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_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_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_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_5(self):
"""
formula for alpha5: (rank((open - (sum(vwap, 10) / 10)))*(-1*abs(rank((close - vwap)))))
:return time series rank of a window rank
"""
return (_utils.rank(self.open -
(_utils.ts_sum(self.vwap, window=10) / 10)) * -1 *
abs(_utils.rank(self.close - self.vwap)))
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_15(self):
"""
formula: (-1 * sum(rank(correlation(rank(high), rank(volume), 3)), 3))
"""
df = _utils.correlation(_utils.rank(self.high),
_utils.rank(self.volume), 3)
df = df.replace([-np.inf, np.inf], 0).fillna(method='backfill')
return (-1 * _utils.ts_sum(_utils.rank(df), 3)).fillna(method='ffill')
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')