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13283fbfce
* BIGGGG LINTING * fixing lints * fixing lints * black * very ruff * no export * fix hedge_view again * lints * platform lints * lints * black * black it @hjoaquim * fix some more linting --------- Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt>
350 lines
9.8 KiB
Python
350 lines
9.8 KiB
Python
"""Volatility Technical Indicators View"""
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__docformat__ = "numpy"
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import logging
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import os
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from typing import Optional, Union
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import pandas as pd
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from openbb_terminal import OpenBBFigure
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from openbb_terminal.common.technical_analysis import ta_helpers, volatility_model
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from openbb_terminal.core.plots.plotly_ta.ta_class import PlotlyTA
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import (
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export_data,
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)
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logger = logging.getLogger(__name__)
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# pylint: disable=too-many-arguments
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@log_start_end(log=logger)
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def display_bbands(
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data: pd.DataFrame,
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symbol: str = "",
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window: int = 15,
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n_std: float = 2,
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mamode: str = "sma",
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export: str = "",
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sheet_name: Optional[str] = None,
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external_axes: bool = False,
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) -> Union[OpenBBFigure, None]:
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"""Plots bollinger bands
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Parameters
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----------
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data : pd.DataFrame
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Dataframe of ohlc prices
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symbol : str
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Ticker symbol
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window : int
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Length of window to calculate BB
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n_std : float
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Number of standard deviations to show
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mamode : str
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Method of calculating average
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export : str
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Format of export file
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external_axes : bool, optional
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Whether to return the figure object or not, by default False
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"""
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ta = PlotlyTA()
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fig = ta.plot(
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data,
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dict(bbands=dict(length=window, std=n_std, mamode=mamode)),
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f"{symbol.upper()} Bollinger Bands",
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False,
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volume=False,
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)
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if ta_helpers.check_columns(data, high=False, low=False) is None:
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return None
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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"bbands",
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ta.df_ta,
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sheet_name,
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fig,
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)
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return fig.show(external=external_axes)
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@log_start_end(log=logger)
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def display_donchian(
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data: pd.DataFrame,
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symbol: str = "",
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upper_length: int = 20,
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lower_length: int = 20,
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export: str = "",
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sheet_name: Optional[str] = None,
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external_axes: bool = False,
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) -> Union[OpenBBFigure, None]:
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"""Plots donchian channels
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Parameters
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----------
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data : pd.DataFrame
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Dataframe of ohlc prices
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symbol : str
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Ticker symbol
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upper_length : int
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Length of window to calculate upper channel
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lower_length : int
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Length of window to calculate lower channel
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export : str
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Format of export file
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external_axes : bool, optional
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Whether to return the figure object or not, by default False
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"""
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ta = PlotlyTA()
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fig = ta.plot(
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data,
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dict(donchian=dict(upper_length=upper_length, lower_length=lower_length)),
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f"{symbol.upper()} Donchian Channels",
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False,
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volume=False,
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)
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if ta_helpers.check_columns(data) is None:
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return None
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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"donchian",
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ta.df_ta,
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sheet_name,
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fig,
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)
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return fig.show(external=external_axes)
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@log_start_end(log=logger)
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def view_kc(
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data: pd.DataFrame,
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window: int = 20,
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scalar: float = 2,
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mamode: str = "ema",
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offset: int = 0,
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symbol: str = "",
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export: str = "",
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sheet_name: Optional[str] = None,
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external_axes: bool = False,
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) -> Union[OpenBBFigure, None]:
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"""Plots Keltner Channels Indicator
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Parameters
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----------
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data: pd.DataFrame
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Dataframe of ohlc prices
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window: int
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Length of window
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scalar: float
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Scalar value
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mamode: str
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Type of filter
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offset: int
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Offset value
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symbol: str
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Ticker symbol
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sheet_name: str
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Optionally specify the name of the sheet the data is exported to.
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export: str
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Format to export data
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external_axes : bool, optional
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Whether to return the figure object or not, by default False
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"""
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ta = PlotlyTA()
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fig = ta.plot(
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data,
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dict(kc=dict(length=window, scalar=scalar, mamode=mamode, offset=offset)),
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f"{symbol.upper()} Keltner Channels",
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False,
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volume=False,
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)
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if ta_helpers.check_columns(data) is None:
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return None
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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"kc",
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ta.df_ta,
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sheet_name,
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fig,
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)
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return fig.show(external=external_axes)
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@log_start_end(log=logger)
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def display_atr(
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data: pd.DataFrame,
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symbol: str = "",
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window: int = 14,
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mamode: str = "sma",
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offset: int = 0,
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export: str = "",
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sheet_name: Optional[str] = None,
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external_axes: bool = False,
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) -> Union[OpenBBFigure, None]:
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"""Plots ATR
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Parameters
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----------
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data : pd.DataFrame
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Dataframe of ohlc prices
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symbol : str
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Ticker symbol
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window : int
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Length of window to calculate upper channel
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export : str
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Format of export file
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external_axes : bool, optional
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Whether to return the figure object or not, by default False
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"""
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ta = PlotlyTA()
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fig = ta.plot(
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data,
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dict(atr=dict(length=window, mamode=mamode, offset=offset)),
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f"{symbol.upper()} Average True Range",
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False,
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volume=False,
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)
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if ta_helpers.check_columns(data) is None:
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return None
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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"atr",
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ta.df_ta,
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sheet_name,
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fig,
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)
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return fig.show(external=external_axes)
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@log_start_end(log=logger)
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def display_cones(
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data: pd.DataFrame,
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symbol: str = "",
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lower_q: float = 0.25,
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upper_q: float = 0.75,
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model: str = "STD",
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is_crypto: bool = False,
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export: str = "",
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sheet_name: Optional[str] = None,
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external_axes: bool = False,
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) -> Union[OpenBBFigure, None]:
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"""Plots the realized volatility quantiles for the loaded ticker.
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The model used to calculate the volatility is selectable.
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Parameters
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----------
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data: pd.DataFrame
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DataFrame of OHLC prices.
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symbol: str (default = "")
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The ticker symbol.
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lower_q: float (default = 0.25)
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The lower quantile to calculate for.
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upper_q: float (default = 0.75)
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The upper quantile to for.
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is_crypto: bool (default = False)
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If true, volatility is calculated for 365 days instead of 252.
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model: str (default = "STD")
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The model to use for volatility calculation. Choices are:
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["STD", "Parkinson", "Garman-Klass", "Hodges-Tompkins", "Rogers-Satchell", "Yang-Zhang"]
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Standard deviation measures how widely returns are dispersed from the average return.
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It is the most common (and biased) estimator of volatility.
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Parkinson volatility uses the high and low price of the day rather than just close to close prices.
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It is useful for capturing large price movements during the day.
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Garman-Klass volatility extends Parkinson volatility by taking into account the opening and closing price.
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As markets are most active during the opening and closing of a trading session;
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it makes volatility estimation more accurate.
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Hodges-Tompkins volatility is a bias correction for estimation using an overlapping data sample.
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It produces unbiased estimates and a substantial gain in efficiency.
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Rogers-Satchell is an estimator for measuring the volatility with an average return not equal to zero.
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Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term,
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mean return not equal to zero.
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Yang-Zhang volatility is the combination of the overnight (close-to-open volatility).
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It is a weighted average of the Rogers-Satchell volatility and the open-to-close volatility.
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export : str
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Format of export file
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sheet_name: str
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Optionally specify the name of the sheet the data is exported to.
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external_axes : bool, optional
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Whether to return the figure object or not, by default False
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Examples
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--------
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df_ta = openbb.stocks.load('XLY')
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openbb.ta.cones_chart(data = df_ta, symbol = 'XLY')
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df_ta = openbb.stocks.load('XLE')
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openbb.ta.cones_chart(data = df_ta, symbol = "XLE", lower_q = 0.10, upper_q = 0.90)
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openbb.ta.cones_chart(data = df_ta, symbol = "XLE", model = "Garman-Klass")
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"""
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if lower_q > upper_q:
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lower_q, upper_q = upper_q, lower_q
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df_ta = volatility_model.cones(
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data, lower_q=lower_q, upper_q=upper_q, is_crypto=is_crypto, model=model
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)
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lower_q_label = str(int(lower_q * 100))
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upper_q_label = str(int(upper_q * 100))
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if df_ta.empty:
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return None
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fig = OpenBBFigure(xaxis_title="Window of Time (in days)")
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fig.set_title(f"{symbol} - Realized Volatility Cones - {model} Model", x=0.5)
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fig.add_scatter(x=df_ta.index, y=df_ta.Min, name="Min")
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fig.add_scatter(x=df_ta.index, y=df_ta.Max, name="Max")
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fig.add_scatter(x=df_ta.index, y=df_ta.Median, name="Median")
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fig.add_scatter(
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x=df_ta.index,
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y=df_ta["Upper " f"{upper_q_label}" "%"],
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name="Upper " f"{upper_q_label}" "%",
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)
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fig.add_scatter(
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x=df_ta.index,
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y=df_ta["Lower " f"{lower_q_label}" "%"],
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name="Lower " f"{lower_q_label}" "%",
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)
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fig.add_scatter(x=df_ta.index, y=df_ta.Realized, name="Realized")
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fig.update_xaxes(tickmode="array", tickvals=df_ta.index, ticktext=df_ta.index)
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fig.horizontal_legend(x=1, y=1, yanchor="top")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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"cones",
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df_ta,
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sheet_name,
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fig,
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)
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return fig.show(external=external_axes)
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