def alpha75(df):
"""
Alpha#75(df)
(rank(correlation(vwap, volume, 4.24304))
< rank(correlation(rank(low), rank(adv50), 12.4413)))
"""
temp1 = u.rank(u.corr(df.vwap, df.volume, 4))
temp2 = u.rank(u.corr(u.rank(df.low), u.rank(u.adv(df, 50)), 12))
return (temp1 < temp2)
def alpha78(df):
"""
Alpha#78
(rank(correlation(sum(((low * 0.352233) + (vwap * (1 - 0.352233))), 19.7428),
sum(adv40, 19.7428), 6.83313))^rank(correlation(rank(vwap), rank(volume), 5.77492)))
"""
temp1 = u.ts_sum(((df.low * 0.352233) + (df.vwap * (1 - 0.352233))), 20)
temp2 = u.rank(u.corr(u.rank(df.vwap), u.rank(df.volume), 6))
temp3 = u.rank(u.corr(temp1, u.ts_sum(u.adv(df, 40), 20), 7))
return (temp3**temp2)
def alpha45(df):
"""
Alpha#45
(-1 * ((rank((sum(delay(close, 5), 20) / 20)) * correlation(close, volume, 2))
* rank(correlation(sum(close, 5), sum(close, 20), 2))))
"""
temp1 = u.rank((u.ts_sum(u.delay(df.close, 5), 20) / 20))
temp2 = u.corr(df.close, df.volume, 2)
temp3 = u.rank(u.corr(u.ts_sum(df.close, 5), u.ts_sum(df.close, 20), 2))
return (-1 * ((temp1 * temp2) * temp3))
def alpha74(df):
"""
Alpha#74
((rank(correlation(close, sum(adv30, 37.4843), 15.1365))
< rank(correlation(rank(((high * 0.0261661)
+ (vwap * (1 - 0.0261661)))), rank(volume), 11.4791))) * -1)
"""
temp1 = u.rank(u.corr(df.close, u.ts_sum(u.adv(df, 30), 37), 15))
temp2 = u.rank(
u.corr(u.rank(((df.high * 0.0261661) + (df.vwap * (1 - 0.0261661)))),
u.rank(df.volume), 11))
return ((temp1 < temp2) * -1)
def alpha99(df):
"""
Alpha#99
((rank(correlation(sum(((high + low) / 2), 19.8975), sum(adv60, 19.8975), 8.8136))
< rank(correlation(low, volume, 6.28259))) * -1)
"""
temp1 = u.rank(
u.corr(u.ts_sum(((df.high + df.low) / 2), 20),
u.ts_sum(u.adv(df, 60), 20), 9))
temp2 = u.rank(u.corr(df.low, df.volume, 6))
return pd.Series(np.where(temp1 < temp2, temp1 * -1, temp2 * -1),
index=df.index)
def alpha98(df):
"""
Alpha#98
(rank(decay_linear(correlation(vwap, sum(adv5, 26.4719), 4.58418), 7.18088)) -
rank(decay_linear(Ts_Rank(Ts_ArgMin(correlation(rank(open), rank(adv15), 20.8187), 8.62571),
6.95668), 8.07206)))
"""
temp1 = u.ts_rank(
u.ts_argmin(u.corr(u.rank(df.open), u.rank(u.adv(df, 15)), 21), 9), 7)
temp2 = u.rank(u.decay_linear(temp1, 8.07206))
temp3 = u.rank(
u.decay_linear(u.corr(df.vwap, u.ts_sum(u.adv(df, 5), 26), 5), 7))
return (temp3 - temp2)
def alpha96(df):
"""
Alpha#96
(max(Ts_Rank(decay_linear(correlation(rank(vwap), rank(volume), 3.83878),
4.16783), 8.38151), Ts_Rank(decay_linear(Ts_ArgMax(correlation(Ts_Rank(close, 7.45404),
Ts_Rank(adv60, 4.13242), 3.65459), 12.6556), 14.0365), 13.4143)) * -1)
"""
temp1 = u.ts_rank(
u.decay_linear(u.corr(u.rank(df.vwap), u.rank(df.volume), 4), 4.16783),
8)
temp2 = u.corr(u.ts_rank(df.close, 7), u.ts_rank(u.adv(df, 60), 4), 4)
temp3 = u.ts_rank(u.decay_linear(u.ts_argmax(temp2, 13), 14.0365), 13)
return pd.Series(np.where(temp1 > temp3, temp1, temp3), index=df.index)
def alpha81(df):
"""
Alpha#81
((rank(Log(product(rank((rank(correlation(vwap, sum(adv10, 49.6054),
8.47743))^4)), 14.9655))) < rank(correlation(rank(vwap), rank(volume), 5.07914))) * -1)
"""
temp = u.rank(
np.log(
u.product(
u.rank(
u.rank(u.corr(df.vwap, u.ts_sum(u.adv(df, 10), 50),
8))**4), 15)))
return ((temp < u.rank(u.corr(u.rank(df.vwap), u.rank(df.volume), 5))) *
-1)
def alpha85(df):
"""
Alpha#85
(rank(correlation(((high * 0.876703) + (close * (1 - 0.876703))), adv30,
9.61331))^rank(correlation(Ts_Rank(((high + low) / 2), 3.70596),
Ts_Rank(volume, 10.1595), 7.11408)))
"""
temp1 = u.rank(
u.corr(((df.high * 0.876703) + (df.close * (1 - 0.876703))),
u.adv(df, 30), 10))
temp2 = u.rank(
u.corr(u.ts_rank(((df.high + df.low) / 2), 4),
u.ts_rank(df.volume, 10), 7))
return (temp1**temp2)
def alpha72(df):
"""
Alpha#72
(rank(decay_linear(correlation(((high + low) / 2), adv40, 8.93345), 10.1519)) /
rank(decay_linear(correlation(Ts_Rank(vwap, 3.72469),
Ts_Rank(volume, 18.5188), 6.86671),2.95011)))
"""
temp1 = u.rank(
u.decay_linear(u.corr(((df.high + df.low) / 2), u.adv(df, 40), 9), 10))
temp2 = u.rank(
u.decay_linear(
u.corr(u.ts_rank(df.vwap, 4), u.ts_rank(df.volume, 19), 7),
2.95011))
return (temp1 / temp2)
def orth_matrix(n=10):
Y = utils.rand(n, 1)
X = utils.zeros(n, n)
if n > 2:
for j in xrange(n - 1):
x = utils.rand(n, 1)
while abs(abs(utils.corr(x, Y)) - j / (n - 1.0)) > 0.005:
x = utils.rand(n, 1)
if utils.corr(x, Y) < 0:
x *= -1
X[:, j] = x.ravel()
X[:, n - 1] = Y.ravel()
return X, Y
def orth_matrix(n=10):
Y = utils.rand(n, 1)
X = utils.zeros(n, n)
if n > 2:
for j in xrange(n - 1):
x = utils.rand(n, 1)
while abs(abs(utils.corr(x, Y)) - j / (n - 1.0)) > 0.005:
x = utils.rand(n, 1)
if utils.corr(x, Y) < 0:
x *= -1
X[:, j] = x.ravel()
X[:, n - 1] = Y.ravel()
return X, Y
def alpha36(df):
"""
Alpha#36
(((((2.21 * rank(correlation((close - open), delay(volume, 1), 15))) +
(0.7 * rank((open - close)))) + (0.73 * rank(Ts_Rank(delay((-1 * returns), 6), 5)))) +
rank(abs(correlation(vwap, adv20, 6)))) + (0.6 * rank((((sum(close, 200) / 200) - open) * (close - open)))))
"""
temp1 = (2.21 *
u.rank(u.corr((df.close - df.open), u.delay(df.volume, 1), 15)))
temp2 = (0.7 * u.rank((df.open - df.close)))
temp3 = (0.73 * u.rank(u.ts_rank(u.delay((-1 * df.returns), 6), 5)))
temp4 = u.rank(abs(u.corr(df.vwap, u.adv(df, 20), 6)))
temp5 = (0.6 * u.rank(
(((sum(df.close, 200) / 200) - df.open) * (df.close - df.open))))
return ((((temp1 + temp2) + temp3) + temp4) + temp5)
def alpha40(df):
"""
Alpha#40
((-1 * rank(stddev(high, 10))) * correlation(high, volume, 10))
"""
return ((-1 * u.rank(u.stddev(df.high, 10))) *
u.corr(df.high, df.volume, 10))
def alpha28(df):
"""
Alpha#28
scale(((correlation(adv20, low, 5) + ((high + low) / 2)) - close))
"""
return u.scale(((u.corr(u.adv(df, 20), df.low, 5) +
((df.high + df.low) / 2)) - df.close))
def alpha50(df):
"""
Alpha#50
(-1 * ts_max(rank(correlation(rank(volume), rank(vwap), 5)), 5))
"""
return (-1 *
u.ts_max(u.rank(u.corr(u.rank(df.volume), u.rank(df.vwap), 5)), 5))
def alpha14(df):
"""
Alpha#14
((-1 * rank(delta(returns, 3))) * correlation(open, volume, 10))
"""
return ((-1 * u.rank(u.delta(df.returns, 3))) *
u.corr(df.open, df.volume, 10))
def alpha22(df):
"""
Alpha#22
(-1 * (delta(correlation(high, volume, 5), 5) * rank(stddev(close, 20))))
"""
return (-1 * (u.delta(u.corr(df.high, df.volume, 5), 5) *
u.rank(u.stddev(df.close, 20))))
def alpha2(df):
"""
Alpha#2
(-1 * correlation(rank(delta(log(volume), 2)), rank(((close - open) / open)), 6))
"""
tmp_1 = u.rank(u.delta(np.log(df.volume), 2))
tmp_2 = u.rank(((df.close - df.open) / df.open))
return (-1 * u.corr(tmp_1, tmp_2, 6))
def alpha26(df):
"""
Alpha#26
(-1 * ts_max(correlation(ts_rank(volume, 5), ts_rank(high, 5), 5), 3))
"""
return (
-1 *
u.ts_max(u.corr(u.ts_rank(df.volume, 5), u.ts_rank(df.high, 5), 5), 3))
def alpha37(df):
"""
Alpha#37
(rank(correlation(delay((open - close), 1), close, 200)) + rank((open - close)))
"""
return (u.rank(u.corr(u.delay(
(df.open - df.close), 1), df.close, 200)) + u.rank(
(df.open - df.close)))
def alpha86(df):
"""
Alpha#86
((Ts_Rank(correlation(close, sum(adv20, 14.7444), 6.00049), 20.4195)
< rank(((open + close) - (vwap + open)))) * -1)
"""
temp1 = u.ts_rank(u.corr(df.close, u.ts_sum(u.adv(df, 20), 15), 6), 20)
temp2 = u.rank(((df.open + df.close) - (df.vwap + df.open)))
return ((temp1 < temp2) * -1)
def alpha27(df):
"""
Alpha#27
((0.5 < rank((sum(correlation(rank(volume), rank(vwap), 6), 2) / 2.0))) ? (-1 * 1) : 1)
"""
temp = np.where((0.5 < u.rank(
(u.ts_sum(u.corr(u.rank(df.volume), u.rank(df.vwap), 6), 2) / 2.0))),
(-1 * 1), 1)
return pd.Series(temp, index=df.index)
def alpha32(df):
"""
Alpha#32
(scale(((sum(close, 7) / 7) - close)) +
(20 * scale(correlation(vwap, delay(close, 5), 230))))
"""
temp1 = u.scale(((u.ts_sum(df.close, 7) / 7) - df.close))
temp2 = (20 * u.scale(u.corr(df.vwap, u.delay(df.close, 5), 230)))
return temp1 + temp2
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 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 alpha62(df):
"""
Alpha#62
((rank(correlation(vwap, sum(adv20, 22.4101), 9.91009)) < rank(((rank(open)
+ rank(open)) < (rank(((high + low) / 2)) + rank(high))))) * -1)
"""
temp1 = u.rank(u.corr(df.vwap, u.ts_sum(u.adv(df, 20), 22), 10))
temp2 = u.rank(((u.rank(df.open) + u.rank(df.open)) < (u.rank(
((df.high + df.low) / 2)) + u.rank(df.high))))
return ((temp1 < temp2) * -1)
def alpha18(df):
"""
Alpha#18
(-1 * rank(((stddev(abs((close - open)), 5) + (close - open)) +
correlation(close, open, 10))))
"""
temp1 = u.stddev(abs((df.close - df.open)), 5)
temp2 = df.close - df.open
temp3 = u.corr(df.close, df.open, 10)
return (-1 * u.rank(temp1 + temp2 + temp3))
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 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 orth_matrix(n=10):
Y = utils.rand(n, 1)
X = utils.zeros(n, n)
if n > 2:
for j in xrange(n - 1):
x = utils.rand(n, 1)
while abs(abs(utils.corr(x, Y)) - j / (n - 1.0)) > 0.005:
x = utils.rand(n, 1)
if utils.corr(x, Y) < 0:
x *= -1
X[:, j] = x.ravel()
X[:, n - 1] = Y.ravel()
return X, Y
#def check_ortho(M, err_msg):
# K = np.dot(M.T, M)
# assert_array_almost_equal(K, np.diag(np.diag(K)), err_msg=err_msg)
