曾氏通道的python实现

1、概述

网上关于曾氏通道的描述并不多，似乎应用也很少，还没有深入研究，只是转成了python形式

2、公式

excel格式的：https://share.weiyun.com/fciZSeNL

3、python实现，基于极星量化

import talib
from scipy import stats
import math
import numpy
from scipy.stats import norm
 
XseqG = []
YlogG = []
Regression_Line_yG = []
DiffG = []
per95G = []
per75G = []
fper75G = []
fper95G = []
 
hisSize = '20220105'
backSize = 1000
 
 
# 策略开始运行时执行该函数一次
def initialize(context): 
    SetBarInterval('HKEX|F|HSI|2202','M',10,hisSize,backSize)
    SetTriggerType(5)
    SetOrderWay(2)
    pass
 
 
# 策略触发事件每次触发时都会执行该函数
def handle_data(context):
    global XseqG,YlogG,Regression_Line_yG,DiffG,per95G,per75G,fper75G,fper95G
    if len(Close())<1:
        return
    #根据close的数量，每次都重新生成Xseq
    count = 0
    XseqG = []
    for c in Close():
        count = count + 1
        XseqG.append(count)
    #根据Close的最新值，重新计算Ylog
    YlogG = []
    for c in Close():
        #Ylog = LN(Close)
        Ylog = math.log(c,math.e)
        YlogG.append(Ylog)
    #Slope = SLOPE(Ylog,Close)
    slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(Close(),YlogG)
    Slope = slope1
    #y-intercept = INTERCEPT(Ylog,Xseq)
    slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(XseqG, YlogG)
    y_intercept = intercept2
    #Regression_Line_y = Slope*Xseq + y_intercept 注意每次都需要根据最新的Slope和y_intercept做重新计算
    Regression_Line_yG = []
    for Xseq in XseqG:
        Regression_Line_y = Slope*Xseq + y_intercept
        Regression_Line_yG.append(Regression_Line_y)
    #Diff = Regression_Line_y - Ylog 注意每次都需要根据最新的Regression_Line_y做重新计算
    DiffG = []
    for i in range(0,len(Regression_Line_yG)):
        Diff = Regression_Line_yG[i] - YlogG[i]
        DiffG.append(Diff)
    #SD = STDEV(Diff)
    SD = numpy.std(DiffG,ddof=1)
    #95% = NORMINV(0.95,Regression_Line_y,SD)   注意每次根据最新的Regression_Line_yG重新计算
    per95G = []
    for Regression_Line_y in Regression_Line_yG:
        per95 = norm.ppf(0.95,Regression_Line_y, SD)
        per95G.append(per95)
    #75% = NORMINV(0.75,Regression_Line_y,SD)
    per75G = []
    for Regression_Line_y in Regression_Line_yG:
        per75 = norm.ppf(0.75,Regression_Line_y, SD)
        per75G.append(per75)
    #-75% = NORMINV(0.25,Regression_Line_y,SD)
    fper75G = []
    for Regression_Line_y in Regression_Line_yG:
        fper75 = norm.ppf(0.25,Regression_Line_y, SD)
        fper75G.append(fper75)
    #-95% = NORMINV(0.05,Regression_Line_y,SD)
    fper95G = []
    for Regression_Line_y in Regression_Line_yG:
        fper95 = norm.ppf(0.05,Regression_Line_y, SD)
        fper95G.append(fper95)
 
    PlotNumeric('Ylog',YlogG[-1],RGB_Red(),False)
    PlotNumeric('Regression_Line_y',Regression_Line_yG[-1],RGB_Blue(),False)
    PlotNumeric('per95',per95G[-1],RGB_Brown(),False)
    PlotNumeric('per75',per75G[-1],RGB_Green(),False)
    PlotNumeric('fper75',fper75G[-1],RGB_Purple(),False)
    PlotNumeric('fper95',fper95G[-1],RGB_Yellow(),False)
    #回溯一遍，重新画
    for X in XseqG:
        i = X*-1
        barsBack = X-1
        UnPlotNumeric('Regression_Line_y',False,barsBack)
        PlotNumeric('Regression_Line_y',Regression_Line_yG[i],RGB_Blue(),False,False,barsBack)
        UnPlotNumeric('per95',False,barsBack)
        PlotNumeric('per95',per95G[i],RGB_Brown(),False,False,barsBack)
        UnPlotNumeric('per75',False,barsBack)
        PlotNumeric('per75',per75G[i],RGB_Green(),False,False,barsBack)
        UnPlotNumeric('fper75',False,barsBack)
        PlotNumeric('fper75',fper75G[i],RGB_Purple(),False,False,barsBack)
        UnPlotNumeric('fper95',False,barsBack)
        PlotNumeric('fper95',fper95G[i],RGB_Yellow(),False,False,barsBack)
 
 
    pass
 
 
# 历史回测阶段结束时执行该函数一次
def hisover_callback(context):
    pass
 
# 策略退出前执行该函数一次
def exit_callback(context):
    pass
 
 

 

4、运行结果

 

 

5、要点

需要注意，excel计算时是静态数据，python计算是动态数据，为了与excel结果保持一致，每当有close更新时都重新将所有数据计算一遍，否则无法保持一致。

依赖scipy这个包，需要安装，网络不好还装不上。

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