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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nDGS10.csv\nREADME.md","is_error":false}]},"uuid":"59a17f53-2f9a-4ebe-9879-bd8d99e6d6b6","timestamp":"2026-10-30T05:18:02.246Z","toolUseResult":{"stdout":"# F6-conf-01 — Does the day of the week carry predictive content for daily changes in the 10-year Treasury yield?\n\n## Scientific question\nFinancial folklore attributes systematic \"Monday\" or \"Friday\" patterns to daily asset-price changes. Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n 5218 data/DGS10.csv\n 215","is_error":false}]},"uuid":"606426b0-0a14-41c7-98d0-c73fb82d977e","timestamp":"2026-10-30T05:18:04.015Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-01\n\n## `DGS10.csv` (5217 rows)\nObserved data: *Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis* (FRED series `DGS10`, daily, percent, not seasonally adjusted). Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\nobservation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n 5218 data/DGS10.csv\n 215","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a2e5e581-416b-40fa-8e55-b5b62ed68513","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","hash":"a307458eb7a4027644b5a7e83dd2df6ff19602f92581ef17a3f78751a963f401","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0187ZazCWxf1D2zdAU2AwkW5","name":"Bash","input":{"command":"cat data/README.md; 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cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01: weekday effects in daily DGS10 changes.\"\"\"\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\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"DGS10.csv\"), dtype=str, keep_default_na=False)\nn_input = len(raw)\nissues = []\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"])\nmiss = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(miss.sum())\nissues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n \"handling\": \"dropped before differencing; change computed between consecutive available observations (so post-holiday changes span >1 business day)\"})\ndf = raw.loc[~miss].copy()\ndf[\"y\"] = pd.to_numeric(df[\"DGS10\"], errors=\"coerce\")\nbad = int(df[\"y\"].isna().sum())\nif bad:\n issues.append({\"issue\": f\"{bad} non-numeric DGS10 values\", \"handling\": \"dropped\"})\n df = df.dropna(subset=[\"y\"])\ndup = int(df[\"date\"].duplicated().sum())\nif dup:\n issues.append({\"issue\": f\"{dup} duplicated dates\", \"handling\": \"kept first\"})\n df = df.drop_duplicates(\"date\")\nif not df[\"date\"].is_monotonic_increasing:\n issues.append({\"issue\": \"dates not sorted\", \"handling\": \"sorted\"})\ndf = df.sort_values(\"date\").reset_index(drop=True)\nwkend = int((df[\"date\"].dt.dayofweek >= 5).sum())\nassert wkend == 0, \"weekend observations present\"\n\ndf[\"dy\"] = 100 * df[\"y\"].diff()\ndf[\"dy\"] = df[\"dy\"].round(6) # remove float noise from decimal differencing\nch = df.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\ngap = (ch[\"date\"] - df[\"date\"].shift(1).loc[ch.index]).dt.days\nn_long = int(((gap > 1) & ~((ch[\"wd\"] == 0) & (gap == 3))).sum())\nissues.append({\"issue\": f\"{n_long} changes span a holiday gap (longer than the normal 1-day or Monday 3-day gap)\",\n \"handling\": \"kept, per task definition (attributed to weekday of t); noted as limitation\"})\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\n\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Monday contrast: OLS on Monday indicator with HC3 robust SE\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nfit = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HC3\")\neff = float(fit.params[1]); lo, hi = map(float, fit.conf_int(0.05)[1])\n# Welch cross-check\nw = stats.ttest_ind(groups[0], np.concatenate(groups[1:]), equal_var=False)\n\ntm = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(tm)\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"] ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"] - pred) ** 2)))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\ntab = pd.DataFrame({\"weekday\": names,\n \"n\": [len(g) for g in groups],\n \"mean_bp\": [g.mean() for g in groups],\n \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\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)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean daily change in 10y yield (bp)\")\nax.set_title(f\"DGS10 daily change by weekday, 2000-2019 (95% CI)\\nANOVA F={F:.2f}, p={p:.3f}\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nres = {\"task_id\": \"F6-conf-01\", \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n \"effect_estimate\": eff, \"effect_ci_low\": lo, \"effect_ci_high\": hi,\n \"baseline_metric\": rmse_base, \"model_metric\": rmse_model,\n \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": {\"DGS10.csv\": n_input},\n \"n_analysed\": int(len(ch)), \"random_seed\": None,\n \"supplementary\": {\"welch_t\": float(w.statistic), \"welch_p\": float(w.pvalue),\n \"monday_contrast_hc3_p\": float(fit.pvalues[1]),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in tm.items()},\n \"rmse_ratio_model_over_baseline\": rmse_model / rmse_base,\n \"test_mean_change_bp\": float(test[\"dy\"].mean())}}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2)); print(tab)\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":811,"cache_read_input_tokens":4606,"output_tokens":2467,"output_tokens_details":{"thinking_tokens":23},"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":811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrbT51j3ysPFn3hMkx3","requestRef":"f2c57e01-3f4e-498a-96d5-8ae5ff3f4542","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b91fba08-5e61-469f-bd0e-6cc0f39f6b5e","timestamp":"2026-10-30T05:18:23.399Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"b91fba08-5e61-469f-bd0e-6cc0f39f6b5e","isSidechain":false,"promptId":"709d4322-c2ed-4ad4-9524-235a0a40d6b3","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017bMdqrWZ6UPLhiZuRgjQVF","type":"tool_result","content":"{\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": 215,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401699385,\n \"effect_ci_low\": -0.3192109323240765,\n \"effect_ci_high\": 0.4195511291274753,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 field (US market holidays)\",\n \"handling\": \"dropped before differencing; change computed between consecutive available observations (so post-holiday changes span >1 business day)\"\n },\n {\n \"issue\": \"214 changes span a holiday gap (longer than the normal 1-day or Monday 3-day gap)\",\n \"handling\": \"kept, per task definition (attributed to weekday of t); noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"supplementary\": {\n \"welch_t\": 0.2663245398237406,\n \"welch_p\": 0.7900247217974709,\n \"monday_contrast_hc3_p\": 0.7900802719442238,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695652,\n \"Tuesday\": -0.40545808966861596,\n \"Wednesday\": -0.2054263565891473,\n \"Thursday\": 0.01383399209486166,\n \"Friday\": 0.11683168316831684\n },\n \"rmse_ratio_model_over_baseline\": 1.0016689625518014,\n \"test_mean_change_bp\": -0.07716913234706117\n }\n}\n weekday n mean_bp se_bp\n0 Monday 919 -0.052231 0.164386\n1 Tuesday 1028 -0.219844 0.177007\n2 Wednesday 1030 0.016505 0.182346\n3 Thursday 1012 -0.059289 0.186918\n4 Friday 1012 -0.147233 0.189918","is_error":false}]},"uuid":"12433ccc-ffcf-4d8d-b797-4e28b3141c85","timestamp":"2026-10-30T05:18:24.618Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": 215,\n \"n_train\": 2500,\n \"n_test\": 2501,\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401699385,\n \"effect_ci_low\": -0.3192109323240765,\n \"effect_ci_high\": 0.4195511291274753,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 field (US market holidays)\",\n \"handling\": \"dropped before differencing; change computed between consecutive available observations (so post-holiday changes span >1 business day)\"\n },\n {\n \"issue\": \"214 changes span a holiday gap (longer than the normal 1-day or Monday 3-day gap)\",\n \"handling\": \"kept, per task definition (attributed to weekday of t); noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"supplementary\": {\n \"welch_t\": 0.2663245398237406,\n \"welch_p\": 0.7900247217974709,\n \"monday_contrast_hc3_p\": 0.7900802719442238,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695652,\n \"Tuesday\": -0.40545808966861596,\n \"Wednesday\": -0.2054263565891473,\n \"Thursday\": 0.01383399209486166,\n \"Friday\": 0.11683168316831684\n },\n \"rmse_ratio_model_over_baseline\": 1.0016689625518014,\n \"test_mean_change_bp\": -0.07716913234706117\n }\n}\n weekday n mean_bp se_bp\n0 Monday 919 -0.052231 0.164386\n1 Tuesday 1028 -0.219844 0.177007\n2 Wednesday 1030 0.016505 0.182346\n3 Thursday 1012 -0.059289 0.186918\n4 Friday 1012 -0.147233 0.189918","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b91fba08-5e61-469f-bd0e-6cc0f39f6b5e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","hash":"2f940f4f6adc60c6f51b923eb7047eef260c297ea62ae7c2e130e9605e5d6751","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_017bMdqrWZ6UPLhiZuRgjQVF","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-01: weekday effects in daily DGS10 changes.\"\"\"\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\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"DGS10.csv\"), dtype=str, keep_default_na=False)\nn_input = len(raw)\nissues = []\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"])\nmiss = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(miss.sum())\nissues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n \"handling\": \"dropped before differencing; change computed between consecutive available observations (so post-holiday changes span >1 business day)\"})\ndf = raw.loc[~miss].copy()\ndf[\"y\"] = pd.to_numeric(df[\"DGS10\"], errors=\"coerce\")\nbad = int(df[\"y\"].isna().sum())\nif bad:\n issues.append({\"issue\": f\"{bad} non-numeric DGS10 values\", \"handling\": \"dropped\"})\n df = df.dropna(subset=[\"y\"])\ndup = int(df[\"date\"].duplicated().sum())\nif dup:\n issues.append({\"issue\": f\"{dup} duplicated dates\", \"handling\": \"kept first\"})\n df = df.drop_duplicates(\"date\")\nif not df[\"date\"].is_monotonic_increasing:\n issues.append({\"issue\": \"dates not sorted\", \"handling\": \"sorted\"})\ndf = df.sort_values(\"date\").reset_index(drop=True)\nwkend = int((df[\"date\"].dt.dayofweek >= 5).sum())\nassert wkend == 0, \"weekend observations present\"\n\ndf[\"dy\"] = 100 * df[\"y\"].diff()\ndf[\"dy\"] = df[\"dy\"].round(6) # remove float noise from decimal differencing\nch = df.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\ngap = (ch[\"date\"] - df[\"date\"].shift(1).loc[ch.index]).dt.days\nn_long = int(((gap > 1) & ~((ch[\"wd\"] == 0) & (gap == 3))).sum())\nissues.append({\"issue\": f\"{n_long} changes span a holiday gap (longer than the normal 1-day or Monday 3-day gap)\",\n \"handling\": \"kept, per task definition (attributed to weekday of t); noted as limitation\"})\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\n\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Monday contrast: OLS on Monday indicator with HC3 robust SE\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nfit = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HC3\")\neff = float(fit.params[1]); lo, hi = map(float, fit.conf_int(0.05)[1])\n# Welch cross-check\nw = stats.ttest_ind(groups[0], np.concatenate(groups[1:]), equal_var=False)\n\ntm = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(tm)\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"] ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"] - pred) ** 2)))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\ntab = pd.DataFrame({\"weekday\": names,\n \"n\": [len(g) for g in groups],\n \"mean_bp\": [g.mean() for g in groups],\n \"se_bp\": [g.std(ddof=1) / np.sqrt(len(g)) for g in groups]})\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\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)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean daily change in 10y yield (bp)\")\nax.set_title(f\"DGS10 daily change by weekday, 2000-2019 (95% CI)\\nANOVA F={F:.2f}, p={p:.3f}\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nres = {\"task_id\": \"F6-conf-01\", \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n \"effect_estimate\": eff, \"effect_ci_low\": lo, \"effect_ci_high\": hi,\n \"baseline_metric\": rmse_base, \"model_metric\": rmse_model,\n \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": {\"DGS10.csv\": n_input},\n \"n_analysed\": int(len(ch)), \"random_seed\": None,\n \"supplementary\": {\"welch_t\": float(w.statistic), \"welch_p\": float(w.pvalue),\n \"monday_contrast_hc3_p\": float(fit.pvalues[1]),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in tm.items()},\n \"rmse_ratio_model_over_baseline\": rmse_model / rmse_base,\n \"test_mean_change_bp\": float(test[\"dy\"].mean())}}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2)); 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Estimands: (i) joint equality of the five weekday means (one-way ANOVA); (ii) the Monday contrast (Monday mean minus mean of other weekdays) with a 95% CI; (iii) out-of-sample RMSE of weekday-mean forecasts vs a zero-change random-walk forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5217 rows (all weekdays 2000-01-03 … 2019-12-31). 215 rows have an empty DGS10 field (market holidays) and were dropped, leaving 5002 yields and 5001 changes.\n- No non-numeric values, duplicated dates, unsorted dates or weekend dates were found (checked in code).\n- Changes are computed between consecutive available observations and attributed to the weekday of t; 214 changes span a holiday gap (e.g. Tuesday after a Monday holiday) and are kept as specified.\n- Estimation period (≤ 2009-12-31): n_train = 2500 changes; evaluation period (≥ 2010-01-01): n_test = 2501.\n\n## Method\n- Joint test: classical equal-variance one-way ANOVA F-test (scipy `f_oneway`) on all 5001 changes, F(4, 4996).\n- Monday contrast: OLS of Δy on a constant and a Monday indicator; the slope equals mean(Monday) − mean(other days). 95% CI uses HC3 heteroskedasticity-robust SEs (normal critical values). Welch two-sample t-test reported as a cross-check.\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change of its weekday; compared with the zero-change forecast by RMSE (bp). Weekday means use only the estimation period.\n- Conclusion rule: ANOVA at 5%; if it rejected, sign taken from the Monday-contrast CI. No randomness is involved (no seed).\n\n## Results\nTable 1 (`out/table1.csv`), all changes, bp:\n\n| weekday | n | mean | SE |\n|---|---|---|---|\n| Monday | 919 | −0.052 | 0.164 |\n| Tuesday | 1028 | −0.220 | 0.177 |\n| Wednesday | 1030 | 0.017 | 0.182 |\n| Thursday | 1012 | −0.059 | 0.187 |\n| Friday | 1012 | −0.147 | 0.190 |\n\n- ANOVA: F = 0.263, p = 0.902 — no evidence that weekday means differ.\n- Monday contrast: +0.050 bp, 95% CI [−0.319, 0.420] bp (HC3; Welch p = 0.79).\n- Out-of-sample RMSE: zero-change baseline 4.876 bp; weekday-mean model 4.884 bp (ratio 1.0017) — the weekday model is slightly *worse* than the random walk.\n- Conclusion code: `no_detectable_effect`. Figure 1 (`out/figure1.png`) shows weekday means with 95% CIs, all overlapping zero.\n\n## Uncertainty\nWeekday means have SEs of ~0.16–0.19 bp against a daily standard deviation of ~5–6 bp, so the design can detect only weekday differences of roughly ≥0.5 bp; smaller effects cannot be excluded. The Monday contrast CI (width ~0.74 bp) rules out a Monday effect larger than about ±0.4 bp/day. The RMSE comparison is a single 2010–2019 split without a formal test (e.g. Diebold–Mariano); the 0.17% difference is negligible either way.\n\n## Limitations and open issues\n- **What would invalidate the result:** strong volatility clustering and serial dependence in daily yield changes violate the ANOVA's i.i.d./equal-variance assumptions; heavy tails (crisis days in 2008, 2001) could distort the F-test's size. The HC3 interval addresses heteroskedasticity but not autocorrelation or GARCH-type dependence; a HAC or block-bootstrap check was not done.\n- **Not checked:** subperiod stability of weekday patterns (effects could exist and cancel across regimes); interaction with macro announcement calendars (e.g. payrolls on Fridays, FOMC days on Wednesdays), which are the plausible mechanism behind any weekday pattern; distributional (variance) differences by weekday — only means were tested.\n- **Measurement threats:** holiday-gap changes (214) mix multi-day moves into specific weekdays (notably Tuesdays after Monday holidays); Monday changes span the weekend by construction. H.15 yields are rounded to 0.01 pp (1 bp), coarse relative to the effect sizes considered. FRED data may be revised; results correspond to the bundled file.\n- **Multiple testing / data-mining:** the weekday hypothesis is one of many calendar anomalies; a null here is consistent with the anomaly literature's fragility, but a significant result would have needed out-of-sample corroboration, which the RMSE comparison does not provide.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Weekday means of daily 10-year yield changes (2000-2019) are not jointly different at the 5% level (ANOVA F=0.26, p=0.90).\", \"results_keys\": [\"anova_f_stat\", \"anova_p_value\", \"n_analysed\", \"conclusion\"], \"status\": \"supported\", \"caveat\": \"Classical ANOVA assumes i.i.d. equal-variance errors; volatility clustering not addressed.\"},\n {\"id\": \"C2\", \"text\": \"The Monday contrast is +0.05 bp with 95% CI [-0.32, 0.42] bp, i.e. no detectable Monday effect.\", \"results_keys\": [\"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\"], \"status\": \"supported\", \"caveat\": \"HC3 robust SE; does not account for serial dependence. Effects below ~0.4 bp cannot be ruled out.\"},\n {\"id\": \"C3\", \"text\": \"Weekday-specific estimation-period means do not improve out-of-sample forecasts over a zero-change baseline (RMSE 4.884 vs 4.876 bp in 2010-2019).\", \"results_keys\": [\"baseline_metric\", \"model_metric\", \"n_train\", \"n_test\"], \"status\": \"supported\", \"caveat\": \"Single split; no formal forecast-comparison test.\"}\n]\nEOF\nls out","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3281,"cache_read_input_tokens":5417,"output_tokens":2453,"output_tokens_details":{"thinking_tokens":61},"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":3281},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrcyCmyZS1DgdxYxU9h","requestRef":"6443f9a6-65d3-4ad7-833e-24108dd98984","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b9929f74-b0d4-4f78-aa73-bccad13f6225","timestamp":"2026-10-30T05:18:46.150Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"b9929f74-b0d4-4f78-aa73-bccad13f6225","isSidechain":false,"promptId":"709d4322-c2ed-4ad4-9524-235a0a40d6b3","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PntayvNKgwpcaHkaDbYjMp","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","is_error":false}]},"uuid":"2fd9ba0f-f199-41a6-9844-c84fc4818dc8","timestamp":"2026-10-30T05:18:46.193Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b9929f74-b0d4-4f78-aa73-bccad13f6225","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","hash":"89d517a005ec43eb27dc36dfcda49dd885fed19c699f9856a27a2bd0ec43b9e9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01PntayvNKgwpcaHkaDbYjMp","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Day-of-week effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the weekday carry predictive content for the one-day change Δy_t = 100·(y_t − y_{t−1}) (bp) in the 10-year constant-maturity Treasury yield (FRED DGS10), 2000–2019? Estimands: (i) joint equality of the five weekday means (one-way ANOVA); (ii) the Monday contrast (Monday mean minus mean of other weekdays) with a 95% CI; (iii) out-of-sample RMSE of weekday-mean forecasts vs a zero-change random-walk forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5217 rows (all weekdays 2000-01-03 … 2019-12-31). 215 rows have an empty DGS10 field (market holidays) and were dropped, leaving 5002 yields and 5001 changes.\n- No non-numeric values, duplicated dates, unsorted dates or weekend dates were found (checked in code).\n- Changes are computed between consecutive available observations and attributed to the weekday of t; 214 changes span a holiday gap (e.g. Tuesday after a Monday holiday) and are kept as specified.\n- Estimation period (≤ 2009-12-31): n_train = 2500 changes; evaluation period (≥ 2010-01-01): n_test = 2501.\n\n## Method\n- Joint test: classical equal-variance one-way ANOVA F-test (scipy `f_oneway`) on all 5001 changes, F(4, 4996).\n- Monday contrast: OLS of Δy on a constant and a Monday indicator; the slope equals mean(Monday) − mean(other days). 95% CI uses HC3 heteroskedasticity-robust SEs (normal critical values). Welch two-sample t-test reported as a cross-check.\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change of its weekday; compared with the zero-change forecast by RMSE (bp). Weekday means use only the estimation period.\n- Conclusion rule: ANOVA at 5%; if it rejected, sign taken from the Monday-contrast CI. No randomness is involved (no seed).\n\n## Results\nTable 1 (`out/table1.csv`), all changes, bp:\n\n| weekday | n | mean | SE |\n|---|---|---|---|\n| Monday | 919 | −0.052 | 0.164 |\n| Tuesday | 1028 | −0.220 | 0.177 |\n| Wednesday | 1030 | 0.017 | 0.182 |\n| Thursday | 1012 | −0.059 | 0.187 |\n| Friday | 1012 | −0.147 | 0.190 |\n\n- ANOVA: F = 0.263, p = 0.902 — no evidence that weekday means differ.\n- Monday contrast: +0.050 bp, 95% CI [−0.319, 0.420] bp (HC3; Welch p = 0.79).\n- Out-of-sample RMSE: zero-change baseline 4.876 bp; weekday-mean model 4.884 bp (ratio 1.0017) — the weekday model is slightly *worse* than the random walk.\n- Conclusion code: `no_detectable_effect`. Figure 1 (`out/figure1.png`) shows weekday means with 95% CIs, all overlapping zero.\n\n## Uncertainty\nWeekday means have SEs of ~0.16–0.19 bp against a daily standard deviation of ~5–6 bp, so the design can detect only weekday differences of roughly ≥0.5 bp; smaller effects cannot be excluded. The Monday contrast CI (width ~0.74 bp) rules out a Monday effect larger than about ±0.4 bp/day. The RMSE comparison is a single 2010–2019 split without a formal test (e.g. Diebold–Mariano); the 0.17% difference is negligible either way.\n\n## Limitations and open issues\n- **What would invalidate the result:** strong volatility clustering and serial dependence in daily yield changes violate the ANOVA's i.i.d./equal-variance assumptions; heavy tails (crisis days in 2008, 2001) could distort the F-test's size. The HC3 interval addresses heteroskedasticity but not autocorrelation or GARCH-type dependence; a HAC or block-bootstrap check was not done.\n- **Not checked:** subperiod stability of weekday patterns (effects could exist and cancel across regimes); interaction with macro announcement calendars (e.g. payrolls on Fridays, FOMC days on Wednesdays), which are the plausible mechanism behind any weekday pattern; distributional (variance) differences by weekday — only means were tested.\n- **Measurement threats:** holiday-gap changes (214) mix multi-day moves into specific weekdays (notably Tuesdays after Monday holidays); Monday changes span the weekend by construction. H.15 yields are rounded to 0.01 pp (1 bp), coarse relative to the effect sizes considered. FRED data may be revised; results correspond to the bundled file.\n- **Multiple testing / data-mining:** the weekday hypothesis is one of many calendar anomalies; a null here is consistent with the anomaly literature's fragility, but a significant result would have needed out-of-sample corroboration, which the RMSE comparison does not provide.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Weekday means of daily 10-year yield changes (2000-2019) are not jointly different at the 5% level (ANOVA F=0.26, p=0.90).\", \"results_keys\": [\"anova_f_stat\", \"anova_p_value\", \"n_analysed\", \"conclusion\"], \"status\": \"supported\", \"caveat\": \"Classical ANOVA assumes i.i.d. equal-variance errors; volatility clustering not addressed.\"},\n {\"id\": \"C2\", \"text\": \"The Monday contrast is +0.05 bp with 95% CI [-0.32, 0.42] bp, i.e. no detectable Monday effect.\", \"results_keys\": [\"effect_estimate\", \"effect_ci_low\", \"effect_ci_high\"], \"status\": \"supported\", \"caveat\": \"HC3 robust SE; does not account for serial dependence. Effects below ~0.4 bp cannot be ruled out.\"},\n {\"id\": \"C3\", \"text\": \"Weekday-specific estimation-period means do not improve out-of-sample forecasts over a zero-change baseline (RMSE 4.884 vs 4.876 bp in 2010-2019).\", \"results_keys\": [\"baseline_metric\", \"model_metric\", \"n_train\", \"n_test\"], \"status\": \"supported\", \"caveat\": \"Single split; no formal forecast-comparison test.\"}\n]\nEOF\nls out","description":"Write report and claims"}}]}}31{"type":"api-request","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","id":"f381490e-4121-4c1b-9428-fd7a8be1edc2","timestamp":"2026-10-30T05:18:46.197Z","version":"2.1.314","querySource":"sdk","shapeHash":"44be0083a36c6d5cb96a2f884bb5db2b6fb7a145f30b754600c13dee00348d71","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":"6443f9a6-65d3-4ad7-833e-24108dd98984","keep":8,"tail":["89d517a005ec43eb27dc36dfcda49dd885fed19c699f9856a27a2bd0ec43b9e9","62903e8149877dfb824ad7a5e44d034942109e3d51b1b925bb64693c795111cd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"593fce21a67f02b9756bf2788f837b52814640fa71c3749804ef98f690d8dad1"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2fd9ba0f-f199-41a6-9844-c84fc4818dc8","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f"}33{"type":"atis-latch","atis":"","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f"}34{"parentUuid":"2fd9ba0f-f199-41a6-9844-c84fc4818dc8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXreZdVpi9bNJsrvrPBF","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":2488,"cache_read_input_tokens":8698,"output_tokens":828,"output_tokens_details":{"thinking_tokens":56},"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":2488},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6630,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXreZMNbYA6JeMQ7Z73S","requestRef":"f381490e-4121-4c1b-9428-fd7a8be1edc2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b0961fea-345b-4b02-8f21-707aed469210","timestamp":"2026-10-30T05:18:54.045Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"b0961fea-345b-4b02-8f21-707aed469210","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXreZdVpi9bNJsrvrPBF","type":"message","role":"assistant","content":[{"type":"text","text":"The day of the week has no detectable predictive content for daily changes in the 10-year Treasury yield over 2000–2019. All deliverables are written to `out/`, and `out/analysis.py` regenerates the results, table and figure from `data/`.\n\n**Data:** the file has 5,217 rows. 215 of them have no yield (US market holidays) and were dropped, leaving 5,001 daily changes: 2,500 in the 2000–2009 estimation period and 2,501 in the 2010–2019 evaluation period. The code found no duplicate, unsorted, weekend or non-numeric dates.\n\n**Results:**\n- **Joint test:** the weekday means are not distinguishable (F = 0.263, p = 0.90).\n- **Monday contrast:** Mondays average +0.05 basis points (bp) more than the other weekdays, with a 95% interval of −0.32 to +0.42 bp. I used a regression on a Monday indicator with heteroskedasticity-robust standard errors; a Welch two-sample t-test agrees (p = 0.79).\n- **Out-of-sample forecast:** predicting each 2010–2019 change by its weekday's 2000–2009 average gives an error (RMSE) of 4.884 bp. That is slightly worse than predicting no change at all (4.876 bp).\n- **Conclusion code:** `no_detectable_effect`.\n\n**Limitations:**\n- The tests assume changes are independent with equal variance. That does not hold for yields, which have calm and volatile spells, and I did not run a check that allows for this.\n- The data can only detect weekday differences of about 0.5 bp or more. A Monday effect larger than about ±0.4 bp a day is ruled out, but smaller ones are not.\n- 214 changes span a holiday, so they cover more than one trading day. I kept them as the task specifies.\n- I did not check whether the pattern differs between sub-periods, whether it is driven by data-release days (such as Friday jobs reports), or whether volatility differs by weekday.\n- The forecast comparison uses a single split and has no formal significance test.\n\nAll of these are set out in the report's \"Limitations and open issues\" section.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `figure1.png`\n- `table1.csv`\n- `claims.json`\n- `analysis.py`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2488,"cache_read_input_tokens":8698,"output_tokens":828,"output_tokens_details":{"thinking_tokens":56},"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":2488},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXreZMNbYA6JeMQ7Z73S","requestRef":"f381490e-4121-4c1b-9428-fd7a8be1edc2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"286130c4-8057-49c9-b7fc-2767bfc919d4","timestamp":"2026-10-30T05:18:54.282Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0266","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","version":"2.1.314","gitBranch":"HEAD"}36{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"286130c4-8057-49c9-b7fc-2767bfc919d4","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f"}37{"type":"cost-state","sessionId":"aeba681d-7d5c-414c-a064-d702e1f6b95f","totalCostUSD":0.1734498,"totalAPIDuration":52354,"totalAPIDurationWithoutRetries":52351,"totalToolDuration":1344,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":53892,"startTime":1793337480396,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":6025,"thinkingTokens":140,"cacheReadInputTokens":22459,"cacheCreationInputTokens":9682,"cacheCreationEphemeral5mInputTokens":9682,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1734498}},"hasUnknownModelCost":false}38