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* hugo server * Update test.yml * Update test.yml * Create gh-pages.yml * Update gh-pages.yml * Update config.toml * Update config.toml * update themes and public * Update gh-pages.yml * Update gh-pages.yml * Update gh-pages.yml * remove themes * add themes * Update gh-pages.yml * Update config.toml * Update config.toml * remove * re-add theme * Update config.toml * Update config.toml * Update config.toml * Update gh-pages.yml * Update gh-pages.yml * Update config.toml * Update config.toml * Update config.toml * Update _index.md * Update config.toml * Update gh-pages.yml * Update README.md * Update README.md * add Keltner Channels to TA menu * add ```if other_args: to call functions Co-authored-by: didier <dro.lopes@campus.fct.unl.pt>
139 lines
2.9 KiB
Python
139 lines
2.9 KiB
Python
"""Volatility Technical Indicators"""
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__docformat__ = "numpy"
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import pandas as pd
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import pandas_ta as ta
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def bbands(
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s_interval: str, df_stock: pd.DataFrame, length: int, n_std: float, mamode: str
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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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s_interval : str
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Interval of stock data
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df_stock : pd.DataFrame
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DataFrame of stock data
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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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# Daily
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if s_interval == "1440min":
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df_ta = ta.bbands(
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close=df_stock["Adj Close"],
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length=length,
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std=n_std,
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mamode=mamode,
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).dropna()
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# Intraday
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else:
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df_ta = ta.bbands(
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close=df_stock["Close"],
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length=length,
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std=n_std,
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mamode=mamode,
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).dropna()
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return df_ta
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def donchian(
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df_stock: pd.DataFrame,
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upper_length: int,
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lower_length: int,
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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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s_interval : str
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Interval of stock data
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df_stock : pd.DataFrame
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DataFrame of stock data
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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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df_ta: pd.DataFrame
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Dataframe of upper and lower channels
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"""
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df_ta = ta.donchian(
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high=df_stock["High"],
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low=df_stock["Low"],
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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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return df_ta
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def kc(
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s_interval: str,
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df_stock: pd.DataFrame,
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length: int,
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scalar: float,
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mamode: str,
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offset: int,
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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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s_interval : str
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Interval of stock data
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df_stock : pd.DataFrame
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Dataframe of 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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if s_interval == "1440min":
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df_kc = ta.kc(
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high=df_stock["High"],
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low=df_stock["Low"],
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close=df_stock["Adj Close"],
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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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# Intraday
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else:
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df_kc = ta.kc(
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high=df_stock["High"],
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low=df_stock["Low"],
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close=df_stock["Close"],
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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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return df_kc
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