python实现MACD策略背离点的判断

MACD策略python实现背离点的判断

话不多说直接贴代码和运行结果！！
****运行环境：**PyCharm2017.1

import baostock as bs
import pandas as pd
import talib as ta
import matplotlib.pyplot as plt
def computeMACD(code,startdate,enddate):
    login_result = bs.login(user_id='anonymous', password='123456')
    print(login_result)
    ###获取股票日K线数据###
    rs = bs.query_history_k_data(code,
        "date,code,close,tradeStatus",
        start_date=startdate, end_date=enddate, 
        frequency="d", adjustflag="3")
    #### 打印结果集 ####
    result_list = []
    while (rs.error_code == '0') & rs.next():
        # 获取一条记录，将记录合并在一起
        result_list.append(rs.get_row_data())
    df = pd.DataFrame(result_list, columns=rs.fields)
    #剔除停盘数据
    #print(df)
    df2 = df[df['tradeStatus']=='1']#交易日
    #获取dif,dea,hist，它们的数据类似是tuple，且跟df2的date日期一一对应
    #记住了dif,dea,hist前33个为Nan，所以推荐用于计算的数据量一般为你所求日期之间数据量的3倍
    #这里计算的hist就是dif-dea,而很多证券商计算的MACD=hist*2=(dif-dea)*2
    dif, dea, hist= ta.MACD(df2['close'].astype(float).values, fastperiod=12, slowperiod=26, signalperiod=9)
    df3 = pd.DataFrame({
     'dif':dif[33:],'dea':dea[33:],'hist':hist[33:]},
                       index=df2['date'][33:],columns=['dif','dea','hist'])
    df4 = pd.merge(df3, df2, on='date', how='left')
    #print(df)
    print(df2)
    print(df3)
    print(df4)
    df4.to_csv("./out_df4.csv", index=False)
    df2.to_csv("./out_df2.csv", index=False)
    hist=2*(df4['dif']-df4['dea'])
    df3.plot(title='MACD')
    plt.show()
    #寻找MACD金叉和死叉
    datenumber = int(df3.shape[0])
    lastdif=None
    lastclose = None
    #tlist = []
    print(df3.iloc[0,0])
    for i in range(datenumber-1):
        if (df4.iloc[i, 1]<=df4.iloc[i, 2])&\
                (df4.iloc[i+1, 1]>=df4.iloc[i+1, 2])&\
                    greaterThan(lastdif, df4.iloc[i+1,1]) &\
                        greaterThan(df4['close'][i+1],lastclose):#上一次下穿时间的收盘价小于当日收盘价
                lastdif = df4.iloc[i , 1]
                lastclose = df4['close'][i]
                #tlist.append(df3.index[i+1])
                print("期货代码:{},顶背离时间：{}, 价格：{}".format(code,df4['date'][i + 1], df4['close'][i+1]))
        if ((df4.iloc[i,1]>=df4.iloc[i,2])&(df4.iloc[i+1,1]<=df4.iloc[i+1,2])&greaterThan(df4.iloc[i+1,1],lastdif) &greaterThan(lastclose,df4['close'][i+1])):
            lastdif = df4['dif'][i]
            lastclose = df4['close'][i]
            #tlist.append(df4['date'][i+1])
            print("期货代码:{},底背离时间：{}, 价格：{}".format(code, df4['date'][i + 1], df4['close'][i+1]))

    bs.logout()
    return(dif,dea,hist)

def greaterThan(a, b):
        if(not a )|( not b):
            return True
        elif(a > b):
            return True
        else:
            return False

if __name__ == '__main__':
    code = 'sh.600000'          #浦发银行股票代码
    startdate = '2019-06-08'
    enddate = '2020-01-08'
    (dif,dea,hist) = computeMACD(code,startdate,enddate)![在这里插入图片描述](https://img-blog.csdnimg.cn/20200110140633281.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNDIwOTI5,size_16,color_FFFFFF,t_70)

我测的是浦发银行的，日线。
引用到的包链接baostock
python各种扩展包