def alpha_42(self):
"""
formula: (ts_rank((volume / adv20), 20) * ts_rank((-1 * delta(close, 7)), 8))
:return:
"""
adv20 = _utils.ts_ma(self.volume, 20)
return _utils.ts_rank(self.volume / adv20, 20) * _utils.ts_rank(
(-1 * _utils.delta(self.close, 7)), 8)
def alpha_35(self):
"""
formula: ((Ts_Rank(volume, 32)* (1 - Ts_Rank(((close + high) - low), 16)))* (1 - Ts_Rank(returns, 32)))
:return: df, need long range to modify nan
"""
return ((_utils.ts_rank(self.volume, 32) *
(1 - _utils.ts_rank(self.close + self.high - self.low, 16))) *
(1 - _utils.ts_rank(self.returns, 32))).fillna(
method='backfill')
def alpha_25(self):
"""
formula: (-1 * ts_max(correlation(ts_rank(volume, 5), ts_rank(high, 5), 5), 3))
"""
return -1 * _utils.ts_max(_utils.correlation(
_utils.ts_rank(self.volume, window=5),
_utils.ts_rank(self.high, window=5),
window=5),
window=3).fillna(method='backfill')
def alpha_26(self):
"""
formula: (-1* ts_max(correlation(ts_rank(volume, 5), ts_rank(high, 5), 5), 3))
"""
df = _utils.correlation(_utils.ts_rank(self.volume, window=5),
_utils.ts_rank(self.high, window=5),
window=3)
df = df.replace([-np.inf, np.inf], 0)
df = -1 * _utils.ts_max(df.fillna(method='backfill'), window=3)
return df.fillna(0.5)
def alpha_17(self):
"""
formula: (((-1 * rank(ts_rank(close, 10))) *
rank(delta(delta(close, 1), 1)))
*rank(ts_rank((volume / adv20), 5)))
:return:
"""
adv20 = _utils.ts_ma(self.volume, 20)
return -1 * (
_utils.rank(_utils.ts_rank(self.close, 10)) * _utils.rank(
_utils.delta(_utils.delta(self.close, period=1), period=1)) *
_utils.rank(_utils.ts_rank(self.volume / adv20, window=5)))
def alpha_4(self):
"""
formula for alpha4: (-1*Ts_Rank(rank(low), 9))
:return time series rank of a window rank
"""
df = -1 * _utils.ts_rank(_utils.rank(self.low), 9)
return df.replace([-np.inf, np.inf], 0)
def alpha_37(self):
"""
formula: ((-1 * rank(Ts_Rank(close, 10))) * rank((close / open)))
:return:
"""
inner = self.close / self.open
inner = inner.replace([-np.inf, np.inf], 1).fillna(value=1)
return -1 * _utils.rank(_utils.ts_rank(self.open,
10)) * _utils.rank(inner)
def alpha_7(self):
"""
formula for alpha7: ((adv20 < volume) ? ((-1*ts_rank(abs(delta(close, 7)), 60))
*sign(delta(close, 7))) : (-1*1))
careful, the self.close should be len>60, so that nan won't generated
"""
adv20 = _utils.ts_ma(self.volume, 20)
alpha = -1 * _utils.ts_rank(abs(_utils.delta(self.close, 7)),
60) * np.sign(_utils.delta(self.close, 7))
alpha[adv20 >= self.volume] = -1
return alpha
def alpha_50(self):
"""
formula: ((((-1 * ts_min(low, 5)) + delay(ts_min(low, 5), 5)) * rank(((sum(returns, 240) -sum(returns, 20))
/ 220))) * ts_rank(volume, 5))
:return: long range data needed
"""
return ((
(-1 * _utils.delta(_utils.ts_min(self.low, 5), 5)) * _utils.rank(
((_utils.ts_sum(self.returns, 240) -
_utils.ts_sum(self.returns, 20)) / 220))) *
_utils.ts_rank(self.volume, 5))
def alpha_29(self):
"""
formula: (min(product(rank(rank(scale(log(sum(ts_min(rank(
rank((-1*rank(delta((close - 1), 5))))), 2), 1))))), 1), 5) +
ts_rank(delay((-1* returns), 6), 5))
"""
df = _utils.ts_min(
_utils.rank(
_utils.rank(
_utils.scale(
np.log(
_utils.ts_sum(
_utils.rank(
_utils.rank(-1 * _utils.rank(
_utils.delta(self.close - 1, 5)))),
2))))), 5) + _utils.ts_rank(
_utils.delay(-1 * self.returns, 6), 5)
return df.fillna(method='backfill')