mirror of
https://github.com/OpenBB-finance/OpenBB.git
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6426b76771
* decorated functions and removed assertion tests that are now caught by decorator logs * modified functions with @property * fixed bc of coingecko * fixed outedated tests
127 lines
2.6 KiB
Python
127 lines
2.6 KiB
Python
"""Volatility Technical Indicators"""
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__docformat__ = "numpy"
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import logging
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import pandas as pd
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import pandas_ta as ta
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from gamestonk_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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MAMODES = ["ema", "sma", "wma", "hma", "zlma"]
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@log_start_end(log=logger)
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def bbands(
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close_values: pd.Series, length: int = 15, n_std: float = 2, mamode: str = "ema"
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) -> pd.DataFrame:
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"""Calculate Bollinger Bands
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Parameters
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----------
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close_values : pd.DataFrame
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DataFrame of sclose prices
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length : 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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Returns
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-------
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df_ta: pd.DataFrame
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Dataframe of bollinger band data
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"""
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return pd.DataFrame(
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ta.bbands(
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close=close_values,
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length=length,
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std=n_std,
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mamode=mamode,
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)
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).dropna()
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@log_start_end(log=logger)
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def donchian(
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high_prices: pd.Series,
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low_prices: pd.Series,
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upper_length: int = 20,
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lower_length: int = 20,
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) -> pd.DataFrame:
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"""Calculate Donchian Channels
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Parameters
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----------
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high_prices : pd.DataFrame
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High prices
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low_prices : pd.DataFrame
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Low prices
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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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Returns
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-------
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pd.DataFrame
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Dataframe of upper and lower channels
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"""
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return pd.DataFrame(
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ta.donchian(
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high=high_prices,
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low=low_prices,
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upper_length=upper_length,
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lower_length=lower_length,
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).dropna()
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)
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@log_start_end(log=logger)
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def kc(
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high_prices: pd.Series,
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low_prices: pd.Series,
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close_prices: pd.Series,
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length: 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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) -> pd.DataFrame:
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"""Keltner Channels
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Parameters
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----------
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high_values : pd.DataFrame
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High prices
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low_values : pd.DataFrame
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Low prices
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close_values : pd.DataFrame
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Close prices
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length : int
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Length of window
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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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Returns
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-------
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df_en : pd.DataFrame
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Dataframe of rolling kc
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"""
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# Daily
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return pd.DataFrame(
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ta.kc(
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high=high_prices,
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low=low_prices,
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close=close_prices,
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length=length,
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scalar=scalar,
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mamode=mamode,
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offset=offset,
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).dropna()
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)
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