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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.\n\nCareful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.\n\nData deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.\n\nSimple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.\n\nUncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.\n\nReproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.\n\nFinally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. When a task cannot be completed as specified, saying so, and saying why, is the correct outcome.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. 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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/RA0024","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3"}12{"parentUuid":"9c3388db-2bdd-48c8-9876-28a51b7dd3ab","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt6jtUH3eLcaWP2mbr7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TH3yho3kA6uvegkgYxLzxv","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/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nDGS10.csv\nREADME.md\nbundle.json\ndata\ngate_spec.json\nTASK.md","is_error":false}]},"uuid":"16c9c726-d6eb-425f-a63f-a2f63ddbf812","timestamp":"2026-10-30T05:37:50.013Z","toolUseResult":{"stdout":"# F6-conf-01 — Does the day of the week carry predictive content for daily changes in the 10-year Treasury yield?\n\n## Scientific question\nFinancial folklore attributes systematic \"Monday\" or \"Friday\" patterns to daily asset-price changes. Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n 5218 data/DGS10.csv\n(5217, 2) ['observation_date', 'DGS10']\n0 2000-01-03 2019-12-31\n215 215 count 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\nDGS10\n 215\nName: count, dtype: int64\nobservation_date\n0 1044\n1 1044\n2 1043\n3 1043\n4 1043\nName: count, dtype: int64\nTrue","is_error":false}]},"uuid":"a35bbf4e-dbc9-4616-80be-b65a858a35f3","timestamp":"2026-10-30T05:37:53.498Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-01\n\n## `DGS10.csv` (5217 rows)\nObserved data: *Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis* (FRED series `DGS10`, daily, percent, not seasonally adjusted). Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n 5218 data/DGS10.csv\n(5217, 2) ['observation_date', 'DGS10']\n0 2000-01-03 2019-12-31\n215 215 count 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\nDGS10\n 215\nName: count, dtype: int64\nobservation_date\n0 1044\n1 1044\n2 1043\n3 1043\n4 1043\nName: count, dtype: int64\nTrue","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"54a4918e-c5a9-4a97-bb8e-ccb037872802","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0024/data","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","hash":"c6625ccd23500172a7b61dc526ef895a5f36a3595235eb9e474cb49952573440","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017zEfdQbeLBdDLdt2M26cAs","name":"Bash","input":{"command":"cat data/README.md; 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Writing the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1137,"cache_read_input_tokens":5553,"output_tokens":2725,"output_tokens_details":{"thinking_tokens":88},"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":1137},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt78iere78rPLPVKTh7","requestRef":"49039613-c32d-4122-ba24-0d0d3568da85","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"63f1efe5-01b3-4a5a-8146-e8ba8438e199","timestamp":"2026-10-30T05:37:55.527Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0024/data","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"63f1efe5-01b3-4a5a-8146-e8ba8438e199","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt794VZZpen7e6TfemG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012RnTKgDoGgGtet9ctk3LNx","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\"\"\"\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\nSEED = None # no randomness used\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_input = len(raw)\nassert not raw.observation_date.duplicated().any()\nn_missing = int((raw.DGS10.str.strip() == \"\").sum())\nd = raw[raw.DGS10.str.strip() != \"\"].copy()\nd[\"date\"] = pd.to_datetime(d.observation_date)\nd[\"y\"] = d.DGS10.astype(float)\nd = d.sort_values(\"date\").reset_index(drop=True)\nd[\"dy\"] = 100 * d.y.diff()\nd[\"gap_days\"] = d.date.diff().dt.days\nd = d.dropna(subset=[\"dy\"]).copy()\nd[\"dy\"] = d.dy.round(10)\nd[\"wd\"] = d.date.dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\nassert d.wd.isin(range(5)).all()\n\ntrain = d[d.date <= \"2009-12-31\"]; test = d[d.date >= \"2010-01-01\"]\n\n# (i) one-way ANOVA, all changes\ngroups = [d.dy[d.wd == k].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon = d.dy[d.wd == 0].values; oth = d.dy[d.wd != 0].values\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) out-of-sample\nwd_means = train.groupby(\"wd\").dy.mean()\npred = test.wd.map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(test.dy.values ** 2)))\nrmse1 = float(np.sqrt(np.mean((test.dy.values - pred) ** 2)))\n# Diebold-Mariano (squared loss, h=1) as supplementary\ndl = (test.dy.values - pred) ** 2 - test.dy.values ** 2\ndm = dl.mean() / (dl.std(ddof=1) / np.sqrt(len(dl)))\ndm_p = 2 * stats.norm.sf(abs(dm))\n# supplementary: train-only mean (unconditional) forecast\nrmse_mean = float(np.sqrt(np.mean((test.dy.values - train.dy.mean()) ** 2)))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\n# table + figure\ntab = pd.DataFrame({\"weekday\": names,\n \"n\": [len(g) for g in groups],\n \"mean_bp\": [g.mean() for g in groups],\n \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(\"out/table1.csv\", index=False, float_format=\"%.6f\")\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, n - 1) * s for n, s in zip(tab.n, tab.se_bp)]\nax.errorbar(range(5), tab.mean_bp, yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean one-day change in 10y yield (bp)\")\nax.set_title(f\"DGS10 daily changes by weekday, 2000-2019 (n={len(d)})\\n95% CIs; ANOVA F={F:.2f}, p={p:.3f}\")\nfig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=150)\n\nmulti_gap = int((d.gap_days > 3).sum() + ((d.gap_days == 2) | (d.gap_days == 3)).sum() - (d.gap_days == 3).sum() * 0)\npost_hol = int(((d.gap_days > 1) & ~((d.wd == 0) & (d.gap_days == 3))).sum())\nres = {\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n \"effect_estimate\": float(eff), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0, \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before differencing, as prescribed; changes computed between consecutive available observations.\"},\n {\"issue\": f\"{post_hol} changes (excluding ordinary Fri->Mon weekends) span a holiday gap and thus cover more than one business day\",\n \"handling\": \"Kept and attributed to the weekday of t, as prescribed by TASK.md; noted as a limitation (inflates variance, mostly for Tuesdays after Monday holidays).\"},\n ],\n \"n_input_rows\": {\"DGS10.csv\": n_input},\n \"n_analysed\": int(len(d)),\n \"random_seed\": SEED,\n \"supplementary\": {\n \"monday_contrast_method\": \"Welch two-sample t interval (unequal variances)\",\n \"welch_se_bp\": float(se), \"welch_df\": float(df),\n \"welch_p_value\": float(2 * stats.t.sf(abs(eff / se), df)),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wd_means.items()},\n \"rmse_train_unconditional_mean_bp\": rmse_mean,\n \"dm_stat_model_minus_zero_sqloss\": float(dm), \"dm_p_value\": float(dm_p),\n \"levene_p_value\": float(stats.levene(*groups).pvalue),\n \"kruskal_p_value\": float(stats.kruskal(*groups).pvalue),\n },\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2)); print(tab)\nEOF\n~/wp29/venv/bin/python out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1137,"cache_read_input_tokens":5553,"output_tokens":2725,"output_tokens_details":{"thinking_tokens":88},"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":1137},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXt78iere78rPLPVKTh7","requestRef":"49039613-c32d-4122-ba24-0d0d3568da85","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"646701c1-f01e-4904-83e0-407634faa9ce","timestamp":"2026-10-30T05:38:14.755Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0024/data","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"646701c1-f01e-4904-83e0-407634faa9ce","isSidechain":false,"promptId":"dc504920-9d2b-47cc-a038-b16b8420bf8f","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RA0024/data/out/analysis.py\", line 12, in <module>\n raw = 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_012RnTKgDoGgGtet9ctk3LNx"}]},"uuid":"a0ecf6fb-cae2-4c6d-8cfc-cacc759ca1d6","timestamp":"2026-10-30T05:38:15.383Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RA0024/data/out/analysis.py\", line 12, in <module>\n raw = 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'","sandboxed":false,"sourceToolAssistantUUID":"646701c1-f01e-4904-83e0-407634faa9ce","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0024/data","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","hash":"3a54ae488b0e5919eac8898d3c157f0d5c07a934c5d827466d31f8b55cca8ec3","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Data look clean (no duplicates, all weekdays, 215 holidays). Writing the analysis script."},{"type":"tool_use","id":"toolu_012RnTKgDoGgGtet9ctk3LNx","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\"\"\"\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\nSEED = None # no randomness used\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_input = len(raw)\nassert not raw.observation_date.duplicated().any()\nn_missing = int((raw.DGS10.str.strip() == \"\").sum())\nd = raw[raw.DGS10.str.strip() != \"\"].copy()\nd[\"date\"] = pd.to_datetime(d.observation_date)\nd[\"y\"] = d.DGS10.astype(float)\nd = d.sort_values(\"date\").reset_index(drop=True)\nd[\"dy\"] = 100 * d.y.diff()\nd[\"gap_days\"] = d.date.diff().dt.days\nd = d.dropna(subset=[\"dy\"]).copy()\nd[\"dy\"] = d.dy.round(10)\nd[\"wd\"] = d.date.dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\nassert d.wd.isin(range(5)).all()\n\ntrain = d[d.date <= \"2009-12-31\"]; test = d[d.date >= \"2010-01-01\"]\n\n# (i) one-way ANOVA, all changes\ngroups = [d.dy[d.wd == k].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon = d.dy[d.wd == 0].values; oth = d.dy[d.wd != 0].values\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) out-of-sample\nwd_means = train.groupby(\"wd\").dy.mean()\npred = test.wd.map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(test.dy.values ** 2)))\nrmse1 = float(np.sqrt(np.mean((test.dy.values - pred) ** 2)))\n# Diebold-Mariano (squared loss, h=1) as supplementary\ndl = (test.dy.values - pred) ** 2 - test.dy.values ** 2\ndm = dl.mean() / (dl.std(ddof=1) / np.sqrt(len(dl)))\ndm_p = 2 * stats.norm.sf(abs(dm))\n# supplementary: train-only mean (unconditional) forecast\nrmse_mean = float(np.sqrt(np.mean((test.dy.values - train.dy.mean()) ** 2)))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\n# table + figure\ntab = pd.DataFrame({\"weekday\": names,\n \"n\": [len(g) for g in groups],\n \"mean_bp\": [g.mean() for g in groups],\n \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(\"out/table1.csv\", index=False, float_format=\"%.6f\")\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, n - 1) * s for n, s in zip(tab.n, tab.se_bp)]\nax.errorbar(range(5), tab.mean_bp, yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean one-day change in 10y yield (bp)\")\nax.set_title(f\"DGS10 daily changes by weekday, 2000-2019 (n={len(d)})\\n95% CIs; ANOVA F={F:.2f}, p={p:.3f}\")\nfig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=150)\n\nmulti_gap = int((d.gap_days > 3).sum() + ((d.gap_days == 2) | (d.gap_days == 3)).sum() - (d.gap_days == 3).sum() * 0)\npost_hol = int(((d.gap_days > 1) & ~((d.wd == 0) & (d.gap_days == 3))).sum())\nres = {\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n \"effect_estimate\": float(eff), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0, \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before differencing, as prescribed; changes computed between consecutive available observations.\"},\n {\"issue\": f\"{post_hol} changes (excluding ordinary Fri->Mon weekends) span a holiday gap and thus cover more than one business day\",\n \"handling\": \"Kept and attributed to the weekday of t, as prescribed by TASK.md; noted as a limitation (inflates variance, mostly for Tuesdays after Monday holidays).\"},\n ],\n \"n_input_rows\": {\"DGS10.csv\": n_input},\n \"n_analysed\": int(len(d)),\n \"random_seed\": SEED,\n \"supplementary\": {\n \"monday_contrast_method\": \"Welch two-sample t interval (unequal variances)\",\n \"welch_se_bp\": float(se), \"welch_df\": float(df),\n \"welch_p_value\": float(2 * stats.t.sf(abs(eff / se), df)),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wd_means.items()},\n \"rmse_train_unconditional_mean_bp\": rmse_mean,\n \"dm_stat_model_minus_zero_sqloss\": float(dm), \"dm_p_value\": float(dm_p),\n \"levene_p_value\": float(stats.levene(*groups).pvalue),\n \"kruskal_p_value\": float(stats.kruskal(*groups).pvalue),\n },\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2)); print(tab)\nEOF\n~/wp29/venv/bin/python out/analysis.py"}}]}}27{"type":"api-request","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","id":"bfb38a37-94d7-47e0-b3b3-9b6d3d553946","timestamp":"2026-10-30T05:38:15.386Z","version":"2.1.314","querySource":"sdk","shapeHash":"232ff3c3c6ebf4e9c02f230e346262a16e6a5ec1505f8ec85ce50a3e0e4cf1c1","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":"49039613-c32d-4122-ba24-0d0d3568da85","keep":6,"tail":["3a54ae488b0e5919eac8898d3c157f0d5c07a934c5d827466d31f8b55cca8ec3","0146cdc4d844cb93b5f75987b382d683e76ed44e52bf18ca0dbd8261119aeb71"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"79a417f937e9bfdc5b254dffdb071a59e4d45d6696191c511a9aeb92efdeac48"}28{"parentUuid":"a0ecf6fb-cae2-4c6d-8cfc-cacc759ca1d6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt8kZDCmX6cRNbMyPnE","type":"message","role":"assistant","content":[{"type":"text","text":"Oops, shell was still in `data/`. 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changes computed between consecutive available observations.\"\n },\n {\n \"issue\": \"214 changes (excluding ordinary Fri->Mon weekends) span a holiday gap and thus cover more than one business day\",\n \"handling\": \"Kept and attributed to the weekday of t, as prescribed by TASK.md; noted as a limitation (inflates variance, mostly for Tuesdays after Monday holidays).\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"supplementary\": {\n \"monday_contrast_method\": \"Welch two-sample t interval (unequal variances)\",\n \"welch_se_bp\": 0.18837955539096196,\n \"welch_df\": 1548.9581965107577,\n \"welch_p_value\": 0.7900247217974709,\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_train_unconditional_mean_bp\": 4.8751986528852305,\n \"dm_stat_model_minus_zero_sqloss\": 1.8573748978469748,\n \"dm_p_value\": 0.06325783524850241,\n \"levene_p_value\": 1.0122651168712971e-07,\n \"kruskal_p_value\": 0.5176806169324906\n }\n}\n weekday n mean_bp se_bp\n0 Monday 919 -0.052231 0.164386\n1 Tuesday 1028 -0.219844 0.177007\n2 Wednesday 1030 0.016505 0.182346\n3 Thursday 1012 -0.059289 0.186918\n4 Friday 1012 -0.147233 0.189918","is_error":false}]},"uuid":"6e676ff7-4a76-4eec-8107-eb0a91e959e1","timestamp":"2026-10-30T05:38:18.515Z","toolUseResult":{"stdout":"DGS10.csv\nREADME.md\n{\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": 215,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before differencing, as prescribed; changes computed between consecutive available observations.\"\n },\n {\n \"issue\": \"214 changes (excluding ordinary Fri->Mon weekends) span a holiday gap and thus cover more than one business day\",\n \"handling\": \"Kept and attributed to the weekday of t, as prescribed by TASK.md; noted as a limitation (inflates variance, mostly for Tuesdays after Monday holidays).\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"supplementary\": {\n \"monday_contrast_method\": \"Welch two-sample t interval (unequal variances)\",\n \"welch_se_bp\": 0.18837955539096196,\n \"welch_df\": 1548.9581965107577,\n \"welch_p_value\": 0.7900247217974709,\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_train_unconditional_mean_bp\": 4.8751986528852305,\n \"dm_stat_model_minus_zero_sqloss\": 1.8573748978469748,\n \"dm_p_value\": 0.06325783524850241,\n \"levene_p_value\": 1.0122651168712971e-07,\n \"kruskal_p_value\": 0.5176806169324906\n }\n}\n weekday n mean_bp se_bp\n0 Monday 919 -0.052231 0.164386\n1 Tuesday 1028 -0.219844 0.177007\n2 Wednesday 1030 0.016505 0.182346\n3 Thursday 1012 -0.059289 0.186918\n4 Friday 1012 -0.147233 0.189918","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"966a990a-8d98-4f28-ae61-ed49f9f12917","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0024","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","hash":"27da8ade823840a398c6b6d8d41b2dda6563f8d87f50b58018b578536395d099","message":{"role":"assistant","content":[{"type":"text","text":"Oops, shell was still in `data/`. 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> out/report.md <<'EOF'\n# F6-conf-01 — Day-of-week effects in daily 10-year Treasury yield changes\n\n## Question\nUsing daily FRED DGS10 data for 2000–2019, does the weekday help predict the one-day change in the 10-year Treasury yield, Δy_t = 100·(y_t − y_{t−1}) in basis points? We look at three things: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on the other weekdays) with a 95% CI; (iii) whether forecasting each change with its weekday mean improves out-of-sample RMSE over a zero-change forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 rows (every weekday from 2000-01-03 to 2019-12-31). There are no duplicate dates, the dates are in increasing order, and each weekday appears 1,043–1,044 times. Values range from 1.37% to 6.79%, which is plausible.\n- 215 rows have an empty `DGS10` field (market holidays). As the task prescribes, these were dropped before differencing. That leaves 5,002 levels and 5,001 changes (`n_analysed`).\n- Estimation period (changes dated ≤ 2009-12-31): n_train = 2,500. Evaluation period (≥ 2010-01-01): n_test = 2,501.\n- Data issue: 214 changes span a holiday gap, not counting the ordinary Friday→Monday weekend. Each of these covers more than one business day; most are Tuesdays after a Monday holiday. Following the task rules, they were kept and attributed to the weekday of t. As a result, there are fewer Mondays (919) than other weekdays (1,012–1,030).\n\n## Method\n- Baseline: a zero-change (random-walk) forecast.\n- (i) Classical one-way equal-variance ANOVA F-test over all 5,001 changes, using F(4, 4996).\n- (ii) Monday contrast: the Monday mean minus the pooled mean of Tuesday–Friday. The 95% interval is a **Welch** two-sample t interval, which allows unequal variances; Welch–Satterthwaite df ≈ 1549.\n- (iii) Out-of-sample: each evaluation-period change is forecast by the mean change for its weekday, estimated from 2000–2009 only. RMSE is compared with the zero-change forecast over 2010–2019.\n- Supplementary checks, not part of the decision rule: a Diebold–Mariano test on squared loss, Levene's test for equal variances, a Kruskal–Wallis test, and an unconditional training-mean forecast.\n- The conclusion follows the task's decision rule: ANOVA at 5%, with the sign taken from the Monday interval if the ANOVA rejects.\n- No randomness is involved, so no seed is set. `out/analysis.py` regenerates every number.\n\n## Results\nTable 1 (`out/table1.csv`) gives the mean change by weekday over all changes. Figure 1 (`out/figure1.png`) plots these means with 95% CIs.\n\n| weekday | n | mean (bp) | SE (bp) |\n|---|---|---|---|\n| Monday | 919 | −0.052 | 0.164 |\n| Tuesday | 1028 | −0.220 | 0.177 |\n| Wednesday | 1030 | 0.017 | 0.182 |\n| Thursday | 1012 | −0.059 | 0.187 |\n| Friday | 1012 | −0.147 | 0.190 |\n\n- **Joint ANOVA:** F = 0.263, p = 0.902. The equal-means hypothesis is not rejected.\n- **Monday contrast:** +0.050 bp, 95% Welch CI [−0.319, +0.420] bp (p = 0.79). This is indistinguishable from zero. It is also economically negligible next to a daily standard deviation of roughly 5 bp.\n- **Out-of-sample:** the zero-change RMSE is 4.876 bp. The weekday-mean RMSE is 4.884 bp, so it is slightly *worse*. The Diebold–Mariano statistic is +1.86 (p = 0.063), pointing toward worse but not significant at 5%. An unconditional training-mean forecast gives 4.875 bp.\n- **Conclusion code:** `no_detectable_effect`. The weekday carries no detectable predictive content for daily changes in the 10-year yield over 2000–2019.\n\n## Uncertainty\n- The SEs of the weekday means are about 0.16–0.19 bp. The Monday-contrast CI is about ±0.37 bp wide, so any true Monday effect larger than roughly 0.4 bp per day in absolute value is unlikely under the model's assumptions.\n- The ANOVA assumes equal variances, and Levene's test rejects that assumption (p ≈ 1e-7). The Welch interval for the contrast does not rely on the assumption.\n- The rank-based Kruskal–Wallis test also finds no difference (p = 0.52), so the null result does not depend on normality or equal variances.\n- The intervals treat daily changes as independent. Daily yield changes have little autocorrelation but strong volatility clustering. HAC standard errors were not computed, but they would most likely widen the intervals somewhat and would not overturn a result with p ≈ 0.9.\n\n## Limitations and open issues\n- **What would invalidate the result:**\n - Strong serial dependence or volatility clustering that makes the iid SEs badly wrong. This is a concern for inference in general, but it cannot manufacture a rejection here; it could only mask a small effect.\n - A data-revision or vintage problem in the FRED series. This was not checked: only one vintage was available, and we cannot verify the SHA beyond what the README states.\n- **Holiday-spanning changes:** these mix one-day and multi-day changes. Under the prescribed rule they are attributed to the weekday of t, which mostly inflates Tuesday variance. A per-business-day normalisation or excluding these changes was not tried.\n- **Not checked:**\n - Sub-period stability, e.g. 2008 crisis versus calm periods, or the pre/post-2010 weekday means separately beyond the training means.\n - Interaction with macro-announcement days. Employment reports fall on Fridays and FOMC decisions mid-week, so any weekday pattern could reflect an announcement calendar rather than the weekday itself.\n - Robust or median-based contrasts, and HAC or GARCH-adjusted inference.\n- **Power:** with about 1,000 observations per weekday and a daily SD of about 5 bp, effects smaller than about 0.4–0.5 bp cannot be detected. \"No detectable effect\" therefore does not mean \"exactly zero\".\n- **Multiple comparisons and folklore:** the Monday contrast was pre-specified by the task. Any other weekday contrast singled out from Table 1 (e.g. Tuesday's −0.22 bp) would be data-mined and is not significant anyway.\n- **Generality:** the result covers one maturity, one 20-year window and the mean only. It says nothing about weekday patterns in volatility, which Levene's test suggests do exist.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA does not reject equal weekday mean changes (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption is rejected by Levene's test; Kruskal-Wallis (supplementary) agrees with the null.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, indistinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"iid assumption; no HAC adjustment for volatility clustering.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample RMSE over the zero-change forecast (4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Difference is not significant at 5% by a supplementary Diebold-Mariano test (p=0.063).\"},\n {\"id\":\"C4\",\"text\":\"Overall, the day of the week carries no detectable predictive content for daily 10-year yield changes in 2000-2019.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Absence of evidence: effects below about 0.4 bp/day cannot be ruled out.\"}\n]\nEOF\nmd5 out/results.json; 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We look at three things: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on the other weekdays) with a 95% CI; (iii) whether forecasting each change with its weekday mean improves out-of-sample RMSE over a zero-change forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 rows (every weekday from 2000-01-03 to 2019-12-31). There are no duplicate dates, the dates are in increasing order, and each weekday appears 1,043–1,044 times. Values range from 1.37% to 6.79%, which is plausible.\n- 215 rows have an empty `DGS10` field (market holidays). As the task prescribes, these were dropped before differencing. That leaves 5,002 levels and 5,001 changes (`n_analysed`).\n- Estimation period (changes dated ≤ 2009-12-31): n_train = 2,500. Evaluation period (≥ 2010-01-01): n_test = 2,501.\n- Data issue: 214 changes span a holiday gap, not counting the ordinary Friday→Monday weekend. Each of these covers more than one business day; most are Tuesdays after a Monday holiday. Following the task rules, they were kept and attributed to the weekday of t. As a result, there are fewer Mondays (919) than other weekdays (1,012–1,030).\n\n## Method\n- Baseline: a zero-change (random-walk) forecast.\n- (i) Classical one-way equal-variance ANOVA F-test over all 5,001 changes, using F(4, 4996).\n- (ii) Monday contrast: the Monday mean minus the pooled mean of Tuesday–Friday. The 95% interval is a **Welch** two-sample t interval, which allows unequal variances; Welch–Satterthwaite df ≈ 1549.\n- (iii) Out-of-sample: each evaluation-period change is forecast by the mean change for its weekday, estimated from 2000–2009 only. RMSE is compared with the zero-change forecast over 2010–2019.\n- Supplementary checks, not part of the decision rule: a Diebold–Mariano test on squared loss, Levene's test for equal variances, a Kruskal–Wallis test, and an unconditional training-mean forecast.\n- The conclusion follows the task's decision rule: ANOVA at 5%, with the sign taken from the Monday interval if the ANOVA rejects.\n- No randomness is involved, so no seed is set. `out/analysis.py` regenerates every number.\n\n## Results\nTable 1 (`out/table1.csv`) gives the mean change by weekday over all changes. Figure 1 (`out/figure1.png`) plots these means with 95% CIs.\n\n| weekday | n | mean (bp) | SE (bp) |\n|---|---|---|---|\n| Monday | 919 | −0.052 | 0.164 |\n| Tuesday | 1028 | −0.220 | 0.177 |\n| Wednesday | 1030 | 0.017 | 0.182 |\n| Thursday | 1012 | −0.059 | 0.187 |\n| Friday | 1012 | −0.147 | 0.190 |\n\n- **Joint ANOVA:** F = 0.263, p = 0.902. The equal-means hypothesis is not rejected.\n- **Monday contrast:** +0.050 bp, 95% Welch CI [−0.319, +0.420] bp (p = 0.79). This is indistinguishable from zero. It is also economically negligible next to a daily standard deviation of roughly 5 bp.\n- **Out-of-sample:** the zero-change RMSE is 4.876 bp. The weekday-mean RMSE is 4.884 bp, so it is slightly *worse*. The Diebold–Mariano statistic is +1.86 (p = 0.063), pointing toward worse but not significant at 5%. An unconditional training-mean forecast gives 4.875 bp.\n- **Conclusion code:** `no_detectable_effect`. The weekday carries no detectable predictive content for daily changes in the 10-year yield over 2000–2019.\n\n## Uncertainty\n- The SEs of the weekday means are about 0.16–0.19 bp. The Monday-contrast CI is about ±0.37 bp wide, so any true Monday effect larger than roughly 0.4 bp per day in absolute value is unlikely under the model's assumptions.\n- The ANOVA assumes equal variances, and Levene's test rejects that assumption (p ≈ 1e-7). The Welch interval for the contrast does not rely on the assumption.\n- The rank-based Kruskal–Wallis test also finds no difference (p = 0.52), so the null result does not depend on normality or equal variances.\n- The intervals treat daily changes as independent. Daily yield changes have little autocorrelation but strong volatility clustering. HAC standard errors were not computed, but they would most likely widen the intervals somewhat and would not overturn a result with p ≈ 0.9.\n\n## Limitations and open issues\n- **What would invalidate the result:**\n - Strong serial dependence or volatility clustering that makes the iid SEs badly wrong. This is a concern for inference in general, but it cannot manufacture a rejection here; it could only mask a small effect.\n - A data-revision or vintage problem in the FRED series. This was not checked: only one vintage was available, and we cannot verify the SHA beyond what the README states.\n- **Holiday-spanning changes:** these mix one-day and multi-day changes. Under the prescribed rule they are attributed to the weekday of t, which mostly inflates Tuesday variance. A per-business-day normalisation or excluding these changes was not tried.\n- **Not checked:**\n - Sub-period stability, e.g. 2008 crisis versus calm periods, or the pre/post-2010 weekday means separately beyond the training means.\n - Interaction with macro-announcement days. Employment reports fall on Fridays and FOMC decisions mid-week, so any weekday pattern could reflect an announcement calendar rather than the weekday itself.\n - Robust or median-based contrasts, and HAC or GARCH-adjusted inference.\n- **Power:** with about 1,000 observations per weekday and a daily SD of about 5 bp, effects smaller than about 0.4–0.5 bp cannot be detected. \"No detectable effect\" therefore does not mean \"exactly zero\".\n- **Multiple comparisons and folklore:** the Monday contrast was pre-specified by the task. Any other weekday contrast singled out from Table 1 (e.g. Tuesday's −0.22 bp) would be data-mined and is not significant anyway.\n- **Generality:** the result covers one maturity, one 20-year window and the mean only. It says nothing about weekday patterns in volatility, which Levene's test suggests do exist.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA does not reject equal weekday mean changes (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption is rejected by Levene's test; Kruskal-Wallis (supplementary) agrees with the null.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, indistinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"iid assumption; no HAC adjustment for volatility clustering.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample RMSE over the zero-change forecast (4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Difference is not significant at 5% by a supplementary Diebold-Mariano test (p=0.063).\"},\n {\"id\":\"C4\",\"text\":\"Overall, the day of the week carries no detectable predictive content for daily 10-year yield changes in 2000-2019.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Absence of evidence: effects below about 0.4 bp/day cannot be ruled out.\"}\n]\nEOF\nmd5 out/results.json; 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All required files are written, and rerunning `out/analysis.py` produced an identical `results.json`.\n\n| Estimand | Result |\n|---|---|\n| Equal weekday means (ANOVA, all 5,001 changes) | F = 0.263, p = 0.902, not rejected |\n| Monday minus the other weekdays | +0.050 bp, 95% interval [−0.319, +0.420] bp |\n| Out-of-sample error (RMSE), 2010–2019 | weekday means 4.884 bp vs zero-change forecast 4.876 bp |\n\n- **Conclusion code:** `no_detectable_effect`.\n- **Data:** the input file has 5,217 rows. 215 have no yield (market holidays) and were dropped before computing changes, as the task requires. That leaves 2,500 changes for estimation (to 2009) and 2,501 for evaluation (from 2010).\n- **Monday interval:** I used a Welch two-sample interval, which doesn't assume equal variances.\n- **Out-of-sample:** forecasting with the weekday means is slightly worse than predicting no change. An extra check (Diebold–Mariano) puts that difference at p = 0.063, so it is not significant at 5%.\n\nLimitations:\n- **Unequal variances:** Levene's test strongly rejects equal variance across weekdays (p ≈ 1e-7), so the ANOVA's equal-variance assumption doesn't hold. A rank-based check (Kruskal–Wallis, p = 0.52) agrees with the null result.\n- **Holiday gaps:** 214 changes span a holiday and cover more than one business day, mostly Tuesdays after a Monday holiday. As the task prescribes, I kept them and assigned them to the weekday they end on.\n- **Independence assumed:** the intervals treat daily changes as independent. I did not adjust for volatility clustering, which would probably widen them somewhat.\n- **Not checked:** stability across sub-periods (e.g. the 2008 crisis) and confounding by macro announcement days (jobs reports fall on Fridays). Both are flagged in the report.\n- **Power:** a true weekday effect smaller than about 0.4 bp per day could not be detected, so this is not evidence that the effect is exactly zero.\n\nFiles are in `out/`:\n- analysis.py\n- results.json\n- table1.csv\n- figure1.png\n- report.md\n- claims.json"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3321,"cache_read_input_tokens":11290,"output_tokens":877,"output_tokens_details":{"thinking_tokens":36},"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":3321},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtBC1hyduuXXiQwY2Sd","requestRef":"36d75df8-ba40-43e5-81e2-a65e951db25a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d432ea48-75b1-4cf3-8e14-89784c2d07a4","timestamp":"2026-10-30T05:38:56.761Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0024","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","version":"2.1.314","gitBranch":"HEAD"}43{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"d432ea48-75b1-4cf3-8e14-89784c2d07a4","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3"}44{"type":"cost-state","sessionId":"48a2ebe8-2c4d-4b43-a407-2f0e518437e3","totalCostUSD":0.2218044,"totalAPIDuration":66639,"totalAPIDurationWithoutRetries":66636,"totalToolDuration":2189,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":72020,"startTime":1793338664765,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":7427,"thinkingTokens":259,"cacheReadInputTokens":38367,"cacheCreationInputTokens":13107,"cacheCreationEphemeral5mInputTokens":13107,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2218044}},"hasUnknownModelCost":false}45