spb/wp29_uqo

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An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`.\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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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980"}12{"parentUuid":"8b37cfa3-b976-4e84-a018-070e008a0fcc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrhNyd5KiQrDSP6Kegy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Tp9QP1YL5ypBhfcXyZ3ym9","name":"Bash","input":{"command":"cat TASK.md; 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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":"eafbe429-6389-463e-b75b-4681d535f327","timestamp":"2026-10-30T05:19:26.147Z","toolUseResult":{"stdout":"# F6-conf-01 — Does the day of the week carry predictive content for daily changes in the 10-year Treasury yield?\n\n## Scientific question\nFinancial folklore attributes systematic \"Monday\" or \"Friday\" patterns to daily asset-price changes. Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nDGS10.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c819c4fa-cbb3-440d-9f51-85432a125f9a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","hash":"1faf777ea4eabdf7dc56483790c9e9e737aaabb9ca245614da3703e7136f1244","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Tp9QP1YL5ypBhfcXyZ3ym9","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50","description":"Read task and list data"}}]}}15{"type":"api-request","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","id":"ae3cfa29-8b84-4dab-a8b6-31526df4399a","timestamp":"2026-10-30T05:19:26.150Z","version":"2.1.314","querySource":"sdk","shapeHash":"ce75c271715e690fc73b4c7147c41ecebe8870d2a2f5f628f96bc664fedc652f","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":"eb715e82-7dfe-4966-afdf-87ee04c72abf","keep":2,"tail":["1faf777ea4eabdf7dc56483790c9e9e737aaabb9ca245614da3703e7136f1244","06263f3c066ad7c6065ffc7713caf11e18fa973c8df30192cd67b628420b8150"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2006567d526a049d65894ae70d22d9bce4eddd4c7c4c09c69c74996a13238197"}16{"parentUuid":"eafbe429-6389-463e-b75b-4681d535f327","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrhWMNvGGVSqZecFivW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T4L4hhUNCYz38UTmWjXYm5","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"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2372,"cache_read_input_tokens":2234,"output_tokens":188,"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":2372},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrhW5W6w599LERNB6fx","requestRef":"ae3cfa29-8b84-4dab-a8b6-31526df4399a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"15e10c29-36e4-4e4b-add0-0bbd8c495825","timestamp":"2026-10-30T05:19:28.133Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"15e10c29-36e4-4e4b-add0-0bbd8c495825","isSidechain":false,"promptId":"a4f9c9c6-193a-4191-ac02-8b576c4fe0bf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T4L4hhUNCYz38UTmWjXYm5","type":"tool_result","content":"# 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","is_error":false}]},"uuid":"44a87208-f6ea-4adb-bd4b-101a4c1b4940","timestamp":"2026-10-30T05:19:28.200Z","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":"15e10c29-36e4-4e4b-add0-0bbd8c495825","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","hash":"13e1f4fe891ec34454ef20e1e4fa651b890e6b95ffcad1d5a449744ddd2c48d7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01T4L4hhUNCYz38UTmWjXYm5","name":"Bash","input":{"command":"cat data/README.md; 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cat > out/analysis.py <<'EOF'\n\"\"\"Replication script for F6-conf-01: day-of-week effects in daily 10y Treasury yield changes.\"\"\"\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\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_input = len(raw)\nissues = []\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nissues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / no publication)\",\n               \"handling\": \"rows dropped before computing changes, as required; the change after a gap spans >1 business day and is attributed to the weekday of its end date\"})\nvals = pd.to_numeric(raw.loc[~empty, \"DGS10\"], errors=\"coerce\")\nn_nonnum = int(vals.isna().sum())\nif n_nonnum:\n    issues.append({\"issue\": f\"{n_nonnum} non-numeric DGS10 values\", \"handling\": \"treated as missing and dropped\"})\ndf = raw.loc[~empty].assign(y=vals).dropna(subset=[\"y\"]).sort_values(\"date\")\nn_dup = int(df[\"date\"].duplicated().sum())\nif n_dup:\n    issues.append({\"issue\": f\"{n_dup} duplicated dates\", \"handling\": \"kept first occurrence\"})\n    df = df.drop_duplicates(\"date\")\nn_weekend = int((df[\"date\"].dt.dayofweek >= 5).sum())\nif n_weekend:\n    issues.append({\"issue\": f\"{n_weekend} weekend observations\", \"handling\": \"dropped\"})\n    df = df[df[\"date\"].dt.dayofweek < 5]\ndf[\"dy\"] = 100 * df[\"y\"].diff()\ndf = df.dropna(subset=[\"dy\"]).copy()\ngap = df[\"date\"].diff().dt.days\ndf[\"wd\"] = df[\"date\"].dt.dayofweek\n# count changes spanning a holiday (calendar gap larger than normal)\nnormal = np.where(df[\"wd\"] == 0, 3, 1)\nprev_dates = df[\"date\"].shift(1)\nspan = (df[\"date\"] - prev_dates).dt.days.values\nlong_span = int(np.nansum(span[1:] > normal[1:]))\nissues.append({\"issue\": f\"{long_span} changes span a holiday gap (longer than the usual 1 or 3 calendar days); this mixes horizons across weekdays (e.g. Tuesdays after Monday holidays)\",\n               \"handling\": \"kept as specified by TASK.md; noted as a limitation\"})\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\ndf[\"weekday\"] = df[\"wd\"].map(dict(enumerate(names)))\n\ntrain = df[df[\"date\"] <= \"2009-12-31\"]; test = df[df[\"date\"] >= \"2010-01-01\"]\n\ngroups = [df.loc[df[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\nmon = df.loc[df[\"wd\"] == 0, \"dy\"].values; oth = df.loc[df[\"wd\"] != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndof = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, dof)\nlo, hi = est - tq * se, est + tq * se\nwelch_p = float(2 * stats.t.sf(abs(est / se), dof))\n\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans).values\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"].values - pred) ** 2)))\n# supplementary: training-mean (no weekday) forecast\nrmse_mean = float(np.sqrt(np.mean((test[\"dy\"].values - train[\"dy\"].mean()) ** 2)))\n\nif p < 0.05:\n    concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n    concl = \"no_detectable_effect\"\n\ntab = pd.DataFrame({\"weekday\": names, \"n\": [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, float_format=\"%.6f\")\n\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, ls=\"--\")\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield change by weekday, 2000-2019\\n(95% CI; ANOVA F={F:.2f}, p={p:.3f})\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150)\n\nres = {\"task_id\": \"F6-conf-01\", \"n_missing_rows\": n_missing,\n       \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n       \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n       \"effect_estimate\": float(est), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n       \"baseline_metric\": rmse0, \"model_metric\": rmse1, \"conclusion\": concl,\n       \"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n       \"n_input_rows\": {\"DGS10.csv\": int(n_input)}, \"n_analysed\": int(len(df)), \"random_seed\": None,\n       \"supplementary\": {\"monday_contrast_method\": \"Welch two-sample t (Monday vs other weekdays)\",\n                         \"welch_se_bp\": float(se), \"welch_df\": float(dof), \"welch_p_value\": welch_p,\n                         \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wmeans.items()},\n                         \"rmse_train_overall_mean_bp\": rmse_mean,\n                         \"rmse_ratio_model_to_baseline\": rmse1 / rmse0,\n                         \"n_valid_obs\": int(len(df) + 1)}}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2)); print(tab)\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":811,"cache_read_input_tokens":4606,"output_tokens":2791,"output_tokens_details":{"thinking_tokens":97},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrhetb4XyKNNyV9Ztox","requestRef":"67cb118f-a9c6-4ba7-9df0-8cd8a231f644","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"079ef23f-6292-494a-9057-3c8c1241ff9b","timestamp":"2026-10-30T05:19:49.825Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"079ef23f-6292-494a-9057-3c8c1241ff9b","isSidechain":false,"promptId":"a4f9c9c6-193a-4191-ac02-8b576c4fe0bf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VU6XVf2tuvKtyd8TCcvArb","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\": \"rows dropped before computing changes, as required; the change after a gap spans >1 business day and is attributed to the weekday of its end date\"\n    },\n    {\n      \"issue\": \"214 changes span a holiday gap (longer than the usual 1 or 3 calendar days); this mixes horizons across weekdays (e.g. Tuesdays after Monday holidays)\",\n      \"handling\": \"kept as specified by TASK.md; noted as a limitation\"\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 (Monday vs other weekdays)\",\n    \"welch_se_bp\": 0.18837955539096202,\n    \"welch_df\": 1548.9581965107568,\n    \"welch_p_value\": 0.7900247217974673,\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    \"rmse_train_overall_mean_bp\": 4.87519865288523,\n    \"rmse_ratio_model_to_baseline\": 1.0016689625518016,\n    \"n_valid_obs\": 5002\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":"290066d3-5f8f-4942-be59-3e3687bae3f5","timestamp":"2026-10-30T05:19:50.487Z","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\": \"rows dropped before computing changes, as required; the change after a gap spans >1 business day and is attributed to the weekday of its end date\"\n    },\n    {\n      \"issue\": \"214 changes span a holiday gap (longer than the usual 1 or 3 calendar days); this mixes horizons across weekdays (e.g. Tuesdays after Monday holidays)\",\n      \"handling\": \"kept as specified by TASK.md; noted as a limitation\"\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 (Monday vs other weekdays)\",\n    \"welch_se_bp\": 0.18837955539096202,\n    \"welch_df\": 1548.9581965107568,\n    \"welch_p_value\": 0.7900247217974673,\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    \"rmse_train_overall_mean_bp\": 4.87519865288523,\n    \"rmse_ratio_model_to_baseline\": 1.0016689625518016,\n    \"n_valid_obs\": 5002\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":"079ef23f-6292-494a-9057-3c8c1241ff9b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","hash":"1ce91340a547ef02ee4397291c0c1f8e4e6fe6616f7c3b3ea4566d363713106c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01VU6XVf2tuvKtyd8TCcvArb","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication script for F6-conf-01: day-of-week effects in daily 10y Treasury yield changes.\"\"\"\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\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_input = len(raw)\nissues = []\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nissues.append({\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays / no publication)\",\n               \"handling\": \"rows dropped before computing changes, as required; the change after a gap spans >1 business day and is attributed to the weekday of its end date\"})\nvals = pd.to_numeric(raw.loc[~empty, \"DGS10\"], errors=\"coerce\")\nn_nonnum = int(vals.isna().sum())\nif n_nonnum:\n    issues.append({\"issue\": f\"{n_nonnum} non-numeric DGS10 values\", \"handling\": \"treated as missing and dropped\"})\ndf = raw.loc[~empty].assign(y=vals).dropna(subset=[\"y\"]).sort_values(\"date\")\nn_dup = int(df[\"date\"].duplicated().sum())\nif n_dup:\n    issues.append({\"issue\": f\"{n_dup} duplicated dates\", \"handling\": \"kept first occurrence\"})\n    df = df.drop_duplicates(\"date\")\nn_weekend = int((df[\"date\"].dt.dayofweek >= 5).sum())\nif n_weekend:\n    issues.append({\"issue\": f\"{n_weekend} weekend observations\", \"handling\": \"dropped\"})\n    df = df[df[\"date\"].dt.dayofweek < 5]\ndf[\"dy\"] = 100 * df[\"y\"].diff()\ndf = df.dropna(subset=[\"dy\"]).copy()\ngap = df[\"date\"].diff().dt.days\ndf[\"wd\"] = df[\"date\"].dt.dayofweek\n# count changes spanning a holiday (calendar gap larger than normal)\nnormal = np.where(df[\"wd\"] == 0, 3, 1)\nprev_dates = df[\"date\"].shift(1)\nspan = (df[\"date\"] - prev_dates).dt.days.values\nlong_span = int(np.nansum(span[1:] > normal[1:]))\nissues.append({\"issue\": f\"{long_span} changes span a holiday gap (longer than the usual 1 or 3 calendar days); this mixes horizons across weekdays (e.g. Tuesdays after Monday holidays)\",\n               \"handling\": \"kept as specified by TASK.md; noted as a limitation\"})\nnames = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\ndf[\"weekday\"] = df[\"wd\"].map(dict(enumerate(names)))\n\ntrain = df[df[\"date\"] <= \"2009-12-31\"]; test = df[df[\"date\"] >= \"2010-01-01\"]\n\ngroups = [df.loc[df[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\n\nmon = df.loc[df[\"wd\"] == 0, \"dy\"].values; oth = df.loc[df[\"wd\"] != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndof = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, dof)\nlo, hi = est - tq * se, est + tq * se\nwelch_p = float(2 * stats.t.sf(abs(est / se), dof))\n\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\npred = test[\"wd\"].map(wmeans).values\nrmse0 = float(np.sqrt(np.mean(test[\"dy\"].values ** 2)))\nrmse1 = float(np.sqrt(np.mean((test[\"dy\"].values - pred) ** 2)))\n# supplementary: training-mean (no weekday) forecast\nrmse_mean = float(np.sqrt(np.mean((test[\"dy\"].values - train[\"dy\"].mean()) ** 2)))\n\nif p < 0.05:\n    concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nelse:\n    concl = \"no_detectable_effect\"\n\ntab = pd.DataFrame({\"weekday\": names, \"n\": [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, float_format=\"%.6f\")\n\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, ls=\"--\")\nax.set_xticks(range(5)); ax.set_xticklabels(names)\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield change by weekday, 2000-2019\\n(95% CI; ANOVA F={F:.2f}, p={p:.3f})\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150)\n\nres = {\"task_id\": \"F6-conf-01\", \"n_missing_rows\": n_missing,\n       \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n       \"anova_f_stat\": float(F), \"anova_p_value\": float(p),\n       \"effect_estimate\": float(est), \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n       \"baseline_metric\": rmse0, \"model_metric\": rmse1, \"conclusion\": concl,\n       \"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n       \"n_input_rows\": {\"DGS10.csv\": int(n_input)}, \"n_analysed\": int(len(df)), \"random_seed\": None,\n       \"supplementary\": {\"monday_contrast_method\": \"Welch two-sample t (Monday vs other weekdays)\",\n                         \"welch_se_bp\": float(se), \"welch_df\": float(dof), \"welch_p_value\": welch_p,\n                         \"train_weekday_means_bp\": {names[k]: float(v) for k, v in wmeans.items()},\n                         \"rmse_train_overall_mean_bp\": rmse_mean,\n                         \"rmse_ratio_model_to_baseline\": rmse1 / rmse0,\n                         \"n_valid_obs\": int(len(df) + 1)}}\nwith open(f\"{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"}}]}}26{"type":"api-request","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","id":"9457a3b2-6ba4-493c-9355-bf6fd69b3bb2","timestamp":"2026-10-30T05:19:50.489Z","version":"2.1.314","querySource":"sdk","shapeHash":"ce75c271715e690fc73b4c7147c41ecebe8870d2a2f5f628f96bc664fedc652f","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":"67cb118f-a9c6-4ba7-9df0-8cd8a231f644","keep":6,"tail":["1ce91340a547ef02ee4397291c0c1f8e4e6fe6616f7c3b3ea4566d363713106c","2681b383b87131e299ea4b77d66ca6d6e6fb23287612b763a2ff44fb18719fd3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c582d8ba61a5b94a672d45370ea78cf5eff0924250267f9ef02fb17b0267057f"}27{"parentUuid":"290066d3-5f8f-4942-be59-3e3687bae3f5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrjJSL2HQcof1X5Gno6","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":3688,"cache_read_input_tokens":5417,"output_tokens":2694,"output_tokens_details":{"thinking_tokens":75},"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":3688},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":572,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrjJ9iav3SVDrpTa2oz","requestRef":"9457a3b2-6ba4-493c-9355-bf6fd69b3bb2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0df3910b-671a-49a2-a197-439e3706bc93","timestamp":"2026-10-30T05:19:52.707Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"0df3910b-671a-49a2-a197-439e3706bc93","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrjJSL2HQcof1X5Gno6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L2vcAkxmwnihjfjUt4PYLL","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) a joint test of equal weekday means; (ii) the Monday contrast (Monday mean minus mean of other weekdays) with a 95% CI; (iii) out-of-sample RMSE of weekday-specific means vs. a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 weekday rows (header excluded), 2000-01-03 … 2019-12-31.\n- 215 rows have an empty `DGS10` field (US market holidays); they were dropped first, as required. No non-numeric values, duplicated dates or weekend rows were found (checked in code).\n- Changes Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations: 5,001 changes (first observation has no change). Estimation period (≤ 2009-12-31): n_train = 2,500; evaluation period (≥ 2010-01-01): n_test = 2,501.\n- 214 changes span a holiday gap (e.g. the Tuesday after a Monday holiday covers Friday→Tuesday). They are kept and attributed to the weekday of their end date, per the task rules.\n\n## Method\n- Joint test: classical one-way equal-variance ANOVA F-test of equal weekday means on all 5,001 changes, F(4, 4996).\n- Monday contrast: difference of sample means (Monday vs. all other weekdays pooled), with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- Out-of-sample: weekday means estimated on the estimation period only, used to forecast each evaluation-period change; compared to the zero-change forecast by RMSE (bp). No randomness is involved (no seed).\n- Decision rule: ANOVA at 5%; only if it rejects is the sign taken from the Monday-contrast CI.\n\n## Results\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\n(Table: `out/table1.csv`; figure: `out/figure1.png`.)\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).\n- Out-of-sample (2010–2019): zero-change RMSE = 4.876 bp; weekday-mean RMSE = 4.884 bp (ratio 1.0017). Weekday-specific means slightly *worsen* forecasts relative to the random walk.\n- **Conclusion: `no_detectable_effect`.** Over 2000–2019 the day of the week has no detectable in-sample or out-of-sample predictive content for daily 10-year yield changes.\n\n## Uncertainty\nThe weekday means are all within about ±0.25 bp of zero, while the daily standard deviation is about 5 bp; the standard errors (~0.16–0.19 bp) are comparable to or larger than the means. The Monday-contrast CI half-width is ≈0.37 bp, so Monday effects larger than roughly ±0.4 bp/day can be ruled out at 95% confidence (under the i.i.d.-type assumptions of the Welch interval), but smaller effects cannot be. The RMSE difference (0.008 bp) is tiny; no formal forecast-comparison test (e.g. Diebold–Mariano) was run, so we do not claim the weekday model is significantly *worse*, only that it is not better.\n\n## Limitations and open issues\n- **Dependence and heteroskedasticity:** daily yield changes show volatility clustering (e.g. 2008–09, 2013 taper tantrum). The ANOVA assumes equal variances and independence; the Welch interval allows unequal group variances but not serial correlation or time-varying volatility. HAC-based inference was not run; it could change p-values somewhat, but with F ≈ 0.26 a reversal to significance is implausible.\n- **Holiday-spanning changes:** 214 changes cover more than the normal horizon and are concentrated on Tuesdays/Fridays/Mondays adjacent to holidays; this mixes horizons across weekdays. A sensitivity analysis excluding them was not performed.\n- **Weekend horizon:** Monday changes cover three calendar days, so a variance difference (not a mean difference) would be expected; we tested means only, not variances.\n- **Non-stationarity:** the 2000–2019 period has a strong downward yield trend (~−0.2 bp/day on average) and regime changes (ZLB, QE); weekday effects could exist within sub-periods and cancel overall. Sub-period stability was not checked beyond the train/test split.\n- **Multiple testing / specification:** only the Monday contrast was examined as a specific contrast; other contrasts (e.g. Friday) were not pre-specified and were not tested.\n- **What would invalidate the result:** errors in the source data (FRED revisions), or a different change definition (e.g. dropping holiday-gap changes) materially altering means — unlikely given the size of the SEs relative to the means, but unchecked.\n- External validity: results concern the 10y CMT yield in 2000–2019 only; not other maturities, assets or periods.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Weekday mean daily changes in the 10y yield do not differ significantly (ANOVA F=0.26, p=0.90) over 2000-2019.\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Classical ANOVA assumes independence and equal variances; volatility clustering not modelled.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, i.e. not distinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Welch interval ignores serial dependence and time-varying volatility.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve out-of-sample RMSE over a zero-change forecast in 2010-2019 (4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"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.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Small effects (< ~0.4 bp/day) cannot be ruled out.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3688,"cache_read_input_tokens":5417,"output_tokens":2694,"output_tokens_details":{"thinking_tokens":75},"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":3688},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrjJ9iav3SVDrpTa2oz","requestRef":"9457a3b2-6ba4-493c-9355-bf6fd69b3bb2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1b11786a-e0fd-499b-b056-c1860f6466d9","timestamp":"2026-10-30T05:20:14.716Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"1b11786a-e0fd-499b-b056-c1860f6466d9","isSidechain":false,"promptId":"a4f9c9c6-193a-4191-ac02-8b576c4fe0bf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L2vcAkxmwnihjfjUt4PYLL","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","is_error":false}]},"uuid":"3970817b-e889-4a80-9b8f-cb2a57106ef9","timestamp":"2026-10-30T05:20:14.785Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1b11786a-e0fd-499b-b056-c1860f6466d9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0268","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"7b90def7-bb19-4745-b194-4bd93c19e980","hash":"e46b5b4810a52fe5864a8aa8f30e346d17b170a06e2831049b79a07e66f06bdb","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01L2vcAkxmwnihjfjUt4PYLL","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) a joint test of equal weekday means; (ii) the Monday contrast (Monday mean minus mean of other weekdays) with a 95% CI; (iii) out-of-sample RMSE of weekday-specific means vs. a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 weekday rows (header excluded), 2000-01-03 … 2019-12-31.\n- 215 rows have an empty `DGS10` field (US market holidays); they were dropped first, as required. No non-numeric values, duplicated dates or weekend rows were found (checked in code).\n- Changes Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations: 5,001 changes (first observation has no change). Estimation period (≤ 2009-12-31): n_train = 2,500; evaluation period (≥ 2010-01-01): n_test = 2,501.\n- 214 changes span a holiday gap (e.g. the Tuesday after a Monday holiday covers Friday→Tuesday). They are kept and attributed to the weekday of their end date, per the task rules.\n\n## Method\n- Joint test: classical one-way equal-variance ANOVA F-test of equal weekday means on all 5,001 changes, F(4, 4996).\n- Monday contrast: difference of sample means (Monday vs. all other weekdays pooled), with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- Out-of-sample: weekday means estimated on the estimation period only, used to forecast each evaluation-period change; compared to the zero-change forecast by RMSE (bp). No randomness is involved (no seed).\n- Decision rule: ANOVA at 5%; only if it rejects is the sign taken from the Monday-contrast CI.\n\n## Results\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\n(Table: `out/table1.csv`; figure: `out/figure1.png`.)\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).\n- Out-of-sample (2010–2019): zero-change RMSE = 4.876 bp; weekday-mean RMSE = 4.884 bp (ratio 1.0017). Weekday-specific means slightly *worsen* forecasts relative to the random walk.\n- **Conclusion: `no_detectable_effect`.** Over 2000–2019 the day of the week has no detectable in-sample or out-of-sample predictive content for daily 10-year yield changes.\n\n## Uncertainty\nThe weekday means are all within about ±0.25 bp of zero, while the daily standard deviation is about 5 bp; the standard errors (~0.16–0.19 bp) are comparable to or larger than the means. The Monday-contrast CI half-width is ≈0.37 bp, so Monday effects larger than roughly ±0.4 bp/day can be ruled out at 95% confidence (under the i.i.d.-type assumptions of the Welch interval), but smaller effects cannot be. The RMSE difference (0.008 bp) is tiny; no formal forecast-comparison test (e.g. Diebold–Mariano) was run, so we do not claim the weekday model is significantly *worse*, only that it is not better.\n\n## Limitations and open issues\n- **Dependence and heteroskedasticity:** daily yield changes show volatility clustering (e.g. 2008–09, 2013 taper tantrum). The ANOVA assumes equal variances and independence; the Welch interval allows unequal group variances but not serial correlation or time-varying volatility. HAC-based inference was not run; it could change p-values somewhat, but with F ≈ 0.26 a reversal to significance is implausible.\n- **Holiday-spanning changes:** 214 changes cover more than the normal horizon and are concentrated on Tuesdays/Fridays/Mondays adjacent to holidays; this mixes horizons across weekdays. A sensitivity analysis excluding them was not performed.\n- **Weekend horizon:** Monday changes cover three calendar days, so a variance difference (not a mean difference) would be expected; we tested means only, not variances.\n- **Non-stationarity:** the 2000–2019 period has a strong downward yield trend (~−0.2 bp/day on average) and regime changes (ZLB, QE); weekday effects could exist within sub-periods and cancel overall. Sub-period stability was not checked beyond the train/test split.\n- **Multiple testing / specification:** only the Monday contrast was examined as a specific contrast; other contrasts (e.g. Friday) were not pre-specified and were not tested.\n- **What would invalidate the result:** errors in the source data (FRED revisions), or a different change definition (e.g. dropping holiday-gap changes) materially altering means — unlikely given the size of the SEs relative to the means, but unchecked.\n- External validity: results concern the 10y CMT yield in 2000–2019 only; not other maturities, assets or periods.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Weekday mean daily changes in the 10y yield do not differ significantly (ANOVA F=0.26, p=0.90) over 2000-2019.\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Classical ANOVA assumes independence and equal variances; volatility clustering not modelled.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.05 bp with 95% Welch CI [-0.32, 0.42] bp, i.e. not distinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Welch interval ignores serial dependence and time-varying volatility.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific means estimated on 2000-2009 do not improve out-of-sample RMSE over a zero-change forecast in 2010-2019 (4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"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.\",\"results_keys\":[\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Small effects (< ~0.4 bp/day) cannot be ruled out.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out","description":"Write 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file /Users/simon-pierreboucher/wp29/runs/RC0268/out/report.md has been updated successfully."}]},"uuid":"09ca5108-bf28-4ac5-b5e6-9a911c860ca8","timestamp":"2026-10-30T05:20:19.693Z","toolUseResult":{"filePath":"/Users/simon-pierreboucher/wp29/runs/RC0268/out/report.md","oldString":"(~−0.2 bp/day on average)","newString":"(≈−0.09 bp/day on average, from 6.58% to 1.92%)","originalFile":"# 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) a joint test of equal weekday means; (ii) the Monday contrast (Monday mean minus mean of other weekdays) with a 95% CI; (iii) out-of-sample RMSE of weekday-specific means vs. a zero-change (random-walk) forecast.\n\n## Data and exclusions\n- `data/DGS10.csv`: 5,217 weekday rows (header excluded), 2000-01-03 … 2019-12-31.\n- 215 rows have an empty `DGS10` field (US market holidays); they were dropped first, as required. No non-numeric values, duplicated dates or weekend rows were found (checked in code).\n- Changes Δy_t = 100·(y_t − y_{t−1}) between consecutive available observations: 5,001 changes (first observation has no change). Estimation period (≤ 2009-12-31): n_train = 2,500; evaluation period (≥ 2010-01-01): n_test = 2,501.\n- 214 changes span a holiday gap (e.g. the Tuesday after a Monday holiday covers Friday→Tuesday). They are kept and attributed to the weekday of their end date, per the task rules.\n\n## Method\n- Joint test: classical one-way equal-variance ANOVA F-test of equal weekday means on all 5,001 changes, F(4, 4996).\n- Monday contrast: difference of sample means (Monday vs. all other weekdays pooled), with a **Welch** two-sample t interval (unequal variances, Welch–Satterthwaite df).\n- Out-of-sample: weekday means estimated on the estimation period only, used to forecast each evaluation-period change; compared to the zero-change forecast by RMSE (bp). No randomness is involved (no seed).\n- Decision rule: ANOVA at 5%; only if it rejects is the sign taken from the Monday-contrast CI.\n\n## Results\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\n(Table: `out/table1.csv`; figure: `out/figure1.png`.)\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).\n- Out-of-sample (2010–2019): zero-change RMSE = 4.876 bp; weekday-mean RMSE = 4.884 bp (ratio 1.0017). Weekday-specific means slightly *worsen* forecasts relative to the random walk.\n- **Conclusion: `no_detectable_effect`.** Over 2000–2019 the day of the week has no detectable in-sample or out-of-sample predictive content for daily 10-year yield changes.\n\n## Uncertainty\nThe weekday means are all within about ±0.25 bp of zero, while the daily standard deviation is about 5 bp; the standard errors (~0.16–0.19 bp) are comparable to or larger than the means. The Monday-contrast CI half-width is ≈0.37 bp, so Monday effects larger than roughly ±0.4 bp/day can be ruled out at 95% confidence (under the i.i.d.-type assumptions of the Welch interval), but smaller effects cannot be. The RMSE difference (0.008 bp) is tiny; no formal forecast-comparison test (e.g. Diebold–Mariano) was run, so we do not claim the weekday model is significantly *worse*, only that it is not better.\n\n## Limitations and open issues\n- **Dependence and heteroskedasticity:** daily yield changes show volatility clustering (e.g. 2008–09, 2013 taper tantrum). The ANOVA assumes equal variances and independence; the Welch interval allows unequal group variances but not serial correlation or time-varying volatility. HAC-based inference was not run; it could change p-values somewhat, but with F ≈ 0.26 a reversal to significance is implausible.\n- **Holiday-spanning changes:** 214 changes cover more than the normal horizon and are concentrated on Tuesdays/Fridays/Mondays adjacent to holidays; this mixes horizons across weekdays. A sensitivity analysis excluding them was not performed.\n- **Weekend horizon:** Monday changes cover three calendar days, so a variance difference (not a mean difference) would be expected; we tested means only, not variances.\n- **Non-stationarity:** the 2000–2019 period has a strong downward yield trend (~−0.2 bp/day on average) and regime changes (ZLB, QE); weekday effects could exist within sub-periods and cancel overall. Sub-period stability was not checked beyond the train/test split.\n- **Multiple testing / specification:** only the Monday contrast was examined as a specific contrast; other contrasts (e.g. Friday) were not pre-specified and were not tested.\n- **What would invalidate the result:** errors in the source data (FRED revisions), or a different change definition (e.g. dropping holiday-gap changes) materially altering means — unlikely given the size of the SEs relative to the means, but unchecked.\n- External validity: results concern the 10y CMT yield in 2000–2019 only; not other maturities, assets or periods.\n","structuredPatch":[{"oldStart":38,"oldLines":7,"newStart":38,"newLines":7,"lines":[" - **Dependence and heteroskedasticity:** daily yield changes show volatility clustering (e.g. 2008–09, 2013 taper tantrum). The ANOVA assumes equal variances and independence; the Welch interval allows unequal group variances but not serial correlation or time-varying volatility. HAC-based inference was not run; it could change p-values somewhat, but with F ≈ 0.26 a reversal to significance is implausible."," - **Holiday-spanning changes:** 214 changes cover more than the normal horizon and are concentrated on Tuesdays/Fridays/Mondays adjacent to holidays; this mixes horizons across weekdays. A sensitivity analysis excluding them was not performed."," - **Weekend horizon:** Monday changes cover three calendar days, so a variance difference (not a mean difference) would be expected; we tested means only, not variances.","-- **Non-stationarity:** the 2000–2019 period has a strong downward yield trend (~−0.2 bp/day on average) and regime changes (ZLB, QE); weekday effects could exist within sub-periods and cancel overall. 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found no detectable day-of-week effect: across 2000–2019, the weekday doesn't predict daily changes in the 10-year Treasury yield, either within the sample or out of sample. All the required files are in `out/`, and `analysis.py` regenerates the results, figure and table from `data/`.\n\n**Data:** 5,217 rows read. 215 had no yield (US market holidays) and were dropped, which leaves 5,001 daily changes: 2,500 in 2000–2009 (estimation) and 2,501 in 2010–2019 (evaluation).\n\n**Results:**\n\n| Estimand | Result |\n|---|---|\n| (i) Are the five weekday means equal? (one-way ANOVA, all changes) | F = 0.263, p = 0.902: no evidence they differ |\n| (ii) Monday contrast: Monday mean minus the other four weekdays | +0.050 bp, 95% CI [−0.319, +0.420] bp |\n| (iii) Out-of-sample error (RMSE) in 2010–2019 | Zero-change forecast 4.876 bp; weekday-mean forecast 4.884 bp (slightly worse) |\n\n- **Conclusion code:** `no_detectable_effect`. The ANOVA does not reject at the 5% level.\n- **Size of what's ruled out:** all weekday means are within about ±0.25 bp of zero, against a daily standard deviation of about 5 bp. Monday effects larger than roughly ±0.4 bp a day are excluded at 95% confidence, but smaller ones are not.\n- **Interval method:** I used a Welch two-sample interval, which allows the two groups to have different variances.\n\n**Limitations** (discussed in the report):\n- The tests assume changes are independent, but yields go through calm and volatile periods. I didn't run tests that allow for this, though with F ≈ 0.26 a switch to a significant result is very unlikely.\n- 214 changes span a holiday, so some weekdays include longer horizons (e.g. the Tuesday after a Monday holiday). I kept them as the task requires and didn't check whether dropping them changes anything.\n- I didn't test whether the pattern holds within sub-periods, didn't compare variances across weekdays, and didn't run a formal significance test on the 0.008 bp RMSE difference.\n\nThe output files are `results.json`, `report.md`, `figure1.png`, `table1.csv`, `claims.json` and `analysis.py`. 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