Source code for pandas_ta.volatility.massi

# -*- coding: utf-8 -*-
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, non_zero_range, verify_series

[docs]def massi(high, low, fast=None, slow=None, offset=None, **kwargs): """Indicator: Mass Index (MASSI)""" # Validate arguments fast = int(fast) if fast and fast > 0 else 9 slow = int(slow) if slow and slow > 0 else 25 if slow < fast: fast, slow = slow, fast _length = max(fast, slow) high = verify_series(high, _length) low = verify_series(low, _length) offset = get_offset(offset) if "length" in kwargs: kwargs.pop("length") if high is None or low is None: return # Calculate Result high_low_range = non_zero_range(high, low) hl_ema1 = ema(close=high_low_range, length=fast, **kwargs) hl_ema2 = ema(close=hl_ema1, length=fast, **kwargs) hl_ratio = hl_ema1 / hl_ema2 massi = hl_ratio.rolling(slow, min_periods=slow).sum() # Offset if offset != 0: massi = massi.shift(offset) # Handle fills if "fillna" in kwargs: massi.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: massi.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"MASSI_{fast}_{slow}" massi.category = "volatility" return massi
massi.__doc__ = \ """Mass Index (MASSI) The Mass Index is a non-directional volatility indicator that utilitizes the High-Low Range to identify trend reversals based on range expansions. Sources: mi = sum(ema(high - low, 9) / ema(ema(high - low, 9), 9), length) Calculation: Default Inputs: fast: 9, slow: 25 EMA = Exponential Moving Average hl = high - low hl_ema1 = EMA(hl, fast) hl_ema2 = EMA(hl_ema1, fast) hl_ratio = hl_ema1 / hl_ema2 MASSI = SUM(hl_ratio, slow) Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's fast (int): The short period. Default: 9 slow (int): The long period. Default: 25 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. """