布林线的python定义

sphinxrascal168

已于 2022-02-11 10:04:16 修改

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分类专栏：大幅度文章标签：深度学习

于 2022-02-11 09:50:52 首次发布

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该博客探讨了布林带（BBANDS）指标在金融数据分析中的运用，包括计算上、中、下轨，计算斜率以及开口变化。通过线性回归计算中轨斜率，并设置阈值判断斜率变化。进一步，分析了开口变化趋势，利用SUM函数对5天和10天内的变化进行计数，从而识别开口的扩大或缩小趋势。最终，根据计数结果确定开口变化状态，并据此进行数据筛选。

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#布林线相关指标,shift(-1),向上移动1格，也就当前行变成下一行，在这里就是未来的某一天，+1就是向下移动，就是过去的一天
df1['upper_BBANDS'],df1['mid_BBANDS'],df1['lower_BBANDS'] = ta.BBANDS(df1['close'].values,timeperiod=5,nbdevup=2,nbdevdn=2,matype=0)

df1['mid_BBANDS_xielv'] =ta.LINEARREG_SLOPE(df1['mid_BBANDS'],timeperiod=5)

df1['kaikou_BBANDS'] = df1['upper_BBANDS'] -df1['lower_BBANDS']

df1['kaikou_BBANDS_bianhua'] = df1['kaikou_BBANDS'] - df1['kaikou_BBANDS'].shift(1)
#mid是大于0还是小于等于0
df1['mid_BBANDS_xielv_flag '] = np.where(df1['mid_BBANDS_xielv']>0.001,1,0)
df1['kaikou_bianhua_flag'] = np.where(df1['kaikou_BBANDS_bianhua']>0.001,1,0)
#开口在5天内，变大了还是变小了
df1['kaikou_bianhua_flag_10count'] = ta.SUM(df1['kaikou_bianhua_flag'], timeperiod=10)
df1['kaikou_bianhua_flag_5count'] = ta.SUM(df1['kaikou_bianhua_flag'], timeperiod=5)
df1['kaikou_bianhua_count'] = np.select([((df1['kaikou_bianhua_flag_5count']==0) & (df1['kaikou_bianhua_flag_10count']==0)),
((df1['kaikou_bianhua_flag_5count']==0) & (df1['kaikou_bianhua_flag_10count'] >0)),
((df1['kaikou_bianhua_flag_5count'] >0) & (df1['kaikou_bianhua_flag_10count']==0)),
((df1['kaikou_bianhua_flag_5count'] >0) & (df1['kaikou_bianhua_flag_10count']>0))],[0,-99,99,1])
df1 = df1.drop(['upper_BBANDS','mid_BBANDS','lower_BBANDS','kaikou_BBANDS','kaikou_BBANDS_bianhua','kaikou_bianhua_flag','mid_BBANDS_xielv',
'kaikou_bianhua_flag_10count','kaikou_bianhua_flag_5count'],axis=1)