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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 136\ndrwxr-xr-x  11 simon-pierreboucher  staff    352 oct.  30 01:23 .\ndrwxr-xr-x   7 simon-pierreboucher  staff    224 oct.  30 01:21 ..\n-rw-r--r--   1 simon-pierreboucher  staff   6783 oct.  30 01:21 analysis.py\n-rw-r--r--   1 simon-pierreboucher  staff   1487 oct.  30 01:22 claims.json\n-rw-r--r--   1 simon-pierreboucher  staff    225 oct.  30 01:22 exclusions.csv\n-rw-r--r--   1 simon-pierreboucher  staff  35987 oct.  30 01:22 figure1.png\n-rw-r--r--   1 simon-pierreboucher  staff   6133 oct.  30 01:22 report.md\n-rw-r--r--   1 simon-pierreboucher  staff   2145 oct.  30 01:22 results.json\ndrwxr-xr-x   2 simon-pierreboucher  staff     64 oct.  30 01:23 review\ndrwxr-xr-x   8 simon-pierreboucher  staff    256 oct.  30 01:22 state\n-rw-r--r--   1 simon-pierreboucher  staff    183 oct.  30 01:22 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 field (US market holidays)\",\n      \"handling\": \"dropped before differencing, per TASK rule\"\n    },\n    {\n      \"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n      \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"\n    },\n    {\n      \"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n      \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"\n    },\n    {\n      \"issue\": \"yields recorded to 0.01 pct, 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\": 0.2634657168,\n  \"anova_p_value\": 0.9015060508,\n  \"effect_estimate\": 0.0501700984,\n  \"effect_ci_low\": -0.3193357757,\n  \"effect_ci_high\": 0.4196759725,\n  \"baseline_metric\": 4.8757041546,\n  \"model_metric\": 4.8838415223,\n  \"conclusion\": \"no_detectable_effect\",\n  \"effect_se_welch\": 0.1883795554,\n  \"effect_df_welch\": 1548.9581965108,\n  \"effect_p_welch\": 0.7900247218,\n  \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n  \"sens_hac_effect_se\": 0.1936007292,\n  \"sens_hac_effect_p\": 0.7955256074,\n  \"sens_kruskal_H\": 3.2450803566,\n  \"sens_kruskal_p\": 0.5176806169,\n  \"sens_welch_anova_F\": 0.2621528465,\n  \"sens_welch_anova_p\": 0.9023065014,\n  \"sens_levene_W\": 9.588190973,\n  \"sens_levene_p\": 1.012e-07,\n  \"oos_rmse_ratio_model_over_baseline\": 1.0016689626,\n  \"oos_rmse_unconditional_train_mean\": 4.8751986529,\n  \"oos_dm_mean_loss_diff\": 0.0794170112,\n  \"oos_dm_t\": 1.8931519972,\n  \"oos_dm_p\": 0.058337659,\n  \"train_weekday_means_bp\": {\n    \"Monday\": -0.0543478261,\n    \"Tuesday\": -0.4054580897,\n    \"Wednesday\": -0.2054263566,\n    \"Thursday\": 0.0138339921,\n    \"Friday\": 0.1168316832\n  },\n  \"n_changes_total\": 5001\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=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption violated (Levene); Welch ANOVA and Kruskal-Wallis agree.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, i.e. not distinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_p_welch\"],\"status\":\"supported\",\"caveat\":\"Effects smaller than ~0.4 bp/day cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"oos_rmse_ratio_model_over_baseline\",\"oos_dm_p\"],\"status\":\"supported\",\"caveat\":\"Single train/test split; the small deterioration is not significant at 5% (DM p=0.058).\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion code: no_detectable_effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Decided by the ANOVA at 5% as the task prescribes.\"},\n {\"id\":\"C5\",\"text\":\"The null result is robust to unequal variances, non-normality and autocorrelation in the Monday contrast SE.\",\"results_keys\":[\"sens_welch_anova_p\",\"sens_kruskal_p\",\"sens_levene_p\",\"sens_hac_effect_p\"],\"status\":\"supported\",\"caveat\":\"HAC with 5 lags only; no subperiod analysis.\"}\n]","is_error":false}]},"uuid":"ae051e61-f9ad-44b2-9cb1-02ca40243f79","timestamp":"2026-10-30T05:23:04.617Z","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 136\ndrwxr-xr-x  11 simon-pierreboucher  staff    352 oct.  30 01:23 .\ndrwxr-xr-x   7 simon-pierreboucher  staff    224 oct.  30 01:21 ..\n-rw-r--r--   1 simon-pierreboucher  staff   6783 oct.  30 01:21 analysis.py\n-rw-r--r--   1 simon-pierreboucher  staff   1487 oct.  30 01:22 claims.json\n-rw-r--r--   1 simon-pierreboucher  staff    225 oct.  30 01:22 exclusions.csv\n-rw-r--r--   1 simon-pierreboucher  staff  35987 oct.  30 01:22 figure1.png\n-rw-r--r--   1 simon-pierreboucher  staff   6133 oct.  30 01:22 report.md\n-rw-r--r--   1 simon-pierreboucher  staff   2145 oct.  30 01:22 results.json\ndrwxr-xr-x   2 simon-pierreboucher  staff     64 oct.  30 01:23 review\ndrwxr-xr-x   8 simon-pierreboucher  staff    256 oct.  30 01:22 state\n-rw-r--r--   1 simon-pierreboucher  staff    183 oct.  30 01:22 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 field (US market holidays)\",\n      \"handling\": \"dropped before differencing, per TASK rule\"\n    },\n    {\n      \"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n      \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"\n    },\n    {\n      \"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n      \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"\n    },\n    {\n      \"issue\": \"yields recorded to 0.01 pct, 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\": 0.2634657168,\n  \"anova_p_value\": 0.9015060508,\n  \"effect_estimate\": 0.0501700984,\n  \"effect_ci_low\": -0.3193357757,\n  \"effect_ci_high\": 0.4196759725,\n  \"baseline_metric\": 4.8757041546,\n  \"model_metric\": 4.8838415223,\n  \"conclusion\": \"no_detectable_effect\",\n  \"effect_se_welch\": 0.1883795554,\n  \"effect_df_welch\": 1548.9581965108,\n  \"effect_p_welch\": 0.7900247218,\n  \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n  \"sens_hac_effect_se\": 0.1936007292,\n  \"sens_hac_effect_p\": 0.7955256074,\n  \"sens_kruskal_H\": 3.2450803566,\n  \"sens_kruskal_p\": 0.5176806169,\n  \"sens_welch_anova_F\": 0.2621528465,\n  \"sens_welch_anova_p\": 0.9023065014,\n  \"sens_levene_W\": 9.588190973,\n  \"sens_levene_p\": 1.012e-07,\n  \"oos_rmse_ratio_model_over_baseline\": 1.0016689626,\n  \"oos_rmse_unconditional_train_mean\": 4.8751986529,\n  \"oos_dm_mean_loss_diff\": 0.0794170112,\n  \"oos_dm_t\": 1.8931519972,\n  \"oos_dm_p\": 0.058337659,\n  \"train_weekday_means_bp\": {\n    \"Monday\": -0.0543478261,\n    \"Tuesday\": -0.4054580897,\n    \"Wednesday\": -0.2054263566,\n    \"Thursday\": 0.0138339921,\n    \"Friday\": 0.1168316832\n  },\n  \"n_changes_total\": 5001\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=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption violated (Levene); Welch ANOVA and Kruskal-Wallis agree.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, i.e. not distinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_p_welch\"],\"status\":\"supported\",\"caveat\":\"Effects smaller than ~0.4 bp/day cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"oos_rmse_ratio_model_over_baseline\",\"oos_dm_p\"],\"status\":\"supported\",\"caveat\":\"Single train/test split; the small deterioration is not significant at 5% (DM p=0.058).\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion code: no_detectable_effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Decided by the ANOVA at 5% as the task prescribes.\"},\n {\"id\":\"C5\",\"text\":\"The null result is robust to unequal variances, non-normality and autocorrelation in the Monday contrast SE.\",\"results_keys\":[\"sens_welch_anova_p\",\"sens_kruskal_p\",\"sens_levene_p\",\"sens_hac_effect_p\"],\"status\":\"supported\",\"caveat\":\"HAC with 5 lags only; 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writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"], format=\"%Y-%m-%d\")\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"dy\"] = d[\"dy\"].round(8)  # remove float noise; data have 1 bp resolution\nch = d.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\n\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with empty DGS10 field (holidays)\", n_before=n_raw,\n         n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: no published yield\"),\n    dict(step=2, rule=\"first available observation has no change\", n_before=n_raw - n_missing,\n         n_after=len(ch), n_removed=n_raw - n_missing - len(ch), rationale=\"TASK rule: change needs y_{t-1}\"),\n]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\nkw_H, kw_p = stats.kruskal(*groups)                       # sensitivity (rank-based)\nlev_W, lev_p = stats.levene(*groups, center=\"median\")     # equal-variance check\nfrom statsmodels.stats.oneway import anova_oneway\nwa = anova_oneway(groups, use_var=\"unequal\")              # Welch ANOVA sensitivity\n\n# (ii) Monday contrast, Welch two-sample interval (primary)\nmon = ch.loc[ch[\"wd\"] == 0, \"dy\"].values; oth = ch.loc[ch[\"wd\"] != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df_w)\nlo, hi = est - tq * se, est + tq * se\nwelch_p = 2 * stats.t.sf(abs(est / se), df_w)\n# sensitivity: OLS on Monday dummy with HAC (Newey-West, 5 lags) SE\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nhac = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\n\n# (iii) out-of-sample\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nf_model = test[\"wd\"].map(wmeans).values\ne0 = test[\"dy\"].values; e1 = e0 - f_model\nrmse0 = float(np.sqrt(np.mean(e0 ** 2))); rmse1 = float(np.sqrt(np.mean(e1 ** 2)))\ne_unc = e0 - train[\"dy\"].mean()\nrmse_unc = float(np.sqrt(np.mean(e_unc ** 2)))\n# Diebold-Mariano on squared-error loss differential (model - baseline), HAC 5 lags\ndl = e1 ** 2 - e0 ** 2\ndm = sm.OLS(dl, np.ones_like(dl)).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\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\n# table + figure\ntab = ch.groupby(\"wd\")[\"dy\"].agg(n=\"count\", mean_bp=\"mean\", sd=\"std\").reindex(range(5))\ntab[\"se_bp\"] = tab[\"sd\"] / np.sqrt(tab[\"n\"])\ntab.insert(0, \"weekday\", DAYS)\ntab[[\"weekday\", \"n\", \"mean_bp\", \"se_bp\"]].to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\ncrit = stats.t.ppf(0.975, tab[\"n\"] - 1)\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=crit * tab[\"se_bp\"], fmt=\"o\", capsize=5, color=\"C0\")\nax.axhline(0, color=\"grey\", lw=0.8, ls=\"--\")\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(f\"10y Treasury yield changes by weekday, 2000-2019 (n={len(ch)})\\n\"\n             f\"95% CI; one-way ANOVA F={F:.2f}, p={p:.3f}\", fontsize=9)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150); plt.close(fig)\n\nr = lambda x: float(round(float(x), 10))\nres = {\n    \"task_id\": \"F6-conf-01\", \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n         \"handling\": \"dropped before differencing, per TASK rule\"},\n        {\"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n         \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"},\n        {\"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n         \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"},\n        {\"issue\": \"yields recorded to 0.01 pct, 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(len(ch)), \"random_seed\": None,\n    \"n_missing_rows\": n_missing, \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n    \"anova_f_stat\": r(F), \"anova_p_value\": r(p),\n    \"effect_estimate\": r(est), \"effect_ci_low\": r(lo), \"effect_ci_high\": r(hi),\n    \"baseline_metric\": r(rmse0), \"model_metric\": r(rmse1),\n    \"conclusion\": concl,\n    # supplementary\n    \"effect_se_welch\": r(se), \"effect_df_welch\": r(df_w), \"effect_p_welch\": r(welch_p),\n    \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n    \"sens_hac_effect_se\": r(hac.bse[1]), \"sens_hac_effect_p\": r(hac.pvalues[1]),\n    \"sens_kruskal_H\": r(kw_H), \"sens_kruskal_p\": r(kw_p),\n    \"sens_welch_anova_F\": r(wa.statistic), \"sens_welch_anova_p\": r(wa.pvalue),\n    \"sens_levene_W\": r(lev_W), \"sens_levene_p\": r(lev_p),\n    \"oos_rmse_ratio_model_over_baseline\": r(rmse1 / rmse0),\n    \"oos_rmse_unconditional_train_mean\": r(rmse_unc),\n    \"oos_dm_mean_loss_diff\": r(dm.params[0]), \"oos_dm_t\": r(dm.tvalues[0]), \"oos_dm_p\": r(dm.pvalues[0]),\n    \"train_weekday_means_bp\": {DAYS[k]: r(v) for k, v in wmeans.items()},\n    \"n_changes_total\": int(len(ch)),\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_analysed\", \"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 carry predictive content for the one-day change Δy_t (bp) of the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? Three estimands: (i) joint equality of the five weekday means (classical one-way ANOVA, all changes); (ii) the Monday contrast (mean Monday change − mean Tue–Fri change) with a 95% interval; (iii) out-of-sample RMSE (2010–2019) of weekday-specific means estimated on 2000–2009, compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\nSingle input `data/DGS10.csv` (SHA-256 verified against the README), 5217 rows (`n_input_rows`), every weekday from 2000-01-03 to 2019-12-31, no duplicate dates. 215 rows have an empty yield field (US market holidays; `n_missing_rows`) and were dropped before differencing, as the task requires. Changes were then taken between consecutive available observations; the first observation has no change. That leaves 5001 changes (`n_analysed`): 2500 in the estimation period ≤2009-12-31 (`n_train`) and 2501 in the evaluation period ≥2010-01-01 (`n_test`). The exclusion ledger (`out/exclusions.csv`) adds up: 5217 − 215 − 1 = 5001. Data issues found and how each was handled are listed in `data_issues`: holiday gaps, multi-day post-holiday changes, heavy tails (−51 bp on 2009-03-18), and 1 bp discreteness. No extreme value was removed. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Joint test: `scipy.stats.f_oneway` (equal-variance one-way ANOVA, F(4, 4996)) on all 5001 changes, grouped by the weekday of t.\n- Monday contrast: difference of sample means, all changes. The 95% interval is a **Welch** two-sample t interval (Monday n=919 vs Tue–Fri n=4082), chosen because it does not assume equal variances. As a sensitivity check, an OLS regression on a Monday dummy with Newey–West (HAC, 5 lags) SE.\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change for its weekday (`train_weekday_means_bp`). The baseline forecast is zero. Both are scored by RMSE over the 2501 evaluation changes. A Diebold–Mariano-type test on the squared-error differential (HAC, 5 lags) is supplementary.\n- Decision rule: `conclusion` comes from the ANOVA at the 5% level. If the ANOVA rejected, the sign would come from the Monday interval.\n- Sensitivity checks on the ANOVA: Welch ANOVA, Kruskal–Wallis, and a Levene (median) test of equal variances.\n- No randomness is involved (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n- Weekday means (table1.csv, figure1.png) range from −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each 95% interval covers zero.\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.902 (`anova_p_value`). Equality of the weekday means is not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, +0.420] bp (`effect_ci_low`, `effect_ci_high`), p = 0.79 (`effect_p_welch`).\n- Out-of-sample: zero-change RMSE 4.876 bp (`baseline_metric`), weekday-mean RMSE 4.884 bp (`model_metric`), a ratio of 1.0017 (`oos_rmse_ratio_model_over_baseline`). The weekday model is slightly *worse* than the random walk. The DM statistic is t = 1.89, p = 0.058 (`oos_dm_t`, `oos_dm_p`), so the deterioration is borderline and not significant at 5%. Using the overall training mean instead gives an RMSE of 4.875 bp (`oos_rmse_unconditional_train_mean`).\n- **Conclusion: `no_detectable_effect`** (`conclusion`). These data show no evidence that the weekday predicts daily 10-year yield changes, and weekday-specific means do not beat a zero forecast out of sample.\n\n## Uncertainty\n- The Welch SE of the Monday contrast is 0.188 bp (`effect_se_welch`). The HAC SE is 0.194 bp (`sens_hac_effect_se`, p = 0.80, `sens_hac_effect_p`), so allowing for autocorrelation and heteroskedasticity barely changes the inference.\n- The CI half-width is about 0.37 bp. A Monday effect larger than about ±0.4 bp/day is therefore inconsistent with these data at the 95% level. Smaller effects cannot be ruled out, but they would be tiny next to the ~5 bp daily standard deviation.\n- The equal-variance assumption of the classical ANOVA is violated: Levene p ≈ 1e-7 (`sens_levene_p`). The conclusion holds anyway under Welch ANOVA (p = 0.902, `sens_welch_anova_p`) and Kruskal–Wallis (p = 0.52, `sens_kruskal_p`).\n- The OOS comparison rests on a single split, with no resampling-based interval for the RMSE difference beyond the DM test.\n\n## Limitations and open issues\n- **What would invalidate the result.** Strong dependence or volatility clustering beyond what 5-lag HAC captures could make the SEs too small. Since the effect is null, though, too-small SEs would only make it *easier* to find an effect, so the null conclusion is conservative in that direction. Changes in the weekday pattern over time (e.g., FOMC calendars, the 2008–09 crisis, QE) could hide period-specific effects that cancel in the pooled 20-year sample. No subperiod tests were run beyond the train/test split.\n- **Measurement convention.** Post-holiday changes span more than one trading day; for example, Tuesday after a Monday holiday captures Fri→Tue. This inflates the variance of some weekday groups (one reason for the Levene rejection) and slightly mixes weekday attributions. I followed the task rule and did not adjust for it. DGS10 is an end-of-day constant-maturity interpolated yield, not a traded price, and is discretised at 1 bp.\n- **Not checked.** Event-day effects such as FOMC days or payroll Fridays, intraday timing, other maturities, and any multiple-testing adjustment across folklore hypotheses (only the Monday contrast was tested specifically). The classical ANOVA is the mandated test even though its equal-variance assumption fails; robust alternatives agree with it.\n- **Main threats.** The power to detect sub-0.4 bp effects is limited. A single OOS split can be sensitive to regime (2010–2019 is a low-rate era). Heavy tails make RMSE sensitive to a few large days.\n- No review issues have been received yet.\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 field (holidays),5217,5002,215,TASK rule: no published yield\n2,first available observation has no 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":"947b71c0-6206-4408-be69-1ae9aa7caa14","timestamp":"2026-10-30T05:23:06.520Z","toolUseResult":{"stdout":"\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"], format=\"%Y-%m-%d\")\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"dy\"] = d[\"dy\"].round(8)  # remove float noise; data have 1 bp resolution\nch = d.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\n\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with empty DGS10 field (holidays)\", n_before=n_raw,\n         n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: no published yield\"),\n    dict(step=2, rule=\"first available observation has no change\", n_before=n_raw - n_missing,\n         n_after=len(ch), n_removed=n_raw - n_missing - len(ch), rationale=\"TASK rule: change needs y_{t-1}\"),\n]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\nkw_H, kw_p = stats.kruskal(*groups)                       # sensitivity (rank-based)\nlev_W, lev_p = stats.levene(*groups, center=\"median\")     # equal-variance check\nfrom statsmodels.stats.oneway import anova_oneway\nwa = anova_oneway(groups, use_var=\"unequal\")              # Welch ANOVA sensitivity\n\n# (ii) Monday contrast, Welch two-sample interval (primary)\nmon = ch.loc[ch[\"wd\"] == 0, \"dy\"].values; oth = ch.loc[ch[\"wd\"] != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df_w)\nlo, hi = est - tq * se, est + tq * se\nwelch_p = 2 * stats.t.sf(abs(est / se), df_w)\n# sensitivity: OLS on Monday dummy with HAC (Newey-West, 5 lags) SE\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nhac = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\n\n# (iii) out-of-sample\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nf_model = test[\"wd\"].map(wmeans).values\ne0 = test[\"dy\"].values; e1 = e0 - f_model\nrmse0 = float(np.sqrt(np.mean(e0 ** 2))); rmse1 = float(np.sqrt(np.mean(e1 ** 2)))\ne_unc = e0 - train[\"dy\"].mean()\nrmse_unc = float(np.sqrt(np.mean(e_unc ** 2)))\n# Diebold-Mariano on squared-error loss differential (model - baseline), HAC 5 lags\ndl = e1 ** 2 - e0 ** 2\ndm = sm.OLS(dl, np.ones_like(dl)).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\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\n# table + figure\ntab = ch.groupby(\"wd\")[\"dy\"].agg(n=\"count\", mean_bp=\"mean\", sd=\"std\").reindex(range(5))\ntab[\"se_bp\"] = tab[\"sd\"] / np.sqrt(tab[\"n\"])\ntab.insert(0, \"weekday\", DAYS)\ntab[[\"weekday\", \"n\", \"mean_bp\", \"se_bp\"]].to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\ncrit = stats.t.ppf(0.975, tab[\"n\"] - 1)\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=crit * tab[\"se_bp\"], fmt=\"o\", capsize=5, color=\"C0\")\nax.axhline(0, color=\"grey\", lw=0.8, ls=\"--\")\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(f\"10y Treasury yield changes by weekday, 2000-2019 (n={len(ch)})\\n\"\n             f\"95% CI; one-way ANOVA F={F:.2f}, p={p:.3f}\", fontsize=9)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150); plt.close(fig)\n\nr = lambda x: float(round(float(x), 10))\nres = {\n    \"task_id\": \"F6-conf-01\", \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n         \"handling\": \"dropped before differencing, per TASK rule\"},\n        {\"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n         \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"},\n        {\"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n         \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"},\n        {\"issue\": \"yields recorded to 0.01 pct, 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(len(ch)), \"random_seed\": None,\n    \"n_missing_rows\": n_missing, \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n    \"anova_f_stat\": r(F), \"anova_p_value\": r(p),\n    \"effect_estimate\": r(est), \"effect_ci_low\": r(lo), \"effect_ci_high\": r(hi),\n    \"baseline_metric\": r(rmse0), \"model_metric\": r(rmse1),\n    \"conclusion\": concl,\n    # supplementary\n    \"effect_se_welch\": r(se), \"effect_df_welch\": r(df_w), \"effect_p_welch\": r(welch_p),\n    \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n    \"sens_hac_effect_se\": r(hac.bse[1]), \"sens_hac_effect_p\": r(hac.pvalues[1]),\n    \"sens_kruskal_H\": r(kw_H), \"sens_kruskal_p\": r(kw_p),\n    \"sens_welch_anova_F\": r(wa.statistic), \"sens_welch_anova_p\": r(wa.pvalue),\n    \"sens_levene_W\": r(lev_W), \"sens_levene_p\": r(lev_p),\n    \"oos_rmse_ratio_model_over_baseline\": r(rmse1 / rmse0),\n    \"oos_rmse_unconditional_train_mean\": r(rmse_unc),\n    \"oos_dm_mean_loss_diff\": r(dm.params[0]), \"oos_dm_t\": r(dm.tvalues[0]), \"oos_dm_p\": r(dm.pvalues[0]),\n    \"train_weekday_means_bp\": {DAYS[k]: r(v) for k, v in wmeans.items()},\n    \"n_changes_total\": int(len(ch)),\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_analysed\", \"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 carry predictive content for the one-day change Δy_t (bp) of the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? Three estimands: (i) joint equality of the five weekday means (classical one-way ANOVA, all changes); (ii) the Monday contrast (mean Monday change − mean Tue–Fri change) with a 95% interval; (iii) out-of-sample RMSE (2010–2019) of weekday-specific means estimated on 2000–2009, compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\nSingle input `data/DGS10.csv` (SHA-256 verified against the README), 5217 rows (`n_input_rows`), every weekday from 2000-01-03 to 2019-12-31, no duplicate dates. 215 rows have an empty yield field (US market holidays; `n_missing_rows`) and were dropped before differencing, as the task requires. Changes were then taken between consecutive available observations; the first observation has no change. That leaves 5001 changes (`n_analysed`): 2500 in the estimation period ≤2009-12-31 (`n_train`) and 2501 in the evaluation period ≥2010-01-01 (`n_test`). The exclusion ledger (`out/exclusions.csv`) adds up: 5217 − 215 − 1 = 5001. Data issues found and how each was handled are listed in `data_issues`: holiday gaps, multi-day post-holiday changes, heavy tails (−51 bp on 2009-03-18), and 1 bp discreteness. No extreme value was removed. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Joint test: `scipy.stats.f_oneway` (equal-variance one-way ANOVA, F(4, 4996)) on all 5001 changes, grouped by the weekday of t.\n- Monday contrast: difference of sample means, all changes. The 95% interval is a **Welch** two-sample t interval (Monday n=919 vs Tue–Fri n=4082), chosen because it does not assume equal variances. As a sensitivity check, an OLS regression on a Monday dummy with Newey–West (HAC, 5 lags) SE.\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change for its weekday (`train_weekday_means_bp`). The baseline forecast is zero. Both are scored by RMSE over the 2501 evaluation changes. A Diebold–Mariano-type test on the squared-error differential (HAC, 5 lags) is supplementary.\n- Decision rule: `conclusion` comes from the ANOVA at the 5% level. If the ANOVA rejected, the sign would come from the Monday interval.\n- Sensitivity checks on the ANOVA: Welch ANOVA, Kruskal–Wallis, and a Levene (median) test of equal variances.\n- No randomness is involved (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n- Weekday means (table1.csv, figure1.png) range from −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each 95% interval covers zero.\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.902 (`anova_p_value`). Equality of the weekday means is not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, +0.420] bp (`effect_ci_low`, `effect_ci_high`), p = 0.79 (`effect_p_welch`).\n- Out-of-sample: zero-change RMSE 4.876 bp (`baseline_metric`), weekday-mean RMSE 4.884 bp (`model_metric`), a ratio of 1.0017 (`oos_rmse_ratio_model_over_baseline`). The weekday model is slightly *worse* than the random walk. The DM statistic is t = 1.89, p = 0.058 (`oos_dm_t`, `oos_dm_p`), so the deterioration is borderline and not significant at 5%. Using the overall training mean instead gives an RMSE of 4.875 bp (`oos_rmse_unconditional_train_mean`).\n- **Conclusion: `no_detectable_effect`** (`conclusion`). These data show no evidence that the weekday predicts daily 10-year yield changes, and weekday-specific means do not beat a zero forecast out of sample.\n\n## Uncertainty\n- The Welch SE of the Monday contrast is 0.188 bp (`effect_se_welch`). The HAC SE is 0.194 bp (`sens_hac_effect_se`, p = 0.80, `sens_hac_effect_p`), so allowing for autocorrelation and heteroskedasticity barely changes the inference.\n- The CI half-width is about 0.37 bp. A Monday effect larger than about ±0.4 bp/day is therefore inconsistent with these data at the 95% level. Smaller effects cannot be ruled out, but they would be tiny next to the ~5 bp daily standard deviation.\n- The equal-variance assumption of the classical ANOVA is violated: Levene p ≈ 1e-7 (`sens_levene_p`). The conclusion holds anyway under Welch ANOVA (p = 0.902, `sens_welch_anova_p`) and Kruskal–Wallis (p = 0.52, `sens_kruskal_p`).\n- The OOS comparison rests on a single split, with no resampling-based interval for the RMSE difference beyond the DM test.\n\n## Limitations and open issues\n- **What would invalidate the result.** Strong dependence or volatility clustering beyond what 5-lag HAC captures could make the SEs too small. Since the effect is null, though, too-small SEs would only make it *easier* to find an effect, so the null conclusion is conservative in that direction. Changes in the weekday pattern over time (e.g., FOMC calendars, the 2008–09 crisis, QE) could hide period-specific effects that cancel in the pooled 20-year sample. No subperiod tests were run beyond the train/test split.\n- **Measurement convention.** Post-holiday changes span more than one trading day; for example, Tuesday after a Monday holiday captures Fri→Tue. This inflates the variance of some weekday groups (one reason for the Levene rejection) and slightly mixes weekday attributions. I followed the task rule and did not adjust for it. DGS10 is an end-of-day constant-maturity interpolated yield, not a traded price, and is discretised at 1 bp.\n- **Not checked.** Event-day effects such as FOMC days or payroll Fridays, intraday timing, other maturities, and any multiple-testing adjustment across folklore hypotheses (only the Monday contrast was tested specifically). The classical ANOVA is the mandated test even though its equal-variance assumption fails; robust alternatives agree with it.\n- **Main threats.** The power to detect sub-0.4 bp effects is limited. A single OOS split can be sensitive to regime (2010–2019 is a low-rate era). Heavy tails make RMSE sensitive to a few large days.\n- No review issues have been received yet.\nweekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 field (holidays),5217,5002,215,TASK rule: no published yield\n2,first available observation has no change,5002,5001,1,TASK rule: change needs 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","media_type":"image/png"}}]}]},"uuid":"e29425a5-68ed-4dad-97c1-04285ae1072b","timestamp":"2026-10-30T05:23:19.465Z","toolUseResult":{"type":"image","file":{"base64":"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> out/review/review.json <<'EOF'\n{\n  \"verdict\": \"accept\",\n  \"issues\": [\n    {\n      \"id\": \"R1\",\n      \"severity\": \"minor\",\n      \"location\": \"claims.json C5\",\n      \"issue\": \"C5 says the null is 'robust to ... autocorrelation', but the autocorrelation-robust check is applied only to the Monday contrast, using Newey-West with a fixed 5 lags. The Welch ANOVA and Kruskal-Wallis checks of the joint test still assume independent observations.\",\n      \"suggested_fix\": \"Limit the wording to 'Monday-contrast SE robust to HAC(5)', or add an autocorrelation-robust joint Wald test (OLS on weekday dummies with HAC covariance).\"\n    },\n    {\n      \"id\": \"R2\",\n      \"severity\": \"minor\",\n      \"location\": \"report.md Uncertainty\",\n      \"issue\": \"The report calls the daily standard deviation '~5 bp'. The full-sample SD of the changes is about 5.7 bp; 4.9 bp is the evaluation-period RMSE. This is loose wording, not an error.\",\n      \"suggested_fix\": \"State the full-sample SD (about 5.7 bp) or say which period the ~5 bp figure refers to.\"\n    },\n    {\n      \"id\": \"R3\",\n      \"severity\": \"minor\",\n      \"location\": \"report.md Limitations\",\n      \"issue\": \"The line 'No review issues have been received yet' describes the process, not a scientific limitation.\",\n      \"suggested_fix\": \"Remove it.\"\n    }\n  ],\n  \"summary\": \"Rerunning analysis.py in a scratch copy reproduced results.json and table1.csv exactly. The data hash matches data/README.md. Independent checks confirm the counts: 5217 rows, 215 empty, 5001 changes split 2500/2501, and the -51 bp extreme on 2009-03-18. The code follows the TASK rules. Empty rows are dropped before differencing, and each change is dated to the weekday of t. Weekday forecast means come from changes dated on or before 2009-12-31 only. The joint test is the classical equal-variance f_oneway on all changes, and the Monday interval is a Welch interval, as the report states. All required keys are present. The conclusion code follows the prescribed rule (ANOVA p=0.90). The out-of-sample comparison against the zero-change baseline is reported, including the slight deterioration and a DM-type test (p=0.058). The table, figure and report sections meet the spec. The claims match the numbers and carry fitting caveats. The limitations are honest: Levene rejects equal variances, the mandated test is kept anyway, and the report notes multi-day post-holiday changes, the single split, and limited power below about 0.4 bp. Only minor wording issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1208,"cache_read_input_tokens":14301,"output_tokens":1231,"output_tokens_details":{"thinking_tokens":182},"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":1208},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrzhpfTBeLueUc4fABa","requestRef":"6299ff70-c808-4018-b3b9-17ec71b1eaff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"51d2ec4a-9add-46f8-9902-cbb1f5c4bd3a","timestamp":"2026-10-30T05:23:31.618Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"51d2ec4a-9add-46f8-9902-cbb1f5c4bd3a","isSidechain":false,"promptId":"d4c9d550-1614-4c8e-a552-bfc46e0451c5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Pt2deA3nczLMgw5XorHVYC","type":"tool_result","content":"ok","is_error":false}]},"uuid":"f9bcc26f-ce72-4189-bc55-0b1423b99b5a","timestamp":"2026-10-30T05:23:31.676Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"51d2ec4a-9add-46f8-9902-cbb1f5c4bd3a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","hash":"3464ccaebd1986bd7f920d14725cae46f5107ff7e486fe15c9baca6c7bd07714","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Pt2deA3nczLMgw5XorHVYC","name":"Bash","input":{"command":"cat > out/review/review.json <<'EOF'\n{\n  \"verdict\": \"accept\",\n  \"issues\": [\n    {\n      \"id\": \"R1\",\n      \"severity\": \"minor\",\n      \"location\": \"claims.json C5\",\n      \"issue\": \"C5 says the null is 'robust to ... autocorrelation', but the autocorrelation-robust check is applied only to the Monday contrast, using Newey-West with a fixed 5 lags. The Welch ANOVA and Kruskal-Wallis checks of the joint test still assume independent observations.\",\n      \"suggested_fix\": \"Limit the wording to 'Monday-contrast SE robust to HAC(5)', or add an autocorrelation-robust joint Wald test (OLS on weekday dummies with HAC covariance).\"\n    },\n    {\n      \"id\": \"R2\",\n      \"severity\": \"minor\",\n      \"location\": \"report.md Uncertainty\",\n      \"issue\": \"The report calls the daily standard deviation '~5 bp'. The full-sample SD of the changes is about 5.7 bp; 4.9 bp is the evaluation-period RMSE. This is loose wording, not an error.\",\n      \"suggested_fix\": \"State the full-sample SD (about 5.7 bp) or say which period the ~5 bp figure refers to.\"\n    },\n    {\n      \"id\": \"R3\",\n      \"severity\": \"minor\",\n      \"location\": \"report.md Limitations\",\n      \"issue\": \"The line 'No review issues have been received yet' describes the process, not a scientific limitation.\",\n      \"suggested_fix\": \"Remove it.\"\n    }\n  ],\n  \"summary\": \"Rerunning analysis.py in a scratch copy reproduced results.json and table1.csv exactly. The data hash matches data/README.md. Independent checks confirm the counts: 5217 rows, 215 empty, 5001 changes split 2500/2501, and the -51 bp extreme on 2009-03-18. The code follows the TASK rules. Empty rows are dropped before differencing, and each change is dated to the weekday of t. Weekday forecast means come from changes dated on or before 2009-12-31 only. The joint test is the classical equal-variance f_oneway on all changes, and the Monday interval is a Welch interval, as the report states. All required keys are present. The conclusion code follows the prescribed rule (ANOVA p=0.90). The out-of-sample comparison against the zero-change baseline is reported, including the slight deterioration and a DM-type test (p=0.058). The table, figure and report sections meet the spec. The claims match the numbers and carry fitting caveats. The limitations are honest: Levene rejects equal variances, the mandated test is kept anyway, and the report notes multi-day post-holiday changes, the single split, and limited power below about 0.4 bp. Only minor wording issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate"}}]}}42{"type":"api-request","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","id":"22fad4ff-9b2a-4726-9743-4aab3f2eee6b","timestamp":"2026-10-30T05:23:31.679Z","version":"2.1.314","querySource":"sdk","shapeHash":"2aa7d557decaeb5e09a41329481f86f2d44dac122864685236901c4c661f6349","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":"6299ff70-c808-4018-b3b9-17ec71b1eaff","keep":10,"tail":["3464ccaebd1986bd7f920d14725cae46f5107ff7e486fe15c9baca6c7bd07714","ef67ee85c05775945d7a554e287abb75f064b90e36620ef715d476dbd1e8d04e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e7167bc7f976e1848e2712444dba74d55c42a7526d235ba3ebf0ffc7ba7cf352"}43{"parentUuid":"f9bcc26f-ce72-4189-bc55-0b1423b99b5a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs1cJZXM1VNJC5MB1V2","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":1238,"cache_read_input_tokens":15509,"output_tokens":678,"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":1238},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5758,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs1c2hCK8Fd89nZ5jCx","requestRef":"22fad4ff-9b2a-4726-9743-4aab3f2eee6b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8e1b59e0-c5fa-49b9-b65c-9596aba9a7eb","timestamp":"2026-10-30T05:23:38.740Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"8e1b59e0-c5fa-49b9-b65c-9596aba9a7eb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs1cJZXM1VNJC5MB1V2","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**, with three minor issues, and the review is written to `out/review/review.json`.\n\nI re-ran `out/analysis.py` on a scratch copy. It reproduced `results.json` and `table1.csv` exactly, and the data file's checksum matches the one in `data/README.md`. I checked the counts against the raw data myself: 5217 rows, 215 empty, 5001 changes split 2500 before 2010 and 2501 after.\n\n- **Rules followed:** Empty rows are dropped before the changes are computed, and each change is assigned to the weekday it falls on. The weekday means used for forecasting come only from 2009 and earlier. The joint test is the classical equal-variance ANOVA required by the task. The Monday interval uses the Welch method, which the report states.\n- **Results:** All required keys are present. The conclusion `no_detectable_effect` follows the task's rule (ANOVA p = 0.90). The Monday effect is +0.05 bp with a 95% interval of −0.32 to +0.42 bp.\n- **Baseline:** The comparison with the zero-change forecast is there. The weekday model does slightly worse (error 4.884 vs 4.876 bp), and that gap is not significant at 5% (p = 0.058).\n- **Limitations:** The report is open about them. A test shows the weekdays have unequal variances, so the required ANOVA's assumption fails; checks that don't rely on it give the same answer. It also covers changes that span a holiday, the single before/after-2010 split, and the lack of power to detect effects smaller than about 0.4 bp.\n\nThe three minor issues:\n1. **Claim C5 overstates robustness to autocorrelation.** That check was applied only to the Monday estimate, not to the joint test.\n2. **The report calls daily volatility \"~5 bp\".** The full-sample figure is about 5.7 bp; 4.9 bp is the post-2010 forecast error.\n3. **A line about review status sits in the limitations section.** It should be removed."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1238,"cache_read_input_tokens":15509,"output_tokens":678,"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":1238},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs1c2hCK8Fd89nZ5jCx","requestRef":"22fad4ff-9b2a-4726-9743-4aab3f2eee6b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"964c1ffb-cfc3-41ef-93be-76bcce421a59","timestamp":"2026-10-30T05:23:38.749Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","version":"2.1.314","gitBranch":"HEAD"}45{"type":"last-prompt","lastPrompt":"Review","leafUuid":"964c1ffb-cfc3-41ef-93be-76bcce421a59","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844"}46{"type":"cost-state","sessionId":"20ae43ed-f95c-4260-a406-35bf7f265844","totalCostUSD":0.1530914,"totalAPIDuration":35853,"totalAPIDurationWithoutRetries":35849,"totalToolDuration":1166,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":37156,"startTime":1793337781607,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":3306,"thinkingTokens":838,"cacheReadInputTokens":53502,"cacheCreationInputTokens":15243,"cacheCreationEphemeral5mInputTokens":15243,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1530914}},"hasUnknownModelCost":false}47