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 get_vol_kdj_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)
# 忽略当前volume
data_df = data_df[:-1]
data_df['v5'] = data_df['volume'].rolling(center=False,
min_periods=1,
window=5).sum()
data_df['v15'] = data_df['volume'].rolling(center=False,
min_periods=1,
window=15).sum()
zb_df = ZB.kdj(data_df)
zb_df['direction'] = 1
# 向下的趋势
xd1 = (zb_df['kdj_d'] < zb_df['kdj_d'].shift(1)) | (
zb_df['kdj_k'] < zb_df['kdj_k'].shift(1))
zb_df.ix[xd1, 'direction'] = -1
zb_df['energy_d'] = zb_df['direction'] * zb_df['kdj_d']
data_df['direction'] = zb_df['direction']
data_df['energy_kdj_d'] = zb_df['energy_d']
data_df['energy_vol_kdj_d'] = data_df['energy_kdj_d'] * (
data_df['v5'] / data_df['v15'])
cns = ['direction', 'energy_kdj_d', 'energy_vol_kdj_d']
return cls._extract(data_df, cns)
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_x003(cls, st_df):
"""
k30
"""
name = "selected"
mdi_df = ZB.mdi(st_df)
x1 = mdi_df["DIFF"] > mdi_df["DIFF"].shift(1)
x2 = ZB.cross(mdi_df['ADXR'], mdi_df['ADX'])
t1 = (mdi_df.index.hour > 10) & (mdi_df.index.hour <= 14)
mdi_df[name] = False
mdi_df.ix[t1 & x1 & x2, name] = True
return mdi_df.ix[:, [name]]
def select_x003(cls, st_df):
"""
k30
"""
name = "s003"
kdj_df = ZB.kdj(st_df)
x1 = ZB.cross(kdj_df["kdj_k"], kdj_df["kdj_d"])
x2 = kdj_df["kdj_k"] <= 60
t1 = kdj_df.index.hour >= 11
kdj_df[name] = False
kdj_df.ix[t1 & x1 & x2, name] = True
return kdj_df.ix[:, [name]]
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):
"""
"""
name = "s004"
kdj_df = ZB.kdj(st_df)
x1 = kdj_df["kdj_k"] < kdj_df['kdj_d']
x2 = kdj_df["kdj_k"] >= kdj_df["kdj_k"].shift(1)
x3 = ZB.cross(kdj_df["kdj_k"], kdj_df["kdj_d"])
t1 = kdj_df.index.hour >= 13
kdj_df[name] = False
kdj_df.ix[t1 & x1 & x2, name] = True
kdj_df.ix[t1 & x3, name] = True
return kdj_df.ix[:, [name]]
def select_x003(cls, st_df):
"""
k30
"""
name = "selected"
mdi_df = ZB.mdi(st_df)
ma_df = ZB.ma(st_df)
close = st_df['close']
x1 = ZB.cross(mdi_df['ADX'], mdi_df['ADXR']) & (mdi_df['ADXR'] < 30)
x2 = mdi_df['DIFF'] > mdi_df['DIFF'].shift(1)
x21 = (mdi_df['PDI'] > mdi_df['ADXR']) & (mdi_df['PDI'] > mdi_df['MDI'])
x3 = (ma_df['ma50'] <= close + (close * 0.02)) & (ma_df['ma50'] <= close - (close * 0.02))
t1 = (mdi_df.index.hour > 10) & (mdi_df.index.hour <= 14)
mdi_df[name] = False
mdi_df.ix[t1 & x1 & x2 & x21 & x3, 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]]
def select_x004(cls, st_df):
"""
k60
"""
name = "selected"
mdi_df = ZB.mdi(st_df)
ma_df = ZB.ma(st_df)
x1 = mdi_df["DIFF"] > mdi_df["DIFF"].shift(1)
close = st_df['close']
mdi_df['MAX_ADXR'] = mdi_df['ADXR'].rolling(center=False, min_periods=1, window=10).max()
#
# x2 = (mdi_df['ADXR'] < 24) & (mdi_df['DIFF'] > -30)
x3 = (ma_df['ma50'] <= close + (close * 0.02)) & (ma_df['ma50'] <= close - (close * 0.02))
# x4 = mdi_df['MAX_ADXR'] < 30
mdi_df[name] = False
# mdi_df.ix[x1 & x2 & x3 & kk, name] = True
mdi_df.ix[x1 & x3, name] = True
return mdi_df.ix[:, [name]]
def select_x004(cls, st_df):
"""
k60
"""
name = "selected"
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_x005(cls, st_df):
"""
kd
"""
name = "selected"
kdj_df = ZB.kdj(st_df)
x1 = kdj_df['kdj_k'] > kdj_df['kdj_k'].shift()
kdj_df[name] = False
kdj_df.ix[x1, name] = True
return kdj_df.ix[:, [name]]
def select_x004(cls, st_df):
"""
"""
name = "s004"
kdj_df = ZB.kdj(st_df)
diff = kdj_df['kdj_k'] - kdj_df['kdj_d']
kdj_df["diff"] = diff.ewm(com=1).mean()
# 高位, 确认趋势变坏
x1 = kdj_df["diff"] > 0
x2 = kdj_df["diff"] < kdj_df["diff"].shift(1)
x3 = kdj_df["diff"].shift(1) <= kdj_df["diff"].shift(2)
c1 = ZB.cross(kdj_df['kdj_d'], kdj_df['kdj_k'])
c2 = kdj_df['kdj_k'] >= 30
kdj_df[name] = False
kdj_df.ix[x1 & x2 & x3, name] = True
kdj_df.ix[c1 & c2, name] = True
return kdj_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_x005(cls, st_df):
"""
kd
"""
name = "selected"
mdi_df = ZB.mdi(st_df)
# x1 = kdj_df['kdj_k'] > kdj_df['kdj_k'].shift()
mdi_df[name] = True
x1 = mdi_df["DIFF"] > mdi_df["DIFF"].shift(1)
mdi_df.ix[x1, name] = True
return mdi_df.ix[:, [name]]
def select_x005(cls, st_df):
"""
kd
"""
name = "s005"
kdj_df = ZB.kdj(st_df)
x1 = kdj_df["kdj_k"] > kdj_df["kdj_k"].shift(1)
# x2 = kdj_df["kdj_k"] <= 80
kdj_df[name] = False
kdj_df.ix[x1, name] = True
return kdj_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 = "s004"
kdj_df = ZB.kdj(st_df)
x1 = kdj_df["kdj_k"] > kdj_df["kdj_k"].shift(1)
x2 = kdj_df["kdj_k"] <= 50
kdj_df[name] = False
kdj_df.ix[x1 & x2, name] = True
return kdj_df.ix[:, [name]]
def get_kdj_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.kdj(data_df)
cur_d = zb_df['kdj_d']
max_d = zb_df['kdj_d'].rolling(center=False, min_periods=1,
window=20).max()
min_d = zb_df['kdj_d'].rolling(center=False, min_periods=1,
window=20).max()
zb_df['up_ratio'] = (cur_d - min_d) / cur_d
zb_df['down_ratio'] = (max_d - cur_d) / max_d
cns = ['kdj_k', 'kdj_d', 'up_ratio', 'down_ratio']
return cls._extract(zb_df, cns)
def select_x005(cls, st_df):
"""
k60
"""
name = "s005"
kdj_df = ZB.kdj(st_df)
diff = kdj_df['kdj_k'] - kdj_df['kdj_d']
kdj_df["diff"] = diff.ewm(com=2).mean()
x10 = kdj_df["diff"] > kdj_df["diff"].shift(1)
x11 = (kdj_df["kdj_k"] > kdj_df["kdj_k"].shift(1)) & (
kdj_df["kdj_k"] > kdj_df["kdj_k"].shift(2))
x2 = kdj_df["diff"] <= 0
kdj_df[name] = False
kdj_df.ix[x2 & x10 & x11, name] = True
return kdj_df.ix[:, [name]]
def get_ma_values(cls, base_df, dt, ktype="60"):
"""
描述了均线的分布情况
:param base_df:
:param dt:
:param ktype:
:return:
"""
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)
ma_df = ZB.ma(data_df)
ma_df['close'] = data_df['close']
ma_df['m_1_5'] = (ma_df['close'] - ma_df['ma5']) / ma_df['ma5'] * 100
ma_df['m_1_10'] = (ma_df['close'] -
ma_df['ma10']) / ma_df['ma10'] * 100
ma_df['m_5_10'] = (ma_df['ma5'] - ma_df['ma10']) / ma_df['ma10'] * 100
ma_df['m_5_20'] = (ma_df['ma5'] - ma_df['ma20']) / ma_df['ma20'] * 100
ma_df['m_10_20'] = (ma_df['ma10'] -
ma_df['ma20']) / ma_df['ma20'] * 100
ma_df['m_10_50'] = (ma_df['ma10'] -
ma_df['ma50']) / ma_df['ma50'] * 100
ma_df['avg'] = (ma_df['m_1_5'] + ma_df['m_1_10'] + ma_df['m_5_10'] +
ma_df['m_5_20'] + ma_df['m_10_20'] +
ma_df['m_10_50']) / 6
ma_df['abs'] = (ma_df['m_1_5'].abs() + ma_df['m_1_10'].abs() +
ma_df['m_5_10'].abs() + ma_df['m_5_20'].abs() +
ma_df['m_10_20'].abs() + ma_df['m_10_50'].abs()) / 6
cns = [
'm_1_5', 'm_1_10', 'm_5_10', 'm_5_20', 'm_10_20', 'm_10_50', 'avg',
'abs'
]
return cls._extract(ma_df, cns)
# coding=utf-8
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"]