Ta-lib 函数一览

import tkinter as tk
from tkinter import ttk
import matplotlib.pyplot as plt

import numpy as np
import talib as ta

series = np.random.choice([1, -1], size=200)
close = np.cumsum(series).astype(float)

# 重叠指标
def overlap_process(event):
    print(event.widget.get())
    overlap = event.widget.get()

    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    fig, axes = plt.subplots(2, 1, sharex=True)
    ax1, ax2 = axes[0], axes[1]
    axes[0].plot(close, ‘rd-‘, markersize=3)
    axes[0].plot(upperband, ‘y-‘)
    axes[0].plot(middleband, ‘b-‘)
    axes[0].plot(lowerband, ‘y-‘)
    axes[0].set_title(overlap, fontproperties="SimHei")

    if overlap == ‘布林线‘:
        pass
    elif overlap == ‘双指数移动平均线‘:
        real = ta.DEMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘指数移动平均线 ‘:
        real = ta.EMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘希尔伯特变换——瞬时趋势线‘:
        real = ta.HT_TRENDLINE(close)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘考夫曼自适应移动平均线‘:
        real = ta.KAMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘移动平均线‘:
        real = ta.MA(close, timeperiod=30, matype=0)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘MESA自适应移动平均‘:
        mama, fama = ta.MAMA(close, fastlimit=0, slowlimit=0)
        axes[1].plot(mama, ‘r-‘)
        axes[1].plot(fama, ‘g-‘)
    elif overlap == ‘变周期移动平均线‘:
        real = ta.MAVP(close, periods, minperiod=2, maxperiod=30, matype=0)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘简单移动平均线‘:
        real = ta.SMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘三指数移动平均线(T3)‘:
        real = ta.T3(close, timeperiod=5, vfactor=0)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘三指数移动平均线‘:
        real = ta.TEMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘三角形加权法 ‘:
        real = ta.TRIMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif overlap == ‘加权移动平均数‘:
        real = ta.WMA(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    plt.show()

# 动量指标
def momentum_process(event):
    print(event.widget.get())
    momentum = event.widget.get()

    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    fig, axes = plt.subplots(2, 1, sharex=True)
    ax1, ax2 = axes[0], axes[1]
    axes[0].plot(close, ‘rd-‘, markersize=3)
    axes[0].plot(upperband, ‘y-‘)
    axes[0].plot(middleband, ‘b-‘)
    axes[0].plot(lowerband, ‘y-‘)
    axes[0].set_title(momentum, fontproperties="SimHei")

    if momentum == ‘绝对价格振荡器‘:
        real = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘钱德动量摆动指标‘:
        real = ta.CMO(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘移动平均收敛/散度‘:
        macd, macdsignal, macdhist = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
        axes[1].plot(macd, ‘r-‘)
        axes[1].plot(macdsignal, ‘g-‘)
        axes[1].plot(macdhist, ‘b-‘)
    elif momentum == ‘带可控MA类型的MACD‘:
        macd, macdsignal, macdhist = ta.MACDEXT(close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)
        axes[1].plot(macd, ‘r-‘)
        axes[1].plot(macdsignal, ‘g-‘)
        axes[1].plot(macdhist, ‘b-‘)
    elif momentum == ‘移动平均收敛/散度 固定 12/26‘:
        macd, macdsignal, macdhist = ta.MACDFIX(close, signalperiod=9)
        axes[1].plot(macd, ‘r-‘)
        axes[1].plot(macdsignal, ‘g-‘)
        axes[1].plot(macdhist, ‘b-‘)
    elif momentum == ‘动量‘:
        real = ta.MOM(close, timeperiod=10)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘比例价格振荡器‘:
        real = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘变化率‘:
        real = ta.ROC(close, timeperiod=10)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘变化率百分比‘:
        real = ta.ROCP(close, timeperiod=10)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘变化率的比率‘:
        real = ta.ROCR(close, timeperiod=10)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘变化率的比率100倍‘:
        real = ta.ROCR100(close, timeperiod=10)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘相对强弱指数‘:
        real = ta.RSI(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif momentum == ‘随机相对强弱指标‘:
        fastk, fastd = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
        axes[1].plot(fastk, ‘r-‘)
        axes[1].plot(fastd, ‘r-‘)
    elif momentum == ‘三重光滑EMA的日变化率‘:
        real = ta.TRIX(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)

    plt.show()

# 周期指标
def cycle_process(event):
    print(event.widget.get())
    cycle = event.widget.get()

    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    fig, axes = plt.subplots(2, 1, sharex=True)
    ax1, ax2 = axes[0], axes[1]
    axes[0].plot(close, ‘rd-‘, markersize=3)
    axes[0].plot(upperband, ‘y-‘)
    axes[0].plot(middleband, ‘b-‘)
    axes[0].plot(lowerband, ‘y-‘)
    axes[0].set_title(cycle, fontproperties="SimHei")

    if cycle == ‘希尔伯特变换——主要的循环周期‘:
        real = ta.HT_DCPERIOD(close)
        axes[1].plot(real, ‘r-‘)
    elif cycle == ‘希尔伯特变换,占主导地位的周期阶段‘:
        real = ta.HT_DCPHASE(close)
        axes[1].plot(real, ‘r-‘)
    elif cycle == ‘希尔伯特变换——相量组件‘:
        inphase, quadrature = ta.HT_PHASOR(close)
        axes[1].plot(inphase, ‘r-‘)
        axes[1].plot(quadrature, ‘g-‘)
    elif cycle == ‘希尔伯特变换——正弦曲线‘:
        sine, leadsine = ta.HT_SINE(close)
        axes[1].plot(sine, ‘r-‘)
        axes[1].plot(leadsine, ‘g-‘)
    elif cycle == ‘希尔伯特变换——趋势和周期模式‘:
        integer = ta.HT_TRENDMODE(close)
        axes[1].plot(integer, ‘r-‘)

    plt.show()

# 统计功能
def statistic_process(event):
    print(event.widget.get())
    statistic = event.widget.get()

    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    fig, axes = plt.subplots(2, 1, sharex=True)
    ax1, ax2 = axes[0], axes[1]
    axes[0].plot(close, ‘rd-‘, markersize=3)
    axes[0].plot(upperband, ‘y-‘)
    axes[0].plot(middleband, ‘b-‘)
    axes[0].plot(lowerband, ‘y-‘)
    axes[0].set_title(statistic, fontproperties="SimHei")

    if statistic == ‘线性回归‘:
        real = ta.LINEARREG(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif statistic == ‘线性回归角度‘:
        real = ta.LINEARREG_ANGLE(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif statistic == ‘线性回归截距‘:
        real = ta.LINEARREG_INTERCEPT(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif statistic == ‘线性回归斜率‘:
        real = ta.LINEARREG_SLOPE(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif statistic == ‘标准差‘:
        real = ta.STDDEV(close, timeperiod=5, nbdev=1)
        axes[1].plot(real, ‘r-‘)
    elif statistic == ‘时间序列预测‘:
        real = ta.TSF(close, timeperiod=14)
        axes[1].plot(real, ‘r-‘)
    elif statistic == ‘方差‘:
        real = ta.VAR(close, timeperiod=5, nbdev=1)
        axes[1].plot(real, ‘r-‘)

    plt.show()

# 数学变换
def math_transform_process(event):
    print(event.widget.get())
    math_transform = event.widget.get()

    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    fig, axes = plt.subplots(2, 1, sharex=True)
    ax1, ax2 = axes[0], axes[1]
    axes[0].plot(close, ‘rd-‘, markersize=3)
    axes[0].plot(upperband, ‘y-‘)
    axes[0].plot(middleband, ‘b-‘)
    axes[0].plot(lowerband, ‘y-‘)
    axes[0].set_title(math_transform, fontproperties="SimHei")

    if math_transform == ‘反余弦‘:
        real = ta.ACOS(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘反正弦‘:
        real = ta.ASIN(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘反正切‘:
        real = ta.ATAN(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘向上取整‘:
        real = ta.CEIL(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘余弦‘:
        real = ta.COS(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘双曲余弦‘:
        real = ta.COSH(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘指数‘:
        real = ta.EXP(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘向下取整‘:
        real = ta.FLOOR(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘自然对数‘:
        real = ta.LN(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘常用对数‘:
        real = ta.LOG10(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘正弦‘:
        real = ta.SIN(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘双曲正弦‘:
        real = ta.SINH(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘平方根‘:
        real = ta.SQRT(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘正切‘:
        real = ta.TAN(close)
        axes[1].plot(real, ‘r-‘)
    elif math_transform == ‘双曲正切‘:
        real = ta.TANH(close)
        axes[1].plot(real, ‘r-‘)

    plt.show()

# 数学操作
def math_operator_process(event):
    print(event.widget.get())
    math_operator = event.widget.get()

    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    fig, axes = plt.subplots(2, 1, sharex=True)
    ax1, ax2 = axes[0], axes[1]
    axes[0].plot(close, ‘rd-‘, markersize=3)
    axes[0].plot(upperband, ‘y-‘)
    axes[0].plot(middleband, ‘b-‘)
    axes[0].plot(lowerband, ‘y-‘)
    axes[0].set_title(math_operator, fontproperties="SimHei")

    if math_operator == ‘指定的期间的最大值‘:
        real = ta.MAX(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif math_operator == ‘指定的期间的最大值的索引‘:
        integer = ta.MAXINDEX(close, timeperiod=30)
        axes[1].plot(integer, ‘r-‘)
    elif math_operator == ‘指定的期间的最小值‘:
        real = ta.MIN(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)
    elif math_operator == ‘指定的期间的最小值的索引‘:
        integer = ta.MININDEX(close, timeperiod=30)
        axes[1].plot(integer, ‘r-‘)
    elif math_operator == ‘指定的期间的最小和最大值‘:
        min, max = ta.MINMAX(close, timeperiod=30)
        axes[1].plot(min, ‘r-‘)
        axes[1].plot(max, ‘r-‘)
    elif math_operator == ‘指定的期间的最小和最大值的索引‘:
        minidx, maxidx = ta.MINMAXINDEX(close, timeperiod=30)
        axes[1].plot(minidx, ‘r-‘)
        axes[1].plot(maxidx, ‘r-‘)
    elif math_operator == ‘合计‘:
        real = ta.SUM(close, timeperiod=30)
        axes[1].plot(real, ‘r-‘)

    plt.show()

root = tk.Tk()

# 第一行：重叠指标
rowframe1 = tk.Frame(root)
rowframe1.pack(side=tk.TOP, ipadx=3, ipady=3)
tk.Label(rowframe1, text="重叠指标").pack(side=tk.LEFT)

overlap_indicator = tk.StringVar() # 重叠指标
combobox1 = ttk.Combobox(rowframe1, textvariable=overlap_indicator)
combobox1[‘values‘] = [‘布林线‘,‘双指数移动平均线‘,‘指数移动平均线 ‘,‘希尔伯特变换——瞬时趋势线‘,
                       ‘考夫曼自适应移动平均线‘,‘移动平均线‘,‘MESA自适应移动平均‘,‘变周期移动平均线‘,
                       ‘简单移动平均线‘,‘三指数移动平均线(T3)‘,‘三指数移动平均线‘,‘三角形加权法 ‘,‘加权移动平均数‘]
combobox1.current(0)
combobox1.pack(side=tk.LEFT)

combobox1.bind(‘<<ComboboxSelected>>‘, overlap_process)

# 第二行：动量指标
rowframe2 = tk.Frame(root)
rowframe2.pack(side=tk.TOP, ipadx=3, ipady=3)
tk.Label(rowframe2, text="动量指标").pack(side=tk.LEFT)

momentum_indicator = tk.StringVar() # 动量指标
combobox2 = ttk.Combobox(rowframe2, textvariable=momentum_indicator)
combobox2[‘values‘] = [‘绝对价格振荡器‘,‘钱德动量摆动指标‘,‘移动平均收敛/散度‘,‘带可控MA类型的MACD‘,
                       ‘移动平均收敛/散度 固定 12/26‘,‘动量‘,‘比例价格振荡器‘,‘变化率‘,‘变化率百分比‘,
                       ‘变化率的比率‘,‘变化率的比率100倍‘,‘相对强弱指数‘,‘随机相对强弱指标‘,‘三重光滑EMA的日变化率‘]

combobox2.current(0)
combobox2.pack(side=tk.LEFT)

combobox2.bind(‘<<ComboboxSelected>>‘, momentum_process)

# 第三行：周期指标
rowframe3 = tk.Frame(root)
rowframe3.pack(side=tk.TOP, ipadx=3, ipady=3)
tk.Label(rowframe3, text="周期指标").pack(side=tk.LEFT)

cycle_indicator = tk.StringVar() # 周期指标
combobox3 = ttk.Combobox(rowframe3, textvariable=cycle_indicator)
combobox3[‘values‘] = [‘希尔伯特变换——主要的循环周期‘,‘希尔伯特变换——主要的周期阶段‘,‘希尔伯特变换——相量组件‘,
                       ‘希尔伯特变换——正弦曲线‘,‘希尔伯特变换——趋势和周期模式‘]

combobox3.current(0)
combobox3.pack(side=tk.LEFT)

combobox3.bind(‘<<ComboboxSelected>>‘, cycle_process)

# 第四行：统计功能
rowframe4 = tk.Frame(root)
rowframe4.pack(side=tk.TOP, ipadx=3, ipady=3)
tk.Label(rowframe4, text="统计功能").pack(side=tk.LEFT)

statistic_indicator = tk.StringVar() # 统计功能
combobox4 = ttk.Combobox(rowframe4, textvariable=statistic_indicator)
combobox4[‘values‘] = [‘贝塔系数；投资风险与股市风险系数‘,‘皮尔逊相关系数‘,‘线性回归‘,‘线性回归角度‘,
                       ‘线性回归截距‘,‘线性回归斜率‘,‘标准差‘,‘时间序列预测‘,‘方差‘]

combobox4.current(0)
combobox4.pack(side=tk.LEFT)

combobox4.bind(‘<<ComboboxSelected>>‘, statistic_process)

# 第五行：数学变换
rowframe5 = tk.Frame(root)
rowframe5.pack(side=tk.TOP, ipadx=3, ipady=3)
tk.Label(rowframe5, text="数学变换").pack(side=tk.LEFT)

math_transform = tk.StringVar() # 数学变换
combobox5 = ttk.Combobox(rowframe5, textvariable=math_transform_process)
combobox5[‘values‘] = [‘反余弦‘,‘反正弦‘,‘反正切‘,‘向上取整‘,‘余弦‘,‘双曲余弦‘,‘指数‘,‘向下取整‘,
                       ‘自然对数‘,‘常用对数‘,‘正弦‘,‘双曲正弦‘,‘平方根‘,‘正切‘,‘双曲正切‘]

combobox5.current(0)
combobox5.pack(side=tk.LEFT)

combobox5.bind(‘<<ComboboxSelected>>‘, math_transform_process)

# 第六行：数学操作
rowframe6 = tk.Frame(root)
rowframe6.pack(side=tk.TOP, ipadx=3, ipady=3)
tk.Label(rowframe6, text="数学操作").pack(side=tk.LEFT)

math_operator = tk.StringVar() # 数学操作
combobox6 = ttk.Combobox(rowframe6, textvariable=math_operator_process)
combobox6[‘values‘] = [‘指定期间的最大值‘,‘指定期间的最大值的索引‘,‘指定期间的最小值‘,‘指定期间的最小值的索引‘,
                       ‘指定期间的最小和最大值‘,‘指定期间的最小和最大值的索引‘,‘合计‘]

combobox6.current(0)
combobox6.pack(side=tk.LEFT)

combobox6.bind(‘<<ComboboxSelected>>‘, math_operator_process)

root.mainloop()
时间： 2024-08-05 04:43:07

Ta-lib 函数一览的相关文章

&#8203;Linux下C如何调用PCI Lib函数

Linux下C如何调用PCI Lib函数 在Linux下,可以通过"setpci"和"setpci"命令来访问PCI设备的配置空间,那么能否用程序来访问PCI 配置空间呢?答案当然是肯定的,linux下提供了多个pci库以供应用程序访问.下面就以最常见的为例,从安装.使用和编译的角度分别进行说明. 安装在centos中,用超级用户权限,可用下面的命令查看到和pci访问相关的库包括:libpciaccess.i686 : PCI access librarylibpc

【UEFI】---BIOS中对Guid的使用以及Lib函数的使用总结

---恢复内容开始--- BIOS发展至今传统的汇编实现早已被抛弃,UEFI作为目前一套主流的标准定义接口,被广泛使用.之前被一些有关GUID和一些Lib函数的使用以及跨Pkg调用给折腾的不行,每次改代码都要build好多次,才能build过.趁着这次的一个Bug,针对GUID和Lib的跨Pkg使用做下小结. /*GUID*/ 顾名思义--globally unique identifier,全局唯一标识符.GUID是一个由128bit=16Byte的二进制数据组成,此数据的生成一般由专门的GU

jquery 常用函数一览

可以打印后慢慢一个一个查找,有利于记忆. 核心 ·         jQuery 核心函数 o    jQuery([sel,[context]]) o    jQuery(html,[ownerDoc])1.8* o    jQuery(callback) o    jQuery.holdReady(hold)1.6+ ·         jQuery 对象访问 o    each(callback) o    size() o    length o    selector o    con

VS2005环境下采用makefile编译、使用libjpeg.lib函数库

1.从www.ijg.org下载源码,解压后得到文件夹jpeg-8d 2.在文件夹里新建jconfig.h文件,将jconfig.vc里的内容拷到jconfig.h中 3.编译. Run->CMD->进入到C:\Program Files\Microsoft Visual Studio 8\VC\bin目录(不同的VC编译器目录也不一样)下 ->运行vcvars32.bat文件http://www.playerflash.org/,此时 这里的先进入makfefile.vc目录下 --&

C++库常用函数一览

本文中提到的函数库有:<string> <cctype> <algorithm> <cmath> <cstdlib> <iomanip> <numeric> 1.   标准C++库字符串类std::string的用法 begin       得到指向字符串开头的Iteratorend         得到指向字符串结尾的Iteratorrbegin      得到指向反向字符串开头的Iteratorrend        

openCV各函数一览

CVAPI(void)  cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2,                          CvScalar color, int thickness CV_DEFAULT(1),                          int line_type CV_DEFAULT(8),                          int shift CV_DEFAULT(0)); img图像. pt1

Matlab绘图函数一览

要查看Matlab所有绘图函数,请从Matlab主界面菜单查看"绘图目录",或从Matlab帮助文档查看"Types of MATLAB Plots"(在线版本).本文的图和英文解释摘自Matlab帮助文档. 类别 Function 图 维度 描述 曲线 plot 2 绘制曲线,相邻点之间被插值 fplot     输入函数或函数句柄.自变量取值区间,绘制曲线 plotyy 2 双纵坐标图,两个纵坐标的数量级不同 plot3 3 绘制3D曲线 loglog 2 X,

SQLite核心函数一览

abs(X) abs(X)返回 X 的绝对值. Abs(X) returns NULL if X is NULL. Abs(X) return 0.0  if X is a string or blob that cannot be converted to a numeric value. If X is  the integer -9223372036854775807 then abs(X) throws an integer overflow error  since there is

Delphi子类调用祖父类的虚函数

因为看Delphi书的时候,就产生了疑惑.老讲调用父类虚函数,但是万一需要调用祖父虚函数怎么办?后来又经常在C++里看到,就更疑惑了 type TA = class procedure ShowMsg; virtual; end; TAClass = class of TA; TB = class(TA) procedure ShowMsg; override; end; TShowMsg = procedure of object; TC = class(TB) private FGrandF