Files
pandas-ta/pandas_ta/trend/vortex.py
T
2019-05-20 09:08:51 -07:00

91 lines
2.7 KiB
Python

# -*- coding: utf-8 -*-
from pandas import DataFrame
from ..volatility.true_range import true_range
from ..utils import get_drift, get_offset, verify_series, zero
def vortex(high, low, close, length=None, drift=None, offset=None, **kwargs):
"""Indicator: Vortex"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
length = length if length and length > 0 else 14
min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
tr = true_range(high=high, low=low, close=close)
tr_sum = tr.rolling(length, min_periods=min_periods).sum()
vmp = (high - low.shift(drift)).abs()
vmm = (low - high.shift(drift)).abs()
vip = vmp.rolling(length, min_periods=min_periods).sum() / tr_sum
vim = vmm.rolling(length, min_periods=min_periods).sum() / tr_sum
# Offset
if offset != 0:
vip = vip.shift(offset)
vim = vim.shift(offset)
# Handle fills
if 'fillna' in kwargs:
vip.fillna(kwargs['fillna'], inplace=True)
vim.fillna(kwargs['fillna'], inplace=True)
if 'fill_method' in kwargs:
vip.fillna(method=kwargs['fill_method'], inplace=True)
vim.fillna(method=kwargs['fill_method'], inplace=True)
# Name and Categorize it
vip.name = f"VTXP_{length}"
vim.name = f"VTXM_{length}"
vip.category = vim.category = 'trend'
# Prepare DataFrame to return
data = {vip.name: vip, vim.name: vim}
vtxdf = DataFrame(data)
vtxdf.name = f"VTX_{length}"
vtxdf.category = 'trend'
return vtxdf
vortex.__doc__ = \
"""Vortex
Two oscillators that capture positive and negative trend movement.
Sources:
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
Calculation:
Default Inputs:
length=14, drift=1
TR = True Range
SMA = Simple Moving Average
tr = TR(high, low, close)
tr_sum = tr.rolling(length).sum()
vmp = (high - low.shift(drift)).abs()
vmn = (low - high.shift(drift)).abs()
VIP = vmp.rolling(length).sum() / tr_sum
VIM = vmn.rolling(length).sum() / tr_sum
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
length (int): ROC 1 period. Default: 14
drift (int): The difference period. Default: 1
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.DataFrame: vip and vim columns
"""