Source code for pandas_ta.volatility.donchian

# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.utils import get_offset, verify_series

[docs]def donchian(high, low, lower_length=None, upper_length=None, offset=None, **kwargs): """Indicator: Donchian Channels (DC)""" # Validate arguments lower_length = int(lower_length) if lower_length and lower_length > 0 else 20 upper_length = int(upper_length) if upper_length and upper_length > 0 else 20 lower_min_periods = int(kwargs["lower_min_periods"]) if "lower_min_periods" in kwargs and kwargs["lower_min_periods"] is not None else lower_length upper_min_periods = int(kwargs["upper_min_periods"]) if "upper_min_periods" in kwargs and kwargs["upper_min_periods"] is not None else upper_length _length = max(lower_length, lower_min_periods, upper_length, upper_min_periods) high = verify_series(high, _length) low = verify_series(low, _length) offset = get_offset(offset) if high is None or low is None: return # Calculate Result lower = low.rolling(lower_length, min_periods=lower_min_periods).min() upper = high.rolling(upper_length, min_periods=upper_min_periods).max() mid = 0.5 * (lower + upper) # Handle fills if "fillna" in kwargs: lower.fillna(kwargs["fillna"], inplace=True) mid.fillna(kwargs["fillna"], inplace=True) upper.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: lower.fillna(method=kwargs["fill_method"], inplace=True) mid.fillna(method=kwargs["fill_method"], inplace=True) upper.fillna(method=kwargs["fill_method"], inplace=True) # Offset if offset != 0: lower = lower.shift(offset) mid = mid.shift(offset) upper = upper.shift(offset) # Name and Categorize it = f"DCL_{lower_length}_{upper_length}" = f"DCM_{lower_length}_{upper_length}" = f"DCU_{lower_length}_{upper_length}" mid.category = upper.category = lower.category = "volatility" # Prepare DataFrame to return data = { lower, mid, upper} dcdf = DataFrame(data) = f"DC_{lower_length}_{upper_length}" dcdf.category = mid.category return dcdf
donchian.__doc__ = \ """Donchian Channels (DC) Donchian Channels are used to measure volatility, similar to Bollinger Bands and Keltner Channels. Sources: Calculation: Default Inputs: lower_length=upper_length=20 LOWER = low.rolling(lower_length).min() UPPER = high.rolling(upper_length).max() MID = 0.5 * (LOWER + UPPER) Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's lower_length (int): The short period. Default: 20 upper_length (int): The short period. Default: 20 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: lower, mid, upper columns. """