"exclusions.csv\", index=False)\nassert n2 == n_train_last\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\"))}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | tail -12","description":"Write and run analysis script"}}]}}
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{"parentUuid":"fc487303-5503-4b12-9fb7-08225bd7c39b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmbtPS7kh1kCzeSEkYP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GRetATjhiU1bBLaHruN5fD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03 — Ridge vs naive / expanding-mean forecasts of US IP growth (h = 1)\n\n## Question\nDoes a ridge regression with a fixed penalty (λ = 50, `lambda`) on 8 standardised monthly predictors (`n_features`) forecast next month's US industrial production growth (100·Δln INDPRO, percent per month) better than the naive last-value forecast and the expanding historical mean? There are 60 rolling origins (`n_origins`), from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, so the targets run from 2021-10 to 2026-09.\n\n## Data and exclusions\n- The input is `data/ip_predictors.csv` (`n_input_rows` = 417 rows, 1992-01 to 2026-09). Its SHA-256 matches the README. The months are contiguous and there are no duplicates. Details are in `out/state/data_audit.md`.\n- The only missing values are `neworder_chg` for 1992-01 and 1992-02. Both months fall before the training start specified in the task (1992-03), so no imputation was needed.\n- The README says the 1992-01 value of every change series is empty, but `ip_growth`, `oil_chg` and `permit_chg` do have 1992-01 values. I used them as given, and only `ip_growth` 1992-01 enters the model (as the second lag of the first training row). This and the vintage caveat are recorded in `data_issues`.\n- Exclusion ledger (`out/exclusions.csv`): 417 raw rows, minus 2 months before 1992-03, minus 2 months whose target falls after the last origin, gives 413 training rows at the last origin (`n_analysed`).\n\n## Method\n- **Naive forecast:** `ip_growth` at origin t.\n- **Expanding-mean forecast:** the mean of `ip_growth` over 1992-04 to t.\n- **Ridge regression:**\n  - At each origin, the training rows are s ∈ [1992-03, t−1], with target `ip_growth` at s+1. The features are ip_growth at s, s−1 and s−2, plus oil_chg, fedfunds, permit_chg, neworder_chg and tcu at s.\n  - Each feature is standardised with the mean and the sample SD (ddof = 1) of that origin's training rows (`normalisation_window` = \"expanding\"). The target is centred on its training mean ȳ.\n  - Coefficients: b = (Z′Z + 50·I)⁻¹Z′(y−ȳ). Forecast: ȳ + z̃_t·b, where z̃_t is the origin's feature vector standardised with the same mean and SD.\n- **Errors:** RMSE and MAE over the 60 one-step errors, computed on the current-vintage data.\n- **Determinism:** the procedure is deterministic and uses no randomness (`random_seed` = null). `out/analysis.py` regenerates `results.json`, `forecasts.csv` and `exclusions.csv`.\n\n## Results\n| model | RMSE (pp/month) | MAE (pp/month) |\n|---|---|---|\n| naive | 0.8831 (`rmse_naive`) | 0.6990 (`mae_naive`) |\n| expanding mean | 0.5439 (`rmse_mean`) | 0.4293 (`mae_mean`) |\n| ridge λ=50 | 0.5608 (`rmse_ridge`) | 0.4514 (`mae_ridge`) |\n\n- Compared with the naive forecast, ridge cuts RMSE by 36.5% (`rel_improvement_vs_naive` = 0.3649).\n- Compared with the expanding mean, ridge is worse: RMSE is 3.1% higher (`rel_improvement_vs_mean` = −0.0310), and its MAE is also higher (`mae_ridge` > `mae_mean`).\n- **Answer:** the ridge model beats the naive baseline but not the expanding-mean baseline over this evaluation window.\n\n## Uncertainty\n- The evaluation uses only 60 monthly errors. As a supplementary check that the task did not require, I ran Diebold–Mariano-type tests on squared-error loss differentials (h = 1, simple variance, t-reference).\n  - Ridge vs naive: the gain is clearly distinguishable from zero (`supplementary.dm_stat_ridge_vs_naive` = 3.36, p = 0.0014, `supplementary.dm_pvalue_ridge_vs_naive`).\n  - Ridge vs expanding mean: the difference is not distinguishable from zero (`supplementary.dm_stat_ridge_vs_mean` = −0.54, p = 0.59, `supplementary.dm_pvalue_ridge_vs_mean`). The small loss relative to the mean should therefore be read as \"no evidence of improvement\", not as evidence that ridge is genuinely worse.\n- No confidence intervals for the RMSEs were requested or computed.\n\n## Limitations and open issues\n- **Not real-time:** the data are the current vintage, and publication lags are ignored by design. INDPRO, TCU and NEWORDER are heavily revised, so these results do not measure real-time forecast performance.\n- **Fixed specification:** λ = 50 is fixed by the task and not tuned. Performance against the mean baseline may depend on this choice and on the 2020 COVID observations in the training windows (`ip_growth` minimum −14.14 in 2020-04), which were not treated in any special way.\n- **Single evaluation window:** results cover 2021-10 to 2026-09 only and may not generalise to other periods.\n- **Small discrepancy:** forecasts in `forecasts.csv` are rounded to 8 decimals, so metrics recomputed from that file differ from `results.json` by less than 1e-9.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Ridge (lambda=50) RMSE is 36.5% lower than the naive forecast RMSE over 60 origins.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, publication lags ignored.\"},\n {\"id\":\"C2\",\"text\":\"Ridge does not beat the expanding-mean baseline: its RMSE is 3.1% higher and its MAE also higher.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"rel_improvement_vs_mean\",\"mae_ridge\",\"mae_mean\"],\"status\":\"supported\",\"caveat\":\"Single 60-month window; fixed lambda.\"},\n {\"id\":\"C3\",\"text\":\"The ridge-vs-naive gain is statistically distinguishable from zero (DM p=0.0014), while the ridge-vs-mean difference is not (DM p=0.59).\",\"results_keys\":[\"supplementary\"],\"status\":\"supported\",\"caveat\":\"Supplementary, simple DM test with t reference on 60 errors; not required by the task.\"},\n {\"id\":\"C4\",\"text\":\"All standardisation statistics are computed from each origin's training window only.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Verified by a future-data perturbation test recorded in out/state/checks.md.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\"","description":"Write report and claims; validate claim keys"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1958,"cache_read_input_tokens":16944,"output_tokens":2619,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1958},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmbt7pfb32eeJnDKYQp","requestRef":"9c722f88-249f-4daa-9a50-917234a79c21","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7e47f3cf-928d-41c4-8aad-d0fd080ffe3f","timestamp":"2026-10-30T04:12:56.398Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
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{"type":"api-request-blob","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","hash":"fdfffab8c8c1e5386d9d7e7a1571fc9fadebede384ec123032f151a088a1d63a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01GRetATjhiU1bBLaHruN5fD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03 — Ridge vs naive / expanding-mean forecasts of US IP growth (h = 1)\n\n## Question\nDoes a ridge regression with a fixed penalty (λ = 50, `lambda`) on 8 standardised monthly predictors (`n_features`) forecast next month's US industrial production growth (100·Δln INDPRO, percent per month) better than the naive last-value forecast and the expanding historical mean? There are 60 rolling origins (`n_origins`), from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, so the targets run from 2021-10 to 2026-09.\n\n## Data and exclusions\n- The input is `data/ip_predictors.csv` (`n_input_rows` = 417 rows, 1992-01 to 2026-09). Its SHA-256 matches the README. The months are contiguous and there are no duplicates. Details are in `out/state/data_audit.md`.\n- The only missing values are `neworder_chg` for 1992-01 and 1992-02. Both months fall before the training start specified in the task (1992-03), so no imputation was needed.\n- The README says the 1992-01 value of every change series is empty, but `ip_growth`, `oil_chg` and `permit_chg` do have 1992-01 values. I used them as given, and only `ip_growth` 1992-01 enters the model (as the second lag of the first training row). This and the vintage caveat are recorded in `data_issues`.\n- Exclusion ledger (`out/exclusions.csv`): 417 raw rows, minus 2 months before 1992-03, minus 2 months whose target falls after the last origin, gives 413 training rows at the last origin (`n_analysed`).\n\n## Method\n- **Naive forecast:** `ip_growth` at origin t.\n- **Expanding-mean forecast:** the mean of `ip_growth` over 1992-04 to t.\n- **Ridge regression:**\n  - At each origin, the training rows are s ∈ [1992-03, t−1], with target `ip_growth` at s+1. The features are ip_growth at s, s−1 and s−2, plus oil_chg, fedfunds, permit_chg, neworder_chg and tcu at s.\n  - Each feature is standardised with the mean and the sample SD (ddof = 1) of that origin's training rows (`normalisation_window` = \"expanding\"). The target is centred on its training mean ȳ.\n  - Coefficients: b = (Z′Z + 50·I)⁻¹Z′(y−ȳ). Forecast: ȳ + z̃_t·b, where z̃_t is the origin's feature vector standardised with the same mean and SD.\n- **Errors:** RMSE and MAE over the 60 one-step errors, computed on the current-vintage data.\n- **Determinism:** the procedure is deterministic and uses no randomness (`random_seed` = null). `out/analysis.py` regenerates `results.json`, `forecasts.csv` and `exclusions.csv`.\n\n## Results\n| model | RMSE (pp/month) | MAE (pp/month) |\n|---|---|---|\n| naive | 0.8831 (`rmse_naive`) | 0.6990 (`mae_naive`) |\n| expanding mean | 0.5439 (`rmse_mean`) | 0.4293 (`mae_mean`) |\n| ridge λ=50 | 0.5608 (`rmse_ridge`) | 0.4514 (`mae_ridge`) |\n\n- Compared with the naive forecast, ridge cuts RMSE by 36.5% (`rel_improvement_vs_naive` = 0.3649).\n- Compared with the expanding mean, ridge is worse: RMSE is 3.1% higher (`rel_improvement_vs_mean` = −0.0310), and its MAE is also higher (`mae_ridge` > `mae_mean`).\n- **Answer:** the ridge model beats the naive baseline but not the expanding-mean baseline over this evaluation window.\n\n## Uncertainty\n- The evaluation uses only 60 monthly errors. As a supplementary check that the task did not require, I ran Diebold–Mariano-type tests on squared-error loss differentials (h = 1, simple variance, t-reference).\n  - Ridge vs naive: the gain is clearly distinguishable from zero (`supplementary.dm_stat_ridge_vs_naive` = 3.36, p = 0.0014, `supplementary.dm_pvalue_ridge_vs_naive`).\n  - Ridge vs expanding mean: the difference is not distinguishable from zero (`supplementary.dm_stat_ridge_vs_mean` = −0.54, p = 0.59, `supplementary.dm_pvalue_ridge_vs_mean`). The small loss relative to the mean should therefore be read as \"no evidence of improvement\", not as evidence that ridge is genuinely worse.\n- No confidence intervals for the RMSEs were requested or computed.\n\n## Limitations and open issues\n- **Not real-time:** the data are the current vintage, and publication lags are ignored by design. INDPRO, TCU and NEWORDER are heavily revised, so these results do not measure real-time forecast performance.\n- **Fixed specification:** λ = 50 is fixed by the task and not tuned. Performance against the mean baseline may depend on this choice and on the 2020 COVID observations in the training windows (`ip_growth` minimum −14.14 in 2020-04), which were not treated in any special way.\n- **Single evaluation window:** results cover 2021-10 to 2026-09 only and may not generalise to other periods.\n- **Small discrepancy:** forecasts in `forecasts.csv` are rounded to 8 decimals, so metrics recomputed from that file differ from `results.json` by less than 1e-9.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Ridge (lambda=50) RMSE is 36.5% lower than the naive forecast RMSE over 60 origins.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, publication lags ignored.\"},\n {\"id\":\"C2\",\"text\":\"Ridge does not beat the expanding-mean baseline: its RMSE is 3.1% higher and its MAE also higher.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"rel_improvement_vs_mean\",\"mae_ridge\",\"mae_mean\"],\"status\":\"supported\",\"caveat\":\"Single 60-month window; fixed lambda.\"},\n {\"id\":\"C3\",\"text\":\"The ridge-vs-naive gain is statistically distinguishable from zero (DM p=0.0014), while the ridge-vs-mean difference is not (DM p=0.59).\",\"results_keys\":[\"supplementary\"],\"status\":\"supported\",\"caveat\":\"Supplementary, simple DM test with t reference on 60 errors; not required by the task.\"},\n {\"id\":\"C4\",\"text\":\"All standardisation statistics are computed from each origin's training window only.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Verified by a future-data perturbation test recorded in out/state/checks.md.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\"","description":"Write report and claims; validate claim keys"}}]}}
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{"parentUuid":"a8311eb8-ab71-41af-8ef2-5c0d169916a6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmdR4BcuqJxAVw3PZwP","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the forecast comparison. Ridge forecasts next month's US industrial production growth much better than the naive (last-value) forecast, but not better than the expanding historical mean.\n\n| Forecast | RMSE (pp/month) | MAE (pp/month) |\n|---|---|---|\n| Naive (last value) | 0.883 | 0.699 |\n| Expanding mean | 0.544 | 0.429 |\n| Ridge (λ = 50) | 0.561 | 0.451 |\n\n- **Against the naive forecast:** ridge cuts RMSE by 36.5%. An extra significance test I ran, which the task didn't ask for, gives p ≈ 0.001 for this difference.\n- **Against the expanding mean:** ridge's RMSE is 3.1% higher, and its MAE is higher too. The same test gives p ≈ 0.59 here, so read this as \"no evidence that ridge helps\" rather than \"ridge is worse\". The test results are stored under an extra `supplementary` key in `out/results.json`.\n\nThe model was built exactly as specified: 413 training rows at the last origin, and the means and standard deviations used for scaling are recomputed from each origin's own training data. A clean re-run of `out/analysis.py` produces byte-identical outputs. A separately written version of the ridge model gave the same forecasts even after I scrambled all data after each origin, so no future data leaks in. All checks passed and every phase is marked passed.\n\nLimitations:\n- **Not real-time:** the data are today's revised figures and publication delays are ignored, as the task specifies.\n- **Fixed settings:** λ = 50 was not tuned.\n- **Small sample:** there are only 60 forecast errors.\n- **COVID months kept:** the large 2020 swings stay in the training data untreated.\n- **README mismatch:** the README says the first month (1992-01) of each change series is empty, but three of them have values. I used the data as given and recorded this under `data_issues`.\n\nEverything is in `out/`:\n- `analysis.py` (the one script that regenerates the results)\n- `results.json`\n- `forecasts.csv` (180 rows)\n- `exclusions.csv`\n- `report.md`\n- `claims.json`\n- `state/` (contract, data audit, checks, phase ledger, log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2625,"cache_read_input_tokens":18902,"output_tokens":783,"output_tokens_details":{"thinking_tokens":27},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2625},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmdQm66txoTBypoCxF2","requestRef":"6ecec515-8b58-42a8-81f9-fd6d00f3e7e0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"404655b3-a39a-42b0-a074-34d9f21ffb7d","timestamp":"2026-10-30T04:13:04.316Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0033","sessionId":"da9f39fb-e7f8-4e5b-a974-63979ec5f532","version":"2.1.314","gitBranch":"HEAD"}
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