def alpha_34(self):
"""
formula: rank(((1 - rank((stddev(returns, 2) / stddev(returns, 5)))) + (1 - rank(delta(close, 1)))))
:return:
"""
inner = _utils.stddev(self.returns, 2) / _utils.stddev(self.returns, 5)
inner = inner.replace([-np.inf, np.inf], 1).fillna(value=1)
return _utils.rank(2 - _utils.rank(inner) -
_utils.rank(_utils.delta(self.close, 1)))
def alpha_39(self):
"""
formula: ((-1 * rank(stddev(high, 10))) * correlation(high, volume, 10))
:return:
"""
return -1 * _utils.rank(_utils.stddev(
self.high, 10)) * _utils.correlation(self.high, self.volume, 10)
def alpha_22(self):
"""
formula: (-1 * (delta(correlation(high, volume, 5), 5) * rank(stddev(close, 20))))
"""
df = _utils.correlation(self.high, self.volume, 5)
df = df.replace([-np.inf, np.inf], 0).fillna(value=0)
return -1 * _utils.delta(df, 5) * _utils.rank(
_utils.stddev(self.close, 20))
def alpha_18(self):
"""
formula: (-1 * rank(((stddev(abs((close - open)), 5) + (close - open)) + correlation(close, open,10))))
:return
"""
df = _utils.correlation(self.close, self.open, 10)
df = df.replace([-np.inf, np.inf], 0).fillna(value=0)
return -1 * _utils.rank(
_utils.stddev(abs(self.close - self.open), window=5) + self.close -
self.open) + df
def alpha_1(self):
"""
Wrapper formula for alpha1, (rank(Ts_ArgMax(SignedPower(((returns < 0)
? stddev(returns, 20) : close), 2.), 5)) - 0.5)
:return: time series alpha feature
"""
inner = self.close
# replace all the date where return<0 to std
# 这里会出warning,不过没什么大事
inner[self.returns < 0] = _utils.stddev(self.returns, window=20)
# calculate which day max the time series
ts_argmax = _utils.ts_argmax(inner**2, 5)
return _utils.rank(ts_argmax) - 0.5
def alpha_21(self):
"""
formula: ((((sum(close, 8) / 8) + stddev(close, 8)) < (sum(close, 2) / 2))
? (-1* 1) : (((sum(close, 2) / 2) <
((sum(close, 8) / 8) - stddev(close, 8))) ? 1 : (((1 < (volume / adv20)) or ((volume / adv20) == 1)) ?
1 : (-1* 1))))
need to reconsider
"""
condition_1 = _utils.ts_ma(self.close, 8) + _utils.stddev(
self.close, 8) < _utils.ts_ma(self.close, 2)
condition_2 = _utils.ts_ma(self.volume, 20) / self.volume < 1
alpha = pd.DataFrame(np.ones_like(self.close),
index=self.close.index,
columns=self.close.columns)
alpha[condition_1 | condition_2] = -1
return alpha