004 | 掌握金融量化交易库Talib

    ta.MA(close,timeperiod=30,matype=0)
    
    
      
    
    AI助手

    import pandas as pd  
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline   
    #正常显示画图时出现的中文和负号
    from pylab import mpl
    mpl.rcParams['font.sans-serif']=['SimHei']
    mpl.rcParams['axes.unicode_minus']=False
    #引入TA-Lib库
    import talib as ta
    #使用tushare获取上证指数数据作为示例
    import tushare as ts
    df=ts.get_k_data('sh',start='2000-01-01')
    df.index=pd.to_datetime(df.date)
    df=df.sort_index()
    
    types=['SMA','EMA','WMA','DEMA','TEMA','TRIMA','KAMA','MAMA','T3']
    df_ma=pd.DataFrame(df.close)
    for i in range(len(types)):
    df_ma[types[i]]=ta.MA(df.close,timeperiod=5,matype=i)
    df_ma.tail()
    df_ma.loc['2018-08-01':].plot(figsize=(16,6))  
    plt.title('上证指数各种类型移动平均线',fontsize=15)
    plt.show()
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    AI助手

    ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
    
    
      
    
    AI助手

    H_line,M_line,L_line=ta.BBANDS(df.close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
    df1=pd.DataFrame(df.close,index=df.index,columns=['close'])
    df1['H_line']=H_line
    df1['M_line']=M_line
    df1['L_line']=L_line
    df1.tail()
    df1.loc['2019-01-01':'2019-12-30'].plot(figsize=(16,6)) 
    plt.title('上证指数布林带',fontsize=15)
    plt.show()
    
    
      
      
      
      
      
      
      
      
      
    
    AI助手

    #获取上证指数收盘价、最高、最低价格
    df=df[['open','close','high','low']]
    #开盘价，最高价，最低价，收盘价的均值
    df['average']=ta.AVGPRICE(df.open,df.high,df.low,df.close)
    #最高价，最低价的均值
    df['median']=ta.MEDPRICE(df.high,df.low)
    #最高价，最低价，收盘价的均值
    df['typical']=ta.TYPPRICE(df.high,df.low,df.close)
    #最高价，最低价，收盘价的加权
    df['weight']=ta.WCLPRICE(df.high,df.low,df.close)
    df.loc['2019-12-01':,['close','average','median','typical','weight']].plot(figsize=(12,6)) 
    plt.title('上证指数及其价格转换',fontsize=15)
    plt.show()
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    AI助手

    df['dcperiod']=ta.HT_DCPERIOD(df.close)
    df['dcphase']=ta.HT_DCPHASE(df.close)
    df['inhpase'],df['quadrature']=ta.HT_PHASOR(df.close)
    df['sine'],df['leadsine']=sine, leadsine = ta.HT_SINE(df.close)
    df['trendmode']=ta.HT_TRENDMODE(df.close)
    #将上述函数计算得到的结果进行可视化
    df.loc['2019-12-01':,['close','dcperiod','dcphase','inhpase','quadrature','sine',
    'leadsine','trendmode']].plot(figsize=(20,18),subplots = True,layout=(4, 2))
    plt.subplots_adjust(wspace=0,hspace=0.2)
    
    
      
      
      
      
      
      
      
      
      
    
    AI助手

    df['atr']=ta.ATR(df.high, df.low, df.close, timeperiod=14)
    df['natr']=ta.NATR(df.high, df.low, df.close, timeperiod=14)
    df['trange']=ta.TRANGE(df.high, df.low, df.close)
    df.loc['2019-12-01':,['atr','natr','trange']].plot(figsize=(10,8),subplots=True,layout=(3,1))
    
    
      
      
      
      
    
    AI助手

    #最高价与最低价之和
    df['add']=ta.ADD(df.high,df.low)
    #最高价与最低价之差
    df['sub']=ta.SUB(df.high,df.low)
    #最高价与最低价之乘
    df['mult']=ta.MULT(df.high,df.low)
    #最高价与最低价之除
    df['div']=ta.DIV(df.high,df.low)
    #收盘价的每30日移动求和
    df['sum']=ta.SUM(df.close, timeperiod=30)
    #收盘价的每30日内的最大最小值
    df['min'], df['max'] = ta.MINMAX(df.close, timeperiod=30)
    #收盘价的每30日内的最大最小值对应的索引值（第N行）
    df['minidx'], df['maxidx'] = ta.MINMAXINDEX(df.close, timeperiod=30)
    df.tail()
    #将上述函数计算得到的结果进行可视化
    df.loc['2019-12-01':,['close','add','sub','mult','div','sum','min','max']].plot(figsize=(15,8), subplots = True,layout=(4, 2))
    plt.subplots_adjust(wspace=0,hspace=0.2)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    AI助手

    #收盘价对时间t的线性回归预测值
    df['linearreg']=ta.LINEARREG(df.close, timeperiod=14)
    #时间序列预测值
    df['tsf']=ta.TSF(df.close, timeperiod=14)
    #画图
    df.loc['2019-12-01':,['close','linearreg','tsf']].plot(figsize=(12,6))
    
    
      
      
      
      
      
      
    
    AI助手