def alpha_20(self):
"""
formula: (((-1 * rank((open - delay(high, 1)))) *
rank((open - delay(close, 1)))) *
rank((open -delay(low, 1))))
"""
return -1 * (_utils.rank(self.open - _utils.delay(self.high, 1)) *
_utils.rank(self.open - _utils.delay(self.close, 1)) *
_utils.rank(self.open - _utils.delay(self.low, 1))
).fillna(method='backfill')
def alpha_30(self):
"""
formula: (((1.0 - rank(((sign((close - delay(close, 1))) + sign((delay(close, 1) - delay(close, 2)))) +
sign((delay(close, 2) - delay(close, 3))))))* sum(volume, 5)) / sum(volume, 20))
"""
delta_close = _utils.delta(self.close, 1) # (close - delay(close, 1)
inner = np.sign(delta_close) + np.sign(_utils.delay(
delta_close, 1)) + np.sign(_utils.delay(delta_close, 2))
df = (1.0 - _utils.rank(inner)) * _utils.ts_sum(
self.volume, 5) / _utils.ts_sum(self.volume, 20)
return df.fillna(method='backfill')
def alpha_49(self):
"""
formula: (((((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)) < (-1 *0.05)) ?
1 : ((-1 * 1) * (close - delay(close, 1))))
:return:
"""
inner = ((
(_utils.delay(self.close, 20) - _utils.delay(self.close, 10)) / 10)
- ((_utils.delay(self.close, 10) - self.close) / 10))
alpha = (-1 * _utils.delta(self.close))
alpha[inner < -0.05] = 1
return alpha
def alpha_45(self):
"""
formula: (0.25 < (((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10))) ?
(-1 * 1) : (((((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)) < 0) ?
1 :((-1 * 1) * (close - delay(close, 1)))))
:return dataframe， inner has lot of nan
"""
inner = ((_utils.delay(self.close, 20) - _utils.delay(self.close, 10)) / 10) - \
((_utils.delay(self.close, 10) - self.close) / 10)
alpha = (-1 * _utils.delta(self.close))
alpha[inner < 0] = 1
alpha[inner > 0.25] = -1
return alpha
def alpha_19(self):
"""
formula: ((-1 * sign(((close - delay(close, 7)) + delta(close, 7)))) * (1 + rank((1 + sum(returns,250)))))
be careful with the time range, less than 250 will be nan
"""
return ((-1 * np.sign((self.close - _utils.delay(self.close, 7)) +
_utils.delta(self.close, 7))) *
(1 + _utils.rank(1 + _utils.ts_sum(self.returns, 250))))
def alpha_36(self):
"""
formula: (rank(correlation(delay((open - close), 1), close, 200)) + rank((open - close)))
:return:dataframe, need long range
"""
return (_utils.rank(
_utils.correlation(_utils.delay(self.open - self.close, 1),
self.close, 200)) +
_utils.rank(self.open - self.close)).fillna(method='backfill')
def alpha_32(self):
"""
formula: (scale(((sum(close, 7) / 7) - close)) + (20* scale(correlation(vwap, delay(close, 5), 230))))
long time series
:return:
"""
return _utils.scale(
((_utils.ts_ma(self.close, 7) / 7) - self.close)
) + (20 * _utils.scale(
_utils.correlation(self.vwap, _utils.delay(self.close, 5), 230)))
def alpha_8(self):
"""
formula: (-1*rank(((sum(open, 5)*sum(returns, 5)) - delay((sum(open, 5)*sum(returns, 5)), 10))))
"""
df = -1 * _utils.rank(
_utils.ts_sum(self.open, window=5) *
_utils.ts_sum(self.returns, window=5) -
_utils.delay(_utils.ts_sum(self.open, window=5) *
_utils.ts_sum(self.returns, window=5),
period=10))
return df.fillna(method='backfill')
def alpha_24(self):
"""
formula: ((((delta((sum(close, 100) / 100), 100) / delay(close, 100)) < 0.05) or
((delta((sum(close, 100) / 100), 100) / delay(close, 100)) == 0.05)) ?
(-1* (close - ts_min(close, 100))) : (-1* delta(close, 3)))
"""
condition = _utils.delta(_utils.ts_ma(self.close, 100),
100) / _utils.delay(self.close, 100) <= 0.05
alpha = -1 * _utils.delta(self.close, 3)
alpha[condition] = -1 * (self.close - _utils.ts_min(self.close, 100))
return alpha
def alpha_46(self):
"""
formula: ((((rank((1 / close)) * volume) / adv20) * ((high * rank((high - close)))
/ (sum(high, 5) /5))) - rank((vwap - delay(vwap, 5))))
:return:
"""
adv20 = _utils.ts_ma(self.volume, 20)
df = ((((_utils.rank((1 / self.close)) * self.volume) / adv20) *
((self.high * _utils.rank((self.high - self.close))) /
(_utils.ts_ma(self.high, 5) / 5))) - _utils.rank(
(self.vwap - _utils.delay(self.vwap, 5))))
return df.fillna(method='backfill')
def alpha_44(self):
"""
formula: (-1 * ((rank((sum(delay(close, 5), 20) / 20)) * correlation(close, volume, 2))
*rank(correlation(sum(close, 5), sum(close, 20), 2))))
:return: dataframe, need more stock and long range
"""
df = _utils.correlation(self.close, self.volume, 2)
df = df.replace([-np.inf, np.inf], 0).fillna(value=0)
df = -1 * (_utils.rank(_utils.ts_ma(_utils.delay(self.close, 5), 20)) *
df * _utils.rank(
_utils.correlation(_utils.ts_sum(self.close, 5),
_utils.ts_sum(self.close, 20), 2)))
return df.fillna(method='backfill')
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')
