Skip to content
New: Star-import, signal unraveling, Python 3.12 support, and more

#
pivotlb module

Module with PIVOTLB.

## PIVOTLB class

    PIVOTLB(
        wrapper,
        input_list,
        input_mapper,
        in_output_list,
        output_list,
        param_list,
        mapper_list,
        short_name,
        level_names
    )

Label generator based on pivots_nb().

Superclasses

- Analyzable
- AttrResolverMixin
- Cacheable
- Chainable
- Comparable
- Configured
- ExtPandasIndexer
- HasSettings
- IndexingBase
- IndicatorBase
- PandasIndexer
- Pickleable
- PlotsBuilderMixin
- Prettified
- StatsBuilderMixin
- Wrapping
- `vectorbtpro.labels.generators.pivotlb.ParamIndexer`

Inherited members

- AttrResolverMixin.deep_getattr()
- AttrResolverMixin.post_resolve_attr()
- AttrResolverMixin.pre_resolve_attr()
- AttrResolverMixin.resolve_attr()
- AttrResolverMixin.resolve_shortcut_attr()
- Cacheable.get_ca_setup()
- Chainable.pipe()
- Configured.copy()
- Configured.equals()
- Configured.get_writeable_attrs()
- Configured.prettify()
- Configured.replace()
- Configured.resolve_merge_kwargs()
- Configured.update_config()
- HasSettings.get_path_setting()
- HasSettings.get_path_settings()
- HasSettings.get_setting()
- HasSettings.get_settings()
- HasSettings.has_path_setting()
- HasSettings.has_path_settings()
- HasSettings.has_setting()
- HasSettings.has_settings()
- HasSettings.reset_settings()
- HasSettings.resolve_setting()
- HasSettings.resolve_settings_paths()
- HasSettings.set_settings()
- IndexingBase.indexing_setter_func()
- IndicatorBase.cls_dir
- IndicatorBase.column_only_select
- IndicatorBase.column_stack()
- IndicatorBase.config
- IndicatorBase.dropna()
- IndicatorBase.group_select
- IndicatorBase.iloc
- IndicatorBase.in_output_names
- IndicatorBase.indexing_func()
- IndicatorBase.indexing_kwargs
- IndicatorBase.input_names
- IndicatorBase.lazy_output_names
- IndicatorBase.level_names
- IndicatorBase.loc
- IndicatorBase.output_flags
- IndicatorBase.output_names
- IndicatorBase.param_names
- IndicatorBase.plots_defaults
- IndicatorBase.range_only_select
- IndicatorBase.rec_state
- IndicatorBase.row_stack()
- IndicatorBase.run_pipeline()
- IndicatorBase.self_aliases
- IndicatorBase.short_name
- IndicatorBase.stats_defaults
- IndicatorBase.to_dict()
- IndicatorBase.to_frame()
- IndicatorBase.unpack()
- IndicatorBase.wrapper
- IndicatorBase.xloc
- PandasIndexer.xs()
- Pickleable.decode_config()
- Pickleable.decode_config_node()
- Pickleable.dumps()
- Pickleable.encode_config()
- Pickleable.encode_config_node()
- Pickleable.file_exists()
- Pickleable.getsize()
- Pickleable.load()
- Pickleable.loads()
- Pickleable.modify_state()
- Pickleable.resolve_file_path()
- Pickleable.save()
- PlotsBuilderMixin.build_subplots_doc()
- PlotsBuilderMixin.override_subplots_doc()
- PlotsBuilderMixin.plots()
- StatsBuilderMixin.build_metrics_doc()
- StatsBuilderMixin.override_metrics_doc()
- StatsBuilderMixin.stats()
- Wrapping.regroup()
- Wrapping.resample()
- Wrapping.resolve_column_stack_kwargs()
- Wrapping.resolve_row_stack_kwargs()
- Wrapping.resolve_self()
- Wrapping.resolve_stack_kwargs()
- Wrapping.select_col()
- Wrapping.select_col_from_obj()
- Wrapping.split()

Subclasses

- `vectorbtpro.labels.generators.pivotlb._PIVOTLB`

### apply_func method

    PIVOTLB.apply_func(
        high,
        low,
        up_th,
        down_th
    )

2-dim version of `pivots_1d_nb`.

### cache_func class variable

### custom_func method

    IndicatorFactory.with_apply_func.<locals>.custom_func(
        input_tuple,
        in_output_tuple,
        param_tuple,
        *_args,
        input_shape=None,
        per_column=False,
        split_columns=False,
        skipna=False,
        return_cache=False,
        use_cache=True,
        jitted_loop=False,
        jitted_warmup=False,
        execute_kwargs=None,
        **_kwargs
    )

Custom function that forwards inputs and parameters to `apply_func`.

### down_th_list property

List of `down_th` values.

### high property

Input array.

### high_above method

    PIVOTLB.high_above(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `high` is above `other`.

See combine_objs().

### high_below method

    PIVOTLB.high_below(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `high` is below `other`.

See combine_objs().

### high_crossed_above method

    PIVOTLB.high_crossed_above(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `high` is crossed_above `other`.

See combine_objs().

### high_crossed_below method

    PIVOTLB.high_crossed_below(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `high` is crossed_below `other`.

See combine_objs().

### high_equal method

    PIVOTLB.high_equal(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `high` is equal `other`.

See combine_objs().

### high_stats method

    PIVOTLB.high_stats(
        *args,
        **kwargs
    )

Stats of `high` as generic.

### labels property

Output array.

### labels_above method

    PIVOTLB.labels_above(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `labels` is above `other`.

See combine_objs().

### labels_below method

    PIVOTLB.labels_below(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `labels` is below `other`.

See combine_objs().

### labels_crossed_above method

    PIVOTLB.labels_crossed_above(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `labels` is crossed_above `other`.

See combine_objs().

### labels_crossed_below method

    PIVOTLB.labels_crossed_below(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `labels` is crossed_below `other`.

See combine_objs().

### labels_equal method

    PIVOTLB.labels_equal(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `labels` is equal `other`.

See combine_objs().

### labels_stats method

    PIVOTLB.labels_stats(
        *args,
        **kwargs
    )

Stats of `labels` as generic.

### low property

Input array.

### low_above method

    PIVOTLB.low_above(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `low` is above `other`.

See combine_objs().

### low_below method

    PIVOTLB.low_below(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `low` is below `other`.

See combine_objs().

### low_crossed_above method

    PIVOTLB.low_crossed_above(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `low` is crossed_above `other`.

See combine_objs().

### low_crossed_below method

    PIVOTLB.low_crossed_below(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `low` is crossed_below `other`.

See combine_objs().

### low_equal method

    PIVOTLB.low_equal(
        other,
        level_name=None,
        allow_multiple=True,
        **kwargs
    )

Return True for each element where `low` is equal `other`.

See combine_objs().

### low_stats method

    PIVOTLB.low_stats(
        *args,
        **kwargs
    )

Stats of `low` as generic.

### param_select_func_nb method

    PIVOTLB.param_select_func_nb(
        i,
        args_before,
        high,
        low,
        up_th,
        down_th,
        *args
    )

### plot method

    _PIVOTLB.plot(
        column=None,
        **kwargs
    )

Plot the median of PIVOTLB.high and PIVOTLB.low, and overlay it with the heatmap of PIVOTLB.labels.

`**kwargs` are passed to GenericAccessor.overlay_with_heatmap().

Usage

    >>> vbt.PIVOTLB.run(ohlcv['High'], ohlcv['Low'], up_th=0.2, down_th=0.2).plot().show()

### run class method

    PIVOTLB.run(
        high,
        low,
        up_th,
        down_th,
        short_name='pivotlb',
        hide_params=None,
        hide_default=True,
        **kwargs
    )

Run PIVOTLB indicator.

- Inputs: `high`, `low`
- Parameters: `up_th`, `down_th`
- Outputs: `labels`

Pass a list of parameter names as `hide_params` to hide their column levels, or True to hide all. Set `hide_default` to False to show the column levels of the parameters with a default value.

Other keyword arguments are passed to IndicatorBase.run_pipeline().

### run_combs class method

    PIVOTLB.run_combs(
        high,
        low,
        up_th,
        down_th,
        r=2,
        param_product=False,
        comb_func=itertools.combinations,
        run_unique=True,
        short_names=None,
        hide_params=None,
        hide_default=True,
        **kwargs
    )

Create a combination of multiple PIVOTLB indicators using function `comb_func`.

- Inputs: `high`, `low`
- Parameters: `up_th`, `down_th`
- Outputs: `labels`

`comb_func` must accept an iterable of parameter tuples and `r`. Also accepts all combinatoric iterators from itertools such as `itertools.combinations`. Pass `r` to specify how many indicators to run. Pass `short_names` to specify the short name for each indicator. Set `run_unique` to True to first compute raw outputs for all parameters, and then use them to build each indicator (faster).

Other keyword arguments are passed to PIVOTLB.run().

Note

This method should only be used when multiple indicators are needed. To test multiple parameters, pass them as lists to PIVOTLB.run().

### up_th_list property

List of `up_th` values.

#### Cookie consent

We use cookies to recognize your repeated visits and preferences, as well as to measure the effectiveness of our documentation and whether users find what they're searching for. With your consent, you're helping us to make our documentation better.

- Google Analytics
- GitHub

Manage settings