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In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. 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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 01:22 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:20 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 5715 oct. 30 01:21 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1442 oct. 30 01:22 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 233 oct. 30 01:21 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 29925 oct. 30 01:21 figure1.png\n-rw-r--r--@ 1 simon-pierreboucher staff 5860 oct. 30 01:22 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 1655 oct. 30 01:21 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:22 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 01:21 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)\",\n \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"\n },\n {\n \"issue\": \"214 changes span a holiday (gap longer than a normal weekday/weekend gap); 0 span more than 4 calendar days\",\n \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"\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\": 0.05017009840169969,\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 \"supp_effect_se\": 0.18837955539096202,\n \"supp_effect_welch_df\": 1548.9581965107568,\n \"supp_effect_welch_p\": 0.7900247217974672,\n \"supp_rmse_ratio_model_over_baseline\": 1.0016689625518016,\n \"supp_rmse_train_mean_forecast\": 4.87519865288523,\n \"supp_levene_p\": 1.0122651168712971e-07,\n \"supp_kruskal_p\": 0.47102318097227847,\n \"supp_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 \"supp_n_holiday_spanning_changes\": 214\n}[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal mean daily changes across the five weekdays (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption is violated (Levene p~1e-7); serial dependence ignored.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, which contains zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"supp_effect_se\",\"supp_effect_welch_p\"],\"status\":\"supported\",\"caveat\":\"Assumes independent daily changes; effects below ~0.4 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve on the zero-change forecast over 2010-2019 (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"supp_rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal forecast-comparison test; single fixed split.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect in daily changes of the 10-year Treasury yield, 2000-2019.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Non-rejection, not proof of zero effect.\"},\n {\"id\":\"C5\",\"text\":\"A rank-based Kruskal-Wallis test also does not reject equal weekday distributions.\",\"results_keys\":[\"supp_kruskal_p\"],\"status\":\"supported\",\"caveat\":\"Robustness check only.\"}\n]","is_error":false}]},"uuid":"b8a039e0-d46c-4ebf-af0f-c590e0db8343","timestamp":"2026-10-30T05:22:38.317Z","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 01:22 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:20 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 5715 oct. 30 01:21 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1442 oct. 30 01:22 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 233 oct. 30 01:21 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 29925 oct. 30 01:21 figure1.png\n-rw-r--r--@ 1 simon-pierreboucher staff 5860 oct. 30 01:22 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 1655 oct. 30 01:21 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:22 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 01:21 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)\",\n \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"\n },\n {\n \"issue\": \"214 changes span a holiday (gap longer than a normal weekday/weekend gap); 0 span more than 4 calendar days\",\n \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"\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\": 0.05017009840169969,\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 \"supp_effect_se\": 0.18837955539096202,\n \"supp_effect_welch_df\": 1548.9581965107568,\n \"supp_effect_welch_p\": 0.7900247217974672,\n \"supp_rmse_ratio_model_over_baseline\": 1.0016689625518016,\n \"supp_rmse_train_mean_forecast\": 4.87519865288523,\n \"supp_levene_p\": 1.0122651168712971e-07,\n \"supp_kruskal_p\": 0.47102318097227847,\n \"supp_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 \"supp_n_holiday_spanning_changes\": 214\n}[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal mean daily changes across the five weekdays (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption is violated (Levene p~1e-7); serial dependence ignored.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, which contains zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"supp_effect_se\",\"supp_effect_welch_p\"],\"status\":\"supported\",\"caveat\":\"Assumes independent daily changes; effects below ~0.4 bp cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve on the zero-change forecast over 2010-2019 (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"supp_rmse_ratio_model_over_baseline\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal forecast-comparison test; single fixed split.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect in daily changes of the 10-year Treasury yield, 2000-2019.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Non-rejection, not proof of zero effect.\"},\n {\"id\":\"C5\",\"text\":\"A rank-based Kruskal-Wallis test also does not reject equal weekday distributions.\",\"results_keys\":[\"supp_kruskal_p\"],\"status\":\"supported\",\"caveat\":\"Robustness check only.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"64db709d-21c9-427a-bbe7-922674cdc022","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52","hash":"72304a239c8d938136c881f6235cdd2d4c0a5bafe1afd5b7d94831d292646450","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01YRG91Sf6GtKRtjzpA9gUY5","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/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nSEED = None # no randomness used\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\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\"] = d[\"DGS10\"].astype(float)\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd = d.reset_index(drop=True)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap_days\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\nn_ch = len(ch)\n\nexcl = pd.DataFrame([\n {\"step\": 1, \"rule\": \"drop rows with empty DGS10 (market holidays)\", \"n_before\": n_raw,\n \"n_after\": n_raw - n_missing, \"n_removed\": n_missing, \"rationale\": \"no yield published; required by TASK info rules\"},\n {\"step\": 2, \"rule\": \"first available observation has no change\", \"n_before\": n_raw - n_missing,\n \"n_after\": n_ch, \"n_removed\": n_raw - n_missing - n_ch, \"rationale\": \"difference undefined\"},\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# table 1\ntab = pd.DataFrame({\"weekday\": DAYS,\n \"n\": [len(g) for g in groups],\n \"mean_bp\": [g.mean() for g in groups],\n \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon, oth = groups[0], np.concatenate(groups[1:])\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_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntcrit = stats.t.ppf(0.975, df_w)\nlo, hi = eff - tcrit * se, eff + tcrit * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), df_w)\n\n# (iii) out-of-sample\ncut = pd.Timestamp(\"2009-12-31\")\ntr, te = ch[ch[\"date\"] <= cut], ch[ch[\"date\"] > cut]\nwd_means = tr.groupby(\"wd\")[\"dy\"].mean()\nassert tr[\"date\"].max() < te[\"date\"].min()\npred = te[\"wd\"].map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(te[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((te[\"dy\"].values - pred) ** 2)))\n# supplementary: unconditional train-mean forecast\nrmse_mean = float(np.sqrt(np.mean((te[\"dy\"].values - tr[\"dy\"].mean()) ** 2)))\n\nif p >= 0.05:\n concl = \"no_detectable_effect\"\nelif lo > 0:\n concl = \"positive_effect\"\nelif hi < 0:\n concl = \"negative_effect\"\nelse:\n concl = \"no_detectable_effect\"\n\nn_long_gap = int((ch[\"gap_days\"] > 4).sum())\nn_holiday_spanning = int(((ch[\"gap_days\"] > 1) & ~((ch[\"wd\"] == 0) & (ch[\"gap_days\"] == 3))).sum())\nissues = [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays)\",\n \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"},\n {\"issue\": f\"{n_holiday_spanning} changes span a holiday (gap longer than a normal weekday/weekend gap); {n_long_gap} span more than 4 calendar days\",\n \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"},\n]\n\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n \"n_analysed\": int(n_ch),\n \"random_seed\": SEED,\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(tr)),\n \"n_test\": int(len(te)),\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 # supplementary diagnostics\n \"supp_effect_se\": float(se),\n \"supp_effect_welch_df\": float(df_w),\n \"supp_effect_welch_p\": float(welch_p),\n \"supp_rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"supp_rmse_train_mean_forecast\": rmse_mean,\n \"supp_levene_p\": float(stats.levene(*groups, center=\"median\").pvalue),\n \"supp_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n \"supp_train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wd_means.items()},\n \"supp_n_holiday_spanning_changes\": n_holiday_spanning,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\n\n# figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y Treasury yield: mean daily change by weekday, 2000-2019\\n(95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\",\n \"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nprint(tab.to_string(index=False))\n# F6-conf-01 — Day-of-week effects in daily changes of the 10-year Treasury yield (2000–2019)\n\n## Question\nCan the day of the week predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10)? Three estimands: (i) a joint test that all five weekday means are equal; (ii) the Monday contrast (mean Monday change minus mean change on Tue–Fri) with a 95% interval; (iii) whether weekday-specific means estimated on 2000–2009 forecast 2010–2019 changes better than a zero-change (random-walk) forecast.\n\n## Data and exclusions\nInput: `data/DGS10.csv`, 5217 weekday rows, 2000-01-03 to 2019-12-31 (`n_input_rows`). SHA-256 matches the README. There are no duplicate dates and no values that fail to parse. 215 rows have an empty DGS10 field because they are US market holidays (`n_missing_rows`). These rows were dropped before differencing, as the task requires. The first available observation has no change, so 5001 daily changes remain (`n_analysed`). Of these, 2500 are dated on or before 2009-12-31 (`n_train`) and 2501 on or after 2010-01-01 (`n_test`). The steps are recorded in `out/exclusions.csv`. 214 changes span a holiday (`supp_n_holiday_spanning_changes`), meaning they cover more calendar time than a normal day or weekend. Following the task rules, these are kept and attributed to the weekday of t (`data_issues`).\n\n## Method\n- Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t.\n- Joint test: classical one-way equal-variance ANOVA F-test (scipy `f_oneway`) on all 5001 changes, with F(4, 4996). The result was cross-checked with an OLS regression on weekday dummies (statsmodels), which gives the same F and p.\n- Monday contrast: Welch two-sample comparison of Monday changes against all Tue–Fri changes, with a Welch–Satterthwaite t interval. I chose Welch because it does not assume equal variances. An HC1-robust regression on a Monday dummy gives practically the same interval (see `out/state/checks.md`).\n- Out-of-sample test: each 2010–2019 change is forecast by the 2000–2009 mean change for its weekday (`supp_train_weekday_means_bp`), and compared with a zero-change forecast. Both are scored by RMSE over the evaluation period. Only estimation-period data enter the forecasts; the code asserts this.\n- Conclusion rule: if the ANOVA p ≥ 0.05 → `no_detectable_effect`; otherwise the sign comes from the Monday-contrast interval.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n- Weekday means (`out/table1.csv`, `out/figure1.png`) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each standard error is about 0.16–0.19 bp.\n- Joint ANOVA: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). The test does not reject equal weekday means.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`), Welch p = 0.79 (`supp_effect_welch_p`).\n- Out-of-sample: the zero-change RMSE is 4.876 bp (`baseline_metric`). The weekday-mean forecast RMSE is 4.884 bp (`model_metric`), a ratio of 1.0017 (`supp_rmse_ratio_model_over_baseline`). The weekday means do slightly *worse* than the random walk.\n- Conclusion: `no_detectable_effect` (`conclusion`). In this sample, the day of the week has no detectable predictive content for daily changes in the 10-year yield.\n\n## Uncertainty\n- The Monday-contrast interval is about ±0.37 bp wide (`effect_ci_low`, `effect_ci_high`; SE `supp_effect_se` = 0.188 bp). The data therefore rule out Monday effects larger than roughly 0.4 bp per day in either direction. Smaller effects cannot be excluded.\n- The out-of-sample RMSE difference (0.008 bp) is tiny compared with the daily volatility (~4.9 bp). No formal forecast-comparison test (e.g. Diebold–Mariano) was run. The evidence is that weekday means add nothing, not a significance statement.\n- Robustness: a Kruskal–Wallis rank test also does not reject equal weekday distributions (p = 0.47, `supp_kruskal_p`).\n\n## Limitations and open issues\n- **Assumption violation:** Levene's test strongly rejects equal variances across weekdays (p ≈ 1e-7, `supp_levene_p`). The classical ANOVA required by the task therefore runs under a violated assumption. With roughly balanced group sizes (919–1030) and p = 0.90, this is unlikely to change the conclusion. A Welch ANOVA was not run. The Monday contrast uses Welch, and the HC1 regression check agrees with it.\n- **Serial dependence / volatility clustering:** daily yield changes show conditional heteroskedasticity, and their autocorrelation may not be exactly zero. The ANOVA and Welch standard errors assume independence. HAC/GARCH-robust inference was not checked.\n- **Holiday-spanning changes:** 214 changes cover more than one trading day and are assigned to the weekday of t. This mixes multi-day changes into Tuesdays (after Monday holidays) and other days. The task rules require this treatment. A sensitivity analysis excluding these changes was not done.\n- **Regime changes:** the 2000–2019 period includes the zero-lower-bound and QE years. A weekday pattern could exist in sub-periods and cancel out overall. Only the fixed 2009/2010 split was examined.\n- **What would invalidate the result:** errors in the FRED file (stale or revised values), or a definition of the change different from the prescribed one (e.g. calendar-day rather than trading-day changes). Calendar effects not tested here, such as FOMC days, auction days, or month-end, could be correlated with weekdays. Because the null is not rejected, such confounding could hide an effect rather than create one.\n- Power: the study cannot detect weekday effects much smaller than ~0.4 bp/day. A non-rejection is not proof that the effect is zero.\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 (market holidays),5217,5002,215,no yield published; required by TASK info rules\n2,first available observation has no change,5002,5001,1,difference undefined\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49","is_error":false}]},"uuid":"d111b2a7-7353-436c-8f11-18ebe7070362","timestamp":"2026-10-30T05:22:39.953Z","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/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nSEED = None # no randomness used\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\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\"] = d[\"DGS10\"].astype(float)\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd = d.reset_index(drop=True)\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"gap_days\"] = d[\"date\"].diff().dt.days\nch = d.iloc[1:].copy() # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\nn_ch = len(ch)\n\nexcl = pd.DataFrame([\n {\"step\": 1, \"rule\": \"drop rows with empty DGS10 (market holidays)\", \"n_before\": n_raw,\n \"n_after\": n_raw - n_missing, \"n_removed\": n_missing, \"rationale\": \"no yield published; required by TASK info rules\"},\n {\"step\": 2, \"rule\": \"first available observation has no change\", \"n_before\": n_raw - n_missing,\n \"n_after\": n_ch, \"n_removed\": n_raw - n_missing - n_ch, \"rationale\": \"difference undefined\"},\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# table 1\ntab = pd.DataFrame({\"weekday\": DAYS,\n \"n\": [len(g) for g in groups],\n \"mean_bp\": [g.mean() for g in groups],\n \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# (ii) Monday contrast, Welch two-sample interval\nmon, oth = groups[0], np.concatenate(groups[1:])\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_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntcrit = stats.t.ppf(0.975, df_w)\nlo, hi = eff - tcrit * se, eff + tcrit * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), df_w)\n\n# (iii) out-of-sample\ncut = pd.Timestamp(\"2009-12-31\")\ntr, te = ch[ch[\"date\"] <= cut], ch[ch[\"date\"] > cut]\nwd_means = tr.groupby(\"wd\")[\"dy\"].mean()\nassert tr[\"date\"].max() < te[\"date\"].min()\npred = te[\"wd\"].map(wd_means).values\nrmse0 = float(np.sqrt(np.mean(te[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((te[\"dy\"].values - pred) ** 2)))\n# supplementary: unconditional train-mean forecast\nrmse_mean = float(np.sqrt(np.mean((te[\"dy\"].values - tr[\"dy\"].mean()) ** 2)))\n\nif p >= 0.05:\n concl = \"no_detectable_effect\"\nelif lo > 0:\n concl = \"positive_effect\"\nelif hi < 0:\n concl = \"negative_effect\"\nelse:\n concl = \"no_detectable_effect\"\n\nn_long_gap = int((ch[\"gap_days\"] > 4).sum())\nn_holiday_spanning = int(((ch[\"gap_days\"] > 1) & ~((ch[\"wd\"] == 0) & (ch[\"gap_days\"] == 3))).sum())\nissues = [\n {\"issue\": f\"{n_missing} rows with empty DGS10 (US market holidays)\",\n \"handling\": \"dropped before differencing, as required; changes computed between consecutive available observations\"},\n {\"issue\": f\"{n_holiday_spanning} changes span a holiday (gap longer than a normal weekday/weekend gap); {n_long_gap} span more than 4 calendar days\",\n \"handling\": \"kept and attributed to the weekday of t per the task rules; noted as limitation (multi-day changes have larger variance)\"},\n]\n\nres = {\n \"task_id\": \"F6-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n \"n_analysed\": int(n_ch),\n \"random_seed\": SEED,\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(tr)),\n \"n_test\": int(len(te)),\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 # supplementary diagnostics\n \"supp_effect_se\": float(se),\n \"supp_effect_welch_df\": float(df_w),\n \"supp_effect_welch_p\": float(welch_p),\n \"supp_rmse_ratio_model_over_baseline\": rmse1 / rmse0,\n \"supp_rmse_train_mean_forecast\": rmse_mean,\n \"supp_levene_p\": float(stats.levene(*groups, center=\"median\").pvalue),\n \"supp_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n \"supp_train_weekday_means_bp\": {DAYS[k]: float(v) for k, v in wd_means.items()},\n \"supp_n_holiday_spanning_changes\": n_holiday_spanning,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\n\n# figure 1\nci = stats.t.ppf(0.975, tab[\"n\"] - 1) * tab[\"se_bp\"]\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(DAYS)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(\"10y Treasury yield: mean daily change by weekday, 2000-2019\\n(95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_train\", \"n_test\", \"anova_f_stat\", \"anova_p_value\",\n \"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nprint(tab.to_string(index=False))\n# F6-conf-01 — Day-of-week effects in daily changes of the 10-year Treasury yield (2000–2019)\n\n## Question\nCan the day of the week predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10)? Three estimands: (i) a joint test that all five weekday means are equal; (ii) the Monday contrast (mean Monday change minus mean change on Tue–Fri) with a 95% interval; (iii) whether weekday-specific means estimated on 2000–2009 forecast 2010–2019 changes better than a zero-change (random-walk) forecast.\n\n## Data and exclusions\nInput: `data/DGS10.csv`, 5217 weekday rows, 2000-01-03 to 2019-12-31 (`n_input_rows`). SHA-256 matches the README. There are no duplicate dates and no values that fail to parse. 215 rows have an empty DGS10 field because they are US market holidays (`n_missing_rows`). These rows were dropped before differencing, as the task requires. The first available observation has no change, so 5001 daily changes remain (`n_analysed`). Of these, 2500 are dated on or before 2009-12-31 (`n_train`) and 2501 on or after 2010-01-01 (`n_test`). The steps are recorded in `out/exclusions.csv`. 214 changes span a holiday (`supp_n_holiday_spanning_changes`), meaning they cover more calendar time than a normal day or weekend. Following the task rules, these are kept and attributed to the weekday of t (`data_issues`).\n\n## Method\n- Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t.\n- Joint test: classical one-way equal-variance ANOVA F-test (scipy `f_oneway`) on all 5001 changes, with F(4, 4996). The result was cross-checked with an OLS regression on weekday dummies (statsmodels), which gives the same F and p.\n- Monday contrast: Welch two-sample comparison of Monday changes against all Tue–Fri changes, with a Welch–Satterthwaite t interval. I chose Welch because it does not assume equal variances. An HC1-robust regression on a Monday dummy gives practically the same interval (see `out/state/checks.md`).\n- Out-of-sample test: each 2010–2019 change is forecast by the 2000–2009 mean change for its weekday (`supp_train_weekday_means_bp`), and compared with a zero-change forecast. Both are scored by RMSE over the evaluation period. Only estimation-period data enter the forecasts; the code asserts this.\n- Conclusion rule: if the ANOVA p ≥ 0.05 → `no_detectable_effect`; otherwise the sign comes from the Monday-contrast interval.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n- Weekday means (`out/table1.csv`, `out/figure1.png`) range from about −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each standard error is about 0.16–0.19 bp.\n- Joint ANOVA: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). The test does not reject equal weekday means.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`), Welch p = 0.79 (`supp_effect_welch_p`).\n- Out-of-sample: the zero-change RMSE is 4.876 bp (`baseline_metric`). The weekday-mean forecast RMSE is 4.884 bp (`model_metric`), a ratio of 1.0017 (`supp_rmse_ratio_model_over_baseline`). The weekday means do slightly *worse* than the random walk.\n- Conclusion: `no_detectable_effect` (`conclusion`). In this sample, the day of the week has no detectable predictive content for daily changes in the 10-year yield.\n\n## Uncertainty\n- The Monday-contrast interval is about ±0.37 bp wide (`effect_ci_low`, `effect_ci_high`; SE `supp_effect_se` = 0.188 bp). The data therefore rule out Monday effects larger than roughly 0.4 bp per day in either direction. Smaller effects cannot be excluded.\n- The out-of-sample RMSE difference (0.008 bp) is tiny compared with the daily volatility (~4.9 bp). No formal forecast-comparison test (e.g. Diebold–Mariano) was run. The evidence is that weekday means add nothing, not a significance statement.\n- Robustness: a Kruskal–Wallis rank test also does not reject equal weekday distributions (p = 0.47, `supp_kruskal_p`).\n\n## Limitations and open issues\n- **Assumption violation:** Levene's test strongly rejects equal variances across weekdays (p ≈ 1e-7, `supp_levene_p`). The classical ANOVA required by the task therefore runs under a violated assumption. With roughly balanced group sizes (919–1030) and p = 0.90, this is unlikely to change the conclusion. A Welch ANOVA was not run. The Monday contrast uses Welch, and the HC1 regression check agrees with it.\n- **Serial dependence / volatility clustering:** daily yield changes show conditional heteroskedasticity, and their autocorrelation may not be exactly zero. The ANOVA and Welch standard errors assume independence. HAC/GARCH-robust inference was not checked.\n- **Holiday-spanning changes:** 214 changes cover more than one trading day and are assigned to the weekday of t. This mixes multi-day changes into Tuesdays (after Monday holidays) and other days. The task rules require this treatment. A sensitivity analysis excluding these changes was not done.\n- **Regime changes:** the 2000–2019 period includes the zero-lower-bound and QE years. A weekday pattern could exist in sub-periods and cancel out overall. Only the fixed 2009/2010 split was examined.\n- **What would invalidate the result:** errors in the FRED file (stale or revised values), or a definition of the change different from the prescribed one (e.g. calendar-day rather than trading-day changes). Calendar effects not tested here, such as FOMC days, auction days, or month-end, could be correlated with weekdays. Because the null is not rejected, such confounding could hide an effect rather than create one.\n- Power: the study cannot detect weekday effects much smaller than ~0.4 bp/day. A non-rejection is not proof that the effect is zero.\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 (market holidays),5217,5002,215,no yield published; required by TASK info rules\n2,first available observation has no change,5002,5001,1,difference undefined\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"09a0bfbc-e83e-4248-9dd3-e97fd0daa8c7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52","version":"2.1.314","gitBranch":"HEAD"}20{"type":"api-request-blob","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52","hash":"867ff23169ad4d936d396bf7c157d4e794e6cd6a64c13b059b1b3bb992f455bf","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01QFyEYBP2sW5fy9ARmaQJ6N","name":"Bash","input":{"command":"cat out/analysis.py; cat out/report.md; cat out/table1.csv out/exclusions.csv; ls out/state; head -3 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/tmp && rm -rf rv && mkdir rv && cd rv && cp -r ~/wp29/runs/RC0274/data . && mkdir out && cp ~/wp29/runs/RC0274/out/analysis.py out/ && ~/wp29/venv/bin/python out/analysis.py >/dev/null && diff out/results.json ~/wp29/runs/RC0274/out/results.json && diff out/table1.csv ~/wp29/runs/RC0274/out/table1.csv && echo SAME; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np,statsmodels.formula.api as smf\nd=pd.read_csv('data/DGS10.csv').dropna();d['date']=pd.to_datetime(d.observation_date);d['dy']=100*d.DGS10.diff();d=d.iloc[1:];d['mon']=(d.date.dt.dayofweek==0).astype(int)\nr=smf.ols('dy~mon',d).fit(cov_type='HC1');print(r.params['mon'],r.conf_int().loc['mon'].values)\nprint(len(d[d.date<='2009-12-31']),(d.date.dt.dayofweek).value_counts().sort_index().values)\n\"; grep -c '' data/DGS10.csv; ~/wp29/runs/RC0274/out/../../../venv/bin/python -c 1 2>/dev/null; cat ~/wp29/runs/RC0274/out/state/checks.md | head 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[-0.31895707 0.41929727]\n2500 [ 919 1028 1030 1012 1012]\n5218\n# Verification checks (P4)\n1. Clean re-run: deleted out/results.json, ran analysis.py twice; byte-identical outputs (cmp) — PASS.\n2. Counts: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500+2501) = sum of table1 n — PASS. Exclusion ledger sums — PASS.\n3. CI contains estimate (−0.319 < 0.050 < 0.420) — PASS. All SEs > 0 — PASS.\n4. Monday contrast recomputed from table1 weighted means: 0.050170 = effect_estimate — PASS.\n5. Independent cross-check with statsmodels OLS dy~C(weekday): F=0.26347, p=0.90151, identical to scipy f_oneway — PASS. HC1 regression on Monday dummy: 0.0502, CI [−0.319, 0.419], agrees with Welch — PASS.\n6. Chronology: max train date < min test date (assert in code); weekday means estimated from train only — PASS.\n7. Magnitudes: RMSE ≈ 4.88 bp is plausible for daily 10y changes; model RMSE (4.8838) > baseline (4.8757), ratio 1.0017 — consistent with no effect.\n8. Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect — PASS.\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0274","is_error":false}]},"uuid":"778470bc-cdc0-456c-a5ed-6f24cc763a14","timestamp":"2026-10-30T05:22:50.477Z","toolUseResult":{"stdout":"SAME\n0.050170098401700364 [-0.31895707 0.41929727]\n2500 [ 919 1028 1030 1012 1012]\n5218\n# Verification checks (P4)\n1. Clean re-run: deleted out/results.json, ran analysis.py twice; byte-identical outputs (cmp) — PASS.\n2. Counts: 5217 − 215 − 1 = 5001 = n_analysed = n_train + n_test (2500+2501) = sum of table1 n — PASS. Exclusion ledger sums — PASS.\n3. CI contains estimate (−0.319 < 0.050 < 0.420) — PASS. All SEs > 0 — PASS.\n4. Monday contrast recomputed from table1 weighted means: 0.050170 = effect_estimate — PASS.\n5. Independent cross-check with statsmodels OLS dy~C(weekday): F=0.26347, p=0.90151, identical to scipy f_oneway — PASS. HC1 regression on Monday dummy: 0.0502, CI [−0.319, 0.419], agrees with Welch — PASS.\n6. Chronology: max train date < min test date (assert in code); weekday means estimated from train only — PASS.\n7. Magnitudes: RMSE ≈ 4.88 bp is plausible for daily 10y changes; model RMSE (4.8838) > baseline (4.8757), ratio 1.0017 — consistent with no effect.\n8. Conclusion rule: ANOVA p=0.90 ≥ 0.05 → no_detectable_effect — PASS.","stderr":"\nShell cwd was reset to 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> out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Uncertainty) / claims.json C2\",\n \"issue\": \"The report says the data 'rule out Monday effects larger than roughly 0.4 bp per day'. That bound comes from an i.i.d. Welch/HC1 standard error. The report itself says volatility clustering and serial dependence were not addressed, so the bound is conditional on independence and should not be stated as a firm exclusion.\",\n \"suggested_fix\": \"Qualify the bound as holding under independence, or add a HAC (Newey-West) standard error for the Monday-dummy regression.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations, holiday-spanning bullet)\",\n \"issue\": \"The report says the 214 holiday-spanning changes 'cover more than one trading day'. By construction each change is between consecutive trading days. What these changes span is extra calendar days, not extra trading days.\",\n \"suggested_fix\": \"Reword to 'span more calendar days than a normal weekday or weekend gap'.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"claims.json C3 / report Uncertainty\",\n \"issue\": \"The out-of-sample result (RMSE 4.884 vs 4.876) is reported without any test or interval. The caveat says so, so this is correctly hedged. Still, 'do not improve' is a point-estimate comparison only.\",\n \"suggested_fix\": \"Optionally add a Diebold-Mariano test, or a block-bootstrap interval for the RMSE difference.\"\n }\n ],\n \"summary\": \"Re-running analysis.py in a scratch copy reproduces results.json and table1.csv byte-for-byte. Counts are consistent: 5217 rows minus 215 missing minus 1 first observation gives 5001 changes, split 2500 + 2501, and table1 n sums to 5001. The ANOVA is the required classical F-test over all changes. 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That bound comes from an i.i.d. Welch/HC1 standard error. The report itself says volatility clustering and serial dependence were not addressed, so the bound is conditional on independence and should not be stated as a firm exclusion.\",\n \"suggested_fix\": \"Qualify the bound as holding under independence, or add a HAC (Newey-West) standard error for the Monday-dummy regression.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations, holiday-spanning bullet)\",\n \"issue\": \"The report says the 214 holiday-spanning changes 'cover more than one trading day'. By construction each change is between consecutive trading days. 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The review is in `out/review/review.json`.\n\n- **Results reproduce exactly:** I re-ran `out/analysis.py` in a scratch copy and it regenerated `results.json` and `table1.csv` byte-for-byte. Every required key is present.\n- **Counts add up:** 5217 rows, minus 215 empty ones, minus the first observation, gives 5001 changes. These split into 2500 before 2010 and 2501 from 2010 on, and the per-weekday counts in `table1.csv` also sum to 5001.\n- **The rules in `TASK.md` are followed:**\n - The joint test is the required classical analysis of variance over all changes (F = 0.263, p = 0.90).\n - The Monday contrast uses Welch's method, and the report says so: +0.050 basis points, 95% interval [−0.319, 0.420]. My own check, a robust-error regression on a Monday indicator, gave the same estimate and interval [−0.319, 0.419].\n - The weekday-mean forecasts use only data up to the end of 2009, which the code enforces.\n - The zero-change baseline is there (error 4.876 basis points against 4.884 for the weekday means).\n - `no_detectable_effect` is the correct conclusion under the task's rule.\n- **Report and claims:** the report has the six required sections in order, the claims match the numbers, and the limitations section is honest. It covers unequal variances across weekdays, day-to-day dependence, changes that span holidays, shifts between market regimes, and the limited power to detect small effects.\n\nThe three minor issues:\n1. **The ±0.4 basis-point bound is stated too firmly.** The report says the data \"rule out\" Monday effects larger than about 0.4 basis points. That relies on treating daily changes as independent, which the report itself says was not checked. It should be qualified, or backed by a dependence-robust standard error.\n2. **One phrase is inaccurate.** The 214 holiday-spanning changes are described as covering \"more than one trading day\". Each change is between consecutive trading days by construction; what they span is extra calendar days.\n3. **The forecast comparison has no formal test.** Saying weekday means \"do not improve\" on the zero-change forecast is a comparison of two point values with no test or interval. The claim's caveat already says this."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1269,"cache_read_input_tokens":14245,"output_tokens":789,"output_tokens_details":{"thinking_tokens":5},"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":1269},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXryps4QvstvwDVkYzqc","requestRef":"98e5fd7e-9301-4276-a2a5-69db0adfac83","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7063b517-759d-431e-bc2d-6ed2aab45ac3","timestamp":"2026-10-30T05:23:16.408Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0274","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52","version":"2.1.314","gitBranch":"HEAD"}44{"type":"last-prompt","lastPrompt":"Review","leafUuid":"7063b517-759d-431e-bc2d-6ed2aab45ac3","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52"}45{"type":"cost-state","sessionId":"9881728c-6f8d-45bb-ab24-b318cfcf7e52","totalCostUSD":0.1483122,"totalAPIDuration":39377,"totalAPIDurationWithoutRetries":39371,"totalToolDuration":1911,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":41478,"startTime":1793337754940,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":3412,"thinkingTokens":840,"cacheReadInputTokens":49831,"cacheCreationInputTokens":14010,"cacheCreationEphemeral5mInputTokens":14010,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1483122}},"hasUnknownModelCost":false}46