def run(self, hd):
'''
Calculate!
'''
delay = 1
rank1 = Op.rank_col(hd['volume'])
rank2 = Op.rank_col(hd['vwap'])
corr = rank1.rolling(5).corr(rank2)
rank3 = Op.rank_col(corr)
tsmax = rank3.rolling(5).max()
data = -tsmax
start = hd['startidx']
end = hd['endidx']
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
data1 = (hd['high'] - hd['low']) / (hd['close'].rolling(5).sum() / 5)
#rank1 = self.rank_col(data1)
rank1 = Op.rank_col(data1.shift(2))
rank2 = Op.rank_col(hd['volume'])
data3 = data1 / (hd['vwap'] - hd['close'])
data3[data3 == 0] = np.nan
data = rank1 * rank2 / data3
start = hd['startidx']
end = hd['endidx']
#self.alpha = data.iloc[start-delay:end+1,:]
alpha = np.full([self._alpha.shape[0], self._alpha.shape[1]], np.nan)
for di in range(start, end + 1):
alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
alpha = Op.Neutralize('IND', alpha, start, end)
print("Neutralize is finished!")
self._alpha = Op.personalize(alpha)
print("Person is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
#data = self.close.shift(5)-self.close
data1 = hd['vwap'] - hd['close']
data1[data1 < 3] = 3
rank1 = Op.rank_col(data1)
data2 = hd['vwap'] - hd['close']
data2[data2 > 3] = 3
rank2 = Op.rank_col(data2)
volume = hd['volume']
volume[volume == 0] = np.nan
rank3 = Op.rank_col(volume - volume.shift(3))
data = rank1 + rank2 * rank3
start = hd['startidx']
end = hd['endidx']
#self.alpha = data.iloc[start-delay:end+1,:]
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
self._alpha = Op.personalize(self._alpha)
print("Personize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
#data = self.close.shift(5)-self.close
rank1 = Op.rank_col(hd['volume'])
rank2 = Op.rank_col(hd['vwap'])
corr = rank1.rolling(5).corr(rank2)
rank3 = Op.rank_col(corr)
data = -1 * rank3.rolling(5).max()
#for ii in range(window, corr.shape[0]+1):
start = hd['startidx']
end = hd['endidx']
#self.alpha = data.iloc[start-delay:end+1,:]
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
self._alpha = Op.personalize(self._alpha)
print("Personize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
#data = self.close.shift(5)-self.close
lnvol = np.log(hd['volume'])
lnvol_d = lnvol - lnvol.shift()
rank1 = Op.rank_col(lnvol_d)
rank2 = Op.rank_col((hd['close'] - hd['open']) / hd['open'])
corr = rank1.rolling(6).corr(rank2)
data = -corr
#data = self.Rank(data)
start = hd['startidx']
end = hd['endidx']
#self.alpha = data.iloc[start-delay:end+1,:]
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
self._alpha = Op.personalize(self._alpha)
print("Personize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
self.volume[self.volume == 0] = np.nan
data1 = self.volume.rolling(5).mean()
data1 = data1.rolling(26).sum()
corr = self.vwap.rolling(5).corr(data1)
data1 = corr.ewm(7).mean()
rank1 = self.rank_col(data1)
rank1 = np.array(rank1)
rank2 = self.rank_col(self.open)
rank3 = self.rank_col(self.volume.rolling(15).mean())
corr2 = rank2.rolling(21).corr(rank3)
window = 7
rank4 = Op.tsrank(corr2, window)
#rank4 = np.full([corr2.shape[0], corr2.shape[1]], np.nan)
#for ii in range(window, corr2.shape[0]+1):
# tmp = corr2.iloc[ii-window:ii,:]
# rank4[ii-1,:] = self.rank_row(tmp).iloc[-1,:]
data2 = pd.DataFrame(rank4)
data2 = data2.ewm(8).mean()
rank5 = self.rank_col(data2)
rank5 = np.array(rank5)
data = rank5 - rank1
data = pd.DataFrame(data)
#start = np.where(data.index == self.start)[0].tolist()[0]
#end = np.where(data.index == self.end)[0].tolist()[0]
#self.alpha = data.iloc[start-delay:end+1,:]
start = hd['startidx']
end = hd['endidx']
for di in range(start, end + 1):
self.alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
#data = self.close.shift(5)-self.close
data = hd['close'].rolling(10).corr(hd['volume'])
data = -Op.rank_col(data)
start = hd['startidx']
end = hd['endidx']
#self.alpha = data.iloc[start-delay:end+1,:]
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
#data = self.close.shift(5)-self.close
data = -hd['amount'].rolling(10).std()
#data = self.Rank(data)
start = hd['startidx']
end = hd['endidx']
#self.alpha = data.iloc[start-delay:end+1,:]
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
self._alpha = Op.personalize(self._alpha)
print("Personize is finished!")
def predict(self):
nums = self.col #each time feed how much data
for key, model in self._models.items():
for ii in range(self.row):
x = self._x[ii * nums:(ii + 1) * nums, :]
xnanrow = np.where(pd.isnull(x))[0]
nanrow = np.unique(xnanrow)
x[pd.isnull(x)] = 0
y = model.predict(x)
y[nanrow] = np.nan
self._alpha[ii, :] = y.reshape(1, -1)
self._alpha = Op.Neutralize('IND', self._alpha, self.start, self.end)
print("Neutralize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 1
#data = self.close.shift(5)-self.close
data1 = ((hd['close'] - hd['low']) -
(hd['high'] - hd['close'])) / (hd['high'] - hd['low'])
data = (data1 - data1.shift()) * -1
#data = self.Rank(data)
start = hd['startidx']
end = hd['endidx']
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
print("Neutralize is finished!")
def run(self, hd):
'''
Calculate!
'''
delay = 0
#data = self.close.shift(5)-self.close
data = hd['close'] - hd['open']
start = hd['startidx']
end = hd['endidx']
#start = np.where(data.index == self.start)[0].tolist()[0]
#end = np.where(data.index == self.end)[0].tolist()[0]
#self.alpha = data.iloc[start-delay:end+1,:]
for di in range(start, end + 1):
self._alpha[di - start, :] = data.iloc[di - delay, :]
print("Alpha is finished!")
self._alpha = Op.Neutralize('IND', self._alpha, start, end)
def run():
pro = ts.pro_api()
today = time.strftime('%Y.%m.%d', time.localtime(time.time()))
today = today.replace('.', '') #consider +1
lastMonth = time.strftime('%Y.%m.%d',
time.localtime(time.time() - 2592000))
lastMonth = lastMonth.replace('.', '')
close = pd.DataFrame()
volume = pd.DataFrame()
stkdata = pro.query('stock_basic',
exchange='',
list_status='L',
fields='ts_code, name, industry')
i = 1
for code in stkdata['ts_code']:
if (i % 1000 == 0):
print("finish", i // 1000, "* 25% work")
i = i + 1
try:
df = ts.pro_bar(ts_code=code,
adj='hfq',
start_date=lastMonth,
end_date=today)
close[code] = df['close'].iloc[::-1]
volume[code] = (df['amount'] / df['close'] * 10).iloc[::-1]
except Exception as e:
print(e)
# close = oclose.copy()
# volume = ovolume.copy()
cols = np.where(pd.isnull(close) | pd.isnull(volume))[1]
colnames = close.columns[cols].unique()
for name in colnames:
del close[name]
del volume[name]
rank1 = Op.rank_col(volume)
rank2 = Op.rank_col(close)
corr = rank1.rolling(5).corr(rank2)
rank3 = Op.rank_col(corr)
tsmax = rank3.rolling(5).max()
data = -tsmax
#alpha = np.full([1, close.shape[1]], np.nan)
alpha = data.iloc[-1, :]
print("Alpha is finished!")
alpha = Neutralize(alpha, stkdata)
#print("Neutralize is finished!")
alpha[pd.isnull(alpha)] = -10
idx = np.argpartition(alpha, -5)[-5:]
stks = alpha[idx]
inx = pro.index_daily(ts_code='000905.SH',
start_date=lastMonth,
end_date=today)['close'][::-1]
trend = (inx.rolling(5).mean() - 1.01 * inx.rolling(10).mean()).iloc[-1]
content = 'trend: ' + str(trend) + '\n'
for ii in range(stks.shape[0]):
content = content + str(stks.index[ii]) + ':' + str(
stks.iloc[ii]) + '\n'
send(content)
return
def __init__(self, v: Operator.Op, pos: int = None) -> None:
super().__init__(v, pos)
self.__chd: List[Tok] = []
self.__argc: int = v.argc()
def v(self, v: Operator.Op) -> None:
Tok.v.fset(self, v)
self.__argc: int = v.argc()