def alpha55(df):
"""
Alpha#55
(-1 * correlation(rank(((close - ts_min(low, 12)) / (ts_max(high, 12)
- ts_min(low, 12)))), rank(volume), 6))
"""
temp1 = (df.close - u.ts_min(df.low, 12))
temp2 = (u.ts_max(df.high, 12) - u.ts_min(df.low, 12))
return (-1 * u.corr(u.rank((temp1 / temp2)), u.rank(df.volume), 6))
def alpha52(df):
"""
Alpha#52
((((-1 * ts_min(low, 5)) + delay(ts_min(low, 5), 5)) * rank(((sum(returns, 240)
- sum(returns, 20)) / 220))) * ts_rank(volume, 5))
"""
temp1 = ((-1 * u.ts_min(df.low, 5)) + u.delay(u.ts_min(df.low, 5), 5))
temp2 = u.rank(
((u.ts_sum(df.returns, 240) - u.ts_sum(df.returns, 20)) / 220))
return ((temp1 * temp2) * u.ts_rank(df.volume, 5))
def alpha61(df):
"""
Alpha#61
(rank((vwap - ts_min(vwap, 16.1219))) < rank(correlation(vwap, adv180, 17.9282)))
Rounded the days to int since partial lookback
"""
return (u.rank((df.vwap - u.ts_min(df.vwap, 16))) < u.rank(
u.corr(df.vwap, u.adv(df, 180), 18)))
def alpha65(df):
"""
Alpha#65
((rank(correlation(((open * 0.00817205) +
(vwap * (1 - 0.00817205))), sum(adv60, 8.6911), 6.40374)) < rank((open - ts_min(open, 13.635)))) * -1)
"""
temp1 = (df.open * 0.00817205) + (df.vwap * (1 - 0.00817205))
temp2 = u.rank((df.open - u.ts_min(df.open, 14)))
return ((u.rank(u.corr(temp1, u.ts_sum(u.adv(df, 60), 9), 6)) < temp2) *
-1)
def alpha11(df):
"""
Alpha#11
((rank(ts_max((vwap - close), 3)) +
rank(ts_min((vwap - close), 3))) * rank(delta(volume, 3)))
"""
temp1 = u.rank(u.ts_max((df.vwap - df.close), 3))
temp2 = u.rank(u.ts_min((df.vwap - df.close), 3))
temp3 = u.rank(u.delta(df.volume, 3))
return temp1 + (temp2 * temp3)
def alpha95(df):
"""
Alpha#95
(rank((open - ts_min(open, 12.4105))) < Ts_Rank((rank(correlation(sum(((high + low)
/ 2), 19.1351), sum(adv40, 19.1351), 12.8742))^5), 11.7584))
"""
temp1 = u.rank((df.open - u.ts_min(df.open, 12)))
temp2 = u.corr(u.ts_sum(((df.high + df.low) / 2), 19),
u.ts_sum(u.adv(df, 40), 19), 13)
return (temp1 < u.ts_rank((u.rank(temp2)**5), 12))
def alpha94(df):
"""
Alpha#94
((rank((vwap - ts_min(vwap, 11.5783)))^Ts_Rank(correlation(Ts_Rank(vwap,
19.6462), Ts_Rank(adv60, 4.02992), 18.0926), 2.70756)) * -1)
"""
temp1 = u.rank((df.vwap - u.ts_min(df.vwap, 12)))
temp2 = u.ts_rank(
u.corr(u.ts_rank(df.vwap, 20), u.ts_rank(u.adv(df, 60), 4), 18), 3)
return ((temp1**temp2) * -1)
def alpha9(df):
"""
Alpha#9
((0 < ts_min(delta(close, 1), 5)) ? delta(close, 1) :
((ts_max(delta(close, 1), 5) < 0) ? delta(close, 1) : (-1 * delta(close, 1))))
"""
tempd1 = u.delta(df.close, 1)
tempmin = u.ts_min(tempd1, 5)
tempmax = u.ts_max(tempd1, 5)
return pd.Series(
np.where(tempmin > 0, tempd1,
np.where(tempmax < 0, tempd1, (-1 * tempd1))), df.index)
def alpha24(df):
"""
Alpha#24
Can be shortened without the || (or operator) and just use the <= statement.
((((delta((sum(close, 100) / 100), 100) / delay(close, 100)) < 0.05) ||
((delta((sum(close, 100) / 100), 100) / delay(close, 100)) == 0.05)) ?
(-1 * (close - ts_min(close, 100))) : (-1 * delta(close, 3)))
"""
decision = u.delta(
(u.ts_sum(df.close, 100) / 100), 100) / u.delay(df.close, 100) <= 0.05
if_true = (-1 * (df.close - u.ts_min(df.close, 100)))
if_false = (-1 * u.delta(df.close, 3))
return pd.Series(np.where(decision, if_true, if_false), df.index)
def alpha10(df):
"""
Alpha#10
rank(((0 < ts_min(delta(close, 1), 4)) ? delta(close, 1) : ((ts_max(delta(close, 1), 4) < 0)
? delta(close, 1) : (-1 * delta(close, 1)))))
"""
tempd1 = u.delta(df.close, 1)
tempmin = u.ts_min(tempd1, 4)
tempmax = u.ts_max(tempd1, 4)
return u.rank(
pd.Series(
np.where(tempmin > 0, tempd1,
np.where(tempmax < 0, tempd1, (-1 * tempd1))), df.index))
def alpha29(df):
"""
Alpha#29
(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))
"""
temp1 = u.scale(
np.log(
u.ts_sum(
u.ts_min(
u.rank(u.rank((-1 * u.rank(u.delta((df.close - 1), 5))))),
2), 1)))
temp2 = u.product(u.rank(u.rank(temp1)), 1)
temp3 = u.ts_rank(u.delay((-1 * df.returns), 6), 5)
return (np.where(temp1 < temp2, temp1, temp2) + temp3)
