Ta-lib 函数一览

Posted 2020-07-27 罗兵の水库

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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\',\'Ta-lib函数功能列表

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