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The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. 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Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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{"parentUuid":"73f9eba0-09f4-43d0-88d8-94b415b815b0","isSidechain":false,"promptId":"c0c04d08-9d1b-4223-9eea-8d546255eff2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011uwEkrevqthaD6GTMm5YJA","type":"tool_result","content":"# F6-conf-01 — Does the day of the week carry predictive content for daily changes in the 10-year Treasury yield?\n\n## Scientific question\nFinancial folklore attributes systematic \"Monday\" or \"Friday\" patterns to daily asset-price changes. Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:42 .\ndrwxr-xr-x  153 simon-pierreboucher  staff  4896 oct.  30 01:18 ..\n-rw-r--r--    1 simon-pierreboucher  staff   509 oct.  29 23:42 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:27 data\n-rw-r--r--    1 simon-pierreboucher  staff  2209 oct.  29 23:35 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  6307 oct.  29 23:35 TASK.md\n\n./data:\ntotal 176\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:27 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:42 ..\n-rw-r--r--  1 simon-pierreboucher  staff  82635 oct.  29 23:27 DGS10.csv\n-rw-r--r--  1 simon-pierreboucher  staff   1162 oct.  29 23:27 README.md\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"87e50bd7-5a7e-4533-9efb-88f898112d70","timestamp":"2026-10-30T05:19:00.078Z","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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{"parentUuid":"07ebdbc2-2dec-4587-b9e8-b2b4b3f15cc2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrgZdnuMdWWPKwPcCNS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AGCPm7TwfyHdyLNg22Fgb8","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, hashlib, 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\nDATA = \"data/DGS10.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(DATA, dtype=str, keep_default_na=False)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\ndf = raw.loc[~empty].copy()\ndf[\"y\"] = pd.to_numeric(df[\"DGS10\"], errors=\"raise\")\ndf = df.sort_values(\"date\").reset_index(drop=True)\n\n# audit facts\ndata_issues = []\nn_dup = int(raw[\"observation_date\"].duplicated().sum())\nn_weekend = int((raw[\"date\"].dt.dayofweek >= 5).sum())\ndata_issues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n                    \"handling\": \"dropped before differencing, per task rule; change after a holiday spans the gap\"})\nif n_dup: data_issues.append({\"issue\": f\"{n_dup} duplicated dates\", \"handling\": \"reported only\"})\nif n_weekend: data_issues.append({\"issue\": f\"{n_weekend} weekend dates\", \"handling\": \"reported only\"})\n\ndf[\"dy\"] = 100.0 * df[\"y\"].diff()\ndf[\"dy\"] = df[\"dy\"].round(10)  # remove float noise from percent->bp conversion\nch = df.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.day_name()\nassert set(ch[\"wd\"]) <= set(DAYS)\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\n\nexcl = pd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10\", n_raw, n_raw - n_missing, n_missing, \"no published yield (holiday); task rule\"],\n    [\"2\", \"first available observation has no change\", n_raw - n_missing, len(ch), 1, \"differencing\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch.wd == d, \"dy\"].values for d in DAYS]\nF, p = stats.f_oneway(*groups)\n\n# table\ntab = pd.DataFrame({\"weekday\": DAYS, \"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(f\"{OUT}/table1.csv\", index=False)\n\n# (ii) Monday contrast, Welch two-sample\nmon = ch.loc[ch.wd == \"Monday\", \"dy\"].values; oth = ch.loc[ch.wd != \"Monday\", \"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))\ntq = stats.t.ppf(0.975, dfw)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) OOS\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans)\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"] ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"] - pred) ** 2)))\n\nif p >= 0.05: concl = \"no_detectable_effect\"\nelif lo > 0: concl = \"positive_effect\"\nelif hi < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\n# figure\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab.mean_bp, yerr=1.96 * tab.se_bp, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([d[:3] for d in DAYS])\nax.set_ylabel(\"Mean daily change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield, 2000-2019 (n={len(ch)}); 95% CI\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"n_missing_rows\": n_missing, \"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    \"effect_se\": float(se), \"effect_welch_df\": float(dfw),\n    \"baseline_metric\": rmse0, \"model_metric\": rmse1,\n    \"model_minus_baseline_rmse\": rmse1 - rmse0,\n    \"train_weekday_means_bp\": {d: float(wmeans[d]) for d in DAYS},\n    \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": {\"DGS10.csv\": n_raw},\n    \"n_analysed\": int(len(ch)), \"random_seed\": SEED,\n    \"input_sha256\": {\"DGS10.csv\": sha},\n    \"first_change_date\": str(ch.date.min().date()), \"last_change_date\": str(ch.date.max().date()),\n    \"last_train_date\": str(train.date.max().date()), \"first_test_date\": str(test.date.min().date()),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f: json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"anova_f_stat\", \"anova_p_value\", \"effect_estimate\", \"effect_ci_low\",\n                  \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && cat out/results.json | head -60","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1139,"cache_read_input_tokens":7471,"output_tokens":2651,"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":1139},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrgZMS2aL46z3ytm5qG","requestRef":"832aeea4-6e97-4052-ba2e-580a9ca74eae","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d61f97bf-bb66-48b2-9358-48d80bc8d6f5","timestamp":"2026-10-30T05:19:33.379Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270/out/state","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"d61f97bf-bb66-48b2-9358-48d80bc8d6f5","isSidechain":false,"promptId":"c0c04d08-9d1b-4223-9eea-8d546255eff2","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0270/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01AGCPm7TwfyHdyLNg22Fgb8"}]},"uuid":"9d4b1047-f7e6-44b7-be4f-ac77f37732c1","timestamp":"2026-10-30T05:19:33.426Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0270/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"d61f97bf-bb66-48b2-9358-48d80bc8d6f5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270/out/state","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","hash":"bf77ddbd437c445fde11e247da166cac84bb419d9704bd74af3ce0db3346ce97","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (it also handles the audit computations)."},{"type":"tool_use","id":"toolu_01AGCPm7TwfyHdyLNg22Fgb8","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, hashlib, 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\nDATA = \"data/DGS10.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(DATA, dtype=str, keep_default_na=False)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\ndf = raw.loc[~empty].copy()\ndf[\"y\"] = pd.to_numeric(df[\"DGS10\"], errors=\"raise\")\ndf = df.sort_values(\"date\").reset_index(drop=True)\n\n# audit facts\ndata_issues = []\nn_dup = int(raw[\"observation_date\"].duplicated().sum())\nn_weekend = int((raw[\"date\"].dt.dayofweek >= 5).sum())\ndata_issues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n                    \"handling\": \"dropped before differencing, per task rule; change after a holiday spans the gap\"})\nif n_dup: data_issues.append({\"issue\": f\"{n_dup} duplicated dates\", \"handling\": \"reported only\"})\nif n_weekend: data_issues.append({\"issue\": f\"{n_weekend} weekend dates\", \"handling\": \"reported only\"})\n\ndf[\"dy\"] = 100.0 * df[\"y\"].diff()\ndf[\"dy\"] = df[\"dy\"].round(10)  # remove float noise from percent->bp conversion\nch = df.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.day_name()\nassert set(ch[\"wd\"]) <= set(DAYS)\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\n\nexcl = pd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10\", n_raw, n_raw - n_missing, n_missing, \"no published yield (holiday); task rule\"],\n    [\"2\", \"first available observation has no change\", n_raw - n_missing, len(ch), 1, \"differencing\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch.wd == d, \"dy\"].values for d in DAYS]\nF, p = stats.f_oneway(*groups)\n\n# table\ntab = pd.DataFrame({\"weekday\": DAYS, \"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(f\"{OUT}/table1.csv\", index=False)\n\n# (ii) Monday contrast, Welch two-sample\nmon = ch.loc[ch.wd == \"Monday\", \"dy\"].values; oth = ch.loc[ch.wd != \"Monday\", \"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))\ntq = stats.t.ppf(0.975, dfw)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) OOS\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans)\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"] ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"] - pred) ** 2)))\n\nif p >= 0.05: concl = \"no_detectable_effect\"\nelif lo > 0: concl = \"positive_effect\"\nelif hi < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\n# figure\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab.mean_bp, yerr=1.96 * tab.se_bp, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([d[:3] for d in DAYS])\nax.set_ylabel(\"Mean daily change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield, 2000-2019 (n={len(ch)}); 95% CI\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"n_missing_rows\": n_missing, \"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    \"effect_se\": float(se), \"effect_welch_df\": float(dfw),\n    \"baseline_metric\": rmse0, \"model_metric\": rmse1,\n    \"model_minus_baseline_rmse\": rmse1 - rmse0,\n    \"train_weekday_means_bp\": {d: float(wmeans[d]) for d in DAYS},\n    \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": {\"DGS10.csv\": n_raw},\n    \"n_analysed\": int(len(ch)), \"random_seed\": SEED,\n    \"input_sha256\": {\"DGS10.csv\": sha},\n    \"first_change_date\": str(ch.date.min().date()), \"last_change_date\": str(ch.date.max().date()),\n    \"last_train_date\": str(train.date.max().date()), \"first_test_date\": str(test.date.min().date()),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f: json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"anova_f_stat\", \"anova_p_value\", \"effect_estimate\", \"effect_ci_low\",\n                  \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && cat out/results.json | head -60","description":"Write and run analysis script"}}]}}
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change after a holiday spans the gap\"})\nif n_dup: data_issues.append({\"issue\": f\"{n_dup} duplicated dates\", \"handling\": \"reported only\"})\nif n_weekend: data_issues.append({\"issue\": f\"{n_weekend} weekend dates\", \"handling\": \"reported only\"})\n\ndf[\"dy\"] = 100.0 * df[\"y\"].diff()\ndf[\"dy\"] = df[\"dy\"].round(10)  # remove float noise from percent->bp conversion\nch = df.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.day_name()\nassert set(ch[\"wd\"]) <= set(DAYS)\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\n\nexcl = pd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10\", n_raw, n_raw - n_missing, n_missing, \"no published yield (holiday); task rule\"],\n    [\"2\", \"first available observation has no change\", n_raw - n_missing, len(ch), 1, \"differencing\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch.wd == d, \"dy\"].values for d in DAYS]\nF, p = stats.f_oneway(*groups)\n\n# table\ntab = pd.DataFrame({\"weekday\": DAYS, \"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(f\"{OUT}/table1.csv\", index=False)\n\n# (ii) Monday contrast, Welch two-sample\nmon = ch.loc[ch.wd == \"Monday\", \"dy\"].values; oth = ch.loc[ch.wd != \"Monday\", \"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))\ntq = stats.t.ppf(0.975, dfw)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) OOS\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans)\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"] ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"] - pred) ** 2)))\n\nif p >= 0.05: concl = \"no_detectable_effect\"\nelif lo > 0: concl = \"positive_effect\"\nelif hi < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\n# figure\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab.mean_bp, yerr=1.96 * tab.se_bp, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([d[:3] for d in DAYS])\nax.set_ylabel(\"Mean daily change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield, 2000-2019 (n={len(ch)}); 95% CI\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"n_missing_rows\": n_missing, \"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    \"effect_se\": float(se), \"effect_welch_df\": float(dfw),\n    \"baseline_metric\": rmse0, \"model_metric\": rmse1,\n    \"model_minus_baseline_rmse\": rmse1 - rmse0,\n    \"train_weekday_means_bp\": {d: float(wmeans[d]) for d in DAYS},\n    \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": {\"DGS10.csv\": n_raw},\n    \"n_analysed\": int(len(ch)), \"random_seed\": SEED,\n    \"input_sha256\": {\"DGS10.csv\": sha},\n    \"first_change_date\": str(ch.date.min().date()), \"last_change_date\": str(ch.date.max().date()),\n    \"last_train_date\": str(train.date.max().date()), \"first_test_date\": str(test.date.min().date()),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f: json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"anova_f_stat\", \"anova_p_value\", \"effect_estimate\", \"effect_ci_low\",\n                  \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && cat out/results.json","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":184,"cache_read_input_tokens":11423,"output_tokens":2657,"output_tokens_details":{"thinking_tokens":28},"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":184},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXriBWKuMsk4oAieXGvn","requestRef":"e6fbd9c6-a507-4d33-8f98-0bb5f4ad869d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2efec437-1440-425e-9fa6-59dbec43c10b","timestamp":"2026-10-30T05:19:53.155Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"2efec437-1440-425e-9fa6-59dbec43c10b","isSidechain":false,"promptId":"c0c04d08-9d1b-4223-9eea-8d546255eff2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014TanEg8ZeXda3UxGJZCFJb","type":"tool_result","content":"{\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.05223068552774755,0.16438585639710285\nTuesday,1028,-0.2198443579766537,0.17700678391028105\nWednesday,1030,0.01650485436893204,0.18234593373486246\nThursday,1012,-0.05928853754940711,0.18691776551235453\nFriday,1012,-0.14723320158102768,0.18991766797184337\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10,5217,5002,215,no published yield (holiday); task rule\n2,first available observation has no change,5002,5001,1,differencing\n{\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\": 0.050170098401698796,\n  \"effect_ci_low\": -0.31933577569409977,\n  \"effect_ci_high\": 0.41967597249749733,\n  \"effect_se\": 0.18837955539096196,\n  \"effect_welch_df\": 1548.9581965107577,\n  \"baseline_metric\": 4.875704154642544,\n  \"model_metric\": 4.883841522290305,\n  \"model_minus_baseline_rmse\": 0.00813736764776074,\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  \"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, per task rule; change after a holiday spans the gap\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"DGS10.csv\": 5217\n  },\n  \"n_analysed\": 5001,\n  \"random_seed\": null,\n  \"input_sha256\": {\n    \"DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\"\n  },\n  \"first_change_date\": \"2000-01-04\",\n  \"last_change_date\": \"2019-12-31\",\n  \"last_train_date\": \"2009-12-31\",\n  \"first_test_date\": \"2010-01-04\"\n}","is_error":false}]},"uuid":"f435cad5-2bac-4453-9284-e86a9d61943f","timestamp":"2026-10-30T05:19:54.163Z","toolUseResult":{"stdout":"{\n \"anova_f_stat\": 0.263465716807118,\n \"anova_p_value\": 0.9015060507654888,\n \"effect_estimate\": 0.050170098401698796,\n \"effect_ci_low\": -0.31933577569409977,\n \"effect_ci_high\": 0.41967597249749733,\n \"baseline_metric\": 4.875704154642544,\n \"model_metric\": 4.883841522290305,\n \"conclusion\": \"no_detectable_effect\"\n}\nweekday,n,mean_bp,se_bp\nMonday,919,-0.05223068552774755,0.16438585639710285\nTuesday,1028,-0.2198443579766537,0.17700678391028105\nWednesday,1030,0.01650485436893204,0.18234593373486246\nThursday,1012,-0.05928853754940711,0.18691776551235453\nFriday,1012,-0.14723320158102768,0.18991766797184337\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10,5217,5002,215,no published yield (holiday); task rule\n2,first available observation has no change,5002,5001,1,differencing\n{\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\": 0.050170098401698796,\n  \"effect_ci_low\": -0.31933577569409977,\n  \"effect_ci_high\": 0.41967597249749733,\n  \"effect_se\": 0.18837955539096196,\n  \"effect_welch_df\": 1548.9581965107577,\n  \"baseline_metric\": 4.875704154642544,\n  \"model_metric\": 4.883841522290305,\n  \"model_minus_baseline_rmse\": 0.00813736764776074,\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  \"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, per task rule; change after a holiday spans the gap\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"DGS10.csv\": 5217\n  },\n  \"n_analysed\": 5001,\n  \"random_seed\": null,\n  \"input_sha256\": {\n    \"DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\"\n  },\n  \"first_change_date\": \"2000-01-04\",\n  \"last_change_date\": \"2019-12-31\",\n  \"last_train_date\": \"2009-12-31\",\n  \"first_test_date\": \"2010-01-04\"\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2efec437-1440-425e-9fa6-59dbec43c10b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
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OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(DATA, dtype=str, keep_default_na=False)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\ndf = raw.loc[~empty].copy()\ndf[\"y\"] = pd.to_numeric(df[\"DGS10\"], errors=\"raise\")\ndf = df.sort_values(\"date\").reset_index(drop=True)\n\n# audit facts\ndata_issues = []\nn_dup = int(raw[\"observation_date\"].duplicated().sum())\nn_weekend = int((raw[\"date\"].dt.dayofweek >= 5).sum())\ndata_issues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n                    \"handling\": \"dropped before differencing, per task rule; change after a holiday spans the gap\"})\nif n_dup: data_issues.append({\"issue\": f\"{n_dup} duplicated dates\", \"handling\": \"reported only\"})\nif n_weekend: data_issues.append({\"issue\": f\"{n_weekend} weekend dates\", \"handling\": \"reported only\"})\n\ndf[\"dy\"] = 100.0 * df[\"y\"].diff()\ndf[\"dy\"] = df[\"dy\"].round(10)  # remove float noise from percent->bp conversion\nch = df.iloc[1:].copy()  # first available obs has no change\nch[\"wd\"] = ch[\"date\"].dt.day_name()\nassert set(ch[\"wd\"]) <= set(DAYS)\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\n\nexcl = pd.DataFrame([\n    [\"1\", \"drop rows with empty DGS10\", n_raw, n_raw - n_missing, n_missing, \"no published yield (holiday); task rule\"],\n    [\"2\", \"first available observation has no change\", n_raw - n_missing, len(ch), 1, \"differencing\"],\n], columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# (i) ANOVA, all changes\ngroups = [ch.loc[ch.wd == d, \"dy\"].values for d in DAYS]\nF, p = stats.f_oneway(*groups)\n\n# table\ntab = pd.DataFrame({\"weekday\": DAYS, \"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(f\"{OUT}/table1.csv\", index=False)\n\n# (ii) Monday contrast, Welch two-sample\nmon = ch.loc[ch.wd == \"Monday\", \"dy\"].values; oth = ch.loc[ch.wd != \"Monday\", \"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))\ntq = stats.t.ppf(0.975, dfw)\nlo, hi = eff - tq * se, eff + tq * se\n\n# (iii) OOS\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans)\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"] ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"] - pred) ** 2)))\n\nif p >= 0.05: concl = \"no_detectable_effect\"\nelif lo > 0: concl = \"positive_effect\"\nelif hi < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\n# figure\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab.mean_bp, yerr=1.96 * tab.se_bp, fmt=\"o\", capsize=5)\nax.axhline(0, color=\"grey\", lw=0.8)\nax.set_xticks(range(5)); ax.set_xticklabels([d[:3] for d in DAYS])\nax.set_ylabel(\"Mean daily change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield, 2000-2019 (n={len(ch)}); 95% CI\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nres = {\n    \"task_id\": \"F6-conf-01\",\n    \"n_missing_rows\": n_missing, \"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    \"effect_se\": float(se), \"effect_welch_df\": float(dfw),\n    \"baseline_metric\": rmse0, \"model_metric\": rmse1,\n    \"model_minus_baseline_rmse\": rmse1 - rmse0,\n    \"train_weekday_means_bp\": {d: float(wmeans[d]) for d in DAYS},\n    \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": {\"DGS10.csv\": n_raw},\n    \"n_analysed\": int(len(ch)), \"random_seed\": SEED,\n    \"input_sha256\": {\"DGS10.csv\": sha},\n    \"first_change_date\": str(ch.date.min().date()), \"last_change_date\": str(ch.date.max().date()),\n    \"last_train_date\": str(train.date.max().date()), \"first_test_date\": str(test.date.min().date()),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f: json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"anova_f_stat\", \"anova_p_value\", \"effect_estimate\", \"effect_ci_low\",\n                  \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv && cat out/results.json","description":"Write and run analysis script"}}]}}
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{"parentUuid":"e8f8a3d6-0ed2-4592-baf2-07d6cbe8e744","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrjaB7cqpfg7SKh3aYn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QxuR3u41smfMzUYV9xLP5H","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; shasum out/table1.csv > /tmp/t1; rm out/results.json out/table1.csv out/figure1.png out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && echo RESULTS_IDENTICAL; shasum -c /tmp/t1\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json, statsmodels.formula.api as smf\nfrom scipy import stats\nr=json.load(open(\"out/results.json\"))\nd=pd.read_csv(\"data/DGS10.csv\"); print(\"rows\",len(d),\"dups\",d.observation_date.duplicated().sum(),\"range\",d.observation_date.min(),d.observation_date.max())\nd[\"date\"]=pd.to_datetime(d.observation_date); print(\"weekend rows\",(d.date.dt.dayofweek>=5).sum())\nd=d.dropna().reset_index(drop=True); print(\"y min/max\",d.DGS10.min(),d.DGS10.max())\nd[\"dy\"]=100*d.DGS10.diff(); c=d.iloc[1:].copy(); c[\"gap\"]=(c.date-d.date.shift().iloc[1:]).dt.days\nprint(\"dy sd\",c.dy.std(),\"min\",c.dy.min(),\"max\",c.dy.max(),\"n\",len(c))\nc[\"mon\"]=(c.date.dt.dayofweek==0).astype(int); c[\"wd\"]=c.date.dt.dayofweek\nm=smf.ols(\"dy~mon\",c).fit(cov_type=\"HC1\"); print(\"HC1 mon\",m.params.mon,m.conf_int().loc[\"mon\"].tolist())\nm2=smf.ols(\"dy~mon\",c).fit(); print(\"pooled\",m2.conf_int().loc[\"mon\"].tolist())\na=smf.ols(\"dy~C(wd)\",c).fit(); print(\"OLS F\",a.fvalue,a.f_pvalue)\nprint(\"Levene\",stats.levene(*[c.dy[c.wd==k] for k in range(5)]))\nprint(\"Kruskal\",stats.kruskal(*[c.dy[c.wd==k] for k in range(5)]))\nprint(\"sd by wd\",c.groupby(\"wd\").dy.std().round(3).tolist())\nprint(\"lag1 autocorr\",c.dy.autocorr())\ncc=c[c.gap<=3]; print(\"excluding post-holiday changes n\",len(cc),stats.f_oneway(*[cc.dy[cc.wd==k] for k in range(5)]))\nfor per,s in [(\"train\",c[c.date<=\"2009-12-31\"]),(\"test\",c[c.date>=\"2010-01-01\"])]:\n  print(per,stats.f_oneway(*[s.dy[s.wd==k] for k in range(5)]))\nprint(\"table sum n\",pd.read_csv(\"out/table1.csv\").n.sum(), \"train+test\",r[\"n_train\"]+r[\"n_test\"])\nEOF","description":"Rerun for reproducibility and run cross-checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3728,"cache_read_input_tokens":11607,"output_tokens":1335,"output_tokens_details":{"thinking_tokens":212},"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":3728},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrjZuzKT5K8YoFqfkKY","requestRef":"62bf50f8-e0ed-4913-a0b8-788dbae7099f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6b43695d-42aa-4cce-a081-73bfcd472adf","timestamp":"2026-10-30T05:20:06.157Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"6b43695d-42aa-4cce-a081-73bfcd472adf","isSidechain":false,"promptId":"c0c04d08-9d1b-4223-9eea-8d546255eff2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QxuR3u41smfMzUYV9xLP5H","type":"tool_result","content":"RESULTS_IDENTICAL\nout/table1.csv: OK\nrows 5217 dups 0 range 2000-01-03 2019-12-31\nweekend rows 0\ny min/max 1.37 6.79\ndy sd 5.723645297963581 min -51.00000000000002 max 25.0 n 5001\nHC1 mon 0.050170098401700364 [-0.3189570734770597, 0.4192972702804604]\npooled [-0.3595630204880429, 0.45990321729144357]\nOLS F 0.26346571680719233 0.9015060507654888\nLevene LeveneResult(statistic=np.float64(9.58819097304059), pvalue=np.float64(1.0122651168712971e-07))\nKruskal KruskalResult(statistic=np.float64(3.5453047275711587), pvalue=np.float64(0.47102318097227847))\nsd by wd [4.983, 5.675, 5.852, 5.946, 6.042]\nlag1 autocorr -0.007698981093822477\nexcluding post-holiday changes n 4846 F_onewayResult(statistic=np.float64(0.3375705435041147), pvalue=np.float64(0.852772948769423))\ntrain F_onewayResult(statistic=np.float64(0.5063447393817322), pvalue=np.float64(0.7310927218686241))\ntest F_onewayResult(statistic=np.float64(1.1624668716464337), pvalue=np.float64(0.32542536024549934))\ntable sum n 5001 train+test 5001","is_error":false}]},"uuid":"db66a90d-cb46-4dfa-9a5f-13eee5e079ef","timestamp":"2026-10-30T05:20:07.888Z","toolUseResult":{"stdout":"RESULTS_IDENTICAL\nout/table1.csv: OK\nrows 5217 dups 0 range 2000-01-03 2019-12-31\nweekend rows 0\ny min/max 1.37 6.79\ndy sd 5.723645297963581 min -51.00000000000002 max 25.0 n 5001\nHC1 mon 0.050170098401700364 [-0.3189570734770597, 0.4192972702804604]\npooled [-0.3595630204880429, 0.45990321729144357]\nOLS F 0.26346571680719233 0.9015060507654888\nLevene LeveneResult(statistic=np.float64(9.58819097304059), pvalue=np.float64(1.0122651168712971e-07))\nKruskal KruskalResult(statistic=np.float64(3.5453047275711587), pvalue=np.float64(0.47102318097227847))\nsd by wd [4.983, 5.675, 5.852, 5.946, 6.042]\nlag1 autocorr -0.007698981093822477\nexcluding post-holiday changes n 4846 F_onewayResult(statistic=np.float64(0.3375705435041147), pvalue=np.float64(0.852772948769423))\ntrain F_onewayResult(statistic=np.float64(0.5063447393817322), pvalue=np.float64(0.7310927218686241))\ntest F_onewayResult(statistic=np.float64(1.1624668716464337), pvalue=np.float64(0.32542536024549934))\ntable sum n 5001 train+test 5001","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6b43695d-42aa-4cce-a081-73bfcd472adf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"bda71125-b28c-404f-be16-b790e9253c1c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrkaspioByrzvJXYAJx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019geR7tSd9uRYeLX8DFfY51","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport re\ns=open(\"out/analysis.py\").read()\nadd='''\n# ---- robustness / diagnostics (reported, do not change the primary conclusion) ----\nimport statsmodels.formula.api as smf\nch[\"mon\"] = (ch.wd == \"Monday\").astype(int)\nhc = smf.ols(\"dy ~ mon\", ch).fit(cov_type=\"HC1\")\npooled = smf.ols(\"dy ~ mon\", ch).fit()\nprev_date = df[\"date\"].shift().iloc[1:]\nch[\"gap_days\"] = (ch[\"date\"] - prev_date).dt.days\nnoh = ch[ch.gap_days <= 3]\ndef anova_p(s): return float(stats.f_oneway(*[s.loc[s.wd == d, \"dy\"].values for d in DAYS]).pvalue)\nimin = ch[\"dy\"].idxmin(); imax = ch[\"dy\"].idxmax()\nrobust = {\n    \"robust_mon_hc1_ci\": [float(x) for x in hc.conf_int().loc[\"mon\"]],\n    \"robust_mon_pooled_ci\": [float(x) for x in pooled.conf_int().loc[\"mon\"]],\n    \"robust_kruskal_p\": float(stats.kruskal(*groups).pvalue),\n    \"diag_levene_p\": float(stats.levene(*groups).pvalue),\n    \"diag_sd_by_weekday_bp\": {d: float(g.std(ddof=1)) for d, g in zip(DAYS, groups)},\n    \"diag_lag1_autocorr\": float(ch[\"dy\"].autocorr()),\n    \"robust_anova_p_excl_post_holiday\": anova_p(noh),\n    \"robust_n_excl_post_holiday\": int(len(noh)),\n    \"robust_anova_p_train\": anova_p(train), \"robust_anova_p_test\": anova_p(test),\n    \"diag_dy_sd_bp\": float(ch[\"dy\"].std(ddof=1)),\n    \"diag_dy_min_bp\": [float(ch.loc[imin, \"dy\"]), str(ch.loc[imin, \"date\"].date())],\n    \"diag_dy_max_bp\": [float(ch.loc[imax, \"dy\"]), str(ch.loc[imax, \"date\"].date())],\n}\n'''\ns=s.replace(\"res = {\", add+\"\\nres = {\",1)\ns=s.replace('with open(f\"{OUT}/results.json\", \"w\")','res.update(robust)\\nwith open(f\"{OUT}/results.json\", \"w\")')\nopen(\"out/analysis.py\",\"w\").write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && cp out/results.json /tmp/r2.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r2.json && echo IDENT && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));[print(k,v) for k,v in r.items() if k.startswith(('robust','diag'))]\"","description":"Add robustness keys to script and rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1850,"cache_read_input_tokens":15335,"output_tokens":1289,"output_tokens_details":{"thinking_tokens":158},"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":1850},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrkabhAuM5sbCB6bdMH","requestRef":"25d69979-88ac-4a8c-84be-5940a538b777","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7fb6d306-2e88-4426-9545-3315e0941180","timestamp":"2026-10-30T05:20:18.882Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"4b802f51-8281-46b8-8b50-09665e6544c0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrmYP27bXdc4CivxCHV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CrrKSQ7RxV8UUNAtwBk8Mc","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0270\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README) | 5217 | 2 |\n| data/README.md | provenance text only | – | – |\n\n- Schema: `observation_date` (YYYY-MM-DD), `DGS10` (percent, 2 decimals). Date range 2000-01-03 … 2019-12-31.\n- Duplicated dates: 0. Weekend dates: 0. Non-numeric non-empty values: 0.\n- Empty DGS10: 215 rows (US market holidays) → dropped per task rule (exclusions.csv step 1).\n- Yield range 1.37–6.79 %. Δy (bp): sd 5.72, min −51 (2009-03-18, FOMC QE announcement – genuine), max +25 (2001-11-15). No anomalies requiring removal.\n- Changes after a holiday span >1 weekday (155 changes with gap > 3 calendar days); kept per task rule, sensitivity reported.\n- Info-availability: single permitted input; no violations. Forecast weekday means use only changes dated ≤ 2009-12-31 (last train date 2009-12-31, first test date 2010-01-04).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run: deleted results.json/table1.csv/figure1.png/exclusions.csv, re-ran analysis.py → results.json byte-identical (cmp), table1.csv sha identical. PASS (repeated after adding robustness keys: PASS).\n2. Counts: 5217 raw − 215 empty − 1 (first obs) = 5001 = n_analysed = n_train(2500)+n_test(2501) = sum of table1 n. PASS.\n3. Date ordering: last_train_date 2009-12-31 < first_test_date 2010-01-04; weekday means for forecast computed from train subset only. PASS.\n4. ANOVA cross-check: statsmodels OLS dy~C(weekday) overall F = 0.26347, p = 0.9015, identical to scipy f_oneway. PASS.\n5. Monday contrast: Welch CI [−0.319, 0.420] contains estimate 0.050; SE 0.188 > 0; HC1 regression gives the same point estimate and nearly identical CI; pooled CI slightly wider. PASS.\n6. Magnitudes: model RMSE (4.884) vs baseline RMSE (4.876) — difference 0.008 bp, plausible given weekday means of order 0.1–0.4 bp vs sd ≈ 5 bp. PASS.\n7. Conclusion rule: ANOVA p = 0.90 ≥ 0.05 → no_detectable_effect; consistent with Monday CI containing 0. PASS.\n8. Note: an ad-hoc check without rounding gave Kruskal p 0.471 vs 0.518 in the script; difference is due to float noise breaking ties in Δy (script rounds to 1e-10). ANOVA/means unaffected. Documented, not a failure.\n9. Assumption check: Levene p ≈ 1e-7 → weekday variances unequal (Monday sd 4.98 vs Friday 6.04); classical ANOVA's equal-variance assumption is violated, but Kruskal–Wallis (p 0.52) and Welch/HC1 Monday contrast agree on no effect. Reported as limitation.\nEOF\ncat > out/report.md <<'EOF'\n# Day-of-week effects in daily changes of the 10-year Treasury yield (F6-conf-01)\n\n## Question\nDoes the weekday predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? The three estimands are: (i) a joint test of equal weekday means; (ii) the Monday contrast (mean change on Mondays minus the mean on Tuesday–Friday) with a 95% interval; (iii) the out-of-sample RMSE of a forecast from weekday-specific means, compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 rows (`n_input_rows`), and its SHA-256 matches the README (`input_sha256`). 215 rows have an empty yield field because of US market holidays (`n_missing_rows`); they are dropped. Removing the first available observation, which has no change, leaves 5001 daily changes (`n_analysed`), dated `first_change_date` (2000-01-04) to `last_change_date` (2019-12-31). Of these, 2500 fall in the estimation period ≤2009-12-31 (`n_train`) and 2501 in the evaluation period ≥2010-01-01 (`n_test`). The ledger is in `out/exclusions.csv`. There are no duplicated dates, no weekend dates and no non-numeric values. The largest moves are genuine: −51 bp on 2009-03-18 (`diag_dy_min_bp`) and +25 bp on 2001-11-15 (`diag_dy_max_bp`).\n\n## Method\n- Δy_t = 100·(y_t − y_{t−1}), taken between consecutive available observations and attributed to the weekday of t. This follows the task rule, so a change after a holiday covers more than one trading day.\n- (i) Classical one-way ANOVA F-test (equal variances) of the five weekday means, using all 5001 changes, with reference distribution F(4, 4996).\n- (ii) Monday contrast: Welch two-sample t interval (unequal variances; Welch df in `effect_welch_df`). This was chosen because weekday variances differ (see Uncertainty).\n- (iii) The forecast for each evaluation-period change is the estimation-period mean change for its weekday (`train_weekday_means_bp`). It is compared with a zero-change forecast by RMSE over 2010–2019. Nothing from the evaluation period enters the forecast.\n- Decision rule: if the ANOVA has p ≥ 0.05 the conclusion is `no_detectable_effect`; otherwise the sign is taken from the Monday interval.\n- Sensitivity checks reported below: OLS with HC1 robust SE, pooled-variance interval, Kruskal–Wallis test, ANOVA excluding post-holiday changes, and ANOVA separately for each period. The analysis uses no randomness (`random_seed` = null). Outputs are `out/figure1.png` and `out/table1.csv`.\n\n## Results\n- Weekday means are all within about ±0.22 bp of zero (Tuesday −0.22, Wednesday +0.02, Friday −0.15; see `out/table1.csv` and `out/figure1.png`). By comparison, the daily standard deviation is 5.72 bp (`diag_dy_sd_bp`).\n- Joint test: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equal weekday means are not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`).\n- Out of sample (2010–2019): zero-change RMSE 4.8757 bp (`baseline_metric`); weekday-mean RMSE 4.8838 bp (`model_metric`). The weekday model is slightly *worse*, by 0.008 bp (`model_minus_baseline_rmse`).\n- **Conclusion: `no_detectable_effect`** (`conclusion`). In this sample, the weekday has no detectable in-sample or out-of-sample predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday interval is about ±0.37 bp wide (`effect_se` = 0.188). Any Monday effect larger than about 0.4 bp in absolute value is therefore inconsistent with the data. This is small compared with the 5.7 bp daily volatility.\n- The result holds across the checks: HC1 regression CI [−0.319, 0.419] (`robust_mon_hc1_ci`); pooled CI [−0.360, 0.460] (`robust_mon_pooled_ci`); Kruskal–Wallis p = 0.52 (`robust_kruskal_p`); ANOVA without the 155 post-holiday changes (n = 4846, `robust_n_excl_post_holiday`) p = 0.85 (`robust_anova_p_excl_post_holiday`); ANOVA p = 0.73 in 2000–2009 (`robust_anova_p_train`) and 0.33 in 2010–2019 (`robust_anova_p_test`).\n- Monday's training-period mean (−0.05 bp) and the training means for other days, which ranged from −0.41 to +0.12 bp (`train_weekday_means_bp`), did not carry over out of sample. This is consistent with them being noise.\n- The RMSE difference (0.008 bp) has no interval attached; no Diebold–Mariano test was run. It is not evidence of a real difference in either direction.\n\n## Limitations and open issues\n- **Assumption violation:** Weekday variances differ significantly (Levene p ≈ 1e-7, `diag_levene_p`). Monday changes are the least volatile (sd 4.98 vs 6.04 bp on Friday, `diag_sd_by_weekday_bp`). The classical equal-variance ANOVA required by the task is therefore misspecified in its variance assumption. The Welch, HC1 and rank-based checks agree with it, but a heteroskedasticity-robust joint test (e.g. Welch ANOVA) was not computed. The weekday pattern in *volatility* is a real feature of the data. It is outside the mean-change estimand and was not studied further.\n- **Dependence and fat tails:** Lag-1 autocorrelation is negligible (−0.008, `diag_lag1_autocorr`). Volatility clustering (GARCH-type dependence) and fat tails were not modelled. They could make the iid-based standard errors somewhat optimistic, but with p = 0.90 this would not change the conclusion.\n- **Definition choices:** Changes after a holiday cover several days but are attributed to a single weekday (task rule). Excluding them does not change the result. Rounding the series to 2 decimals (1 bp resolution) is negligible next to the 5.7 bp volatility.\n- **What would invalidate the result:** a wrong weekday assignment (checked: every date is a weekday); look-ahead in the forecast (checked: training means use only changes ≤2009-12-31); or a weekday effect confined to sub-periods or regimes (e.g. FOMC or data-release days, which cluster on certain weekdays). Sub-period instability was checked only with the two-period split. Calendar-event confounding was not checked.\n- **Power:** Absence of evidence is not proof of zero. Effects smaller than about 0.3–0.4 bp/day cannot be ruled out, but they would be economically trivial relative to daily volatility and to transaction costs.\n- **Scope:** These conclusions apply to the 10-year CMT yield, 2000–2019, only. Other maturities, other periods and intraday data were not examined.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equality of the five weekday mean changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption violated (Levene); rank-based and robust checks agree.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with a 95% Welch CI of [-0.32, 0.42] bp, which contains zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"iid assumption; volatility clustering not modelled.\"},\n {\"id\":\"C3\",\"text\":\"Out of sample (2010-2019), weekday-specific means estimated on 2000-2009 do not improve on the zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"model_minus_baseline_rmse\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal forecast-comparison test; difference is tiny.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect on the mean daily change of the 10-year yield.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Effects below ~0.4 bp/day cannot be excluded.\"},\n {\"id\":\"C5\",\"text\":\"The null result holds under HC1, pooled, Kruskal-Wallis, post-holiday exclusion and per-period analyses.\",\"results_keys\":[\"robust_mon_hc1_ci\",\"robust_mon_pooled_ci\",\"robust_kruskal_p\",\"robust_anova_p_excl_post_holiday\",\"robust_anova_p_train\",\"robust_anova_p_test\"],\"status\":\"supported\",\"caveat\":\"Welch-ANOVA joint test not computed.\"},\n {\"id\":\"C6\",\"text\":\"Weekday variances of the daily change differ significantly, with Mondays least volatile.\",\"results_keys\":[\"diag_levene_p\",\"diag_sd_by_weekday_bp\"],\"status\":\"supported\",\"caveat\":\"Side observation; outside the mean-change estimand.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open(\"out/results.json\")); c=json.load(open(\"out/claims.json\"))\nassert all(k in r for x in c for k in x[\"results_keys\"]); print(\"claims keys ok\")\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"5217 rows, 0 dups, 0 weekend, 215 empty\",\"exclusions.csv sums: 5217-216=5001\"],\"notes\":\"data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change RMSE on evaluation period = baseline_metric 4.8757\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, Agg backend, no RNG\",\"writes results.json, table1.csv, figure1.png\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical results.json\",\"counts identity\",\"date ordering\",\"ANOVA cross-check statsmodels\",\"CI contains estimate, SE>0\",\"conclusion rule\"],\"notes\":\"see checks.md\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md six sections in order\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit done, SHA ok, 215 holiday rows dropped.\n- 2026-10-30 P2 passed: zero-change RMSE baseline 4.876 bp.\n- 2026-10-30 P3 passed: analysis.py written; Welch chosen for Monday CI; robustness keys added.\n- 2026-10-30 P4 passed: reproducibility and consistency checks (checks.md).\n- 2026-10-30 P5 passed: report, claims written. Task complete.\nEOF\ngrep '^## ' out/report.md; ls out","description":"Write audit, checks, report, claims, ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1669,"cache_read_input_tokens":17185,"output_tokens":5668,"output_tokens_details":{"thinking_tokens":183},"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":1669},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrmY6ezydew4jstt2Km","requestRef":"dc75a04a-1967-46e9-a517-b47b2b5e5981","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bae6a6ed-b07c-4759-857a-f8da13333cca","timestamp":"2026-10-30T05:21:08.398Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
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I'm noting this in the checks file, then writing the state files and report.\n\n","signature":"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"},{"type":"tool_use","id":"toolu_01CrrKSQ7RxV8UUNAtwBk8Mc","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0270\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/DGS10.csv | 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches README) | 5217 | 2 |\n| data/README.md | provenance text only | – | – |\n\n- Schema: `observation_date` (YYYY-MM-DD), `DGS10` (percent, 2 decimals). Date range 2000-01-03 … 2019-12-31.\n- Duplicated dates: 0. Weekend dates: 0. Non-numeric non-empty values: 0.\n- Empty DGS10: 215 rows (US market holidays) → dropped per task rule (exclusions.csv step 1).\n- Yield range 1.37–6.79 %. Δy (bp): sd 5.72, min −51 (2009-03-18, FOMC QE announcement – genuine), max +25 (2001-11-15). No anomalies requiring removal.\n- Changes after a holiday span >1 weekday (155 changes with gap > 3 calendar days); kept per task rule, sensitivity reported.\n- Info-availability: single permitted input; no violations. Forecast weekday means use only changes dated ≤ 2009-12-31 (last train date 2009-12-31, first test date 2010-01-04).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run: deleted results.json/table1.csv/figure1.png/exclusions.csv, re-ran analysis.py → results.json byte-identical (cmp), table1.csv sha identical. PASS (repeated after adding robustness keys: PASS).\n2. Counts: 5217 raw − 215 empty − 1 (first obs) = 5001 = n_analysed = n_train(2500)+n_test(2501) = sum of table1 n. PASS.\n3. Date ordering: last_train_date 2009-12-31 < first_test_date 2010-01-04; weekday means for forecast computed from train subset only. PASS.\n4. ANOVA cross-check: statsmodels OLS dy~C(weekday) overall F = 0.26347, p = 0.9015, identical to scipy f_oneway. PASS.\n5. Monday contrast: Welch CI [−0.319, 0.420] contains estimate 0.050; SE 0.188 > 0; HC1 regression gives the same point estimate and nearly identical CI; pooled CI slightly wider. PASS.\n6. Magnitudes: model RMSE (4.884) vs baseline RMSE (4.876) — difference 0.008 bp, plausible given weekday means of order 0.1–0.4 bp vs sd ≈ 5 bp. PASS.\n7. Conclusion rule: ANOVA p = 0.90 ≥ 0.05 → no_detectable_effect; consistent with Monday CI containing 0. PASS.\n8. Note: an ad-hoc check without rounding gave Kruskal p 0.471 vs 0.518 in the script; difference is due to float noise breaking ties in Δy (script rounds to 1e-10). ANOVA/means unaffected. Documented, not a failure.\n9. Assumption check: Levene p ≈ 1e-7 → weekday variances unequal (Monday sd 4.98 vs Friday 6.04); classical ANOVA's equal-variance assumption is violated, but Kruskal–Wallis (p 0.52) and Welch/HC1 Monday contrast agree on no effect. Reported as limitation.\nEOF\ncat > out/report.md <<'EOF'\n# Day-of-week effects in daily changes of the 10-year Treasury yield (F6-conf-01)\n\n## Question\nDoes the weekday predict the one-day change Δy_t (basis points) in the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? The three estimands are: (i) a joint test of equal weekday means; (ii) the Monday contrast (mean change on Mondays minus the mean on Tuesday–Friday) with a 95% interval; (iii) the out-of-sample RMSE of a forecast from weekday-specific means, compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\n`data/DGS10.csv` has 5217 rows (`n_input_rows`), and its SHA-256 matches the README (`input_sha256`). 215 rows have an empty yield field because of US market holidays (`n_missing_rows`); they are dropped. Removing the first available observation, which has no change, leaves 5001 daily changes (`n_analysed`), dated `first_change_date` (2000-01-04) to `last_change_date` (2019-12-31). Of these, 2500 fall in the estimation period ≤2009-12-31 (`n_train`) and 2501 in the evaluation period ≥2010-01-01 (`n_test`). The ledger is in `out/exclusions.csv`. There are no duplicated dates, no weekend dates and no non-numeric values. The largest moves are genuine: −51 bp on 2009-03-18 (`diag_dy_min_bp`) and +25 bp on 2001-11-15 (`diag_dy_max_bp`).\n\n## Method\n- Δy_t = 100·(y_t − y_{t−1}), taken between consecutive available observations and attributed to the weekday of t. This follows the task rule, so a change after a holiday covers more than one trading day.\n- (i) Classical one-way ANOVA F-test (equal variances) of the five weekday means, using all 5001 changes, with reference distribution F(4, 4996).\n- (ii) Monday contrast: Welch two-sample t interval (unequal variances; Welch df in `effect_welch_df`). This was chosen because weekday variances differ (see Uncertainty).\n- (iii) The forecast for each evaluation-period change is the estimation-period mean change for its weekday (`train_weekday_means_bp`). It is compared with a zero-change forecast by RMSE over 2010–2019. Nothing from the evaluation period enters the forecast.\n- Decision rule: if the ANOVA has p ≥ 0.05 the conclusion is `no_detectable_effect`; otherwise the sign is taken from the Monday interval.\n- Sensitivity checks reported below: OLS with HC1 robust SE, pooled-variance interval, Kruskal–Wallis test, ANOVA excluding post-holiday changes, and ANOVA separately for each period. The analysis uses no randomness (`random_seed` = null). Outputs are `out/figure1.png` and `out/table1.csv`.\n\n## Results\n- Weekday means are all within about ±0.22 bp of zero (Tuesday −0.22, Wednesday +0.02, Friday −0.15; see `out/table1.csv` and `out/figure1.png`). By comparison, the daily standard deviation is 5.72 bp (`diag_dy_sd_bp`).\n- Joint test: F = 0.263 (`anova_f_stat`), p = 0.90 (`anova_p_value`). Equal weekday means are not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, 0.420] bp (`effect_ci_low`, `effect_ci_high`).\n- Out of sample (2010–2019): zero-change RMSE 4.8757 bp (`baseline_metric`); weekday-mean RMSE 4.8838 bp (`model_metric`). The weekday model is slightly *worse*, by 0.008 bp (`model_minus_baseline_rmse`).\n- **Conclusion: `no_detectable_effect`** (`conclusion`). In this sample, the weekday has no detectable in-sample or out-of-sample predictive content for daily 10-year yield changes.\n\n## Uncertainty\n- The Monday interval is about ±0.37 bp wide (`effect_se` = 0.188). Any Monday effect larger than about 0.4 bp in absolute value is therefore inconsistent with the data. This is small compared with the 5.7 bp daily volatility.\n- The result holds across the checks: HC1 regression CI [−0.319, 0.419] (`robust_mon_hc1_ci`); pooled CI [−0.360, 0.460] (`robust_mon_pooled_ci`); Kruskal–Wallis p = 0.52 (`robust_kruskal_p`); ANOVA without the 155 post-holiday changes (n = 4846, `robust_n_excl_post_holiday`) p = 0.85 (`robust_anova_p_excl_post_holiday`); ANOVA p = 0.73 in 2000–2009 (`robust_anova_p_train`) and 0.33 in 2010–2019 (`robust_anova_p_test`).\n- Monday's training-period mean (−0.05 bp) and the training means for other days, which ranged from −0.41 to +0.12 bp (`train_weekday_means_bp`), did not carry over out of sample. This is consistent with them being noise.\n- The RMSE difference (0.008 bp) has no interval attached; no Diebold–Mariano test was run. It is not evidence of a real difference in either direction.\n\n## Limitations and open issues\n- **Assumption violation:** Weekday variances differ significantly (Levene p ≈ 1e-7, `diag_levene_p`). Monday changes are the least volatile (sd 4.98 vs 6.04 bp on Friday, `diag_sd_by_weekday_bp`). The classical equal-variance ANOVA required by the task is therefore misspecified in its variance assumption. The Welch, HC1 and rank-based checks agree with it, but a heteroskedasticity-robust joint test (e.g. Welch ANOVA) was not computed. The weekday pattern in *volatility* is a real feature of the data. It is outside the mean-change estimand and was not studied further.\n- **Dependence and fat tails:** Lag-1 autocorrelation is negligible (−0.008, `diag_lag1_autocorr`). Volatility clustering (GARCH-type dependence) and fat tails were not modelled. They could make the iid-based standard errors somewhat optimistic, but with p = 0.90 this would not change the conclusion.\n- **Definition choices:** Changes after a holiday cover several days but are attributed to a single weekday (task rule). Excluding them does not change the result. Rounding the series to 2 decimals (1 bp resolution) is negligible next to the 5.7 bp volatility.\n- **What would invalidate the result:** a wrong weekday assignment (checked: every date is a weekday); look-ahead in the forecast (checked: training means use only changes ≤2009-12-31); or a weekday effect confined to sub-periods or regimes (e.g. FOMC or data-release days, which cluster on certain weekdays). Sub-period instability was checked only with the two-period split. Calendar-event confounding was not checked.\n- **Power:** Absence of evidence is not proof of zero. Effects smaller than about 0.3–0.4 bp/day cannot be ruled out, but they would be economically trivial relative to daily volatility and to transaction costs.\n- **Scope:** These conclusions apply to the 10-year CMT yield, 2000–2019, only. Other maturities, other periods and intraday data were not examined.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equality of the five weekday mean changes (F=0.263, p=0.90).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption violated (Levene); rank-based and robust checks agree.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with a 95% Welch CI of [-0.32, 0.42] bp, which contains zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"iid assumption; volatility clustering not modelled.\"},\n {\"id\":\"C3\",\"text\":\"Out of sample (2010-2019), weekday-specific means estimated on 2000-2009 do not improve on the zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"model_minus_baseline_rmse\",\"n_train\",\"n_test\"],\"status\":\"supported\",\"caveat\":\"No formal forecast-comparison test; difference is tiny.\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion: no detectable day-of-week effect on the mean daily change of the 10-year yield.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Effects below ~0.4 bp/day cannot be excluded.\"},\n {\"id\":\"C5\",\"text\":\"The null result holds under HC1, pooled, Kruskal-Wallis, post-holiday exclusion and per-period analyses.\",\"results_keys\":[\"robust_mon_hc1_ci\",\"robust_mon_pooled_ci\",\"robust_kruskal_p\",\"robust_anova_p_excl_post_holiday\",\"robust_anova_p_train\",\"robust_anova_p_test\"],\"status\":\"supported\",\"caveat\":\"Welch-ANOVA joint test not computed.\"},\n {\"id\":\"C6\",\"text\":\"Weekday variances of the daily change differ significantly, with Mondays least volatile.\",\"results_keys\":[\"diag_levene_p\",\"diag_sd_by_weekday_bp\"],\"status\":\"supported\",\"caveat\":\"Side observation; outside the mean-change estimand.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open(\"out/results.json\")); c=json.load(open(\"out/claims.json\"))\nassert all(k in r for x in c for k in x[\"results_keys\"]); print(\"claims keys ok\")\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"5217 rows, 0 dups, 0 weekend, 215 empty\",\"exclusions.csv sums: 5217-216=5001\"],\"notes\":\"data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change RMSE on evaluation period = baseline_metric 4.8757\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, Agg backend, no RNG\",\"writes results.json, table1.csv, figure1.png\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical results.json\",\"counts identity\",\"date ordering\",\"ANOVA cross-check statsmodels\",\"CI contains estimate, SE>0\",\"conclusion rule\"],\"notes\":\"see checks.md\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md six sections in order\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit done, SHA ok, 215 holiday rows dropped.\n- 2026-10-30 P2 passed: zero-change RMSE baseline 4.876 bp.\n- 2026-10-30 P3 passed: analysis.py written; Welch chosen for Monday CI; robustness keys added.\n- 2026-10-30 P4 passed: reproducibility and consistency checks (checks.md).\n- 2026-10-30 P5 passed: report, claims written. Task complete.\nEOF\ngrep '^## ' out/report.md; ls out","description":"Write audit, checks, report, claims, ledger"}}]}}
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{"parentUuid":"6d7d010d-152a-43c7-9073-e0c9a4bfbebc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrqBiv6jyD2cXp6hmkR","type":"message","role":"assistant","content":[{"type":"text","text":"I found no detectable day-of-week effect on daily changes in the 10-year Treasury yield over 2000–2019. All required files are written, and all six protocol phases (P0–P5) passed with their checks recorded.\n\n**Data:** the file has 5,217 rows and its checksum matches the provenance note. I dropped 215 holiday rows with no yield, which leaves 5,001 daily changes: 2,500 in the 2000–2009 estimation period and 2,501 in the 2010–2019 evaluation period.\n\n**Findings:**\n- **Joint test:** the standard test of equal weekday means does not come close to rejecting (F = 0.263, p = 0.90). Every weekday's average change is within about ±0.22 basis points of zero, against a typical daily move of 5.7 bp.\n- **Monday vs other days:** Mondays average +0.05 bp more than the other four days. The 95% interval is −0.32 to +0.42 bp, which includes zero. I used a Welch two-sample interval, which allows the weekdays to have different variances.\n- **Forecasting 2010–2019:** using each weekday's 2000–2009 average as the forecast does slightly worse than forecasting no change (RMSE 4.884 vs 4.876 bp). I did not run a formal forecast-comparison test, so this tiny gap means nothing in either direction.\n\nSeveral alternative versions of the analysis give the same answer:\n- a regression with robust standard errors, and a pooled-variance interval;\n- a rank-based test (p = 0.52);\n- leaving out the 155 changes that span a holiday;\n- testing each decade separately.\n\n**Limitations:**\n- **Unequal variances:** the task's required test assumes every weekday has the same variance, and that assumption fails here. Mondays are clearly less volatile (standard deviation 4.98 bp, against 6.04 on Fridays). The alternative tests above agree with the main result, but I did not compute a joint test designed for unequal variances.\n- **Not modelled:** periods of high and low volatility, extreme moves, and events that fall on particular weekdays (such as Fed meetings or data releases).\n- **Power:** effects smaller than about 0.4 bp a day cannot be ruled out, though they would be economically trivial.\n\nEverything is in `out/`. Running `~/wp29/venv/bin/python out/analysis.py` regenerates the results, table and figure, and a re-run from scratch reproduced the results file exactly.\n- `results.json`\n- `report.md`\n- `claims.json`\n- `table1.csv`\n- `figure1.png`\n- `exclusions.csv`\n- `analysis.py`\n- `state/` (the audit, checks, phase ledger and log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":567,"cache_read_input_tokens":24613,"output_tokens":929,"output_tokens_details":{"thinking_tokens":35},"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":567},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrqBSoPhSxtsnpe6Qz8","requestRef":"e96d558b-8ff9-40d8-8130-73b42d5e62b2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ed2bc147-cf89-4aec-b071-4bc6140948e8","timestamp":"2026-10-30T05:21:20.383Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0270","sessionId":"6c8a2d21-000c-4624-9b1e-6eec9b974acf","version":"2.1.314","gitBranch":"HEAD"}
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