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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. 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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.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:42 .\ndrwxr-xr-x 151 simon-pierreboucher staff 4832 oct. 30 01:17 ..\n-rw-r--r-- 1 simon-pierreboucher staff 509 oct. 29 23:42 bundle.json\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:27 data\n-rw-r--r-- 1 simon-pierreboucher staff 2209 oct. 29 23:35 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 6307 oct. 29 23:35 TASK.md\ndata:\ntotal 176\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:27 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:42 ..\n-rw-r--r-- 1 simon-pierreboucher staff 82635 oct. 29 23:27 DGS10.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1162 oct. 29 23:27 README.md","is_error":true,"tool_use_id":"toolu_01TGq9ZRVFTmf3JotVVnyraB"}]},"uuid":"c0b7e2ac-158f-4d47-ba6d-0963c0b1b64b","timestamp":"2026-10-30T05:17:34.016Z","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`\n{\n \"task_id\": \"F6-conf-01\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\",\n \"out/figure1.png\",\n \"out/table1.csv\"\n ],\n \"results_keys\": {\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"n_missing_rows\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_train\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_test\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"anova_f_stat\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"anova_p_value\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_estimate\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_low\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_high\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"baseline_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"model_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"ci_contains_estimate\",\n \"positive_se\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\n \"exclusion_ledger_sums\"\n ],\n \"ci_pairs\": [\n [\n \"effect_estimate\",\n \"effect_ci_low\",\n \"effect_ci_high\"\n ]\n ],\n \"positive_keys\": [\n \"anova_f_stat\",\n \"baseline_metric\",\n \"model_metric\"\n ],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}{\n \"task_id\": \"F6-conf-01\",\n \"family\": \"F6\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n \"files\": {\n \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n \"gate_spec.json\": \"d53691940951460bcc45cb08c958917841ae1bfbc8d242d37ded795a3e998eb7\"\n }\n}observation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n2019-12-27,1.88\n2019-12-30,1.90\n2019-12-31,1.92\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a data/README.md","is_error":false}]},"uuid":"cb915f6c-8b6f-4013-a8b8-25644b6f9e84","timestamp":"2026-10-30T05:17:36.143Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-01\n\n## `DGS10.csv` (5217 rows)\nObserved data: *Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis* (FRED series `DGS10`, daily, percent, not seasonally adjusted). Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\n{\n \"task_id\": \"F6-conf-01\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\",\n \"out/figure1.png\",\n \"out/table1.csv\"\n ],\n \"results_keys\": {\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"n_missing_rows\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_train\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_test\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"anova_f_stat\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"anova_p_value\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_estimate\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_low\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_high\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"baseline_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"model_metric\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"ci_contains_estimate\",\n \"positive_se\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\n \"exclusion_ledger_sums\"\n ],\n \"ci_pairs\": [\n [\n \"effect_estimate\",\n \"effect_ci_low\",\n \"effect_ci_high\"\n ]\n ],\n \"positive_keys\": [\n \"anova_f_stat\",\n \"baseline_metric\",\n \"model_metric\"\n ],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}{\n \"task_id\": \"F6-conf-01\",\n \"family\": \"F6\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n \"files\": {\n \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n \"gate_spec.json\": 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> out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday of a trading day predict the one-day change in the 10-year Treasury CMT yield (FRED DGS10), 2000–2019?\n**Estimands.** (i) Joint equality of the 5 weekday conditional means of Δy_t (bp), classical one-way equal-variance ANOVA F(4, n−5) on all changes. (ii) Monday contrast = mean(Mon) − mean(Tue–Fri), all changes, 95% CI (choice: Welch two-sample). (iii) Out-of-sample RMSE (2010–2019 changes) of weekday means estimated on ≤2009-12-31 changes vs zero-change forecast.\nMeaningful under: changes roughly independent across days (ANOVA/Welch assume independence; ANOVA assumes equal variances), stable weekday effect across periods for (iii).\n**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 has no change. Train = changes dated ≤ 2009-12-31; test ≥ 2010-01-01. 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; otherwise sign from Monday CI (if CI straddles zero after ANOVA rejects, report no_detectable_effect for the Monday contrast and explain).\n**Invalidity.** Missing/unparsable file, no weekday variation, or using test info in forecasts.\nEOF\ncat > 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 and bundle.json) | 5217 | 2 |\n| data/README.md | 32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a | – | – |\n\n- Schema: observation_date (YYYY-MM-DD, weekdays only, sorted, no duplicates), DGS10 (percent, 2 decimals).\n- Range 2000-01-03 … 2019-12-31. Weekday counts of rows: Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043.\n- Empty DGS10: 215 rows (US market holidays, incl. 2001-09-11/12 closure). No other non-numeric values.\n- Available yields: 5002, range 1.37–6.79 %, plausible.\n- Calendar gaps between consecutive available obs: 1 day 3899, 2 days 58, 3 days 889, 4 days 155. Changes after a holiday span >1 trading day; per the task rules they are kept and attributed to the weekday of t (documented as a data issue).\n- No input violates availability rules; the train/test split is enforced in code.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/missing/duplicates/gaps audited\"],\"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, P1 passed. Decision: Welch CI for Monday contrast; keep post-holiday multi-day changes per rules.\" > out/state/log.md","description":"Write contract, audit, 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":912,"cache_read_input_tokens":9011,"output_tokens":1678,"output_tokens_details":{"thinking_tokens":59},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":912},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrZoEur4feYrLXrww4U","requestRef":"07776a52-6b63-4b61-89cf-7955e88423d6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3fa4d905-c99d-4c82-83df-e1649bd01175","timestamp":"2026-10-30T05:17:57.091Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"3fa4d905-c99d-4c82-83df-e1649bd01175","isSidechain":false,"promptId":"327a991c-8554-47e0-aa15-adf0169d3f83","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MeGyUeJYCaLSVeEUggDDq2","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"5386d255-d728-4038-b028-1278aa1d0779","timestamp":"2026-10-30T05:17:57.139Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3fa4d905-c99d-4c82-83df-e1649bd01175","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","hash":"e993d5b28feb5a44e6ce85ff5a1bcf8153f5d055e8e9a56aab35becd8c015193","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01MeGyUeJYCaLSVeEUggDDq2","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday of a trading day predict the one-day change in the 10-year Treasury CMT yield (FRED DGS10), 2000–2019?\n**Estimands.** (i) Joint equality of the 5 weekday conditional means of Δy_t (bp), classical one-way equal-variance ANOVA F(4, n−5) on all changes. (ii) Monday contrast = mean(Mon) − mean(Tue–Fri), all changes, 95% CI (choice: Welch two-sample). (iii) Out-of-sample RMSE (2010–2019 changes) of weekday means estimated on ≤2009-12-31 changes vs zero-change forecast.\nMeaningful under: changes roughly independent across days (ANOVA/Welch assume independence; ANOVA assumes equal variances), stable weekday effect across periods for (iii).\n**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 has no change. Train = changes dated ≤ 2009-12-31; test ≥ 2010-01-01. 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; otherwise sign from Monday CI (if CI straddles zero after ANOVA rejects, report no_detectable_effect for the Monday contrast and explain).\n**Invalidity.** Missing/unparsable file, no weekday variation, or using test info in forecasts.\nEOF\ncat > 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 and bundle.json) | 5217 | 2 |\n| data/README.md | 32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a | – | – |\n\n- Schema: observation_date (YYYY-MM-DD, weekdays only, sorted, no duplicates), DGS10 (percent, 2 decimals).\n- Range 2000-01-03 … 2019-12-31. Weekday counts of rows: Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043.\n- Empty DGS10: 215 rows (US market holidays, incl. 2001-09-11/12 closure). No other non-numeric values.\n- Available yields: 5002, range 1.37–6.79 %, plausible.\n- Calendar gaps between consecutive available obs: 1 day 3899, 2 days 58, 3 days 889, 4 days 155. Changes after a holiday span >1 trading day; per the task rules they are kept and attributed to the weekday of t (documented as a data issue).\n- No input violates availability rules; the train/test split is enforced in code.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/missing/duplicates/gaps audited\"],\"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, P1 passed. 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Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nmissing = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(missing.sum())\nd = raw[~missing].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = d[\"DGS10\"].astype(float)\nd = d.sort_values(\"date\").reset_index(drop=True)\ngapdays = d[\"date\"].diff().dt.days\nd[\"dy\"] = 100 * d[\"y\"].diff()\nch = d.iloc[1:].copy()\nch[\"dy\"] = ch[\"dy\"].round(10) # remove float noise from 2-decimal inputs\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\nn_changes = len(ch)\n\n# exclusion ledger\nex = pd.DataFrame([\n [\"1\", \"drop rows with empty DGS10\", n_raw, n_raw - n_missing, n_missing, \"no yield published (market holiday)\"],\n [\"2\", \"first available observation has no change\", n_raw - n_missing, n_changes, 1, \"change undefined\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nex.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]\ntest = ch[ch[\"date\"] >= \"2010-01-01\"]\n\n# ANOVA (classical, equal variance), all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Monday contrast, Welch\nmon = ch.loc[ch.wd == 0, \"dy\"].values\noth = ch.loc[ch.wd != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df)\nlo, hi = est - tq * se, est + tq * se\n\n# Out-of-sample\nwd_means = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wd_means).values\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - pred) ** 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\n# Table and figure\ntab = pd.DataFrame({\n \"weekday\": names,\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],\n})\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\ncrit = [stats.t.ppf(0.975, n - 1) for n in tab[\"n\"]]\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=np.array(crit) * tab[\"se_bp\"], fmt=\"o\", capsize=4)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([n[:3] for n in names])\nax.set_ylabel(\"Mean daily change in 10y yield (bp)\")\nax.set_title(\"DGS10 daily change by weekday, 2000-2019 (95% CI)\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nn_multiday = int(((gapdays.iloc[1:] == 2) | ((gapdays.iloc[1:] >= 3) & (d[\"date\"].iloc[1:].dt.dayofweek != 0))\n | (gapdays.iloc[1:] >= 4)).sum())\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays, incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, as required by TASK.md\"},\n {\"issue\": f\"{n_multiday} changes span more than one trading day because the previous weekday was a holiday\",\n \"handling\": \"kept and attributed to weekday of t, per TASK.md rules; noted as limitation\"},\n ],\n \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n \"n_analysed\": int(n_changes),\n \"random_seed\": None,\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)),\n \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F),\n \"anova_p_value\": float(p),\n \"effect_estimate\": float(est),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse_base,\n \"model_metric\": rmse_model,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wd_means.items()},\n \"rmse_ratio_model_over_baseline\": rmse_model / rmse_base,\n \"test_mean_bp\": float(test[\"dy\"].mean()),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv out/exclusions.csv; cat out/results.json | head -30","description":"Write and run the 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":1693,"cache_read_input_tokens":9923,"output_tokens":2514,"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":1693},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrawhvGTnD6gRu1TfSm","requestRef":"db20d958-2fe0-47a3-bda8-530eca0e25bb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"196ec8c7-faa4-451f-9461-69e2064ba615","timestamp":"2026-10-30T05:18:15.942Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"196ec8c7-faa4-451f-9461-69e2064ba615","isSidechain":false,"promptId":"327a991c-8554-47e0-aa15-adf0169d3f83","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FMfitAaaZJvPkETwqnrgoq","type":"tool_result","content":"{\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.05223068552774755,0.16438585639710285\nTuesday,1028,-0.2198443579766537,0.17700678391028105\nWednesday,1030,0.01650485436893204,0.18234593373486246\nThursday,1012,-0.05928853754940711,0.18691776551235453\nFriday,1012,-0.14723320158102768,0.18991766797184337\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10,5217,5002,215,no yield published (market holiday)\n2,first available observation has no change,5002,5001,1,change undefined\n{\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 (US market holidays, incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, as required by TASK.md\"\n },\n {\n \"issue\": \"214 changes span more than one trading day because the previous weekday was a holiday\",\n \"handling\": \"kept and attributed to weekday of t, per TASK.md rules; noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"n_missing_rows\": 215,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",","is_error":false}]},"uuid":"22e72c7f-5540-4ac7-a531-c1ce186a46ff","timestamp":"2026-10-30T05:18:16.985Z","toolUseResult":{"stdout":"{\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.05223068552774755,0.16438585639710285\nTuesday,1028,-0.2198443579766537,0.17700678391028105\nWednesday,1030,0.01650485436893204,0.18234593373486246\nThursday,1012,-0.05928853754940711,0.18691776551235453\nFriday,1012,-0.14723320158102768,0.18991766797184337\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10,5217,5002,215,no yield published (market holiday)\n2,first available observation has no change,5002,5001,1,change undefined\n{\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 (US market holidays, incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, as required by TASK.md\"\n },\n {\n \"issue\": \"214 changes span more than one trading day because the previous weekday was a holiday\",\n \"handling\": \"kept and attributed to weekday of t, per TASK.md rules; noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"n_missing_rows\": 215,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"196ec8c7-faa4-451f-9461-69e2064ba615","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","hash":"3faa7279f1be770f839cab08465d88af8eed6560ed1b3b5065225e55ed5f9035","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01FMfitAaaZJvPkETwqnrgoq","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nmissing = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(missing.sum())\nd = raw[~missing].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = d[\"DGS10\"].astype(float)\nd = d.sort_values(\"date\").reset_index(drop=True)\ngapdays = d[\"date\"].diff().dt.days\nd[\"dy\"] = 100 * d[\"y\"].diff()\nch = d.iloc[1:].copy()\nch[\"dy\"] = ch[\"dy\"].round(10) # remove float noise from 2-decimal inputs\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\nn_changes = len(ch)\n\n# exclusion ledger\nex = pd.DataFrame([\n [\"1\", \"drop rows with empty DGS10\", n_raw, n_raw - n_missing, n_missing, \"no yield published (market holiday)\"],\n [\"2\", \"first available observation has no change\", n_raw - n_missing, n_changes, 1, \"change undefined\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nex.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]\ntest = ch[ch[\"date\"] >= \"2010-01-01\"]\n\n# ANOVA (classical, equal variance), all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Monday contrast, Welch\nmon = ch.loc[ch.wd == 0, \"dy\"].values\noth = ch.loc[ch.wd != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df)\nlo, hi = est - tq * se, est + tq * se\n\n# Out-of-sample\nwd_means = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wd_means).values\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - pred) ** 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\n# Table and figure\ntab = pd.DataFrame({\n \"weekday\": names,\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],\n})\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\ncrit = [stats.t.ppf(0.975, n - 1) for n in tab[\"n\"]]\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=np.array(crit) * tab[\"se_bp\"], fmt=\"o\", capsize=4)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([n[:3] for n in names])\nax.set_ylabel(\"Mean daily change in 10y yield (bp)\")\nax.set_title(\"DGS10 daily change by weekday, 2000-2019 (95% CI)\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nn_multiday = int(((gapdays.iloc[1:] == 2) | ((gapdays.iloc[1:] >= 3) & (d[\"date\"].iloc[1:].dt.dayofweek != 0))\n | (gapdays.iloc[1:] >= 4)).sum())\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays, incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, as required by TASK.md\"},\n {\"issue\": f\"{n_multiday} changes span more than one trading day because the previous weekday was a holiday\",\n \"handling\": \"kept and attributed to weekday of t, per TASK.md rules; noted as limitation\"},\n ],\n \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n \"n_analysed\": int(n_changes),\n \"random_seed\": None,\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)),\n \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F),\n \"anova_p_value\": float(p),\n \"effect_estimate\": float(est),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse_base,\n \"model_metric\": rmse_model,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wd_means.items()},\n \"rmse_ratio_model_over_baseline\": rmse_model / rmse_base,\n \"test_mean_bp\": float(test[\"dy\"].mean()),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv out/exclusions.csv; cat out/results.json | head -30","description":"Write and run the analysis 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n- Clean re-run of out/analysis.py (results/table/figure deleted first): results.json byte-identical — PASS.\n- n_train + n_test = n_analysed = 5217 − 215 − 1 = 5001 — PASS; exclusion ledger 5217 − 216 = 5001 — PASS.\n- table1 n sums to 5001; all se_bp > 0 — PASS.\n- CI [-0.319, 0.420] contains estimate 0.050 — PASS.\n- Cross-check of Monday contrast with OLS on Monday dummy, HC1 SE: 0.0502, CI [-0.319, 0.419] — agrees with Welch — PASS.\n- Chronology: weekday means used for forecasting estimated on dates ≤ 2009-12-31 only; test dates ≥ 2010-01-01 — PASS (by construction in code).\n- Magnitudes: RMSE ~4.9 bp/day plausible for 10y yield; model RMSE ≥ baseline by 0.008 bp — consistent with null ANOVA.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Write checks and run the gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":738,"cache_read_input_tokens":14986,"output_tokens":506,"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":738},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrd2D6Hbk4QgE2wea9N","requestRef":"f202f502-803b-4e0a-8f0d-c1fb50edd076","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cd3b2dfb-ff76-4115-bb2d-9be397691d15","timestamp":"2026-10-30T05:18:30.346Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"cd3b2dfb-ff76-4115-bb2d-9be397691d15","isSidechain":false,"promptId":"327a991c-8554-47e0-aa15-adf0169d3f83","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014yrgxiXsCWUp9VtpCmXGJB","type":"tool_result","content":" [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: ['P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:effect_estimate: -0.31933577569409977 <= 0.050170098401698796 <= 0.41967597249749733\n [ok] positive:anova_f_stat: 0.263465716807118\n [ok] positive:baseline_metric: 4.875704154642544\n [ok] positive:model_metric: 4.883841522290305\n [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0265/out/state/gate_receipts/20261030T051831.371237Z.json","is_error":false}]},"uuid":"cb110cf1-a318-47a9-b87e-13c1282b03ae","timestamp":"2026-10-30T05:18:31.378Z","toolUseResult":{"stdout":" [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: ['P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:effect_estimate: -0.31933577569409977 <= 0.050170098401698796 <= 0.41967597249749733\n [ok] positive:anova_f_stat: 0.263465716807118\n [ok] positive:baseline_metric: 4.875704154642544\n [ok] positive:model_metric: 4.883841522290305\n [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: 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PASS.\n- n_train + n_test = n_analysed = 5217 − 215 − 1 = 5001 — PASS; exclusion ledger 5217 − 216 = 5001 — PASS.\n- table1 n sums to 5001; all se_bp > 0 — PASS.\n- CI [-0.319, 0.420] contains estimate 0.050 — PASS.\n- Cross-check of Monday contrast with OLS on Monday dummy, HC1 SE: 0.0502, CI [-0.319, 0.419] — agrees with Welch — PASS.\n- Chronology: weekday means used for forecasting estimated on dates ≤ 2009-12-31 only; test dates ≥ 2010-01-01 — PASS (by construction in code).\n- Magnitudes: RMSE ~4.9 bp/day plausible for 10y yield; model RMSE ≥ baseline by 0.008 bp — consistent with null ANOVA.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Write checks and run the 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failures at this point only concern P5 deliverables, as expected. 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":1162,"cache_read_input_tokens":15724,"output_tokens":2441,"output_tokens_details":{"thinking_tokens":35},"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":1162},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrdU5bZUa3UDaJhsvA9","requestRef":"51fb32e0-c4a5-4a99-8618-2b05ed5e6622","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"550b392e-98e8-4922-b8b2-9cde1bc61541","timestamp":"2026-10-30T05:18:33.378Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"550b392e-98e8-4922-b8b2-9cde1bc61541","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrdUMD6imz5sJ3Xo2t1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0128pSPQvg8vckGGxZ2xMQvi","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\n\n## Question\nDoes the weekday carry predictive content for the one-day change Δy_t (bp) of the 10-year US Treasury CMT yield (FRED DGS10), 2000–2019? Estimands: (i) joint test of equal weekday means; (ii) Monday contrast (Monday mean minus mean of Tue–Fri) with a 95% CI; (iii) out-of-sample RMSE of estimation-period weekday means vs a zero-change forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 weekday rows (`n_input_rows`), SHA-256 matches the provenance file. 215 rows have an empty yield (market holidays; `n_missing_rows`) and were dropped before differencing; the first available observation has no change, leaving 5001 changes (`n_analysed`), of which 2500 are dated ≤ 2009-12-31 (`n_train`) and 2501 ≥ 2010-01-01 (`n_test`). Ledger: `out/exclusions.csv`. 214 changes span a holiday (previous weekday missing) and are kept per the task rules (`data_issues`). Weekday counts are in `out/table1.csv`.\n\n## Method\nΔy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t. (i) Classical one-way equal-variance ANOVA across the five weekdays, all 5001 changes, F(4, 4996). (ii) Monday contrast with a Welch two-sample t interval (unequal variances); I cross-checked it with OLS on a Monday indicator using HC1 SEs, which gave the same estimate and an almost identical interval (`out/state/checks.md`). (iii) Forecast each evaluation-period change by the mean change of its weekday in the estimation period (`train_weekday_means_bp`); baseline forecast = 0. Metric: RMSE over the 2501 evaluation changes. The conclusion code comes from the ANOVA at 5%. No randomness is involved (`random_seed` = null).\n\n## Results\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equal weekday means are not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`).\n- Out of sample: zero-change RMSE 4.876 bp (`baseline_metric`), weekday-mean RMSE 4.884 bp (`model_metric`). Weekday means are slightly *worse* than the random walk (ratio 1.0017, `rmse_ratio_model_over_baseline`).\n- Conclusion: `no_detectable_effect` (`conclusion`).\n- Figure: `out/figure1.png` (weekday means with 95% CIs). Table: `out/table1.csv`. All weekday means are within ±0.22 bp of zero, and every 95% interval contains zero.\n\n## Uncertainty\nThe Monday-contrast CI is about ±0.37 bp wide, so any Monday effect larger than roughly 0.4 bp/day in either direction is ruled out at the 95% level. That is small next to the ~4.9 bp daily standard deviation of changes (`baseline_metric`). The ANOVA p-value of 0.90 means the weekday means vary less than chance alone would typically produce. The out-of-sample RMSE difference (0.008 bp) is tiny, and I did not run a formal forecast-comparison (Diebold–Mariano) test. Its sign still goes against weekday predictability.\n\n## Limitations and open issues\n- **Assumptions.** The ANOVA and Welch tests assume independent changes. Volatility clustering (heteroskedasticity over time) breaks the ANOVA's equal-variance assumption, and its i.i.d. assumption too. With a p-value this far from 0.05 the conclusion is unlikely to flip, but the stated p-value is not exact. The HC1 cross-check only addresses heteroskedasticity across the Monday indicator, not serial dependence.\n- **Holiday-spanning changes.** 214 changes cover two trading days and Monday changes cover a weekend. Following the rules, these are attributed to the weekday of t. This mixes horizon length with weekday. Normalising per calendar day was not done.\n- **Not checked.** Stability across subperiods or rate regimes (for example the zero-lower-bound years), effects around macro release days (many US releases fall on Fridays), other maturities, and a formal test of forecast-accuracy differences. A weekday effect that reverses between periods could cancel in the pooled ANOVA. The out-of-sample comparison partly addresses this, but it is not a direct test.\n- **What would invalidate the result.** Errors in the FRED series or in its revision vintage. A different holiday treatment. A day-of-week effect that is genuinely present but smaller than about 0.4 bp. This study cannot detect an effect that small, so \"no detectable effect\" is not proof of no effect.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday mean changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Assumes independent, equal-variance changes; volatility clustering makes the p-value approximate.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with a 95% Welch CI of [-0.319, 0.420] bp, which includes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Monday changes span weekends; effects below ~0.4 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Out of sample (2010-2019), weekday-specific means from 2000-2009 do not beat the zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal Diebold-Mariano test; the difference is tiny.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect in daily 10-year yield changes.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Absence of evidence within the precision of the data, not proof of zero effect.\"}\n]\nEOF\necho ok","description":"Write report and claims ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1162,"cache_read_input_tokens":15724,"output_tokens":2441,"output_tokens_details":{"thinking_tokens":35},"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":1162},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrdU5bZUa3UDaJhsvA9","requestRef":"51fb32e0-c4a5-4a99-8618-2b05ed5e6622","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bc420815-458a-4eb6-8338-1419c08b97d9","timestamp":"2026-10-30T05:18:53.419Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"bc420815-458a-4eb6-8338-1419c08b97d9","isSidechain":false,"promptId":"327a991c-8554-47e0-aa15-adf0169d3f83","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0128pSPQvg8vckGGxZ2xMQvi","type":"tool_result","content":"ok","is_error":false}]},"uuid":"29ced077-5f42-49ff-99a5-9c6b4d1447df","timestamp":"2026-10-30T05:18:53.472Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bc420815-458a-4eb6-8338-1419c08b97d9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","hash":"bc6d95fcb3cac141a11a2ca86b8185bddba7095065cc42704c5b7d32a939a183","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Gate failures at this point only concern P5 deliverables, as expected. Now the report and claims."},{"type":"tool_use","id":"toolu_0128pSPQvg8vckGGxZ2xMQvi","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\n\n## Question\nDoes the weekday carry predictive content for the one-day change Δy_t (bp) of the 10-year US Treasury CMT yield (FRED DGS10), 2000–2019? Estimands: (i) joint test of equal weekday means; (ii) Monday contrast (Monday mean minus mean of Tue–Fri) with a 95% CI; (iii) out-of-sample RMSE of estimation-period weekday means vs a zero-change forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 weekday rows (`n_input_rows`), SHA-256 matches the provenance file. 215 rows have an empty yield (market holidays; `n_missing_rows`) and were dropped before differencing; the first available observation has no change, leaving 5001 changes (`n_analysed`), of which 2500 are dated ≤ 2009-12-31 (`n_train`) and 2501 ≥ 2010-01-01 (`n_test`). Ledger: `out/exclusions.csv`. 214 changes span a holiday (previous weekday missing) and are kept per the task rules (`data_issues`). Weekday counts are in `out/table1.csv`.\n\n## Method\nΔy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t. (i) Classical one-way equal-variance ANOVA across the five weekdays, all 5001 changes, F(4, 4996). (ii) Monday contrast with a Welch two-sample t interval (unequal variances); I cross-checked it with OLS on a Monday indicator using HC1 SEs, which gave the same estimate and an almost identical interval (`out/state/checks.md`). (iii) Forecast each evaluation-period change by the mean change of its weekday in the estimation period (`train_weekday_means_bp`); baseline forecast = 0. Metric: RMSE over the 2501 evaluation changes. The conclusion code comes from the ANOVA at 5%. No randomness is involved (`random_seed` = null).\n\n## Results\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equal weekday means are not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`).\n- Out of sample: zero-change RMSE 4.876 bp (`baseline_metric`), weekday-mean RMSE 4.884 bp (`model_metric`). Weekday means are slightly *worse* than the random walk (ratio 1.0017, `rmse_ratio_model_over_baseline`).\n- Conclusion: `no_detectable_effect` (`conclusion`).\n- Figure: `out/figure1.png` (weekday means with 95% CIs). Table: `out/table1.csv`. All weekday means are within ±0.22 bp of zero, and every 95% interval contains zero.\n\n## Uncertainty\nThe Monday-contrast CI is about ±0.37 bp wide, so any Monday effect larger than roughly 0.4 bp/day in either direction is ruled out at the 95% level. That is small next to the ~4.9 bp daily standard deviation of changes (`baseline_metric`). The ANOVA p-value of 0.90 means the weekday means vary less than chance alone would typically produce. The out-of-sample RMSE difference (0.008 bp) is tiny, and I did not run a formal forecast-comparison (Diebold–Mariano) test. Its sign still goes against weekday predictability.\n\n## Limitations and open issues\n- **Assumptions.** The ANOVA and Welch tests assume independent changes. Volatility clustering (heteroskedasticity over time) breaks the ANOVA's equal-variance assumption, and its i.i.d. assumption too. With a p-value this far from 0.05 the conclusion is unlikely to flip, but the stated p-value is not exact. The HC1 cross-check only addresses heteroskedasticity across the Monday indicator, not serial dependence.\n- **Holiday-spanning changes.** 214 changes cover two trading days and Monday changes cover a weekend. Following the rules, these are attributed to the weekday of t. This mixes horizon length with weekday. Normalising per calendar day was not done.\n- **Not checked.** Stability across subperiods or rate regimes (for example the zero-lower-bound years), effects around macro release days (many US releases fall on Fridays), other maturities, and a formal test of forecast-accuracy differences. A weekday effect that reverses between periods could cancel in the pooled ANOVA. The out-of-sample comparison partly addresses this, but it is not a direct test.\n- **What would invalidate the result.** Errors in the FRED series or in its revision vintage. A different holiday treatment. A day-of-week effect that is genuinely present but smaller than about 0.4 bp. This study cannot detect an effect that small, so \"no detectable effect\" is not proof of no effect.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday mean changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Assumes independent, equal-variance changes; volatility clustering makes the p-value approximate.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with a 95% Welch CI of [-0.319, 0.420] bp, which includes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Monday changes span weekends; effects below ~0.4 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Out of sample (2010-2019), weekday-specific means from 2000-2009 do not beat the zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal Diebold-Mariano test; the difference is tiny.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect in daily 10-year yield changes.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Absence of evidence within the precision of the data, not proof of zero effect.\"}\n]\nEOF\necho ok","description":"Write report and claims ledger"}}]}}54{"type":"api-request","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","id":"e05f8375-d2df-43a4-9d4e-7ed2e19ddd60","timestamp":"2026-10-30T05:18:53.476Z","version":"2.1.314","querySource":"sdk","shapeHash":"20a5fa83bb67ad22d0a9b7cd11f356934f458c07daeb4d792185b429ef02b693","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":"51fb32e0-c4a5-4a99-8618-2b05ed5e6622","keep":16,"tail":["bc6d95fcb3cac141a11a2ca86b8185bddba7095065cc42704c5b7d32a939a183","7c7a1d27fddfe0accd088359322a367016d1ab6fc35413b306b2df3e61257823"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7af5a46961ca1c88943fb9c30843be0b3c408ac91cabd7d89eecb7316f407575"}55{"parentUuid":"29ced077-5f42-49ff-99a5-9c6b4d1447df","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrf6qhbL6vJfPV43aUx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016Zenz3immYTsN8Zwpr39LP","name":"Bash","input":{"command":"R1=out/state/gate_receipts/20261030T051831.371237Z.json\ncat > out/state/phases.json <<EOF\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/missing/duplicates/gaps audited\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE computed on evaluation period: baseline_metric=4.876\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results/table/figure; no randomness\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\",\"HC1 cross-check agrees\",\"gate run at end of P4: $R1 (only P5-deliverable failures, as expected before P5)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md 6 sections\",\"claims.json 4 claims referencing existing keys\",\"final gate run: see log.md\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2-P4 passed (gate receipt $R1). 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All six phases are marked passed and the final gate run passed (0 of 39 checks failed). Re-running `out/analysis.py` from scratch reproduces `out/results.json` exactly.\n\n**Data:** the file has 5,217 rows. I dropped the 215 with no yield (market holidays), which leaves 5,001 daily changes: 2,500 in 2000–2009 for estimation and 2,501 in 2010–2019 for evaluation.\n\n| Question | Result |\n|---|---|\n| Do the five weekdays have equal mean changes? (standard one-way ANOVA test) | Not rejected: F = 0.263, p = 0.90 |\n| Monday contrast (Monday mean minus the other four days) | +0.050 bp, 95% interval [−0.319, 0.420] bp (Welch two-sample interval) |\n| Error of a \"no change\" forecast, 2010–2019 | 4.876 bp (RMSE) |\n| Error when forecasting each day by its weekday's 2000–2009 average, 2010–2019 | 4.884 bp (RMSE), slightly worse than predicting no change |\n\n- **Conclusion code:** `no_detectable_effect`.\n- **Size of what's ruled out:** any Monday effect larger than about 0.4 bp a day in either direction is excluded at the 95% level. That is small next to the roughly 4.9 bp typical daily move. Smaller effects can't be excluded.\n- **Cross-check:** a regression on a Monday indicator with heteroskedasticity-robust standard errors gave the same estimate and almost the same interval.\n- **Weekday means:** all five are within ±0.22 bp of zero, and every 95% interval contains zero.\n\n**Limitations:**\n- The ANOVA and the Monday interval assume independent changes with constant variance. Yield volatility comes in clusters, so the p-value is approximate, though at 0.90 the conclusion is unlikely to change.\n- Following the task rules, 214 changes that span a holiday, and every Monday change (which spans a weekend), are counted under the weekday they end on. This mixes the length of the interval with the weekday.\n- I did not run a formal test of whether the two forecast errors differ, check whether the pattern is stable across sub-periods, or look at macro release days.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `figure1.png`\n- `table1.csv`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, log, phase ledger and gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":293,"cache_read_input_tokens":20026,"output_tokens":919,"output_tokens_details":{"thinking_tokens":23},"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":293},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrfqeSCRCjGKvuVJLAv","requestRef":"1c3b9b75-d947-40cf-80fa-25fc9121cefd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e0cb03e2-0fe6-437f-864a-1158c0bec3a2","timestamp":"2026-10-30T05:19:13.480Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0265","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","version":"2.1.314","gitBranch":"HEAD"}65{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e0cb03e2-0fe6-437f-864a-1158c0bec3a2","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab"}66{"type":"atis-latch","atis":"","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab"}67{"type":"cost-state","sessionId":"aaac4563-0b45-441a-bed7-ece72f7d68ab","totalCostUSD":0.3275778,"totalAPIDuration":95201,"totalAPIDurationWithoutRetries":95193,"totalToolDuration":6264,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":101662,"startTime":1793337451826,"modelUsage":{"claude-opus-5-5":{"inputTokens":24,"outputTokens":10370,"thinkingTokens":370,"cacheReadInputTokens":130034,"cacheCreationInputTokens":18815,"cacheCreationEphemeral5mInputTokens":18815,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.3275778}},"hasUnknownModelCost":false}68