mirror of
https://github.com/Ichinga-Samuel/aiomql.git
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116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import copy, cos, exp, zeros_like
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from numba import njit
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from pandas import Series
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from pandas_ta._typing import Array, DictLike, Int, IntFloat
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from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
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# Ehlers's Super Smoother Filter
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# http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html
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@njit(cache=True)
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def nb_ssf(x, n, pi, sqrt2):
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m, ratio, result = x.size, sqrt2 / n, copy(x)
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a = exp(-pi * ratio)
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b = 2 * a * cos(180 * ratio)
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c = a * a - b + 1
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# result[:2] = x[:2]
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for i in range(2, m):
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result[i] = 0.5 * c * (x[i] + x[i - 1]) + b * result[i - 1] \
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- a * a * result[i - 2]
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return result
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# John F. Ehlers's Super Smoother Filter by Everget (2 poles), Tradingview
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# https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/
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@njit(cache=True)
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def nb_ssf_everget(x, n, pi, sqrt2):
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m, arg, result = x.size, pi * sqrt2 / n, copy(x)
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a = exp(-arg)
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b = 2 * a * cos(arg)
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# result[:2] = x[:2]
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for i in range(2, m):
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result[i] = 0.5 * (a * a - b + 1) * (x[i] + x[i - 1]) \
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+ b * result[i - 1] - a * a * result[i - 2]
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return result
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def ssf(
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close: Series, length: Int = None,
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everget: bool = None, pi: IntFloat = None, sqrt2: IntFloat = None,
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offset: Int = None, **kwargs: DictLike
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) -> Series:
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"""Ehlers's Super Smoother Filter
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This indicator, by John F. Ehlers's © 2013, is a (Recursive) Digital
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Filter that attempts to reduce lag and remove aliases. This version
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has two poles.
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Sources:
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* [mql5](https://www.mql5.com/en/code/588)
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* [traders.com](http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html)
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* [tradingview](https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/)
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Parameters:
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close (pd.Series): ```close``` Series
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length (int): The period. Default: ```20```
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everget (bool): Everget's implementation of ssf that uses pi
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instead of 180 for the b factor of ssf. Default: ```False```
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pi (float): The default is Ehlers's truncated value: ```3.14159```.
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Default: ```3.14159```
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sqrt2 (float): The default is Ehlers's truncated value: ```1.414```.
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Default: ```1.414```
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offset (int): Post shift. Default: ```0```
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Other Parameters:
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fillna (value): ```pd.DataFrame.fillna(value)```
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Returns:
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(pd.Series): 1 column
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Note:
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* Everget's calculation on TradingView:
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```pi=np.pi```, ```sqrt2=np.sqrt(2)```
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Danger:
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Possible Data Leak
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"""
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# Validate
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length = v_pos_default(length, 20)
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close = v_series(close, length)
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if close is None:
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return
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pi = v_pos_default(pi, 3.14159)
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sqrt2 = v_pos_default(sqrt2, 1.414)
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everget = v_bool(everget, False)
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offset = v_offset(offset)
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# Calculate
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np_close = close.to_numpy()
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if everget:
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ssf = nb_ssf_everget(np_close, length, pi, sqrt2)
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else:
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ssf = nb_ssf(np_close, length, pi, sqrt2)
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ssf = Series(ssf, index=close.index)
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# Offset
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if offset != 0:
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ssf = ssf.shift(offset)
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# Fill
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if "fillna" in kwargs:
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ssf.fillna(kwargs["fillna"], inplace=True)
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# Name and Category
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ssf.name = f"SSF{'e' if everget else ''}_{length}"
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ssf.category = "overlap"
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return ssf
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