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fmean module

Module with FMEAN.

FMEAN class

FMEAN(
    wrapper,
    input_list,
    input_mapper,
    in_output_list,
    output_list,
    param_list,
    mapper_list,
    short_name,
    **kwargs
)

Look-ahead indicator based on future_mean_nb.

Superclasses

Inherited members

Subclasses

  • vectorbtpro.labels.generators.fmean._FMEAN

apply_func method

FMEAN.apply_func(
    close,
    window=14,
    wtype=0,
    wait=1,
    minp=None,
    adjust=False
)

Apply function.

cache_func NoneType

Cache function.

close class property

Input array.

close_above method

FMEAN.close_above(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where close is above other.

See combine_objs.

close_below method

FMEAN.close_below(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where close is below other.

See combine_objs.

close_crossed_above method

FMEAN.close_crossed_above(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where close is crossed_above other.

See combine_objs.

close_crossed_below method

FMEAN.close_crossed_below(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where close is crossed_below other.

See combine_objs.

close_equal method

FMEAN.close_equal(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where close is equal other.

See combine_objs.

close_stats method

FMEAN.close_stats(
    *args,
    **kwargs
)

Stats of close as generic.

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,
    param_index=None,
    final_index=None,
    single_comb=False,
    execute_kwargs=None,
    **_kwargs
)

Custom function.

fmean class property

Output array.

fmean_above method

FMEAN.fmean_above(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where fmean is above other.

See combine_objs.

fmean_below method

FMEAN.fmean_below(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where fmean is below other.

See combine_objs.

fmean_crossed_above method

FMEAN.fmean_crossed_above(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where fmean is crossed_above other.

See combine_objs.

fmean_crossed_below method

FMEAN.fmean_crossed_below(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where fmean is crossed_below other.

See combine_objs.

fmean_equal method

FMEAN.fmean_equal(
    other,
    level_name=None,
    allow_multiple=True,
    **kwargs
)

Return True for each element where fmean is equal other.

See combine_objs.

fmean_stats method

FMEAN.fmean_stats(
    *args,
    **kwargs
)

Stats of fmean as generic.

param_select_func_nb method

FMEAN.param_select_func_nb(
    i,
    args_before,
    close,
    window,
    wtype,
    wait,
    *args
)

Parameter selection function.

plot method

_FMEAN.plot(
    column=None,
    plot_close=True,
    close_trace_kwargs=None,
    fmean_trace_kwargs=None,
    add_trace_kwargs=None,
    fig=None,
    **layout_kwargs
)

Plot FMEAN.fmean against FMEAN.close.

Args

column : str
Name of the column to plot.
plot_close : bool
Whether to plot FMEAN.close.
close_trace_kwargs : dict
Keyword arguments passed to plotly.graph_objects.Scatter for FMEAN.close.
fmean_trace_kwargs : dict
Keyword arguments passed to plotly.graph_objects.Scatter for FMEAN.fmean.
add_trace_kwargs : dict
Keyword arguments passed to fig.add_trace when adding each trace.
fig : Figure or FigureWidget
Figure to add traces to.
**layout_kwargs
Keyword arguments passed to fig.update_layout.

Usage

>>> vbt.FMEAN.run(ohlcv['Close']).plot().show()

run class method

FMEAN.run(
    close,
    window=Default(value=14),
    wtype=Default(value='simple'),
    wait=Default(value=1),
    short_name='fmean',
    hide_params=None,
    hide_default=True,
    **kwargs
)

Run FMEAN indicator.

  • Inputs: close
  • Parameters: window, wtype, wait
  • Outputs: fmean

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

FMEAN.run_combs(
    close,
    window=Default(value=14),
    wtype=Default(value='simple'),
    wait=Default(value=1),
    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 FMEAN indicators using function comb_func.

  • Inputs: close
  • Parameters: window, wtype, wait
  • Outputs: fmean

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 FMEAN.run.

Note

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

wait_list class property

List of wait values.

window_list class property

List of window values.

wtype_list class property

List of wtype values.