spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:18:00.220Z","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:18:00.226Z","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99"}4{"parentUuid":null,"isSidechain":false,"promptId":"a4725974-bf1c-4e74-8b21-54588870ae55","type":"user","message":{"role":"user","content":"Begin"},"uuid":"884cb224-1b8d-42ce-96d2-ce7f9415886a","timestamp":"2026-10-30T05:18:00.232Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","shapeHash":"ee1d5fa0eb3853afa67f40cbbe99707f143ef28607552948c33b918d64a34cfd","timestamp":"2026-10-30T05:18:00.245Z","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/RC0267\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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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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/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99"}12{"parentUuid":"597bfab4-9693-4ba3-8b36-53bc928ae035","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrbC3suuJgHepLuBcfB","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":2603,"cache_read_input_tokens":1504,"output_tokens":117,"output_tokens_details":{"thinking_tokens":15},"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":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":468,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrbBrDzj85LqsdMuC76","requestRef":"13856ee2-3eaf-4ff6-9189-9e953d8537c7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ad660b91-d630-4a54-8f2f-98a94d13e651","timestamp":"2026-10-30T05:18:01.620Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"ad660b91-d630-4a54-8f2f-98a94d13e651","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrbC3suuJgHepLuBcfB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01X5Thb8yXWVHpkWMX7ZTAAS","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`\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\n    5218 data/DGS10.csv","is_error":false}]},"uuid":"bf0c3a41-60f8-4faa-98eb-d0e3f73c5a14","timestamp":"2026-10-30T05:18:03.786Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-01\n\n## `DGS10.csv` (5217 rows)\nObserved data: *Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis* (FRED series `DGS10`, daily, percent, not seasonally adjusted). Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\n{\n  \"task_id\": \"F6-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"n_missing_rows\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_train\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_test\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"anova_f_stat\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"anova_p_value\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"anova_f_stat\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F6-conf-01\",\n  \"family\": \"F6\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n  \"files\": {\n    \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n    \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n    \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n    \"gate_spec.json\": \"d53691940951460bcc45cb08c958917841ae1bfbc8d242d37ded795a3e998eb7\"\n  }\n}observation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n2019-12-27,1.88\n2019-12-30,1.90\n2019-12-31,1.92\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv\n32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a  data/README.md\n    5218 data/DGS10.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"599b0090-c57a-47a0-aba6-32714806587f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","hash":"ae7ad91a796987626337ceaeae888c3c0b327bd2a5ae8f8ea6720a6bc72ddc3f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0188mcXtZZZDyfhKpgSGnBSS","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json; cat bundle.json; head -5 data/DGS10.csv; tail -3 data/DGS10.csv; shasum -a 256 data/*; wc -l data/DGS10.csv","description":"Inspect provenance, spec, data head"}}]}}22{"type":"api-request","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","id":"45cd4c40-d91c-4436-8c03-37c788c322ff","timestamp":"2026-10-30T05:18:03.790Z","version":"2.1.314","querySource":"sdk","shapeHash":"ee1d5fa0eb3853afa67f40cbbe99707f143ef28607552948c33b918d64a34cfd","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":"9739274a-c797-4db8-ad81-4a0db8421558","keep":4,"tail":["ae7ad91a796987626337ceaeae888c3c0b327bd2a5ae8f8ea6720a6bc72ddc3f","87d1c149664b9553f69ba5829d0b8695a0228e2fbcb38ae86e6814c87eeb2929"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f422fcfe6d1bd7517c664150ed93b7d7ee82ff700c7d59a4ed41199662f095f1"}23{"parentUuid":"bf0c3a41-60f8-4faa-98eb-d0e3f73c5a14","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrbT7zRqEJW5zyLMSjB","type":"message","role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2128,"cache_read_input_tokens":6933,"output_tokens":1123,"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":2128},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrbSu6nkbitDPgAUKGW","requestRef":"45cd4c40-d91c-4436-8c03-37c788c322ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9024e93a-70a6-4fce-bf41-e07d0788fa03","timestamp":"2026-10-30T05:18:05.634Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"9024e93a-70a6-4fce-bf41-e07d0788fa03","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrbT7zRqEJW5zyLMSjB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WRiPSLqCiyKipUysZC8CFh","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday carry predictive content for the one-day change in the 10-year Treasury CMT yield (DGS10), 2000–2019?\n\n**Estimands.**\n1. Joint test of equal weekday conditional means of Δy_t (bp), classical one-way equal-variance ANOVA F(4, n−5) over all changes.\n2. Monday contrast: mean(Δy | Monday) − mean(Δy | Tue–Fri), all changes, 95% CI (chosen: Welch two-sample interval).\n3. Out-of-sample: RMSE over the evaluation period (changes dated ≥ 2010-01-01) of (a) zero-change forecast, (b) estimation-period (≤ 2009-12-31) weekday means.\nAssumptions: changes are approximately independent with finite variance; ANOVA additionally assumes equal variance & approximate normality (robust in large n); weekday means are stationary across periods for (3) to be meaningful.\n\n**Information rules.** Drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available observations, dated t, weekday of t. First available obs has no change. Weekday means for forecasting from estimation period only. Only data/DGS10.csv used.\n\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; out/exclusions.csv; out/state/*.\nConclusion code: ANOVA at 5%; if not rejected → no_detectable_effect; if rejected, sign from Monday CI (positive/negative_effect; if CI covers zero while ANOVA rejects, I will report no_detectable_effect for the Monday-sign and document it).\n\n**Invalidity.** Missing/unparseable file, non-weekday dates, duplicate dates, too few observations per weekday, or using 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.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2128,"cache_read_input_tokens":6933,"output_tokens":1123,"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":2128},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrbSu6nkbitDPgAUKGW","requestRef":"45cd4c40-d91c-4436-8c03-37c788c322ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"022214e5-1a75-45a2-adb8-dc672703a297","timestamp":"2026-10-30T05:18:14.199Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"022214e5-1a75-45a2-adb8-dc672703a297","isSidechain":false,"promptId":"a4725974-bf1c-4e74-8b21-54588870ae55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WRiPSLqCiyKipUysZC8CFh","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"a75d3ae3-c5e0-4740-81f0-1e11cb3a9939","timestamp":"2026-10-30T05:18:14.226Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"022214e5-1a75-45a2-adb8-dc672703a297","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267/out/state","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","hash":"24f56cd4cb0e16f3b4cf16a2571dfc6401d6f936a556389f64857b3230551c73","message":{"role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."},{"type":"tool_use","id":"toolu_01WRiPSLqCiyKipUysZC8CFh","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does the weekday carry predictive content for the one-day change in the 10-year Treasury CMT yield (DGS10), 2000–2019?\n\n**Estimands.**\n1. Joint test of equal weekday conditional means of Δy_t (bp), classical one-way equal-variance ANOVA F(4, n−5) over all changes.\n2. Monday contrast: mean(Δy | Monday) − mean(Δy | Tue–Fri), all changes, 95% CI (chosen: Welch two-sample interval).\n3. Out-of-sample: RMSE over the evaluation period (changes dated ≥ 2010-01-01) of (a) zero-change forecast, (b) estimation-period (≤ 2009-12-31) weekday means.\nAssumptions: changes are approximately independent with finite variance; ANOVA additionally assumes equal variance & approximate normality (robust in large n); weekday means are stationary across periods for (3) to be meaningful.\n\n**Information rules.** Drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available observations, dated t, weekday of t. First available obs has no change. Weekday means for forecasting from estimation period only. Only data/DGS10.csv used.\n\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; out/exclusions.csv; out/state/*.\nConclusion code: ANOVA at 5%; if not rejected → no_detectable_effect; if rejected, sign from Monday CI (positive/negative_effect; if CI covers zero while ANOVA rejects, I will report no_detectable_effect for the Monday-sign and document it).\n\n**Invalidity.** Missing/unparseable file, non-weekday dates, duplicate dates, too few observations per weekday, or using 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.\" > log.md","description":"Write contract, phase ledger, 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code 1\nTraceback (most recent call last):\n  File \"<string>\", line 3, in <module>\n    d=pd.read_csv('data/DGS10.csv',dtype=str,keep_default_na=False)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/DGS10.csv'","is_error":true,"tool_use_id":"toolu_01JTtLjD1E3KNN4c6MLWNq2Z"}]},"uuid":"4bb58dfc-188e-4ee9-8093-30f097dfa2ba","timestamp":"2026-10-30T05:18:18.990Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"<string>\", line 3, in <module>\n    d=pd.read_csv('data/DGS10.csv',dtype=str,keep_default_na=False)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 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2) ['observation_date', 'DGS10']\nbad dates 0 dups 0 monotone True 2000-01-03 00:00:00 2019-12-31 00:00:00\nweekdays {0: 1044, 1: 1044, 2: 1043, 3: 1043, 4: 1043}\nempty 215 nonnum nonempty 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\n488     25.0\n2270    27.0\n2318    28.0\n2402    51.0\n0        NaN\nName: DGS10, dtype: float64\nobservation_date\n07-04    16\n12-25    15\n01-01    14\n11-11    13\n09-03     4\n02-18     4\n01-21     4\n11-22     4\n11-12     4\n10-08     4\n05-28     4\n09-05     3\nName: count, dtype: int64","is_error":false}]},"uuid":"2c82c3ce-fcd0-4984-83b7-b50ca2c318e3","timestamp":"2026-10-30T05:18:23.241Z","toolUseResult":{"stdout":"(5217, 2) ['observation_date', 'DGS10']\nbad dates 0 dups 0 monotone True 2000-01-03 00:00:00 2019-12-31 00:00:00\nweekdays {0: 1044, 1: 1044, 2: 1043, 3: 1043, 4: 1043}\nempty 215 nonnum nonempty 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\n488     25.0\n2270    27.0\n2318    28.0\n2402    51.0\n0        NaN\nName: DGS10, dtype: float64\nobservation_date\n07-04    16\n12-25    15\n01-01    14\n11-11    13\n09-03     4\n02-18     4\n01-21     4\n11-22     4\n11-12     4\n10-08     4\n05-28     4\n09-05     3\nName: count, dtype: int64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7da97738-09ef-47cb-b55f-cfbc2ce8ebca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","hash":"6b0feced8766c4b00a572767f4e3518eb0e159cd7f8b17346bc663479bdee8fc","message":{"role":"assistant","content":[{"type":"text","text":"The earlier `cd` persisted; returning to the workspace root."},{"type":"tool_use","id":"toolu_01SRop5mdjo1EwQRbttnxv9p","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0267 && ~/wp29/venv/bin/python -c \"\nimport pandas as 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> out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10-year Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nassert raw[\"date\"].is_monotonic_increasing and not raw[\"date\"].duplicated().any()\nassert (raw[\"date\"].dt.dayofweek < 5).all()\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nobs = raw.loc[~empty].copy()\nobs[\"y\"] = obs[\"DGS10\"].astype(float)\nobs = obs.reset_index(drop=True)\n\n# One-day change between consecutive available observations, dated t\nobs[\"dy\"] = 100.0 * obs[\"y\"].diff()\nobs[\"gap_days\"] = obs[\"date\"].diff().dt.days\nch = obs.iloc[1:].copy()  # first available observation has no change\nch[\"dy\"] = ch[\"dy\"].round(10)  # remove float noise from 2-decimal percent data\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10 (market holidays)\", n_raw, n_raw - n_missing, n_missing,\n     \"TASK rule: no yield published; dropped before differencing\"],\n    [\"2\", \"first available observation has no change\", n_raw - n_missing, n_changes, 1,\n     \"Δy undefined for first observation\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == n_changes\n\n# (i) one-way ANOVA (equal variance), all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Table 1\ntab = pd.DataFrame({\"weekday\": DAYS, \"n\": [len(g) for g in groups],\n                    \"mean_bp\": [g.mean() for g in groups],\n                    \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\n\n# (ii) Monday contrast with Welch 95% CI\nmon, oth = groups[0], np.concatenate(groups[1:])\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\nwelch_p = 2 * stats.t.sf(abs(est / se), df_w)\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans).to_numpy()\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"].to_numpy() ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"].to_numpy() - pred) ** 2)))\n# Diebold-Mariano-type check on squared-error loss differential (HAC not needed for h=1; iid SE)\nd = (test[\"dy\"].to_numpy() - pred) ** 2 - test[\"dy\"].to_numpy() ** 2\ndm_t = d.mean() / (d.std(ddof=1) / np.sqrt(len(d)))\ndm_p = 2 * stats.norm.sf(abs(dm_t))\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\ngap_long = ch[ch[\"gap_days\"] > 3]\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 field (US market holidays)\",\n         \"handling\": \"dropped before differencing, per TASK rule\"},\n        {\"issue\": f\"{len(gap_long)} changes span more than 3 calendar days (holiday gaps, e.g. after Monday holidays or 2001-09-11 closure)\",\n         \"handling\": \"kept as one-day changes between consecutive available observations, attributed to weekday of t, per TASK rule\"},\n        {\"issue\": \"heavy-tailed changes (max |dy| = %.0f bp on %s)\" % (ch.dy.abs().max(), ch.loc[ch.dy.abs().idxmax(), \"observation_date\"]),\n         \"handling\": \"verified as genuine market moves; kept; Welch CI used for the contrast\"},\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\": rmse0,\n    \"model_metric\": rmse1,\n    \"conclusion\": conclusion,\n    # supplementary\n    \"effect_se\": float(se),\n    \"effect_welch_df\": float(df_w),\n    \"effect_welch_p_value\": float(welch_p),\n    \"oos_mse_diff_mean\": float(d.mean()),\n    \"oos_dm_t_stat\": float(dm_t),\n    \"oos_dm_p_value\": float(dm_p),\n    \"train_weekday_means_bp\": {DAYS[k]: float(wmeans[k]) for k in range(5)},\n    \"rmse_ratio_model_to_baseline\": rmse1 / rmse0,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# Figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield change by weekday, 2000-2019 (n={n_changes})\\nANOVA F={F:.2f}, p={p:.3f}; bars = 95% CI\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"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 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":942,"cache_read_input_tokens":11427,"output_tokens":3297,"output_tokens_details":{"thinking_tokens":170},"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":942},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrct6wGoF7tTHHvhvuF","requestRef":"54adbb2c-1f02-4f44-88f2-e9f1cd55ee52","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2868bce8-f514-4c54-8804-a39678ce71e8","timestamp":"2026-10-30T05:18:48.468Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"2868bce8-f514-4c54-8804-a39678ce71e8","isSidechain":false,"promptId":"a4725974-bf1c-4e74-8b21-54588870ae55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QL2YbvvQMBheCkf2uo18wk","type":"tool_result","content":"{\n \"n_analysed\": 5001,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (market holidays),5217,5002,215,TASK rule: no yield published; dropped before differencing\n2,first available observation has no change,5002,5001,1,Δy undefined for first observation\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 field (US market holidays)\",\n      \"handling\": \"dropped before differencing, per TASK rule\"\n    },\n    {\n      \"issue\": \"155 changes span more than 3 calendar days (holiday gaps, e.g. after Monday holidays or 2001-09-11 closure)\",\n      \"handling\": \"kept as one-day changes between consecutive available observations, attributed to weekday of t, per TASK rule\"\n    },\n    {\n      \"issue\": \"heavy-tailed changes (max |dy| = 51 bp on 2009-03-18)\",\n      \"handling\": \"verified as genuine market moves; kept; Welch CI used for the contrast\"\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,","is_error":false}]},"uuid":"8c45e14e-9324-4a26-86d9-edef0a47dce3","timestamp":"2026-10-30T05:18:49.346Z","toolUseResult":{"stdout":"{\n \"n_analysed\": 5001,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (market holidays),5217,5002,215,TASK rule: no yield published; dropped before differencing\n2,first available observation has no change,5002,5001,1,Δy undefined for first observation\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 field (US market holidays)\",\n      \"handling\": \"dropped before differencing, per TASK rule\"\n    },\n    {\n      \"issue\": \"155 changes span more than 3 calendar days (holiday gaps, e.g. after Monday holidays or 2001-09-11 closure)\",\n      \"handling\": \"kept as one-day changes between consecutive available observations, attributed to weekday of t, per TASK rule\"\n    },\n    {\n      \"issue\": \"heavy-tailed changes (max |dy| = 51 bp on 2009-03-18)\",\n      \"handling\": \"verified as genuine market moves; kept; Welch CI used for the contrast\"\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,","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2868bce8-f514-4c54-8804-a39678ce71e8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}43{"type":"api-request-blob","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","hash":"3aac90e1a8393c131ddd7387601d844bfc5ec7dc7bf10cdca8c8185feb731431","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01QL2YbvvQMBheCkf2uo18wk","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10-year Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nassert raw[\"date\"].is_monotonic_increasing and not raw[\"date\"].duplicated().any()\nassert (raw[\"date\"].dt.dayofweek < 5).all()\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nobs = raw.loc[~empty].copy()\nobs[\"y\"] = obs[\"DGS10\"].astype(float)\nobs = obs.reset_index(drop=True)\n\n# One-day change between consecutive available observations, dated t\nobs[\"dy\"] = 100.0 * obs[\"y\"].diff()\nobs[\"gap_days\"] = obs[\"date\"].diff().dt.days\nch = obs.iloc[1:].copy()  # first available observation has no change\nch[\"dy\"] = ch[\"dy\"].round(10)  # remove float noise from 2-decimal percent data\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10 (market holidays)\", n_raw, n_raw - n_missing, n_missing,\n     \"TASK rule: no yield published; dropped before differencing\"],\n    [\"2\", \"first available observation has no change\", n_raw - n_missing, n_changes, 1,\n     \"Δy undefined for first observation\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == n_changes\n\n# (i) one-way ANOVA (equal variance), all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Table 1\ntab = pd.DataFrame({\"weekday\": DAYS, \"n\": [len(g) for g in groups],\n                    \"mean_bp\": [g.mean() for g in groups],\n                    \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\n\n# (ii) Monday contrast with Welch 95% CI\nmon, oth = groups[0], np.concatenate(groups[1:])\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\nwelch_p = 2 * stats.t.sf(abs(est / se), df_w)\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans).to_numpy()\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"].to_numpy() ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"].to_numpy() - pred) ** 2)))\n# Diebold-Mariano-type check on squared-error loss differential (HAC not needed for h=1; iid SE)\nd = (test[\"dy\"].to_numpy() - pred) ** 2 - test[\"dy\"].to_numpy() ** 2\ndm_t = d.mean() / (d.std(ddof=1) / np.sqrt(len(d)))\ndm_p = 2 * stats.norm.sf(abs(dm_t))\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\ngap_long = ch[ch[\"gap_days\"] > 3]\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 field (US market holidays)\",\n         \"handling\": \"dropped before differencing, per TASK rule\"},\n        {\"issue\": f\"{len(gap_long)} changes span more than 3 calendar days (holiday gaps, e.g. after Monday holidays or 2001-09-11 closure)\",\n         \"handling\": \"kept as one-day changes between consecutive available observations, attributed to weekday of t, per TASK rule\"},\n        {\"issue\": \"heavy-tailed changes (max |dy| = %.0f bp on %s)\" % (ch.dy.abs().max(), ch.loc[ch.dy.abs().idxmax(), \"observation_date\"]),\n         \"handling\": \"verified as genuine market moves; kept; Welch CI used for the contrast\"},\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\": rmse0,\n    \"model_metric\": rmse1,\n    \"conclusion\": conclusion,\n    # supplementary\n    \"effect_se\": float(se),\n    \"effect_welch_df\": float(df_w),\n    \"effect_welch_p_value\": float(welch_p),\n    \"oos_mse_diff_mean\": float(d.mean()),\n    \"oos_dm_t_stat\": float(dm_t),\n    \"oos_dm_p_value\": float(dm_p),\n    \"train_weekday_means_bp\": {DAYS[k]: float(wmeans[k]) for k in range(5)},\n    \"rmse_ratio_model_to_baseline\": rmse1 / rmse0,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# Figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield change by weekday, 2000-2019 (n={n_changes})\\nANOVA F={F:.2f}, p={p:.3f}; bars = 95% CI\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"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 analysis 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 \"anova_p_value\": 0.9015060507654888,\n  \"effect_estimate\": 0.050170098401698796,\n  \"effect_ci_low\": -0.31933577569409977,\n  \"effect_ci_high\": 0.41967597249749733,\n  \"baseline_metric\": 4.875704154642544,\n  \"model_metric\": 4.883841522290305,\n  \"conclusion\": \"no_detectable_effect\",\n  \"effect_se\": 0.18837955539096196,\n  \"effect_welch_df\": 1548.9581965107577,\n  \"effect_welch_p_value\": 0.7900247217974709,\n  \"oos_mse_diff_mean\": 0.0794170112483237,\n  \"oos_dm_t_stat\": 1.8573748978469748,\n  \"oos_dm_p_value\": 0.06325783524850241,\n  \"train_weekday_means_bp\": {\n    \"Monday\": -0.05434782608695652,\n    \"Tuesday\": -0.40545808966861596,\n    \"Wednesday\": -0.2054263565891473,\n    \"Thursday\": 0.01383399209486166,\n    \"Friday\": 0.11683168316831684\n  },\n  \"rmse_ratio_model_to_baseline\": 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out/state && cat > data_audit.md <<'EOF'\n# Data audit — F6-conf-01\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README & bundle.json) | 5217 | 2 |\n| data/README.md | 32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a | provenance text | – |\n\n- Schema: `observation_date` (YYYY-MM-DD, all parse, strictly increasing, no duplicates, all weekdays: Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043); `DGS10` (percent, 2 decimals).\n- Date range 2000-01-03 … 2019-12-31.\n- Missing: 215 empty DGS10 fields (holidays: Jul-4 16, Dec-25 15, Jan-1 14, Nov-11 13, other Monday holidays...). No non-numeric non-empty values.\n- Level range 1.37–6.79 %, mean 3.43 %.\n- Changes (bp, n=5001): mean −0.093, sd 5.72, min −51 (2009-03-18, FOMC QE announcement), max +25. Heavy tails; kept (no outlier rule in TASK).\n- 155 changes span >3 calendar days (holiday gaps); per TASK rule they are treated as one-day changes attributed to the weekday of t. Mondays therefore have fewer changes (919) because Monday holidays are dropped.\n- Information availability: only DGS10.csv used; split train ≤ 2009-12-31 (last 2009-12-31), test ≥ 2010-01-01 (first 2010-01-04). Forecast weekday means use train only. No violations.\nEOF\ncat > checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json/table1/figure1, re-ran) gives byte-identical results.json | PASS |\n| ANOVA F/p cross-checked with statsmodels OLS dy~C(weekday) (F=0.26347, p=0.9015) | PASS |\n| Monday contrast cross-checked with OLS on Monday dummy, HC1 SE: CI [-0.319, 0.419] vs Welch [-0.319, 0.420] | PASS (choice-insensitive) |\n| Counts: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500+2501) = sum of table1 n | PASS |\n| exclusions.csv sums: 5217 − (215+1) = 5001 | PASS |\n| CI contains estimate; all weekday SE > 0; RMSEs > 0 | PASS |\n| Chronology: max train date 2009-12-31 < min test date 2010-01-04; forecast means from train only | PASS |\n| Magnitude: model RMSE (4.884) vs baseline (4.876) bp — same order, model slightly worse | PASS (plausible) |\n| Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect | PASS |\nEOF\ncd ../.. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write audit and checks, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":786,"cache_read_input_tokens":17372,"output_tokens":1266,"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":786},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrfkPApBGY1noaH4RbD","requestRef":"17fdb3bb-5b44-4780-9589-bc082fcfad07","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6eef3e20-dc43-42c0-be48-0c78aff40fec","timestamp":"2026-10-30T05:19:13.137Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"6eef3e20-dc43-42c0-be48-0c78aff40fec","isSidechain":false,"promptId":"a4725974-bf1c-4e74-8b21-54588870ae55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V95fqdd4pebaNNBGU28gQP","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 39 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/figure1.png: present\n  [ok] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.31933577569409977 <= 0.050170098401698796 <= 0.41967597249749733\n  [ok] positive:anova_f_stat: 0.263465716807118\n  [ok] positive:baseline_metric: 4.875704154642544\n  [ok] positive:model_metric: 4.883841522290305\n  [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0267/out/state/gate_receipts/20261030T051914.078859Z.json\n20261030T051914.078859Z.json","is_error":false}]},"uuid":"46e811e6-cc2f-40dd-aaa8-e552dd4ca120","timestamp":"2026-10-30T05:19:14.088Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 39 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/figure1.png: present\n  [ok] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.31933577569409977 <= 0.050170098401698796 <= 0.41967597249749733\n  [ok] positive:anova_f_stat: 0.263465716807118\n  [ok] positive:baseline_metric: 4.875704154642544\n  [ok] positive:model_metric: 4.883841522290305\n  [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0267/out/state/gate_receipts/20261030T051914.078859Z.json\n20261030T051914.078859Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6eef3e20-dc43-42c0-be48-0c78aff40fec","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}59{"type":"api-request-blob","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","hash":"a0fb8a5be618b4de3b42ab66c40416eebf8fe98546320bda5555e16b65db6167","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01V95fqdd4pebaNNBGU28gQP","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit — F6-conf-01\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README & bundle.json) | 5217 | 2 |\n| data/README.md | 32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a | provenance text | – |\n\n- Schema: `observation_date` (YYYY-MM-DD, all parse, strictly increasing, no duplicates, all weekdays: Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043); `DGS10` (percent, 2 decimals).\n- Date range 2000-01-03 … 2019-12-31.\n- Missing: 215 empty DGS10 fields (holidays: Jul-4 16, Dec-25 15, Jan-1 14, Nov-11 13, other Monday holidays...). No non-numeric non-empty values.\n- Level range 1.37–6.79 %, mean 3.43 %.\n- Changes (bp, n=5001): mean −0.093, sd 5.72, min −51 (2009-03-18, FOMC QE announcement), max +25. Heavy tails; kept (no outlier rule in TASK).\n- 155 changes span >3 calendar days (holiday gaps); per TASK rule they are treated as one-day changes attributed to the weekday of t. Mondays therefore have fewer changes (919) because Monday holidays are dropped.\n- Information availability: only DGS10.csv used; split train ≤ 2009-12-31 (last 2009-12-31), test ≥ 2010-01-01 (first 2010-01-04). Forecast weekday means use train only. No violations.\nEOF\ncat > checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json/table1/figure1, re-ran) gives byte-identical results.json | PASS |\n| ANOVA F/p cross-checked with statsmodels OLS dy~C(weekday) (F=0.26347, p=0.9015) | PASS |\n| Monday contrast cross-checked with OLS on Monday dummy, HC1 SE: CI [-0.319, 0.419] vs Welch [-0.319, 0.420] | PASS (choice-insensitive) |\n| Counts: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500+2501) = sum of table1 n | PASS |\n| exclusions.csv sums: 5217 − (215+1) = 5001 | PASS |\n| CI contains estimate; all weekday SE > 0; RMSEs > 0 | PASS |\n| Chronology: max train date 2009-12-31 < min test date 2010-01-04; forecast means from train only | PASS |\n| Magnitude: model RMSE (4.884) vs baseline (4.876) bp — same order, model slightly worse | PASS (plausible) |\n| Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect | PASS |\nEOF\ncd ../.. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write audit and checks, run gate"}}]}}60{"type":"api-request","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","id":"9d0d2443-b3fa-4727-9d60-647a9d626c24","timestamp":"2026-10-30T05:19:14.092Z","version":"2.1.314","querySource":"sdk","shapeHash":"ee1d5fa0eb3853afa67f40cbbe99707f143ef28607552948c33b918d64a34cfd","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":"17fdb3bb-5b44-4780-9589-bc082fcfad07","keep":18,"tail":["a0fb8a5be618b4de3b42ab66c40416eebf8fe98546320bda5555e16b65db6167","19a12df580761bfe11dac136984ab4b8b186f684d08a6313a36f3b4e4bb8e392"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"beffa0a101b6ad93d7b4788ff5548411e74497ed19d09e51f3b6d881c2698a80"}61{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"46e811e6-cc2f-40dd-aaa8-e552dd4ca120","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99"}62{"type":"atis-latch","atis":"","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99"}63{"parentUuid":"46e811e6-cc2f-40dd-aaa8-e552dd4ca120","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrgdgskQKE7nakvb2wL","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":2146,"cache_read_input_tokens":18158,"output_tokens":3834,"output_tokens_details":{"thinking_tokens":55},"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":2146},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":626,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrgdWDapVyRigqL2tPo","requestRef":"9d0d2443-b3fa-4727-9d60-647a9d626c24","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"68ac7208-020c-4eb1-a5f8-7a3216e17cf2","timestamp":"2026-10-30T05:19:15.667Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"68ac7208-020c-4eb1-a5f8-7a3216e17cf2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrgdgskQKE7nakvb2wL","type":"message","role":"assistant","content":[{"type":"text","text":"Remaining failures are the P5 deliverables, as expected at the end of P4. Writing the report, claims, and ledger now."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2146,"cache_read_input_tokens":18158,"output_tokens":3834,"output_tokens_details":{"thinking_tokens":55},"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":2146},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrgdWDapVyRigqL2tPo","requestRef":"9d0d2443-b3fa-4727-9d60-647a9d626c24","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"eafef777-bffb-49b1-a5fc-96916cbb2023","timestamp":"2026-10-30T05:19:15.668Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}65{"parentUuid":"eafef777-bffb-49b1-a5fc-96916cbb2023","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrgdgskQKE7nakvb2wL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018cFPZZexAme6a1o5hp4Do4","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nCan the day of the week predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? We look at three things: (i) a joint test of equal weekday means, (ii) the Monday contrast (Monday mean minus the mean of Tuesday–Friday) with a 95% interval, and (iii) out-of-sample forecasts from weekday means compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`). The SHA-256 matches the README. Dates run from 2000-01-03 to 2019-12-31, every date is a weekday, and there are no duplicate dates.\n- 215 rows have an empty yield field because markets were closed for US holidays (`n_missing_rows` = 215). We dropped these rows before differencing. The first available observation has no change, so 1 more row is lost. That leaves 5001 daily changes (`n_analysed`). The arithmetic is in `out/exclusions.csv`.\n- Split: 2500 changes in the estimation period, dated on or before 2009-12-31 (`n_train`), and 2501 in the evaluation period, dated on or after 2010-01-01 (`n_test`).\n- Data issues are listed in `data_issues`:\n  - 155 changes span holiday gaps of more than 3 calendar days. Following the task rule, we kept them and dated each to its weekday. Because Monday holidays are dropped, Monday has the fewest changes (919; `out/table1.csv`).\n  - The changes have heavy tails. The largest move is −51 bp on 2009-03-18, the day of an FOMC announcement. We removed no outliers.\n\n## Method\n- Joint test: a classical one-way, equal-variance ANOVA of Δy on weekday over all 5001 changes, giving an F(4, 4996) statistic (scipy `f_oneway`). We checked the result against statsmodels OLS and got the same answer.\n- Monday contrast: the mean on Mondays minus the mean on Tuesday–Friday, with a **Welch** two-sample 95% t-interval. We chose Welch because it does not assume equal variances. As a sensitivity check, an OLS regression on a Monday dummy with HC1 standard errors gives almost the same interval (see `out/state/checks.md`).\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change for its weekday. We compare the RMSE of this forecast (`model_metric`) with the RMSE of a zero-change forecast (`baseline_metric`). As a supplement, a Diebold–Mariano-type t-test is applied to the difference in squared errors.\n- Decision rule: the `conclusion` code comes from the ANOVA at the 5% level. Only if the ANOVA rejects does the sign of the Monday interval set the code.\n- No randomness is used (`random_seed` = null). Every output is produced by `out/analysis.py`.\n\n## Results\n- Weekday means (`out/table1.csv`, `out/figure1.png`) are all within ±0.22 bp of zero. Their standard errors are about 0.16–0.19 bp.\n- **Joint test:** F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). There is no evidence that mean changes differ across weekdays.\n- **Monday contrast:** +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`), Welch p = 0.79 (`effect_welch_p_value`).\n- **Out-of-sample (2010–2019):**\n\n  | forecast | RMSE (bp) | key |\n  |---|---|---|\n  | zero change | 4.876 | `baseline_metric` |\n  | weekday means | 4.884 | `model_metric` |\n\n  The weekday-mean forecast is slightly *worse*: its RMSE is 1.0017 times the baseline (`rmse_ratio_model_to_baseline`). On the Diebold–Mariano-type test of squared errors, t = 1.86 and p = 0.063 (`oos_dm_t_stat`, `oos_dm_p_value`).\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this sample, the day of the week has no detectable predictive content for daily changes in the 10-year yield.\n\n## Uncertainty\n- The 95% interval for the Monday contrast, [−0.32, +0.42] bp, rules out Monday effects larger than about 0.4 bp per day in either direction. That is small compared with the 5.7 bp standard deviation of daily changes.\n- The ANOVA p-value of 0.90 is far from any conventional threshold.\n- The out-of-sample RMSE gap (0.008 bp) is small, and the supplementary test does not establish it at 5%. What we can say is that the weekday means did not *improve* on the random walk.\n- The Welch and HC1 intervals agree to within 0.001 bp, so the result does not depend on which two-sample method is used.\n\n## Limitations and open issues\n- **Assumptions behind the inference:**\n  - The ANOVA assumes equal variances and roughly independent errors. Daily yield changes show volatility clustering, and their variance shifts between regimes (2008–09 crisis versus the 2010s).\n  - The Welch interval relaxes equal variances but not independence. We did not check for serial correlation or apply HAC/block-bootstrap intervals.\n  - With n ≈ 1000 per weekday the central limit theorem makes the mean tests robust to heavy tails. Even so, a few extreme days such as 2009-03-18 can move individual weekday means by roughly 0.05 bp.\n- **Calendar conventions:** changes after holidays cover more than one trading interval but are attributed to the weekday of t, as the task requires. This mechanically mixes \"post-holiday\" and \"Monday\" effects. We did not test any other convention, such as excluding multi-day gaps.\n- **What we did not check:**\n  - We did not check whether a weekday effect exists within sub-periods. The estimation-period weekday means differ in sign from the full-sample means, which suggests instability or noise rather than a stable effect.\n  - We did not look at intraday or announcement-day effects. Many macro releases, such as payrolls, fall on Fridays, so an effect from announcements could be hidden in, or confused with, a weekday effect.\n  - We did not test multiple weekday contrasts beyond Monday.\n- **What would invalidate the result:** errors or revisions in the FRED series, a different holiday treatment, or a much larger sample that is sensitive to effects below 0.3 bp. Power is the main threat to a \"no effect\" reading: very small true effects, around 0.1 bp, cannot be excluded.\n- **No forecasting value:** the out-of-sample comparison uses a single split. The weekday-mean forecast was slightly worse than the random walk, so the data give no support for using weekday as a predictor.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Mean daily changes in the 10-year yield do not differ detectably across weekdays (one-way ANOVA F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Assumes equal variance and independence; volatility clustering not modelled.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, which includes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Welch interval ignores serial dependence; effects below ~0.3 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Out-of-sample (2010-2019) weekday-mean forecasts estimated on 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_to_baseline\",\"oos_dm_p_value\"],\"status\":\"supported\",\"caveat\":\"Single split; the RMSE difference itself is not significant at 5% (p=0.063).\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Absence of evidence within the precision of a 20-year daily sample.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/dates/dups/missing audited (data_audit.md)\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"no availability violations\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE computed on evaluation period (baseline_metric=4.876)\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results/table/figure; no seed needed\"],\"notes\":\"Welch CI for Monday contrast\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"statsmodels cross-check of F and HC1 contrast\",\"count identities\",\"CI contains estimate\",\"chronology\",\"gate run: \"+r[0]+\" (failures only for P5 artefacts not yet written)\"],\"notes\":\"see checks.md\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md six sections\",\"claims.json referencing existing keys\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit written, 215 missing rows, no violations.\n- 2026-10-30 P2/P3 passed: analysis.py written; baseline RMSE and weekday model computed; Welch chosen for contrast.\n- 2026-10-30 P4 passed: reproducibility & cross-checks; gate run 1 (P5 files pending).\n- 2026-10-30 P5: report and claims written; final gate run.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\"","description":"Write report, claims, ledger; run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2146,"cache_read_input_tokens":18158,"output_tokens":3834,"output_tokens_details":{"thinking_tokens":55},"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":2146},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrgdWDapVyRigqL2tPo","requestRef":"9d0d2443-b3fa-4727-9d60-647a9d626c24","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f2c4d8c3-1630-4c0f-b41e-fe0ea063b26f","timestamp":"2026-10-30T05:19:48.959Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"f2c4d8c3-1630-4c0f-b41e-fe0ea063b26f","isSidechain":false,"promptId":"a4725974-bf1c-4e74-8b21-54588870ae55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018cFPZZexAme6a1o5hp4Do4","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0267/out/state/gate_receipts/20261030T051949.894936Z.json","is_error":false}]},"uuid":"6d1d779b-00a0-40b5-b6bd-55cc3b50fd51","timestamp":"2026-10-30T05:19:49.902Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 39 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0267/out/state/gate_receipts/20261030T051949.894936Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f2c4d8c3-1630-4c0f-b41e-fe0ea063b26f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0267","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","version":"2.1.314","gitBranch":"HEAD"}67{"type":"api-request-blob","sessionId":"f1ad7023-0e1c-47e8-b14b-91a9ab80fb99","hash":"5ff20f363fd4181776c6136e8aec3aa9991ad38e0105e8c7238b6fd0695849a9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Remaining failures are the P5 deliverables, as expected at the end of P4. Writing the report, claims, and ledger now."},{"type":"tool_use","id":"toolu_018cFPZZexAme6a1o5hp4Do4","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nCan the day of the week predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? We look at three things: (i) a joint test of equal weekday means, (ii) the Monday contrast (Monday mean minus the mean of Tuesday–Friday) with a 95% interval, and (iii) out-of-sample forecasts from weekday means compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`). The SHA-256 matches the README. Dates run from 2000-01-03 to 2019-12-31, every date is a weekday, and there are no duplicate dates.\n- 215 rows have an empty yield field because markets were closed for US holidays (`n_missing_rows` = 215). We dropped these rows before differencing. The first available observation has no change, so 1 more row is lost. That leaves 5001 daily changes (`n_analysed`). The arithmetic is in `out/exclusions.csv`.\n- Split: 2500 changes in the estimation period, dated on or before 2009-12-31 (`n_train`), and 2501 in the evaluation period, dated on or after 2010-01-01 (`n_test`).\n- Data issues are listed in `data_issues`:\n  - 155 changes span holiday gaps of more than 3 calendar days. Following the task rule, we kept them and dated each to its weekday. Because Monday holidays are dropped, Monday has the fewest changes (919; `out/table1.csv`).\n  - The changes have heavy tails. The largest move is −51 bp on 2009-03-18, the day of an FOMC announcement. We removed no outliers.\n\n## Method\n- Joint test: a classical one-way, equal-variance ANOVA of Δy on weekday over all 5001 changes, giving an F(4, 4996) statistic (scipy `f_oneway`). We checked the result against statsmodels OLS and got the same answer.\n- Monday contrast: the mean on Mondays minus the mean on Tuesday–Friday, with a **Welch** two-sample 95% t-interval. We chose Welch because it does not assume equal variances. As a sensitivity check, an OLS regression on a Monday dummy with HC1 standard errors gives almost the same interval (see `out/state/checks.md`).\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change for its weekday. We compare the RMSE of this forecast (`model_metric`) with the RMSE of a zero-change forecast (`baseline_metric`). As a supplement, a Diebold–Mariano-type t-test is applied to the difference in squared errors.\n- Decision rule: the `conclusion` code comes from the ANOVA at the 5% level. Only if the ANOVA rejects does the sign of the Monday interval set the code.\n- No randomness is used (`random_seed` = null). Every output is produced by `out/analysis.py`.\n\n## Results\n- Weekday means (`out/table1.csv`, `out/figure1.png`) are all within ±0.22 bp of zero. Their standard errors are about 0.16–0.19 bp.\n- **Joint test:** F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). There is no evidence that mean changes differ across weekdays.\n- **Monday contrast:** +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`), Welch p = 0.79 (`effect_welch_p_value`).\n- **Out-of-sample (2010–2019):**\n\n  | forecast | RMSE (bp) | key |\n  |---|---|---|\n  | zero change | 4.876 | `baseline_metric` |\n  | weekday means | 4.884 | `model_metric` |\n\n  The weekday-mean forecast is slightly *worse*: its RMSE is 1.0017 times the baseline (`rmse_ratio_model_to_baseline`). On the Diebold–Mariano-type test of squared errors, t = 1.86 and p = 0.063 (`oos_dm_t_stat`, `oos_dm_p_value`).\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this sample, the day of the week has no detectable predictive content for daily changes in the 10-year yield.\n\n## Uncertainty\n- The 95% interval for the Monday contrast, [−0.32, +0.42] bp, rules out Monday effects larger than about 0.4 bp per day in either direction. That is small compared with the 5.7 bp standard deviation of daily changes.\n- The ANOVA p-value of 0.90 is far from any conventional threshold.\n- The out-of-sample RMSE gap (0.008 bp) is small, and the supplementary test does not establish it at 5%. What we can say is that the weekday means did not *improve* on the random walk.\n- The Welch and HC1 intervals agree to within 0.001 bp, so the result does not depend on which two-sample method is used.\n\n## Limitations and open issues\n- **Assumptions behind the inference:**\n  - The ANOVA assumes equal variances and roughly independent errors. Daily yield changes show volatility clustering, and their variance shifts between regimes (2008–09 crisis versus the 2010s).\n  - The Welch interval relaxes equal variances but not independence. We did not check for serial correlation or apply HAC/block-bootstrap intervals.\n  - With n ≈ 1000 per weekday the central limit theorem makes the mean tests robust to heavy tails. Even so, a few extreme days such as 2009-03-18 can move individual weekday means by roughly 0.05 bp.\n- **Calendar conventions:** changes after holidays cover more than one trading interval but are attributed to the weekday of t, as the task requires. This mechanically mixes \"post-holiday\" and \"Monday\" effects. We did not test any other convention, such as excluding multi-day gaps.\n- **What we did not check:**\n  - We did not check whether a weekday effect exists within sub-periods. The estimation-period weekday means differ in sign from the full-sample means, which suggests instability or noise rather than a stable effect.\n  - We did not look at intraday or announcement-day effects. Many macro releases, such as payrolls, fall on Fridays, so an effect from announcements could be hidden in, or confused with, a weekday effect.\n  - We did not test multiple weekday contrasts beyond Monday.\n- **What would invalidate the result:** errors or revisions in the FRED series, a different holiday treatment, or a much larger sample that is sensitive to effects below 0.3 bp. Power is the main threat to a \"no effect\" reading: very small true effects, around 0.1 bp, cannot be excluded.\n- **No forecasting value:** the out-of-sample comparison uses a single split. The weekday-mean forecast was slightly worse than the random walk, so the data give no support for using weekday as a predictor.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Mean daily changes in the 10-year yield do not differ detectably across weekdays (one-way ANOVA F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Assumes equal variance and independence; volatility clustering not modelled.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, which includes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Welch interval ignores serial dependence; effects below ~0.3 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Out-of-sample (2010-2019) weekday-mean forecasts estimated on 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_to_baseline\",\"oos_dm_p_value\"],\"status\":\"supported\",\"caveat\":\"Single split; the RMSE difference itself is not significant at 5% (p=0.063).\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Absence of evidence within the precision of a 20-year daily sample.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/dates/dups/missing audited (data_audit.md)\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"no availability violations\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"zero-change baseline RMSE computed on evaluation period (baseline_metric=4.876)\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results/table/figure; no seed needed\"],\"notes\":\"Welch CI for Monday contrast\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"statsmodels cross-check of F and HC1 contrast\",\"count identities\",\"CI contains estimate\",\"chronology\",\"gate run: \"+r[0]+\" (failures only for P5 artefacts not yet written)\"],\"notes\":\"see checks.md\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md six sections\",\"claims.json referencing existing keys\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit written, 215 missing rows, no violations.\n- 2026-10-30 P2/P3 passed: analysis.py written; baseline RMSE and weekday model computed; Welch chosen for contrast.\n- 2026-10-30 P4 passed: reproducibility & cross-checks; gate run 1 (P5 files pending).\n- 2026-10-30 P5: report and claims written; final gate run.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\"","description":"Write report, claims, ledger; run final 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The required `conclusion` code is `no_detectable_effect`. All required files are written, the gate passes (0 of 39 checks failed), and re-running `out/analysis.py` from scratch reproduces `out/results.json` exactly.\n\n**Data:** the file has 5,217 rows. I dropped 215 rows with no yield (US market holidays), and the first remaining day has no prior day to compare with, which leaves 5,001 daily changes. Of these, 2,500 fall in 2000–2009 (estimation) and 2,501 in 2010–2019 (evaluation).\n\n**Results:**\n- **Do weekday means differ at all?** No. The joint test (one-way analysis of variance) gives F = 0.263, p = 0.90. Every weekday's average change is within ±0.22 basis points of zero.\n- **Monday versus the other four days:** Mondays average +0.050 basis points more, with a 95% interval of −0.32 to +0.42 (Welch method), so zero is well inside the range. A regression with robust standard errors gives almost the same interval.\n- **Forecasting 2010–2019:** using each weekday's 2000–2009 average did slightly worse than simply predicting no change. The forecast error (RMSE) was 4.884 basis points against 4.876. That gap is not statistically significant at 5% (p = 0.063).\n\n**Main limitations (discussed in the report):**\n- The tests treat days as independent and don't account for volatility clustering. I didn't apply interval methods that allow for serial correlation.\n- As the task required, a change after a holiday covers more than one day but counts as the weekday it ends on. 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