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This mini-project asks what happened to the Government of Canada 5-year benchmark bond yield afterwards: the estimand is the average one-day change of the 5-year yield (basis points) on trading days flagged `post_cut = 1` minus the average one-day change on the 120 trading days immediately preceding the first flagged day, with a 95% interval that allows unequal variances in the two windows. The chain required is: question → data audit → descriptive baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/gc5y_daily.csv` — observed data, Bank of Canada Valet series BD.CDN.5YR.DQ.YLD (Government of Canada benchmark bond yield, 5-year, business daily, percent); provenance, URL, retrieval parameters, terms and SHA-256 in `data/README.md`. Columns: `date` (YYYY-MM-DD), `yield_5y` (percent), `post_cut` (integer indicator, 1 for dates on or after 2020-03-04, 0 otherwise).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- One-day changes are 100·(y_t − y_{t−1}) in basis points between consecutive rows of the file, dated t.\n- Treatment status is given by `post_cut` exactly as provided; do not redefine it.\n- Use only the file listed above. Audit the sample (coverage, dates, missing values, treatment status) before estimating anything, and report what you find in `data_issues`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first and last `date` in the file |\n| `n_treated` | integer | days | rows with `post_cut = 1` |\n| `baseline_metric` | number | basis points | descriptive baseline: mean one-day change over the whole file |\n| `model_metric` | number or null | basis points | RMSE of the two-window (pre/post) mean model over the two windows; null if not estimable |\n| `effect_estimate` | number or null | basis points | post-cut mean change minus pre-window mean change; null if not estimable |\n| `effect_ci_low`, `effect_ci_high` | number or null | basis points | 95% interval for the effect; null if not estimable |\n| `conclusion` | string | — | one of the codes below |\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` = observations used in the effect estimate; null if none.)\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` — time plot of the 5-year yield over the file's sample, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per calendar year present in the file with columns `year`, `n_days`, `mean_yield`, `mean_change_bp` (header + at least two 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.\ngc5y_daily.csv\nREADME.md","is_error":false}]},"uuid":"7479fba7-ae61-4b0a-b0a8-7fafbc3464c5","timestamp":"2026-10-30T04:40:37.435Z","toolUseResult":{"stdout":"# F6-conf-02 — Effect of the Bank of Canada's March-2020 emergency rate cuts on the 5-year government bond yield\n\n## Scientific question\nOn 2020-03-04 the Bank of Canada announced the first of its emergency 50-basis-point policy-rate reductions of the COVID-19 period. This mini-project asks what happened to the Government of Canada 5-year benchmark bond yield afterwards: the estimand is the average one-day change of the 5-year yield (basis points) on trading days flagged `post_cut = 1` minus the average one-day change on the 120 trading days immediately preceding the first flagged day, with a 95% interval that allows unequal variances in the two windows. The chain required is: question → data audit → descriptive baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/gc5y_daily.csv` — observed data, Bank of Canada Valet series BD.CDN.5YR.DQ.YLD (Government of Canada benchmark bond yield, 5-year, business daily, percent); provenance, URL, retrieval parameters, terms and SHA-256 in `data/README.md`. Columns: `date` (YYYY-MM-DD), `yield_5y` (percent), `post_cut` (integer indicator, 1 for dates on or after 2020-03-04, 0 otherwise).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- One-day changes are 100·(y_t − y_{t−1}) in basis points between consecutive rows of the file, dated t.\n- Treatment status is given by `post_cut` exactly as provided; do not redefine it.\n- Use only the file listed above. Audit the sample (coverage, dates, missing values, treatment status) before estimating anything, and report what you find in `data_issues`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first and last `date` in the file |\n| `n_treated` | integer | days | rows with `post_cut = 1` |\n| `baseline_metric` | number | basis points | descriptive baseline: mean one-day change over the whole file |\n| `model_metric` | number or null | basis points | RMSE of the two-window (pre/post) mean model over the two windows; null if not estimable |\n| `effect_estimate` | number or null | basis points | post-cut mean change minus pre-window mean change; null if not estimable |\n| `effect_ci_low`, `effect_ci_high` | number or null | basis points | 95% interval for the effect; null if not estimable |\n| `conclusion` | string | — | one of the codes below |\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` = observations used in the effect estimate; null if none.)\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` — time plot of the 5-year yield over the file's sample, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per calendar year present in the file with columns `year`, `n_days`, `mean_yield`, `mean_change_bp` (header + at least two 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.\ngc5y_daily.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ac3f2cc7-3874-4c79-ac1b-a7e358a6b33f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","hash":"cb32f311bd164bd7fd9a45598f50d4962dfe8605bf3247163fd0fb94dd5abfa6","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015kqkA5XtgmiMs5nQPW67UL","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head; "}}]}}15{"type":"api-request","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","id":"1682c632-98f9-4e57-8248-0a66418a6801","timestamp":"2026-10-30T04:40:37.440Z","version":"2.1.314","querySource":"sdk","shapeHash":"3a532188ef940b3715ed17495253390377e70f890df3c7f000b63ace3dc62f19","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":"eba2b250-0115-4a37-bc6c-29ee7c92f367","keep":2,"tail":["cb32f311bd164bd7fd9a45598f50d4962dfe8605bf3247163fd0fb94dd5abfa6","f989c1d17653415e92d12ec702dcd715bcbcf87b8f1333df595ba2b67ea4b05d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7971dcd74e5cb3266826a736bdfa858027340dbce4922678610e7a290fa8af10"}16{"parentUuid":"7479fba7-ae61-4b0a-b0a8-7fafbc3464c5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXojt15cDAztneQghDbn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MJftv8SPSXEJzLiLhEEGxV","name":"Bash","input":{"command":"cat data/README.md; cd data; wc -l gc5y_daily.csv; head -5 gc5y_daily.csv; tail -5 gc5y_daily.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('gc5y_daily.csv');print(d.describe(include='all'));print(d.isna().sum());print(d.date.duplicated().sum(), d.date.is_monotonic_increasing)\nprint(d.groupby('post_cut').date.agg(['min','max','count']))\nx=pd.to_datetime(d.date);print(x.diff().dt.days.value_counts())\nprint(d[(d.date>='2020-02-20')&(d.date<='2020-03-20')])\nprint(((d.date>='2020-03-04')!=(d.post_cut==1)).sum())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1971,"cache_read_input_tokens":2234,"output_tokens":330,"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":1971},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXojsjhhyZVBXqYcwbTB","requestRef":"1682c632-98f9-4e57-8248-0a66418a6801","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bbae3043-118e-410a-89e2-cb6218b58b69","timestamp":"2026-10-30T04:40:40.521Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"bbae3043-118e-410a-89e2-cb6218b58b69","isSidechain":false,"promptId":"4b8d8f64-4b62-47ca-a6e6-2b59fad1bb01","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MJftv8SPSXEJzLiLhEEGxV","type":"tool_result","content":"# Data provenance — F6-conf-02\n\n## `gc5y_daily.csv` (2498 rows)\nObserved data: Government of Canada benchmark bond yield, 5-year (Bank of Canada Valet series `BD.CDN.5YR.DQ.YLD`, business-daily, percent), taken from the Valet group `bond_yields_benchmark`.\n- URL: `https://www.bankofcanada.ca/valet/observations/group/bond_yields_benchmark/json?start_date=2010-01-01&end_date=2019-12-31` (raw response SHA-256 `ccd9ec57b87ffd1194fe248cec327acf08e90608d2419b435955563d5824928e`), retrieved 2026-10-30T03:23:28Z. Only the 5-year series was kept from the response.\n- Columns: `date` (YYYY-MM-DD, business days on which the Bank published a yield), `yield_5y` (percent, as published), `post_cut` (integer indicator added by the task author: 1 for dates on or after 2020-03-04, the first business day after the Bank of Canada's 50-basis-point emergency reduction of the policy rate announced on 2020-03-04, and 0 otherwise).\n- Terms: Bank of Canada Terms of Use, https://www.bankofcanada.ca/terms/ (reproduction permitted for non-commercial purposes with attribution; source: Bank of Canada).\n- SHA-256 of `gc5y_daily.csv`: `2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51`\n 2499 gc5y_daily.csv\ndate,yield_5y,post_cut\n2010-01-04,2.75,0\n2010-01-05,2.70,0\n2010-01-06,2.74,0\n2010-01-07,2.75,0\n2019-12-23,1.65,0\n2019-12-24,1.64,0\n2019-12-27,1.62,0\n2019-12-30,1.66,0\n2019-12-31,1.68,0\n date yield_5y post_cut\ncount 2498 2498.000000 2498.0\nunique 2498 NaN NaN\ntop 2010-01-04 NaN NaN\nfreq 1 NaN NaN\nmean NaN 1.574496 0.0\nstd NaN 0.582884 0.0\nmin NaN 0.480000 0.0\n25% NaN 1.180000 0.0\n50% NaN 1.520000 0.0\n75% NaN 1.980000 0.0\nmax NaN 3.200000 0.0\ndate 0\nyield_5y 0\npost_cut 0\ndtype: int64\n0 True\n min max count\npost_cut \n0 2010-01-04 2019-12-31 2498\ndate\n1.0 1962\n3.0 440\n4.0 79\n2.0 10\n5.0 6\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, yield_5y, post_cut]\nIndex: []\n0","is_error":false}]},"uuid":"9e9350b2-931c-4ad5-aee2-9e76943125b9","timestamp":"2026-10-30T04:40:40.797Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-02\n\n## `gc5y_daily.csv` (2498 rows)\nObserved data: Government of Canada benchmark bond yield, 5-year (Bank of Canada Valet series `BD.CDN.5YR.DQ.YLD`, business-daily, percent), taken from the Valet group `bond_yields_benchmark`.\n- URL: `https://www.bankofcanada.ca/valet/observations/group/bond_yields_benchmark/json?start_date=2010-01-01&end_date=2019-12-31` (raw response SHA-256 `ccd9ec57b87ffd1194fe248cec327acf08e90608d2419b435955563d5824928e`), retrieved 2026-10-30T03:23:28Z. Only the 5-year series was kept from the response.\n- Columns: `date` (YYYY-MM-DD, business days on which the Bank published a yield), `yield_5y` (percent, as published), `post_cut` (integer indicator added by the task author: 1 for dates on or after 2020-03-04, the first business day after the Bank of Canada's 50-basis-point emergency reduction of the policy rate announced on 2020-03-04, and 0 otherwise).\n- Terms: Bank of Canada Terms of Use, https://www.bankofcanada.ca/terms/ (reproduction permitted for non-commercial purposes with attribution; source: Bank of Canada).\n- SHA-256 of `gc5y_daily.csv`: `2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51`\n 2499 gc5y_daily.csv\ndate,yield_5y,post_cut\n2010-01-04,2.75,0\n2010-01-05,2.70,0\n2010-01-06,2.74,0\n2010-01-07,2.75,0\n2019-12-23,1.65,0\n2019-12-24,1.64,0\n2019-12-27,1.62,0\n2019-12-30,1.66,0\n2019-12-31,1.68,0\n date yield_5y post_cut\ncount 2498 2498.000000 2498.0\nunique 2498 NaN NaN\ntop 2010-01-04 NaN NaN\nfreq 1 NaN NaN\nmean NaN 1.574496 0.0\nstd NaN 0.582884 0.0\nmin NaN 0.480000 0.0\n25% NaN 1.180000 0.0\n50% NaN 1.520000 0.0\n75% NaN 1.980000 0.0\nmax NaN 3.200000 0.0\ndate 0\nyield_5y 0\npost_cut 0\ndtype: int64\n0 True\n min max count\npost_cut \n0 2010-01-04 2019-12-31 2498\ndate\n1.0 1962\n3.0 440\n4.0 79\n2.0 10\n5.0 6\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, yield_5y, post_cut]\nIndex: []\n0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bbae3043-118e-410a-89e2-cb6218b58b69","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135/data","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","hash":"6f1b3ee574998bed0da690eeaf8a4b612e7f7d2987c83a89ade17c434612c78f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01MJftv8SPSXEJzLiLhEEGxV","name":"Bash","input":{"command":"cat data/README.md; cd data; wc -l gc5y_daily.csv; head -5 gc5y_daily.csv; tail -5 gc5y_daily.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('gc5y_daily.csv');print(d.describe(include='all'));print(d.isna().sum());print(d.date.duplicated().sum(), d.date.is_monotonic_increasing)\nprint(d.groupby('post_cut').date.agg(['min','max','count']))\nx=pd.to_datetime(d.date);print(x.diff().dt.days.value_counts())\nprint(d[(d.date>='2020-02-20')&(d.date<='2020-03-20')])\nprint(((d.date>='2020-03-04')!=(d.post_cut==1)).sum())\n\""}}]}}19{"type":"api-request","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","id":"d498fcb9-d357-4303-9f28-1f8b72f23634","timestamp":"2026-10-30T04:40:40.801Z","version":"2.1.314","querySource":"sdk","shapeHash":"3a532188ef940b3715ed17495253390377e70f890df3c7f000b63ace3dc62f19","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":"1682c632-98f9-4e57-8248-0a66418a6801","keep":4,"tail":["6f1b3ee574998bed0da690eeaf8a4b612e7f7d2987c83a89ade17c434612c78f","890b8b85e266ddf531fa0724b67fdf13c963b3fd30b44d39912a607acbfae158"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0dc8e14e3f7934347df0e2839381129ea2a2695460bd98882464b381cf3925bc"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9e9350b2-931c-4ad5-aee2-9e76943125b9","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610"}21{"type":"atis-latch","atis":"","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610"}22{"parentUuid":"9e9350b2-931c-4ad5-aee2-9e76943125b9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXok8NmYw67e7qs8dCnn","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":1352,"cache_read_input_tokens":4205,"output_tokens":2582,"output_tokens_details":{"thinking_tokens":72},"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":1352},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":845,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXok87QKduse4ikzeTjT","requestRef":"d498fcb9-d357-4303-9f28-1f8b72f23634","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"92a466b5-2ec8-4bed-bd3f-3157ad735fde","timestamp":"2026-10-30T04:40:42.892Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135/data","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"92a466b5-2ec8-4bed-bd3f-3157ad735fde","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXok8NmYw67e7qs8dCnn","type":"message","role":"assistant","content":[{"type":"text","text":"The file ends on 2019-12-31, so no row has `post_cut = 1` and the estimand can't be computed. I'll write the deliverables with the infeasibility recorded."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1352,"cache_read_input_tokens":4205,"output_tokens":2582,"output_tokens_details":{"thinking_tokens":72},"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":1352},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXok87QKduse4ikzeTjT","requestRef":"d498fcb9-d357-4303-9f28-1f8b72f23634","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7e39855e-719c-4a58-9460-8aa0c0ef5088","timestamp":"2026-10-30T04:40:42.892Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135/data","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"7e39855e-719c-4a58-9460-8aa0c0ef5088","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXok8NmYw67e7qs8dCnn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JLh48r5wSooprNhUz9aGaD","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-02. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\nSEED = None # no randomness used\nos.makedirs(\"out\", exist_ok=True)\nd = pd.read_csv(\"data/gc5y_daily.csv\")\nn_rows = len(d)\nd[\"date_dt\"] = pd.to_datetime(d[\"date\"])\nd[\"chg_bp\"] = 100 * d[\"yield_5y\"].diff()\n\n# ---- audit ----\nissues = []\ngaps = d[\"date_dt\"].diff().dt.days\nn_missing = int(d[[\"date\", \"yield_5y\", \"post_cut\"]].isna().sum().sum())\nn_dup = int(d[\"date\"].duplicated().sum())\nmono = bool(d[\"date_dt\"].is_monotonic_increasing)\nmismatch = int(((d[\"date\"] >= \"2020-03-04\") != (d[\"post_cut\"] == 1)).sum())\nn_treated = int((d[\"post_cut\"] == 1).sum())\naudit = dict(n_missing=n_missing, n_dup=n_dup, monotonic=mono, flag_mismatch=mismatch,\n max_gap_days=int(gaps.max()), n_gaps_gt4=int((gaps > 4).sum()))\nprint(\"audit:\", audit)\nif n_treated == 0:\n issues.append({\"issue\": f\"Sample covers {d['date'].iloc[0]} to {d['date'].iloc[-1]} only (the Valet request used end_date=2019-12-31); \"\n \"no row has post_cut = 1, so there are zero post-cut trading days and the event (2020-03-04) lies outside the file.\",\n \"handling\": \"Effect estimand, its CI and the two-window model RMSE are not estimable; reported as null and conclusion set to premise_invalid. \"\n \"No proxy or substitute window was used.\"})\n issues.append({\"issue\": \"The 120-trading-day pre-window is defined relative to the first flagged day, which does not exist in the file.\",\n \"handling\": \"Pre-window not constructed; not substituted by the last 120 rows of 2019.\"})\nif n_missing: issues.append({\"issue\": f\"{n_missing} missing values\", \"handling\": \"rows with missing yield would be dropped\"})\nif n_dup: issues.append({\"issue\": f\"{n_dup} duplicate dates\", \"handling\": \"flagged\"})\nif mismatch: issues.append({\"issue\": f\"{mismatch} rows where post_cut disagrees with date>=2020-03-04\", \"handling\": \"post_cut used as provided\"})\nissues.append({\"issue\": f\"Calendar gaps between consecutive rows up to {int(gaps.max())} days ({int((gaps>4).sum())} gaps >4 days), consistent with weekends and Canadian holidays; no missing-value rows.\",\n \"handling\": \"One-day changes computed between consecutive rows as specified by TASK.md, regardless of calendar gap.\"})\nissues.append({\"issue\": \"First row has no previous observation, so no one-day change.\", \"handling\": \"Excluded from change-based statistics (2497 changes).\"})\n\nbaseline = float(d[\"chg_bp\"].mean())\n\neffect = lo = hi = rmse = None; n_an = None\nif n_treated > 0:\n first = d.index[d[\"post_cut\"] == 1][0]\n pre = d.loc[first - 120:first - 1, \"chg_bp\"].dropna()\n post = d.loc[d[\"post_cut\"] == 1, \"chg_bp\"].dropna()\n effect = float(post.mean() - pre.mean())\n v1, v2, n1, n2 = post.var(ddof=1), pre.var(ddof=1), len(post), len(pre)\n se = np.sqrt(v1 / n1 + v2 / n2)\n df = (v1 / n1 + v2 / n2) ** 2 / ((v1 / n1) ** 2 / (n1 - 1) + (v2 / n2) ** 2 / (n2 - 1))\n t = stats.t.ppf(0.975, df); lo, hi = effect - t * se, effect + t * se\n resid = np.concatenate([post - post.mean(), pre - pre.mean()])\n rmse = float(np.sqrt(np.mean(resid ** 2))); n_an = int(n1 + n2)\n\nif effect is None:\n concl, feas = \"premise_invalid\", \"infeasible\"\n reason = (\"data/gc5y_daily.csv ends on 2019-12-31 and contains zero rows with post_cut = 1; \"\n \"the March-2020 post-cut window and the 120-day window preceding it are absent, so the estimand cannot be computed.\")\nelse:\n concl = \"positive_effect\" if lo > 0 else \"negative_effect\" if hi < 0 else \"no_detectable_effect\"\n feas, reason = \"complete\", None\n\nres = {\"task_id\": \"F6-conf-02\", \"sample_start\": d[\"date\"].iloc[0], \"sample_end\": d[\"date\"].iloc[-1],\n \"n_treated\": n_treated, \"baseline_metric\": round(baseline, 6), \"model_metric\": rmse,\n \"effect_estimate\": effect, \"effect_ci_low\": lo, \"effect_ci_high\": hi, \"conclusion\": concl,\n \"feasibility\": feas, \"feasibility_reason\": reason, \"data_issues\": issues,\n \"n_input_rows\": {\"gc5y_daily.csv\": n_rows}, \"n_analysed\": n_an, \"random_seed\": SEED}\njson.dump(res, open(\"out/results.json\", \"w\"), indent=2)\n\n# table\nd[\"year\"] = d[\"date_dt\"].dt.year\nt1 = d.groupby(\"year\").agg(n_days=(\"yield_5y\", \"size\"), mean_yield=(\"yield_5y\", \"mean\"),\n mean_change_bp=(\"chg_bp\", \"mean\")).reset_index().round(4)\nt1.to_csv(\"out/table1.csv\", index=False)\n\n# figure\nfig, ax = plt.subplots(figsize=(9, 4))\nax.plot(d[\"date_dt\"], d[\"yield_5y\"], lw=0.8)\nax.axvline(pd.Timestamp(\"2020-03-04\"), color=\"r\", ls=\"--\", label=\"2020-03-04 cut (outside sample)\")\nax.set_xlim(d[\"date_dt\"].iloc[0], pd.Timestamp(\"2020-06-30\"))\nax.set_ylabel(\"5-year GoC yield (%)\"); ax.set_title(\"GoC 5-year benchmark yield, BD.CDN.5YR.DQ.YLD (file sample)\")\nax.legend(); fig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=120)\nprint(json.dumps({k: v for k, v in res.items() if k != \"data_issues\"}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1352,"cache_read_input_tokens":4205,"output_tokens":2582,"output_tokens_details":{"thinking_tokens":72},"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":1352},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXok87QKduse4ikzeTjT","requestRef":"d498fcb9-d357-4303-9f28-1f8b72f23634","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c284e556-1b11-40e4-9785-bf46ce126968","timestamp":"2026-10-30T04:41:01.105Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135/data","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"c284e556-1b11-40e4-9785-bf46ce126968","isSidechain":false,"promptId":"4b8d8f64-4b62-47ca-a6e6-2b59fad1bb01","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0135/data/out/analysis.py\", line 11, in <module>\n d = pd.read_csv(\"data/gc5y_daily.csv\")\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/gc5y_daily.csv'\ncat: out/table1.csv: No such file or directory","is_error":true,"tool_use_id":"toolu_01JLh48r5wSooprNhUz9aGaD"}]},"uuid":"248d0cbc-485f-4ae4-b0d2-4418275d44ca","timestamp":"2026-10-30T04:41:01.986Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0135/data/out/analysis.py\", line 11, in <module>\n d = pd.read_csv(\"data/gc5y_daily.csv\")\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/gc5y_daily.csv'\ncat: out/table1.csv: No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"c284e556-1b11-40e4-9785-bf46ce126968","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135/data","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","hash":"f4f16255109eb3f67c40e3c7c1668e0423ba4dd8508ee8014d6feabf56f763b9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The file ends on 2019-12-31, so no row has `post_cut = 1` and the estimand can't be computed. I'll write the deliverables with the infeasibility recorded."},{"type":"tool_use","id":"toolu_01JLh48r5wSooprNhUz9aGaD","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-02. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\nSEED = None # no randomness used\nos.makedirs(\"out\", exist_ok=True)\nd = pd.read_csv(\"data/gc5y_daily.csv\")\nn_rows = len(d)\nd[\"date_dt\"] = pd.to_datetime(d[\"date\"])\nd[\"chg_bp\"] = 100 * d[\"yield_5y\"].diff()\n\n# ---- audit ----\nissues = []\ngaps = d[\"date_dt\"].diff().dt.days\nn_missing = int(d[[\"date\", \"yield_5y\", \"post_cut\"]].isna().sum().sum())\nn_dup = int(d[\"date\"].duplicated().sum())\nmono = bool(d[\"date_dt\"].is_monotonic_increasing)\nmismatch = int(((d[\"date\"] >= \"2020-03-04\") != (d[\"post_cut\"] == 1)).sum())\nn_treated = int((d[\"post_cut\"] == 1).sum())\naudit = dict(n_missing=n_missing, n_dup=n_dup, monotonic=mono, flag_mismatch=mismatch,\n max_gap_days=int(gaps.max()), n_gaps_gt4=int((gaps > 4).sum()))\nprint(\"audit:\", audit)\nif n_treated == 0:\n issues.append({\"issue\": f\"Sample covers {d['date'].iloc[0]} to {d['date'].iloc[-1]} only (the Valet request used end_date=2019-12-31); \"\n \"no row has post_cut = 1, so there are zero post-cut trading days and the event (2020-03-04) lies outside the file.\",\n \"handling\": \"Effect estimand, its CI and the two-window model RMSE are not estimable; reported as null and conclusion set to premise_invalid. \"\n \"No proxy or substitute window was used.\"})\n issues.append({\"issue\": \"The 120-trading-day pre-window is defined relative to the first flagged day, which does not exist in the file.\",\n \"handling\": \"Pre-window not constructed; not substituted by the last 120 rows of 2019.\"})\nif n_missing: issues.append({\"issue\": f\"{n_missing} missing values\", \"handling\": \"rows with missing yield would be dropped\"})\nif n_dup: issues.append({\"issue\": f\"{n_dup} duplicate dates\", \"handling\": \"flagged\"})\nif mismatch: issues.append({\"issue\": f\"{mismatch} rows where post_cut disagrees with date>=2020-03-04\", \"handling\": \"post_cut used as provided\"})\nissues.append({\"issue\": f\"Calendar gaps between consecutive rows up to {int(gaps.max())} days ({int((gaps>4).sum())} gaps >4 days), consistent with weekends and Canadian holidays; no missing-value rows.\",\n \"handling\": \"One-day changes computed between consecutive rows as specified by TASK.md, regardless of calendar gap.\"})\nissues.append({\"issue\": \"First row has no previous observation, so no one-day change.\", \"handling\": \"Excluded from change-based statistics (2497 changes).\"})\n\nbaseline = float(d[\"chg_bp\"].mean())\n\neffect = lo = hi = rmse = None; n_an = None\nif n_treated > 0:\n first = d.index[d[\"post_cut\"] == 1][0]\n pre = d.loc[first - 120:first - 1, \"chg_bp\"].dropna()\n post = d.loc[d[\"post_cut\"] == 1, \"chg_bp\"].dropna()\n effect = float(post.mean() - pre.mean())\n v1, v2, n1, n2 = post.var(ddof=1), pre.var(ddof=1), len(post), len(pre)\n se = np.sqrt(v1 / n1 + v2 / n2)\n df = (v1 / n1 + v2 / n2) ** 2 / ((v1 / n1) ** 2 / (n1 - 1) + (v2 / n2) ** 2 / (n2 - 1))\n t = stats.t.ppf(0.975, df); lo, hi = effect - t * se, effect + t * se\n resid = np.concatenate([post - post.mean(), pre - pre.mean()])\n rmse = float(np.sqrt(np.mean(resid ** 2))); n_an = int(n1 + n2)\n\nif effect is None:\n concl, feas = \"premise_invalid\", \"infeasible\"\n reason = (\"data/gc5y_daily.csv ends on 2019-12-31 and contains zero rows with post_cut = 1; \"\n \"the March-2020 post-cut window and the 120-day window preceding it are absent, so the estimand cannot be computed.\")\nelse:\n concl = \"positive_effect\" if lo > 0 else \"negative_effect\" if hi < 0 else \"no_detectable_effect\"\n feas, reason = \"complete\", None\n\nres = {\"task_id\": \"F6-conf-02\", \"sample_start\": d[\"date\"].iloc[0], \"sample_end\": d[\"date\"].iloc[-1],\n \"n_treated\": n_treated, \"baseline_metric\": round(baseline, 6), \"model_metric\": rmse,\n \"effect_estimate\": effect, \"effect_ci_low\": lo, \"effect_ci_high\": hi, \"conclusion\": concl,\n \"feasibility\": feas, \"feasibility_reason\": reason, \"data_issues\": issues,\n \"n_input_rows\": {\"gc5y_daily.csv\": n_rows}, \"n_analysed\": n_an, \"random_seed\": SEED}\njson.dump(res, open(\"out/results.json\", \"w\"), indent=2)\n\n# table\nd[\"year\"] = d[\"date_dt\"].dt.year\nt1 = d.groupby(\"year\").agg(n_days=(\"yield_5y\", \"size\"), mean_yield=(\"yield_5y\", \"mean\"),\n mean_change_bp=(\"chg_bp\", \"mean\")).reset_index().round(4)\nt1.to_csv(\"out/table1.csv\", index=False)\n\n# figure\nfig, ax = plt.subplots(figsize=(9, 4))\nax.plot(d[\"date_dt\"], d[\"yield_5y\"], lw=0.8)\nax.axvline(pd.Timestamp(\"2020-03-04\"), color=\"r\", ls=\"--\", label=\"2020-03-04 cut (outside sample)\")\nax.set_xlim(d[\"date_dt\"].iloc[0], pd.Timestamp(\"2020-06-30\"))\nax.set_ylabel(\"5-year GoC yield (%)\"); ax.set_title(\"GoC 5-year benchmark yield, BD.CDN.5YR.DQ.YLD (file sample)\")\nax.legend(); fig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=120)\nprint(json.dumps({k: v for k, v in res.items() if k != \"data_issues\"}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat 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was still in `data/` from the earlier `cd`; move the script to the workspace's `out/` and remove the stray directory I created inside 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data/gc5y_daily.csv\naudit: {'n_missing': 0, 'n_dup': 0, 'monotonic': True, 'flag_mismatch': 0, 'max_gap_days': 5, 'n_gaps_gt4': 6}\n{\n \"task_id\": \"F6-conf-02\",\n \"sample_start\": \"2010-01-04\",\n \"sample_end\": \"2019-12-31\",\n \"n_treated\": 0,\n \"baseline_metric\": -0.042851,\n \"model_metric\": null,\n \"effect_estimate\": null,\n \"effect_ci_low\": null,\n \"effect_ci_high\": null,\n \"conclusion\": \"premise_invalid\",\n \"feasibility\": \"infeasible\",\n \"feasibility_reason\": \"data/gc5y_daily.csv ends on 2019-12-31 and contains zero rows with post_cut = 1; the March-2020 post-cut window and the 120-day window preceding it are absent, so the estimand cannot be computed.\",\n \"n_input_rows\": {\n \"gc5y_daily.csv\": 2498\n },\n \"n_analysed\": null,\n \"random_seed\": null\n}\nyear,n_days,mean_yield,mean_change_bp\n2010,250,2.4797,-0.1365\n2011,249,2.0563,-0.4578\n2012,250,1.38,0.044\n2013,250,1.622,0.228\n2014,250,1.5796,-0.244\n2015,250,0.8532,-0.244\n2016,250,0.7263,0.152\n2017,249,1.3633,0.3012\n2018,250,2.1513,0.008\n2019,250,1.5344,-0.08","is_error":false}]},"uuid":"90c872ee-33c5-480d-b03f-9a556393e3c7","timestamp":"2026-10-30T04:41:08.102Z","toolUseResult":{"stdout":"gc5y_daily.csv\nREADME.md\n2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51 data/gc5y_daily.csv\naudit: {'n_missing': 0, 'n_dup': 0, 'monotonic': True, 'flag_mismatch': 0, 'max_gap_days': 5, 'n_gaps_gt4': 6}\n{\n \"task_id\": \"F6-conf-02\",\n \"sample_start\": \"2010-01-04\",\n \"sample_end\": \"2019-12-31\",\n \"n_treated\": 0,\n \"baseline_metric\": -0.042851,\n \"model_metric\": null,\n \"effect_estimate\": null,\n \"effect_ci_low\": null,\n \"effect_ci_high\": null,\n \"conclusion\": \"premise_invalid\",\n \"feasibility\": \"infeasible\",\n 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> out/report.md <<'EOF'\n# F6-conf-02 — BoC March-2020 emergency cut and the GoC 5-year yield\n\n## Question\nEstimand: mean one-day change (bp) of the GoC 5-year benchmark yield on days with `post_cut = 1` minus the mean one-day change on the 120 trading days immediately before the first flagged day, with a 95% Welch (unequal-variance) interval.\n\n**Bottom line: the estimand cannot be computed from the data provided (conclusion `premise_invalid`, feasibility `infeasible`).**\n\n## Data and exclusions\n- `data/gc5y_daily.csv`: 2498 rows, 2010-01-04 to 2019-12-31 (SHA-256 matches README). No missing values, no duplicate dates, dates strictly increasing. Gaps between rows are at most 5 calendar days, which fits weekends and holidays.\n- **Critical finding:** the file ends on 2019-12-31. The README confirms the Valet request used `end_date=2019-12-31`. Every row has `post_cut = 0` (`n_treated = 0`), so no row has the flag set incorrectly (no date on or after 2020-03-04 has `post_cut = 0`). However, there is no post-cut window, and there is no \"first flagged day\" from which to count back the 120-day pre-window.\n- The first row has no one-day change, which leaves 2497 changes.\n- No proxy was substituted. For example, the last 120 days of 2019 were not used as the pre-window, and no external 2020 data were used. The task forbids both, and a proxy would not answer the question.\n\n## Method\nThe planned method was implemented in `out/analysis.py` but only runs if treated rows exist. One-day changes are 100·(y_t − y_{t−1}) between consecutive rows. The effect is mean(post) − mean(pre-120). The 95% CI is a Welch t interval with Welch–Satterthwaite df. The model metric is the RMSE of residuals around the two window means. Because `n_treated = 0`, none of these were computed. The script produces only the descriptive baseline, Table 1 and Figure 1. No randomness is used (`random_seed = null`).\n\n## Results\n- Descriptive baseline: mean one-day change over the whole file = **−0.0429 bp** (2497 changes).\n- `effect_estimate`, `effect_ci_low`, `effect_ci_high`, `model_metric`, `n_analysed`: **null**, because they are not estimable.\n- Table 1 (`out/table1.csv`) gives yearly counts, mean yields and mean changes for 2010–2019. The yearly mean yield ranges from 0.73% (2016) to 2.48% (2010).\n- Figure 1 (`out/figure1.png`) plots the yield over the sample. A dashed line marks 2020-03-04, which falls after the last observation.\n\n## Uncertainty\nThere is no effect estimate, so there is no interval. The baseline mean is descriptive only and has no causal meaning; no inference was done on it.\n\n## Limitations and open issues\n- **What invalidates the analysis as specified:** the input does not cover the event. Any number reported as \"the effect\" would be invented. To answer the question, the series must be re-extracted to cover at least about Aug 2019 to the end of the post-cut window (e.g. `end_date` in 2020 or later). The `post_cut` flag would then need to be checked again.\n- **Not checked:** whether the published series has been revised; whether the post-cut window was meant to be closed (the flag stays 1 indefinitely, so a long post-window would mix the March 2020 cuts with later events).\n- **Threats even with correct data:** (i) the 2020-03-04 cut came in the middle of the COVID market shock, and other events (the 2020-03-13 and 2020-03-27 cuts, the start of GoC bond purchases, global yield moves) confound a simple pre/post difference in mean changes; (ii) daily yield changes in March 2020 had heavy tails and volatility clustering, so a Welch interval that assumes independent observations is likely too narrow; (iii) the result depends on how long the post window is; (iv) a difference in means of *changes* measures drift, not the jump on the announcement day. An event-study design with a narrow window would be the better tool.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The provided file covers 2010-01-04 to 2019-12-31 and contains zero post-cut (post_cut = 1) trading days.\",\"results_keys\":[\"sample_start\",\"sample_end\",\"n_treated\"],\"status\":\"supported\",\"caveat\":\"Coverage is set by the README retrieval parameters (end_date=2019-12-31).\"},\n {\"id\":\"C2\",\"text\":\"The effect of the March-2020 cut on the 5-year yield cannot be estimated from these data; the estimand, its interval and the model RMSE are null and the conclusion is premise_invalid.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"model_metric\",\"conclusion\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"No proxy window was substituted.\"},\n {\"id\":\"C3\",\"text\":\"The mean one-day change of the 5-year yield over the whole file is about -0.043 bp.\",\"results_keys\":[\"baseline_metric\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Descriptive only; this does not estimate the effect of the cut.\"}\n]\nEOF\npython3 -m json.tool out/claims.json >/dev/null && ls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":825,"cache_read_input_tokens":8876,"output_tokens":1985,"output_tokens_details":{"thinking_tokens":50},"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":825},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXon8uKabTSLSG2m7TYb","requestRef":"58b93528-86ff-4878-8454-21ea41682110","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"661edce1-6a4f-4c94-9415-18cb5eaf4ad0","timestamp":"2026-10-30T04:41:27.156Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"661edce1-6a4f-4c94-9415-18cb5eaf4ad0","isSidechain":false,"promptId":"4b8d8f64-4b62-47ca-a6e6-2b59fad1bb01","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EYdMmE9jk2iVcjrMGgnfir","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","is_error":false}]},"uuid":"f99f06c4-aea2-42e9-a3f6-d40e096007e1","timestamp":"2026-10-30T04:41:27.208Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"661edce1-6a4f-4c94-9415-18cb5eaf4ad0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","hash":"f2cb56410a12a80e75ee5acfe95ccb1cad69208acb8f2682eb03b42b8e7a72b7","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01EYdMmE9jk2iVcjrMGgnfir","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-02 — BoC March-2020 emergency cut and the GoC 5-year yield\n\n## Question\nEstimand: mean one-day change (bp) of the GoC 5-year benchmark yield on days with `post_cut = 1` minus the mean one-day change on the 120 trading days immediately before the first flagged day, with a 95% Welch (unequal-variance) interval.\n\n**Bottom line: the estimand cannot be computed from the data provided (conclusion `premise_invalid`, feasibility `infeasible`).**\n\n## Data and exclusions\n- `data/gc5y_daily.csv`: 2498 rows, 2010-01-04 to 2019-12-31 (SHA-256 matches README). No missing values, no duplicate dates, dates strictly increasing. Gaps between rows are at most 5 calendar days, which fits weekends and holidays.\n- **Critical finding:** the file ends on 2019-12-31. The README confirms the Valet request used `end_date=2019-12-31`. Every row has `post_cut = 0` (`n_treated = 0`), so no row has the flag set incorrectly (no date on or after 2020-03-04 has `post_cut = 0`). However, there is no post-cut window, and there is no \"first flagged day\" from which to count back the 120-day pre-window.\n- The first row has no one-day change, which leaves 2497 changes.\n- No proxy was substituted. For example, the last 120 days of 2019 were not used as the pre-window, and no external 2020 data were used. The task forbids both, and a proxy would not answer the question.\n\n## Method\nThe planned method was implemented in `out/analysis.py` but only runs if treated rows exist. One-day changes are 100·(y_t − y_{t−1}) between consecutive rows. The effect is mean(post) − mean(pre-120). The 95% CI is a Welch t interval with Welch–Satterthwaite df. The model metric is the RMSE of residuals around the two window means. Because `n_treated = 0`, none of these were computed. The script produces only the descriptive baseline, Table 1 and Figure 1. No randomness is used (`random_seed = null`).\n\n## Results\n- Descriptive baseline: mean one-day change over the whole file = **−0.0429 bp** (2497 changes).\n- `effect_estimate`, `effect_ci_low`, `effect_ci_high`, `model_metric`, `n_analysed`: **null**, because they are not estimable.\n- Table 1 (`out/table1.csv`) gives yearly counts, mean yields and mean changes for 2010–2019. The yearly mean yield ranges from 0.73% (2016) to 2.48% (2010).\n- Figure 1 (`out/figure1.png`) plots the yield over the sample. A dashed line marks 2020-03-04, which falls after the last observation.\n\n## Uncertainty\nThere is no effect estimate, so there is no interval. The baseline mean is descriptive only and has no causal meaning; no inference was done on it.\n\n## Limitations and open issues\n- **What invalidates the analysis as specified:** the input does not cover the event. Any number reported as \"the effect\" would be invented. To answer the question, the series must be re-extracted to cover at least about Aug 2019 to the end of the post-cut window (e.g. `end_date` in 2020 or later). The `post_cut` flag would then need to be checked again.\n- **Not checked:** whether the published series has been revised; whether the post-cut window was meant to be closed (the flag stays 1 indefinitely, so a long post-window would mix the March 2020 cuts with later events).\n- **Threats even with correct data:** (i) the 2020-03-04 cut came in the middle of the COVID market shock, and other events (the 2020-03-13 and 2020-03-27 cuts, the start of GoC bond purchases, global yield moves) confound a simple pre/post difference in mean changes; (ii) daily yield changes in March 2020 had heavy tails and volatility clustering, so a Welch interval that assumes independent observations is likely too narrow; (iii) the result depends on how long the post window is; (iv) a difference in means of *changes* measures drift, not the jump on the announcement day. An event-study design with a narrow window would be the better tool.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The provided file covers 2010-01-04 to 2019-12-31 and contains zero post-cut (post_cut = 1) trading days.\",\"results_keys\":[\"sample_start\",\"sample_end\",\"n_treated\"],\"status\":\"supported\",\"caveat\":\"Coverage is set by the README retrieval parameters (end_date=2019-12-31).\"},\n {\"id\":\"C2\",\"text\":\"The effect of the March-2020 cut on the 5-year yield cannot be estimated from these data; the estimand, its interval and the model RMSE are null and the conclusion is premise_invalid.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"model_metric\",\"conclusion\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"No proxy window was substituted.\"},\n {\"id\":\"C3\",\"text\":\"The mean one-day change of the 5-year yield over the whole file is about -0.043 bp.\",\"results_keys\":[\"baseline_metric\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Descriptive only; this does not estimate the effect of the cut.\"}\n]\nEOF\npython3 -m json.tool out/claims.json >/dev/null && ls out"}}]}}37{"type":"api-request","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","id":"1697d0b7-0ca6-4bad-9bec-83cf4d519c1f","timestamp":"2026-10-30T04:41:27.211Z","version":"2.1.314","querySource":"sdk","shapeHash":"3a532188ef940b3715ed17495253390377e70f890df3c7f000b63ace3dc62f19","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":"58b93528-86ff-4878-8454-21ea41682110","keep":10,"tail":["f2cb56410a12a80e75ee5acfe95ccb1cad69208acb8f2682eb03b42b8e7a72b7","e0feb86bab4a6341fab722397de246cb821a794ada102ea8fdf5823d10baa557"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9708922291f5382f1aa4cf51143dccad049f24d1a5da09618becbd3643898d74"}38{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f99f06c4-aea2-42e9-a3f6-d40e096007e1","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610"}39{"type":"atis-latch","atis":"","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610"}40{"parentUuid":"f99f06c4-aea2-42e9-a3f6-d40e096007e1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXooYoXg7HCbvVRbhvqz","type":"message","role":"assistant","content":[{"type":"text","text":"I couldn't estimate the effect of the Bank of Canada's March 2020 cut on the 5-year yield, because the data file stops before the cut. `data/gc5y_daily.csv` runs from 2010-01-04 to 2019-12-31, and the README confirms the data was downloaded with an end date of 2019-12-31. Every one of its 2,498 rows has `post_cut = 0`. That means there is no post-cut window, and no first post-cut day to count the 120-day comparison window back from.\n\nSo `results.json` records the conclusion as `premise_invalid` and feasibility as `infeasible`. The effect, its 95% interval, the model RMSE and `n_analysed` are all `null`. I did not swap in a substitute, such as the last 120 days of 2019, because the task forbids it and it wouldn't answer the question.\n\nWhat I could produce:\n- **Data check:** no missing values, no duplicate dates, dates in order, and no row flagged wrongly. Gaps between rows are at most 5 calendar days, which fits weekends and holidays. The file's checksum matches the README, and `data/` is unchanged.\n- **Baseline:** the average one-day change over the whole file is −0.0429 basis points. This is descriptive only and says nothing about the cut.\n- **Table and figure:** `table1.csv` has one row per year for 2010–2019. `figure1.png` plots the yield, with the cut date marked after the last observation.\n- **Analysis script:** `out/analysis.py` regenerates the results, table and figure. It already includes the pre/post comparison with an unequal-variance interval, so it will produce the estimate if run on data that covers 2020.\n- **Report and claims:** `report.md` has the six required sections. Its last section explains what data is needed and the main threats to the analysis even with correct data: other events in March 2020 (two further cuts, government bond purchases, global market moves), heavy-tailed and clustered daily changes, and the post-cut window having no end date. `claims.json` holds three claims, all supported by the numbers.\n\nTo answer the question, the yield series needs to be downloaded again with an end date in 2020 or later.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `figure1.png`\n- `table1.csv`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1991,"cache_read_input_tokens":9701,"output_tokens":764,"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":1991},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXooYYfEb57Af1dqpm7R","requestRef":"1697d0b7-0ca6-4bad-9bec-83cf4d519c1f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b6729ae9-aa6e-4c9d-b659-c069b87e1068","timestamp":"2026-10-30T04:41:34.346Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0135","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b6729ae9-aa6e-4c9d-b659-c069b87e1068","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610"}42{"type":"cost-state","sessionId":"bd1468a1-24ba-4151-aaea-0e50cedbf610","totalCostUSD":0.17609139999999998,"totalAPIDuration":56458,"totalAPIDurationWithoutRetries":56455,"totalToolDuration":2116,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":58725,"startTime":1793335235629,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":5934,"thinkingTokens":122,"cacheReadInputTokens":32077,"cacheCreationInputTokens":10188,"cacheCreationEphemeral5mInputTokens":10188,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.17609139999999998}},"hasUnknownModelCost":false}43