spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:20:05.555Z","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:20:05.561Z","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4"}4{"parentUuid":null,"isSidechain":false,"promptId":"39b6d381-dba9-4b04-9b12-5d1486a03713","type":"user","message":{"role":"user","content":"Begin"},"uuid":"255ab5c7-d553-4464-a01d-e8b5dce80d6f","timestamp":"2026-10-30T05:20:05.566Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","shapeHash":"6021bf1b8343b1d28ec57d1551ceb9091da08b61b46a3a9fd92444bf56c25382","timestamp":"2026-10-30T05:20:05.579Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0271\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. 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"}],"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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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4"}12{"parentUuid":"50edad3b-39c2-46b9-a911-94220dca9ac0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkS2kyR8tEC3nwb4Ks","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":4,"cache_creation_input_tokens":2209,"cache_read_input_tokens":1504,"output_tokens":129,"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":514,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrkRkPPUBTVFzTi9mwy","requestRef":"1aa0beca-b116-4816-8e1f-a0750f06599e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bab7db66-3778-4536-9cbf-465fb1d111a8","timestamp":"2026-10-30T05:20:06.819Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"bab7db66-3778-4536-9cbf-465fb1d111a8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkS2kyR8tEC3nwb4Ks","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018T2YmWVyYUdWhRMzS7cACJ","name":"Bash","input":{"command":"cat TASK.md; 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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n2019-12-27,1.88\n2019-12-30,1.90\n2019-12-31,1.92\n    5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv\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}","is_error":false}]},"uuid":"96da8068-ac7d-4af2-9b5b-33b3110b4106","timestamp":"2026-10-30T05:20:09.023Z","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\n2019-12-27,1.88\n2019-12-30,1.90\n2019-12-31,1.92\n    5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv\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}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5af6c286-b3ea-42e6-9c11-51b65399bdcc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","hash":"ee7ff7ebaa045ca2eb27b5a0e23136587d5fa75ede17ccee28c1a3afea2849ef","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01EqpWCMQ9wt8gShu4L52HXp","name":"Bash","input":{"command":"cat data/README.md; head -5 data/DGS10.csv; tail -3 data/DGS10.csv; wc -l data/DGS10.csv; shasum -a 256 data/DGS10.csv; cat bundle.json","description":"Inspect data and provenance"}}]}}22{"type":"api-request","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","id":"50efca5a-dc6a-4802-9861-8454043ab0dd","timestamp":"2026-10-30T05:20:09.026Z","version":"2.1.314","querySource":"sdk","shapeHash":"6021bf1b8343b1d28ec57d1551ceb9091da08b61b46a3a9fd92444bf56c25382","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":"e728d2d3-9e28-4e89-95bd-1bc303a48fdd","keep":4,"tail":["ee7ff7ebaa045ca2eb27b5a0e23136587d5fa75ede17ccee28c1a3afea2849ef","4a0af1c09416b55f88bbfb76f392d8f989e055e407398374e5bd7f3cede3b5d0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f21b898b794f6115fb364ca14d6c0c1683725bda905cd52a4ef0b7bcdda8596e"}23{"parentUuid":"96da8068-ac7d-4af2-9b5b-33b3110b4106","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkgcbapkwBHw8FDPxV","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":1155,"cache_read_input_tokens":6381,"output_tokens":1550,"output_tokens_details":{"thinking_tokens":30},"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":1155},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":405,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrkgMy3CAaw77mqBJhA","requestRef":"50efca5a-dc6a-4802-9861-8454043ab0dd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cd19bffc-60cd-483c-80fd-c592d7c44afb","timestamp":"2026-10-30T05:20:10.318Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"cd19bffc-60cd-483c-80fd-c592d7c44afb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkgcbapkwBHw8FDPxV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SUzSAbeFVCoUaXevc1t5az","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0) — F6-conf-01\n**Question:** Does the weekday carry predictive content for the daily change in the 10y Treasury CMT yield (DGS10), 2000–2019?\n**Estimands:**\n1. Joint test of equal weekday means of Δy_t (bp) — classical one-way equal-variance ANOVA F, F(4, n−5), all changes.\n2. Monday contrast = mean(Δy | Monday) − mean(Δy | Tue–Fri), all changes, with 95% CI. Choice: Welch two-sample t interval (unequal variances, Welch–Satterthwaite df).\n3. Out-of-sample: RMSE over evaluation period (changes dated >= 2010-01-01) of zero-change forecast (baseline) vs. weekday-specific means estimated on changes dated <= 2009-12-31.\nAssumptions: changes treated as independent draws within weekday (ANOVA additionally assumes equal variances, approx. normality — large n mitigates); stationarity of weekday means across periods for OOS.\n**Information rules:** drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available obs, attributed to weekday of t; first obs has no change. Weekday means for forecasting from estimation period only. Only DGS10.csv used.\n**Outputs:** out/results.json (n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (weekday,n,mean_bp,se_bp); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Conclusion rule:** ANOVA p>=0.05 → no_detectable_effect; if rejects, sign from Monday CI (positive/negative; if CI covers zero despite ANOVA rejection → no_detectable_effect for the Monday-contrast estimand, documented).\n**Invalidating conditions:** missing/corrupt file, hash mismatch, no data in either period, weekdays missing, use of evaluation-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\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written. Decision: Welch interval for Monday contrast.\" > log.md\ncd ../.. && ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/DGS10.csv',dtype=str,keep_default_na=False)\nprint(d.shape, d.columns.tolist())\nprint('empty',(d.DGS10.str.strip()=='').sum(), 'dots',(d.DGS10=='.').sum())\nv=pd.to_numeric(d.DGS10,errors='coerce'); print('nonnumeric nonempty',((v.isna())&(d.DGS10!='')).sum())\ndt=pd.to_datetime(d.observation_date); print(dt.min(),dt.max(),'dup',dt.duplicated().sum(),'sorted',dt.is_monotonic_increasing,'weekend',(dt.dt.weekday>4).sum())\nprint(v.describe()); dy=100*v.dropna().diff(); print(dy.describe()); print(dy.abs().nlargest(5))\nprint(d[d.DGS10==''].observation_date.tolist()[:20])\nimport numpy as np; dd=dt[v.notna()].diff().dt.days; print(dd.value_counts())\n\"","description":"Write contract/ledger and audit data"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1155,"cache_read_input_tokens":6381,"output_tokens":1550,"output_tokens_details":{"thinking_tokens":30},"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":1155},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrkgMy3CAaw77mqBJhA","requestRef":"50efca5a-dc6a-4802-9861-8454043ab0dd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"941fd4e8-1963-4e99-b958-29a5b7099563","timestamp":"2026-10-30T05:20:21.971Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"941fd4e8-1963-4e99-b958-29a5b7099563","isSidechain":false,"promptId":"39b6d381-dba9-4b04-9b12-5d1486a03713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SUzSAbeFVCoUaXevc1t5az","type":"tool_result","content":"(5217, 2) ['observation_date', 'DGS10']\nempty 215 dots 0\nnonnumeric nonempty 0\n2000-01-03 00:00:00 2019-12-31 00:00:00 dup 0 sorted True weekend 0\ncount    5002.000000\nmean        3.432167\nstd         1.239314\nmin         1.370000\n25%         2.340000\n50%         3.360000\n75%         4.410000\nmax         6.790000\nName: DGS10, dtype: float64\ncount    5001.000000\nmean       -0.093181\nstd         5.723645\nmin       -51.000000\n25%        -3.000000\n50%         0.000000\n75%         3.000000\nmax        25.000000\nName: DGS10, dtype: float64\n2402    51.0\n2318    28.0\n2270    27.0\n488     25.0\n2381    25.0\nName: DGS10, dtype: float64\n['2000-01-17', '2000-02-21', '2000-04-21', '2000-05-29', '2000-07-04', '2000-09-04', '2000-10-09', '2000-11-23', '2000-12-25', '2001-01-01', '2001-01-15', '2001-02-19', '2001-04-13', '2001-05-28', '2001-07-04', '2001-09-03', '2001-09-11', '2001-09-12', '2001-10-08', '2001-11-12']\nobservation_date\n1.0    3899\n3.0     889\n4.0     155\n2.0      58\nName: count, dtype: int64","is_error":false}]},"uuid":"3965e17a-b1f4-4dd9-a733-c0cecd2d81c6","timestamp":"2026-10-30T05:20:22.232Z","toolUseResult":{"stdout":"(5217, 2) ['observation_date', 'DGS10']\nempty 215 dots 0\nnonnumeric nonempty 0\n2000-01-03 00:00:00 2019-12-31 00:00:00 dup 0 sorted True weekend 0\ncount    5002.000000\nmean        3.432167\nstd         1.239314\nmin         1.370000\n25%         2.340000\n50%         3.360000\n75%         4.410000\nmax         6.790000\nName: DGS10, dtype: float64\ncount    5001.000000\nmean       -0.093181\nstd         5.723645\nmin       -51.000000\n25%        -3.000000\n50%         0.000000\n75%         3.000000\nmax        25.000000\nName: DGS10, dtype: float64\n2402    51.0\n2318    28.0\n2270    27.0\n488     25.0\n2381    25.0\nName: DGS10, dtype: float64\n['2000-01-17', '2000-02-21', '2000-04-21', '2000-05-29', '2000-07-04', '2000-09-04', '2000-10-09', '2000-11-23', '2000-12-25', '2001-01-01', '2001-01-15', '2001-02-19', '2001-04-13', '2001-05-28', '2001-07-04', '2001-09-03', '2001-09-11', '2001-09-12', '2001-10-08', '2001-11-12']\nobservation_date\n1.0    3899\n3.0     889\n4.0     155\n2.0      58\nName: count, dtype: int64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"941fd4e8-1963-4e99-b958-29a5b7099563","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","hash":"9725e6a0e3ed9dad861a0e0f435b0d46115159023f73b6d5e99a63375e0bdc0e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01SUzSAbeFVCoUaXevc1t5az","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0) — F6-conf-01\n**Question:** Does the weekday carry predictive content for the daily change in the 10y Treasury CMT yield (DGS10), 2000–2019?\n**Estimands:**\n1. Joint test of equal weekday means of Δy_t (bp) — classical one-way equal-variance ANOVA F, F(4, n−5), all changes.\n2. Monday contrast = mean(Δy | Monday) − mean(Δy | Tue–Fri), all changes, with 95% CI. Choice: Welch two-sample t interval (unequal variances, Welch–Satterthwaite df).\n3. Out-of-sample: RMSE over evaluation period (changes dated >= 2010-01-01) of zero-change forecast (baseline) vs. weekday-specific means estimated on changes dated <= 2009-12-31.\nAssumptions: changes treated as independent draws within weekday (ANOVA additionally assumes equal variances, approx. normality — large n mitigates); stationarity of weekday means across periods for OOS.\n**Information rules:** drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available obs, attributed to weekday of t; first obs has no change. Weekday means for forecasting from estimation period only. Only DGS10.csv used.\n**Outputs:** out/results.json (n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (weekday,n,mean_bp,se_bp); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Conclusion rule:** ANOVA p>=0.05 → no_detectable_effect; if rejects, sign from Monday CI (positive/negative; if CI covers zero despite ANOVA rejection → no_detectable_effect for the Monday-contrast estimand, documented).\n**Invalidating conditions:** missing/corrupt file, hash mismatch, no data in either period, weekdays missing, use of evaluation-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\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written. 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-p out && cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01 replication entry point. Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads data/DGS10.csv only; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, hashlib, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data/DGS10.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str, \"DGS10\": str}, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nn_avail = len(d)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"dy\"] = d[\"dy\"].round(6)  # remove float noise from 2-decimal percent inputs\nch = d.iloc[1:].copy()  # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.weekday\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\n\npd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10 (US market holidays)\", n_raw, n_avail, n_raw - n_avail,\n     \"no yield published; required by TASK information rules\"],\n    [\"2\", \"first available observation has no change\", n_avail, len(ch), n_avail - len(ch),\n     \"change undefined for first observation\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n    f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]\ntest = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = groups[0]; oth = 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_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df_w)\nlo, hi = est - tq * se, est + tq * se\n# robustness (reported in stdout only, not a required key): pooled t and HC1 regression\nimport statsmodels.api as sm\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nhc = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HC1\")\nprint(f\"[robustness] HC1 regression Monday coef {hc.params[1]:.4f}, CI {hc.conf_int()[1]}\")\n\n# (iii) out-of-sample\nwd_means_train = train.groupby(\"wd\")[\"dy\"].mean()\nfc = test[\"wd\"].map(wd_means_train)\nassert fc.notna().all()\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - fc.values) ** 2)))\nrmse_trainmean = float(np.sqrt(np.mean((test[\"dy\"].values - train[\"dy\"].mean()) ** 2)))\nprint(f\"[context] RMSE of constant estimation-period mean forecast: {rmse_trainmean:.4f}\")\nprint(f\"[context] estimation-period weekday means (bp): {wd_means_train.round(4).to_dict()}\")\n\n# table + figure\ntab = pd.DataFrame({\n    \"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],\n})\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, n - 1) * s for n, s in zip(tab.n, tab.se_bp)]\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(f\"10y UST daily change by weekday, 2000-2019 (n={len(ch)})\\n95% CIs; ANOVA F={F:.2f}, p={p:.3f}\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nif p < 0.05:\n    conclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n    conclusion = \"no_detectable_effect\"\n\ngaps = d[\"date\"].diff().dt.days.iloc[1:]\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"n_missing_rows\": n_missing,\n    \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n    \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n    \"effect_estimate\": float(est), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n    \"baseline_metric\": rmse_base, \"model_metric\": rmse_model,\n    \"conclusion\": conclusion,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays, incl. 2001-09-11/12)\",\n         \"handling\": \"dropped before differencing, per TASK rules\"},\n        {\"issue\": f\"changes spanning holidays: {int((gaps > 1).sum() - (ch['wd'] == 0).sum() + int(((gaps > 3) & (ch['wd'] == 0).values).sum()) if False else int(((gaps > 1) & (ch['wd'].values != 0)).sum() + ((gaps > 3) & (ch['wd'].values == 0)).sum()))} changes span more than the normal gap (post-holiday days), and Monday changes always span a weekend\",\n         \"handling\": \"kept and attributed to the weekday of t, as specified; no rescaling by elapsed days\"},\n        {\"issue\": \"large outliers, e.g. -51 bp on 2009-03-18 (FOMC QE1 announcement)\",\n         \"handling\": \"kept; verified as genuine market moves, not data errors\"},\n    ],\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(len(ch)),\n    \"random_seed\": None,\n    \"monday_contrast_method\": \"Welch two-sample t interval (unequal variances), Monday vs Tue-Fri, all changes\",\n    \"monday_contrast_se\": float(se), \"monday_contrast_welch_df\": float(df_w),\n    \"robust_hc1_ci_low\": float(hc.conf_int()[1][0]), \"robust_hc1_ci_high\": float(hc.conf_int()[1][1]),\n    \"rmse_ratio_model_over_baseline\": rmse_model / rmse_base,\n    \"rmse_constant_train_mean\": rmse_trainmean,\n    \"data_sha256\": sha,\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","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":341,"cache_read_input_tokens":9583,"output_tokens":3318,"output_tokens_details":{"thinking_tokens":37},"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":341},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrmrtke21R2WDCZkG4c","requestRef":"3f7adf28-2015-4569-b67b-b689046a5412","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"51504700-f440-4479-9921-146ed6c5d485","timestamp":"2026-10-30T05:20:50.863Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}35{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"51504700-f440-4479-9921-146ed6c5d485","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4"}36{"type":"atis-latch","atis":"","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4"}37{"parentUuid":"51504700-f440-4479-9921-146ed6c5d485","isSidechain":false,"promptId":"39b6d381-dba9-4b04-9b12-5d1486a03713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015CgZ4ofMw7PzRM2mstvsAD","type":"tool_result","content":"[robustness] HC1 regression Monday coef 0.0502, CI [-0.31895707  0.41929727]\n[context] RMSE of constant estimation-period mean forecast: 4.8752\n[context] estimation-period weekday means (bp): {0: -0.0543, 1: -0.4055, 2: -0.2054, 3: 0.0138, 4: 0.1168}\n{\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 (US market holidays),5217,5002,215,no yield published; required by TASK information rules\n2,first available observation has no change,5002,5001,1,change undefined for first observation","is_error":false}]},"uuid":"bb040e13-59c6-4770-9d06-33e8e5a57944","timestamp":"2026-10-30T05:20:51.963Z","toolUseResult":{"stdout":"[robustness] HC1 regression Monday coef 0.0502, CI [-0.31895707  0.41929727]\n[context] RMSE of constant estimation-period mean forecast: 4.8752\n[context] estimation-period weekday means (bp): {0: -0.0543, 1: -0.4055, 2: -0.2054, 3: 0.0138, 4: 0.1168}\n{\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 (US market holidays),5217,5002,215,no yield published; required by TASK information rules\n2,first available observation has no change,5002,5001,1,change undefined for first observation","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"51504700-f440-4479-9921-146ed6c5d485","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","hash":"a99bde86b3470bdf076ff834ca1e010e1fd5bcc47cfa18038925413ce3ac0ac1","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_015CgZ4ofMw7PzRM2mstvsAD","name":"Bash","input":{"command":"mkdir -p out && cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01 replication entry point. Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads data/DGS10.csv only; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, hashlib, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data/DGS10.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str, \"DGS10\": str}, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nn_avail = len(d)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"dy\"] = d[\"dy\"].round(6)  # remove float noise from 2-decimal percent inputs\nch = d.iloc[1:].copy()  # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.weekday\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\n\npd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10 (US market holidays)\", n_raw, n_avail, n_raw - n_avail,\n     \"no yield published; required by TASK information rules\"],\n    [\"2\", \"first available observation has no change\", n_avail, len(ch), n_avail - len(ch),\n     \"change undefined for first observation\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n    f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]\ntest = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = groups[0]; oth = 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_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df_w)\nlo, hi = est - tq * se, est + tq * se\n# robustness (reported in stdout only, not a required key): pooled t and HC1 regression\nimport statsmodels.api as sm\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nhc = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HC1\")\nprint(f\"[robustness] HC1 regression Monday coef {hc.params[1]:.4f}, CI {hc.conf_int()[1]}\")\n\n# (iii) out-of-sample\nwd_means_train = train.groupby(\"wd\")[\"dy\"].mean()\nfc = test[\"wd\"].map(wd_means_train)\nassert fc.notna().all()\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - fc.values) ** 2)))\nrmse_trainmean = float(np.sqrt(np.mean((test[\"dy\"].values - train[\"dy\"].mean()) ** 2)))\nprint(f\"[context] RMSE of constant estimation-period mean forecast: {rmse_trainmean:.4f}\")\nprint(f\"[context] estimation-period weekday means (bp): {wd_means_train.round(4).to_dict()}\")\n\n# table + figure\ntab = pd.DataFrame({\n    \"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],\n})\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, n - 1) * s for n, s in zip(tab.n, tab.se_bp)]\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(f\"10y UST daily change by weekday, 2000-2019 (n={len(ch)})\\n95% CIs; ANOVA F={F:.2f}, p={p:.3f}\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nif p < 0.05:\n    conclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n    conclusion = \"no_detectable_effect\"\n\ngaps = d[\"date\"].diff().dt.days.iloc[1:]\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"n_missing_rows\": n_missing,\n    \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n    \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n    \"effect_estimate\": float(est), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n    \"baseline_metric\": rmse_base, \"model_metric\": rmse_model,\n    \"conclusion\": conclusion,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays, incl. 2001-09-11/12)\",\n         \"handling\": \"dropped before differencing, per TASK rules\"},\n        {\"issue\": f\"changes spanning holidays: {int((gaps > 1).sum() - (ch['wd'] == 0).sum() + int(((gaps > 3) & (ch['wd'] == 0).values).sum()) if False else int(((gaps > 1) & (ch['wd'].values != 0)).sum() + ((gaps > 3) & (ch['wd'].values == 0)).sum()))} changes span more than the normal gap (post-holiday days), and Monday changes always span a weekend\",\n         \"handling\": \"kept and attributed to the weekday of t, as specified; no rescaling by elapsed days\"},\n        {\"issue\": \"large outliers, e.g. -51 bp on 2009-03-18 (FOMC QE1 announcement)\",\n         \"handling\": \"kept; verified as genuine market moves, not data errors\"},\n    ],\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(len(ch)),\n    \"random_seed\": None,\n    \"monday_contrast_method\": \"Welch two-sample t interval (unequal variances), Monday vs Tue-Fri, all changes\",\n    \"monday_contrast_se\": float(se), \"monday_contrast_welch_df\": float(df_w),\n    \"robust_hc1_ci_low\": float(hc.conf_int()[1][0]), \"robust_hc1_ci_high\": float(hc.conf_int()[1][1]),\n    \"rmse_ratio_model_over_baseline\": rmse_model / rmse_base,\n    \"rmse_constant_train_mean\": rmse_trainmean,\n    \"data_sha256\": sha,\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","description":"Write and run analysis 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","media_type":"image/png"}}]}]},"uuid":"c06c7215-799a-4896-877f-ac08f7d76ff3","timestamp":"2026-10-30T05:21:08.955Z","toolUseResult":{"type":"image","file":{"base64":"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out && cat > state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README and bundle.json) | 5217 | 2 |\n| data/README.md | provenance only (not analysed) | – | – |\n\n- Schema: `observation_date` (YYYY-MM-DD), `DGS10` (percent, 2 decimals). Range 2000-01-03 … 2019-12-31; sorted; 0 duplicate dates; 0 weekend dates.\n- Missing: 215 empty DGS10 fields (US market holidays, incl. 2001-09-11 and 2001-09-12). No '.' markers, no non-numeric values.\n- Levels: 1.37 … 6.79 %, mean 3.43 %. 5002 available observations → 5001 changes (bp): mean −0.093, sd 5.72, min −51 (2009-03-18, FOMC QE1 announcement), max +25.\n- Calendar gaps between available obs: 1 day 3899, 2 days 58, 3 days 889, 4 days 155. 214 changes span a holiday.\n- Extreme changes (|Δy| ≥ 25 bp): 2001-11-15, 2008-09-15, 2008-11-20, 2009-02-17, 2009-03-18 — all crisis/policy dates, genuine; kept.\n- Availability rules: no input violates them; the only rule-relevant step is the split at 2009-12-31/2010-01-01 enforced in code (asserted). Exclusions in out/exclusions.csv: 5217 − 215 − 1 = 5001 analysed.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run (deleted results.json, table1.csv, figure1.png, exclusions.csv; re-ran analysis.py): results.json byte-identical, table1.csv identical — PASS.\n2. ANOVA recomputed by hand (SSB/SSW): F = 0.263466, p = 0.9015 — matches scipy — PASS.\n3. Counts: sum(table n) = n_analysed = n_train + n_test = 5217 − 215 − 1 = 5001 — PASS. Exclusion ledger adds up — PASS.\n4. Monday contrast recomputed from table1 weighted means = 0.050170 = effect_estimate — PASS.\n5. CI contains estimate; all SEs > 0 — PASS. HC1 regression CI ([-0.319, 0.419]) essentially equal to Welch CI — PASS (robustness).\n6. Information rule: train = dates ≤ 2009-12-31, test ≥ 2010-01-01 (asserted, disjoint, exhaustive); forecast means from train only — PASS.\n7. Magnitudes: weekday means ~±0.2 bp vs daily sd ~5.7 bp; model RMSE 4.884 vs baseline 4.876 bp — plausible — PASS.\n8. Figure inspected visually: five points, 95% bars, all overlapping zero — PASS.\nEOF\ncat > 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) in the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? Three estimands: (i) joint equality of the five weekday means; (ii) the Monday contrast (Monday mean minus the Tue–Fri mean) with a 95% interval; (iii) out-of-sample RMSE of weekday-specific means versus a zero-change forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 rows (`n_input_rows`), with SHA-256 matching the provenance file. 215 rows have an empty yield field (US market holidays; `n_missing_rows`) and were dropped before differencing. The first available observation has no change, which leaves 5001 daily changes (`n_analysed`). These split into 2500 estimation-period changes dated on or before 2009-12-31 (`n_train`) and 2501 evaluation-period changes dated on or after 2010-01-01 (`n_test`). The steps are in `out/exclusions.csv`. Data issues are listed in `data_issues`: 214 changes span a holiday, and there are large but genuine crisis-period moves (−51 bp on 2009-03-18). All of these were kept as specified. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Joint test: classical one-way equal-variance ANOVA F-test of equal weekday means over all 5001 changes, F(4, n−5).\n- Monday contrast: difference in means, Monday versus Tuesday–Friday, all changes. The 95% interval is a Welch two-sample t interval (unequal variances, Welch–Satterthwaite df; `monday_contrast_method`). As a robustness check, an OLS regression on a Monday dummy with HC1 standard errors was also run (`robust_hc1_ci_low`, `robust_hc1_ci_high`).\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change of its weekday (`model_metric`). The comparison is the zero-change forecast (`baseline_metric`). The metric is RMSE in bp.\n- Conclusion rule: if the ANOVA does not reject at 5%, the code is `no_detectable_effect`. Otherwise the sign comes from the Monday interval.\n- The analysis uses no randomness (`random_seed` = null). Everything is regenerated by `out/analysis.py`.\n\n## Results\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.902 (`anova_p_value`). Equal weekday means are not rejected.\n- Weekday means (`out/table1.csv`, Figure 1) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Every weekday's 95% interval includes zero.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, +0.420] bp (`effect_ci_low`, `effect_ci_high`). The HC1 regression interval is practically identical: [−0.319, +0.419] (`robust_hc1_ci_low`, `robust_hc1_ci_high`).\n- Out-of-sample RMSE: weekday means 4.884 bp (`model_metric`) vs zero change 4.876 bp (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday means are slightly *worse* than the random walk.\n- Conclusion: `no_detectable_effect` (`conclusion`).\n\n## Uncertainty\nThe Monday contrast's standard error is 0.188 bp (`monday_contrast_se`, Welch df ≈ `monday_contrast_welch_df`). The interval excludes Monday effects larger than about 0.42 bp in absolute value. That is small next to the daily standard deviation of about 5.7 bp (from the data audit), so the null result is reasonably precise rather than simply underpowered. The RMSE comparison is a single point estimate on one split, with no test of predictive accuracy (e.g. Diebold–Mariano). Because the gap is under 0.2%, it should be read as \"no improvement\", not as evidence that weekday means are harmful.\n\n## Limitations and open issues\n- **What would invalidate the result:**\n  - Strong serial dependence or volatility clustering. Crisis periods such as 2008–09 show both, and the classical ANOVA's independence and equal-variance assumptions would then misstate the p-value. The Welch and HC1 intervals handle unequal variances but not autocorrelation or clustering.\n  - A weekday effect that reverses sign across sub-periods could cancel out in the pooled 20-year means. Only the 2000–09 vs 2010–19 split was examined, and only through the forecasting exercise.\n- **Not checked:**\n  - Sub-period or regime-specific weekday effects.\n  - Weekday effects in volatility rather than in the mean.\n  - Effects tied to FOMC or data-release calendars, which cluster on particular weekdays.\n  - Robustness to winsorising outliers.\n  - HAC standard errors.\n  - Formal forecast-comparison tests.\n  - Changes spanning holidays were attributed to the weekday of t without adjusting for elapsed days, as specified. Monday changes always cover a weekend.\n- **Main threats:**\n  - Mismeasurement from using the constant-maturity yield, an interpolated H.15 series, instead of traded prices.\n  - Small-sample influence of a few large crisis-day moves on the weekday means.\n  - Multiple testing, if one went looking for any weekday contrast. Here only the pre-specified Monday contrast was tested.\n\nNone of these concerns is likely to turn a p-value of 0.90 into a rejection. They do, however, limit how far the result generalises beyond 2000–2019 and beyond the mean.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Classical ANOVA assumes independence and equal variances; volatility clustering not modelled.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, indistinguishable from zero; the HC1 regression CI agrees.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"robust_hc1_ci_low\",\"robust_hc1_ci_high\"],\"status\":\"supported\",\"caveat\":\"No HAC/autocorrelation correction.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample RMSE over the zero-change forecast in 2010-2019 (4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single split; no formal forecast-accuracy test.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Pertains to means over 2000-2019 pooled; sub-period and volatility effects not examined.\"}\n]\nEOF\ncat > 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/bundle\",\"schema, ranges, missing (215), duplicates (0), weekends (0) recorded\",\"outliers inspected and kept\",\"exclusions.csv: 5217-215-1=5001\"],\"notes\":\"No availability-rule violations in inputs.\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE computed on evaluation period = baseline_metric 4.8757\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, Agg backend, no randomness\",\"writes results.json, table1.csv, figure1.png, exclusions.csv\"],\"notes\":\"Welch interval chosen for Monday contrast.\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json and table1.csv\",\"manual ANOVA matches\",\"counts add up\",\"CI contains estimate, SE>0\",\"train/test split asserted\",\"figure inspected\"],\"notes\":\"See checks.md\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md six sections with results keys cited\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}}\nEOF\ncat >> state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit done, hash verified, holidays dropped, outliers kept.\n- 2026-10-30 P2 passed: zero-change RMSE baseline 4.876 bp.\n- 2026-10-30 P3 passed: analysis.py written (Welch CI; HC1 robustness).\n- 2026-10-30 P4 passed: clean rerun identical; consistency checks in checks.md.\n- 2026-10-30 P5 passed: report.md and claims.json written. Conclusion no_detectable_effect.\nEOF\ncd .. && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint([k for x in c for k in x['results_keys'] if k not in r]); print(r['monday_contrast_welch_df'])\"; grep '^## ' out/report.md","description":"Write audit, checks, report, claims, ledger; validate keys"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":604,"cache_read_input_tokens":15656,"output_tokens":4582,"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":604},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrq6c6uLHzmuBQ4rycw","requestRef":"200006d6-25e5-492c-88b3-7376f88a35b8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4c99ebd6-505e-415e-9233-4ebc165e6d15","timestamp":"2026-10-30T05:21:46.984Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"4c99ebd6-505e-415e-9233-4ebc165e6d15","isSidechain":false,"promptId":"39b6d381-dba9-4b04-9b12-5d1486a03713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hbm3aSDZ1DdpzCZP2c1tVX","type":"tool_result","content":"[]\n1548.9581965107577\n## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues","is_error":false}]},"uuid":"4e349c4c-5de9-45cc-a23d-7a7746383596","timestamp":"2026-10-30T05:21:47.028Z","toolUseResult":{"stdout":"[]\n1548.9581965107577\n## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":true},"sandboxed":false,"sourceToolAssistantUUID":"4c99ebd6-505e-415e-9233-4ebc165e6d15","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0271","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"c9b0a644-98b7-45e0-9275-274a45973dd4","hash":"7dd3b5ecceb83b120901ea372d667003d4d2a3737eed4e89e94c925d3589bdc3","message":{"role":"assistant","content":[{"type":"text","text":"Now the state files, report, and claims."},{"type":"tool_use","id":"toolu_01Hbm3aSDZ1DdpzCZP2c1tVX","name":"Bash","input":{"command":"cd out && cat > state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README and bundle.json) | 5217 | 2 |\n| data/README.md | provenance only (not analysed) | – | – |\n\n- Schema: `observation_date` (YYYY-MM-DD), `DGS10` (percent, 2 decimals). Range 2000-01-03 … 2019-12-31; sorted; 0 duplicate dates; 0 weekend dates.\n- Missing: 215 empty DGS10 fields (US market holidays, incl. 2001-09-11 and 2001-09-12). No '.' markers, no non-numeric values.\n- Levels: 1.37 … 6.79 %, mean 3.43 %. 5002 available observations → 5001 changes (bp): mean −0.093, sd 5.72, min −51 (2009-03-18, FOMC QE1 announcement), max +25.\n- Calendar gaps between available obs: 1 day 3899, 2 days 58, 3 days 889, 4 days 155. 214 changes span a holiday.\n- Extreme changes (|Δy| ≥ 25 bp): 2001-11-15, 2008-09-15, 2008-11-20, 2009-02-17, 2009-03-18 — all crisis/policy dates, genuine; kept.\n- Availability rules: no input violates them; the only rule-relevant step is the split at 2009-12-31/2010-01-01 enforced in code (asserted). Exclusions in out/exclusions.csv: 5217 − 215 − 1 = 5001 analysed.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run (deleted results.json, table1.csv, figure1.png, exclusions.csv; re-ran analysis.py): results.json byte-identical, table1.csv identical — PASS.\n2. ANOVA recomputed by hand (SSB/SSW): F = 0.263466, p = 0.9015 — matches scipy — PASS.\n3. Counts: sum(table n) = n_analysed = n_train + n_test = 5217 − 215 − 1 = 5001 — PASS. Exclusion ledger adds up — PASS.\n4. Monday contrast recomputed from table1 weighted means = 0.050170 = effect_estimate — PASS.\n5. CI contains estimate; all SEs > 0 — PASS. HC1 regression CI ([-0.319, 0.419]) essentially equal to Welch CI — PASS (robustness).\n6. Information rule: train = dates ≤ 2009-12-31, test ≥ 2010-01-01 (asserted, disjoint, exhaustive); forecast means from train only — PASS.\n7. Magnitudes: weekday means ~±0.2 bp vs daily sd ~5.7 bp; model RMSE 4.884 vs baseline 4.876 bp — plausible — PASS.\n8. Figure inspected visually: five points, 95% bars, all overlapping zero — PASS.\nEOF\ncat > 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) in the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? Three estimands: (i) joint equality of the five weekday means; (ii) the Monday contrast (Monday mean minus the Tue–Fri mean) with a 95% interval; (iii) out-of-sample RMSE of weekday-specific means versus a zero-change forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 rows (`n_input_rows`), with SHA-256 matching the provenance file. 215 rows have an empty yield field (US market holidays; `n_missing_rows`) and were dropped before differencing. The first available observation has no change, which leaves 5001 daily changes (`n_analysed`). These split into 2500 estimation-period changes dated on or before 2009-12-31 (`n_train`) and 2501 evaluation-period changes dated on or after 2010-01-01 (`n_test`). The steps are in `out/exclusions.csv`. Data issues are listed in `data_issues`: 214 changes span a holiday, and there are large but genuine crisis-period moves (−51 bp on 2009-03-18). All of these were kept as specified. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Joint test: classical one-way equal-variance ANOVA F-test of equal weekday means over all 5001 changes, F(4, n−5).\n- Monday contrast: difference in means, Monday versus Tuesday–Friday, all changes. The 95% interval is a Welch two-sample t interval (unequal variances, Welch–Satterthwaite df; `monday_contrast_method`). As a robustness check, an OLS regression on a Monday dummy with HC1 standard errors was also run (`robust_hc1_ci_low`, `robust_hc1_ci_high`).\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change of its weekday (`model_metric`). The comparison is the zero-change forecast (`baseline_metric`). The metric is RMSE in bp.\n- Conclusion rule: if the ANOVA does not reject at 5%, the code is `no_detectable_effect`. Otherwise the sign comes from the Monday interval.\n- The analysis uses no randomness (`random_seed` = null). Everything is regenerated by `out/analysis.py`.\n\n## Results\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.902 (`anova_p_value`). Equal weekday means are not rejected.\n- Weekday means (`out/table1.csv`, Figure 1) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Every weekday's 95% interval includes zero.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, +0.420] bp (`effect_ci_low`, `effect_ci_high`). The HC1 regression interval is practically identical: [−0.319, +0.419] (`robust_hc1_ci_low`, `robust_hc1_ci_high`).\n- Out-of-sample RMSE: weekday means 4.884 bp (`model_metric`) vs zero change 4.876 bp (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday means are slightly *worse* than the random walk.\n- Conclusion: `no_detectable_effect` (`conclusion`).\n\n## Uncertainty\nThe Monday contrast's standard error is 0.188 bp (`monday_contrast_se`, Welch df ≈ `monday_contrast_welch_df`). The interval excludes Monday effects larger than about 0.42 bp in absolute value. That is small next to the daily standard deviation of about 5.7 bp (from the data audit), so the null result is reasonably precise rather than simply underpowered. The RMSE comparison is a single point estimate on one split, with no test of predictive accuracy (e.g. Diebold–Mariano). Because the gap is under 0.2%, it should be read as \"no improvement\", not as evidence that weekday means are harmful.\n\n## Limitations and open issues\n- **What would invalidate the result:**\n  - Strong serial dependence or volatility clustering. Crisis periods such as 2008–09 show both, and the classical ANOVA's independence and equal-variance assumptions would then misstate the p-value. The Welch and HC1 intervals handle unequal variances but not autocorrelation or clustering.\n  - A weekday effect that reverses sign across sub-periods could cancel out in the pooled 20-year means. Only the 2000–09 vs 2010–19 split was examined, and only through the forecasting exercise.\n- **Not checked:**\n  - Sub-period or regime-specific weekday effects.\n  - Weekday effects in volatility rather than in the mean.\n  - Effects tied to FOMC or data-release calendars, which cluster on particular weekdays.\n  - Robustness to winsorising outliers.\n  - HAC standard errors.\n  - Formal forecast-comparison tests.\n  - Changes spanning holidays were attributed to the weekday of t without adjusting for elapsed days, as specified. Monday changes always cover a weekend.\n- **Main threats:**\n  - Mismeasurement from using the constant-maturity yield, an interpolated H.15 series, instead of traded prices.\n  - Small-sample influence of a few large crisis-day moves on the weekday means.\n  - Multiple testing, if one went looking for any weekday contrast. Here only the pre-specified Monday contrast was tested.\n\nNone of these concerns is likely to turn a p-value of 0.90 into a rejection. They do, however, limit how far the result generalises beyond 2000–2019 and beyond the mean.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Classical ANOVA assumes independence and equal variances; volatility clustering not modelled.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, indistinguishable from zero; the HC1 regression CI agrees.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"robust_hc1_ci_low\",\"robust_hc1_ci_high\"],\"status\":\"supported\",\"caveat\":\"No HAC/autocorrelation correction.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample RMSE over the zero-change forecast in 2010-2019 (4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single split; no formal forecast-accuracy test.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Pertains to means over 2000-2019 pooled; sub-period and volatility effects not examined.\"}\n]\nEOF\ncat > 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/bundle\",\"schema, ranges, missing (215), duplicates (0), weekends (0) recorded\",\"outliers inspected and kept\",\"exclusions.csv: 5217-215-1=5001\"],\"notes\":\"No availability-rule violations in inputs.\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE computed on evaluation period = baseline_metric 4.8757\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, Agg backend, no randomness\",\"writes results.json, table1.csv, figure1.png, exclusions.csv\"],\"notes\":\"Welch interval chosen for Monday contrast.\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json and table1.csv\",\"manual ANOVA matches\",\"counts add up\",\"CI contains estimate, SE>0\",\"train/test split asserted\",\"figure inspected\"],\"notes\":\"See checks.md\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md six sections with results keys cited\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}}\nEOF\ncat >> state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit done, hash verified, holidays dropped, outliers kept.\n- 2026-10-30 P2 passed: zero-change RMSE baseline 4.876 bp.\n- 2026-10-30 P3 passed: analysis.py written (Welch CI; HC1 robustness).\n- 2026-10-30 P4 passed: clean rerun identical; consistency checks in checks.md.\n- 2026-10-30 P5 passed: report.md and claims.json written. Conclusion no_detectable_effect.\nEOF\ncd .. && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint([k for x in c for k in x['results_keys'] if k not in r]); print(r['monday_contrast_welch_df'])\"; grep '^## ' out/report.md","description":"Write audit, checks, report, claims, ledger; validate 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found no day-of-week effect in daily changes of the 10-year Treasury yield over 2000–2019. A clean re-run of `out/analysis.py` reproduced `results.json` and `table1.csv` exactly, and all phases P0–P5 passed their recorded checks.\n\n**Results:**\n- **Equal weekday means (all 5,001 daily changes):** the standard one-way test (ANOVA) gives F = 0.263 and p = 0.902, so equal means are not rejected.\n- **Weekday averages:** these range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday), and every weekday's 95% interval includes zero.\n- **Monday vs. the other four days:** Mondays average 0.050 bp higher, with a 95% interval of −0.319 to +0.420 bp. I used a Welch interval (it allows the two groups to have different variances); a regression with robust standard errors gives almost the same interval. This rules out Monday effects larger than about 0.42 bp, which is small next to the typical daily move of about 5.7 bp. So this is a precise null, not a test too weak to see anything.\n- **Forecasting 2010–2019:** predicting each change from its weekday's 2000–2009 average gives an error (RMSE) of 4.884 bp. Simply predicting no change gives 4.876 bp, so the weekday averages are very slightly worse.\n- **Conclusion code:** `no_detectable_effect`.\n\n**Data:** the file has 5,217 rows. I dropped the 215 with no yield (market holidays, including 11–12 September 2001), and the first remaining day has no change, which leaves 5,001 changes: 2,500 in 2000–2009 and 2,501 in 2010–2019. I kept genuine large moves such as −51 bp on 2009-03-18, the Fed's first quantitative-easing announcement. Changes after a holiday are counted on the weekday they land on, as the task specifies. 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