Source code for pandas_ta.statistics.entropy

# -*- coding: utf-8 -*-
from numpy import log as npLog
from pandas_ta.utils import get_offset, verify_series

[docs]def entropy(close, length=None, base=None, offset=None, **kwargs): """Indicator: Entropy (ENTP)""" # Validate Arguments length = int(length) if length and length > 0 else 10 base = float(base) if base and base > 0 else 2.0 close = verify_series(close, length) offset = get_offset(offset) if close is None: return # Calculate Result p = close / close.rolling(length).sum() entropy = (-p * npLog(p) / npLog(base)).rolling(length).sum() # Offset if offset != 0: entropy = entropy.shift(offset) # Handle fills if "fillna" in kwargs: entropy.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: entropy.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"ENTP_{length}" entropy.category = "statistics" return entropy
entropy.__doc__ = \ """Entropy (ENTP) Introduced by Claude Shannon in 1948, entropy measures the unpredictability of the data, or equivalently, of its average information. A die has higher entropy (p=1/6) versus a coin (p=1/2). Sources: Calculation: Default Inputs: length=10, base=2 P = close / SUM(close, length) E = SUM(-P * npLog(P) / npLog(base), length) Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 base (float): Logarithmic Base. Default: 2 offset (int): How many periods to offset the result. Default: 0 Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: New feature generated. """