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