class_docstr: The plotting method: `df.hvplot(...)` creates a plot similarly to the familiar Pandas
  `df.plot` method.
  
  For more detailed options use a specific plotting method, e.g. `df.hvplot.line`.
  
  Reference: https://hvplot.holoviz.org/reference/index.html
  
  Parameters
  ----------
  x : string, optional
      Field name(s) to draw x-positions from. If not specified, the index is
      used.
  y : string or list, optional
      Field name(s) to draw y-positions from. If not specified, all numerical
      fields are used.
  kind : string, optional
      The kind of plot to generate, e.g. 'area', 'bar', 'line', 'scatter' etc. To see the
      available plots run `print(df.hvplot.__all__)`.
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('scatter')` or similar
      depending on the kind of plot.
  
  Returns
  -------
  A Holoviews object. You can `print` the object to study its composition and run
  
  .. code-block::
  
      import holoviews as hv
      hv.help(the_holoviews_object)
  
  to learn more about its parameters and options.
  
  Examples
  --------
  
  .. code-block::
  
      import pandas as pd
      import hvplot.pandas
  
      df = pd.DataFrame(
          {
              "actual": [100, 150, 125, 140, 145, 135, 123],
              "forecast": [90, 160, 125, 150, 141, 141, 120],
              "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
              "date": pd.date_range("2022-01-03", "2022-01-09"),
              "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
          },
      )
      line = df.hvplot.line(
          x="numerical",
          y=["actual", "forecast"],
          ylabel="value",
          legend="bottom",
          height=500,
          color=["steelblue", "teal"],
          alpha=0.7,
          line_width=5,
      )
      line
  
  You can can add *markers* to a `line` plot by overlaying with a `scatter` plot.
  
  .. code-block::
  
      markers = df.hvplot.scatter(
          x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], size=50
      )
      line * markers
  
  Please note that you can pass widgets or reactive functions as arguments instead of
  literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html.
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docstr: The `ohlc` plot visualizes the open, high, low and close prices of stocks and other assets.
  
  Reference: https://hvplot.holoviz.org/reference/pandas/ohlc.html
  
  Parameters
  ----------
  x : string, optional
      Field name to draw x coordinates from. If not specified, the index is used. Normally
      refers to date values.
  y : list or tuple, optional
      Field names of the OHLC fields. Default is ["open", "high", "low", "close"]
  line_color : string, optional
      The line color. Default is black
  pos_color : string, optional
      The color indicating a positive change. Default is green.
  neg_color : string, optional
      The color indicating a negative change. Default is red.
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('ohlc')`.
  
  Returns
  -------
  A Holoviews object. You can `print` the object to study its composition and run
  
  .. code-block::
  
      import holoviews as hv
      hv.help(the_holoviews_object)
  
  to learn more about its parameters and options.
  
  Example
  -------
  
  .. code-block::
  
      data = pd.DataFrame({
          "open": [100, 101, 102],
          "high": [104, 105, 110],
          "low": [94, 97, 99],
          "close": [101, 99, 103],
      }, index=[pd.Timestamp("2022-08-01"), pd.Timestamp("2022-08-02"), pd.Timestamp("2022-08-03")])
      ohlc = data.hvplot.ohlc(pos_color="#55a194", neg_color="#f16a6f")
      ohlc
  
  References
  ----------
  
  - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/candlestick.html
  - Matplotlib: https://www.statology.org/matplotlib-python-candlestick-chart/
  - Plotly: https://plotly.com/python/ohlc-charts/
  - Wikipedia: https://en.wikipedia.org/wiki/Candlestick_chart
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code: def ohlc(self, x=None, y=None, **kwds):
      """
      The `ohlc` plot visualizes the open, high, low and close prices of stocks and other assets.
  
      Reference: https://hvplot.holoviz.org/reference/pandas/ohlc.html
  
      Parameters
      ----------
      x : string, optional
          Field name to draw x coordinates from. If not specified, the index is used. Normally
          refers to date values.
      y : list or tuple, optional
          Field names of the OHLC fields. Default is ["open", "high", "low", "close"]
      line_color : string, optional
          The line color. Default is black
      pos_color : string, optional
          The color indicating a positive change. Default is green.
      neg_color : string, optional
          The color indicating a negative change. Default is red.
      **kwds : optional
          Additional keywords arguments are documented in `hvplot.help('ohlc')`.
  
      Returns
      -------
      A Holoviews object. You can `print` the object to study its composition and run
  
      .. code-block::
  
          import holoviews as hv
          hv.help(the_holoviews_object)
  
      to learn more about its parameters and options.
  
      Example
      -------
  
      .. code-block::
  
          data = pd.DataFrame({
              "open": [100, 101, 102],
              "high": [104, 105, 110],
              "low": [94, 97, 99],
              "close": [101, 99, 103],
          }, index=[pd.Timestamp("2022-08-01"), pd.Timestamp("2022-08-02"), pd.Timestamp("2022-08-03")])
          ohlc = data.hvplot.ohlc(pos_color="#55a194", neg_color="#f16a6f")
          ohlc
  
      References
      ----------
  
      - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/candlestick.html
      - Matplotlib: https://www.statology.org/matplotlib-python-candlestick-chart/
      - Plotly: https://plotly.com/python/ohlc-charts/
      - Wikipedia: https://en.wikipedia.org/wiki/Candlestick_chart
      """
      return self(kind="ohlc", x=x, y=y, **kwds)
  
imports: import itertools
  from collections import defaultdict
  import param
  from ..converter import HoloViewsConverter
  from ..util import is_list_like, process_dynamic_args
label_desc: Use image node_id 6 for calling the hvPlotTabular obj's underlying member method code with example usage: obj.ohlc(x, y) and returns: self