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77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from pandas_ta.volatility import true_range
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from pandas_ta.utils import get_drift, get_offset, verify_series
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def vortex(high, low, close, length=None, drift=None, offset=None, **kwargs):
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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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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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"""
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# Validate arguments
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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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_length = max(length, min_periods)
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high = verify_series(high, _length)
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low = verify_series(low, _length)
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close = verify_series(close, _length)
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drift = get_drift(drift)
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offset = get_offset(offset)
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if high is None or low is None or close is None: return
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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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