Files
pandas-ta/pandas_ta/volatility/massi.py
T
2019-05-20 13:25:33 -07:00

76 lines
2.2 KiB
Python

# -*- coding: utf-8 -*-
from ..overlap.ema import ema
from ..utils import get_offset, verify_series
def massi(high, low, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Mass Index (MASSI)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
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
min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else fast
offset = get_offset(offset)
# Calculate Result
hl_range = high - low
hl_ema1 = ema(close=hl_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
massi.name = 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:
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index
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.
"""