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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nDGS10.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_01RJZGgJ1bvoixkv5J3LgGDw"}]},"uuid":"d05f3c24-f1ab-4ce5-a2b3-08b995d9939f","timestamp":"2026-10-30T05:20:08.251Z","toolUseResult":"Error: Exit code 1\n# F6-conf-01 — Does the day of the week carry predictive content for daily changes in the 10-year Treasury yield?\n\n## Scientific question\nFinancial folklore attributes systematic \"Monday\" or \"Friday\" patterns to daily asset-price changes. 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`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n    5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a  data/README.md\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\": {","is_error":false}]},"uuid":"9956be7a-1b6b-4696-aaeb-fda09556b1c5","timestamp":"2026-10-30T05:20:12.923Z","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`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n    5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a  data/README.md\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\": 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   \"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}(5217, 2) ['observation_date', 'DGS10']\nDGS10\n        215\n1.92     26\n2.54     25\nName: count, dtype: int64\n215 0 2000-01-03 2019-12-31\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\nEmpty DataFrame\nColumns: [observation_date, DGS10]\nIndex: []\nobservation_date\n0    1044\n1    1044\n2    1043\n3    1043\n4    1043\nName: count, dtype: int64\nTrue","is_error":false}]},"uuid":"0f78ece5-4aeb-474c-9542-3626f4ddfdd4","timestamp":"2026-10-30T05:20:17.893Z","toolUseResult":{"stdout":"    \"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}(5217, 2) ['observation_date', 'DGS10']\nDGS10\n        215\n1.92     26\n2.54     25\nName: count, dtype: int64\n215 0 2000-01-03 2019-12-31\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\nEmpty DataFrame\nColumns: [observation_date, DGS10]\nIndex: []\nobservation_date\n0    1044\n1    1044\n2    1043\n3    1043\n4    1043\nName: count, dtype: 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out/state && cat > contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question:** Does weekday predict the one-day change in the 10y CMT Treasury yield (DGS10), 2000–2019?\n**Estimands:** (i) one-way equal-variance ANOVA F-test of equal weekday means of Δy_t (bp) over all changes; (ii) Monday contrast = mean(Δy | Mon) − mean(Δy | Tue–Fri), all changes, 95% CI (Welch two-sample chosen); (iii) out-of-sample RMSE (2010–2019) of weekday-mean forecast (means estimated on changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes approximately independent across days (ANOVA/Welch validity); ANOVA additionally assumes equal variances and approx normality (large n mitigates).\n**Info rules:** drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available obs, dated t, weekday of t; first obs no change. Train = changes dated ≤ 2009-12-31 (incl. the change on the first 2010 day? No: that is dated 2010 → test). Weekday means for forecasting from train only. Only data/DGS10.csv.\n**Outputs:** out/results.json (keys n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (weekday,n,mean_bp,se_bp); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Conclusion rule:** ANOVA p≥0.05 → no_detectable_effect; else sign from Monday-contrast CI.\n**Invalidating conditions:** unparseable / missing data such that changes can't be formed; using test-period info in forecasts.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed; contract written. P1 started.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":927,"cache_read_input_tokens":8241,"output_tokens":1131,"output_tokens_details":{"thinking_tokens":119},"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":927},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrmKNgSmp4mHeRYSFiA","requestRef":"81668880-0d76-435c-a0f3-7945e0f95ea3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7d0a049f-801e-4ae2-a4cc-5e112ffd92a7","timestamp":"2026-10-30T05:20:29.041Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"7d0a049f-801e-4ae2-a4cc-5e112ffd92a7","isSidechain":false,"promptId":"9121a026-fa9e-43be-bc2a-ff2c83147cdb","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ezuc5JGxgrQZzwP8G4DCTv","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"fcd9b244-752d-47a7-8543-8ff561b56fee","timestamp":"2026-10-30T05:20:29.069Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7d0a049f-801e-4ae2-a4cc-5e112ffd92a7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274/out/state","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","hash":"4f0373e1233ebbe2f39e4b8c9bf5f493a1e8050d44dcf2a2c2f4855be4b917f8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Ezuc5JGxgrQZzwP8G4DCTv","name":"Bash","input":{"command":"cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question:** Does weekday predict the one-day change in the 10y CMT Treasury yield (DGS10), 2000–2019?\n**Estimands:** (i) one-way equal-variance ANOVA F-test of equal weekday means of Δy_t (bp) over all changes; (ii) Monday contrast = mean(Δy | Mon) − mean(Δy | Tue–Fri), all changes, 95% CI (Welch two-sample chosen); (iii) out-of-sample RMSE (2010–2019) of weekday-mean forecast (means estimated on changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes approximately independent across days (ANOVA/Welch validity); ANOVA additionally assumes equal variances and approx normality (large n mitigates).\n**Info rules:** drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available obs, dated t, weekday of t; first obs no change. Train = changes dated ≤ 2009-12-31 (incl. the change on the first 2010 day? No: that is dated 2010 → test). Weekday means for forecasting from train only. Only data/DGS10.csv.\n**Outputs:** out/results.json (keys n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (weekday,n,mean_bp,se_bp); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Conclusion rule:** ANOVA p≥0.05 → no_detectable_effect; else sign from Monday-contrast CI.\n**Invalidating conditions:** unparseable / missing data such that changes can't be formed; using test-period info in forecasts.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed; contract written. P1 started.\" > log.md","description":"Write contract, phase ledger, log"}}]}}31{"type":"api-request","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","id":"eeb2ad5e-f30d-41f3-9609-51bf9ea4f194","timestamp":"2026-10-30T05:20:29.072Z","version":"2.1.314","querySource":"sdk","shapeHash":"24a81578bcf6edd90e1a06cd7ef37180aa5871dec155e0c86754f192454cef10","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":"81668880-0d76-435c-a0f3-7945e0f95ea3","keep":8,"tail":["4f0373e1233ebbe2f39e4b8c9bf5f493a1e8050d44dcf2a2c2f4855be4b917f8","9d3f6350759cbe1b3417735b8c564bea56d04312db143698fd13e54b13e58b3b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"448c95444a40b191ba7210a866fecf190b5870247574262c0bb6ccdc9e75c3b6"}32{"parentUuid":"fcd9b244-752d-47a7-8543-8ff561b56fee","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrn9LDzU4WVXNGWusq1","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (it also writes the exclusion ledger)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1146,"cache_read_input_tokens":9168,"output_tokens":3056,"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":1146},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrn99odsjUxKqsTwVp7","requestRef":"eeb2ad5e-f30d-41f3-9609-51bf9ea4f194","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e6cd55e1-c1bb-41a4-93a0-2adf508f38a5","timestamp":"2026-10-30T05:20:31.377Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274/out/state","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"e6cd55e1-c1bb-41a4-93a0-2adf508f38a5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrn9LDzU4WVXNGWusq1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015TXCn5n7b45NPaAimvcQHf","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nSEED = None  # no randomness used\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = d[\"DGS10\"].astype(float)\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd = d.reset_index(drop=True)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap_days\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\nn_ch = len(ch)\n\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with empty DGS10 (market holidays)\", \"n_before\": n_raw,\n     \"n_after\": n_raw - n_missing, \"n_removed\": n_missing, \"rationale\": \"no yield published; required by TASK info rules\"},\n    {\"step\": 2, \"rule\": \"first available observation has no change\", \"n_before\": n_raw - n_missing,\n     \"n_after\": n_ch, \"n_removed\": n_raw - n_missing - n_ch, \"rationale\": \"difference undefined\"},\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# table 1\ntab = pd.DataFrame({\"weekday\": DAYS,\n                    \"n\": [len(g) for g in groups],\n                    \"mean_bp\": [g.mean() for g in groups],\n                    \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon, oth = groups[0], np.concatenate(groups[1:])\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntcrit = stats.t.ppf(0.975, df_w)\nlo, hi = eff - tcrit * se, eff + tcrit * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), df_w)\n\n# (iii) out-of-sample\ncut = pd.Timestamp(\"2009-12-31\")\ntr, te = ch[ch[\"date\"] <= cut], ch[ch[\"date\"] > cut]\nwd_means = tr.groupby(\"wd\")[\"dy\"].mean()\nassert tr[\"date\"].max() < te[\"date\"].min()\npred = te[\"wd\"].map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(te[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((te[\"dy\"].values - pred) ** 2)))\n# supplementary: unconditional train-mean forecast\nrmse_mean = float(np.sqrt(np.mean((te[\"dy\"].values - tr[\"dy\"].mean()) ** 2)))\n\nif p >= 0.05:\n    concl = \"no_detectable_effect\"\nelif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\nn_long_gap = int((ch[\"gap_days\"] > 4).sum())\nn_holiday_spanning = int(((ch[\"gap_days\"] > 1) & ~((ch[\"wd\"] == 0) & (ch[\"gap_days\"] == 3))).sum())\nissues = [\n    {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays)\",\n     \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"},\n    {\"issue\": f\"{n_holiday_spanning} changes span a holiday (gap longer than a normal weekday/weekend gap); {n_long_gap} span more than 4 calendar days (e.g. Sept 2001 closure)\",\n     \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"},\n]\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(n_ch),\n    \"random_seed\": SEED,\n    \"n_missing_rows\": n_missing,\n    \"n_train\": int(len(tr)),\n    \"n_test\": int(len(te)),\n    \"anova_f_stat\": float(F),\n    \"anova_p_value\": float(p),\n    \"effect_estimate\": float(eff),\n    \"effect_ci_low\": float(lo),\n    \"effect_ci_high\": float(hi),\n    \"baseline_metric\": rmse0,\n    \"model_metric\": rmse1,\n    \"conclusion\": concl,\n    # supplementary diagnostics\n    \"supp_effect_se\": float(se),\n    \"supp_effect_welch_df\": float(df_w),\n    \"supp_effect_welch_p\": float(welch_p),\n    \"supp_rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n    \"supp_rmse_train_mean_forecast\": rmse_mean,\n    \"supp_levene_p\": float(stats.levene(*groups, center=\"median\").pvalue),\n    \"supp_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n    \"supp_train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wd_means.items()},\n    \"supp_n_holiday_spanning_changes\": n_holiday_spanning,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y Treasury yield: mean daily change by weekday, 2000-2019\\n(95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\",\n      \"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nprint(tab.to_string(index=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; grep -A3 supp_ out/results.json | head -30","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":1146,"cache_read_input_tokens":9168,"output_tokens":3056,"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":1146},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrn99odsjUxKqsTwVp7","requestRef":"eeb2ad5e-f30d-41f3-9609-51bf9ea4f194","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d21f6dde-9c3b-413f-946f-81740990fd1a","timestamp":"2026-10-30T05:20:53.609Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274/out/state","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"d21f6dde-9c3b-413f-946f-81740990fd1a","isSidechain":false,"promptId":"9121a026-fa9e-43be-bc2a-ff2c83147cdb","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015TXCn5n7b45NPaAimvcQHf","type":"tool_result","content":"(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0274/out/state/out/analysis.py': [Errno 2] No such file or directory\ncat: out/exclusions.csv: No such file or directory\ngrep: out/results.json: No such file or directory","is_error":false}]},"uuid":"9b024a77-9f49-4edf-9ff5-fb7eb5c718b1","timestamp":"2026-10-30T05:20:53.658Z","toolUseResult":{"stdout":"(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0274/out/state/out/analysis.py': [Errno 2] No such file or directory\ncat: out/exclusions.csv: No such file or directory\ngrep: out/results.json: No such file or directory","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d21f6dde-9c3b-413f-946f-81740990fd1a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274/out/state","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","hash":"a09b601bca7951506d0fe1520ef744238884a0d7c93654abd70183da2003fd2b","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (it also writes the exclusion ledger)."},{"type":"tool_use","id":"toolu_015TXCn5n7b45NPaAimvcQHf","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nSEED = None  # no randomness used\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = d[\"DGS10\"].astype(float)\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd = d.reset_index(drop=True)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap_days\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\nn_ch = len(ch)\n\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with empty DGS10 (market holidays)\", \"n_before\": n_raw,\n     \"n_after\": n_raw - n_missing, \"n_removed\": n_missing, \"rationale\": \"no yield published; required by TASK info rules\"},\n    {\"step\": 2, \"rule\": \"first available observation has no change\", \"n_before\": n_raw - n_missing,\n     \"n_after\": n_ch, \"n_removed\": n_raw - n_missing - n_ch, \"rationale\": \"difference undefined\"},\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# table 1\ntab = pd.DataFrame({\"weekday\": DAYS,\n                    \"n\": [len(g) for g in groups],\n                    \"mean_bp\": [g.mean() for g in groups],\n                    \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon, oth = groups[0], np.concatenate(groups[1:])\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntcrit = stats.t.ppf(0.975, df_w)\nlo, hi = eff - tcrit * se, eff + tcrit * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), df_w)\n\n# (iii) out-of-sample\ncut = pd.Timestamp(\"2009-12-31\")\ntr, te = ch[ch[\"date\"] <= cut], ch[ch[\"date\"] > cut]\nwd_means = tr.groupby(\"wd\")[\"dy\"].mean()\nassert tr[\"date\"].max() < te[\"date\"].min()\npred = te[\"wd\"].map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(te[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((te[\"dy\"].values - pred) ** 2)))\n# supplementary: unconditional train-mean forecast\nrmse_mean = float(np.sqrt(np.mean((te[\"dy\"].values - tr[\"dy\"].mean()) ** 2)))\n\nif p >= 0.05:\n    concl = \"no_detectable_effect\"\nelif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\nn_long_gap = int((ch[\"gap_days\"] > 4).sum())\nn_holiday_spanning = int(((ch[\"gap_days\"] > 1) & ~((ch[\"wd\"] == 0) & (ch[\"gap_days\"] == 3))).sum())\nissues = [\n    {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays)\",\n     \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"},\n    {\"issue\": f\"{n_holiday_spanning} changes span a holiday (gap longer than a normal weekday/weekend gap); {n_long_gap} span more than 4 calendar days (e.g. Sept 2001 closure)\",\n     \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"},\n]\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(n_ch),\n    \"random_seed\": SEED,\n    \"n_missing_rows\": n_missing,\n    \"n_train\": int(len(tr)),\n    \"n_test\": int(len(te)),\n    \"anova_f_stat\": float(F),\n    \"anova_p_value\": float(p),\n    \"effect_estimate\": float(eff),\n    \"effect_ci_low\": float(lo),\n    \"effect_ci_high\": float(hi),\n    \"baseline_metric\": rmse0,\n    \"model_metric\": rmse1,\n    \"conclusion\": concl,\n    # supplementary diagnostics\n    \"supp_effect_se\": float(se),\n    \"supp_effect_welch_df\": float(df_w),\n    \"supp_effect_welch_p\": float(welch_p),\n    \"supp_rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n    \"supp_rmse_train_mean_forecast\": rmse_mean,\n    \"supp_levene_p\": float(stats.levene(*groups, center=\"median\").pvalue),\n    \"supp_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n    \"supp_train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wd_means.items()},\n    \"supp_n_holiday_spanning_changes\": n_holiday_spanning,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y Treasury yield: mean daily change by weekday, 2000-2019\\n(95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\",\n      \"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nprint(tab.to_string(index=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; grep -A3 supp_ out/results.json | head -30","description":"Write and run analysis script"}}]}}36{"type":"api-request","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","id":"e0d9d437-7cf5-4ea2-b210-4f3c8243f1d6","timestamp":"2026-10-30T05:20:53.661Z","version":"2.1.314","querySource":"sdk","shapeHash":"24a81578bcf6edd90e1a06cd7ef37180aa5871dec155e0c86754f192454cef10","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":"eeb2ad5e-f30d-41f3-9609-51bf9ea4f194","keep":10,"tail":["a09b601bca7951506d0fe1520ef744238884a0d7c93654abd70183da2003fd2b","42b51ce53ac01f941c6502d84904565111fc007253430e35d1c04f76ee64dd7e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"27ba194b2fdf662eae64af9b51724317872b7ba158eb921f2b248b74bb80a578"}37{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9b024a77-9f49-4edf-9ff5-fb7eb5c718b1","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa"}38{"type":"atis-latch","atis":"","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa"}39{"parentUuid":"9b024a77-9f49-4edf-9ff5-fb7eb5c718b1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXroxZPVCsp4vsjyzP6Y","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was still in out/state; 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Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nSEED = None  # no randomness used\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = d[\"DGS10\"].astype(float)\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd = d.reset_index(drop=True)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap_days\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\nn_ch = len(ch)\n\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with empty DGS10 (market holidays)\", \"n_before\": n_raw,\n     \"n_after\": n_raw - n_missing, \"n_removed\": n_missing, \"rationale\": \"no yield published; required by TASK info rules\"},\n    {\"step\": 2, \"rule\": \"first available observation has no change\", \"n_before\": n_raw - n_missing,\n     \"n_after\": n_ch, \"n_removed\": n_raw - n_missing - n_ch, \"rationale\": \"difference undefined\"},\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# table 1\ntab = pd.DataFrame({\"weekday\": DAYS,\n                    \"n\": [len(g) for g in groups],\n                    \"mean_bp\": [g.mean() for g in groups],\n                    \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon, oth = groups[0], np.concatenate(groups[1:])\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntcrit = stats.t.ppf(0.975, df_w)\nlo, hi = eff - tcrit * se, eff + tcrit * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), df_w)\n\n# (iii) out-of-sample\ncut = pd.Timestamp(\"2009-12-31\")\ntr, te = ch[ch[\"date\"] <= cut], ch[ch[\"date\"] > cut]\nwd_means = tr.groupby(\"wd\")[\"dy\"].mean()\nassert tr[\"date\"].max() < te[\"date\"].min()\npred = te[\"wd\"].map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(te[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((te[\"dy\"].values - pred) ** 2)))\n# supplementary: unconditional train-mean forecast\nrmse_mean = float(np.sqrt(np.mean((te[\"dy\"].values - tr[\"dy\"].mean()) ** 2)))\n\nif p >= 0.05:\n    concl = \"no_detectable_effect\"\nelif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\nn_long_gap = int((ch[\"gap_days\"] > 4).sum())\nn_holiday_spanning = int(((ch[\"gap_days\"] > 1) & ~((ch[\"wd\"] == 0) & (ch[\"gap_days\"] == 3))).sum())\nissues = [\n    {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays)\",\n     \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"},\n    {\"issue\": f\"{n_holiday_spanning} changes span a holiday (gap longer than a normal weekday/weekend gap); {n_long_gap} span more than 4 calendar days (e.g. Sept 2001 closure)\",\n     \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"},\n]\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(n_ch),\n    \"random_seed\": SEED,\n    \"n_missing_rows\": n_missing,\n    \"n_train\": int(len(tr)),\n    \"n_test\": int(len(te)),\n    \"anova_f_stat\": float(F),\n    \"anova_p_value\": float(p),\n    \"effect_estimate\": float(eff),\n    \"effect_ci_low\": float(lo),\n    \"effect_ci_high\": float(hi),\n    \"baseline_metric\": rmse0,\n    \"model_metric\": rmse1,\n    \"conclusion\": concl,\n    # supplementary diagnostics\n    \"supp_effect_se\": float(se),\n    \"supp_effect_welch_df\": float(df_w),\n    \"supp_effect_welch_p\": float(welch_p),\n    \"supp_rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n    \"supp_rmse_train_mean_forecast\": rmse_mean,\n    \"supp_levene_p\": float(stats.levene(*groups, center=\"median\").pvalue),\n    \"supp_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n    \"supp_train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wd_means.items()},\n    \"supp_n_holiday_spanning_changes\": n_holiday_spanning,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y Treasury yield: mean daily change by weekday, 2000-2019\\n(95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\",\n      \"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nprint(tab.to_string(index=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; grep supp_ -A0 out/results.json; grep -A6 train_weekday out/results.json; grep -A1 '\"issue\"' out/results.json","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":157,"cache_read_input_tokens":13555,"output_tokens":3120,"output_tokens_details":{"thinking_tokens":25},"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":157},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrp7Xsjpt2iq4dkcKHK","requestRef":"1f080e53-47ee-46e7-8914-fac963ccebb3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e43961a2-ef4d-4108-af1f-989e34b48a3b","timestamp":"2026-10-30T05:21:17.580Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"e43961a2-ef4d-4108-af1f-989e34b48a3b","isSidechain":false,"promptId":"9121a026-fa9e-43be-bc2a-ff2c83147cdb","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PztNQKmfSA4RFGcYuPoE1L","type":"tool_result","content":"{\n \"n_analysed\": 5001,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.26346571680712,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169969,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\n  weekday    n   mean_bp    se_bp\n   Monday  919 -0.052231 0.164386\n  Tuesday 1028 -0.219844 0.177007\nWednesday 1030  0.016505 0.182346\n Thursday 1012 -0.059289 0.186918\n   Friday 1012 -0.147233 0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (market holidays),5217,5002,215,no yield published; required by TASK info rules\n2,first available observation has no change,5002,5001,1,difference undefined\n  \"supp_effect_se\": 0.18837955539096202,\n  \"supp_effect_welch_df\": 1548.9581965107568,\n  \"supp_effect_welch_p\": 0.7900247217974672,\n  \"supp_rmse_ratio_model_over_baseline\": 1.0016689625518016,\n  \"supp_rmse_train_mean_forecast\": 4.87519865288523,\n  \"supp_levene_p\": 1.0122651168712971e-07,\n  \"supp_kruskal_p\": 0.47102318097227847,\n  \"supp_train_weekday_means_bp\": {\n  \"supp_n_holiday_spanning_changes\": 214\n  \"supp_train_weekday_means_bp\": {\n    \"Monday\": -0.05434782608695535,\n    \"Tuesday\": -0.40545808966861635,\n    \"Wednesday\": -0.20542635658914643,\n    \"Thursday\": 0.013833992094862349,\n    \"Friday\": 0.11683168316831462\n  },\n      \"issue\": \"215 rows with empty DGS10 (US market holidays)\",\n      \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"\n--\n      \"issue\": \"214 changes span a holiday (gap longer than a normal weekday/weekend gap); 0 span more than 4 calendar days (e.g. Sept 2001 closure)\",\n      \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"","is_error":false}]},"uuid":"a4860e32-6152-4f33-83b9-34963069ef02","timestamp":"2026-10-30T05:21:18.597Z","toolUseResult":{"stdout":"{\n \"n_analysed\": 5001,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.26346571680712,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169969,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\n  weekday    n   mean_bp    se_bp\n   Monday  919 -0.052231 0.164386\n  Tuesday 1028 -0.219844 0.177007\nWednesday 1030  0.016505 0.182346\n Thursday 1012 -0.059289 0.186918\n   Friday 1012 -0.147233 0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (market holidays),5217,5002,215,no yield published; required by TASK info rules\n2,first available observation has no change,5002,5001,1,difference undefined\n  \"supp_effect_se\": 0.18837955539096202,\n  \"supp_effect_welch_df\": 1548.9581965107568,\n  \"supp_effect_welch_p\": 0.7900247217974672,\n  \"supp_rmse_ratio_model_over_baseline\": 1.0016689625518016,\n  \"supp_rmse_train_mean_forecast\": 4.87519865288523,\n  \"supp_levene_p\": 1.0122651168712971e-07,\n  \"supp_kruskal_p\": 0.47102318097227847,\n  \"supp_train_weekday_means_bp\": {\n  \"supp_n_holiday_spanning_changes\": 214\n  \"supp_train_weekday_means_bp\": {\n    \"Monday\": -0.05434782608695535,\n    \"Tuesday\": -0.40545808966861635,\n    \"Wednesday\": -0.20542635658914643,\n    \"Thursday\": 0.013833992094862349,\n    \"Friday\": 0.11683168316831462\n  },\n      \"issue\": \"215 rows with empty DGS10 (US market holidays)\",\n      \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"\n--\n      \"issue\": \"214 changes span a holiday (gap longer than a normal weekday/weekend gap); 0 span more than 4 calendar days (e.g. Sept 2001 closure)\",\n      \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e43961a2-ef4d-4108-af1f-989e34b48a3b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","hash":"db17f8adc77e915840e8c4efe38a6d5a4e94d502815539e9c743297104ac0fef","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01PztNQKmfSA4RFGcYuPoE1L","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nSEED = None  # no randomness used\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = d[\"DGS10\"].astype(float)\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd = d.reset_index(drop=True)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap_days\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\nn_ch = len(ch)\n\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with empty DGS10 (market holidays)\", \"n_before\": n_raw,\n     \"n_after\": n_raw - n_missing, \"n_removed\": n_missing, \"rationale\": \"no yield published; required by TASK info rules\"},\n    {\"step\": 2, \"rule\": \"first available observation has no change\", \"n_before\": n_raw - n_missing,\n     \"n_after\": n_ch, \"n_removed\": n_raw - n_missing - n_ch, \"rationale\": \"difference undefined\"},\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# table 1\ntab = pd.DataFrame({\"weekday\": DAYS,\n                    \"n\": [len(g) for g in groups],\n                    \"mean_bp\": [g.mean() for g in groups],\n                    \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon, oth = groups[0], np.concatenate(groups[1:])\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntcrit = stats.t.ppf(0.975, df_w)\nlo, hi = eff - tcrit * se, eff + tcrit * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), df_w)\n\n# (iii) out-of-sample\ncut = pd.Timestamp(\"2009-12-31\")\ntr, te = ch[ch[\"date\"] <= cut], ch[ch[\"date\"] > cut]\nwd_means = tr.groupby(\"wd\")[\"dy\"].mean()\nassert tr[\"date\"].max() < te[\"date\"].min()\npred = te[\"wd\"].map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(te[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((te[\"dy\"].values - pred) ** 2)))\n# supplementary: unconditional train-mean forecast\nrmse_mean = float(np.sqrt(np.mean((te[\"dy\"].values - tr[\"dy\"].mean()) ** 2)))\n\nif p >= 0.05:\n    concl = \"no_detectable_effect\"\nelif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\nn_long_gap = int((ch[\"gap_days\"] > 4).sum())\nn_holiday_spanning = int(((ch[\"gap_days\"] > 1) & ~((ch[\"wd\"] == 0) & (ch[\"gap_days\"] == 3))).sum())\nissues = [\n    {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays)\",\n     \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"},\n    {\"issue\": f\"{n_holiday_spanning} changes span a holiday (gap longer than a normal weekday/weekend gap); {n_long_gap} span more than 4 calendar days (e.g. Sept 2001 closure)\",\n     \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"},\n]\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(n_ch),\n    \"random_seed\": SEED,\n    \"n_missing_rows\": n_missing,\n    \"n_train\": int(len(tr)),\n    \"n_test\": int(len(te)),\n    \"anova_f_stat\": float(F),\n    \"anova_p_value\": float(p),\n    \"effect_estimate\": float(eff),\n    \"effect_ci_low\": float(lo),\n    \"effect_ci_high\": float(hi),\n    \"baseline_metric\": rmse0,\n    \"model_metric\": rmse1,\n    \"conclusion\": concl,\n    # supplementary diagnostics\n    \"supp_effect_se\": float(se),\n    \"supp_effect_welch_df\": float(df_w),\n    \"supp_effect_welch_p\": float(welch_p),\n    \"supp_rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n    \"supp_rmse_train_mean_forecast\": rmse_mean,\n    \"supp_levene_p\": float(stats.levene(*groups, center=\"median\").pvalue),\n    \"supp_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n    \"supp_train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wd_means.items()},\n    \"supp_n_holiday_spanning_changes\": n_holiday_spanning,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y Treasury yield: mean daily change by weekday, 2000-2019\\n(95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\",\n      \"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nprint(tab.to_string(index=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; grep supp_ -A0 out/results.json; grep -A6 train_weekday out/results.json; grep -A1 '\"issue\"' out/results.json","description":"Write and run analysis 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> out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README) | 5217 | 2 |\n| data/README.md | 32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a | provenance text | – |\n\n- Schema: observation_date (YYYY-MM-DD), DGS10 (percent, string; empty on holidays).\n- Date range 2000-01-03 … 2019-12-31; monotonic increasing; 0 duplicate dates; only weekdays (Mon 1044, Tue 1044, Wed–Fri 1043 each).\n- Missing: 215 empty DGS10 fields (holidays). No non-empty unparseable values. Available values: 5002, range 1.37–6.79 %, mean 3.43 %.\n- After dropping empties and differencing: 5001 changes (2500 dated ≤2009-12-31, 2501 dated ≥2010-01-01).\n- 214 changes span a holiday (gap longer than the normal 1-day / Friday→Monday 3-day gap); none spans more than 4 calendar days. Kept per task rules (attributed to weekday of t).\n- Information-availability: the only input is the permitted file; no violation. The forecasting weekday means use train-period changes only (asserted in code).\n- Anomaly worth noting: Levene test rejects equal variances across weekdays (p≈1e-7), so the classical ANOVA's equal-variance assumption is violated; Kruskal–Wallis and Welch/HC1 checks reported as robustness.\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run: deleted out/results.json, ran analysis.py twice; byte-identical outputs (cmp) — PASS.\n2. Counts: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500+2501) = sum of table1 n — PASS. Exclusion ledger sums — PASS.\n3. CI contains estimate (−0.319 < 0.050 < 0.420) — PASS. All SEs > 0 — PASS.\n4. Monday contrast recomputed from table1 weighted means: 0.050170 = effect_estimate — PASS.\n5. Independent cross-check with statsmodels OLS dy~C(weekday): F=0.26347, p=0.90151, identical to scipy f_oneway — PASS. HC1 regression on Monday dummy: 0.0502, CI [−0.319, 0.419], agrees with Welch — PASS.\n6. Chronology: max train date < min test date (assert in code); weekday means estimated from train only — PASS.\n7. Magnitudes: RMSE ≈ 4.88 bp is plausible for daily 10y changes; model RMSE (4.8838) > baseline (4.8757), ratio 1.0017 — consistent with no effect.\n8. Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect — PASS.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README\",\"rows/cols/dates/missing/duplicates audited (data_audit.md)\",\"exclusion ledger written and sums (5217-216=5001)\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"zero-change RMSE baseline computed over evaluation period: baseline_metric=4.8757 bp\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py implements ANOVA, Welch contrast, OOS RMSE; no randomness (random_seed=null)\",\"figure1.png and table1.csv generated by script\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run identical (cmp)\",\"identity checks in checks.md all PASS\",\"statsmodels cross-check agrees\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1-P3 passed; Welch chosen for Monday contrast. P4 checks done, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write audit, checks, update ledger, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":432,"cache_read_input_tokens":18507,"output_tokens":1668,"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":432},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrrpvpqGhxca6Cd5JMD","requestRef":"351a5903-224d-445e-a3bb-1cd4e539c807","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6fa7c5b2-df2d-4294-803c-1596d53b71da","timestamp":"2026-10-30T05:21:47.950Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"6fa7c5b2-df2d-4294-803c-1596d53b71da","isSidechain":false,"promptId":"9121a026-fa9e-43be-bc2a-ff2c83147cdb","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 39 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/figure1.png: present\n  [ok] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.319335775694099 <= 0.05017009840169969 <= 0.41967597249749833\n  [ok] positive:anova_f_stat: 0.26346571680712\n  [ok] positive:baseline_metric: 4.875704154642543\n  [ok] positive:model_metric: 4.883841522290305\n  [ok] reproducibility: regenerated in 1.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0274/out/state/gate_receipts/20261030T052149.032608Z.json","is_error":true,"tool_use_id":"toolu_01Q3vhNoqK3ayMRufR9nettk"}]},"uuid":"0c222f17-f772-4d0b-be6d-323e86952ab1","timestamp":"2026-10-30T05:21:49.041Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 39 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/figure1.png: present\n  [ok] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.319335775694099 <= 0.05017009840169969 <= 0.41967597249749833\n  [ok] positive:anova_f_stat: 0.26346571680712\n  [ok] positive:baseline_metric: 4.875704154642543\n  [ok] positive:model_metric: 4.883841522290305\n  [ok] reproducibility: regenerated in 1.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0274/out/state/gate_receipts/20261030T052149.032608Z.json","sandboxed":false,"sourceToolAssistantUUID":"6fa7c5b2-df2d-4294-803c-1596d53b71da","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}64{"type":"api-request-blob","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","hash":"74e61f8578186f50ec3e53f17107c8c1e3a37e38c57e683536e4a5ef0d49d460","message":{"role":"assistant","content":[{"type":"text","text":"Now state files, then gate."},{"type":"tool_use","id":"toolu_01Q3vhNoqK3ayMRufR9nettk","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README) | 5217 | 2 |\n| data/README.md | 32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a | provenance text | – |\n\n- Schema: observation_date (YYYY-MM-DD), DGS10 (percent, string; empty on holidays).\n- Date range 2000-01-03 … 2019-12-31; monotonic increasing; 0 duplicate dates; only weekdays (Mon 1044, Tue 1044, Wed–Fri 1043 each).\n- Missing: 215 empty DGS10 fields (holidays). No non-empty unparseable values. Available values: 5002, range 1.37–6.79 %, mean 3.43 %.\n- After dropping empties and differencing: 5001 changes (2500 dated ≤2009-12-31, 2501 dated ≥2010-01-01).\n- 214 changes span a holiday (gap longer than the normal 1-day / Friday→Monday 3-day gap); none spans more than 4 calendar days. Kept per task rules (attributed to weekday of t).\n- Information-availability: the only input is the permitted file; no violation. The forecasting weekday means use train-period changes only (asserted in code).\n- Anomaly worth noting: Levene test rejects equal variances across weekdays (p≈1e-7), so the classical ANOVA's equal-variance assumption is violated; Kruskal–Wallis and Welch/HC1 checks reported as robustness.\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run: deleted out/results.json, ran analysis.py twice; byte-identical outputs (cmp) — PASS.\n2. Counts: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500+2501) = sum of table1 n — PASS. Exclusion ledger sums — PASS.\n3. CI contains estimate (−0.319 < 0.050 < 0.420) — PASS. All SEs > 0 — PASS.\n4. Monday contrast recomputed from table1 weighted means: 0.050170 = effect_estimate — PASS.\n5. Independent cross-check with statsmodels OLS dy~C(weekday): F=0.26347, p=0.90151, identical to scipy f_oneway — PASS. HC1 regression on Monday dummy: 0.0502, CI [−0.319, 0.419], agrees with Welch — PASS.\n6. Chronology: max train date < min test date (assert in code); weekday means estimated from train only — PASS.\n7. Magnitudes: RMSE ≈ 4.88 bp is plausible for daily 10y changes; model RMSE (4.8838) > baseline (4.8757), ratio 1.0017 — consistent with no effect.\n8. Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect — PASS.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README\",\"rows/cols/dates/missing/duplicates audited (data_audit.md)\",\"exclusion ledger written and sums (5217-216=5001)\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"zero-change RMSE baseline computed over evaluation period: baseline_metric=4.8757 bp\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py implements ANOVA, Welch contrast, OOS RMSE; no randomness (random_seed=null)\",\"figure1.png and table1.csv generated by script\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run identical (cmp)\",\"identity checks in checks.md all PASS\",\"statsmodels cross-check agrees\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1-P3 passed; Welch chosen for Monday contrast. P4 checks done, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write audit, checks, update ledger, run gate"}}]}}65{"type":"api-request","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","id":"ac8a8182-5fc8-4977-8c57-88e8ed33409c","timestamp":"2026-10-30T05:21:49.045Z","version":"2.1.314","querySource":"sdk","shapeHash":"24a81578bcf6edd90e1a06cd7ef37180aa5871dec155e0c86754f192454cef10","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":"351a5903-224d-445e-a3bb-1cd4e539c807","keep":20,"tail":["74e61f8578186f50ec3e53f17107c8c1e3a37e38c57e683536e4a5ef0d49d460","8b52b0d1ce60c20774ecb237a23c26cacbf1548864f063c284ba5621bbda9894"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"11500e6f1612301a47135fb54584a42792bb7e799ed0296ba5eede77b384316a"}66{"parentUuid":"0c222f17-f772-4d0b-be6d-323e86952ab1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrt3Kyqx6AYyY6Vucu1","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":2531,"cache_read_input_tokens":18939,"output_tokens":3465,"output_tokens_details":{"thinking_tokens":33},"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":2531},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":593,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrt34cYipmiKk58cUUL","requestRef":"ac8a8182-5fc8-4977-8c57-88e8ed33409c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"dc2d24b9-dbc6-43f3-adc6-b29e814d0413","timestamp":"2026-10-30T05:21:51.207Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"dc2d24b9-dbc6-43f3-adc6-b29e814d0413","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrt3Kyqx6AYyY6Vucu1","type":"message","role":"assistant","content":[{"type":"text","text":"P4's own checks all pass. The remaining gate failures are P5 deliverables that haven't been written yet. Now the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2531,"cache_read_input_tokens":18939,"output_tokens":3465,"output_tokens_details":{"thinking_tokens":33},"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":2531},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrt34cYipmiKk58cUUL","requestRef":"ac8a8182-5fc8-4977-8c57-88e8ed33409c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3ba53417-497a-4d72-8994-116bf74ab073","timestamp":"2026-10-30T05:21:51.207Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"3ba53417-497a-4d72-8994-116bf74ab073","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrt3Kyqx6AYyY6Vucu1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G651Y8ktxtUyAMnRuHXM82","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 Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10)? Three estimands: (i) a joint test that all five weekday means are equal; (ii) the Monday contrast (mean Monday change minus mean change on Tue–Fri) with a 95% interval; (iii) whether weekday-specific means estimated on 2000–2009 forecast 2010–2019 changes better than a zero-change (random-walk) forecast.\n\n## Data and exclusions\nInput: `data/DGS10.csv`, 5217 weekday rows, 2000-01-03 to 2019-12-31 (`n_input_rows`). SHA-256 matches the README. There are no duplicate dates and no values that fail to parse. 215 rows have an empty DGS10 field because they are US market holidays (`n_missing_rows`). These rows were dropped before differencing, as the task requires. The first available observation has no change, so 5001 daily changes remain (`n_analysed`). Of these, 2500 are dated on or before 2009-12-31 (`n_train`) and 2501 on or after 2010-01-01 (`n_test`). The steps are recorded in `out/exclusions.csv`. 214 changes span a holiday (`supp_n_holiday_spanning_changes`), meaning they cover more calendar time than a normal day or weekend. Following the task rules, these are kept and attributed to the weekday of t (`data_issues`).\n\n## Method\n- Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t.\n- Joint test: classical one-way equal-variance ANOVA F-test (scipy `f_oneway`) on all 5001 changes, with F(4, 4996). The result was cross-checked with an OLS regression on weekday dummies (statsmodels), which gives the same F and p.\n- Monday contrast: Welch two-sample comparison of Monday changes against all Tue–Fri changes, with a Welch–Satterthwaite t interval. I chose Welch because it does not assume equal variances. An HC1-robust regression on a Monday dummy gives practically the same interval (see `out/state/checks.md`).\n- Out-of-sample test: each 2010–2019 change is forecast by the 2000–2009 mean change for its weekday (`supp_train_weekday_means_bp`), and compared with a zero-change forecast. Both are scored by RMSE over the evaluation period. Only estimation-period data enter the forecasts; the code asserts this.\n- Conclusion rule: if the ANOVA p ≥ 0.05 → `no_detectable_effect`; otherwise the sign comes from the Monday-contrast interval.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n- Weekday means (`out/table1.csv`, `out/figure1.png`) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each standard error is about 0.16–0.19 bp.\n- Joint ANOVA: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). The test does not reject equal weekday means.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`), Welch p = 0.79 (`supp_effect_welch_p`).\n- Out-of-sample: the zero-change RMSE is 4.876 bp (`baseline_metric`). The weekday-mean forecast RMSE is 4.884 bp (`model_metric`), a ratio of 1.0017 (`supp_rmse_ratio_model_over_baseline`). The weekday means do slightly *worse* than the random walk.\n- Conclusion: `no_detectable_effect` (`conclusion`). In this sample, the day of the week has no detectable predictive content for daily changes in the 10-year yield.\n\n## Uncertainty\n- The Monday-contrast interval is about ±0.37 bp wide (`effect_ci_low`, `effect_ci_high`; SE `supp_effect_se` = 0.188 bp). The data therefore rule out Monday effects larger than roughly 0.4 bp per day in either direction. Smaller effects cannot be excluded.\n- The out-of-sample RMSE difference (0.008 bp) is tiny compared with the daily volatility (~4.9 bp). No formal forecast-comparison test (e.g. Diebold–Mariano) was run. The evidence is that weekday means add nothing, not a significance statement.\n- Robustness: a Kruskal–Wallis rank test also does not reject equal weekday distributions (p = 0.47, `supp_kruskal_p`).\n\n## Limitations and open issues\n- **Assumption violation:** Levene's test strongly rejects equal variances across weekdays (p ≈ 1e-7, `supp_levene_p`). The classical ANOVA required by the task therefore runs under a violated assumption. With roughly balanced group sizes (919–1030) and p = 0.90, this is unlikely to change the conclusion. A Welch ANOVA was not run. The Monday contrast uses Welch, and the HC1 regression check agrees with it.\n- **Serial dependence / volatility clustering:** daily yield changes show conditional heteroskedasticity, and their autocorrelation may not be exactly zero. The ANOVA and Welch standard errors assume independence. HAC/GARCH-robust inference was not checked.\n- **Holiday-spanning changes:** 214 changes cover more than one trading day and are assigned to the weekday of t. This mixes multi-day changes into Tuesdays (after Monday holidays) and other days. The task rules require this treatment. A sensitivity analysis excluding these changes was not done.\n- **Regime changes:** the 2000–2019 period includes the zero-lower-bound and QE years. A weekday pattern could exist in sub-periods and cancel out overall. Only the fixed 2009/2010 split was examined.\n- **What would invalidate the result:** errors in the FRED file (stale or revised values), or a definition of the change different from the prescribed one (e.g. calendar-day rather than trading-day changes). Calendar effects not tested here, such as FOMC days, auction days, or month-end, could be correlated with weekdays. Because the null is not rejected, such confounding could hide an effect rather than create one.\n- Power: the study cannot detect weekday effects much smaller than ~0.4 bp/day. A non-rejection is not proof that the effect is zero.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal mean daily changes across the five weekdays (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption is violated (Levene p~1e-7); serial dependence ignored.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, which contains zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"supp_effect_se\",\"supp_effect_welch_p\"],\"status\":\"supported\",\"caveat\":\"Assumes independent daily changes; effects below ~0.4 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve on the zero-change forecast over 2010-2019 (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"supp_rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal forecast-comparison test; single fixed split.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect in daily changes of the 10-year Treasury yield, 2000-2019.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Non-rejection, not proof of zero effect.\"},\n {\"id\":\"C5\",\"text\":\"A rank-based Kruskal-Wallis test also does not reject equal weekday distributions.\",\"results_keys\":[\"supp_kruskal_p\"],\"status\":\"supported\",\"caveat\":\"Robustness check only.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))[-1]\np['P4']['status']='passed'; p['P4']['checks'].append(f\"gate run at end of P4: {r} (only failures were P5 deliverables not yet written: report, claims, P4/P5 status)\")\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections citing results keys\",\"claims.json written; all keys exist\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt recorded); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report and claims, update ledger, 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":2531,"cache_read_input_tokens":18939,"output_tokens":3465,"output_tokens_details":{"thinking_tokens":33},"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":2531},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrt34cYipmiKk58cUUL","requestRef":"ac8a8182-5fc8-4977-8c57-88e8ed33409c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c60e44b8-819e-4109-b28e-2ce4ab4856ac","timestamp":"2026-10-30T05:22:20.203Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}69{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"c60e44b8-819e-4109-b28e-2ce4ab4856ac","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa"}70{"type":"atis-latch","atis":"","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa"}71{"parentUuid":"c60e44b8-819e-4109-b28e-2ce4ab4856ac","isSidechain":false,"promptId":"9121a026-fa9e-43be-bc2a-ff2c83147cdb","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G651Y8ktxtUyAMnRuHXM82","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0274/out/state/gate_receipts/20261030T052221.275274Z.json","is_error":false}]},"uuid":"fac2416c-6dc7-49a0-be6d-c16a03e7378d","timestamp":"2026-10-30T05:22:21.285Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0274/out/state/gate_receipts/20261030T052221.275274Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c60e44b8-819e-4109-b28e-2ce4ab4856ac","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","version":"2.1.314","gitBranch":"HEAD"}72{"type":"api-request-blob","sessionId":"f9d79e4f-9b84-4f3c-8b0e-d3cf7b7064fa","hash":"b11bb5b8259f89f6575b13b277941610d3d5c22246376a9668e4370e6acfab23","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4's own checks all pass. The remaining gate failures are P5 deliverables that haven't been written yet. Now the report and claims."},{"type":"tool_use","id":"toolu_01G651Y8ktxtUyAMnRuHXM82","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 Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10)? Three estimands: (i) a joint test that all five weekday means are equal; (ii) the Monday contrast (mean Monday change minus mean change on Tue–Fri) with a 95% interval; (iii) whether weekday-specific means estimated on 2000–2009 forecast 2010–2019 changes better than a zero-change (random-walk) forecast.\n\n## Data and exclusions\nInput: `data/DGS10.csv`, 5217 weekday rows, 2000-01-03 to 2019-12-31 (`n_input_rows`). SHA-256 matches the README. There are no duplicate dates and no values that fail to parse. 215 rows have an empty DGS10 field because they are US market holidays (`n_missing_rows`). These rows were dropped before differencing, as the task requires. The first available observation has no change, so 5001 daily changes remain (`n_analysed`). Of these, 2500 are dated on or before 2009-12-31 (`n_train`) and 2501 on or after 2010-01-01 (`n_test`). The steps are recorded in `out/exclusions.csv`. 214 changes span a holiday (`supp_n_holiday_spanning_changes`), meaning they cover more calendar time than a normal day or weekend. Following the task rules, these are kept and attributed to the weekday of t (`data_issues`).\n\n## Method\n- Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t.\n- Joint test: classical one-way equal-variance ANOVA F-test (scipy `f_oneway`) on all 5001 changes, with F(4, 4996). The result was cross-checked with an OLS regression on weekday dummies (statsmodels), which gives the same F and p.\n- Monday contrast: Welch two-sample comparison of Monday changes against all Tue–Fri changes, with a Welch–Satterthwaite t interval. I chose Welch because it does not assume equal variances. An HC1-robust regression on a Monday dummy gives practically the same interval (see `out/state/checks.md`).\n- Out-of-sample test: each 2010–2019 change is forecast by the 2000–2009 mean change for its weekday (`supp_train_weekday_means_bp`), and compared with a zero-change forecast. Both are scored by RMSE over the evaluation period. Only estimation-period data enter the forecasts; the code asserts this.\n- Conclusion rule: if the ANOVA p ≥ 0.05 → `no_detectable_effect`; otherwise the sign comes from the Monday-contrast interval.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n- Weekday means (`out/table1.csv`, `out/figure1.png`) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each standard error is about 0.16–0.19 bp.\n- Joint ANOVA: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). The test does not reject equal weekday means.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`), Welch p = 0.79 (`supp_effect_welch_p`).\n- Out-of-sample: the zero-change RMSE is 4.876 bp (`baseline_metric`). The weekday-mean forecast RMSE is 4.884 bp (`model_metric`), a ratio of 1.0017 (`supp_rmse_ratio_model_over_baseline`). The weekday means do slightly *worse* than the random walk.\n- Conclusion: `no_detectable_effect` (`conclusion`). In this sample, the day of the week has no detectable predictive content for daily changes in the 10-year yield.\n\n## Uncertainty\n- The Monday-contrast interval is about ±0.37 bp wide (`effect_ci_low`, `effect_ci_high`; SE `supp_effect_se` = 0.188 bp). The data therefore rule out Monday effects larger than roughly 0.4 bp per day in either direction. Smaller effects cannot be excluded.\n- The out-of-sample RMSE difference (0.008 bp) is tiny compared with the daily volatility (~4.9 bp). No formal forecast-comparison test (e.g. Diebold–Mariano) was run. The evidence is that weekday means add nothing, not a significance statement.\n- Robustness: a Kruskal–Wallis rank test also does not reject equal weekday distributions (p = 0.47, `supp_kruskal_p`).\n\n## Limitations and open issues\n- **Assumption violation:** Levene's test strongly rejects equal variances across weekdays (p ≈ 1e-7, `supp_levene_p`). The classical ANOVA required by the task therefore runs under a violated assumption. With roughly balanced group sizes (919–1030) and p = 0.90, this is unlikely to change the conclusion. A Welch ANOVA was not run. The Monday contrast uses Welch, and the HC1 regression check agrees with it.\n- **Serial dependence / volatility clustering:** daily yield changes show conditional heteroskedasticity, and their autocorrelation may not be exactly zero. The ANOVA and Welch standard errors assume independence. HAC/GARCH-robust inference was not checked.\n- **Holiday-spanning changes:** 214 changes cover more than one trading day and are assigned to the weekday of t. This mixes multi-day changes into Tuesdays (after Monday holidays) and other days. The task rules require this treatment. A sensitivity analysis excluding these changes was not done.\n- **Regime changes:** the 2000–2019 period includes the zero-lower-bound and QE years. A weekday pattern could exist in sub-periods and cancel out overall. Only the fixed 2009/2010 split was examined.\n- **What would invalidate the result:** errors in the FRED file (stale or revised values), or a definition of the change different from the prescribed one (e.g. calendar-day rather than trading-day changes). Calendar effects not tested here, such as FOMC days, auction days, or month-end, could be correlated with weekdays. Because the null is not rejected, such confounding could hide an effect rather than create one.\n- Power: the study cannot detect weekday effects much smaller than ~0.4 bp/day. A non-rejection is not proof that the effect is zero.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal mean daily changes across the five weekdays (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption is violated (Levene p~1e-7); serial dependence ignored.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, which contains zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"supp_effect_se\",\"supp_effect_welch_p\"],\"status\":\"supported\",\"caveat\":\"Assumes independent daily changes; effects below ~0.4 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve on the zero-change forecast over 2010-2019 (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"supp_rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal forecast-comparison test; single fixed split.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect in daily changes of the 10-year Treasury yield, 2000-2019.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Non-rejection, not proof of zero effect.\"},\n {\"id\":\"C5\",\"text\":\"A rank-based Kruskal-Wallis test also does not reject equal weekday distributions.\",\"results_keys\":[\"supp_kruskal_p\"],\"status\":\"supported\",\"caveat\":\"Robustness check only.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))[-1]\np['P4']['status']='passed'; p['P4']['checks'].append(f\"gate run at end of P4: {r} (only failures were P5 deliverables not yet written: report, claims, P4/P5 status)\")\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections citing results keys\",\"claims.json written; all keys exist\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt recorded); 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All required files are written, and the final gate run passed with no failed checks.\n\n**Data:** the file has 5,217 weekday rows. 215 have no yield (market holidays) and were dropped, which leaves 5,001 daily changes: 2,500 dated up to 2009-12-31 for estimation and 2,501 dated from 2010 for evaluation.\n\n**Results:**\n- **Joint test:** the standard one-way ANOVA does not reject equal weekday means (F = 0.263, p = 0.90). A rank-based check (Kruskal–Wallis) agrees (p = 0.47).\n- **Monday contrast:** Mondays average +0.050 bp more than the other four weekdays, with a 95% interval of [−0.319, 0.420] bp, so zero is well inside it. I used a Welch two-sample interval; a regression on a Monday indicator with robust standard errors gives nearly the same interval.\n- **Forecasting:** predicting each 2010–2019 change by its weekday's 2000–2009 average gives a typical error (RMSE) of 4.884 bp. 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