{"record":{"id":"667ea8d80a22b56f","repo":"HKUDS/Vibe-Trading","slug":"no-event-could-be-measured-reasons-reasons","errorCode":null,"errorMessage":"no event could be measured; reasons: {reasons}","messagePattern":"no event could be measured; reasons: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"agent/src/quantlib/eventstudy.py","lineNumber":448,"sourceCode":"\n        outcomes.append(\n            (\n                EventOutcome(\n                    symbol=symbol,\n                    event_date=index[position],\n                    abnormal_returns=pd.Series(abnormal, index=relative_days, name=symbol),\n                    car=car,\n                    car_std_error=car_se,\n                    standardised_car=standardised,\n                    fit=fit,\n                ),\n                est_residuals,\n                abnormal,\n            )\n        )\n\n    if not outcomes:\n        raise ValueError(\n            \"no event could be measured; reasons: \"\n            + \"; \".join(f\"{s}@{d}: {r}\" for s, d, r in dropped)\n        )\n\n    n_events = len(outcomes)\n    event_outcomes = [o[0] for o in outcomes]\n    ar_matrix = np.vstack([o.abnormal_returns.to_numpy() for o in event_outcomes])\n    aar = pd.Series(ar_matrix.mean(axis=0), index=relative_days, name=\"aar\")\n    caar = pd.Series(np.cumsum(aar.to_numpy()), index=relative_days, name=\"caar\")\n\n    cars = np.array([o.car for o in event_outcomes])\n    if n_events > 1:\n        car_sd = float(cars.std(ddof=1))\n        t_stat = float(cars.mean() / (car_sd / np.sqrt(n_events))) if car_sd > 0 else float(\"nan\")\n        t_p = float(2 * student_t.sf(abs(t_stat), df=n_events - 1)) if np.isfinite(t_stat) else float(\"nan\")\n    else:\n        t_stat, t_p = float(\"nan\"), float(\"nan\")\n","sourceCodeStart":430,"sourceCodeEnd":466,"githubUrl":"https://github.com/HKUDS/Vibe-Trading/blob/80ffdda44c5c4db0dd84d70e051cca591cea67df/agent/src/quantlib/eventstudy.py#L430-L466","documentation":"After per-event processing, if every event was dropped (window falls outside the data, estimation window has too few observations, etc.), event_study has no outcomes to aggregate and raises this error, enumerating each dropped event with its reason.","triggerScenarios":"Event dates near the start of the sample such that the estimation window (event-relative days minus gap and window length) predates the first observation, or NaNs wiping out an estimation window for every event.","commonSituations":"Events at the very beginning of a backtest, a returns frame that starts later than expected after data-cleaning, or timezone shifts moving event dates outside the index.","solutions":["Read the per-event reasons in the message: symbol@date: reason tells you exactly which constraint failed.","Trim events whose date is earlier than estimation_window + estimation_gap + |start| periods into the sample, or extend the returns history backwards."],"exampleFix":"# before\nevents = {\"AAPL\": [\"2010-01-05\"]}  # too early for the sample\n# after\nmin_days = estimation_window + estimation_gap + abs(event_window[0])\nevents = {s: [d for d in ds if returns.index.get_loc(d) >= min_days] for s, ds in events.items()}","handlingStrategy":"try-catch","validationCode":"min_days = estimation_window + estimation_gap + abs(event_window[0])\nevents = {s: [d for d in ds if returns.index.get_loc(d) >= min_days] for s, ds in events.items()}","typeGuard":null,"tryCatchPattern":"try:\n    res = event_study(...)\nexcept ValueError as e:\n    if \"no event could be measured\" in str(e):\n        logger.warning(\"all events dropped: %s\", e)\n    else:\n        raise","preventionTips":["Trim events too close to the sample start.","Extend returns history backwards or shift the study window."],"tags":["event-study","no-valid-events"],"backgroundTag":"insufficient-data-validation","analyzedSha":"80ffdda44c5c4db0dd84d70e051cca591cea67df","analyzedAt":"2026-08-28T12:46:38.989Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}