本文主要是介绍Ta-lib 函数一览，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
import tkinter as tkfrom 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()
这篇关于Ta-lib 函数一览的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
