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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. 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. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. 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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\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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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\n{\n \"task_id\": \"F6-conf-01\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\",\n \"out/figure1.png\",\n \"out/table1.csv\"\n ],\n \"results_keys\": {\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"n_missing_rows\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_train\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_test\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"anova_f_stat\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"anova_p_value\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_estimate\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_low\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_high\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"baseline_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"model_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"ci_contains_estimate\",\n \"positive_se\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\n \"exclusion_ledger_sums\"\n ],\n \"ci_pairs\": [\n [\n \"effect_estimate\",\n \"effect_ci_low\",\n \"effect_ci_high\"\n ]\n ],\n \"positive_keys\": [\n \"anova_f_stat\",\n \"baseline_metric\",\n \"model_metric\"\n ],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}{\n \"task_id\": \"F6-conf-01\",\n \"family\": \"F6\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n \"files\": {\n \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n \"gate_spec.json\": \"d53691940951460bcc45cb08c958917841ae1bfbc8d242d37ded795a3e998eb7\"\n }\n}observation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n2019-12-27,1.88\n2019-12-30,1.90\n2019-12-31,1.92\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a data/README.md","is_error":false}]},"uuid":"ba713f4d-3e3f-40de-9311-677235b1dcce","timestamp":"2026-10-30T05:19:44.639Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-01\n\n## `DGS10.csv` (5217 rows)\nObserved data: *Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis* (FRED series `DGS10`, daily, percent, not seasonally adjusted). Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\n{\n \"task_id\": \"F6-conf-01\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\",\n \"out/figure1.png\",\n \"out/table1.csv\"\n ],\n \"results_keys\": {\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"n_missing_rows\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_train\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_test\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"anova_f_stat\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"anova_p_value\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_estimate\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_low\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_high\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"baseline_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"model_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"ci_contains_estimate\",\n \"positive_se\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\n \"exclusion_ledger_sums\"\n ],\n \"ci_pairs\": [\n [\n \"effect_estimate\",\n \"effect_ci_low\",\n \"effect_ci_high\"\n ]\n ],\n \"positive_keys\": [\n \"anova_f_stat\",\n \"baseline_metric\",\n \"model_metric\"\n ],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}{\n \"task_id\": \"F6-conf-01\",\n \"family\": \"F6\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n \"files\": {\n \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n \"gate_spec.json\": \"d53691940951460bcc45cb08c958917841ae1bfbc8d242d37ded795a3e998eb7\"\n }\n}observation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n2019-12-27,1.88\n2019-12-30,1.90\n2019-12-31,1.92\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"107cef99-32bf-4854-bbf5-8253050cf284","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","hash":"0645cd394e9e0573c3bcdbb16808137791acb141058956a48421285a2dd0c737","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LstxHJs4XRNFUvNv3YgHi3","name":"Bash","input":{"command":"cat data/README.md gate_spec.json bundle.json; head -5 data/DGS10.csv; tail -3 data/DGS10.csv; shasum -a 256 data/*","description":"Read provenance, gate spec, data head"}}]}}22{"type":"api-request","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","id":"f17934f8-ec2d-4ddd-b0fd-bea0ee65217a","timestamp":"2026-10-30T05:19:44.643Z","version":"2.1.314","querySource":"sdk","shapeHash":"c59902ee8879832d5b19c8ab363e92f3befcd9063c14accfcda845bf0d3d8301","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"5c48cecd-071c-4737-a8e9-a1216ec0446f","keep":4,"tail":["0645cd394e9e0573c3bcdbb16808137791acb141058956a48421285a2dd0c737","ee92c367416f875597ede51ed3f1e100dbe3100a9324ebb90cb21d6dc44bb4c8"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2404e69ae3fe4ec9a1d8f3d55a882fe2b50ab82530ce00c39abb8d29715b8e4f"}23{"parentUuid":"ba713f4d-3e3f-40de-9311-677235b1dcce","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrisZcG5H5jKU5a9RpH","type":"message","role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2094,"cache_read_input_tokens":7172,"output_tokens":1474,"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":2094},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrisJERiz9uqYyGSMPA","requestRef":"f17934f8-ec2d-4ddd-b0fd-bea0ee65217a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f8a96e18-a523-4668-bae5-357f909baf89","timestamp":"2026-10-30T05:19:45.769Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"f8a96e18-a523-4668-bae5-357f909baf89","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrisZcG5H5jKU5a9RpH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FmthtoTim749eZ9fsigQcn","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday carry predictive content for the daily change in the 10y Treasury CMT yield (FRED DGS10), 2000–2019?\n**Estimands.** (i) Joint equality of weekday conditional means of Δy_t (bp) — classical one-way equal-variance ANOVA F over all changes (F(4,n−5)); (ii) Monday contrast = mean(Δy|Mon) − mean(Δy|Tue–Fri), all changes, with 95% CI (choice: Welch two-sample t interval); (iii) out-of-sample RMSE (2010–2019 changes) of weekday-mean forecast (means from changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes approximately independent across days for the classical tests (serial dependence/heteroskedasticity is a threat); ANOVA assumes equal variance (Welch ANOVA as robustness only).\n**Info rules.** Drop empty DGS10 rows first; Δy between consecutive available obs, dated at t, weekday of t; first obs has no change. Train = changes dated ≤2009-12-31 (including the change on the first 2010 day? no: that change is dated in 2010 so it's test, its y_{t-1} from 2009 is legitimately known). Weekday means for forecasting from train only. Only data/DGS10.csv.\n**Outputs.** out/results.json (keys: n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/figure1.png, out/table1.csv (weekday,n,mean_bp,se_bp), out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Conclusion rule.** ANOVA p≥0.05 → no_detectable_effect; else sign from Monday CI (positive/negative; if CI covers zero despite ANOVA rejection → no_detectable_effect, documented).\n**Invalidity.** Missing/unparseable data making changes uncomputable → premise_invalid; using test-period info in weekday means → invalid.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\nd=pd.read_csv('data/DGS10.csv',dtype=str,keep_default_na=False)\nprint(d.shape, d.observation_date.duplicated().sum())\ndt=pd.to_datetime(d.observation_date); print(dt.min(),dt.max(), dt.is_monotonic_increasing, dt.dt.dayofweek.value_counts().sort_index().to_dict())\ne=d.DGS10.str.strip()==''; print('empty',e.sum()); \nv=pd.to_numeric(d.DGS10[~e],errors='coerce'); print('nonnum',v.isna().sum(), v.describe())\nprint(sorted(set(d.DGS10[~e])-set(d.DGS10[~e][v.notna()])))\nx=v.dropna(); dx=(x.diff()*100); print(dx.describe()); print(dx.abs().sort_values().tail(5))\nprint(d[e].observation_date.tolist()[:20])\nEOF","description":"Write contract, ledger; audit data"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2094,"cache_read_input_tokens":7172,"output_tokens":1474,"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":2094},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrisJERiz9uqYyGSMPA","requestRef":"f17934f8-ec2d-4ddd-b0fd-bea0ee65217a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"177c711b-8f6b-46a5-8fcf-a792c38e5f81","timestamp":"2026-10-30T05:19:57.552Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"177c711b-8f6b-46a5-8fcf-a792c38e5f81","isSidechain":false,"promptId":"f2e39458-5dbd-4540-bb0c-348bf9a7d84f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FmthtoTim749eZ9fsigQcn","type":"tool_result","content":"(5217, 2) 0\n2000-01-03 00:00:00 2019-12-31 00:00:00 True {0: 1044, 1: 1044, 2: 1043, 3: 1043, 4: 1043}\nempty 215\nnonnum 0 count 5002.000000\nmean 3.432167\nstd 1.239314\nmin 1.370000\n25% 2.340000\n50% 3.360000\n75% 4.410000\nmax 6.790000\nName: DGS10, dtype: float64\n[]\ncount 5001.000000\nmean -0.093181\nstd 5.723645\nmin -51.000000\n25% -3.000000\n50% 0.000000\n75% 3.000000\nmax 25.000000\nName: DGS10, dtype: float64\n488 25.0\n2270 27.0\n2318 28.0\n2402 51.0\n0 NaN\nName: DGS10, dtype: float64\n['2000-01-17', '2000-02-21', '2000-04-21', '2000-05-29', '2000-07-04', '2000-09-04', '2000-10-09', '2000-11-23', '2000-12-25', '2001-01-01', '2001-01-15', '2001-02-19', '2001-04-13', '2001-05-28', '2001-07-04', '2001-09-03', '2001-09-11', '2001-09-12', '2001-10-08', '2001-11-12']","is_error":false}]},"uuid":"c21f28f0-7be4-40e6-bb25-df6ed99bded0","timestamp":"2026-10-30T05:19:57.866Z","toolUseResult":{"stdout":"(5217, 2) 0\n2000-01-03 00:00:00 2019-12-31 00:00:00 True {0: 1044, 1: 1044, 2: 1043, 3: 1043, 4: 1043}\nempty 215\nnonnum 0 count 5002.000000\nmean 3.432167\nstd 1.239314\nmin 1.370000\n25% 2.340000\n50% 3.360000\n75% 4.410000\nmax 6.790000\nName: DGS10, dtype: float64\n[]\ncount 5001.000000\nmean -0.093181\nstd 5.723645\nmin -51.000000\n25% -3.000000\n50% 0.000000\n75% 3.000000\nmax 25.000000\nName: DGS10, dtype: float64\n488 25.0\n2270 27.0\n2318 28.0\n2402 51.0\n0 NaN\nName: DGS10, dtype: float64\n['2000-01-17', '2000-02-21', '2000-04-21', '2000-05-29', '2000-07-04', '2000-09-04', '2000-10-09', '2000-11-23', '2000-12-25', '2001-01-01', '2001-01-15', '2001-02-19', '2001-04-13', '2001-05-28', '2001-07-04', '2001-09-03', '2001-09-11', '2001-09-12', '2001-10-08', '2001-11-12']","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"177c711b-8f6b-46a5-8fcf-a792c38e5f81","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","hash":"ecd4c07b1eba19a9aa48815153e70ab8a75860b28a41ef860a07a9f30f8d2b35","message":{"role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."},{"type":"tool_use","id":"toolu_01FmthtoTim749eZ9fsigQcn","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday carry predictive content for the daily change in the 10y Treasury CMT yield (FRED DGS10), 2000–2019?\n**Estimands.** (i) Joint equality of weekday conditional means of Δy_t (bp) — classical one-way equal-variance ANOVA F over all changes (F(4,n−5)); (ii) Monday contrast = mean(Δy|Mon) − mean(Δy|Tue–Fri), all changes, with 95% CI (choice: Welch two-sample t interval); (iii) out-of-sample RMSE (2010–2019 changes) of weekday-mean forecast (means from changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes approximately independent across days for the classical tests (serial dependence/heteroskedasticity is a threat); ANOVA assumes equal variance (Welch ANOVA as robustness only).\n**Info rules.** Drop empty DGS10 rows first; Δy between consecutive available obs, dated at t, weekday of t; first obs has no change. Train = changes dated ≤2009-12-31 (including the change on the first 2010 day? no: that change is dated in 2010 so it's test, its y_{t-1} from 2009 is legitimately known). Weekday means for forecasting from train only. Only data/DGS10.csv.\n**Outputs.** out/results.json (keys: n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/figure1.png, out/table1.csv (weekday,n,mean_bp,se_bp), out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Conclusion rule.** ANOVA p≥0.05 → no_detectable_effect; else sign from Monday CI (positive/negative; if CI covers zero despite ANOVA rejection → no_detectable_effect, documented).\n**Invalidity.** Missing/unparseable data making changes uncomputable → premise_invalid; using test-period info in weekday means → invalid.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\nd=pd.read_csv('data/DGS10.csv',dtype=str,keep_default_na=False)\nprint(d.shape, d.observation_date.duplicated().sum())\ndt=pd.to_datetime(d.observation_date); 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Mon 1044, Tue 1044, Wed/Thu/Fri 1043 rows); `DGS10` percent, 2-decimal (1 bp resolution).\n- Missing: 215 rows with empty DGS10 (US holidays, also 2001-09-11/12 closures). No non-numeric values. Range 1.37–6.79%.\n- After dropping empties: 5002 observations → 5001 changes (first obs has no change).\n- Gaps between consecutive available obs (calendar days): 1d 3899, 2d 58, 3d 889 (weekends), 4d 155 (long weekends). Changes after holidays span >1 trading day; per TASK rules they are kept and attributed to weekday of t (e.g. Tuesday after a Monday holiday) — documented as data issue.\n- Extreme changes |Δy|≥25bp: 2001-11-15 +25, 2008-09-15 −27 (Lehman), 2008-11-20 −28, 2009-02-17 −25, 2009-03-18 −51 (Fed QE1 announcement). Genuine market events; kept (no outlier removal); robustness noted in report.\n- Info-availability: no input violates the rules; only DGS10.csv used. Weekday means for forecasting use changes dated ≤2009-12-31 only.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1: audit done; decision: keep holiday-spanning changes and extreme values (rules prescribe changes between consecutive available obs). Monday CI method: Welch two-sample t.\nEOF\ncat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\nSPLIT = pd.Timestamp(\"2009-12-31\")\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nassert not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 (no yield published)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: drop empty rows first\"),\n dict(step=2, rule=\"first available observation has no one-day change\", n_before=n_raw - n_missing,\n n_after=n_changes, n_removed=n_raw - n_missing - n_changes, rationale=\"TASK rule: change needs y_{t-1}\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= SPLIT]; test = ch[ch[\"date\"] > SPLIT]\nassert train[\"date\"].max() < test[\"date\"].min()\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n# robustness (not a required key): Welch-type check via Kruskal-Wallis and Levene, reported in stdout only\nkw = stats.kruskal(*groups); lev = stats.levene(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.wd == 0, \"dy\"].to_numpy(); oth = ch.loc[ch.wd != 0, \"dy\"].to_numpy()\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nfc = test[\"wd\"].map(wmeans).to_numpy()\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"].to_numpy() ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"].to_numpy() - fc) ** 2)))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\n# table and figure\nrows = []\nfor k, g in enumerate(groups):\n rows.append(dict(weekday=DAYS[k], n=len(g), mean_bp=g.mean(), se_bp=g.std(ddof=1) / np.sqrt(len(g))))\ntab = pd.DataFrame(rows)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, r[\"n\"] - 1) * r[\"se_bp\"] for r in rows]\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95% CI, all %d changes)\" % n_changes)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\ngap_counts = ch[\"gap\"].value_counts().sort_index()\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"},\n {\"issue\": f\"{int((ch.gap > 3).sum() + ((ch.gap == 2)).sum())} changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"},\n {\"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"},\n {\"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"},\n ],\n \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n \"n_analysed\": int(n_changes),\n \"random_seed\": None,\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)),\n \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F),\n \"anova_p_value\": float(p),\n \"effect_estimate\": float(eff),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0,\n \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wmeans.items()},\n \"rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"robust_kruskal_wallis_p\": float(kw.pvalue),\n \"robust_levene_p\": float(lev.pvalue),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && grep -A12 train_week out/results.json","description":"Write audit and analysis script; run it"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":595,"cache_read_input_tokens":11196,"output_tokens":3755,"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":595},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrkAB1rvpgSznAK8AEZ","requestRef":"5fb65edb-c540-4e2d-aa8c-81a84482925a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"99500f93-c2f0-41dd-8fe1-d89b5338c5f6","timestamp":"2026-10-30T05:20:31.749Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"99500f93-c2f0-41dd-8fe1-d89b5338c5f6","isSidechain":false,"promptId":"f2e39458-5dbd-4540-bb0c-348bf9a7d84f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GTYgcAhMJ3Rt8o6xEHTEb3","type":"tool_result","content":"Traceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0273/out/analysis.py\", line 81, in <module>\n ax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95% CI, all %d changes)\" % n_changes)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: unsupported format character 'C' (0x43) at index 60","is_error":false}]},"uuid":"bb310259-b699-4795-89c5-3fd2a6c72153","timestamp":"2026-10-30T05:20:32.646Z","toolUseResult":{"stdout":"Traceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0273/out/analysis.py\", line 81, in <module>\n ax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95% CI, all %d changes)\" % n_changes)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: unsupported format character 'C' (0x43) at index 60","stderr":"","interrupted":false,"isImage":false,"returnCodeInterpretation":"No matches found","noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"99500f93-c2f0-41dd-8fe1-d89b5338c5f6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","hash":"172ed760c8cfc407bb7c249f6dacf34e02926cfba3b42cd31b438c02d35b7bf0","message":{"role":"assistant","content":[{"type":"text","text":"Now the audit file and the analysis script."},{"type":"tool_use","id":"toolu_01GTYgcAhMJ3Rt8o6xEHTEb3","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README and bundle.json) | 5217 | 2 |\n| data/README.md | 32b825f0...e561a (matches bundle.json) | provenance text | – |\n\n- Schema: `observation_date` (YYYY-MM-DD, all weekdays 2000-01-03 … 2019-12-31, strictly increasing, 0 duplicate dates; Mon 1044, Tue 1044, Wed/Thu/Fri 1043 rows); `DGS10` percent, 2-decimal (1 bp resolution).\n- Missing: 215 rows with empty DGS10 (US holidays, also 2001-09-11/12 closures). No non-numeric values. Range 1.37–6.79%.\n- After dropping empties: 5002 observations → 5001 changes (first obs has no change).\n- Gaps between consecutive available obs (calendar days): 1d 3899, 2d 58, 3d 889 (weekends), 4d 155 (long weekends). Changes after holidays span >1 trading day; per TASK rules they are kept and attributed to weekday of t (e.g. Tuesday after a Monday holiday) — documented as data issue.\n- Extreme changes |Δy|≥25bp: 2001-11-15 +25, 2008-09-15 −27 (Lehman), 2008-11-20 −28, 2009-02-17 −25, 2009-03-18 −51 (Fed QE1 announcement). Genuine market events; kept (no outlier removal); robustness noted in report.\n- Info-availability: no input violates the rules; only DGS10.csv used. Weekday means for forecasting use changes dated ≤2009-12-31 only.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1: audit done; decision: keep holiday-spanning changes and extreme values (rules prescribe changes between consecutive available obs). Monday CI method: Welch two-sample t.\nEOF\ncat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\nSPLIT = pd.Timestamp(\"2009-12-31\")\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nassert not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 (no yield published)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: drop empty rows first\"),\n dict(step=2, rule=\"first available observation has no one-day change\", n_before=n_raw - n_missing,\n n_after=n_changes, n_removed=n_raw - n_missing - n_changes, rationale=\"TASK rule: change needs y_{t-1}\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= SPLIT]; test = ch[ch[\"date\"] > SPLIT]\nassert train[\"date\"].max() < test[\"date\"].min()\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n# robustness (not a required key): Welch-type check via Kruskal-Wallis and Levene, reported in stdout only\nkw = stats.kruskal(*groups); lev = stats.levene(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.wd == 0, \"dy\"].to_numpy(); oth = ch.loc[ch.wd != 0, \"dy\"].to_numpy()\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nfc = test[\"wd\"].map(wmeans).to_numpy()\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"].to_numpy() ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"].to_numpy() - fc) ** 2)))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\n# table and figure\nrows = []\nfor k, g in enumerate(groups):\n rows.append(dict(weekday=DAYS[k], n=len(g), mean_bp=g.mean(), se_bp=g.std(ddof=1) / np.sqrt(len(g))))\ntab = pd.DataFrame(rows)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, r[\"n\"] - 1) * r[\"se_bp\"] for r in rows]\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95% CI, all %d changes)\" % n_changes)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\ngap_counts = ch[\"gap\"].value_counts().sort_index()\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"},\n {\"issue\": f\"{int((ch.gap > 3).sum() + ((ch.gap == 2)).sum())} changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"},\n {\"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"},\n {\"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"},\n ],\n \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n \"n_analysed\": int(n_changes),\n \"random_seed\": None,\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)),\n \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F),\n \"anova_p_value\": float(p),\n \"effect_estimate\": float(eff),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0,\n \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wmeans.items()},\n \"rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"robust_kruskal_wallis_p\": float(kw.pvalue),\n \"robust_levene_p\": float(lev.pvalue),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && grep -A12 train_week out/results.json","description":"Write audit and analysis script; run it"}}]}}39{"type":"api-request","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","id":"37501530-9a0d-4e3d-8e53-e525a72a351e","timestamp":"2026-10-30T05:20:32.650Z","version":"2.1.314","querySource":"sdk","shapeHash":"c59902ee8879832d5b19c8ab363e92f3befcd9063c14accfcda845bf0d3d8301","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"5fb65edb-c540-4e2d-aa8c-81a84482925a","keep":10,"tail":["172ed760c8cfc407bb7c249f6dacf34e02926cfba3b42cd31b438c02d35b7bf0","980ded9766a2ae64fb60f6bffe48daa7e52ef05d95b58f44db447c91e3bc0a91"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e436d2572627ff2fc62368be4fd41a83ac0223bb65fbd25b4e51d32e948db008"}40{"parentUuid":"bb310259-b699-4795-89c5-3fd2a6c72153","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrnQotSzEhwvENJ8Pma","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01D3abaXGxFwqi9WF1EqaSAT","name":"Bash","input":{"command":"sed -i '' 's/(95% CI, all %d changes)\" % n_changes/(95%% CI, all %d changes)\" % n_changes/' out/analysis.py && ~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && grep -A12 train_week out/results.json","description":"Fix format string and 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\"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.26346571680712,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169966,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052230685527746824,0.1643858563971029\nTuesday,1028,-0.2198443579766538,0.17700678391028105\nWednesday,1030,0.01650485436893226,0.18234593373486246\nThursday,1012,-0.059288537549406724,0.18691776551235453\nFriday,1012,-0.14723320158102893,0.18991766797184334\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (no yield published),5217,5002,215,TASK rule: drop empty rows first\n2,first available observation has no one-day change,5002,5001,1,TASK rule: change needs y_{t-1}\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695535,\n \"Tuesday\": -0.40545808966861635,\n \"Wednesday\": -0.20542635658914643,\n \"Thursday\": 0.013833992094862349,\n \"Friday\": 0.11683168316831462\n },\n \"rmse_ratio_model_over_baseline\": 1.0016689625518016,\n \"robust_kruskal_wallis_p\": 0.47102318097227847,\n \"robust_levene_p\": 1.0122651168712971e-07\n}","is_error":false}]},"uuid":"5fae6324-39a3-4dfa-88a5-6d6dc9126319","timestamp":"2026-10-30T05:20:35.381Z","toolUseResult":{"stdout":"{\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.26346571680712,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169966,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052230685527746824,0.1643858563971029\nTuesday,1028,-0.2198443579766538,0.17700678391028105\nWednesday,1030,0.01650485436893226,0.18234593373486246\nThursday,1012,-0.059288537549406724,0.18691776551235453\nFriday,1012,-0.14723320158102893,0.18991766797184334\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (no yield published),5217,5002,215,TASK rule: drop empty rows first\n2,first available observation has no one-day change,5002,5001,1,TASK rule: change needs y_{t-1}\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695535,\n \"Tuesday\": -0.40545808966861635,\n \"Wednesday\": -0.20542635658914643,\n \"Thursday\": 0.013833992094862349,\n \"Friday\": 0.11683168316831462\n },\n \"rmse_ratio_model_over_baseline\": 1.0016689625518016,\n \"robust_kruskal_wallis_p\": 0.47102318097227847,\n \"robust_levene_p\": 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out/results.json /tmp/r1.json; rm -f out/results.json out/figure1.png out/table1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np,statsmodels.formula.api as smf\nr=json.load(open('out/results.json'))\nd=pd.read_csv('data/DGS10.csv').dropna(); d['dt']=pd.to_datetime(d.observation_date); d['dy']=d.DGS10.diff()*100; c=d.iloc[1:].copy()\nc['wd']=c.dt.dt.dayofweek; c['mon']=(c.wd==0).astype(int)\nm=smf.ols('dy~C(wd)',c).fit(); print('OLS F',m.fvalue,m.f_pvalue, m.df_resid)\nh=smf.ols('dy~mon',c).fit(cov_type='HC1'); print('HC1 mon',h.params.mon,h.conf_int().loc['mon'].tolist())\nnw=smf.ols('dy~mon',c).fit(cov_type='HAC',cov_kwds={'maxlags':5}); print('NW5',nw.conf_int().loc['mon'].tolist())\nt=pd.read_csv('out/table1.csv'); print('table n sum',t.n.sum(), r['n_train']+r['n_test'], r['n_analysed'])\nprint('ci contains', r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high'])\ntr=c[c.dt<='2009-12-31']; print('train max',tr.dt.max(),'test min',c[c.dt>'2009-12-31'].dt.min())\nprint('ex-crisis anova', __import__('scipy.stats').stats.f_oneway(*[g.dy for _,g in c[(c.dt<'2008-09-01')|(c.dt>'2009-06-30')].groupby('wd')]))\nEOF","description":"Reproducibility and independent 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F 0.26346571680719233 0.9015060507654888 4996.0\nHC1 mon 0.050170098401700364 [-0.3189570734770597, 0.4192972702804604]\nNW5 [-0.3292803583071403, 0.429620555110541]\ntable n sum 5001 5001 5001\nci contains True\ntrain max 2009-12-31 00:00:00 test min 2010-01-04 00:00:00\nex-crisis anova F_onewayResult(statistic=np.float64(0.7050503861677778), pvalue=np.float64(0.5883921797034571))","is_error":false}]},"uuid":"93b9aaf1-5630-4d97-b1ec-d779976d93ef","timestamp":"2026-10-30T05:20:45.529Z","toolUseResult":{"stdout":"IDENTICAL\nOLS F 0.26346571680719233 0.9015060507654888 4996.0\nHC1 mon 0.050170098401700364 [-0.3189570734770597, 0.4192972702804604]\nNW5 [-0.3292803583071403, 0.429620555110541]\ntable n sum 5001 5001 5001\nci contains True\ntrain max 2009-12-31 00:00:00 test min 2010-01-04 00:00:00\nex-crisis anova F_onewayResult(statistic=np.float64(0.7050503861677778), pvalue=np.float64(0.5883921797034571))","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3e7c97a5-d888-410e-a9ae-0168feb42b0c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","hash":"413cb57aa9a37b3221a781c7857acc965cbb23e064dd283e8d9c47740b90d948","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KFQzQSG4GA9koyoG46bZje","name":"Bash","input":{"command":"cp 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r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high'])\ntr=c[c.dt<='2009-12-31']; print('train max',tr.dt.max(),'test min',c[c.dt>'2009-12-31'].dt.min())\nprint('ex-crisis anova', __import__('scipy.stats').stats.f_oneway(*[g.dy for _,g in c[(c.dt<'2008-09-01')|(c.dt>'2009-06-30')].groupby('wd')]))\nEOF","description":"Reproducibility and independent 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run of out/analysis.py (deleted outputs) regenerates byte-identical results.json | PASS |\n| 2 | Exclusion ledger: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500 + 2501) = sum of table1 n | PASS |\n| 3 | ANOVA F cross-checked with statsmodels OLS dy~C(weekday) overall F (0.26347, p 0.9015, df 4/4996) | PASS (identical to 1e-12) |\n| 4 | Monday contrast Welch CI contains estimate; CI agrees with HC1 regression CI and Newey-West(5) CI to ~0.01 bp | PASS |\n| 5 | SEs positive (table1 se_bp, effect_se_welch) | PASS |\n| 6 | Chronology: max train date 2009-12-31 < min test date 2010-01-04; weekday means computed from train only (train_weekday_means_bp) | PASS |\n| 7 | Magnitudes: baseline RMSE 4.88 bp vs full-sample sd of changes 5.72 bp (2000-09 more volatile) — plausible; model/baseline ratio ≈1.002 | PASS |\n| 8 | Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect; Monday CI also covers 0 (consistent) | PASS |\n| 9 | Equal-variance assumption of classical ANOVA: Levene p≈1e-7 rejects equal variances → ANOVA kept as prescribed; Kruskal-Wallis (p 0.47) and HC/HAC contrast CIs agree in not rejecting | NOTED (limitation) |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"row/col/missing/duplicate/gap/extreme audit in data_audit.md\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE over evaluation period computed (baseline_metric)\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results.json/table1.csv/figure1.png; no randomness\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"checks.md 1-9 executed\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed; robustness keys added; P4 checks done, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Record checks, update ledger, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1265,"cache_read_input_tokens":17751,"output_tokens":1128,"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":1265},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrp87r9LVUvCWZQkW1u","requestRef":"c741a251-e3fb-4c4e-8904-62a2076fe439","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"762834db-14d7-497c-9098-056d02b83fe7","timestamp":"2026-10-30T05:21:05.572Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"762834db-14d7-497c-9098-056d02b83fe7","isSidechain":false,"promptId":"f2e39458-5dbd-4540-bb0c-348bf9a7d84f","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 39 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/figure1.png: present\n [ok] file:out/table1.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [ok] key:n_missing_rows: ok\n [ok] key:n_train: ok\n [ok] key:n_test: ok\n [ok] key:anova_f_stat: ok\n [ok] key:anova_p_value: ok\n [ok] key:effect_estimate: ok\n [ok] key:effect_ci_low: ok\n [ok] key:effect_ci_high: ok\n [ok] key:baseline_metric: ok\n [ok] key:model_metric: ok\n [ok] key:conclusion: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:effect_estimate: -0.319335775694099 <= 0.05017009840169966 <= 0.41967597249749833\n [ok] positive:anova_f_stat: 0.26346571680712\n [ok] positive:baseline_metric: 4.875704154642543\n [ok] positive:model_metric: 4.883841522290305\n [ok] reproducibility: regenerated in 1.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0273/out/state/gate_receipts/20261030T052106.717419Z.json","is_error":true,"tool_use_id":"toolu_01TUo2oyw6RZy9MSxqJwEWE1"}]},"uuid":"a44f50a2-9c5b-41d2-83c3-829ed593775f","timestamp":"2026-10-30T05:21:06.726Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 39 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/figure1.png: present\n [ok] file:out/table1.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [ok] key:n_missing_rows: ok\n [ok] key:n_train: ok\n [ok] key:n_test: ok\n [ok] key:anova_f_stat: ok\n [ok] key:anova_p_value: ok\n [ok] key:effect_estimate: ok\n [ok] key:effect_ci_low: ok\n [ok] key:effect_ci_high: ok\n [ok] key:baseline_metric: ok\n [ok] key:model_metric: ok\n [ok] key:conclusion: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:effect_estimate: -0.319335775694099 <= 0.05017009840169966 <= 0.41967597249749833\n [ok] positive:anova_f_stat: 0.26346571680712\n [ok] positive:baseline_metric: 4.875704154642543\n [ok] positive:model_metric: 4.883841522290305\n [ok] reproducibility: regenerated in 1.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0273/out/state/gate_receipts/20261030T052106.717419Z.json","sandboxed":false,"sourceToolAssistantUUID":"762834db-14d7-497c-9098-056d02b83fe7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}59{"type":"api-request-blob","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","hash":"20d62493abb854986846f0415dc048c8eeb8fa7e056dfc63d195016d0e5ca548","message":{"role":"assistant","content":[{"type":"text","text":"Now checks, ledger, and the P4 gate run."},{"type":"tool_use","id":"toolu_01TUo2oyw6RZy9MSxqJwEWE1","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run of out/analysis.py (deleted outputs) regenerates byte-identical results.json | PASS |\n| 2 | Exclusion ledger: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500 + 2501) = sum of table1 n | PASS |\n| 3 | ANOVA F cross-checked with statsmodels OLS dy~C(weekday) overall F (0.26347, p 0.9015, df 4/4996) | PASS (identical to 1e-12) |\n| 4 | Monday contrast Welch CI contains estimate; CI agrees with HC1 regression CI and Newey-West(5) CI to ~0.01 bp | PASS |\n| 5 | SEs positive (table1 se_bp, effect_se_welch) | PASS |\n| 6 | Chronology: max train date 2009-12-31 < min test date 2010-01-04; weekday means computed from train only (train_weekday_means_bp) | PASS |\n| 7 | Magnitudes: baseline RMSE 4.88 bp vs full-sample sd of changes 5.72 bp (2000-09 more volatile) — plausible; model/baseline ratio ≈1.002 | PASS |\n| 8 | Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect; Monday CI also covers 0 (consistent) | PASS |\n| 9 | Equal-variance assumption of classical ANOVA: Levene p≈1e-7 rejects equal variances → ANOVA kept as prescribed; Kruskal-Wallis (p 0.47) and HC/HAC contrast CIs agree in not rejecting | NOTED (limitation) |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"row/col/missing/duplicate/gap/extreme audit in data_audit.md\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE over evaluation period computed (baseline_metric)\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results.json/table1.csv/figure1.png; no randomness\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"checks.md 1-9 executed\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed; robustness keys added; P4 checks done, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py 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gate"}}]}}60{"type":"api-request","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","id":"5a4ba109-be1a-4bf6-8170-44f94156dbb3","timestamp":"2026-10-30T05:21:06.729Z","version":"2.1.314","querySource":"sdk","shapeHash":"c59902ee8879832d5b19c8ab363e92f3befcd9063c14accfcda845bf0d3d8301","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"c741a251-e3fb-4c4e-8904-62a2076fe439","keep":18,"tail":["20d62493abb854986846f0415dc048c8eeb8fa7e056dfc63d195016d0e5ca548","c919730f56cc2f4db3dc4d06c4013f7f7e5ec1c43cc9897eb157315a1a598727"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fddb514ffd21fbaa73e11919755e82b700a44e749da177f0f3887f6ba142897a"}61{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a44f50a2-9c5b-41d2-83c3-829ed593775f","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00"}62{"type":"atis-latch","atis":"","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00"}63{"parentUuid":"a44f50a2-9c5b-41d2-83c3-829ed593775f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrpvXd484k8GoFVixdR","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1991,"cache_read_input_tokens":19016,"output_tokens":4051,"output_tokens_details":{"thinking_tokens":65},"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":1991},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":971,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrpvFzoP8jAo62dtJCu","requestRef":"5a4ba109-be1a-4bf6-8170-44f94156dbb3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"08a0b9a0-518f-49d8-8895-254bbc369f13","timestamp":"2026-10-30T05:21:09.011Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"08a0b9a0-518f-49d8-8895-254bbc369f13","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrpvXd484k8GoFVixdR","type":"message","role":"assistant","content":[{"type":"text","text":"The remaining failures are all P5 deliverables, which don't exist yet; the P4-relevant checks pass (keys, CI, reproducibility). Next: write the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1991,"cache_read_input_tokens":19016,"output_tokens":4051,"output_tokens_details":{"thinking_tokens":65},"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":1991},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrpvFzoP8jAo62dtJCu","requestRef":"5a4ba109-be1a-4bf6-8170-44f94156dbb3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e977bbc3-af6b-4846-bdbc-126023734e51","timestamp":"2026-10-30T05:21:09.012Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}65{"parentUuid":"e977bbc3-af6b-4846-bdbc-126023734e51","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrpvXd484k8GoFVixdR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015E2sZ9bs9HMzXbX16opJx3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week help predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10) over 2000–2019? There are three estimands: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on Tuesday–Friday), with a 95% interval; (iii) the out-of-sample RMSE of weekday-mean forecasts against a zero-change forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`.\n- 215 rows have an empty yield field because of US market holidays, including the 2001-09-11/12 closure (`n_missing_rows`). These rows were dropped first. One more observation was lost because the first available observation has no change. This leaves 5001 daily changes (`n_analysed`), see `out/exclusions.csv`.\n- The estimation period (changes dated ≤ 2009-12-31) has 2500 changes (`n_train`). The evaluation period (changes dated ≥ 2010-01-01; the first is 2010-01-04) has 2501 changes (`n_test`).\n- Data issues are listed in `data_issues`:\n - Changes after a holiday span more than one trading session. As the task prescribes, they were kept and attributed to the weekday of t.\n - Crisis-period moves are extreme (for example −51 bp on 2009-03-18). They were kept.\n - Yields are quoted on a 1 bp grid.\n- No values were trimmed or winsorised.\n\n## Method\n- **Joint test (i).** Classical one-way equal-variance ANOVA of Δy on weekday, over all 5001 changes, with an F(4, 4996) reference distribution (scipy `f_oneway`). It was cross-checked against the overall F from an OLS regression on weekday dummies.\n- **Monday contrast (ii).** Difference in means with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- **Out-of-sample comparison (iii).** The model forecast for each evaluation-period change is the estimation-period mean change of its weekday (`train_weekday_means_bp`). The baseline forecast is 0. Both are scored by RMSE over the evaluation period. No evaluation-period information enters the forecasts.\n- **Conclusion rule.** Use the ANOVA at 5%. If it rejects, the sign comes from the Monday interval.\n- **Robustness checks (extra keys, not part of the decision rule):**\n - Monday-indicator OLS with HC1 and Newey–West (5 lags) intervals;\n - Kruskal–Wallis test;\n - Levene test for equal variances;\n - ANOVA excluding the 2008-09 to 2009-06 crisis window.\n- **Outputs and reproducibility.** `out/table1.csv` and `out/figure1.png` (weekday means with 95% t-intervals) are produced by `out/analysis.py`. No randomness is used (`random_seed` = null).\n\n## Results\n- **Joint test.** ANOVA F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equality of the five weekday means is not rejected.\n- **Monday contrast.** +0.05 bp (`effect_estimate`), 95% Welch CI [−0.32, +0.42] bp (`effect_ci_low`, `effect_ci_high`). This is small next to the typical daily move (evaluation-period RMSE of about 4.9 bp, `baseline_metric`), and it is not distinguishable from zero.\n- **Weekday means.** All five lie within about ±0.22 bp of zero; every 95% bar in figure 1 covers zero (`out/table1.csv`).\n- **Out of sample.** The weekday-mean forecast has RMSE 4.884 bp (`model_metric`), against 4.876 bp for the zero-change forecast (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday-specific means estimated in 2000–2009 slightly *worsen* forecasts for 2010–2019.\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this period, the weekday carries no detectable predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday contrast is pinned down to within about ±0.37 bp (half-width of [`effect_ci_low`, `effect_ci_high`]). Weekday effects larger than about 0.4 bp per day on Mondays are therefore inconsistent with these data, but smaller ones cannot be ruled out.\n- The Welch interval agrees with the HC1 interval (`robust_effect_ci_hc1`) and with the Newey–West(5) interval, which allows for short serial correlation (`robust_effect_ci_nw5`).\n- The rank-based Kruskal–Wallis test also does not reject (`robust_kruskal_wallis_p` = 0.47).\n- Excluding the 2008–09 crisis window (4794 changes, `n_excl_crisis`), the ANOVA still does not reject (`robust_anova_p_excl_crisis_2008_09_to_2009_06` = 0.59).\n- The out-of-sample RMSE difference was not given a formal test (for example Diebold–Mariano). The 0.17% gap is descriptive only.\n\n## Limitations and open issues\n- **Equal-variance assumption is violated.** Levene's test strongly rejects equal weekday variances (`robust_levene_p` ≈ 1e-7). The prescribed classical ANOVA p-value is therefore approximate. Heteroskedasticity-robust alternatives (`robust_effect_ci_hc1`, `robust_kruskal_wallis_p`) lead to the same conclusion, but a Welch-type ANOVA for the joint test was not computed.\n- **Dependence and volatility clustering.** The ANOVA and Welch tests assume independent observations. Daily yield changes show strong volatility clustering, especially in 2008–09. Only the Monday contrast was given a HAC check (`robust_effect_ci_nw5`); the joint test was not made HAC-robust.\n- **Holiday-spanning changes.** Changes after a holiday cover two sessions and are attributed to the weekday of t (mostly Tuesdays after Monday holidays). This mixes a calendar-gap effect into the weekday comparison. Excluding or rescaling these changes was not examined.\n- **Weekend changes.** A Monday change spans the weekend (3 calendar days). The data cannot separate weekend news from a genuine Monday effect.\n- **Low power for tiny effects and non-stationarity.** With daily standard deviations of about 5–6 bp, effects of a few tenths of a bp are hard to detect. Weekday patterns could also exist in sub-periods (for example around announcement-heavy days such as payroll Fridays or FOMC Wednesdays) and average out over 20 years. No sub-period or event-day analysis was done beyond the crisis exclusion.\n- **Narrow out-of-sample test.** The comparison uses a single fixed split (2000–2009 vs 2010–2019). Rolling or expanding re-estimation and formal forecast-comparison tests were not done.\n- **Data revisions and other maturities.** FRED vintage effects and other maturities were not checked; the analysis uses only the given file.\n- **What would overturn the result.** A data error in the file (none detected in the audit), or a different definition of the change (for example per-session changes), could alter the result. The conclusion applies only to the stated estimands under the task's definitions.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption rejected by Levene; independence assumed.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, indistinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Small effects below ~0.4 bp cannot be ruled out; Monday changes include weekends.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve 2010-2019 forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single split; no formal forecast-comparison test.\"},\n {\"id\":\"C4\",\"text\":\"The no-effect conclusion is robust to HC1/Newey-West intervals for the Monday contrast, a Kruskal-Wallis test, and excluding the 2008-09 crisis window.\",\"results_keys\":[\"robust_effect_ci_hc1\",\"robust_effect_ci_nw5\",\"robust_kruskal_wallis_p\",\"robust_anova_p_excl_crisis_2008_09_to_2009_06\",\"n_excl_crisis\"],\"status\":\"supported\",\"caveat\":\"Joint test not made HAC/Welch-robust.\"},\n {\"id\":\"C5\",\"text\":\"Weekday variances differ significantly, so the classical ANOVA p-value is approximate.\",\"results_keys\":[\"robust_levene_p\"],\"status\":\"supported\",\"caveat\":\"Levene test also assumes independence.\"},\n {\"id\":\"C6\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Applies to the stated estimands and period only.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"checks.md 1-9 executed\",\"clean re-run identical\",\"gate run \"+r[0]+\": all results/CI/reproducibility checks ok; only P5 artifacts (report, claims) and non-terminal ledger failed, as expected pre-P5\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md 6 sections with key citations\",\"claims.json keys exist\",\"final gate run (see receipt list)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 1); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1991,"cache_read_input_tokens":19016,"output_tokens":4051,"output_tokens_details":{"thinking_tokens":65},"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":1991},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrpvFzoP8jAo62dtJCu","requestRef":"5a4ba109-be1a-4bf6-8170-44f94156dbb3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1f269c1e-e82c-4e34-b53c-a4ae245616bb","timestamp":"2026-10-30T05:21:42.025Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"1f269c1e-e82c-4e34-b53c-a4ae245616bb","isSidechain":false,"promptId":"f2e39458-5dbd-4540-bb0c-348bf9a7d84f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015E2sZ9bs9HMzXbX16opJx3","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0273/out/state/gate_receipts/20261030T052143.167173Z.json","is_error":false}]},"uuid":"6afec1df-af29-485d-9d1b-62e3a3d753d9","timestamp":"2026-10-30T05:21:43.175Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0273/out/state/gate_receipts/20261030T052143.167173Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1f269c1e-e82c-4e34-b53c-a4ae245616bb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","version":"2.1.314","gitBranch":"HEAD"}67{"type":"api-request-blob","sessionId":"d9373c06-70b3-4028-bf02-266b9e78fd00","hash":"31e289b37fb60ba0ab006e0f834c646028effee6ed364b38482fc660648d856b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The remaining failures are all P5 deliverables, which don't exist yet; the P4-relevant checks pass (keys, CI, reproducibility). Next: write the report and claims."},{"type":"tool_use","id":"toolu_015E2sZ9bs9HMzXbX16opJx3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week help predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10) over 2000–2019? There are three estimands: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on Tuesday–Friday), with a 95% interval; (iii) the out-of-sample RMSE of weekday-mean forecasts against a zero-change forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`.\n- 215 rows have an empty yield field because of US market holidays, including the 2001-09-11/12 closure (`n_missing_rows`). These rows were dropped first. One more observation was lost because the first available observation has no change. This leaves 5001 daily changes (`n_analysed`), see `out/exclusions.csv`.\n- The estimation period (changes dated ≤ 2009-12-31) has 2500 changes (`n_train`). The evaluation period (changes dated ≥ 2010-01-01; the first is 2010-01-04) has 2501 changes (`n_test`).\n- Data issues are listed in `data_issues`:\n - Changes after a holiday span more than one trading session. As the task prescribes, they were kept and attributed to the weekday of t.\n - Crisis-period moves are extreme (for example −51 bp on 2009-03-18). They were kept.\n - Yields are quoted on a 1 bp grid.\n- No values were trimmed or winsorised.\n\n## Method\n- **Joint test (i).** Classical one-way equal-variance ANOVA of Δy on weekday, over all 5001 changes, with an F(4, 4996) reference distribution (scipy `f_oneway`). It was cross-checked against the overall F from an OLS regression on weekday dummies.\n- **Monday contrast (ii).** Difference in means with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- **Out-of-sample comparison (iii).** The model forecast for each evaluation-period change is the estimation-period mean change of its weekday (`train_weekday_means_bp`). The baseline forecast is 0. Both are scored by RMSE over the evaluation period. No evaluation-period information enters the forecasts.\n- **Conclusion rule.** Use the ANOVA at 5%. If it rejects, the sign comes from the Monday interval.\n- **Robustness checks (extra keys, not part of the decision rule):**\n - Monday-indicator OLS with HC1 and Newey–West (5 lags) intervals;\n - Kruskal–Wallis test;\n - Levene test for equal variances;\n - ANOVA excluding the 2008-09 to 2009-06 crisis window.\n- **Outputs and reproducibility.** `out/table1.csv` and `out/figure1.png` (weekday means with 95% t-intervals) are produced by `out/analysis.py`. No randomness is used (`random_seed` = null).\n\n## Results\n- **Joint test.** ANOVA F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equality of the five weekday means is not rejected.\n- **Monday contrast.** +0.05 bp (`effect_estimate`), 95% Welch CI [−0.32, +0.42] bp (`effect_ci_low`, `effect_ci_high`). This is small next to the typical daily move (evaluation-period RMSE of about 4.9 bp, `baseline_metric`), and it is not distinguishable from zero.\n- **Weekday means.** All five lie within about ±0.22 bp of zero; every 95% bar in figure 1 covers zero (`out/table1.csv`).\n- **Out of sample.** The weekday-mean forecast has RMSE 4.884 bp (`model_metric`), against 4.876 bp for the zero-change forecast (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday-specific means estimated in 2000–2009 slightly *worsen* forecasts for 2010–2019.\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this period, the weekday carries no detectable predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday contrast is pinned down to within about ±0.37 bp (half-width of [`effect_ci_low`, `effect_ci_high`]). Weekday effects larger than about 0.4 bp per day on Mondays are therefore inconsistent with these data, but smaller ones cannot be ruled out.\n- The Welch interval agrees with the HC1 interval (`robust_effect_ci_hc1`) and with the Newey–West(5) interval, which allows for short serial correlation (`robust_effect_ci_nw5`).\n- The rank-based Kruskal–Wallis test also does not reject (`robust_kruskal_wallis_p` = 0.47).\n- Excluding the 2008–09 crisis window (4794 changes, `n_excl_crisis`), the ANOVA still does not reject (`robust_anova_p_excl_crisis_2008_09_to_2009_06` = 0.59).\n- The out-of-sample RMSE difference was not given a formal test (for example Diebold–Mariano). The 0.17% gap is descriptive only.\n\n## Limitations and open issues\n- **Equal-variance assumption is violated.** Levene's test strongly rejects equal weekday variances (`robust_levene_p` ≈ 1e-7). The prescribed classical ANOVA p-value is therefore approximate. Heteroskedasticity-robust alternatives (`robust_effect_ci_hc1`, `robust_kruskal_wallis_p`) lead to the same conclusion, but a Welch-type ANOVA for the joint test was not computed.\n- **Dependence and volatility clustering.** The ANOVA and Welch tests assume independent observations. Daily yield changes show strong volatility clustering, especially in 2008–09. Only the Monday contrast was given a HAC check (`robust_effect_ci_nw5`); the joint test was not made HAC-robust.\n- **Holiday-spanning changes.** Changes after a holiday cover two sessions and are attributed to the weekday of t (mostly Tuesdays after Monday holidays). This mixes a calendar-gap effect into the weekday comparison. Excluding or rescaling these changes was not examined.\n- **Weekend changes.** A Monday change spans the weekend (3 calendar days). The data cannot separate weekend news from a genuine Monday effect.\n- **Low power for tiny effects and non-stationarity.** With daily standard deviations of about 5–6 bp, effects of a few tenths of a bp are hard to detect. Weekday patterns could also exist in sub-periods (for example around announcement-heavy days such as payroll Fridays or FOMC Wednesdays) and average out over 20 years. No sub-period or event-day analysis was done beyond the crisis exclusion.\n- **Narrow out-of-sample test.** The comparison uses a single fixed split (2000–2009 vs 2010–2019). Rolling or expanding re-estimation and formal forecast-comparison tests were not done.\n- **Data revisions and other maturities.** FRED vintage effects and other maturities were not checked; the analysis uses only the given file.\n- **What would overturn the result.** A data error in the file (none detected in the audit), or a different definition of the change (for example per-session changes), could alter the result. The conclusion applies only to the stated estimands under the task's definitions.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption rejected by Levene; independence assumed.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, indistinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Small effects below ~0.4 bp cannot be ruled out; Monday changes include weekends.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve 2010-2019 forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single split; no formal forecast-comparison test.\"},\n {\"id\":\"C4\",\"text\":\"The no-effect conclusion is robust to HC1/Newey-West intervals for the Monday contrast, a Kruskal-Wallis test, and excluding the 2008-09 crisis window.\",\"results_keys\":[\"robust_effect_ci_hc1\",\"robust_effect_ci_nw5\",\"robust_kruskal_wallis_p\",\"robust_anova_p_excl_crisis_2008_09_to_2009_06\",\"n_excl_crisis\"],\"status\":\"supported\",\"caveat\":\"Joint test not made HAC/Welch-robust.\"},\n {\"id\":\"C5\",\"text\":\"Weekday variances differ significantly, so the classical ANOVA p-value is approximate.\",\"results_keys\":[\"robust_levene_p\"],\"status\":\"supported\",\"caveat\":\"Levene test also assumes independence.\"},\n {\"id\":\"C6\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Applies to the stated estimands and period only.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"checks.md 1-9 executed\",\"clean re-run identical\",\"gate run \"+r[0]+\": all results/CI/reproducibility checks ok; 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All six phases are marked passed, and the final gate run passed with 0 of 39 checks failing.\n\n**Data:** the file has 5,217 rows. I dropped 215 holiday rows with no yield, and the first observation has no change, leaving 5,001 daily changes. 2,500 fall in the 2000–2009 estimation period and 2,501 in the 2010–2019 evaluation period.\n\n| Estimand | Result |\n|---|---|\n| Joint test that all five weekday means are equal (classical ANOVA) | F = 0.263, p = 0.90; not rejected |\n| Monday minus the other four weekdays | +0.05 bp, 95% interval [−0.32, +0.42] bp; indistinguishable from zero |\n| Evaluation-period error, forecasting with 2000–2009 weekday means | 4.884 bp |\n| Evaluation-period error, forecasting zero change | 4.876 bp |\n\nThe weekday-mean forecast is slightly worse than assuming no change, by about 0.17%. I didn't test that gap formally, so it's descriptive only.\n\nFor the Monday interval I used a Welch two-sample interval, which doesn't assume equal variances. As extra checks, I recomputed the interval with heteroskedasticity-robust and serial-correlation-robust (Newey–West, 5 lags) errors and ran a rank-based (Kruskal–Wallis) test. I also reran the ANOVA without the September 2008 to June 2009 crisis months. None of these changes the conclusion.\n\n**Limitations (detailed in the report):**\n- **Equal-variance assumption fails:** the five weekdays clearly have different variances (Levene p ≈ 1e-7). The ANOVA p-value is therefore only approximate, and I didn't compute a version of the joint test that allows for this.\n- **Volatility clustering:** daily yield changes come in calm and volatile stretches. Only the Monday result was checked against this; the joint test assumes independent days.\n- **Holidays and weekends:** a change after a holiday covers two trading sessions but is counted under one weekday, as the task specifies. Monday changes also include the weekend, so weekend news can't be separated from a true Monday effect.\n- **Limited power and a single split:** effects smaller than about 0.4 bp a day can't be ruled out. 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