Source code for pandas_ta.overlap.hilo

# -*- coding: utf-8 -*-
from numpy import nan as npNaN
from pandas import DataFrame, Series
from .ma import ma
from pandas_ta.utils import get_offset, verify_series

[docs]def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offset=None, **kwargs): """Indicator: Gann HiLo (HiLo)""" # Validate Arguments high_length = int(high_length) if high_length and high_length > 0 else 13 low_length = int(low_length) if low_length and low_length > 0 else 21 mamode = mamode.lower() if isinstance(mamode, str) else "sma" _length = max(high_length, low_length) high = verify_series(high, _length) low = verify_series(low, _length) close = verify_series(close, _length) offset = get_offset(offset) if high is None or low is None or close is None: return # Calculate Result m = close.size hilo = Series(npNaN, index=close.index) long = Series(npNaN, index=close.index) short = Series(npNaN, index=close.index) high_ma = ma(mamode, high, length=high_length) low_ma = ma(mamode, low, length=low_length) for i in range(1, m): if close.iloc[i] > high_ma.iloc[i - 1]: hilo.iloc[i] = long.iloc[i] = low_ma.iloc[i] elif close.iloc[i] < low_ma.iloc[i - 1]: hilo.iloc[i] = short.iloc[i] = high_ma.iloc[i] else: hilo.iloc[i] = hilo.iloc[i - 1] long.iloc[i] = short.iloc[i] = hilo.iloc[i - 1] # Offset if offset != 0: hilo = hilo.shift(offset) long = long.shift(offset) short = short.shift(offset) # Handle fills if "fillna" in kwargs: hilo.fillna(kwargs["fillna"], inplace=True) long.fillna(kwargs["fillna"], inplace=True) short.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: hilo.fillna(method=kwargs["fill_method"], inplace=True) long.fillna(method=kwargs["fill_method"], inplace=True) short.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category _props = f"_{high_length}_{low_length}" data = {f"HILO{_props}": hilo, f"HILOl{_props}": long, f"HILOs{_props}": short} df = DataFrame(data, index=close.index) = f"HILO{_props}" df.category = "overlap" return df
hilo.__doc__ = \ """Gann HiLo Activator(HiLo) The Gann High Low Activator Indicator was created by Robert Krausz in a 1998 issue of Stocks & Commodities Magazine. It is a moving average based trend indicator consisting of two different simple moving averages. The indicator tracks both curves (of the highs and the lows). The close of the bar defines which of the two gets plotted. Increasing high_length and decreasing low_length better for short trades, vice versa for long positions. Sources: Calculation: Default Inputs: high_length=13, low_length=21, mamode="sma" EMA = Exponential Moving Average HMA = Hull Moving Average SMA = Simple Moving Average # Default if "ema": high_ma = EMA(high, high_length) low_ma = EMA(low, low_length) elif "hma": high_ma = HMA(high, high_length) low_ma = HMA(low, low_length) else: # "sma" high_ma = SMA(high, high_length) low_ma = SMA(low, low_length) # Similar to Supertrend MA selection hilo = Series(npNaN, index=close.index) for i in range(1, m): if close.iloc[i] > high_ma.iloc[i - 1]: hilo.iloc[i] = low_ma.iloc[i] elif close.iloc[i] < low_ma.iloc[i - 1]: hilo.iloc[i] = high_ma.iloc[i] else: hilo.iloc[i] = hilo.iloc[i - 1] Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's high_length (int): It's period. Default: 13 low_length (int): It's period. Default: 21 mamode (str): See ```help(```. Default: 'sma' offset (int): How many periods to offset the result. Default: 0 Kwargs: adjust (bool): Default: True presma (bool, optional): If True, uses SMA for initial value. fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.DataFrame: HILO (line), HILOl (long), HILOs (short) columns. """