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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"}],"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\n 5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a data/README.md","is_error":false}]},"uuid":"99b9bf19-609a-4b35-837c-2f4e5fc58498","timestamp":"2026-10-30T05:19:44.819Z","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\n 5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e5d9b98e-02e2-4935-8010-b57414173e57","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"dd51b8bc268c752b053397e2d9af59395ff332e6fe287f123dd2d4c84d62f2e8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LxpVnnPpfxCuq3kzg7pR3Q","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json; cat bundle.json; head -5 data/DGS10.csv; tail -3 data/DGS10.csv; wc -l data/DGS10.csv; shasum -a 256 data/*","description":"Inspect provenance, gate spec, data head"}}]}}22{"type":"api-request","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","id":"eef2b0ac-ec11-47e3-907d-e5af51a803ad","timestamp":"2026-10-30T05:19:44.822Z","version":"2.1.314","querySource":"sdk","shapeHash":"0037c9e7a6bc3c6f1429e905ff23f996e85f3f4ec7d6087d94477d2d46619976","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":"a8d4b2a1-f6eb-482e-9df6-8adaa40c0e0a","keep":4,"tail":["dd51b8bc268c752b053397e2d9af59395ff332e6fe287f123dd2d4c84d62f2e8","50d5374c50a7f470eafb2ce16596afeb6dd7abdb512bb199f57db720cd9f3bd3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"520fbfea8da39dbfc7e7aef84787cb7e33f639d90c8a1e33775a3e2664f37b04"}23{"parentUuid":"99b9bf19-609a-4b35-837c-2f4e5fc58498","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXritC4hgUA9uwRTKk8k","type":"message","role":"assistant","content":[{"type":"text","text":"Hash matches. P0: contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2130,"cache_read_input_tokens":6933,"output_tokens":1475,"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":2130},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrisugnoC8Vs6xDWBKf","requestRef":"eef2b0ac-ec11-47e3-907d-e5af51a803ad","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"311838c6-1656-4083-bdce-f874fb6e1b5f","timestamp":"2026-10-30T05:19:46.515Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"311838c6-1656-4083-bdce-f874fb6e1b5f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXritC4hgUA9uwRTKk8k","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NgvQfjoXf5Y8MxYpmG6rvX","name":"Bash","input":{"command":"mkdir -p out/state/gate_receipts\ncat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday predict the one-day change in the 10y Treasury CMT yield (DGS10), 2000–2019?\n**Estimands.** (i) ANOVA (classical equal-variance one-way) F-test of equal weekday means of Δy_t (bp) on all changes; (ii) Monday contrast = mean(Mon) − mean(Tue–Fri), with 95% CI (choice: Welch two-sample t interval); (iii) out-of-sample RMSE (2010–2019 changes) of weekday-mean forecast (means estimated on changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes are roughly independent across days for the ANOVA/Welch inference to be valid (i.i.d. within groups; ANOVA also assumes equal variance); stationarity of weekday means across periods for OOS meaning.\n**Definitions.** Drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available observations; dated/attributed to weekday of t; first obs has no change. Train = changes dated ≤2009-12-31 (includes the first change of 2000; the change dated 2010-01-04 spanning year boundary belongs to test).\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; if rejects, sign by Monday CI (positive/negative; if CI covers zero though ANOVA rejects → no_detectable_effect for the Monday sign, documented).\n**Info rules.** Only data/DGS10.csv; weekday means for forecasting from train period only.\n**Invalidating conditions.** Missing/corrupt file, inability to compute changes; using test data in forecast means.\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\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > 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.columns.tolist())\ndt=pd.to_datetime(d.observation_date)\nprint(dt.min(),dt.max(),dt.duplicated().sum(),dt.is_monotonic_increasing, dt.dt.dayofweek.value_counts().sort_index().tolist())\nv=d.DGS10.str.strip()\nprint('empty',(v=='').sum(), 'nonnumeric', v[(v!='')&pd.to_numeric(v,errors='coerce').isna()].unique())\nx=pd.to_numeric(v,errors='coerce').dropna(); print(x.describe())\ndd=(x.diff()*100).dropna(); print(dd.describe()); print(dd.abs().sort_values().tail(5))\nprint(d.observation_date[v==''].tolist()[:15])\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":2130,"cache_read_input_tokens":6933,"output_tokens":1475,"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":2130},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrisugnoC8Vs6xDWBKf","requestRef":"eef2b0ac-ec11-47e3-907d-e5af51a803ad","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"121e2ba8-f66d-49c3-a7f9-3b42c8c066cd","timestamp":"2026-10-30T05:19:58.104Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"121e2ba8-f66d-49c3-a7f9-3b42c8c066cd","isSidechain":false,"promptId":"7fd1cf74-fdf8-4e1a-b473-09cb021d211d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NgvQfjoXf5Y8MxYpmG6rvX","type":"tool_result","content":"(5217, 2) ['observation_date', 'DGS10']\n2000-01-03 00:00:00 2019-12-31 00:00:00 0 True [1044, 1044, 1043, 1043, 1043]\nempty 215 nonnumeric []\ncount 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\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\n2381 25.0\n488 25.0\n2270 27.0\n2318 28.0\n2402 51.0\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']","is_error":false}]},"uuid":"31dc3c7e-d7e0-4bab-b98d-c76e31960315","timestamp":"2026-10-30T05:19:58.333Z","toolUseResult":{"stdout":"(5217, 2) ['observation_date', 'DGS10']\n2000-01-03 00:00:00 2019-12-31 00:00:00 0 True [1044, 1044, 1043, 1043, 1043]\nempty 215 nonnumeric []\ncount 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\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\n2381 25.0\n488 25.0\n2270 27.0\n2318 28.0\n2402 51.0\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']","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"121e2ba8-f66d-49c3-a7f9-3b42c8c066cd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"955a14b5e5d3de657004e929addf3ddba457d0b6d8d12ee0cc0f32a47ee6f60a","message":{"role":"assistant","content":[{"type":"text","text":"Hash matches. P0: contract and ledger."},{"type":"tool_use","id":"toolu_01NgvQfjoXf5Y8MxYpmG6rvX","name":"Bash","input":{"command":"mkdir -p out/state/gate_receipts\ncat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday predict the one-day change in the 10y Treasury CMT yield (DGS10), 2000–2019?\n**Estimands.** (i) ANOVA (classical equal-variance one-way) F-test of equal weekday means of Δy_t (bp) on all changes; (ii) Monday contrast = mean(Mon) − mean(Tue–Fri), with 95% CI (choice: Welch two-sample t interval); (iii) out-of-sample RMSE (2010–2019 changes) of weekday-mean forecast (means estimated on changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes are roughly independent across days for the ANOVA/Welch inference to be valid (i.i.d. within groups; ANOVA also assumes equal variance); stationarity of weekday means across periods for OOS meaning.\n**Definitions.** Drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available observations; dated/attributed to weekday of t; first obs has no change. Train = changes dated ≤2009-12-31 (includes the first change of 2000; the change dated 2010-01-04 spanning year boundary belongs to test).\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; if rejects, sign by Monday CI (positive/negative; if CI covers zero though ANOVA rejects → no_detectable_effect for the Monday sign, documented).\n**Info rules.** Only data/DGS10.csv; weekday means for forecasting from train period only.\n**Invalidating conditions.** Missing/corrupt file, inability to compute changes; using test data in forecast means.\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\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > 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.columns.tolist())\ndt=pd.to_datetime(d.observation_date)\nprint(dt.min(),dt.max(),dt.duplicated().sum(),dt.is_monotonic_increasing, dt.dt.dayofweek.value_counts().sort_index().tolist())\nv=d.DGS10.str.strip()\nprint('empty',(v=='').sum(), 'nonnumeric', v[(v!='')&pd.to_numeric(v,errors='coerce').isna()].unique())\nx=pd.to_numeric(v,errors='coerce').dropna(); 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dates unique and strictly increasing; weekdays only (Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043).\n- Empty DGS10: 215 rows (US market holidays, plus e.g. 2001-09-11/12 closures). No non-numeric tokens other than empty.\n- Available yields: 5002; range 1.37–6.79 %. Changes: 5001; mean −0.093 bp, sd 5.72 bp, min −51 bp (2009-03-18, FOMC QE announcement — genuine, kept), max +28 bp.\n- Anomaly/feature: changes after a holiday span >1 calendar business day (e.g. Tuesday after a Monday holiday); per TASK they are attributed to the weekday of t. Kept, documented.\n- `data/README.md`: provenance only, not analysed.\n- No input violates the information-availability rules; the only rule-sensitive step is that weekday forecast means use changes dated ≤2009-12-31 only.\nEOF\necho \"- 2026-10-30 P1 passed: audit done, 215 empty rows to drop; no outlier removal.\" >> out/state/log.md","description":"Write data audit"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1889,"cache_read_input_tokens":9063,"output_tokens":732,"output_tokens_details":{"thinking_tokens":105},"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":1889},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrjsgpg2SVMyf2DGxPw","requestRef":"4cec1a9a-40ef-4301-95a3-6c95cba1c25b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5cf8a36e-ce12-4efc-b45a-132bfedaa20b","timestamp":"2026-10-30T05:20:05.283Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"5cf8a36e-ce12-4efc-b45a-132bfedaa20b","isSidechain":false,"promptId":"7fd1cf74-fdf8-4e1a-b473-09cb021d211d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NpLVE9zyHD6UELudmHPwxQ","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"76481f03-8b90-4a1e-8e39-2aa5c31bc5d3","timestamp":"2026-10-30T05:20:05.336Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5cf8a36e-ce12-4efc-b45a-132bfedaa20b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"934b25be18a277a6d1ed77fe826fc5762a16fb4336216cde58a7570511c12590","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSyAcKEAgTGAI4AUIIdGhpbmtpbmcSDOmocT80CpGr286wMhoMEVAOeFl/LqmSEmfPIjAFRWrhBAnosPbXND/Z1MQ4ZmTR9fJwYWFTKn//HXpxGp+fiQXpdk/nxwMUN6iss9wq5QZgB6qaYQ+ORFMqcmB295QfmyIWp3k6J8eiW5azTT3rVhviMMbW196V2BOujgm4Zw7bZ6fPvvw7ubWvOOjW9u6Av/M90CBdn1FlyG3x3MOBNCnchtC3UCbWiyiOiBSHk00O9L16WrGF7BashMZqcJAa9h10aCZm5xXp0b11zvYkDke7mpPMl40zq6/x+eDkMDMpDf+vpIjT+RjQylS3Yb+0+7LwfZHA33cn3Ybbro1gSz35yHYE9PIEIwvonAV/yqz68KfBetIASTSzj1EMzeeM3i8gwlMdaT+mEGWUcEOuwmgRZan7yl5ZhpxohzHtLtC2DDznvC/kIvA4VOiTqJ0/UU9cb72td/yhyIj23To5XGPfAbArvxss83O2+B9Prlt15rGfpL/P+WZTA6iUX/yJKeIxjpibRucshmIv3BSAaLgQIszdmgOM68d3Q5YdvH0pI5ztvIxHft1CIPqXJKAeT+6ZmENkSfYS3w9rZrQHj0sknHL/NSGrlyEy70nLw0dWv4wM/J3mYayyJRZoxY799x9zVoENmm97JMk4wNa7wUJ6rKYsB9fTrr8DVkpQs9yJIguKO/CTLgX2e1T2LGe8UUzXLOU9u/cZjv4k4YWojZKsuwaf1n+S/vdnEHhOLfGU9v/+6aFdLqvpbHgfsU1RmbnQ/Z1qaiioXHx0p3XxdsuI/PU8YGUdI+ZbATh2aYdrwDelAmWZk8TDHxaX54/yJV+3UzuiVobCztxtOfuUC/h1mfz4YW6LvYlUnC187es2a44hkbDBV0rGAyuEff0pAcoqyuiTdkoMW4xalwJwIw8uDdBV1J7caTWHDwjeVowAc0cT7aw4h5IFZEK/WhglkMe66jRXaXUywzdt0pgS4g/lAjT3W9DFd9ABBku3TQDGVzdg57fQ22o5FdkIuVjMNram0IRRs7C1Cmbu9rAx0bwT3JuaHvgvyjTT5trp2ArLLBxDDOsoO9kZsreyHzyg1yX9QJ/en2p4iKCwZdyC5R7CTWK3O420BeiAdy/76jUIviCu39BJJ43E4rmA3pEu7yBD8PGbAFlhEx3ZN1BdXT3KKpaDQmxzuojY7SUrUrSY7iPJWX4Kv8lb+tn6mSZaW7Q8mrSSMMak8yM+ArCA+7AUVcBJqaltNgrktMlVuRknOTlbzxgB"},{"type":"tool_use","id":"toolu_01NpLVE9zyHD6UELudmHPwxQ","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n- `data/DGS10.csv`: SHA-256 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README). 5217 data rows, 2 columns (`observation_date` str YYYY-MM-DD, `DGS10` percent).\n- Date range 2000-01-03 … 2019-12-31; dates unique and strictly increasing; weekdays only (Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043).\n- Empty DGS10: 215 rows (US market holidays, plus e.g. 2001-09-11/12 closures). No non-numeric tokens other than empty.\n- Available yields: 5002; range 1.37–6.79 %. Changes: 5001; mean −0.093 bp, sd 5.72 bp, min −51 bp (2009-03-18, FOMC QE announcement — genuine, kept), max +28 bp.\n- Anomaly/feature: changes after a holiday span >1 calendar business day (e.g. Tuesday after a Monday holiday); per TASK they are attributed to the weekday of t. Kept, documented.\n- `data/README.md`: provenance only, not analysed.\n- No input violates the information-availability rules; the only rule-sensitive step is that weekday forecast means use changes dated ≤2009-12-31 only.\nEOF\necho \"- 2026-10-30 P1 passed: audit done, 215 empty rows to drop; no outlier removal.\" >> out/state/log.md","description":"Write data audit"}}]}}32{"type":"api-request","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","id":"83be8cb0-65ed-4034-8f42-13e73a895519","timestamp":"2026-10-30T05:20:05.339Z","version":"2.1.314","querySource":"sdk","shapeHash":"0037c9e7a6bc3c6f1429e905ff23f996e85f3f4ec7d6087d94477d2d46619976","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":"4cec1a9a-40ef-4301-95a3-6c95cba1c25b","keep":8,"tail":["934b25be18a277a6d1ed77fe826fc5762a16fb4336216cde58a7570511c12590","e7611182730b11ee1c4b4321442bd650dfa56242d8a735625e46e8ff0059a726"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f880738930e2d7ba09d6ffd16a2475752d631f94d00afa66529939fa6ddad41b"}33{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"76481f03-8b90-4a1e-8e39-2aa5c31bc5d3","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4"}34{"type":"atis-latch","atis":"","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4"}35{"parentUuid":"76481f03-8b90-4a1e-8e39-2aa5c31bc5d3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkPvFfPn9XWc8zTaPb","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (baseline + method in one deterministic script)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":747,"cache_read_input_tokens":10952,"output_tokens":3186,"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":747},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrkPePTEWW6wRjHeiML","requestRef":"83be8cb0-65ed-4034-8f42-13e73a895519","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b954b748-6ced-450b-89c7-9cc0820158ca","timestamp":"2026-10-30T05:20:06.472Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"b954b748-6ced-450b-89c7-9cc0820158ca","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkPvFfPn9XWc8zTaPb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KbEyYj3WRHZvDPjX63wZf2","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10), 2000-2019.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv. No 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\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nval = raw[\"DGS10\"].str.strip()\nmissing = val == \"\"\nn_missing = int(missing.sum())\nobs = raw.loc[~missing, [\"date\"]].copy()\nobs[\"y\"] = val[~missing].astype(float).values\nobs = obs.sort_values(\"date\").reset_index(drop=True)\nobs[\"dy\"] = 100.0 * obs[\"y\"].diff()\nch = obs.dropna(subset=[\"dy\"]).copy()\nch[\"dy\"] = ch[\"dy\"].round(10) # remove float noise from percent->bp conversion\nch[\"weekday\"] = ch[\"date\"].dt.dayofweek.map(dict(enumerate(DAYS)))\nassert ch[\"weekday\"].notna().all()\n\n# exclusion ledger\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 field (no published yield)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: holidays/no publication\"),\n dict(step=2, rule=\"first available observation has no prior value -> no change\", n_before=n_raw - n_missing,\n n_after=len(ch), n_removed=n_raw - n_missing - len(ch), rationale=\"TASK rule: change undefined\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ncut = pd.Timestamp(\"2009-12-31\")\ntrain = ch[ch[\"date\"] <= cut]; test = ch[ch[\"date\"] >= pd.Timestamp(\"2010-01-01\")]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.weekday == d, \"dy\"].values for d in DAYS]\nF, p = stats.f_oneway(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.weekday == \"Monday\", \"dy\"].values\noth = ch.loc[ch.weekday != \"Monday\", \"dy\"].values\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndfw = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, dfw)\nlo, hi = eff - tq * se, eff + tq * se\nwelch_p = stats.ttest_ind(mon, oth, equal_var=False).pvalue\n\n# (iii) out-of-sample: baseline zero change vs train weekday means\nwk_means_train = train.groupby(\"weekday\")[\"dy\"].mean()\npred = test[\"weekday\"].map(wk_means_train)\nassert pred.notna().all()\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - pred.values) ** 2)))\n# supplementary: unconditional train mean forecast and DM-type test of model vs zero\nrmse_trainmean = float(np.sqrt(np.mean((test[\"dy\"].values - train[\"dy\"].mean()) ** 2)))\ndloss = (test[\"dy\"].values - pred.values) ** 2 - test[\"dy\"].values ** 2\ndm_stat = dloss.mean() / (dloss.std(ddof=1) / np.sqrt(len(dloss)))\ndm_p = 2 * stats.norm.sf(abs(dm_stat))\n\n# conclusion\nif p >= 0.05:\n conclusion = \"no_detectable_effect\"\nelif lo > 0:\n conclusion = \"positive_effect\"\nelif hi < 0:\n conclusion = \"negative_effect\"\nelse:\n conclusion = \"no_detectable_effect\"\n\n# table + figure\ntab = ch.groupby(\"weekday\")[\"dy\"].agg(n=\"size\", mean_bp=\"mean\", sd=\"std\").reindex(DAYS)\ntab[\"se_bp\"] = tab[\"sd\"] / np.sqrt(tab[\"n\"])\ntab = tab.reset_index()[[\"weekday\", \"n\", \"mean_bp\", \"se_bp\"]]\ntab.to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\ncrit = stats.t.ppf(0.975, tab[\"n\"] - 1)\nfig, ax = plt.subplots(figsize=(6.5, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=crit * tab[\"se_bp\"], fmt=\"o\", capsize=5, color=\"C0\")\nax.axhline(0, color=\"grey\", lw=0.8, ls=\"--\")\nax.set_xticks(range(5)); ax.set_xticklabels([d[:3] for d in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield change by weekday, 2000-2019 (n={len(ch)})\\n\"\n f\"95% CIs; ANOVA F={F:.2f}, p={p:.3f}\", fontsize=9)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150); plt.close(fig)\n\nissues = [\n {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / closures)\",\n \"handling\": \"dropped before computing changes, per TASK rule (see out/exclusions.csv)\"},\n {\"issue\": \"changes following a holiday span more than one business day (e.g. Tuesday after a Monday holiday)\",\n \"handling\": \"kept and attributed to the weekday of t, per TASK definition; noted as a limitation\"},\n {\"issue\": \"large outlier change of -51 bp on 2009-03-18 (FOMC QE announcement)\",\n \"handling\": \"genuine market move; kept (no outlier removal)\"},\n]\nres = dict(\n task_id=\"F6-conf-01\", feasibility=\"complete\", feasibility_reason=None, data_issues=issues,\n n_input_rows={\"DGS10.csv\": int(n_raw)}, n_analysed=int(len(ch)), random_seed=None,\n n_missing_rows=n_missing, n_train=int(len(train)), n_test=int(len(test)),\n anova_f_stat=float(F), anova_p_value=float(p),\n effect_estimate=float(eff), effect_ci_low=float(lo), effect_ci_high=float(hi),\n baseline_metric=rmse_base, model_metric=rmse_model, conclusion=conclusion,\n # supplementary keys\n effect_se=float(se), effect_welch_df=float(dfw), effect_welch_p_value=float(welch_p),\n effect_ci_method=\"Welch two-sample t interval (Monday vs Tue-Fri, all changes)\",\n oos_rmse_ratio_model_to_baseline=rmse_model / rmse_base,\n oos_rmse_train_unconditional_mean=rmse_trainmean,\n oos_dm_stat_model_vs_zero=float(dm_stat), oos_dm_p_value=float(dm_p),\n train_weekday_means_bp={d: float(wk_means_train[d]) for d in DAYS},\n n_changes_total=int(len(ch)),\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 && cat out/results.json | tail -25","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":747,"cache_read_input_tokens":10952,"output_tokens":3186,"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":747},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrkPePTEWW6wRjHeiML","requestRef":"83be8cb0-65ed-4034-8f42-13e73a895519","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7f1b885d-47c8-4d91-9895-2c2af2180de7","timestamp":"2026-10-30T05:20:29.832Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"7f1b885d-47c8-4d91-9895-2c2af2180de7","isSidechain":false,"promptId":"7fd1cf74-fdf8-4e1a-b473-09cb021d211d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KbEyYj3WRHZvDPjX63wZf2","type":"tool_result","content":"{\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 field (no published yield),5217,5002,215,TASK rule: holidays/no publication\n2,first available observation has no prior value -> no change,5002,5001,1,TASK rule: change undefined\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"effect_se\": 0.18837955539096196,\n \"effect_welch_df\": 1548.9581965107577,\n \"effect_welch_p_value\": 0.7900247217974709,\n \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri, all changes)\",\n \"oos_rmse_ratio_model_to_baseline\": 1.0016689625518014,\n \"oos_rmse_train_unconditional_mean\": 4.8751986528852305,\n \"oos_dm_stat_model_vs_zero\": 1.8573748978469748,\n \"oos_dm_p_value\": 0.06325783524850241,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695652,\n \"Tuesday\": -0.40545808966861596,\n \"Wednesday\": -0.2054263565891473,\n \"Thursday\": 0.01383399209486166,\n \"Friday\": 0.11683168316831684\n },\n \"n_changes_total\": 5001\n}","is_error":false}]},"uuid":"f9c7f73d-046d-4ef7-9a2b-e81526aefc4c","timestamp":"2026-10-30T05:20:30.526Z","toolUseResult":{"stdout":"{\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 field (no published yield),5217,5002,215,TASK rule: holidays/no publication\n2,first available observation has no prior value -> no change,5002,5001,1,TASK rule: change undefined\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"effect_se\": 0.18837955539096196,\n \"effect_welch_df\": 1548.9581965107577,\n \"effect_welch_p_value\": 0.7900247217974709,\n \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri, all changes)\",\n \"oos_rmse_ratio_model_to_baseline\": 1.0016689625518014,\n \"oos_rmse_train_unconditional_mean\": 4.8751986528852305,\n \"oos_dm_stat_model_vs_zero\": 1.8573748978469748,\n \"oos_dm_p_value\": 0.06325783524850241,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695652,\n \"Tuesday\": -0.40545808966861596,\n \"Wednesday\": -0.2054263565891473,\n \"Thursday\": 0.01383399209486166,\n \"Friday\": 0.11683168316831684\n },\n \"n_changes_total\": 5001\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7f1b885d-47c8-4d91-9895-2c2af2180de7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"42af89fefaeb573cedb1dca0206067a5b16f1cd3501a28fd849aa5448e9695d1","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (baseline + method in one deterministic script)."},{"type":"tool_use","id":"toolu_01KbEyYj3WRHZvDPjX63wZf2","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10), 2000-2019.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv. No 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\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nval = raw[\"DGS10\"].str.strip()\nmissing = val == \"\"\nn_missing = int(missing.sum())\nobs = raw.loc[~missing, [\"date\"]].copy()\nobs[\"y\"] = val[~missing].astype(float).values\nobs = obs.sort_values(\"date\").reset_index(drop=True)\nobs[\"dy\"] = 100.0 * obs[\"y\"].diff()\nch = obs.dropna(subset=[\"dy\"]).copy()\nch[\"dy\"] = ch[\"dy\"].round(10) # remove float noise from percent->bp conversion\nch[\"weekday\"] = ch[\"date\"].dt.dayofweek.map(dict(enumerate(DAYS)))\nassert ch[\"weekday\"].notna().all()\n\n# exclusion ledger\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 field (no published yield)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: holidays/no publication\"),\n dict(step=2, rule=\"first available observation has no prior value -> no change\", n_before=n_raw - n_missing,\n n_after=len(ch), n_removed=n_raw - n_missing - len(ch), rationale=\"TASK rule: change undefined\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ncut = pd.Timestamp(\"2009-12-31\")\ntrain = ch[ch[\"date\"] <= cut]; test = ch[ch[\"date\"] >= pd.Timestamp(\"2010-01-01\")]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.weekday == d, \"dy\"].values for d in DAYS]\nF, p = stats.f_oneway(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.weekday == \"Monday\", \"dy\"].values\noth = ch.loc[ch.weekday != \"Monday\", \"dy\"].values\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndfw = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, dfw)\nlo, hi = eff - tq * se, eff + tq * se\nwelch_p = stats.ttest_ind(mon, oth, equal_var=False).pvalue\n\n# (iii) out-of-sample: baseline zero change vs train weekday means\nwk_means_train = train.groupby(\"weekday\")[\"dy\"].mean()\npred = test[\"weekday\"].map(wk_means_train)\nassert pred.notna().all()\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - pred.values) ** 2)))\n# supplementary: unconditional train mean forecast and DM-type test of model vs zero\nrmse_trainmean = float(np.sqrt(np.mean((test[\"dy\"].values - train[\"dy\"].mean()) ** 2)))\ndloss = (test[\"dy\"].values - pred.values) ** 2 - test[\"dy\"].values ** 2\ndm_stat = dloss.mean() / (dloss.std(ddof=1) / np.sqrt(len(dloss)))\ndm_p = 2 * stats.norm.sf(abs(dm_stat))\n\n# conclusion\nif p >= 0.05:\n conclusion = \"no_detectable_effect\"\nelif lo > 0:\n conclusion = \"positive_effect\"\nelif hi < 0:\n conclusion = \"negative_effect\"\nelse:\n conclusion = \"no_detectable_effect\"\n\n# table + figure\ntab = ch.groupby(\"weekday\")[\"dy\"].agg(n=\"size\", mean_bp=\"mean\", sd=\"std\").reindex(DAYS)\ntab[\"se_bp\"] = tab[\"sd\"] / np.sqrt(tab[\"n\"])\ntab = tab.reset_index()[[\"weekday\", \"n\", \"mean_bp\", \"se_bp\"]]\ntab.to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\ncrit = stats.t.ppf(0.975, tab[\"n\"] - 1)\nfig, ax = plt.subplots(figsize=(6.5, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=crit * tab[\"se_bp\"], fmt=\"o\", capsize=5, color=\"C0\")\nax.axhline(0, color=\"grey\", lw=0.8, ls=\"--\")\nax.set_xticks(range(5)); ax.set_xticklabels([d[:3] for d in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield change by weekday, 2000-2019 (n={len(ch)})\\n\"\n f\"95% CIs; ANOVA F={F:.2f}, p={p:.3f}\", fontsize=9)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150); plt.close(fig)\n\nissues = [\n {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / closures)\",\n \"handling\": \"dropped before computing changes, per TASK rule (see out/exclusions.csv)\"},\n {\"issue\": \"changes following a holiday span more than one business day (e.g. Tuesday after a Monday holiday)\",\n \"handling\": \"kept and attributed to the weekday of t, per TASK definition; noted as a limitation\"},\n {\"issue\": \"large outlier change of -51 bp on 2009-03-18 (FOMC QE announcement)\",\n \"handling\": \"genuine market move; kept (no outlier removal)\"},\n]\nres = dict(\n task_id=\"F6-conf-01\", feasibility=\"complete\", feasibility_reason=None, data_issues=issues,\n n_input_rows={\"DGS10.csv\": int(n_raw)}, n_analysed=int(len(ch)), random_seed=None,\n n_missing_rows=n_missing, n_train=int(len(train)), n_test=int(len(test)),\n anova_f_stat=float(F), anova_p_value=float(p),\n effect_estimate=float(eff), effect_ci_low=float(lo), effect_ci_high=float(hi),\n baseline_metric=rmse_base, model_metric=rmse_model, conclusion=conclusion,\n # supplementary keys\n effect_se=float(se), effect_welch_df=float(dfw), effect_welch_p_value=float(welch_p),\n effect_ci_method=\"Welch two-sample t interval (Monday vs Tue-Fri, all changes)\",\n oos_rmse_ratio_model_to_baseline=rmse_model / rmse_base,\n oos_rmse_train_unconditional_mean=rmse_trainmean,\n oos_dm_stat_model_vs_zero=float(dm_stat), oos_dm_p_value=float(dm_p),\n train_weekday_means_bp={d: float(wk_means_train[d]) for d in DAYS},\n n_changes_total=int(len(ch)),\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 && cat out/results.json | tail -25","description":"Write and run analysis script"}}]}}39{"type":"api-request","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","id":"7e9eb252-aee4-459a-9917-8799f74991c8","timestamp":"2026-10-30T05:20:30.529Z","version":"2.1.314","querySource":"sdk","shapeHash":"0037c9e7a6bc3c6f1429e905ff23f996e85f3f4ec7d6087d94477d2d46619976","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":"83be8cb0-65ed-4034-8f42-13e73a895519","keep":10,"tail":["42af89fefaeb573cedb1dca0206067a5b16f1cd3501a28fd849aa5448e9695d1","494bd11e674c5852fe1fb0b944c520e4f6cd82da5c052215ac936a4915abaf6b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"84c75ddd501d8cbbc3facdc6350c6dbc6aa30c461d27f7aaef651823e5f1ee1f"}40{"parentUuid":"f9c7f73d-046d-4ef7-9a2b-e81526aefc4c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrnFdk8S6GckWhUkAJD","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS/wYKEAgTGAI4AUIIdGhpbmtpbmcSDOo7LxYNJJmJmqcOoBoMuO4xZuh1Y6NHXTK5IjD1mHFjFqgJg1mwybXth3/6OGuawNN58LBaSs2DUCPrJp+ZlIWZ+2x3/Zrj9uQf/UsqnAatPWyjODdFfo6r04ot4F32qA1frZRyfNoBMr7w8JxID1kBjm1viKItFUavSscZv7PxpjGPM31GhRHmE27dosMhjfcGOsq7nBo/+o/hkJW2WAXy36FbIi8WHppWWD+3Himj/iXytvSZUjOYyBuwJPA5ANxmEPb4eHTn4BbTfxNCRrrOODEXlpK7RUKmWDDBg6FvY9QvhbtEFZy7qJ0vGSdf+54gowTCONxA8L5GNdT1dVqaKV9SM+4k0avjQGZxeDNxYddFaIUPt+Va6Gl9rWJZ9bsuGCoaA2Nd52Ny8rfjRBXd3jwCSiWP88kreExnqP9XSaOYP9DXVX8XuF7T9Vnmm1TXAGIsS/v4+enWfLkgYrrj77tkN67Xv1DrZamzpx0G/u2k5AWzOXUiUGyynsF5AKtZ+Ff80tvzCqPtTCdmLBeJW9TYSftqSksKzJBhwieBs3rSblXpSbECHC7BrttRa52k6BYI8/oaxh1MQnexYVbv62mB935SE8beg2fAZh9iEqsWAo4VAmhOWz751os2SL+zwjPuY5N2s928lCoYf2moSV8HZ+LJkjZtP6q2cQh7NTjwN7hx2reA4/FBCMn9UACdpXhNf7o+d36ZNpTUCnZRxTXS/J7lICNG13RIIkh1PJJqDqPL5nsWDWS6srHtg9wzOc4kil/70ixasw4Ljxmt5Td7LbHD6NxV7dAHENS9mRgx34FbFYrO5M6O2dAHI4PvAffinUfWP4SOOIcnNXX6yiShqaZNrSKqCxAlgF7/8IBkkaHPBsFV83SAvax4i8aMjUGq9OARMfDmxJqgg+b7LWGuUa63l47VQdoYD+ApufoFIxmDWfPFA8LvMEEsmJgrn1sqE60gOtemPqdbJ43ikI/dS6JrXfcUWV1iRWxUaRO2epfw+ZSz8q73+SOAj9cYNjmc6gD+YUtiK1H3OaucAgGx33bi2degtgmw8Q60C4TS/pOUpiP/RN3/saPyKzUueIfbrKpeX16IpOLUMco2n/Tm5HVXLDfrX1d1Pub2X1KQO5Zq1QtxfzjhSJqVjbt4OMGs9GVU0YT3GAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4078,"cache_read_input_tokens":11699,"output_tokens":939,"output_tokens_details":{"thinking_tokens":59},"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":4078},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":705,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrnFLNaL3vaoQVCs5Me","requestRef":"7e9eb252-aee4-459a-9917-8799f74991c8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"29af20a9-61c1-4b4d-923e-40765d3447a5","timestamp":"2026-10-30T05:20:32.937Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"29af20a9-61c1-4b4d-923e-40765d3447a5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrnFdk8S6GckWhUkAJD","type":"message","role":"assistant","content":[{"type":"text","text":"Now verification: clean re-run, consistency checks, and the gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4078,"cache_read_input_tokens":11699,"output_tokens":939,"output_tokens_details":{"thinking_tokens":59},"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":4078},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrnFLNaL3vaoQVCs5Me","requestRef":"7e9eb252-aee4-459a-9917-8799f74991c8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a157f14-7154-4275-957f-cb5078bb84a0","timestamp":"2026-10-30T05:20:32.939Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"6a157f14-7154-4275-957f-cb5078bb84a0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrnFdk8S6GckWhUkAJD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016CuENJ3DtoqKQbcBS3BYsY","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/table1.csv out/figure1.png; ~/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\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"n_train+n_test==n_analysed\", r['n_train']+r['n_test']==r['n_analysed']))\nc.append((\"table n sums to n_analysed\", t.n.sum()==r['n_analysed']))\nc.append((\"exclusion ledger: 5217-216==n_analysed\", r['n_input_rows']['DGS10.csv']-e.n_removed.sum()==r['n_analysed']))\nc.append((\"n_missing==ledger step1\", e.n_removed[0]==r['n_missing_rows']))\nc.append((\"CI contains estimate\", r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high']))\nc.append((\"SE positive\", r['effect_se']>0 and (t.se_bp>0).all()))\nc.append((\"F>0, 0<p<1\", r['anova_f_stat']>0 and 0<r['anova_p_value']<1))\nc.append((\"RMSEs positive, magnitude ~ sd of changes (5.7bp all, lower post-2010)\", 1<r['baseline_metric']<10 and 1<r['model_metric']<10))\nw=t.set_index('weekday'); mon=w.loc['Monday']; o=w.drop('Monday')\nc.append((\"contrast recomputed from table\", abs(mon.mean_bp-(o.mean_bp*o.n).sum()/o.n.sum()-r['effect_estimate'])<1e-5))\nc.append((\"conclusion consistent with rule (p>=0.05 -> no_detectable_effect)\", r['conclusion']=='no_detectable_effect' and r['anova_p_value']>=0.05))\nfor k,v in c: print(k, 'PASS' if v else 'FAIL')\nEOF","description":"Reproducibility and consistency 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PASS\ntable n sums to n_analysed PASS\nexclusion ledger: 5217-216==n_analysed PASS\nn_missing==ledger step1 PASS\nCI contains estimate PASS\nSE positive PASS\nF>0, 0<p<1 PASS\nRMSEs positive, magnitude ~ sd of changes (5.7bp all, lower post-2010) PASS\ncontrast recomputed from table PASS\nconclusion consistent with rule (p>=0.05 -> no_detectable_effect) PASS","is_error":false}]},"uuid":"77d51e5a-4bae-4510-9c76-dcfabb90d917","timestamp":"2026-10-30T05:20:39.775Z","toolUseResult":{"stdout":"IDENTICAL\nn_train+n_test==n_analysed PASS\ntable n sums to n_analysed PASS\nexclusion ledger: 5217-216==n_analysed PASS\nn_missing==ledger step1 PASS\nCI contains estimate PASS\nSE positive PASS\nF>0, 0<p<1 PASS\nRMSEs positive, magnitude ~ sd of changes (5.7bp all, lower post-2010) PASS\ncontrast recomputed from table PASS\nconclusion consistent with rule (p>=0.05 -> no_detectable_effect) 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verification: clean re-run, consistency checks, and the gate."},{"type":"tool_use","id":"toolu_016CuENJ3DtoqKQbcBS3BYsY","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/table1.csv out/figure1.png; ~/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\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"n_train+n_test==n_analysed\", r['n_train']+r['n_test']==r['n_analysed']))\nc.append((\"table n sums to n_analysed\", t.n.sum()==r['n_analysed']))\nc.append((\"exclusion ledger: 5217-216==n_analysed\", r['n_input_rows']['DGS10.csv']-e.n_removed.sum()==r['n_analysed']))\nc.append((\"n_missing==ledger step1\", e.n_removed[0]==r['n_missing_rows']))\nc.append((\"CI contains estimate\", 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\nAll executed 2026-10-30 by code against out/results.json, out/table1.csv, out/exclusions.csv.\n- Clean re-run (deleted results/table/figure, re-ran analysis.py): results.json byte-identical — PASS\n- n_train + n_test == n_analysed (2500+2501=5001) — PASS\n- table1 n sums to n_analysed — PASS\n- Exclusion ledger: 5217 − (215+1) = 5001 = n_analysed — PASS\n- n_missing_rows equals ledger step 1 (215) — PASS\n- Monday-contrast CI contains estimate — PASS\n- SEs positive (contrast and weekday) — PASS\n- F > 0, 0 < p < 1 — PASS\n- RMSE magnitudes plausible vs sd of changes (5.72 bp overall) — PASS\n- Contrast recomputed from table1 weighted means matches effect_estimate — PASS\n- Conclusion follows rule (ANOVA p = 0.90 ≥ 0.05 → no_detectable_effect) — PASS\n- Information rule: train/test split by date with assertion that partition is exhaustive; forecast means computed from `train` only (code inspection) — PASS\n- Baseline comparison: model RMSE 4.8838 vs zero-change 4.8757 (model slightly worse) — consistent with null ANOVA.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run 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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: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:effect_estimate: -0.31933577569409977 <= 0.050170098401698796 <= 0.41967597249749733\n [ok] positive:anova_f_stat: 0.263465716807118\n [ok] positive:baseline_metric: 4.875704154642544\n [ok] positive:model_metric: 4.883841522290305\n [ok] reproducibility: regenerated in 0.6s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0272/out/state/gate_receipts/20261030T052047.518372Z.json\n20261030T052047.518372Z.json","is_error":false}]},"uuid":"56f33b95-e164-40b3-bfd2-2851b40cefc3","timestamp":"2026-10-30T05:20:47.526Z","toolUseResult":{"stdout":"PGAR 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: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:effect_estimate: -0.31933577569409977 <= 0.050170098401698796 <= 0.41967597249749733\n [ok] positive:anova_f_stat: 0.263465716807118\n [ok] positive:baseline_metric: 4.875704154642544\n [ok] positive:model_metric: 4.883841522290305\n [ok] reproducibility: regenerated in 0.6s; identical within tolerance\nreceipt: 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\nAll executed 2026-10-30 by code against out/results.json, out/table1.csv, out/exclusions.csv.\n- Clean re-run (deleted results/table/figure, re-ran analysis.py): results.json byte-identical — PASS\n- n_train + n_test == n_analysed (2500+2501=5001) — PASS\n- table1 n sums to n_analysed — PASS\n- Exclusion ledger: 5217 − (215+1) = 5001 = n_analysed — PASS\n- n_missing_rows equals ledger step 1 (215) — PASS\n- Monday-contrast CI contains estimate — PASS\n- SEs positive (contrast and weekday) — PASS\n- F > 0, 0 < p < 1 — PASS\n- RMSE magnitudes plausible vs sd of changes (5.72 bp overall) — PASS\n- Contrast recomputed from table1 weighted means matches effect_estimate — PASS\n- Conclusion follows rule (ANOVA p = 0.90 ≥ 0.05 → no_detectable_effect) — PASS\n- Information rule: train/test split by date with assertion that partition is exhaustive; forecast means computed from `train` only (code inspection) — PASS\n- Baseline comparison: model RMSE 4.8838 vs zero-change 4.8757 (model slightly worse) — consistent with null ANOVA.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate"}}]}}52{"type":"api-request","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","id":"e536f183-841f-4b71-bf5c-2e927080a394","timestamp":"2026-10-30T05:20:47.528Z","version":"2.1.314","querySource":"sdk","shapeHash":"0037c9e7a6bc3c6f1429e905ff23f996e85f3f4ec7d6087d94477d2d46619976","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":"b209db45-cf0a-4715-8aff-06512037685a","keep":14,"tail":["460c5bfa327eae1b53b531d1332bfa6a321fcae12be49e95f9a0abcbcf0c9e3d","d43ad121b7bb82d101b05a9f0de746035e002ae48aacbf76c58e38000e06b3da"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c6c1b9ed1a852278c792d03a74176d485fbe0f44c18c4516f0842cddb4acbce7"}53{"parentUuid":"56f33b95-e164-40b3-bfd2-2851b40cefc3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXroWTF3472DWJiAh26a","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":1589,"cache_read_input_tokens":16916,"output_tokens":3082,"output_tokens_details":{"thinking_tokens":121},"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":1589},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1803,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXroW98svudFPXKLQBPB","requestRef":"e536f183-841f-4b71-bf5c-2e927080a394","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"134dd8e8-902f-45a3-98e2-6b4d8c4141ea","timestamp":"2026-10-30T05:20:50.582Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"134dd8e8-902f-45a3-98e2-6b4d8c4141ea","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXroWTF3472DWJiAh26a","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The P4 gate run fails only because the P5 report, claims ledger and phase ledger don't exist yet, while reproducibility and the numeric checks pass. I'm writing the report and claims ledger next.\n\n","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":1589,"cache_read_input_tokens":16916,"output_tokens":3082,"output_tokens_details":{"thinking_tokens":121},"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":1589},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1,"thinkingDisplay":"updates","apiBlockIndex":1,"requestId":"req_011CgXroW98svudFPXKLQBPB","requestRef":"e536f183-841f-4b71-bf5c-2e927080a394","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"04897692-829f-4da6-8e80-f005150c59ae","timestamp":"2026-10-30T05:20:50.582Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}55{"parentUuid":"04897692-829f-4da6-8e80-f005150c59ae","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXroWTF3472DWJiAh26a","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QK15ibWGEEqnbYnrSHcTM3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Day-of-week effects in daily changes of the 10-year Treasury yield, 2000–2019\n\n## Question\nCan the day of the week predict the one-day change in the 10-year US Treasury constant-maturity yield (FRED DGS10)? We estimate (i) a joint test that the mean change is the same on all five weekdays; (ii) the \"Monday contrast\" (mean Monday change minus mean change on Tuesday–Friday) with a 95% interval; (iii) whether weekday-specific means from 2000–2009 forecast 2010–2019 changes better than a zero-change (random-walk) forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`. 215 rows have an empty yield field (US market holidays and closures; `n_missing_rows`) and are dropped first. One-day changes Δy_t = 100·(y_t − y_{t−1}) bp are taken between consecutive available observations and assigned to the weekday of t. The first observation has no change. This gives 5001 changes (`n_analysed`): 2500 dated on or before 2009-12-31 (`n_train`) and 2501 dated on or after 2010-01-01 (`n_test`). The exclusion ledger is in `out/exclusions.csv` (5217 − 215 − 1 = 5001). No outliers are removed. The largest move is −51 bp on 2009-03-18 (the FOMC QE announcement), a genuine market move that is kept. Data issues are listed in `data_issues`.\n\n## Method\n- Baseline: zero-change forecast. Its RMSE over the evaluation period is `baseline_metric`.\n- Joint test: classical equal-variance one-way ANOVA of Δy_t on weekday, using all 5001 changes, with F(4, 4996) (`anova_f_stat`, `anova_p_value`).\n- Monday contrast: mean(Monday) − mean(Tue–Fri) over all changes. The 95% interval is a Welch two-sample t interval, chosen because it does not assume equal variances (`effect_estimate`, `effect_ci_low`, `effect_ci_high`; method in `effect_ci_method`).\n- Out-of-sample: each 2010–2019 change is forecast by the mean change of its weekday over 2000–2009 only (`train_weekday_means_bp`). This RMSE is `model_metric`. As a supplement, a Diebold–Mariano-type test compares squared-error losses of this forecast and the zero forecast (normal approximation, no HAC correction; `oos_dm_stat_model_vs_zero`, `oos_dm_p_value`).\n- Conclusion rule (from TASK): if the ANOVA does not reject at 5%, the code is `no_detectable_effect`; otherwise the sign comes from the Monday interval.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n- Weekday means (table: `out/table1.csv`; figure: `out/figure1.png`) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each is within about 1.2 standard errors of zero. A typical daily change has an SD of about 5.7 bp.\n- ANOVA: F = 0.263, p = 0.902 (`anova_f_stat`, `anova_p_value`). This gives no evidence that weekday means differ.\n- Monday contrast: +0.050 bp, 95% CI [−0.319, +0.420] bp (`effect_estimate`, `effect_ci_low`, `effect_ci_high`). Welch p = 0.79 (`effect_welch_p_value`).\n- Out-of-sample RMSE: zero-change forecast 4.876 bp (`baseline_metric`), weekday-means forecast 4.884 bp (`model_metric`). The weekday forecast is slightly worse; the RMSE ratio is 1.0017 (`oos_rmse_ratio_model_to_baseline`). The DM-type test gives stat = 1.86, p = 0.063 (`oos_dm_stat_model_vs_zero`, `oos_dm_p_value`). Weekday means estimated in 2000–2009 did not improve forecasts in 2010–2019.\n- Conclusion code: `no_detectable_effect` (`conclusion`).\n\n## Uncertainty\nThe Monday-contrast interval is narrow in absolute terms: it is about ±0.37 bp, roughly 6% of one daily SD. It rules out Monday effects larger than about 0.4 bp per day in either direction (`effect_ci_low`, `effect_ci_high`, `effect_se`). The ANOVA p-value of 0.90 is far from any conventional threshold. The out-of-sample RMSE difference is 0.008 bp, which is negligible in magnitude. Its sign (weekday means are worse) is what we would expect when noisy in-sample means are used as forecasts and the true effect is absent.\n\n## Limitations and open issues\n- **What could invalidate the result.** Both the ANOVA and the Welch interval assume independent observations. Daily yield changes show volatility clustering (conditional heteroskedasticity), heavy tails, and some weak autocorrelation. The ANOVA also assumes equal variances across weekdays. If variance differs by weekday (for example, on days with employment reports, which are mostly Fridays), the classical F-test can be miscalibrated. With such small F, it is unlikely that a corrected test would reject, but we did **not** run HAC, robust-ANOVA (Welch/Brown–Forsythe), or bootstrap checks.\n- **Definition effects.** Changes after holidays span more than one trading day; for example, the Tuesday after a Monday holiday covers a long weekend. Following the TASK definition, these are attributed to the weekday of t, which mixes \"post-holiday\" and weekday effects. Monday changes also always span a weekend. We did not separate these.\n- **Regime changes.** The sample includes the 2008–09 crisis, QE, and the zero-lower-bound era. Weekday patterns may vary over time; a null over 20 years does not rule out effects in sub-periods, which we did not test. The out-of-sample comparison uses a single split rather than a rolling evaluation.\n- **Not checked.** Other maturities, intraday timing, announcement-day effects, and a power analysis for effects smaller than the CI half-width. Data are taken as published by FRED (H.15); revisions or errors in the source were not checked.\n- **Multiple testing.** Only the Monday contrast was pre-specified. The other pairwise weekday differences were not tested, which avoids a multiple-comparison problem.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA does not reject equal mean daily changes across the five weekdays (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Classical equal-variance F-test assuming independence; heteroskedasticity and volatility clustering not corrected.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with a 95% Welch CI of [-0.319, 0.420] bp, which includes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_welch_p_value\"],\"status\":\"supported\",\"caveat\":\"Welch interval assumes independent observations; Monday changes span weekends by construction.\"},\n {\"id\":\"C3\",\"text\":\"Weekday means estimated on 2000-2009 do not improve out-of-sample forecasts for 2010-2019: RMSE 4.884 bp vs 4.876 bp for zero-change.\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"oos_rmse_ratio_model_to_baseline\",\"oos_dm_p_value\"],\"status\":\"supported\",\"caveat\":\"Single split; DM-type test without HAC correction is supplementary.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect on daily changes in the 10-year Treasury yield, 2000-2019.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Does not rule out effects smaller than about 0.4 bp/day or effects confined to sub-periods.\"}\n]\nEOF","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1589,"cache_read_input_tokens":16916,"output_tokens":3082,"output_tokens_details":{"thinking_tokens":121},"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":1589},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXroW98svudFPXKLQBPB","requestRef":"e536f183-841f-4b71-bf5c-2e927080a394","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8c203a16-7726-4f63-b0a5-f5eef059892c","timestamp":"2026-10-30T05:21:14.814Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"8c203a16-7726-4f63-b0a5-f5eef059892c","isSidechain":false,"promptId":"7fd1cf74-fdf8-4e1a-b473-09cb021d211d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QK15ibWGEEqnbYnrSHcTM3","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"a5e7fd33-1701-42a4-a90b-6767585b2548","timestamp":"2026-10-30T05:21:14.859Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8c203a16-7726-4f63-b0a5-f5eef059892c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}57{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"bc485c66d68d84afc8879cc60223a90ea8c2b79e348db4cfb3cbcbc6cef5095e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"thinking","thinking":"The P4 gate run fails only because the P5 report, claims ledger and phase ledger don't exist yet, while reproducibility and the numeric checks pass. I'm writing the report and claims ledger next.\n\n","signature":"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"},{"type":"tool_use","id":"toolu_01QK15ibWGEEqnbYnrSHcTM3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Day-of-week effects in daily changes of the 10-year Treasury yield, 2000–2019\n\n## Question\nCan the day of the week predict the one-day change in the 10-year US Treasury constant-maturity yield (FRED DGS10)? We estimate (i) a joint test that the mean change is the same on all five weekdays; (ii) the \"Monday contrast\" (mean Monday change minus mean change on Tuesday–Friday) with a 95% interval; (iii) whether weekday-specific means from 2000–2009 forecast 2010–2019 changes better than a zero-change (random-walk) forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`. 215 rows have an empty yield field (US market holidays and closures; `n_missing_rows`) and are dropped first. One-day changes Δy_t = 100·(y_t − y_{t−1}) bp are taken between consecutive available observations and assigned to the weekday of t. The first observation has no change. This gives 5001 changes (`n_analysed`): 2500 dated on or before 2009-12-31 (`n_train`) and 2501 dated on or after 2010-01-01 (`n_test`). The exclusion ledger is in `out/exclusions.csv` (5217 − 215 − 1 = 5001). No outliers are removed. The largest move is −51 bp on 2009-03-18 (the FOMC QE announcement), a genuine market move that is kept. Data issues are listed in `data_issues`.\n\n## Method\n- Baseline: zero-change forecast. Its RMSE over the evaluation period is `baseline_metric`.\n- Joint test: classical equal-variance one-way ANOVA of Δy_t on weekday, using all 5001 changes, with F(4, 4996) (`anova_f_stat`, `anova_p_value`).\n- Monday contrast: mean(Monday) − mean(Tue–Fri) over all changes. The 95% interval is a Welch two-sample t interval, chosen because it does not assume equal variances (`effect_estimate`, `effect_ci_low`, `effect_ci_high`; method in `effect_ci_method`).\n- Out-of-sample: each 2010–2019 change is forecast by the mean change of its weekday over 2000–2009 only (`train_weekday_means_bp`). This RMSE is `model_metric`. As a supplement, a Diebold–Mariano-type test compares squared-error losses of this forecast and the zero forecast (normal approximation, no HAC correction; `oos_dm_stat_model_vs_zero`, `oos_dm_p_value`).\n- Conclusion rule (from TASK): if the ANOVA does not reject at 5%, the code is `no_detectable_effect`; otherwise the sign comes from the Monday interval.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n- Weekday means (table: `out/table1.csv`; figure: `out/figure1.png`) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each is within about 1.2 standard errors of zero. A typical daily change has an SD of about 5.7 bp.\n- ANOVA: F = 0.263, p = 0.902 (`anova_f_stat`, `anova_p_value`). This gives no evidence that weekday means differ.\n- Monday contrast: +0.050 bp, 95% CI [−0.319, +0.420] bp (`effect_estimate`, `effect_ci_low`, `effect_ci_high`). Welch p = 0.79 (`effect_welch_p_value`).\n- Out-of-sample RMSE: zero-change forecast 4.876 bp (`baseline_metric`), weekday-means forecast 4.884 bp (`model_metric`). The weekday forecast is slightly worse; the RMSE ratio is 1.0017 (`oos_rmse_ratio_model_to_baseline`). The DM-type test gives stat = 1.86, p = 0.063 (`oos_dm_stat_model_vs_zero`, `oos_dm_p_value`). Weekday means estimated in 2000–2009 did not improve forecasts in 2010–2019.\n- Conclusion code: `no_detectable_effect` (`conclusion`).\n\n## Uncertainty\nThe Monday-contrast interval is narrow in absolute terms: it is about ±0.37 bp, roughly 6% of one daily SD. It rules out Monday effects larger than about 0.4 bp per day in either direction (`effect_ci_low`, `effect_ci_high`, `effect_se`). The ANOVA p-value of 0.90 is far from any conventional threshold. The out-of-sample RMSE difference is 0.008 bp, which is negligible in magnitude. Its sign (weekday means are worse) is what we would expect when noisy in-sample means are used as forecasts and the true effect is absent.\n\n## Limitations and open issues\n- **What could invalidate the result.** Both the ANOVA and the Welch interval assume independent observations. Daily yield changes show volatility clustering (conditional heteroskedasticity), heavy tails, and some weak autocorrelation. The ANOVA also assumes equal variances across weekdays. If variance differs by weekday (for example, on days with employment reports, which are mostly Fridays), the classical F-test can be miscalibrated. With such small F, it is unlikely that a corrected test would reject, but we did **not** run HAC, robust-ANOVA (Welch/Brown–Forsythe), or bootstrap checks.\n- **Definition effects.** Changes after holidays span more than one trading day; for example, the Tuesday after a Monday holiday covers a long weekend. Following the TASK definition, these are attributed to the weekday of t, which mixes \"post-holiday\" and weekday effects. Monday changes also always span a weekend. We did not separate these.\n- **Regime changes.** The sample includes the 2008–09 crisis, QE, and the zero-lower-bound era. Weekday patterns may vary over time; a null over 20 years does not rule out effects in sub-periods, which we did not test. The out-of-sample comparison uses a single split rather than a rolling evaluation.\n- **Not checked.** Other maturities, intraday timing, announcement-day effects, and a power analysis for effects smaller than the CI half-width. Data are taken as published by FRED (H.15); revisions or errors in the source were not checked.\n- **Multiple testing.** Only the Monday contrast was pre-specified. The other pairwise weekday differences were not tested, which avoids a multiple-comparison problem.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA does not reject equal mean daily changes across the five weekdays (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Classical equal-variance F-test assuming independence; heteroskedasticity and volatility clustering not corrected.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with a 95% Welch CI of [-0.319, 0.420] bp, which includes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_welch_p_value\"],\"status\":\"supported\",\"caveat\":\"Welch interval assumes independent observations; Monday changes span weekends by construction.\"},\n {\"id\":\"C3\",\"text\":\"Weekday means estimated on 2000-2009 do not improve out-of-sample forecasts for 2010-2019: RMSE 4.884 bp vs 4.876 bp for zero-change.\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"oos_rmse_ratio_model_to_baseline\",\"oos_dm_p_value\"],\"status\":\"supported\",\"caveat\":\"Single split; DM-type test without HAC correction is supplementary.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect on daily changes in the 10-year Treasury yield, 2000-2019.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Does not rule out effects smaller than about 0.4 bp/day or effects confined to sub-periods.\"}\n]\nEOF","description":"Write report and claims"}}]}}58{"type":"api-request","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","id":"30884f48-a21e-41bd-a3d6-ae832494273c","timestamp":"2026-10-30T05:21:14.863Z","version":"2.1.314","querySource":"sdk","shapeHash":"0037c9e7a6bc3c6f1429e905ff23f996e85f3f4ec7d6087d94477d2d46619976","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":"e536f183-841f-4b71-bf5c-2e927080a394","keep":16,"tail":["bc485c66d68d84afc8879cc60223a90ea8c2b79e348db4cfb3cbcbc6cef5095e","bfa0a2a5ffaf801ea1878864831f89a8c5715cf118f0fbf5e7d88cd74f01028f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d7e63e9e4f3e2ecf1beac27055d7c8655b36c00f612db20b85cf52e1d875cd0f"}59{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a5e7fd33-1701-42a4-a90b-6767585b2548","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4"}60{"type":"atis-latch","atis":"","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4"}61{"parentUuid":"a5e7fd33-1701-42a4-a90b-6767585b2548","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrqXGuY7fdDFENUoTmA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015CbtkgiNgYpTbto2m8cKGo","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"5217 rows x 2 cols, dates unique/monotonic, weekdays only\",\"215 empty DGS10 rows identified\",\"outlier -51bp 2009-03-18 inspected (genuine)\",\"exclusion ledger sums: 5217-216=5001\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE over 2010-2019 computed: baseline_metric=4.8757\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG (random_seed null)\",\"ANOVA, Welch contrast, OOS RMSE computed; train-only weekday means\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"10 consistency checks PASS (checks.md)\",\"gate run 20261030T052047.518372Z: all results/repro/CI/ledger checks ok; only failures were P5 deliverables (report, claims) and non-terminal phase ledger, expected at that point\"],\"notes\":\"gate receipt out/state/gate_receipts/20261030T052047.518372Z.json\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json 4 claims referencing existing keys\",\"final gate run (see notes)\"],\"notes\":\"final gate receipt recorded below after run\"}}\nEOF\nprintf -- \"- 2026-10-30 P2/P3 passed: analysis.py written; conclusion no_detectable_effect.\\n- 2026-10-30 P4 passed: re-run identical, checks pass, gate run 1 (P5 files absent).\\n- 2026-10-30 P5: report & claims written; final gate run.\\n\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Finalize phase ledger and 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":3097,"cache_read_input_tokens":18505,"output_tokens":812,"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":3097},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrqX4FyHjLuuAigf9GB","requestRef":"30884f48-a21e-41bd-a3d6-ae832494273c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e3ef1db1-c4df-42f1-8153-5b3dc7c69387","timestamp":"2026-10-30T05:21:22.525Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"e3ef1db1-c4df-42f1-8153-5b3dc7c69387","isSidechain":false,"promptId":"7fd1cf74-fdf8-4e1a-b473-09cb021d211d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015CbtkgiNgYpTbto2m8cKGo","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0272/out/state/gate_receipts/20261030T052123.236070Z.json","is_error":false}]},"uuid":"27b66c2e-b7aa-4d29-873f-2705ab691c7b","timestamp":"2026-10-30T05:21:23.241Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0272/out/state/gate_receipts/20261030T052123.236070Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e3ef1db1-c4df-42f1-8153-5b3dc7c69387","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"33ab10f44966bac777c05e68a1a739df52ac14afcb4bd535ca6977b7d1dc365c","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015CbtkgiNgYpTbto2m8cKGo","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"5217 rows x 2 cols, dates unique/monotonic, weekdays only\",\"215 empty DGS10 rows identified\",\"outlier -51bp 2009-03-18 inspected (genuine)\",\"exclusion ledger sums: 5217-216=5001\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE over 2010-2019 computed: baseline_metric=4.8757\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG (random_seed null)\",\"ANOVA, Welch contrast, OOS RMSE computed; train-only weekday means\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"10 consistency checks PASS (checks.md)\",\"gate run 20261030T052047.518372Z: all results/repro/CI/ledger checks ok; only failures were P5 deliverables (report, claims) and non-terminal phase ledger, expected at that point\"],\"notes\":\"gate receipt out/state/gate_receipts/20261030T052047.518372Z.json\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json 4 claims referencing existing keys\",\"final gate run (see notes)\"],\"notes\":\"final gate receipt recorded below after run\"}}\nEOF\nprintf -- \"- 2026-10-30 P2/P3 passed: analysis.py written; conclusion no_detectable_effect.\\n- 2026-10-30 P4 passed: re-run identical, checks pass, gate run 1 (P5 files absent).\\n- 2026-10-30 P5: report & claims written; final gate run.\\n\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Finalize phase ledger and run final gate"}}]}}64{"type":"api-request","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","id":"1e391116-2e7d-4aea-a69c-9c4e005eaa67","timestamp":"2026-10-30T05:21:23.243Z","version":"2.1.314","querySource":"sdk","shapeHash":"0037c9e7a6bc3c6f1429e905ff23f996e85f3f4ec7d6087d94477d2d46619976","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":"30884f48-a21e-41bd-a3d6-ae832494273c","keep":18,"tail":["33ab10f44966bac777c05e68a1a739df52ac14afcb4bd535ca6977b7d1dc365c","d17951c4e909509cfb012960891f0b2fcd8bd7118410a6693b5d20845d3f431e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"07817f7c46b6b118c61b11b67c29c1b9096990dee5041d31e4d4352015a9476f"}65{"parentUuid":"27b66c2e-b7aa-4d29-873f-2705ab691c7b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrr9BenbsoXec6iY63c","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SHHFqPZidp8f4b7cLyQwpE","name":"Bash","input":{"command":"sed -i '' 's|\"final gate receipt recorded below after run\"|\"final gate PASSED 39/39, receipt out/state/gate_receipts/20261030T052123.236070Z.json\"|; 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Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record receipt and reconfirm gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":907,"cache_read_input_tokens":21602,"output_tokens":279,"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":907},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrr8unJC2Xe2duwWGN4","requestRef":"1e391116-2e7d-4aea-a69c-9c4e005eaa67","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b56c9de0-186b-4dc6-9df4-9216e458140a","timestamp":"2026-10-30T05:21:26.155Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"b56c9de0-186b-4dc6-9df4-9216e458140a","isSidechain":false,"promptId":"7fd1cf74-fdf8-4e1a-b473-09cb021d211d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SHHFqPZidp8f4b7cLyQwpE","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)","is_error":false}]},"uuid":"849430d1-b539-4ee5-8bbc-1ef94f086ea5","timestamp":"2026-10-30T05:21:26.931Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b56c9de0-186b-4dc6-9df4-9216e458140a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0272","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","version":"2.1.314","gitBranch":"HEAD"}67{"type":"api-request-blob","sessionId":"2a521b1f-d8f6-43cd-a1d3-182b12debbd4","hash":"b433096ffe62883c10d09a96ca7295bb6f161022a00e00e96d3c6e34148c1375","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01SHHFqPZidp8f4b7cLyQwpE","name":"Bash","input":{"command":"sed -i '' 's|\"final gate receipt recorded below after run\"|\"final gate PASSED 39/39, receipt out/state/gate_receipts/20261030T052123.236070Z.json\"|; s|\"final gate run (see notes)\"|\"final gate run 20261030T052123.236070Z passed\"|' out/state/phases.json\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T052123.236070Z). 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The conclusion code is `no_detectable_effect`. The analysis script reproduces its results exactly on a clean re-run, and the final gate check passed (39/39).\n\n**Data:** the file has 5,217 rows. 215 have no yield (market holidays) and were dropped. That leaves 5,001 daily changes: 2,500 in the 2000–2009 estimation period and 2,501 in the 2010–2019 evaluation period.\n\n| Measure | Result |\n|---|---|\n| Joint test of equal weekday means | F = 0.263, p = 0.902 |\n| Monday minus other weekdays | +0.050 bp, 95% interval [−0.319, +0.420] |\n| Forecast error, zero-change forecast (2010–2019) | 4.876 bp |\n| Forecast error, weekday-mean forecast (2010–2019) | 4.884 bp |\n\n- **Weekday means:** they range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). A typical daily change varies by about 5.7 bp, so these differences are tiny.\n- **Monday interval:** I used a Welch two-sample interval, which doesn't assume equal variances. The interval rules out a Monday effect larger than about 0.4 bp per day in either direction.\n- **Forecasting:** weekday means from 2000–2009 made 2010–2019 forecasts slightly worse than simply predicting no change. An extra comparison test I added (not required by the task) gives p = 0.063.\n\n**Limitations (discussed in the report):**\n- The tests assume each day's change is independent and, for the joint test, that weekdays have equal variance. Neither holds well for yield data, and I didn't run robust or bootstrap versions.\n- As the task specifies, a change after a holiday is assigned to the weekday it lands on, so a Tuesday after a Monday holiday mixes a holiday effect into \"Tuesday\".\n- I used a single estimation/evaluation split and didn't test whether effects exist in shorter sub-periods.\n\nEverything is in `out/`; open `report.md` first. It sits alongside `results.json`, `analysis.py`, `figure1.png`, `table1.csv`, `claims.json` and `exclusions.csv`. 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