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# rolling module

Generic Numba-compiled functions for rolling and expanding windows.

## alpha_from_com_nb function

    alpha_from_com_nb(
        com
    )

Get the smoothing factor `alpha` from a center of mass.

## alpha_from_halflife_nb function

    alpha_from_halflife_nb(
        halflife
    )

Get the smoothing factor `alpha` from a half-life.

## alpha_from_span_nb function

    alpha_from_span_nb(
        span
    )

Get the smoothing factor `alpha` from a span.

## alpha_from_wilder_nb function

    alpha_from_wilder_nb(
        period
    )

Get the smoothing factor `alpha` from a Wilder's period.

## ewm_mean_1d_nb function

    ewm_mean_1d_nb(
        arr,
        span,
        minp=None,
        adjust=False
    )

Compute exponential weighted moving average.

Uses ewm_mean_acc_nb() at each iteration.

Numba equivalent to `pd.Series(arr).ewm(span=span, min_periods=minp, adjust=adjust).mean()`.

Adaptation of `pd._libs.window.aggregations.window_aggregations.ewma` with default arguments.

Note

In contrast to the Pandas implementation, `minp` is effective within `span`.

## ewm_mean_acc_nb function

    ewm_mean_acc_nb(
        in_state
    )

Accumulator of ewm_mean_1d_nb().

Takes a state of type EWMMeanAIS and returns a state of type EWMMeanAOS.

## ewm_mean_nb function

    ewm_mean_nb(
        arr,
        span,
        minp=None,
        adjust=False
    )

2-dim version of ewm_mean_1d_nb().

## ewm_std_1d_nb function

    ewm_std_1d_nb(
        arr,
        span,
        minp=None,
        adjust=False
    )

Compute exponential weighted moving standard deviation.

Uses ewm_std_acc_nb() at each iteration.

Numba equivalent to `pd.Series(arr).ewm(span=span, min_periods=minp).std()`.

Adaptation of `pd._libs.window.aggregations.window_aggregations.ewmcov` with default arguments.

Note

In contrast to the Pandas implementation, `minp` is effective within `span`.

## ewm_std_acc_nb function

    ewm_std_acc_nb(
        in_state
    )

Accumulator of ewm_std_1d_nb().

Takes a state of type EWMStdAIS and returns a state of type EWMStdAOS.

## ewm_std_nb function

    ewm_std_nb(
        arr,
        span,
        minp=None,
        adjust=False
    )

2-dim version of ewm_std_1d_nb().

## expanding_max_1d_nb function

    expanding_max_1d_nb(
        arr,
        minp=1
    )

Compute expanding max.

Numba equivalent to `pd.Series(arr).expanding(min_periods=minp).max()`.

## expanding_max_nb function

    expanding_max_nb(
        arr,
        minp=1
    )

2-dim version of expanding_max_1d_nb().

## expanding_min_1d_nb function

    expanding_min_1d_nb(
        arr,
        minp=1
    )

Compute expanding min.

Numba equivalent to `pd.Series(arr).expanding(min_periods=minp).min()`.

## expanding_min_nb function

    expanding_min_nb(
        arr,
        minp=1
    )

2-dim version of expanding_min_1d_nb().

## ma_1d_nb function

    ma_1d_nb(
        arr,
        window,
        wtype=0,
        minp=None,
        adjust=False
    )

Compute a moving average based on the mode of the type WType.

## ma_nb function

    ma_nb(
        arr,
        window,
        wtype=0,
        minp=None,
        adjust=False
    )

2-dim version of ma_1d_nb().

## msd_1d_nb function

    msd_1d_nb(
        arr,
        window,
        wtype=0,
        minp=None,
        adjust=False,
        ddof=0
    )

Compute a moving standard deviation based on the mode of the type WType.

## msd_nb function

    msd_nb(
        arr,
        window,
        wtype=0,
        minp=None,
        adjust=False,
        ddof=0
    )

2-dim version of msd_1d_nb().

## rolling_all_1d_nb function

    rolling_all_1d_nb(
        arr,
        window
    )

Compute rolling all.

## rolling_all_nb function

    rolling_all_nb(
        arr,
        window
    )

2-dim version of rolling_all_1d_nb().

## rolling_any_1d_nb function

    rolling_any_1d_nb(
        arr,
        window
    )

Compute rolling any.

## rolling_any_nb function

    rolling_any_nb(
        arr,
        window
    )

2-dim version of rolling_any_1d_nb().

## rolling_argmax_1d_nb function

    rolling_argmax_1d_nb(
        arr,
        window,
        minp=None,
        local=False
    )

Compute rolling max index.

## rolling_argmax_nb function

    rolling_argmax_nb(
        arr,
        window,
        minp=None,
        local=False
    )

2-dim version of rolling_argmax_1d_nb().

## rolling_argmin_1d_nb function

    rolling_argmin_1d_nb(
        arr,
        window,
        minp=None,
        local=False
    )

Compute rolling min index.

## rolling_argmin_nb function

    rolling_argmin_nb(
        arr,
        window,
        minp=None,
        local=False
    )

2-dim version of rolling_argmin_1d_nb().

## rolling_corr_1d_nb function

    rolling_corr_1d_nb(
        arr1,
        arr2,
        window,
        minp=None
    )

Compute rolling correlation coefficient.

Numba equivalent to `pd.Series(arr1).rolling(window, min_periods=minp).corr(arr2)`.

## rolling_corr_acc_nb function

    rolling_corr_acc_nb(
        in_state
    )

Accumulator of rolling_corr_1d_nb().

Takes a state of type RollCorrAIS and returns a state of type RollCorrAOS.

## rolling_corr_nb function

    rolling_corr_nb(
        arr1,
        arr2,
        window,
        minp=None
    )

2-dim version of rolling_corr_1d_nb().

## rolling_cov_1d_nb function

    rolling_cov_1d_nb(
        arr1,
        arr2,
        window,
        minp=None,
        ddof=0
    )

Compute rolling covariance.

Numba equivalent to `pd.Series(arr1).rolling(window, min_periods=minp).cov(arr2)`.

## rolling_cov_acc_nb function

    rolling_cov_acc_nb(
        in_state
    )

Accumulator of rolling_cov_1d_nb().

Takes a state of type RollCovAIS and returns a state of type RollCovAOS.

## rolling_cov_nb function

    rolling_cov_nb(
        arr1,
        arr2,
        window,
        minp=None,
        ddof=0
    )

2-dim version of rolling_cov_1d_nb().

## rolling_max_1d_nb function

    rolling_max_1d_nb(
        arr,
        window,
        minp=None
    )

Compute rolling max.

Numba equivalent to `pd.Series(arr).rolling(window, min_periods=minp).max()`.

## rolling_max_nb function

    rolling_max_nb(
        arr,
        window,
        minp=None
    )

2-dim version of rolling_max_1d_nb().

## rolling_mean_1d_nb function

    rolling_mean_1d_nb(
        arr,
        window,
        minp=None
    )

Compute rolling mean.

Uses rolling_mean_acc_nb() at each iteration.

Numba equivalent to `pd.Series(arr).rolling(window, min_periods=minp).mean()`.

## rolling_mean_acc_nb function

    rolling_mean_acc_nb(
        in_state
    )

Accumulator of rolling_mean_1d_nb().

Takes a state of type RollMeanAIS and returns a state of type RollMeanAOS.

## rolling_mean_nb function

    rolling_mean_nb(
        arr,
        window,
        minp=None
    )

2-dim version of rolling_mean_1d_nb().

## rolling_min_1d_nb function

    rolling_min_1d_nb(
        arr,
        window,
        minp=None
    )

Compute rolling min.

Numba equivalent to `pd.Series(arr).rolling(window, min_periods=minp).min()`.

## rolling_min_nb function

    rolling_min_nb(
        arr,
        window,
        minp=None
    )

2-dim version of rolling_min_1d_nb().

## rolling_ols_1d_nb function

    rolling_ols_1d_nb(
        arr1,
        arr2,
        window,
        minp=None
    )

Compute rolling linear regression.

## rolling_ols_acc_nb function

    rolling_ols_acc_nb(
        in_state
    )

Accumulator of rolling_ols_1d_nb().

Takes a state of type RollOLSAIS and returns a state of type RollOLSAOS.

## rolling_ols_nb function

    rolling_ols_nb(
        arr1,
        arr2,
        window,
        minp=None
    )

2-dim version of rolling_ols_1d_nb().

## rolling_pattern_similarity_1d_nb function

    rolling_pattern_similarity_1d_nb(
        arr,
        pattern,
        window=None,
        max_window=None,
        row_select_prob=1.0,
        window_select_prob=1.0,
        interp_mode=3,
        rescale_mode=0,
        vmin=nan,
        vmax=nan,
        pmin=nan,
        pmax=nan,
        invert=False,
        error_type=0,
        distance_measure=0,
        max_error=nan,
        max_error_interp_mode=None,
        max_error_as_maxdist=False,
        max_error_strict=False,
        min_pct_change=nan,
        max_pct_change=nan,
        min_similarity=0.85,
        minp=None
    )

Compute rolling pattern similarity.

Uses pattern_similarity_nb().

## rolling_pattern_similarity_nb function

    rolling_pattern_similarity_nb(
        arr,
        pattern,
        window=None,
        max_window=None,
        row_select_prob=1.0,
        window_select_prob=1.0,
        interp_mode=3,
        rescale_mode=0,
        vmin=nan,
        vmax=nan,
        pmin=nan,
        pmax=nan,
        invert=False,
        error_type=0,
        distance_measure=0,
        max_error=nan,
        max_error_interp_mode=None,
        max_error_as_maxdist=False,
        max_error_strict=False,
        min_pct_change=nan,
        max_pct_change=nan,
        min_similarity=0.85,
        minp=None
    )

2-dim version of rolling_pattern_similarity_1d_nb().

## rolling_prod_1d_nb function

    rolling_prod_1d_nb(
        arr,
        window,
        minp=None
    )

Compute rolling product.

Uses rolling_prod_acc_nb() at each iteration.

Numba equivalent to `pd.Series(arr).rolling(window, min_periods=minp).apply(np.prod)`.

## rolling_prod_acc_nb function

    rolling_prod_acc_nb(
        in_state
    )

Accumulator of rolling_prod_1d_nb().

Takes a state of type RollProdAIS and returns a state of type RollProdAOS.

## rolling_prod_nb function

    rolling_prod_nb(
        arr,
        window,
        minp=None
    )

2-dim version of rolling_prod_1d_nb().

## rolling_rank_1d_nb function

    rolling_rank_1d_nb(
        arr,
        window,
        minp=None,
        pct=False
    )

Rolling version of `rank_1d_nb`.

## rolling_rank_nb function

    rolling_rank_nb(
        arr,
        window,
        minp=None,
        pct=False
    )

2-dim version of rolling_rank_1d_nb().

## rolling_std_1d_nb function

    rolling_std_1d_nb(
        arr,
        window,
        minp=None,
        ddof=0
    )

Compute rolling standard deviation.

Uses rolling_std_acc_nb() at each iteration.

Numba equivalent to `pd.Series(arr).rolling(window, min_periods=minp).std(ddof=ddof)`.

## rolling_std_acc_nb function

    rolling_std_acc_nb(
        in_state
    )

Accumulator of rolling_std_1d_nb().

Takes a state of type RollStdAIS and returns a state of type RollStdAOS.

## rolling_std_nb function

    rolling_std_nb(
        arr,
        window,
        minp=None,
        ddof=0
    )

2-dim version of rolling_std_1d_nb().

## rolling_sum_1d_nb function

    rolling_sum_1d_nb(
        arr,
        window,
        minp=None
    )

Compute rolling sum.

Uses rolling_sum_acc_nb() at each iteration.

Numba equivalent to `pd.Series(arr).rolling(window, min_periods=minp).sum()`.

## rolling_sum_acc_nb function

    rolling_sum_acc_nb(
        in_state
    )

Accumulator of rolling_sum_1d_nb().

Takes a state of type RollSumAIS and returns a state of type RollSumAOS.

## rolling_sum_nb function

    rolling_sum_nb(
        arr,
        window,
        minp=None
    )

2-dim version of rolling_sum_1d_nb().

## rolling_zscore_1d_nb function

    rolling_zscore_1d_nb(
        arr,
        window,
        minp=None,
        ddof=0
    )

Compute rolling z-score.

Uses rolling_zscore_acc_nb() at each iteration.

## rolling_zscore_acc_nb function

    rolling_zscore_acc_nb(
        in_state
    )

Accumulator of rolling_zscore_1d_nb().

Takes a state of type RollZScoreAIS and returns a state of type RollZScoreAOS.

## rolling_zscore_nb function

    rolling_zscore_nb(
        arr,
        window,
        minp=None,
        ddof=0
    )

2-dim version of rolling_zscore_1d_nb().

## vidya_1d_nb function

    vidya_1d_nb(
        arr,
        window,
        minp=None
    )

Compute VIDYA.

Uses vidya_acc_nb() at each iteration.

## vidya_acc_nb function

    vidya_acc_nb(
        in_state
    )

Accumulator of vidya_1d_nb().

Takes a state of type VidyaAIS and returns a state of type VidyaAOS.

## vidya_nb function

    vidya_nb(
        arr,
        window,
        minp=None
    )

2-dim version of vidya_1d_nb().

## wm_mean_1d_nb function

    wm_mean_1d_nb(
        arr,
        window,
        minp=None
    )

Compute weighted moving average.

Uses wm_mean_acc_nb() at each iteration.

## wm_mean_acc_nb function

    wm_mean_acc_nb(
        in_state
    )

Accumulator of wm_mean_1d_nb().

Takes a state of type WMMeanAIS and returns a state of type WMMeanAOS.

## wm_mean_nb function

    wm_mean_nb(
        arr,
        window,
        minp=None
    )

2-dim version of wm_mean_1d_nb().

## wwm_mean_1d_nb function

    wwm_mean_1d_nb(
        arr,
        period,
        minp=None,
        adjust=False
    )

Compute Wilder's exponential weighted moving average.

## wwm_mean_nb function

    wwm_mean_nb(
        arr,
        period,
        minp=None,
        adjust=False
    )

2-dim version of wwm_mean_1d_nb().

## wwm_std_1d_nb function

    wwm_std_1d_nb(
        arr,
        period,
        minp=None,
        adjust=False
    )

Compute Wilder's exponential weighted moving standard deviation.

## wwm_std_nb function

    wwm_std_nb(
        arr,
        period,
        minp=None,
        adjust=False
    )

2-dim version of wwm_std_1d_nb().

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