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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":"8710fbff-14d8-43f1-b88b-89b942fc734e","timestamp":"2026-10-30T05:21:55.067Z","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":"86e5bf5d-b5ca-412f-8e65-b3ce2e48db88","timestamp":"2026-10-30T05:21:57.245Z","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":"056aa436-4535-4dde-b8c0-7b6aa83a952d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","hash":"f2bc68595020a6bb12611b3b31056cd2c8bfb9554f81283e438058f8cd98770a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_013gMjjrsocbsZbPJ8ZfQvnP","name":"Bash","input":{"command":"cat data/README.md; head -5 data/DGS10.csv; wc -l data/DGS10.csv; awk -F, 'NR>1 && $2==\"\"' data/DGS10.csv | wc -l; awk -F, 'NR>1 && $2!=\"\" && $2!~/^[0-9.]+$/' data/DGS10.csv | head","description":"Inspect data"}}]}}19{"type":"api-request","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","id":"c84f5bae-bb7e-42d5-9c85-b97a440e97c4","timestamp":"2026-10-30T05:21:57.248Z","version":"2.1.314","querySource":"sdk","shapeHash":"5d312e25a9816c076bb217e8f96f99b68dbc6a71b742958b2deb574011673cf3","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":"355094a2-676d-43fb-abbc-bf7b132d9130","keep":4,"tail":["f2bc68595020a6bb12611b3b31056cd2c8bfb9554f81283e438058f8cd98770a","45e755f321334c03ca7b3c6502d3f77c64dc76612c3f009d86a412998fbef930"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ff5fd5b1fd8fe90a7d1b1d78eacb2ff266684d8cf143a098796d6cc0593ed301"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"86e5bf5d-b5ca-412f-8e65-b3ce2e48db88","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17"}21{"type":"atis-latch","atis":"","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17"}22{"parentUuid":"86e5bf5d-b5ca-412f-8e65-b3ce2e48db88","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrtfMBCj3JJyWVnLE3j","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":809,"cache_read_input_tokens":4606,"output_tokens":2788,"output_tokens_details":{"thinking_tokens":73},"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":809},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":475,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrtf5JsFDvvB2qCG2c1","requestRef":"c84f5bae-bb7e-42d5-9c85-b97a440e97c4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4bb7caa6-1af8-4a7a-ae2f-b7c3f5490723","timestamp":"2026-10-30T05:21:58.805Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"4bb7caa6-1af8-4a7a-ae2f-b7c3f5490723","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrtfMBCj3JJyWVnLE3j","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GRL1LUWkP79RbUSwbcDoXp","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Day-of-week effect in daily 10y Treasury yield changes (F6-conf-01).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = None # no randomness used\nOUT = \"out\"\nraw = pd.read_csv(\"data/DGS10.csv\", dtype={\"DGS10\": str}, keep_default_na=False)\nn_input = len(raw)\nissues = []\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\nval = raw[\"DGS10\"].str.strip()\nmissing = (val == \"\") | (val == \".\")\nn_missing = int(missing.sum())\nissues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before computing changes, per TASK rules; the change after a holiday spans the gap and is attributed to the weekday of its end date.\"})\ndf = raw.loc[~missing].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.\"})\ndf = df.dropna(subset=[\"y\"])\nndup = int(df[\"observation_date\"].duplicated().sum())\nnwkend = int((df[\"observation_date\"].dt.dayofweek >= 5).sum())\nif ndup or nwkend:\n issues.append({\"issue\": f\"{ndup} duplicate dates, {nwkend} weekend dates\", \"handling\": \"Reported only; none expected.\"})\ndf = df.sort_values(\"observation_date\").reset_index(drop=True)\ndf[\"dy\"] = 100 * df[\"y\"].diff()\ngap = df[\"observation_date\"].diff().dt.days\nn_multi = int(((gap > 1) & (df[\"observation_date\"].dt.dayofweek != 0)).sum() + ((gap > 3) & (df[\"observation_date\"].dt.dayofweek == 0)).sum())\nissues.append({\"issue\": f\"{n_multi} changes span a holiday (more calendar/business days than usual), e.g. Tuesday after a Monday holiday\",\n \"handling\": \"Kept as defined by the TASK (consecutive available observations); noted as a limitation.\"})\nissues.append({\"issue\": \"Yields are quoted to 2 decimals, so changes are discrete in 1 bp steps\",\n \"handling\": \"No action; immaterial for means/ANOVA.\"})\nch = df.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"observation_date\"].dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\n# ANOVA (all changes)\ngroups = [ch.loc[ch.wd == d, \"dy\"].values for d in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Monday contrast: Welch two-sample\nmon, oth = groups[0], ch.loc[ch.wd != 0, \"dy\"].values\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndfw = (v1 + v2) ** 2 / (v1**2 / (len(mon) - 1) + v2**2 / (len(oth) - 1))\nt = stats.t.ppf(0.975, dfw)\nlo, hi = eff - t * se, eff + t * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), dfw)\n\n# Out-of-sample\ntrain = ch[ch.observation_date <= \"2009-12-31\"]\ntest = ch[ch.observation_date >= \"2010-01-01\"]\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans).values\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - pred) ** 2)))\n# Diebold-Mariano (squared loss, simple HAC-free since 1-step) for descriptive purposes\nd = test[\"dy\"].values ** 2 - (test[\"dy\"].values - pred) ** 2\ndm_t = d.mean() / (d.std(ddof=1) / np.sqrt(len(d)))\ndm_p = 2 * stats.norm.sf(abs(dm_t))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\n# Table and figure\ntab = pd.DataFrame({\"weekday\": names, \"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)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, n - 1) * s for n, s in zip(tab.n, tab.se_bp)]\nax.errorbar(range(5), tab.mean_bp, yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean daily change in 10y yield (bp)\")\nax.set_title(\"DGS10 daily change by weekday, 2000-2019 (95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nres = {\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n \"effect_estimate\": float(eff), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse_base, \"model_metric\": rmse_model,\n \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": {\"DGS10.csv\": int(n_input)},\n \"n_analysed\": int(len(ch)), \"random_seed\": SEED,\n \"supplementary\": {\"monday_contrast_method\": \"Welch two-sample t interval\",\n \"welch_se_bp\": float(se), \"welch_df\": float(dfw), \"welch_p_value\": float(welch_p),\n \"dm_stat_squared_loss\": float(dm_t), \"dm_p_value\": float(dm_p),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wmeans.items()},\n \"sd_all_changes_bp\": float(ch.dy.std(ddof=1))},\n}\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":809,"cache_read_input_tokens":4606,"output_tokens":2788,"output_tokens_details":{"thinking_tokens":73},"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":809},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrtf5JsFDvvB2qCG2c1","requestRef":"c84f5bae-bb7e-42d5-9c85-b97a440e97c4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2f9c3d72-b29f-4ca1-b788-8864b10569c8","timestamp":"2026-10-30T05:22:18.413Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"2f9c3d72-b29f-4ca1-b788-8864b10569c8","isSidechain":false,"promptId":"c8854185-afe0-45ce-b9dd-7baab5d446f0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GRL1LUWkP79RbUSwbcDoXp","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\": 0.26346571680711994,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169966,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before computing changes, per TASK rules; the change after a holiday spans the gap and is attributed to the weekday of its end date.\"\n },\n {\n \"issue\": \"214 changes span a holiday (more calendar/business days than usual), e.g. Tuesday after a Monday holiday\",\n \"handling\": \"Kept as defined by the TASK (consecutive available observations); noted as a limitation.\"\n },\n {\n \"issue\": \"Yields are quoted to 2 decimals, so changes are discrete in 1 bp steps\",\n \"handling\": \"No action; immaterial for means/ANOVA.\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"supplementary\": {\n \"monday_contrast_method\": \"Welch two-sample t interval\",\n \"welch_se_bp\": 0.18837955539096202,\n \"welch_df\": 1548.9581965107568,\n \"welch_p_value\": 0.7900247217974673,\n \"dm_stat_squared_loss\": -1.857374897846962,\n \"dm_p_value\": 0.06325783524850424,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695535,\n \"Tuesday\": -0.40545808966861635,\n \"Wednesday\": -0.20542635658914643,\n \"Thursday\": 0.013833992094862349,\n \"Friday\": 0.11683168316831462\n },\n \"sd_all_changes_bp\": 5.723645297963581\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":"57052621-e9b8-4d3e-ae44-7eb3701126ce","timestamp":"2026-10-30T05:22:19.303Z","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\": 0.26346571680711994,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.05017009840169966,\n \"effect_ci_low\": -0.319335775694099,\n \"effect_ci_high\": 0.41967597249749833,\n \"baseline_metric\": 4.875704154642543,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"215 rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before computing changes, per TASK rules; the change after a holiday spans the gap and is attributed to the weekday of its end date.\"\n },\n {\n \"issue\": \"214 changes span a holiday (more calendar/business days than usual), e.g. Tuesday after a Monday holiday\",\n \"handling\": \"Kept as defined by the TASK (consecutive available observations); noted as a limitation.\"\n },\n {\n \"issue\": \"Yields are quoted to 2 decimals, so changes are discrete in 1 bp steps\",\n \"handling\": \"No action; immaterial for means/ANOVA.\"\n }\n ],\n \"n_input_rows\": {\n \"DGS10.csv\": 5217\n },\n \"n_analysed\": 5001,\n \"random_seed\": null,\n \"supplementary\": {\n \"monday_contrast_method\": \"Welch two-sample t interval\",\n \"welch_se_bp\": 0.18837955539096202,\n \"welch_df\": 1548.9581965107568,\n \"welch_p_value\": 0.7900247217974673,\n \"dm_stat_squared_loss\": -1.857374897846962,\n \"dm_p_value\": 0.06325783524850424,\n \"train_weekday_means_bp\": {\n \"Monday\": -0.05434782608695535,\n \"Tuesday\": -0.40545808966861635,\n \"Wednesday\": -0.20542635658914643,\n \"Thursday\": 0.013833992094862349,\n \"Friday\": 0.11683168316831462\n },\n \"sd_all_changes_bp\": 5.723645297963581\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":"2f9c3d72-b29f-4ca1-b788-8864b10569c8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","hash":"33165497447d1d7a719213df59c87d81213031b809901da7a891792831e77ca8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01GRL1LUWkP79RbUSwbcDoXp","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Day-of-week effect in daily 10y Treasury yield changes (F6-conf-01).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = None # no randomness used\nOUT = \"out\"\nraw = pd.read_csv(\"data/DGS10.csv\", dtype={\"DGS10\": str}, keep_default_na=False)\nn_input = len(raw)\nissues = []\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\nval = raw[\"DGS10\"].str.strip()\nmissing = (val == \"\") | (val == \".\")\nn_missing = int(missing.sum())\nissues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / no publication)\",\n \"handling\": \"Dropped before computing changes, per TASK rules; the change after a holiday spans the gap and is attributed to the weekday of its end date.\"})\ndf = raw.loc[~missing].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.\"})\ndf = df.dropna(subset=[\"y\"])\nndup = int(df[\"observation_date\"].duplicated().sum())\nnwkend = int((df[\"observation_date\"].dt.dayofweek >= 5).sum())\nif ndup or nwkend:\n issues.append({\"issue\": f\"{ndup} duplicate dates, {nwkend} weekend dates\", \"handling\": \"Reported only; none expected.\"})\ndf = df.sort_values(\"observation_date\").reset_index(drop=True)\ndf[\"dy\"] = 100 * df[\"y\"].diff()\ngap = df[\"observation_date\"].diff().dt.days\nn_multi = int(((gap > 1) & (df[\"observation_date\"].dt.dayofweek != 0)).sum() + ((gap > 3) & (df[\"observation_date\"].dt.dayofweek == 0)).sum())\nissues.append({\"issue\": f\"{n_multi} changes span a holiday (more calendar/business days than usual), e.g. Tuesday after a Monday holiday\",\n \"handling\": \"Kept as defined by the TASK (consecutive available observations); noted as a limitation.\"})\nissues.append({\"issue\": \"Yields are quoted to 2 decimals, so changes are discrete in 1 bp steps\",\n \"handling\": \"No action; immaterial for means/ANOVA.\"})\nch = df.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"observation_date\"].dt.dayofweek\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\n# ANOVA (all changes)\ngroups = [ch.loc[ch.wd == d, \"dy\"].values for d in range(5)]\nF, p = stats.f_oneway(*groups)\n\n# Monday contrast: Welch two-sample\nmon, oth = groups[0], ch.loc[ch.wd != 0, \"dy\"].values\neff = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndfw = (v1 + v2) ** 2 / (v1**2 / (len(mon) - 1) + v2**2 / (len(oth) - 1))\nt = stats.t.ppf(0.975, dfw)\nlo, hi = eff - t * se, eff + t * se\nwelch_p = 2 * stats.t.sf(abs(eff / se), dfw)\n\n# Out-of-sample\ntrain = ch[ch.observation_date <= \"2009-12-31\"]\ntest = ch[ch.observation_date >= \"2010-01-01\"]\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans).values\nrmse_base = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse_model = float(np.sqrt(np.mean((test[\"dy\"].values - pred) ** 2)))\n# Diebold-Mariano (squared loss, simple HAC-free since 1-step) for descriptive purposes\nd = test[\"dy\"].values ** 2 - (test[\"dy\"].values - pred) ** 2\ndm_t = d.mean() / (d.std(ddof=1) / np.sqrt(len(d)))\ndm_p = 2 * stats.norm.sf(abs(dm_t))\n\nif p < 0.05:\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n concl = \"no_detectable_effect\"\n\n# Table and figure\ntab = pd.DataFrame({\"weekday\": names, \"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)\nfig, ax = plt.subplots(figsize=(6, 4))\nci = [stats.t.ppf(0.975, n - 1) * s for n, s in zip(tab.n, tab.se_bp)]\nax.errorbar(range(5), tab.mean_bp, yerr=ci, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean daily change in 10y yield (bp)\")\nax.set_title(\"DGS10 daily change by weekday, 2000-2019 (95% CI)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nres = {\n \"task_id\": \"F6-conf-01\",\n \"n_missing_rows\": n_missing,\n \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n \"effect_estimate\": float(eff), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n \"baseline_metric\": rmse_base, \"model_metric\": rmse_model,\n \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": {\"DGS10.csv\": int(n_input)},\n \"n_analysed\": int(len(ch)), \"random_seed\": SEED,\n \"supplementary\": {\"monday_contrast_method\": \"Welch two-sample t interval\",\n \"welch_se_bp\": float(se), \"welch_df\": float(dfw), \"welch_p_value\": float(welch_p),\n \"dm_stat_squared_loss\": float(dm_t), \"dm_p_value\": float(dm_p),\n \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wmeans.items()},\n \"sd_all_changes_bp\": float(ch.dy.std(ddof=1))},\n}\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 estimation-period weekday means vs a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 rows read (header excluded), every weekday 2000-01-03 … 2019-12-31.\n- 215 rows have an empty `DGS10` field (US market holidays) and were dropped first, as required. No non-numeric values, duplicate dates or weekend dates were found.\n- Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t; the first observation has no change → 5,001 changes (all used in the ANOVA).\n- Estimation period (≤ 2009-12-31): 2,500 changes; evaluation period (≥ 2010-01-01): 2,501 changes.\n- 214 changes span a holiday gap (e.g. Tuesday after a Monday holiday, covering two business days). They are kept, as the task definition prescribes, and flagged as a limitation.\n\n## Method\n- Baseline: zero-change forecast (random walk in levels), RMSE over the evaluation period.\n- Joint test: classical equal-variance one-way ANOVA F-test (scipy `f_oneway`) of equal weekday means over all 5,001 changes, F(4, 4996).\n- Monday contrast: mean Monday change minus mean change over Tue–Fri pooled, all changes; 95% CI from a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- Out-of-sample model: each evaluation-period change is forecast by the estimation-period (2000–2009) mean change of its weekday; RMSE compared to the baseline. A descriptive Diebold–Mariano statistic (squared loss) is reported as supplementary.\n- Conclusion rule: ANOVA at 5%; if it rejected, the sign would come from the Monday-contrast interval. No randomness is used (`random_seed` = null).\n\n## Results\nTable 1 (`out/table1.csv`), all changes:\n\n| weekday | n | mean (bp) | SE (bp) |\n|---|---|---|---|\n| Monday | 919 | −0.052 | 0.164 |\n| Tuesday | 1028 | −0.220 | 0.177 |\n| Wednesday | 1030 | 0.017 | 0.182 |\n| Thursday | 1012 | −0.059 | 0.187 |\n| Friday | 1012 | −0.147 | 0.190 |\n\nFigure 1 (`out/figure1.png`) plots these means with 95% CIs; all intervals cover zero and overlap heavily.\n\n- ANOVA: F = 0.263, p = 0.902 → no evidence that weekday means differ.\n- Monday contrast: +0.050 bp, 95% Welch CI [−0.319, +0.420] bp (Welch p = 0.79). The daily change has SD ≈ 5.7 bp, so this is tiny and indistinguishable from zero.\n- Out-of-sample (2010–2019): zero-change RMSE = 4.876 bp; weekday-mean RMSE = 4.884 bp. The weekday model is slightly **worse** than the random-walk baseline (DM statistic −1.86, p ≈ 0.063, favouring the baseline at the margin).\n- Conclusion code: **`no_detectable_effect`**.\n\n## Uncertainty\nThe Monday-contrast CI half-width is about 0.37 bp, so effects larger than roughly ±0.4 bp/day on Mondays relative to other days are ruled out at 95% under the stated assumptions. The weekday SEs (~0.16–0.19 bp) are about 30 times smaller than the daily SD, yet no mean is distinguishable from zero. The ANOVA assumes equal variances and independence; the Welch interval relaxes only equal variance. The out-of-sample RMSE gap (0.008 bp) is very small relative to the RMSE level, and its DM p-value is only marginal; the direction (in-sample weekday means do not help out of sample) is the robust takeaway, not its exact size.\n\n## Limitations and open issues\n- **What would invalidate the result:** strong serial dependence or volatility clustering in Δy_t (clearly present in rates data, e.g. 2008–2009) makes the classical ANOVA's i.i.d., equal-variance assumptions wrong; this mostly affects SEs/p-values. Given that F is far below 1 and p ≈ 0.90, a plausible correction would be unlikely to produce rejection, but a HAC/heteroskedasticity-robust Wald test was **not** run.\n- **Holiday-spanning changes:** 214 changes cover more than one business day and are attributed to the post-holiday weekday (often Tuesday), mixing two days of news and inflating that weekday's variance. Not re-analysed with these excluded.\n- **Fat tails:** daily yield changes are heavy-tailed; means are sensitive to a few large moves (e.g. crisis days). No robust (median/trimmed) analysis or permutation test was performed.\n- **Regime dependence:** the effect could exist in sub-periods (e.g. pre-2008 vs zero-lower-bound years) and cancel in aggregate; sub-period stability was not checked beyond the single train/test split.\n- **Multiple comparisons / specification:** only a Monday contrast was pre-specified; individual weekday-vs-rest comparisons (e.g. Tuesday's −0.22 bp) were not tested and should not be over-read.\n- **Data:** DGS10 is a fitted constant-maturity yield rounded to 0.01%, not a traded price; FRED vintages can be revised. Only one maturity was examined, so the result says nothing about other tenors or about intraday/weekend-news timing effects.\n- The out-of-sample comparison uses a single split and RMSE only; economic (trading) value was not evaluated.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA finds no evidence that mean daily 10y yield changes differ across weekdays (F=0.26, p=0.90, 2000-2019).\",\"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 corrected.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, indistinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Welch interval ignores serial dependence; holiday-spanning changes included.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve on a zero-change forecast in 2010-2019 (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single train/test split; difference is tiny and its DM test is only marginal (p~0.06).\"},\n {\"id\":\"C4\",\"text\":\"215 empty (holiday) rows were dropped before computing changes.\",\"results_keys\":[\"n_missing_rows\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Post-holiday changes span multiple business days.\"}\n]\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && ls out","description":"Write report and claims, rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3725,"cache_read_input_tokens":5415,"output_tokens":2880,"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":3725},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrvHLuCTNqMUfxKS2m7","requestRef":"a8ba494f-3652-4254-87dd-348346997da4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a3c48966-a618-47ce-a207-91fb5f763601","timestamp":"2026-10-30T05:22:45.641Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}29{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a3c48966-a618-47ce-a207-91fb5f763601","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17"}30{"type":"atis-latch","atis":"","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17"}31{"parentUuid":"a3c48966-a618-47ce-a207-91fb5f763601","isSidechain":false,"promptId":"c8854185-afe0-45ce-b9dd-7baab5d446f0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Pa4azRFFNmm1ks4aKWYLUd","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","is_error":false}]},"uuid":"3a9f4ee3-0c95-45df-8be7-372954b626d0","timestamp":"2026-10-30T05:22:46.649Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a3c48966-a618-47ce-a207-91fb5f763601","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}32{"type":"api-request-blob","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","hash":"736e3f3592134791a3ba985933eee98b989d10c3b192fdd9441b563e80bab2aa","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Pa4azRFFNmm1ks4aKWYLUd","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Day-of-week effects in daily 10-year Treasury yield changes\n\n## Question\nDoes the weekday carry predictive content for the one-day change Δy_t (bp) in the 10-year US Treasury constant-maturity 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 estimation-period weekday means vs a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 rows read (header excluded), every weekday 2000-01-03 … 2019-12-31.\n- 215 rows have an empty `DGS10` field (US market holidays) and were dropped first, as required. No non-numeric values, duplicate dates or weekend dates were found.\n- Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations, attributed to the weekday of t; the first observation has no change → 5,001 changes (all used in the ANOVA).\n- Estimation period (≤ 2009-12-31): 2,500 changes; evaluation period (≥ 2010-01-01): 2,501 changes.\n- 214 changes span a holiday gap (e.g. Tuesday after a Monday holiday, covering two business days). They are kept, as the task definition prescribes, and flagged as a limitation.\n\n## Method\n- Baseline: zero-change forecast (random walk in levels), RMSE over the evaluation period.\n- Joint test: classical equal-variance one-way ANOVA F-test (scipy `f_oneway`) of equal weekday means over all 5,001 changes, F(4, 4996).\n- Monday contrast: mean Monday change minus mean change over Tue–Fri pooled, all changes; 95% CI from a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- Out-of-sample model: each evaluation-period change is forecast by the estimation-period (2000–2009) mean change of its weekday; RMSE compared to the baseline. A descriptive Diebold–Mariano statistic (squared loss) is reported as supplementary.\n- Conclusion rule: ANOVA at 5%; if it rejected, the sign would come from the Monday-contrast interval. No randomness is used (`random_seed` = null).\n\n## Results\nTable 1 (`out/table1.csv`), all changes:\n\n| weekday | n | mean (bp) | SE (bp) |\n|---|---|---|---|\n| Monday | 919 | −0.052 | 0.164 |\n| Tuesday | 1028 | −0.220 | 0.177 |\n| Wednesday | 1030 | 0.017 | 0.182 |\n| Thursday | 1012 | −0.059 | 0.187 |\n| Friday | 1012 | −0.147 | 0.190 |\n\nFigure 1 (`out/figure1.png`) plots these means with 95% CIs; all intervals cover zero and overlap heavily.\n\n- ANOVA: F = 0.263, p = 0.902 → no evidence that weekday means differ.\n- Monday contrast: +0.050 bp, 95% Welch CI [−0.319, +0.420] bp (Welch p = 0.79). The daily change has SD ≈ 5.7 bp, so this is tiny and indistinguishable from zero.\n- Out-of-sample (2010–2019): zero-change RMSE = 4.876 bp; weekday-mean RMSE = 4.884 bp. The weekday model is slightly **worse** than the random-walk baseline (DM statistic −1.86, p ≈ 0.063, favouring the baseline at the margin).\n- Conclusion code: **`no_detectable_effect`**.\n\n## Uncertainty\nThe Monday-contrast CI half-width is about 0.37 bp, so effects larger than roughly ±0.4 bp/day on Mondays relative to other days are ruled out at 95% under the stated assumptions. The weekday SEs (~0.16–0.19 bp) are about 30 times smaller than the daily SD, yet no mean is distinguishable from zero. The ANOVA assumes equal variances and independence; the Welch interval relaxes only equal variance. The out-of-sample RMSE gap (0.008 bp) is very small relative to the RMSE level, and its DM p-value is only marginal; the direction (in-sample weekday means do not help out of sample) is the robust takeaway, not its exact size.\n\n## Limitations and open issues\n- **What would invalidate the result:** strong serial dependence or volatility clustering in Δy_t (clearly present in rates data, e.g. 2008–2009) makes the classical ANOVA's i.i.d., equal-variance assumptions wrong; this mostly affects SEs/p-values. Given that F is far below 1 and p ≈ 0.90, a plausible correction would be unlikely to produce rejection, but a HAC/heteroskedasticity-robust Wald test was **not** run.\n- **Holiday-spanning changes:** 214 changes cover more than one business day and are attributed to the post-holiday weekday (often Tuesday), mixing two days of news and inflating that weekday's variance. Not re-analysed with these excluded.\n- **Fat tails:** daily yield changes are heavy-tailed; means are sensitive to a few large moves (e.g. crisis days). No robust (median/trimmed) analysis or permutation test was performed.\n- **Regime dependence:** the effect could exist in sub-periods (e.g. pre-2008 vs zero-lower-bound years) and cancel in aggregate; sub-period stability was not checked beyond the single train/test split.\n- **Multiple comparisons / specification:** only a Monday contrast was pre-specified; individual weekday-vs-rest comparisons (e.g. Tuesday's −0.22 bp) were not tested and should not be over-read.\n- **Data:** DGS10 is a fitted constant-maturity yield rounded to 0.01%, not a traded price; FRED vintages can be revised. Only one maturity was examined, so the result says nothing about other tenors or about intraday/weekend-news timing effects.\n- The out-of-sample comparison uses a single split and RMSE only; economic (trading) value was not evaluated.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The one-way ANOVA finds no evidence that mean daily 10y yield changes differ across weekdays (F=0.26, p=0.90, 2000-2019).\",\"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 corrected.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, indistinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Welch interval ignores serial dependence; holiday-spanning changes included.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve on a zero-change forecast in 2010-2019 (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"Single train/test split; difference is tiny and its DM test is only marginal (p~0.06).\"},\n {\"id\":\"C4\",\"text\":\"215 empty (holiday) rows were dropped before computing changes.\",\"results_keys\":[\"n_missing_rows\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Post-holiday changes span multiple business days.\"}\n]\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && ls out","description":"Write report and claims, rerun"}}]}}33{"type":"api-request","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","id":"754ebe45-e15a-438f-8eee-b3b9753099ff","timestamp":"2026-10-30T05:22:46.653Z","version":"2.1.314","querySource":"sdk","shapeHash":"5d312e25a9816c076bb217e8f96f99b68dbc6a71b742958b2deb574011673cf3","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":"a8ba494f-3652-4254-87dd-348346997da4","keep":8,"tail":["736e3f3592134791a3ba985933eee98b989d10c3b192fdd9441b563e80bab2aa","56d523d868566617875f0608dc2d356c272e2745360f110bbabd60c93805c2f4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fa27a0c6299ed7c2dcdcdd25df14afbf1f95e16a02d588ec11d523b037fa18c6"}34{"parentUuid":"3a9f4ee3-0c95-45df-8be7-372954b626d0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrxJY95wt7XNJqSmRN7","type":"message","role":"assistant","content":[{"type":"text","text":"I found no sign that the day of the week predicts daily changes in the 10-year Treasury yield, so the conclusion code is `no_detectable_effect`. All required files are in `out/`, and re-running `out/analysis.py` rebuilds the results, table and figure from `data/`.\n\n**Data:** the file has 5,217 rows. I dropped the 215 empty (holiday) rows, leaving 5,001 daily changes: 2,500 in 2000–2009 for estimation and 2,501 in 2010–2019 for evaluation.\n\n| Question | Result |\n|---|---|\n| Do the five weekday means differ? (one-way ANOVA, all changes) | F = 0.26, p = 0.90 — no evidence of any difference |\n| Monday minus the other four weekdays | +0.05 bp, 95% interval −0.32 to +0.42 bp (Welch two-sample interval) |\n| 2010–2019 forecast error (RMSE), weekday averages vs. \"no change\" | 4.884 bp vs. 4.876 bp — the weekday averages are slightly worse |\n\n- **Size of effect:** a typical daily change varies by about 5.7 bp, so a Monday effect bigger than about ±0.4 bp per day is ruled out.\n- **Forecast comparison:** a test of whether the two forecasts differ in accuracy slightly favours the \"no change\" forecast, but only marginally (p ≈ 0.06). The point is that weekday averages don't help, not the exact size of the gap.\n\n**Limitations (discussed in the report):**\n- The standard ANOVA assumes changes are independent with equal spread across days, which daily rates data violate. I did not run a version robust to this, though with p ≈ 0.90 a correction is unlikely to change the conclusion.\n- 214 changes come right after a holiday and cover two business days. I kept them as the task defines; they often land on a Tuesday.\n- I did not check whether results differ by sub-period, use statistics less sensitive to extreme days, or look at any maturity other than 10 years.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `table1.csv`\n- `figure1.png`\n- `claims.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2915,"cache_read_input_tokens":9140,"output_tokens":735,"output_tokens_details":{"thinking_tokens":0},"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":2915},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrxJHm6edP1sTDHSUVt","requestRef":"754ebe45-e15a-438f-8eee-b3b9753099ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4ea059bb-5501-41b8-9441-5d274d4d454f","timestamp":"2026-10-30T05:22:54.374Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0275","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","version":"2.1.314","gitBranch":"HEAD"}35{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4ea059bb-5501-41b8-9441-5d274d4d454f","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17"}36{"type":"cost-state","sessionId":"7343e2a1-33a5-436f-9380-f76d06720c17","totalCostUSD":0.19094280000000002,"totalAPIDuration":59166,"totalAPIDurationWithoutRetries":59163,"totalToolDuration":1943,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":61274,"startTime":1793337713107,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":6678,"thinkingTokens":129,"cacheReadInputTokens":22899,"cacheCreationInputTokens":10551,"cacheCreationEphemeral5mInputTokens":10551,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.19094280000000002}},"hasUnknownModelCost":false}37