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b71abcfbf4
* quick econ fix * black * keys and feature flags * terminal name 👀 * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
137 lines
3.1 KiB
Python
137 lines
3.1 KiB
Python
"""Rolling Statistics"""
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__docformat__ = "numpy"
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import logging
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from typing import Tuple
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import pandas as pd
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import pandas_ta as ta
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from openbb_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def get_rolling_avg(df: pd.DataFrame, length: int) -> Tuple[pd.DataFrame, pd.DataFrame]:
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"""Return rolling mean and standard deviation
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Parameters
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----------
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df_stock : pd.DataFrame
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Dataframe of target data
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length : int
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Length of rolling window
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Returns
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-------
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pd.DataFrame :
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Dataframe of rolling mean
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pd.DataFrame :
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Dataframe of rolling standard deviation
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"""
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rolling_mean = df.rolling(length, center=True, min_periods=1).mean()
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rolling_std = df.rolling(length, center=True, min_periods=1).std()
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return pd.DataFrame(rolling_mean), pd.DataFrame(rolling_std)
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@log_start_end(log=logger)
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def get_spread(df: pd.DataFrame, length: int) -> Tuple[pd.DataFrame, pd.DataFrame]:
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"""Standard Deviation and Variance
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Parameters
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----------
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df_stock : pd.DataFrame
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DataFrame of targeted data
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Returns
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-------
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df_sd : pd.DataFrame
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Dataframe of rolling standard deviation
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df_var : pd.DataFrame
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Dataframe of rolling standard deviation
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"""
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df_sd = ta.stdev(
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close=df,
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length=length,
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).dropna()
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df_var = ta.variance(
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close=df,
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length=length,
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).dropna()
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return pd.DataFrame(df_sd), pd.DataFrame(df_var)
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@log_start_end(log=logger)
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def get_quantile(
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df: pd.DataFrame, length: int, quantile_pct: float
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) -> Tuple[pd.DataFrame, pd.DataFrame]:
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"""Overlay Median & Quantile
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Parameters
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----------
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df : pd.DataFrame
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Dataframe of targeted data
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length : int
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Length of window
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quantile : float
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Quantile to display
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Returns
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-------
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df_med : pd.DataFrame
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Dataframe of median prices over window
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df_quantile : pd.DataFrame
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Dataframe of gievn quantile prices over window
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"""
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df_med = ta.median(close=df, length=length).dropna()
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df_quantile = ta.quantile(
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df,
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length=length,
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q=quantile_pct,
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).dropna()
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return pd.DataFrame(df_med), pd.DataFrame(df_quantile)
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@log_start_end(log=logger)
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def get_skew(df: pd.DataFrame, length: int) -> pd.DataFrame:
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"""Skewness Indicator
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Parameters
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----------
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df_stock : pd.DataFrame
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Dataframe of targeted data
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length : int
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Length of window
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Returns
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-------
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df_skew : pd.DataFrame
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Dataframe of rolling skew
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"""
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df_skew = ta.skew(close=df, length=length).dropna()
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return df_skew
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@log_start_end(log=logger)
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def get_kurtosis(df: pd.DataFrame, length: int) -> pd.DataFrame:
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"""Kurtosis Indicator
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Parameters
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----------
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df_stock : pd.DataFrame
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Dataframe of targeted data
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length : int
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Length of window
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Returns
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-------
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df_kurt : pd.DataFrame
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Dataframe of rolling kurtosis
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"""
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df_kurt = ta.kurtosis(close=df, length=length).dropna()
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return df_kurt
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