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DynamicSAR.py
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DynamicSAR.py
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# This file is part of Qualified Caching-as-a-Service.
# BSD 3-Clause License
#
# Copyright (c) 2019, Intelligent-distributed Cloud and Security Laboratory (ICNS Lab.)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# title : DynamicSAR.py
# description : python dynamic trend tracker based on moving average and parabolic Stop And Reverse (SAR)
# author : Yunkon(Alvin) Kim
# date : 20190206
# version : 0.1
# python_version : 3.6
# notes : This DynamicSAR is an implementation of trend tracker based on parabolic SAR applying gap of
# short MA and long MA in the Python Programming Language.
# ==============================================================================
import CircularList
class DynamicSAR:
"""
출처: 파라볼릭(Prabolic SAR) 계산해 보기
(http://blog.daum.net/_blog/BlogTypeView.do?blogid=0W7UP&articleno=1833&categoryId=61®dt=20140227005609)
Parabolic SAR (Stop And Reverse)의 개념:
시간의 추이에 따라 주가의 움직임을 변수로 하는 함수를 만들어, 시간의 경과와 일치하는 주가의 변화가 일어나지 않을 경우
이를 추세의 전환시점으로 보고 기존 포지션을 중지하고 그와는 반대의 포지션을 취할 수 있는 기준을 제공해 준다.
계산식:
EP: Extreme Price (상승추세에서는 신고가, 하락추세에서는 신저가)
AF: Acceleration Factor (가속변수)
- 시간경과에 따른 가속 변수
- 0.02부터 시작해서 고가/저가를 갱신할 떄마다 0.02씩 증가
- 최대값(0.2)을 넘어갈 수 없음. (0.02 * 10일 = 0.2, 즉 고가/저가를 10번 갱신하면 가속 변수는 0.2로 고정됨)
변수값
- AF(가속변수): 0.02를 주로 사용
- 최대값(최대AF): 0.2를 주로 사용
"""
def __init__(self, short_term_sampling_size, long_term_sampling_size):
# a temporal list to store data
self.data_list = []
self.data_index = 0
# a circular list of short term moving average
self.short_term = CircularList.CircularList(short_term_sampling_size)
self.__short_term_average = None
# a circular list of long term moving average
self.long_term = CircularList.CircularList(long_term_sampling_size)
self.__long_term_average = None
# The member variables for calculation of Dynamic SAR, which utilizes the moving average and Parabolic SAR
# lsmaaf: Long Short Moving Average Acceleration Factor
self.psar_list = []
self.psarbull_list = []
self.psarbear_list = []
self.bull = True
self.lsmaaf = 0
self.ep = 0
self.af = self.iaf = 0.02
self.maxaf = 0.2
self.reverse = False
def put(self, data):
self.data_list[self.data_index] = data
self.short_term.put(data)
short_list = self.short_term.list
if None not in short_list:
self.__short_term_average = sum(short_list) / float(len(short_list))
self.long_term.put(data)
long_list = self.long_term.list
if None not in long_list:
self.__long_term_average = sum(long_list) / float(len(long_list))
if self.__short_term_average is None or self.__short_term_average is None:
self.psar_list[self.data_index] = self.data_list[self.data_index]
self.ep = self.data_list[self.data_index]
else:
diff_mean = self.__short_term_average - self.__long_term_average
# Normalize is necessary.
self.lsmaaf = self.normalization(diff_mean)
self.parabolic_sar(self.lsmaaf)
self.data_index += 1
def parabolic_sar(self, lsmaaf):
psar = self.psar_list
i = self.data_index
psar[i] = psar[i - 1] + (self.af + lsmaaf) * (self.ep - psar[i - 1])
if self.bull:
if self.data_list[i] < psar[i]:
self.bull = False
self.reverse = True
psar[i] = self.ep
self.ep = self.data_list[i]
self.af = self.iaf
else:
if self.data_list[i] > psar[i]:
self.bull = True
self.reverse = True
psar[i] = self.ep
self.ep = self.data_list[i]
self.af = self.iaf
if not self.reverse:
if self.bull:
if self.data_list[i] > self.ep:
self.ep = self.data_list[i]
self.af = min(self.af + self.iaf, self.maxaf)
if self.data_list[i-1] < psar[i]:
psar[i] = self.data_list[i-1]
if self.data_list[i-2] < psar[i]:
psar[i] = self.data_list[i-2]
else:
if self.data_list[i] < self.ep:
self.ep = self.data_list[i]
self.af = min(self.af + self.iaf, self.maxaf)
if self.data_list[i-1] > psar[i]:
psar[i] = self.data_list[i-1]
if self.data_list[i-2] > psar[i]:
psar[i] = self.data_list[i-2]
if self.bull:
self.psarbull_list[i] = psar[i]
self.psarbear_list[i] = None
else:
self.psarbear_list[i] = psar[i]
self.psarbull_list[i] = None
# The below function should be updated due to unclear gains of normalization, 0.2, 300
@staticmethod
def normalization(num):
if num < 0:
num = -num
temp = (num * 0.2) / 300
return temp
# @property
# def short_term_average(self):
# if self.__short_term_average is not None:
# return self.__short_term_average
# else:
# return "Less sampling data"
#
# @property
# def long_term_average(self):
# if self.__long_term_average is not None:
# return self.__long_term_average
# else:
# return "Less sampling data"