def get_mdi_values(cls, base_df, dt, ktype="60"):
base_df = base_df[base_df.index <= dt].tail(cls.get_max_30k(ktype))
data_df = gkd.Generator.generate_k_data(base_df, ktype=ktype)
zb_df = ZB.mdi(data_df)
zb_df['energy'] = zb_df["DIFF"] * zb_df['ADXR'] / 100
e1 = zb_df['energy']
e3 = zb_df['energy'].shift(3)
e5 = zb_df['energy'].shift(5)
e9 = zb_df['energy'].shift(9)
e15 = zb_df['energy'].shift(15)
zb_df['e_1_3'] = (e1 - e3) / e3 * 100
zb_df['e_1_5'] = (e1 - e5) / e5 * 100
zb_df['e_1_9'] = (e1 - e9) / e9 * 100
zb_df['e_3_5'] = (e3 - e5) / e5 * 100
zb_df['e_3_9'] = (e3 - e9) / e9 * 100
zb_df['e_3_15'] = (e3 - e15) / e15 * 100
zb_df['e_5_9'] = (e5 - e9) / e9 * 100
zb_df['e_5_15'] = (e5 - e15) / e15 * 100
cns = [
'ADX', 'ADXR', 'DIFF', 'energy', 'e_1_3', 'e_1_5', 'e_1_9',
'e_3_5', 'e_3_9', 'e_3_15', 'e_5_9', 'e_5_15'
]
return cls._extract(zb_df, cns)
def select_x005(cls, st_df):
name = "s005"
mdi_df = ZB.mdi(st_df)
x1 = mdi_df["DIFF"] < mdi_df["DIFF"].shift(1)
mdi_df[name] = False
mdi_df.ix[x1, name] = True
return mdi_df.ix[:, [name]]
def select_x003(cls, st_df):
"""
k30
"""
name = "s003"
mdi_df = ZB.mdi(st_df)
x1 = (mdi_df["DIFF"] > mdi_df["DIFF"].shift(1)) & (mdi_df["DIFF"] > mdi_df["DIFF"].shift(2))
x2 = ZB.cross(mdi_df['ADX'], mdi_df['ADXR'])
mdi_df[name] = False
mdi_df.ix[x1 & x2, name] = True
return mdi_df.ix[:, [name]]
def select_x006(cls, st_df):
"""
k60
"""
name = "s006"
mdi_df = ZB.mdi(st_df)
x10 = mdi_df["DIFF"] > mdi_df["DIFF"].shift(1)
mdi_df[name] = False
mdi_df.ix[x10, name] = True
return mdi_df.ix[:, [name]]
def select_x004(cls, st_df):
"""
k60
"""
name = "s004"
mdi_df = ZB.mdi(st_df)
x1 = mdi_df["DIFF"] > mdi_df["DIFF"].shift(1)
# x2 = mdi_df['ADXR'] < mdi_df['ADXR'].shift(1)
mdi_df[name] = False
mdi_df.ix[x1, name] = True
return mdi_df.ix[:, [name]]
def select_x004(cls, st_df):
"""
k60
"""
name = "selected"
mdi_df = ZB.mdi(st_df, d=6)
kdj_df = ZB.kdj(st_df)
x1 = mdi_df["DIFF"] < mdi_df["DIFF"].shift(1)
k1 = kdj_df["kdj_d"] < kdj_df["kdj_d"].shift(1)
max_close = st_df['high'].rolling(center=False,
min_periods=1,
window=6).max()
x2 = st_df['close'] <= (max_close - (max_close * 0.03))
mdi_df[name] = False
mdi_df.ix[(x1 & k1) | x2, name] = True
return mdi_df.ix[:, [name]]
import pandas as pd
import matplotlib.pyplot as plt
import gen_k_data as gkd
from zibiao import ZB
if __name__ == '__main2__':
pd.set_option("display.max_columns", 500)
df = gkd.read_5min_data('files/SH#600280.txt')
df = df.tail(2000)
df = df.head(1000)
df_d = gkd.generate_k_data(df, ktype="30")
df_kdj = ZB.kdj(df_d)
df_mdi = ZB.mdi(df_d)
xx_df = pd.concat([df_kdj, df_mdi], axis=1)
xx_df = xx_df.reset_index(drop=True)
diff1 = xx_df['kdj_k'] - xx_df['kdj_d']
xx_df["diff1"] = diff1.ewm(com=3).mean()
diff2 = xx_df['PDI'] - xx_df['MDI']
xx_df["diff2"] = diff2.ewm(com=3).mean()
del xx_df["PDI"]
del xx_df["MDI"]
del xx_df["ADX"]
del xx_df["ADXR"]