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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 128\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 01:21 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:19 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6376 oct. 30 01:20 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1759 oct. 30 01:21 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 240 oct. 30 01:20 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 29551 oct. 30 01:20 figure1.png\n-rw-r--r-- 1 simon-pierreboucher staff 6762 oct. 30 01:21 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2043 oct. 30 01:20 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:21 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:21 state\n-rw-r--r-- 1 simon-pierreboucher staff 293 oct. 30 01:20 table1.csv\n{\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"\n },\n {\n \"issue\": \"213 changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"\n },\n {\n \"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"\n },\n {\n \"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"\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\": 0.26346571680712,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169966,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"effect_se_welch\": 0.18837955539096202,\n \"effect_df_welch\": 1548.9581965107568,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695535,\n \"Tuesday\": -0.40545808966861635,\n \"Wednesday\": -0.20542635658914643,\n \"Thursday\": 0.013833992094862349,\n \"Friday\": 0.11683168316831462\n },\n \"rmse_ratio_model_over_baseline\": 1.0016689625518016,\n \"robust_kruskal_wallis_p\": 0.47102318097227847,\n \"robust_levene_p\": 1.0122651168712971e-07,\n \"robust_effect_ci_hc1\": [\n -0.3189570734770597,\n 0.4192972702804604\n ],\n \"robust_effect_ci_nw5\": [\n -0.3292803583071403,\n 0.429620555110541\n ],\n \"robust_anova_p_excl_crisis_2008_09_to_2009_06\": 0.5883921797034571,\n \"n_excl_crisis\": 4794\n}[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption rejected by Levene; independence assumed.\"},\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\":\"Small effects below ~0.4 bp cannot be ruled out; Monday changes include weekends.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve 2010-2019 forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single split; no formal forecast-comparison test.\"},\n {\"id\":\"C4\",\"text\":\"The no-effect conclusion is robust to HC1/Newey-West intervals for the Monday contrast, a Kruskal-Wallis test, and excluding the 2008-09 crisis window.\",\"results_keys\":[\"robust_effect_ci_hc1\",\"robust_effect_ci_nw5\",\"robust_kruskal_wallis_p\",\"robust_anova_p_excl_crisis_2008_09_to_2009_06\",\"n_excl_crisis\"],\"status\":\"supported\",\"caveat\":\"Joint test not made HAC/Welch-robust.\"},\n {\"id\":\"C5\",\"text\":\"Weekday variances differ significantly, so the classical ANOVA p-value is approximate.\",\"results_keys\":[\"robust_levene_p\"],\"status\":\"supported\",\"caveat\":\"Levene test also assumes independence.\"},\n {\"id\":\"C6\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Applies to the stated estimands and period only.\"}\n]","is_error":false}]},"uuid":"587e58c7-0b5c-4c27-b748-1f6f0de87c32","timestamp":"2026-10-30T05:21:58.614Z","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/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 128\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 01:21 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:19 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6376 oct. 30 01:20 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1759 oct. 30 01:21 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 240 oct. 30 01:20 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 29551 oct. 30 01:20 figure1.png\n-rw-r--r-- 1 simon-pierreboucher staff 6762 oct. 30 01:21 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2043 oct. 30 01:20 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:21 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:21 state\n-rw-r--r-- 1 simon-pierreboucher staff 293 oct. 30 01:20 table1.csv\n{\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"\n },\n {\n \"issue\": \"213 changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"\n },\n {\n \"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"\n },\n {\n \"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"\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\": 0.26346571680712,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169966,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"effect_se_welch\": 0.18837955539096202,\n \"effect_df_welch\": 1548.9581965107568,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695535,\n \"Tuesday\": -0.40545808966861635,\n \"Wednesday\": -0.20542635658914643,\n \"Thursday\": 0.013833992094862349,\n \"Friday\": 0.11683168316831462\n },\n \"rmse_ratio_model_over_baseline\": 1.0016689625518016,\n \"robust_kruskal_wallis_p\": 0.47102318097227847,\n \"robust_levene_p\": 1.0122651168712971e-07,\n \"robust_effect_ci_hc1\": [\n -0.3189570734770597,\n 0.4192972702804604\n ],\n \"robust_effect_ci_nw5\": [\n -0.3292803583071403,\n 0.429620555110541\n ],\n \"robust_anova_p_excl_crisis_2008_09_to_2009_06\": 0.5883921797034571,\n \"n_excl_crisis\": 4794\n}[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption rejected by Levene; independence assumed.\"},\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\":\"Small effects below ~0.4 bp cannot be ruled out; Monday changes include weekends.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve 2010-2019 forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single split; no formal forecast-comparison test.\"},\n {\"id\":\"C4\",\"text\":\"The no-effect conclusion is robust to HC1/Newey-West intervals for the Monday contrast, a Kruskal-Wallis test, and excluding the 2008-09 crisis window.\",\"results_keys\":[\"robust_effect_ci_hc1\",\"robust_effect_ci_nw5\",\"robust_kruskal_wallis_p\",\"robust_anova_p_excl_crisis_2008_09_to_2009_06\",\"n_excl_crisis\"],\"status\":\"supported\",\"caveat\":\"Joint test not made HAC/Welch-robust.\"},\n {\"id\":\"C5\",\"text\":\"Weekday variances differ significantly, so the classical ANOVA p-value is approximate.\",\"results_keys\":[\"robust_levene_p\"],\"status\":\"supported\",\"caveat\":\"Levene test also assumes independence.\"},\n {\"id\":\"C6\",\"text\":\"Overall conclusion: no detectable day-of-week effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Applies to the stated estimands and period only.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b2175d62-939e-4677-b820-47b20c9232e4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","version":"2.1.314","gitBranch":"HEAD"}14{"type":"last-prompt","lastPrompt":"Review","leafUuid":"587e58c7-0b5c-4c27-b748-1f6f0de87c32","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c"}15{"type":"atis-latch","atis":"","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c"}16{"type":"api-request-blob","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","hash":"460e20fa51e2f8cf060ca1dd1fd7fdd50476636acfe843beddb89ca9ff2c026d","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01843oMrjB3AHx2u3miM6aMK","name":"Bash","input":{"command":"cat TASK.md; 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Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/figure1.png, out/table1.csv, 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 statsmodels.formula.api as smf\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\"]\nSPLIT = pd.Timestamp(\"2009-12-31\")\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nassert not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 (no yield published)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: drop empty rows first\"),\n dict(step=2, rule=\"first available observation has no one-day change\", n_before=n_raw - n_missing,\n n_after=n_changes, n_removed=n_raw - n_missing - n_changes, rationale=\"TASK rule: change needs y_{t-1}\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= SPLIT]; test = ch[ch[\"date\"] > SPLIT]\nassert train[\"date\"].max() < test[\"date\"].min()\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n# robustness (not a required key): Welch-type check via Kruskal-Wallis and Levene, reported in stdout only\nkw = stats.kruskal(*groups); lev = stats.levene(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.wd == 0, \"dy\"].to_numpy(); oth = ch.loc[ch.wd != 0, \"dy\"].to_numpy()\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# robustness (extra keys): Monday-indicator regression with HC1 and Newey-West(5) SEs; ANOVA excluding 2008-09 to 2009-06 crisis window\nch[\"mon\"] = (ch[\"wd\"] == 0).astype(int)\nhc1 = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HC1\").conf_int().loc[\"mon\"].tolist()\nnw5 = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5}).conf_int().loc[\"mon\"].tolist()\nxc = ch[(ch[\"date\"] < \"2008-09-01\") | (ch[\"date\"] > \"2009-06-30\")]\np_xc = stats.f_oneway(*[xc.loc[xc.wd == k, \"dy\"] for k in range(5)]).pvalue\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nfc = 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() - fc) ** 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 and figure\nrows = []\nfor k, g in enumerate(groups):\n rows.append(dict(weekday=DAYS[k], n=len(g), mean_bp=g.mean(), se_bp=g.std(ddof=1) / np.sqrt(len(g))))\ntab = pd.DataFrame(rows)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, r[\"n\"] - 1) * r[\"se_bp\"] for r in rows]\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([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95%% CI, all %d changes)\" % n_changes)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\ngap_counts = ch[\"gap\"].value_counts().sort_index()\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"},\n {\"issue\": f\"{int((ch.gap > 3).sum() + ((ch.gap == 2)).sum())} changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"},\n {\"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"},\n {\"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"},\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(eff),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0,\n \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wmeans.items()},\n \"rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"robust_kruskal_wallis_p\": float(kw.pvalue),\n \"robust_levene_p\": float(lev.pvalue),\n \"robust_effect_ci_hc1\": [float(v) for v in hc1],\n \"robust_effect_ci_nw5\": [float(v) for v in nw5],\n \"robust_anova_p_excl_crisis_2008_09_to_2009_06\": float(p_xc),\n \"n_excl_crisis\": int(len(xc)),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week help predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10) over 2000–2019? There are three estimands: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on Tuesday–Friday), with a 95% interval; (iii) the out-of-sample RMSE of weekday-mean forecasts against a zero-change forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`.\n- 215 rows have an empty yield field because of US market holidays, including the 2001-09-11/12 closure (`n_missing_rows`). These rows were dropped first. One more observation was lost because the first available observation has no change. This leaves 5001 daily changes (`n_analysed`), see `out/exclusions.csv`.\n- The estimation period (changes dated ≤ 2009-12-31) has 2500 changes (`n_train`). The evaluation period (changes dated ≥ 2010-01-01; the first is 2010-01-04) has 2501 changes (`n_test`).\n- Data issues are listed in `data_issues`:\n - Changes after a holiday span more than one trading session. As the task prescribes, they were kept and attributed to the weekday of t.\n - Crisis-period moves are extreme (for example −51 bp on 2009-03-18). They were kept.\n - Yields are quoted on a 1 bp grid.\n- No values were trimmed or winsorised.\n\n## Method\n- **Joint test (i).** Classical one-way equal-variance ANOVA of Δy on weekday, over all 5001 changes, with an F(4, 4996) reference distribution (scipy `f_oneway`). It was cross-checked against the overall F from an OLS regression on weekday dummies.\n- **Monday contrast (ii).** Difference in means with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- **Out-of-sample comparison (iii).** The model forecast for each evaluation-period change is the estimation-period mean change of its weekday (`train_weekday_means_bp`). The baseline forecast is 0. Both are scored by RMSE over the evaluation period. No evaluation-period information enters the forecasts.\n- **Conclusion rule.** Use the ANOVA at 5%. If it rejects, the sign comes from the Monday interval.\n- **Robustness checks (extra keys, not part of the decision rule):**\n - Monday-indicator OLS with HC1 and Newey–West (5 lags) intervals;\n - Kruskal–Wallis test;\n - Levene test for equal variances;\n - ANOVA excluding the 2008-09 to 2009-06 crisis window.\n- **Outputs and reproducibility.** `out/table1.csv` and `out/figure1.png` (weekday means with 95% t-intervals) are produced by `out/analysis.py`. No randomness is used (`random_seed` = null).\n\n## Results\n- **Joint test.** ANOVA F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equality of the five weekday means is not rejected.\n- **Monday contrast.** +0.05 bp (`effect_estimate`), 95% Welch CI [−0.32, +0.42] bp (`effect_ci_low`, `effect_ci_high`). This is small next to the typical daily move (evaluation-period RMSE of about 4.9 bp, `baseline_metric`), and it is not distinguishable from zero.\n- **Weekday means.** All five lie within about ±0.22 bp of zero; every 95% bar in figure 1 covers zero (`out/table1.csv`).\n- **Out of sample.** The weekday-mean forecast has RMSE 4.884 bp (`model_metric`), against 4.876 bp for the zero-change forecast (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday-specific means estimated in 2000–2009 slightly *worsen* forecasts for 2010–2019.\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this period, the weekday carries no detectable predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday contrast is pinned down to within about ±0.37 bp (half-width of [`effect_ci_low`, `effect_ci_high`]). Weekday effects larger than about 0.4 bp per day on Mondays are therefore inconsistent with these data, but smaller ones cannot be ruled out.\n- The Welch interval agrees with the HC1 interval (`robust_effect_ci_hc1`) and with the Newey–West(5) interval, which allows for short serial correlation (`robust_effect_ci_nw5`).\n- The rank-based Kruskal–Wallis test also does not reject (`robust_kruskal_wallis_p` = 0.47).\n- Excluding the 2008–09 crisis window (4794 changes, `n_excl_crisis`), the ANOVA still does not reject (`robust_anova_p_excl_crisis_2008_09_to_2009_06` = 0.59).\n- The out-of-sample RMSE difference was not given a formal test (for example Diebold–Mariano). The 0.17% gap is descriptive only.\n\n## Limitations and open issues\n- **Equal-variance assumption is violated.** Levene's test strongly rejects equal weekday variances (`robust_levene_p` ≈ 1e-7). The prescribed classical ANOVA p-value is therefore approximate. Heteroskedasticity-robust alternatives (`robust_effect_ci_hc1`, `robust_kruskal_wallis_p`) lead to the same conclusion, but a Welch-type ANOVA for the joint test was not computed.\n- **Dependence and volatility clustering.** The ANOVA and Welch tests assume independent observations. Daily yield changes show strong volatility clustering, especially in 2008–09. Only the Monday contrast was given a HAC check (`robust_effect_ci_nw5`); the joint test was not made HAC-robust.\n- **Holiday-spanning changes.** Changes after a holiday cover two sessions and are attributed to the weekday of t (mostly Tuesdays after Monday holidays). This mixes a calendar-gap effect into the weekday comparison. Excluding or rescaling these changes was not examined.\n- **Weekend changes.** A Monday change spans the weekend (3 calendar days). The data cannot separate weekend news from a genuine Monday effect.\n- **Low power for tiny effects and non-stationarity.** With daily standard deviations of about 5–6 bp, effects of a few tenths of a bp are hard to detect. Weekday patterns could also exist in sub-periods (for example around announcement-heavy days such as payroll Fridays or FOMC Wednesdays) and average out over 20 years. No sub-period or event-day analysis was done beyond the crisis exclusion.\n- **Narrow out-of-sample test.** The comparison uses a single fixed split (2000–2009 vs 2010–2019). Rolling or expanding re-estimation and formal forecast-comparison tests were not done.\n- **Data revisions and other maturities.** FRED vintage effects and other maturities were not checked; the analysis uses only the given file.\n- **What would overturn the result.** A data error in the file (none detected in the audit), or a different definition of the change (for example per-session changes), could alter the result. The conclusion applies only to the stated estimands under the task's definitions.\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052230685527746824,0.1643858563971029\nTuesday,1028,-0.2198443579766538,0.17700678391028105\nWednesday,1030,0.01650485436893226,0.18234593373486246\nThursday,1012,-0.059288537549406724,0.18691776551235453\nFriday,1012,-0.14723320158102893,0.18991766797184334\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (no yield published),5217,5002,215,TASK rule: drop empty rows first\n2,first available observation has no one-day change,5002,5001,1,TASK rule: change needs y_{t-1}\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false}]},"uuid":"038afdac-ca46-4639-9e6a-2132c66644cc","timestamp":"2026-10-30T05:21:59.952Z","toolUseResult":{"stdout":"\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/figure1.png, out/table1.csv, 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 statsmodels.formula.api as smf\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\"]\nSPLIT = pd.Timestamp(\"2009-12-31\")\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nassert not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 (no yield published)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: drop empty rows first\"),\n dict(step=2, rule=\"first available observation has no one-day change\", n_before=n_raw - n_missing,\n n_after=n_changes, n_removed=n_raw - n_missing - n_changes, rationale=\"TASK rule: change needs y_{t-1}\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= SPLIT]; test = ch[ch[\"date\"] > SPLIT]\nassert train[\"date\"].max() < test[\"date\"].min()\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n# robustness (not a required key): Welch-type check via Kruskal-Wallis and Levene, reported in stdout only\nkw = stats.kruskal(*groups); lev = stats.levene(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.wd == 0, \"dy\"].to_numpy(); oth = ch.loc[ch.wd != 0, \"dy\"].to_numpy()\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# robustness (extra keys): Monday-indicator regression with HC1 and Newey-West(5) SEs; ANOVA excluding 2008-09 to 2009-06 crisis window\nch[\"mon\"] = (ch[\"wd\"] == 0).astype(int)\nhc1 = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HC1\").conf_int().loc[\"mon\"].tolist()\nnw5 = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5}).conf_int().loc[\"mon\"].tolist()\nxc = ch[(ch[\"date\"] < \"2008-09-01\") | (ch[\"date\"] > \"2009-06-30\")]\np_xc = stats.f_oneway(*[xc.loc[xc.wd == k, \"dy\"] for k in range(5)]).pvalue\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nfc = 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() - fc) ** 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 and figure\nrows = []\nfor k, g in enumerate(groups):\n rows.append(dict(weekday=DAYS[k], n=len(g), mean_bp=g.mean(), se_bp=g.std(ddof=1) / np.sqrt(len(g))))\ntab = pd.DataFrame(rows)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, r[\"n\"] - 1) * r[\"se_bp\"] for r in rows]\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([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95%% CI, all %d changes)\" % n_changes)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\ngap_counts = ch[\"gap\"].value_counts().sort_index()\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"},\n {\"issue\": f\"{int((ch.gap > 3).sum() + ((ch.gap == 2)).sum())} changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"},\n {\"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"},\n {\"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"},\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(eff),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0,\n \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wmeans.items()},\n \"rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"robust_kruskal_wallis_p\": float(kw.pvalue),\n \"robust_levene_p\": float(lev.pvalue),\n \"robust_effect_ci_hc1\": [float(v) for v in hc1],\n \"robust_effect_ci_nw5\": [float(v) for v in nw5],\n \"robust_anova_p_excl_crisis_2008_09_to_2009_06\": float(p_xc),\n \"n_excl_crisis\": int(len(xc)),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week help predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10) over 2000–2019? There are three estimands: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on Tuesday–Friday), with a 95% interval; (iii) the out-of-sample RMSE of weekday-mean forecasts against a zero-change forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`.\n- 215 rows have an empty yield field because of US market holidays, including the 2001-09-11/12 closure (`n_missing_rows`). These rows were dropped first. One more observation was lost because the first available observation has no change. This leaves 5001 daily changes (`n_analysed`), see `out/exclusions.csv`.\n- The estimation period (changes dated ≤ 2009-12-31) has 2500 changes (`n_train`). The evaluation period (changes dated ≥ 2010-01-01; the first is 2010-01-04) has 2501 changes (`n_test`).\n- Data issues are listed in `data_issues`:\n - Changes after a holiday span more than one trading session. As the task prescribes, they were kept and attributed to the weekday of t.\n - Crisis-period moves are extreme (for example −51 bp on 2009-03-18). They were kept.\n - Yields are quoted on a 1 bp grid.\n- No values were trimmed or winsorised.\n\n## Method\n- **Joint test (i).** Classical one-way equal-variance ANOVA of Δy on weekday, over all 5001 changes, with an F(4, 4996) reference distribution (scipy `f_oneway`). It was cross-checked against the overall F from an OLS regression on weekday dummies.\n- **Monday contrast (ii).** Difference in means with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- **Out-of-sample comparison (iii).** The model forecast for each evaluation-period change is the estimation-period mean change of its weekday (`train_weekday_means_bp`). The baseline forecast is 0. Both are scored by RMSE over the evaluation period. No evaluation-period information enters the forecasts.\n- **Conclusion rule.** Use the ANOVA at 5%. If it rejects, the sign comes from the Monday interval.\n- **Robustness checks (extra keys, not part of the decision rule):**\n - Monday-indicator OLS with HC1 and Newey–West (5 lags) intervals;\n - Kruskal–Wallis test;\n - Levene test for equal variances;\n - ANOVA excluding the 2008-09 to 2009-06 crisis window.\n- **Outputs and reproducibility.** `out/table1.csv` and `out/figure1.png` (weekday means with 95% t-intervals) are produced by `out/analysis.py`. No randomness is used (`random_seed` = null).\n\n## Results\n- **Joint test.** ANOVA F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equality of the five weekday means is not rejected.\n- **Monday contrast.** +0.05 bp (`effect_estimate`), 95% Welch CI [−0.32, +0.42] bp (`effect_ci_low`, `effect_ci_high`). This is small next to the typical daily move (evaluation-period RMSE of about 4.9 bp, `baseline_metric`), and it is not distinguishable from zero.\n- **Weekday means.** All five lie within about ±0.22 bp of zero; every 95% bar in figure 1 covers zero (`out/table1.csv`).\n- **Out of sample.** The weekday-mean forecast has RMSE 4.884 bp (`model_metric`), against 4.876 bp for the zero-change forecast (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday-specific means estimated in 2000–2009 slightly *worsen* forecasts for 2010–2019.\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this period, the weekday carries no detectable predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday contrast is pinned down to within about ±0.37 bp (half-width of [`effect_ci_low`, `effect_ci_high`]). Weekday effects larger than about 0.4 bp per day on Mondays are therefore inconsistent with these data, but smaller ones cannot be ruled out.\n- The Welch interval agrees with the HC1 interval (`robust_effect_ci_hc1`) and with the Newey–West(5) interval, which allows for short serial correlation (`robust_effect_ci_nw5`).\n- The rank-based Kruskal–Wallis test also does not reject (`robust_kruskal_wallis_p` = 0.47).\n- Excluding the 2008–09 crisis window (4794 changes, `n_excl_crisis`), the ANOVA still does not reject (`robust_anova_p_excl_crisis_2008_09_to_2009_06` = 0.59).\n- The out-of-sample RMSE difference was not given a formal test (for example Diebold–Mariano). The 0.17% gap is descriptive only.\n\n## Limitations and open issues\n- **Equal-variance assumption is violated.** Levene's test strongly rejects equal weekday variances (`robust_levene_p` ≈ 1e-7). The prescribed classical ANOVA p-value is therefore approximate. Heteroskedasticity-robust alternatives (`robust_effect_ci_hc1`, `robust_kruskal_wallis_p`) lead to the same conclusion, but a Welch-type ANOVA for the joint test was not computed.\n- **Dependence and volatility clustering.** The ANOVA and Welch tests assume independent observations. Daily yield changes show strong volatility clustering, especially in 2008–09. Only the Monday contrast was given a HAC check (`robust_effect_ci_nw5`); the joint test was not made HAC-robust.\n- **Holiday-spanning changes.** Changes after a holiday cover two sessions and are attributed to the weekday of t (mostly Tuesdays after Monday holidays). This mixes a calendar-gap effect into the weekday comparison. Excluding or rescaling these changes was not examined.\n- **Weekend changes.** A Monday change spans the weekend (3 calendar days). The data cannot separate weekend news from a genuine Monday effect.\n- **Low power for tiny effects and non-stationarity.** With daily standard deviations of about 5–6 bp, effects of a few tenths of a bp are hard to detect. Weekday patterns could also exist in sub-periods (for example around announcement-heavy days such as payroll Fridays or FOMC Wednesdays) and average out over 20 years. No sub-period or event-day analysis was done beyond the crisis exclusion.\n- **Narrow out-of-sample test.** The comparison uses a single fixed split (2000–2009 vs 2010–2019). Rolling or expanding re-estimation and formal forecast-comparison tests were not done.\n- **Data revisions and other maturities.** FRED vintage effects and other maturities were not checked; the analysis uses only the given file.\n- **What would overturn the result.** A data error in the file (none detected in the audit), or a different definition of the change (for example per-session changes), could alter the result. The conclusion applies only to the stated estimands under the task's definitions.\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052230685527746824,0.1643858563971029\nTuesday,1028,-0.2198443579766538,0.17700678391028105\nWednesday,1030,0.01650485436893226,0.18234593373486246\nThursday,1012,-0.059288537549406724,0.18691776551235453\nFriday,1012,-0.14723320158102893,0.18991766797184334\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (no yield published),5217,5002,215,TASK rule: drop empty rows first\n2,first available observation has no one-day change,5002,5001,1,TASK rule: change needs y_{t-1}\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"eda10251-a285-4c71-aa32-78c2754ed7de","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"038afdac-ca46-4639-9e6a-2132c66644cc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrtkNnjNWsYr8MbmBiP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FhmRqCE4zqSMocJLt1tX1B","name":"Bash","input":{"command":"rm -rf /tmp/rc && mkdir /tmp/rc && cp -r data out /tmp/rc/ && cd /tmp/rc && ~/wp29/venv/bin/python out/analysis.py >/dev/null && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0273/out/results.json) && echo SAME; 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cat out/report.md; cat out/table1.csv; cat out/exclusions.csv; ls out/state"}},{"type":"tool_use","id":"toolu_01FhmRqCE4zqSMocJLt1tX1B","name":"Bash","input":{"command":"rm -rf /tmp/rc && mkdir /tmp/rc && cp -r data out /tmp/rc/ && cd /tmp/rc && ~/wp29/venv/bin/python out/analysis.py >/dev/null && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0273/out/results.json) && echo SAME; diff out/table1.csv ~/wp29/runs/RC0273/out/table1.csv && echo TSAME\n~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/DGS10.csv');print(len(d),d.DGS10.isna().sum())\ns=d.dropna();y=s.DGS10.values;print(len(s)-1)\n\""}}]}}25{"type":"api-request-blob","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","hash":"d34822675fd423e7c75901142741efa287d19b7f83d06391a9032770e8f1a35a","message":{"role":"user","content":[{"tool_use_id":"toolu_018QrprLTizakCpJPVV7761k","type":"tool_result","content":"\"\"\"Replication entry point for F6-conf-01. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/figure1.png, out/table1.csv, 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 statsmodels.formula.api as smf\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\"]\nSPLIT = pd.Timestamp(\"2009-12-31\")\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw.loc[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"])\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nd = d.sort_values(\"date\").reset_index(drop=True)\nassert not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available observation has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nn_changes = len(ch)\n\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with empty DGS10 (no yield published)\", n_before=n_raw,\n n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: drop empty rows first\"),\n dict(step=2, rule=\"first available observation has no one-day change\", n_before=n_raw - n_missing,\n n_after=n_changes, n_removed=n_raw - n_missing - n_changes, rationale=\"TASK rule: change needs y_{t-1}\"),\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= SPLIT]; test = ch[ch[\"date\"] > SPLIT]\nassert train[\"date\"].max() < test[\"date\"].min()\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch.wd == k, \"dy\"].to_numpy() for k in range(5)]\nF, p = stats.f_oneway(*groups)\n# robustness (not a required key): Welch-type check via Kruskal-Wallis and Levene, reported in stdout only\nkw = stats.kruskal(*groups); lev = stats.levene(*groups)\n\n# (ii) Monday contrast, Welch two-sample t interval\nmon = ch.loc[ch.wd == 0, \"dy\"].to_numpy(); oth = ch.loc[ch.wd != 0, \"dy\"].to_numpy()\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# robustness (extra keys): Monday-indicator regression with HC1 and Newey-West(5) SEs; ANOVA excluding 2008-09 to 2009-06 crisis window\nch[\"mon\"] = (ch[\"wd\"] == 0).astype(int)\nhc1 = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HC1\").conf_int().loc[\"mon\"].tolist()\nnw5 = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5}).conf_int().loc[\"mon\"].tolist()\nxc = ch[(ch[\"date\"] < \"2008-09-01\") | (ch[\"date\"] > \"2009-06-30\")]\np_xc = stats.f_oneway(*[xc.loc[xc.wd == k, \"dy\"] for k in range(5)]).pvalue\n\n# (iii) out-of-sample RMSE\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nfc = 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() - fc) ** 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 and figure\nrows = []\nfor k, g in enumerate(groups):\n rows.append(dict(weekday=DAYS[k], n=len(g), mean_bp=g.mean(), se_bp=g.std(ddof=1) / np.sqrt(len(g))))\ntab = pd.DataFrame(rows)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, r[\"n\"] - 1) * r[\"se_bp\"] for r in rows]\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([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y UST yield: mean daily change by weekday, 2000-2019\\n(95%% CI, all %d changes)\" % n_changes)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\ngap_counts = ch[\"gap\"].value_counts().sort_index()\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays incl. 2001-09-11/12)\",\n \"handling\": \"dropped before differencing, per TASK rules\"},\n {\"issue\": f\"{int((ch.gap > 3).sum() + ((ch.gap == 2)).sum())} changes span a holiday (calendar gap 2 or 4 days), i.e. cover more than one trading session\",\n \"handling\": \"kept and attributed to weekday of t as the TASK prescribes; noted as a limitation\"},\n {\"issue\": \"extreme changes (e.g. -51 bp on 2009-03-18, -27 bp on 2008-09-15) during the 2008-09 crisis\",\n \"handling\": \"kept (genuine market events); no outlier trimming; discussed as limitation\"},\n {\"issue\": \"yields quoted to 0.01 percentage point, so changes are discretised at 1 bp\",\n \"handling\": \"none needed; noted\"},\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(eff),\n \"effect_ci_low\": float(lo),\n \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse0,\n \"model_metric\": rmse1,\n \"conclusion\": concl,\n \"effect_se_welch\": float(se),\n \"effect_df_welch\": float(df),\n \"train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wmeans.items()},\n \"rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"robust_kruskal_wallis_p\": float(kw.pvalue),\n \"robust_levene_p\": float(lev.pvalue),\n \"robust_effect_ci_hc1\": [float(v) for v in hc1],\n \"robust_effect_ci_nw5\": [float(v) for v in nw5],\n \"robust_anova_p_excl_crisis_2008_09_to_2009_06\": float(p_xc),\n \"n_excl_crisis\": int(len(xc)),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week help predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10) over 2000–2019? There are three estimands: (i) a joint test that the five weekday means are equal; (ii) the Monday contrast (mean on Mondays minus mean on Tuesday–Friday), with a 95% interval; (iii) the out-of-sample RMSE of weekday-mean forecasts against a zero-change forecast.\n\n## Data and exclusions\n- Input: `data/DGS10.csv`, 5217 rows (`n_input_rows`), one per weekday from 2000-01-03 to 2019-12-31. Its SHA-256 matches `data/README.md`.\n- 215 rows have an empty yield field because of US market holidays, including the 2001-09-11/12 closure (`n_missing_rows`). These rows were dropped first. One more observation was lost because the first available observation has no change. This leaves 5001 daily changes (`n_analysed`), see `out/exclusions.csv`.\n- The estimation period (changes dated ≤ 2009-12-31) has 2500 changes (`n_train`). The evaluation period (changes dated ≥ 2010-01-01; the first is 2010-01-04) has 2501 changes (`n_test`).\n- Data issues are listed in `data_issues`:\n - Changes after a holiday span more than one trading session. As the task prescribes, they were kept and attributed to the weekday of t.\n - Crisis-period moves are extreme (for example −51 bp on 2009-03-18). They were kept.\n - Yields are quoted on a 1 bp grid.\n- No values were trimmed or winsorised.\n\n## Method\n- **Joint test (i).** Classical one-way equal-variance ANOVA of Δy on weekday, over all 5001 changes, with an F(4, 4996) reference distribution (scipy `f_oneway`). It was cross-checked against the overall F from an OLS regression on weekday dummies.\n- **Monday contrast (ii).** Difference in means with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- **Out-of-sample comparison (iii).** The model forecast for each evaluation-period change is the estimation-period mean change of its weekday (`train_weekday_means_bp`). The baseline forecast is 0. Both are scored by RMSE over the evaluation period. No evaluation-period information enters the forecasts.\n- **Conclusion rule.** Use the ANOVA at 5%. If it rejects, the sign comes from the Monday interval.\n- **Robustness checks (extra keys, not part of the decision rule):**\n - Monday-indicator OLS with HC1 and Newey–West (5 lags) intervals;\n - Kruskal–Wallis test;\n - Levene test for equal variances;\n - ANOVA excluding the 2008-09 to 2009-06 crisis window.\n- **Outputs and reproducibility.** `out/table1.csv` and `out/figure1.png` (weekday means with 95% t-intervals) are produced by `out/analysis.py`. No randomness is used (`random_seed` = null).\n\n## Results\n- **Joint test.** ANOVA F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equality of the five weekday means is not rejected.\n- **Monday contrast.** +0.05 bp (`effect_estimate`), 95% Welch CI [−0.32, +0.42] bp (`effect_ci_low`, `effect_ci_high`). This is small next to the typical daily move (evaluation-period RMSE of about 4.9 bp, `baseline_metric`), and it is not distinguishable from zero.\n- **Weekday means.** All five lie within about ±0.22 bp of zero; every 95% bar in figure 1 covers zero (`out/table1.csv`).\n- **Out of sample.** The weekday-mean forecast has RMSE 4.884 bp (`model_metric`), against 4.876 bp for the zero-change forecast (`baseline_metric`), a ratio of 1.0017 (`rmse_ratio_model_over_baseline`). Weekday-specific means estimated in 2000–2009 slightly *worsen* forecasts for 2010–2019.\n- **Conclusion:** `no_detectable_effect` (`conclusion`). Over this period, the weekday carries no detectable predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday contrast is pinned down to within about ±0.37 bp (half-width of [`effect_ci_low`, `effect_ci_high`]). Weekday effects larger than about 0.4 bp per day on Mondays are therefore inconsistent with these data, but smaller ones cannot be ruled out.\n- The Welch interval agrees with the HC1 interval (`robust_effect_ci_hc1`) and with the Newey–West(5) interval, which allows for short serial correlation (`robust_effect_ci_nw5`).\n- The rank-based Kruskal–Wallis test also does not reject (`robust_kruskal_wallis_p` = 0.47).\n- Excluding the 2008–09 crisis window (4794 changes, `n_excl_crisis`), the ANOVA still does not reject (`robust_anova_p_excl_crisis_2008_09_to_2009_06` = 0.59).\n- The out-of-sample RMSE difference was not given a formal test (for example Diebold–Mariano). The 0.17% gap is descriptive only.\n\n## Limitations and open issues\n- **Equal-variance assumption is violated.** Levene's test strongly rejects equal weekday variances (`robust_levene_p` ≈ 1e-7). The prescribed classical ANOVA p-value is therefore approximate. Heteroskedasticity-robust alternatives (`robust_effect_ci_hc1`, `robust_kruskal_wallis_p`) lead to the same conclusion, but a Welch-type ANOVA for the joint test was not computed.\n- **Dependence and volatility clustering.** The ANOVA and Welch tests assume independent observations. Daily yield changes show strong volatility clustering, especially in 2008–09. Only the Monday contrast was given a HAC check (`robust_effect_ci_nw5`); the joint test was not made HAC-robust.\n- **Holiday-spanning changes.** Changes after a holiday cover two sessions and are attributed to the weekday of t (mostly Tuesdays after Monday holidays). This mixes a calendar-gap effect into the weekday comparison. Excluding or rescaling these changes was not examined.\n- **Weekend changes.** A Monday change spans the weekend (3 calendar days). The data cannot separate weekend news from a genuine Monday effect.\n- **Low power for tiny effects and non-stationarity.** With daily standard deviations of about 5–6 bp, effects of a few tenths of a bp are hard to detect. Weekday patterns could also exist in sub-periods (for example around announcement-heavy days such as payroll Fridays or FOMC Wednesdays) and average out over 20 years. No sub-period or event-day analysis was done beyond the crisis exclusion.\n- **Narrow out-of-sample test.** The comparison uses a single fixed split (2000–2009 vs 2010–2019). Rolling or expanding re-estimation and formal forecast-comparison tests were not done.\n- **Data revisions and other maturities.** FRED vintage effects and other maturities were not checked; the analysis uses only the given file.\n- **What would overturn the result.** A data error in the file (none detected in the audit), or a different definition of the change (for example per-session changes), could alter the result. The conclusion applies only to the stated estimands under the task's definitions.\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052230685527746824,0.1643858563971029\nTuesday,1028,-0.2198443579766538,0.17700678391028105\nWednesday,1030,0.01650485436893226,0.18234593373486246\nThursday,1012,-0.059288537549406724,0.18691776551235453\nFriday,1012,-0.14723320158102893,0.18991766797184334\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 (no yield published),5217,5002,215,TASK rule: drop empty rows first\n2,first available observation has no one-day change,5002,5001,1,TASK rule: change needs y_{t-1}\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false},{"tool_use_id":"toolu_01FhmRqCE4zqSMocJLt1tX1B","type":"tool_result","content":"SAME\nTSAME\n5217 215\n5001\nShell cwd was reset to 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observation_date DGS10 date\n441 2001-09-11 NaN 2001-09-11\n442 2001-09-12 NaN 2001-09-12\n214\n date dy\n468 2001-11-15 25.0\n2177 2008-09-15 -27.0\n2223 2008-11-20 -28.0\n2281 2009-02-17 -25.0\n2302 2009-03-18 -51.0\nout/figure1.png: PNG image data, 720 x 480, 8-bit/color RGBA, non-interlaced\n# Verification checks (P4)\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run of out/analysis.py (deleted outputs) regenerates byte-identical results.json | PASS |\n| 2 | Exclusion ledger: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500 + 2501) = sum of table1 n | PASS |\n| 3 | ANOVA F cross-checked with statsmodels OLS dy~C(weekday) overall F (0.26347, p 0.9015, df 4/4996) | PASS (identical to 1e-12) |\n| 4 | Monday contrast Welch CI contains estimate; CI agrees with HC1 regression CI and Newey-West(5) CI to ~0.01 bp | PASS |\n| 5 | SEs positive (table1 se_bp, effect_se_welch) | PASS |\n| 6 | Chronology: max train date 2009-12-31 < min test date 2010-01-04; weekday means computed from train only (train_weekday_means_bp) | PASS |\n| 7 | Magnitudes: baseline RMSE 4.88 bp vs full-sample sd of changes 5.72 bp (2000-09 more volatile) — plausible; model/baseline ratio ≈1.002 | PASS |\n| 8 | Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect; Monday CI also covers 0 (consistent) | PASS |\n| 9 | Equal-variance assumption of classical ANOVA: Levene p≈1e-7 rejects equal variances → ANOVA kept as prescribed; Kruskal-Wallis (p 0.47) and HC/HAC contrast CIs agree in not rejecting | NOTED (limitation) |","is_error":false}]},"uuid":"2a544b9b-2cbc-4378-a465-4a6205ad268f","timestamp":"2026-10-30T05:22:10.921Z","toolUseResult":{"stdout":" observation_date DGS10 date\n441 2001-09-11 NaN 2001-09-11\n442 2001-09-12 NaN 2001-09-12\n214\n date dy\n468 2001-11-15 25.0\n2177 2008-09-15 -27.0\n2223 2008-11-20 -28.0\n2281 2009-02-17 -25.0\n2302 2009-03-18 -51.0\nout/figure1.png: PNG image data, 720 x 480, 8-bit/color RGBA, non-interlaced\n# Verification checks (P4)\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run of out/analysis.py (deleted outputs) regenerates byte-identical results.json | PASS |\n| 2 | Exclusion ledger: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500 + 2501) = sum of table1 n | PASS |\n| 3 | ANOVA F cross-checked with statsmodels OLS dy~C(weekday) overall F (0.26347, p 0.9015, df 4/4996) | PASS (identical to 1e-12) |\n| 4 | Monday contrast Welch CI contains estimate; CI agrees with HC1 regression CI and Newey-West(5) CI to ~0.01 bp | PASS |\n| 5 | SEs positive (table1 se_bp, effect_se_welch) | PASS |\n| 6 | Chronology: max train date 2009-12-31 < min test date 2010-01-04; weekday means computed from train only (train_weekday_means_bp) | PASS |\n| 7 | Magnitudes: baseline RMSE 4.88 bp vs full-sample sd of changes 5.72 bp (2000-09 more volatile) — plausible; model/baseline ratio ≈1.002 | PASS |\n| 8 | Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect; Monday CI also covers 0 (consistent) | PASS |\n| 9 | Equal-variance assumption of classical ANOVA: Levene p≈1e-7 rejects equal variances → ANOVA kept as prescribed; Kruskal-Wallis (p 0.47) and HC/HAC contrast CIs agree in not rejecting | NOTED (limitation) 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That misses the 2001-09-13 change (Mon 09-10 to Thu 09-13, a 3-day calendar gap that spans two missing sessions). Counting by business days gives 214. This is descriptive only and no estimate is affected.\", \"suggested_fix\": \"Count changes whose previous available observation is not the previous business day, or describe the stated rule exactly.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"report.md:Uncertainty\", \"issue\": \"'Weekday effects larger than about 0.4 bp per day on Mondays are inconsistent with these data' is one-sided. The interval [-0.32, 0.42] also excludes negative Monday contrasts below about -0.32 bp, and it applies to the Monday-vs-rest contrast, not to weekday effects in general.\", \"suggested_fix\": \"Phrase this as: the Monday contrast is constrained to roughly [-0.32, +0.42] bp.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"claims.json:C3\", \"issue\": \"'Do not improve' rests on a single split with a 0.17% RMSE difference and no formal test. The caveat says this, but the claim could say 'slightly worse, not tested'.\", \"suggested_fix\": \"Say the difference is descriptive and untested, for example that no Diebold–Mariano test was run.\"}\n ],\n \"summary\": \"analysis.py re-runs in a scratch copy and reproduces results.json and table1.csv exactly. Independent checks confirm 5217 input rows, 215 empty rows and 5001 changes (2500 train / 2500+1 test). 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Only minor wording and count issues were found.\"}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1546,"cache_read_input_tokens":13478,"output_tokens":1155,"output_tokens_details":{"thinking_tokens":263},"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":1546},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXruemUXujjzs2JHUtox","requestRef":"7e244bc8-7df0-4dfa-9dbe-158d28048547","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4ddc9b71-af7d-47d3-954d-0450eb31ad6e","timestamp":"2026-10-30T05:22:21.943Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"4ddc9b71-af7d-47d3-954d-0450eb31ad6e","isSidechain":false,"promptId":"4391232a-e7fc-4d1a-85bc-0e0e1ceba320","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KcGnoFgQ4jkRc9BTzqARsG","type":"tool_result","content":"ok","is_error":false}]},"uuid":"8dbc48fd-270c-4ede-b0e8-e3b9b5eabb46","timestamp":"2026-10-30T05:22:22.025Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4ddc9b71-af7d-47d3-954d-0450eb31ad6e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","hash":"c47812229af88592f70711f7bf2af94d6afe10ed45342df5098b7901d5be74d9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KcGnoFgQ4jkRc9BTzqARsG","name":"Bash","input":{"command":"cat > out/review/review.json <<'EOF'\n{\"verdict\": \"accept\",\n \"issues\": [\n {\"id\": \"R1\", \"severity\": \"minor\", \"location\": \"results.json:data_issues[1]\", \"issue\": \"The holiday-spanning count (213) uses calendar gaps of 2 or >3 days. That misses the 2001-09-13 change (Mon 09-10 to Thu 09-13, a 3-day calendar gap that spans two missing sessions). Counting by business days gives 214. This is descriptive only and no estimate is affected.\", \"suggested_fix\": \"Count changes whose previous available observation is not the previous business day, or describe the stated rule exactly.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"report.md:Uncertainty\", \"issue\": \"'Weekday effects larger than about 0.4 bp per day on Mondays are inconsistent with these data' is one-sided. The interval [-0.32, 0.42] also excludes negative Monday contrasts below about -0.32 bp, and it applies to the Monday-vs-rest contrast, not to weekday effects in general.\", \"suggested_fix\": \"Phrase this as: the Monday contrast is constrained to roughly [-0.32, +0.42] bp.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"claims.json:C3\", \"issue\": \"'Do not improve' rests on a single split with a 0.17% RMSE difference and no formal test. The caveat says this, but the claim could say 'slightly worse, not tested'.\", \"suggested_fix\": \"Say the difference is descriptive and untested, for example that no Diebold–Mariano test was run.\"}\n ],\n \"summary\": \"analysis.py re-runs in a scratch copy and reproduces results.json and table1.csv exactly. Independent checks confirm 5217 input rows, 215 empty rows and 5001 changes (2500 train / 2500+1 test). The task rules are followed: empty rows are dropped before differencing; weekday means are estimated on the train period only (≤2009-12-31); the prescribed classical ANOVA is used; and the Welch method for the Monday contrast is stated. The conclusion code follows the decision rule. Claims match the numbers and carry appropriate caveats. Limitations are discussed honestly (Levene rejection, volatility clustering, holiday and weekend spans, single split). Only minor wording and count issues were found.\"}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}36{"type":"api-request","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","id":"69b84c69-cd88-46bf-99a9-ec4229266ef5","timestamp":"2026-10-30T05:22:22.029Z","version":"2.1.314","querySource":"sdk","shapeHash":"375d53d492d854cf4cd1cb19f0d60abbc2d11e5d1dc46b66569e19f3907d8111","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":"7e244bc8-7df0-4dfa-9dbe-158d28048547","keep":8,"tail":["c47812229af88592f70711f7bf2af94d6afe10ed45342df5098b7901d5be74d9","f8b00e3d043d83dabc6062afaef554ecfbd9fe07c85302d7f2127869f096420b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"21ea2abe80b91dce24d4a8a7282576b81bd701a3cc7cca2e9530017a19843aef"}37{"type":"last-prompt","lastPrompt":"Review","leafUuid":"8dbc48fd-270c-4ede-b0e8-e3b9b5eabb46","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c"}38{"type":"atis-latch","atis":"","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c"}39{"parentUuid":"8dbc48fd-270c-4ede-b0e8-e3b9b5eabb46","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrvUWcXyhdEuxEh1SfG","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1162,"cache_read_input_tokens":15024,"output_tokens":701,"output_tokens_details":{"thinking_tokens":13},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1162},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5947,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrvUCWcJEX7oZbHZF1K","requestRef":"69b84c69-cd88-46bf-99a9-ec4229266ef5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3de543c1-1009-44b1-80da-eab4c170c44a","timestamp":"2026-10-30T05:22:28.806Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"3de543c1-1009-44b1-80da-eab4c170c44a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrvUWcXyhdEuxEh1SfG","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**. I found only three minor issues and no critical or major ones. The review is written to `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy, and it regenerated `results.json` and `table1.csv` exactly. I also recomputed the counts directly from the data file: 5217 rows read, 215 empty rows, and 5001 daily changes (2500 in the estimation period, 2501 in the evaluation period). These match the figures in the deliverables.\n\nThe code follows the task's rules:\n- Empty rows are dropped before the changes are computed.\n- The weekday means used for forecasting come only from changes dated on or before 2009-12-31.\n- The joint test is the required classical ANOVA, and the report states that the Monday contrast uses a Welch interval.\n- The `no_detectable_effect` conclusion follows the task's decision rule (ANOVA p = 0.90).\n\nThe claims match the numbers in `results.json`, and the limitations section is honest. It notes that the equal-variance assumption is strongly rejected (Levene p ≈ 1e-7), that daily changes cluster in volatility, that changes after holidays and weekends span more than one session, and that the forecast comparison uses a single split with no formal test.\n\nThe three minor issues:\n- **R1, holiday count:** the report says 213 changes span a holiday, but counting by business days gives 214. The change dated 2001-09-13 is missed because it has a 3-day calendar gap but skips two closed sessions. No estimate is affected.\n- **R2, wording in the Uncertainty section:** the report says Monday effects larger than about 0.4 bp are ruled out. The interval of −0.32 to +0.42 bp also rules out negative contrasts below about −0.32 bp, and it covers only Monday against the other days, not weekday effects in general.\n- **R3, claim C3 is slightly strong:** \"do not improve\" rests on a 0.17% difference in forecast error from one split, with no formal test. The claim's caveat says this, but the claim text could say \"slightly worse, not tested\"."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1162,"cache_read_input_tokens":15024,"output_tokens":701,"output_tokens_details":{"thinking_tokens":13},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1162},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrvUCWcJEX7oZbHZF1K","requestRef":"69b84c69-cd88-46bf-99a9-ec4229266ef5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1d0eaf40-6bbf-4d8b-a99d-bf5ad0531d12","timestamp":"2026-10-30T05:22:28.829Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0273","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Review","leafUuid":"1d0eaf40-6bbf-4d8b-a99d-bf5ad0531d12","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c"}42{"type":"cost-state","sessionId":"e0ab03a3-4051-4161-90b9-bc9672c4c54c","totalCostUSD":0.1429356,"totalAPIDuration":30436,"totalAPIDurationWithoutRetries":30431,"totalToolDuration":1795,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":32358,"startTime":1793337716479,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":3084,"thinkingTokens":695,"cacheReadInputTokens":38988,"cacheCreationInputTokens":14682,"cacheCreationEphemeral5mInputTokens":14682,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1429356}},"hasUnknownModelCost":false}43