{"record":{"id":"139e35b4a2568c6d","repo":"HKUDS/Vibe-Trading","slug":"fit-markov-regime-needs-at-least-50-finite-observa","errorCode":null,"errorMessage":"fit_markov_regime needs at least 50 finite observations, got {series.size}","messagePattern":"fit_markov_regime needs at least 50 finite observations, got (.+?)","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"agent/src/quantlib/timeseries.py","lineNumber":550,"sourceCode":"        ``aic``, ``bic`` (float).\n\n    Raises:\n        ImportError: If ``statsmodels`` is not installed.\n        ValueError: If ``n_regimes`` is below 2, or fewer than 50 finite\n            observations survive -- an EM fit on a shorter series produces\n            regimes that are numerically fine and substantively meaningless.\n    \"\"\"\n    markov = _require(\n        \"statsmodels.tsa.regime_switching.markov_regression\",\n        \"statsmodels\",\n        \"fit_markov_regime\",\n    )\n    if n_regimes < 2:\n        raise ValueError(f\"n_regimes must be at least 2, got {n_regimes}\")\n\n    series = pd.Series(returns, dtype=float).dropna()\n    if series.size < 50:\n        raise ValueError(\n            f\"fit_markov_regime needs at least 50 finite observations, got {series.size}\"\n        )\n\n    # Passed as a Series, not an array: statsmodels only names the fitted\n    # parameters (``const[k]``, ``sigma2[k]``) when the input is pandas, and\n    # positional unpacking of that vector would silently break if the package\n    # ever reorders it.\n    model = markov.MarkovRegression(\n        series * 100,\n        k_regimes=n_regimes,\n        trend=\"c\",\n        switching_variance=switching_variance,\n    )\n    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(io.StringIO()):\n        result = model.fit()\n\n    means = np.array(\n        [float(result.params[f\"const[{k}]\"]) / 100 for k in range(n_regimes)]","sourceCodeStart":532,"sourceCodeEnd":568,"githubUrl":"https://github.com/HKUDS/Vibe-Trading/blob/80ffdda44c5c4db0dd84d70e051cca591cea67df/agent/src/quantlib/timeseries.py#L532-L568","documentation":"fit_markov_regime needs at least 50 finite observations after dropna; Markov-switching MLE is badly identified on short samples and statsmodels would emit spurious regimes or convergence failures, so the library enforces a floor.","triggerScenarios":"Passing fewer than 50 non-NaN returns, series with many NaNs that drop below 50, or returns from a short backtest window.","commonSituations":"Testing on a month of daily data (~21 points), passing a series with NaNs from pct_change without dropping them, or slicing a recent window that is too short.","solutions":["Provide at least 50 (preferably 200+) observations","Use returns.dropna() before passing so the count is what you expect","For short windows, use a simpler model (rolling std, GARCH) instead"],"exampleFix":"# before\nres = fit_markov_regime(df['close'].pct_change().dropna().tail(30))\n# after\nrets = df['close'].pct_change().dropna()\nassert rets.size >= 50\nres = fit_markov_regime(rets)","handlingStrategy":"validation","validationCode":"series = pd.Series(returns, dtype=float).dropna()\nassert series.size >= 50, f'Markov fit needs >= 50 obs, got {series.size}'","typeGuard":"def enough_data_for_markov(returns) -> bool:\n    return pd.Series(returns, dtype=float).dropna().size >= 50","tryCatchPattern":"try:\n    fit_markov_regime(returns)\nexcept ValueError as e:\n    if 'at least 50 finite observations' in str(e):\n        # fall back to rolling volatility or GARCH\n        return rolling_vol(returns)\n    raise","preventionTips":["Dropna returns before passing (pct_change creates a leading NaN)","Use >= 200 observations for stable regime estimates","Prefer rolling-std or GARCH on short windows"],"tags":["python","markov-switching","insufficient-data","timeseries"],"backgroundTag":"insufficient-samples-for-model-fit","analyzedSha":"80ffdda44c5c4db0dd84d70e051cca591cea67df","analyzedAt":"2026-08-28T12:46:38.989Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}