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108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from ..utils import get_drift, get_offset, verify_series
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def accbands(high, low, close, length=None, c=None, drift=None, mamode=None, offset=None, **kwargs):
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"""Indicator: Acceleration Bands (ACCBANDS)"""
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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 = int(length) if length and length > 0 else 20
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c = float(c) if c and c > 0 else 4
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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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mamode = mamode.lower() if mamode else 'sma'
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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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hl_ratio = (high - low) / (high + low)
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hl_ratio *= c
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_lower = low * (1 - hl_ratio)
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_upper = high * (1 + hl_ratio)
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if mamode is None or mamode == 'sma':
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lower = _lower.rolling(length, min_periods=min_periods).mean()
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mid = close.rolling(length, min_periods=min_periods).mean()
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upper = _upper.rolling(length, min_periods=min_periods).mean()
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elif mamode == 'ema':
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lower = _lower.ewm(span=length, min_periods=min_periods).mean()
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mid = close.ewm(span=length, min_periods=min_periods).mean()
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upper = _upper.ewm(span=length, min_periods=min_periods).mean()
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# Offset
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if offset != 0:
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lower = lower.shift(offset)
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mid = mid.shift(offset)
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upper = upper.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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lower.fillna(kwargs['fillna'], inplace=True)
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mid.fillna(kwargs['fillna'], inplace=True)
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upper.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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lower.fillna(method=kwargs['fill_method'], inplace=True)
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mid.fillna(method=kwargs['fill_method'], inplace=True)
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upper.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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lower.name = f"ACCBL_{length}"
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mid.name = f"ACCBM_{length}"
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upper.name = f"ACCBU_{length}"
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mid.category = upper.category = lower.category = 'volatility'
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# Prepare DataFrame to return
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data = {lower.name: lower, mid.name: mid, upper.name: upper}
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accbandsdf = DataFrame(data)
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accbandsdf.name = f"ACCBANDS_{length}"
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accbandsdf.category = 'volatility'
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return accbandsdf
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accbands.__doc__ = \
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"""Acceleration Bands (ACCBANDS)
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Acceleration Bands created by Price Headley plots upper and lower envelope
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bands around a simple moving average.
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Sources:
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https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/acceleration-bands-abands/
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Calculation:
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Default Inputs:
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length=10, c=4
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EMA = Exponential Moving Average
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SMA = Simple Moving Average
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HL_RATIO = c * (high - low) / (high + low)
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LOW = low * (1 - HL_RATIO)
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HIGH = high * (1 + HL_RATIO)
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if 'ema':
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LOWER = EMA(LOW, length)
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MID = EMA(close, length)
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UPPER = EMA(HIGH, length)
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else:
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LOWER = SMA(LOW, length)
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MID = SMA(close, length)
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UPPER = SMA(HIGH, length)
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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): It's period. Default: 10
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c (int): Multiplier. Default: 4
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mamode (str): Two options: None or 'ema'. Default: 'ema'
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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: lower, mid, upper columns.
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""" |