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kangchihlun/101Alpha_code_2.py

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101Alpha_code_2.py
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101Alpha_code_2.py
import pandas as pd
import numpy as np
from scipy import stats
def make_factors():
# (rank(Ts_ArgMax(SignedPower(((returns < 0) ? stddev(returns, 20) : close), 2.), 5)) - 0.5)
class Alpha1(CustomFactor):
inputs = [USEquityPricing.close, Returns(window_length=2)]
window_length = 20
def compute(self, today, assets, out, close, returns):
v000 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v000000 = returns[-i0]
v000001 = np.full(out.shape[0], 0.0)
v00000 = v000000 < v000001
v000010 = np.empty((20, out.shape[0]))
for i1 in range(1, 21):
v000010[-i1] = returns[-i1]
v00001 = np.std(v000010, axis=0)
v00002 = close[-i0]
v0000lgcl = np.empty(out.shape[0])
v0000lgcl[v00000] = v00001[v00000]
v0000lgcl[~v00000] = v00002[~v00000]
v0000 = v0000lgcl
v0001 = np.full(out.shape[0], 2.0)
v000[-i0] = np.power(v0000, v0001)
v00 = np.argmax(v000, axis=0)
v0 = stats.rankdata(v00)
v1 = np.full(out.shape[0], 0.5)
out[:] = v0 - v1
# (-1 * correlation(rank(delta(log(volume), 2)), rank(((close - open) / open)), 6))
class Alpha2(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.close, USEquityPricing.open]
window_length = 6
def compute(self, today, assets, out, volume, close, open):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v1000 = np.empty((3, out.shape[0]))
for i1 in range(1, 4):
v10000 = volume[-i1]
v1000[-i1] = np.log(v10000)
v100 = v1000[-1] - v1000[-3]
v10[-i0] = stats.rankdata(v100)
v11 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v11000 = close[-i0]
v11001 = open[-i0]
v1100 = v11000 - v11001
v1101 = open[-i0]
v110 = v1100 / v1101
v11[-i0] = stats.rankdata(v110)
v1 = pd.DataFrame(v10).rolling(window=6).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (-1 * correlation(rank(open), rank(volume), 10))
class Alpha3(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.open]
window_length = 10
def compute(self, today, assets, out, volume, open):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v100 = open[-i0]
v10[-i0] = stats.rankdata(v100)
v11 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v110 = volume[-i0]
v11[-i0] = stats.rankdata(v110)
v1 = pd.DataFrame(v10).rolling(window=10).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (-1 * Ts_Rank(rank(low), 9))
class Alpha4(CustomFactor):
inputs = [USEquityPricing.low]
window_length = 9
def compute(self, today, assets, out, low):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((9, out.shape[0]))
for i0 in range(1, 10):
v100 = low[-i0]
v10[-i0] = stats.rankdata(v100)
v1 = pd.DataFrame(v10).rank().tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (rank((open - (sum(vwap, 10) / 10))) * (-1 * abs(rank((close - vwap)))))
class Alpha5(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.close, USEquityPricing.open, vwap_in]
window_length = 10
def compute(self, today, assets, out, close, open, vwap):
v000 = open[-1]
v00100 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v00100[-i0] = vwap[-i0]
v0010 = v00100.sum(axis=0)
v0011 = np.full(out.shape[0], 10.0)
v001 = v0010 / v0011
v00 = v000 - v001
v0 = stats.rankdata(v00)
v10 = np.full(out.shape[0], -1.0)
v11000 = close[-1]
v11001 = vwap[-1]
v1100 = v11000 - v11001
v110 = stats.rankdata(v1100)
v11 = np.abs(v110)
v1 = v10 * v11
out[:] = v0 * v1
# (-1 * correlation(open, volume, 10))
class Alpha6(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.open]
window_length = 10
def compute(self, today, assets, out, volume, open):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v10[-i0] = open[-i0]
v11 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v11[-i0] = volume[-i0]
v1 = pd.DataFrame(v10).rolling(window=10).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# ((adv20 < volume) ? ((-1 * ts_rank(abs(delta(close, 7)), 60)) * sign(delta(close, 7))) : (-1 * 1))
class Alpha7(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, adv20_in]
window_length = 60
def compute(self, today, assets, out, volume, close, adv20):
v00 = adv20[-1]
v01 = volume[-1]
v0 = v00 < v01
v100 = np.full(out.shape[0], -1.0)
v1010 = np.empty((60, out.shape[0]))
for i0 in range(1, 61):
v101000 = np.empty((8, out.shape[0]))
for i1 in range(1, 9):
v101000[-i1] = close[-i1]
v10100 = v101000[-1] - v101000[-8]
v1010[-i0] = np.abs(v10100)
v101 = pd.DataFrame(v1010).rank().tail(1).as_matrix()[-1]
v10 = v100 * v101
v1100 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v1100[-i0] = close[-i0]
v110 = v1100[-1] - v1100[-8]
v11 = np.sign(v110)
v1 = v10 * v11
v20 = np.full(out.shape[0], -1.0)
v21 = np.full(out.shape[0], 1.0)
v2 = v20 * v21
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# (-1 * rank(((sum(open, 5) * sum(returns, 5)) - delay((sum(open, 5) * sum(returns, 5)), 10))))
class Alpha8(CustomFactor):
inputs = [Returns(window_length=2), USEquityPricing.open]
window_length = 15
def compute(self, today, assets, out, returns, open):
v0 = np.full(out.shape[0], -1.0)
v10000 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v10000[-i0] = open[-i0]
v1000 = v10000.sum(axis=0)
v10010 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v10010[-i0] = returns[-i0]
v1001 = v10010.sum(axis=0)
v100 = v1000 * v1001
v101000 = np.empty((5, out.shape[0]))
for i0 in range(11, 16):
v101000[10-i0] = open[-i0]
v10100 = v101000.sum(axis=0)
v101010 = np.empty((5, out.shape[0]))
for i0 in range(11, 16):
v101010[10-i0] = returns[-i0]
v10101 = v101010.sum(axis=0)
v1010 = v10100 * v10101
v101 = v1010 # delay
v10 = v100 - v101
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# ((0 < ts_min(delta(close, 1), 5)) ? delta(close, 1) : ((ts_max(delta(close, 1), 5) < 0) ? delta(close, 1) : (-1 * delta(close, 1))))
class Alpha9(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 5
def compute(self, today, assets, out, close):
v00 = np.full(out.shape[0], 0.0)
v010 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v0100 = np.empty((2, out.shape[0]))
for i1 in range(1, 3):
v0100[-i1] = close[-i1]
v010[-i0] = v0100[-1] - v0100[-2]
v01 = np.min(v010, axis=0)
v0 = v00 < v01
v10 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v10[-i0] = close[-i0]
v1 = v10[-1] - v10[-2]
v2000 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v20000 = np.empty((2, out.shape[0]))
for i1 in range(1, 3):
v20000[-i1] = close[-i1]
v2000[-i0] = v20000[-1] - v20000[-2]
v200 = np.max(v2000, axis=0)
v201 = np.full(out.shape[0], 0.0)
v20 = v200 < v201
v210 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v210[-i0] = close[-i0]
v21 = v210[-1] - v210[-2]
v220 = np.full(out.shape[0], -1.0)
v2210 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v2210[-i0] = close[-i0]
v221 = v2210[-1] - v2210[-2]
v22 = v220 * v221
v2lgcl = np.empty(out.shape[0])
v2lgcl[v20] = v21[v20]
v2lgcl[~v20] = v22[~v20]
v2 = v2lgcl
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# 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)))))
class Alpha10(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 4
def compute(self, today, assets, out, close):
v000 = np.full(out.shape[0], 0.0)
v0010 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v00100 = np.empty((2, out.shape[0]))
for i1 in range(1, 3):
v00100[-i1] = close[-i1]
v0010[-i0] = v00100[-1] - v00100[-2]
v001 = np.min(v0010, axis=0)
v00 = v000 < v001
v010 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v010[-i0] = close[-i0]
v01 = v010[-1] - v010[-2]
v02000 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v020000 = np.empty((2, out.shape[0]))
for i1 in range(1, 3):
v020000[-i1] = close[-i1]
v02000[-i0] = v020000[-1] - v020000[-2]
v0200 = np.max(v02000, axis=0)
v0201 = np.full(out.shape[0], 0.0)
v020 = v0200 < v0201
v0210 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v0210[-i0] = close[-i0]
v021 = v0210[-1] - v0210[-2]
v0220 = np.full(out.shape[0], -1.0)
v02210 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v02210[-i0] = close[-i0]
v0221 = v02210[-1] - v02210[-2]
v022 = v0220 * v0221
v02lgcl = np.empty(out.shape[0])
v02lgcl[v020] = v021[v020]
v02lgcl[~v020] = v022[~v020]
v02 = v02lgcl
v0lgcl = np.empty(out.shape[0])
v0lgcl[v00] = v01[v00]
v0lgcl[~v00] = v02[~v00]
v0 = v0lgcl
out[:] = stats.rankdata(v0)
# ((rank(ts_max((vwap - close), 3)) + rank(ts_min((vwap - close), 3))) * rank(delta(volume, 3)))
class Alpha11(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, vwap_in]
window_length = 4
def compute(self, today, assets, out, volume, close, vwap):
v0000 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v00000 = vwap[-i0]
v00001 = close[-i0]
v0000[-i0] = v00000 - v00001
v000 = np.max(v0000, axis=0)
v00 = stats.rankdata(v000)
v0100 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v01000 = vwap[-i0]
v01001 = close[-i0]
v0100[-i0] = v01000 - v01001
v010 = np.min(v0100, axis=0)
v01 = stats.rankdata(v010)
v0 = v00 + v01
v100 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v100[-i0] = volume[-i0]
v10 = v100[-1] - v100[-4]
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# (sign(delta(volume, 1)) * (-1 * delta(close, 1)))
class Alpha12(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.close]
window_length = 2
def compute(self, today, assets, out, volume, close):
v000 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v000[-i0] = volume[-i0]
v00 = v000[-1] - v000[-2]
v0 = np.sign(v00)
v10 = np.full(out.shape[0], -1.0)
v110 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v110[-i0] = close[-i0]
v11 = v110[-1] - v110[-2]
v1 = v10 * v11
out[:] = v0 * v1
# (-1 * rank(covariance(rank(close), rank(volume), 5)))
class Alpha13(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.close]
window_length = 5
def compute(self, today, assets, out, volume, close):
v0 = np.full(out.shape[0], -1.0)
v100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1000 = close[-i0]
v100[-i0] = stats.rankdata(v1000)
v101 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1010 = volume[-i0]
v101[-i0] = stats.rankdata(v1010)
v10 = pd.DataFrame(v100).rolling(window=5).corr(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# ((-1 * rank(delta(returns, 3))) * correlation(open, volume, 10))
class Alpha14(CustomFactor):
inputs = [USEquityPricing.volume, Returns(window_length=2), USEquityPricing.open]
window_length = 10
def compute(self, today, assets, out, volume, returns, open):
v00 = np.full(out.shape[0], -1.0)
v0100 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v0100[-i0] = returns[-i0]
v010 = v0100[-1] - v0100[-4]
v01 = stats.rankdata(v010)
v0 = v00 * v01
v10 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v10[-i0] = open[-i0]
v11 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v11[-i0] = volume[-i0]
v1 = pd.DataFrame(v10).rolling(window=10).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (-1 * sum(rank(correlation(rank(high), rank(volume), 3)), 3))
class Alpha15(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.volume]
window_length = 3
def compute(self, today, assets, out, high, volume):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v1000 = np.empty((3, out.shape[0]))
for i1 in range(1, 4):
v10000 = high[-i1]
v1000[-i1] = stats.rankdata(v10000)
v1001 = np.empty((3, out.shape[0]))
for i1 in range(1, 4):
v10010 = volume[-i1]
v1001[-i1] = stats.rankdata(v10010)
v100 = pd.DataFrame(v1000).rolling(window=3).cov(pd.DataFrame(v1001)).tail(1).as_matrix()[-1]
v10[-i0] = stats.rankdata(v100)
v1 = v10.sum(axis=0)
out[:] = v0 * v1
# (-1 * rank(covariance(rank(high), rank(volume), 5)))
class Alpha16(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.volume]
window_length = 5
def compute(self, today, assets, out, high, volume):
v0 = np.full(out.shape[0], -1.0)
v100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1000 = high[-i0]
v100[-i0] = stats.rankdata(v1000)
v101 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1010 = volume[-i0]
v101[-i0] = stats.rankdata(v1010)
v10 = pd.DataFrame(v100).rolling(window=5).corr(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# (((-1 * rank(ts_rank(close, 10))) * rank(delta(delta(close, 1), 1))) * rank(ts_rank((volume / adv20), 5)))
class Alpha17(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, adv20_in]
window_length = 10
def compute(self, today, assets, out, volume, close, adv20):
v000 = np.full(out.shape[0], -1.0)
v00100 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v00100[-i0] = close[-i0]
v0010 = pd.DataFrame(v00100).rank().tail(1).as_matrix()[-1]
v001 = stats.rankdata(v0010)
v00 = v000 * v001
v0100 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v01000 = np.empty((2, out.shape[0]))
for i1 in range(1, 3):
v01000[-i1] = close[-i1]
v0100[-i0] = v01000[-1] - v01000[-2]
v010 = v0100[-1] - v0100[-2]
v01 = stats.rankdata(v010)
v0 = v00 * v01
v100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1000 = volume[-i0]
v1001 = adv20[-i0]
v100[-i0] = v1000 / v1001
v10 = pd.DataFrame(v100).rank().tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# (-1 * rank(((stddev(abs((close - open)), 5) + (close - open)) + correlation(close, open, 10))))
class Alpha18(CustomFactor):
inputs = [USEquityPricing.close, USEquityPricing.open]
window_length = 10
def compute(self, today, assets, out, close, open):
v0 = np.full(out.shape[0], -1.0)
v10000 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1000000 = close[-i0]
v1000001 = open[-i0]
v100000 = v1000000 - v1000001
v10000[-i0] = np.abs(v100000)
v1000 = np.std(v10000, axis=0)
v10010 = close[-1]
v10011 = open[-1]
v1001 = v10010 - v10011
v100 = v1000 + v1001
v1010 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v1010[-i0] = close[-i0]
v1011 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v1011[-i0] = open[-i0]
v101 = pd.DataFrame(v1010).rolling(window=10).cov(pd.DataFrame(v1011)).tail(1).as_matrix()[-1]
v10 = v100 + v101
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# ((-1 * sign(((close - delay(close, 7)) + delta(close, 7)))) * (1 + rank((1 + sum(returns, 250)))))
class Alpha19(CustomFactor):
inputs = [USEquityPricing.close, Returns(window_length=2)]
window_length = 250
def compute(self, today, assets, out, close, returns):
v00 = np.full(out.shape[0], -1.0)
v01000 = close[-1]
v010010 = close[-1]
v01001 = v010010 # delay
v0100 = v01000 - v01001
v01010 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v01010[-i0] = close[-i0]
v0101 = v01010[-1] - v01010[-8]
v010 = v0100 + v0101
v01 = np.sign(v010)
v0 = v00 * v01
v10 = np.full(out.shape[0], 1.0)
v1100 = np.full(out.shape[0], 1.0)
v11010 = np.empty((250, out.shape[0]))
for i0 in range(1, 251):
v11010[-i0] = returns[-i0]
v1101 = v11010.sum(axis=0)
v110 = v1100 + v1101
v11 = stats.rankdata(v110)
v1 = v10 + v11
out[:] = v0 * v1
# (((-1 * rank((open - delay(high, 1)))) * rank((open - delay(close, 1)))) * rank((open - delay(low, 1))))
class Alpha20(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.open, USEquityPricing.low]
window_length = 2
def compute(self, today, assets, out, high, close, open, low):
v000 = np.full(out.shape[0], -1.0)
v00100 = open[-1]
v001010 = high[-1]
v00101 = v001010 # delay
v0010 = v00100 - v00101
v001 = stats.rankdata(v0010)
v00 = v000 * v001
v0100 = open[-1]
v01010 = close[-1]
v0101 = v01010 # delay
v010 = v0100 - v0101
v01 = stats.rankdata(v010)
v0 = v00 * v01
v100 = open[-1]
v1010 = low[-1]
v101 = v1010 # delay
v10 = v100 - v101
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# ((((sum(close, 8) / 8) + stddev(close, 8)) < (sum(close, 2) / 2)) ? (-1 * 1) : (((sum(close, 2) / 2) < ((sum(close, 8) / 8) - stddev(close, 8))) ? 1 : (((1 < (volume / adv20)) || ((volume / adv20) == 1)) ? 1 : (-1 * 1))))
class Alpha21(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, adv20_in]
window_length = 8
def compute(self, today, assets, out, volume, close, adv20):
v00000 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v00000[-i0] = close[-i0]
v0000 = v00000.sum(axis=0)
v0001 = np.full(out.shape[0], 8.0)
v000 = v0000 / v0001
v0010 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v0010[-i0] = close[-i0]
v001 = np.std(v0010, axis=0)
v00 = v000 + v001
v0100 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v0100[-i0] = close[-i0]
v010 = v0100.sum(axis=0)
v011 = np.full(out.shape[0], 2.0)
v01 = v010 / v011
v0 = v00 < v01
v10 = np.full(out.shape[0], -1.0)
v11 = np.full(out.shape[0], 1.0)
v1 = v10 * v11
v20000 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v20000[-i0] = close[-i0]
v2000 = v20000.sum(axis=0)
v2001 = np.full(out.shape[0], 2.0)
v200 = v2000 / v2001
v201000 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v201000[-i0] = close[-i0]
v20100 = v201000.sum(axis=0)
v20101 = np.full(out.shape[0], 8.0)
v2010 = v20100 / v20101
v20110 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v20110[-i0] = close[-i0]
v2011 = np.std(v20110, axis=0)
v201 = v2010 - v2011
v20 = v200 < v201
v21 = np.full(out.shape[0], 1.0)
v22000 = np.full(out.shape[0], 1.0)
v220010 = volume[-1]
v220011 = adv20[-1]
v22001 = v220010 / v220011
v2200 = v22000 < v22001
v220100 = volume[-1]
v220101 = adv20[-1]
v22010 = v220100 / v220101
v22011 = np.full(out.shape[0], 1.0)
v2201 = v22010 == v22011
v220 = v2200 | v2201
v221 = np.full(out.shape[0], 1.0)
v2220 = np.full(out.shape[0], -1.0)
v2221 = np.full(out.shape[0], 1.0)
v222 = v2220 * v2221
v22lgcl = np.empty(out.shape[0])
v22lgcl[v220] = 1
v22lgcl[~v220] = v222[~v220]
v22 = v22lgcl
v2lgcl = np.empty(out.shape[0])
v2lgcl[v20] = 1
v2lgcl[~v20] = v22[~v20]
v2 = v2lgcl
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# (-1 * (delta(correlation(high, volume, 5), 5) * rank(stddev(close, 20))))
class Alpha22(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.volume, USEquityPricing.close]
window_length = 20
def compute(self, today, assets, out, high, volume, close):
v0 = np.full(out.shape[0], -1.0)
v100 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v1000 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v1000[-i1] = high[-i1]
v1001 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v1001[-i1] = volume[-i1]
v100[-i0] = pd.DataFrame(v1000).rolling(window=5).cov(pd.DataFrame(v1001)).tail(1).as_matrix()[-1]
v10 = v100[-1] - v100[-6]
v1100 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v1100[-i0] = close[-i0]
v110 = np.std(v1100, axis=0)
v11 = stats.rankdata(v110)
v1 = v10 * v11
out[:] = v0 * v1
# (((sum(high, 20) / 20) < high) ? (-1 * delta(high, 2)) : 0)
class Alpha23(CustomFactor):
inputs = [USEquityPricing.high]
window_length = 20
def compute(self, today, assets, out, high):
v0000 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v0000[-i0] = high[-i0]
v000 = v0000.sum(axis=0)
v001 = np.full(out.shape[0], 20.0)
v00 = v000 / v001
v01 = high[-1]
v0 = v00 < v01
v10 = np.full(out.shape[0], -1.0)
v110 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v110[-i0] = high[-i0]
v11 = v110[-1] - v110[-3]
v1 = v10 * v11
v2 = np.full(out.shape[0], 0.0)
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = 0
out[:] = vlgcl
# ((((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)))
class Alpha24(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 101
def compute(self, today, assets, out, close):
v00000 = np.empty((101, out.shape[0]))
for i0 in range(1, 102):
v0000000 = np.empty((100, out.shape[0]))
for i1 in range(1, 101):
v0000000[-i1] = close[-i1]
v000000 = v0000000.sum(axis=0)
v000001 = np.full(out.shape[0], 100.0)
v00000[-i0] = v000000 / v000001
v0000 = v00000[-1] - v00000[-101]
v00010 = close[-1]
v0001 = v00010 # delay
v000 = v0000 / v0001
v001 = np.full(out.shape[0], 0.05)
v00 = v000 < v001
v01000 = np.empty((101, out.shape[0]))
for i0 in range(1, 102):
v0100000 = np.empty((100, out.shape[0]))
for i1 in range(1, 101):
v0100000[-i1] = close[-i1]
v010000 = v0100000.sum(axis=0)
v010001 = np.full(out.shape[0], 100.0)
v01000[-i0] = v010000 / v010001
v0100 = v01000[-1] - v01000[-101]
v01010 = close[-1]
v0101 = v01010 # delay
v010 = v0100 / v0101
v011 = np.full(out.shape[0], 0.05)
v01 = v010 == v011
v0 = v00 | v01
v10 = np.full(out.shape[0], -1.0)
v110 = close[-1]
v1110 = np.empty((100, out.shape[0]))
for i0 in range(1, 101):
v1110[-i0] = close[-i0]
v111 = np.min(v1110, axis=0)
v11 = v110 - v111
v1 = v10 * v11
v20 = np.full(out.shape[0], -1.0)
v210 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v210[-i0] = close[-i0]
v21 = v210[-1] - v210[-4]
v2 = v20 * v21
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# rank(((((-1 * returns) * adv20) * vwap) * (high - close)))
class Alpha25(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.close, Returns(window_length=2), adv20_in, vwap_in]
window_length = 2
def compute(self, today, assets, out, high, close, returns, adv20, vwap):
v00000 = np.full(out.shape[0], -1.0)
v00001 = returns[-1]
v0000 = v00000 * v00001
v0001 = adv20[-1]
v000 = v0000 * v0001
v001 = vwap[-1]
v00 = v000 * v001
v010 = high[-1]
v011 = close[-1]
v01 = v010 - v011
v0 = v00 * v01
out[:] = stats.rankdata(v0)
# (-1 * ts_max(correlation(ts_rank(volume, 5), ts_rank(high, 5), 5), 3))
class Alpha26(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.high]
window_length = 5
def compute(self, today, assets, out, volume, high):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v100 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v1000 = np.empty((5, out.shape[0]))
for i2 in range(1, 6):
v1000[-i2] = volume[-i2]
v100[-i1] = pd.DataFrame(v1000).rank().tail(1).as_matrix()[-1]
v101 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v1010 = np.empty((5, out.shape[0]))
for i2 in range(1, 6):
v1010[-i2] = high[-i2]
v101[-i1] = pd.DataFrame(v1010).rank().tail(1).as_matrix()[-1]
v10[-i0] = pd.DataFrame(v100).rolling(window=5).cov(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = np.max(v10, axis=0)
out[:] = v0 * v1
# ((0.5 < rank((sum(correlation(rank(volume), rank(vwap), 6), 2) / 2.0))) ? (-1 * 1) : 1)
class Alpha27(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.volume, vwap_in]
window_length = 6
def compute(self, today, assets, out, volume, vwap):
v00 = np.full(out.shape[0], 0.5)
v01000 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v010000 = np.empty((6, out.shape[0]))
for i1 in range(1, 7):
v0100000 = volume[-i1]
v010000[-i1] = stats.rankdata(v0100000)
v010001 = np.empty((6, out.shape[0]))
for i1 in range(1, 7):
v0100010 = vwap[-i1]
v010001[-i1] = stats.rankdata(v0100010)
v01000[-i0] = pd.DataFrame(v010000).rolling(window=6).cov(pd.DataFrame(v010001)).tail(1).as_matrix()[-1]
v0100 = v01000.sum(axis=0)
v0101 = np.full(out.shape[0], 2.0)
v010 = v0100 / v0101
v01 = stats.rankdata(v010)
v0 = v00 < v01
v10 = np.full(out.shape[0], -1.0)
v11 = np.full(out.shape[0], 1.0)
v1 = v10 * v11
v2 = np.full(out.shape[0], 1.0)
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = 1
out[:] = vlgcl
# scale(((correlation(adv20, low, 5) + ((high + low) / 2)) - close))
class Alpha28(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.close, adv20_in, USEquityPricing.low]
window_length = 5
def compute(self, today, assets, out, high, close, adv20, low):
v0000 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v0000[-i0] = adv20[-i0]
v0001 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v0001[-i0] = low[-i0]
v000 = pd.DataFrame(v0000).rolling(window=5).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00100 = high[-1]
v00101 = low[-1]
v0010 = v00100 + v00101
v0011 = np.full(out.shape[0], 2.0)
v001 = v0010 / v0011
v00 = v000 + v001
v01 = close[-1]
v0 = v00 - v01
out[:] = v0/np.abs(v0).sum()
# (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))
class Alpha29(CustomFactor):
inputs = [USEquityPricing.close, Returns(window_length=2)]
window_length = 7
def compute(self, today, assets, out, close, returns):
v00 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v000 = np.empty((1, out.shape[0]))
for i1 in range(1, 2):
v00000000 = np.empty((1, out.shape[0]))
for i2 in range(1, 2):
v000000000 = np.empty((2, out.shape[0]))
for i3 in range(1, 3):
v000000000000 = np.full(out.shape[0], -1.0)
v00000000000100 = np.empty((6, out.shape[0]))
for i4 in range(1, 7):
v000000000001000 = close[-i4]
v000000000001001 = np.full(out.shape[0], 1.0)
v00000000000100[-i4] = v000000000001000 - v000000000001001
v0000000000010 = v00000000000100[-1] - v00000000000100[-6]
v000000000001 = stats.rankdata(v0000000000010)
v00000000000 = v000000000000 * v000000000001
v0000000000 = stats.rankdata(v00000000000)
v000000000[-i3] = stats.rankdata(v0000000000)
v00000000[-i2] = np.min(v000000000, axis=0)
v0000000 = v00000000.sum(axis=0)
v000000 = np.log(v0000000)
v00000 = v000000/np.abs(v000000).sum()
v0000 = stats.rankdata(v00000)
v000[-i1] = stats.rankdata(v0000)
v00[-i0] = np.prod(v000, axis=0)
v0 = np.min(v00, axis=0)
v10 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1000 = np.full(out.shape[0], -1.0)
v1001 = returns[-1]
v100 = v1000 * v1001
v10[-i0] = v100 # delay
v1 = pd.DataFrame(v10).rank().tail(1).as_matrix()[-1]
out[:] = v0 + v1
# (((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))
class Alpha30(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.close]
window_length = 20
def compute(self, today, assets, out, volume, close):
v000 = np.full(out.shape[0], 1.0)
v00100000 = close[-1]
v001000010 = close[-1]
v00100001 = v001000010 # delay
v0010000 = v00100000 - v00100001
v001000 = np.sign(v0010000)
v001001000 = close[-1]
v00100100 = v001001000 # delay
v001001010 = close[-1]
v00100101 = v001001010 # delay
v0010010 = v00100100 - v00100101
v001001 = np.sign(v0010010)
v00100 = v001000 + v001001
v00101000 = close[-1]
v0010100 = v00101000 # delay
v00101010 = close[-1]
v0010101 = v00101010 # delay
v001010 = v0010100 - v0010101
v00101 = np.sign(v001010)
v0010 = v00100 + v00101
v001 = stats.rankdata(v0010)
v00 = v000 - v001
v010 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v010[-i0] = volume[-i0]
v01 = v010.sum(axis=0)
v0 = v00 * v01
v10 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v10[-i0] = volume[-i0]
v1 = v10.sum(axis=0)
out[:] = v0 / v1
# ((rank(rank(rank(decay_linear((-1 * rank(rank(delta(close, 10)))), 10)))) + rank((-1 * delta(close, 3)))) + sign(scale(correlation(adv20, low, 12))))
class Alpha31(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.close, adv20_in, USEquityPricing.low]
window_length = 12
def compute(self, today, assets, out, close, adv20, low):
v000000 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v0000000 = np.full(out.shape[0], -1.0)
v0000001000 = np.empty((11, out.shape[0]))
for i1 in range(1, 12):
v0000001000[-i1] = close[-i1]
v000000100 = v0000001000[-1] - v0000001000[-11]
v00000010 = stats.rankdata(v000000100)
v0000001 = stats.rankdata(v00000010)
v000000[-i0] = v0000000 * v0000001
v00000 = (v000000 * (np.arange(1.0, 11, 1.0)/55)[:, np.newaxis]).sum(axis=0) # decay_linear
v0000 = stats.rankdata(v00000)
v000 = stats.rankdata(v0000)
v00 = stats.rankdata(v000)
v0100 = np.full(out.shape[0], -1.0)
v01010 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v01010[-i0] = close[-i0]
v0101 = v01010[-1] - v01010[-4]
v010 = v0100 * v0101
v01 = stats.rankdata(v010)
v0 = v00 + v01
v1000 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v1000[-i0] = adv20[-i0]
v1001 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v1001[-i0] = low[-i0]
v100 = pd.DataFrame(v1000).rolling(window=12).cov(pd.DataFrame(v1001)).tail(1).as_matrix()[-1]
v10 = v100/np.abs(v100).sum()
v1 = np.sign(v10)
out[:] = v0 + v1
# (scale(((sum(close, 7) / 7) - close)) + (20 * scale(correlation(vwap, delay(close, 5), 230))))
class Alpha32(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.close, vwap_in]
window_length = 230
def compute(self, today, assets, out, close, vwap):
v00000 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v00000[-i0] = close[-i0]
v0000 = v00000.sum(axis=0)
v0001 = np.full(out.shape[0], 7.0)
v000 = v0000 / v0001
v001 = close[-1]
v00 = v000 - v001
v0 = v00/np.abs(v00).sum()
v10 = np.full(out.shape[0], 20.0)
v1100 = np.empty((230, out.shape[0]))
for i0 in range(1, 231):
v1100[-i0] = vwap[-i0]
v1101 = np.empty((230, out.shape[0]))
for i0 in range(1, 231):
v11010 = close[-1]
v1101[-i0] = v11010 # delay
v110 = pd.DataFrame(v1100).rolling(window=230).cov(pd.DataFrame(v1101)).tail(1).as_matrix()[-1]
v11 = v110/np.abs(v110).sum()
v1 = v10 * v11
out[:] = v0 + v1
# rank((-1 * ((1 - (open / close))^1)))
class Alpha33(CustomFactor):
inputs = [USEquityPricing.close, USEquityPricing.open]
window_length = 2
def compute(self, today, assets, out, close, open):
v00 = np.full(out.shape[0], -1.0)
v0100 = np.full(out.shape[0], 1.0)
v01010 = open[-1]
v01011 = close[-1]
v0101 = v01010 / v01011
v010 = v0100 - v0101
v011 = np.full(out.shape[0], 1.0)
v01 = np.power(v010, v011)
v0 = v00 * v01
out[:] = stats.rankdata(v0)
# rank(((1 - rank((stddev(returns, 2) / stddev(returns, 5)))) + (1 - rank(delta(close, 1)))))
class Alpha34(CustomFactor):
inputs = [USEquityPricing.close, Returns(window_length=2)]
window_length = 5
def compute(self, today, assets, out, close, returns):
v000 = np.full(out.shape[0], 1.0)
v001000 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v001000[-i0] = returns[-i0]
v00100 = np.std(v001000, axis=0)
v001010 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v001010[-i0] = returns[-i0]
v00101 = np.std(v001010, axis=0)
v0010 = v00100 / v00101
v001 = stats.rankdata(v0010)
v00 = v000 - v001
v010 = np.full(out.shape[0], 1.0)
v01100 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v01100[-i0] = close[-i0]
v0110 = v01100[-1] - v01100[-2]
v011 = stats.rankdata(v0110)
v01 = v010 - v011
v0 = v00 + v01
out[:] = stats.rankdata(v0)
# ((Ts_Rank(volume, 32) * (1 - Ts_Rank(((close + high) - low), 16))) * (1 - Ts_Rank(returns, 32)))
class Alpha35(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.close, USEquityPricing.high, Returns(window_length=2), USEquityPricing.low]
window_length = 32
def compute(self, today, assets, out, volume, close, high, returns, low):
v000 = np.empty((32, out.shape[0]))
for i0 in range(1, 33):
v000[-i0] = volume[-i0]
v00 = pd.DataFrame(v000).rank().tail(1).as_matrix()[-1]
v010 = np.full(out.shape[0], 1.0)
v0110 = np.empty((16, out.shape[0]))
for i0 in range(1, 17):
v011000 = close[-i0]
v011001 = high[-i0]
v01100 = v011000 + v011001
v01101 = low[-i0]
v0110[-i0] = v01100 - v01101
v011 = pd.DataFrame(v0110).rank().tail(1).as_matrix()[-1]
v01 = v010 - v011
v0 = v00 * v01
v10 = np.full(out.shape[0], 1.0)
v110 = np.empty((32, out.shape[0]))
for i0 in range(1, 33):
v110[-i0] = returns[-i0]
v11 = pd.DataFrame(v110).rank().tail(1).as_matrix()[-1]
v1 = v10 - v11
out[:] = v0 * v1
# (((((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)))))
class Alpha36(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [adv20_in, vwap_in, USEquityPricing.volume, Returns(window_length=2), USEquityPricing.close, USEquityPricing.open]
window_length = 200
def compute(self, today, assets, out, adv20, vwap, volume, returns, close, open):
v00000 = np.full(out.shape[0], 2.21)
v0000100 = np.empty((15, out.shape[0]))
for i0 in range(1, 16):
v00001000 = close[-i0]
v00001001 = open[-i0]
v0000100[-i0] = v00001000 - v00001001
v0000101 = np.empty((15, out.shape[0]))
for i0 in range(1, 16):
v00001010 = volume[-1]
v0000101[-i0] = v00001010 # delay
v000010 = pd.DataFrame(v0000100).rolling(window=15).cov(pd.DataFrame(v0000101)).tail(1).as_matrix()[-1]
v00001 = stats.rankdata(v000010)
v0000 = v00000 * v00001
v00010 = np.full(out.shape[0], 0.7)
v0001100 = open[-1]
v0001101 = close[-1]
v000110 = v0001100 - v0001101
v00011 = stats.rankdata(v000110)
v0001 = v00010 * v00011
v000 = v0000 + v0001
v0010 = np.full(out.shape[0], 0.73)
v001100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v00110000 = np.full(out.shape[0], -1.0)
v00110001 = returns[-1]
v0011000 = v00110000 * v00110001
v001100[-i0] = v0011000 # delay
v00110 = pd.DataFrame(v001100).rank().tail(1).as_matrix()[-1]
v0011 = stats.rankdata(v00110)
v001 = v0010 * v0011
v00 = v000 + v001
v01000 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v01000[-i0] = vwap[-i0]
v01001 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v01001[-i0] = adv20[-i0]
v0100 = pd.DataFrame(v01000).rolling(window=6).cov(pd.DataFrame(v01001)).tail(1).as_matrix()[-1]
v010 = np.abs(v0100)
v01 = stats.rankdata(v010)
v0 = v00 + v01
v10 = np.full(out.shape[0], 0.6)
v1100000 = np.empty((200, out.shape[0]))
for i0 in range(1, 201):
v1100000[-i0] = close[-i0]
v110000 = v1100000.sum(axis=0)
v110001 = np.full(out.shape[0], 200.0)
v11000 = v110000 / v110001
v11001 = open[-1]
v1100 = v11000 - v11001
v11010 = close[-1]
v11011 = open[-1]
v1101 = v11010 - v11011
v110 = v1100 * v1101
v11 = stats.rankdata(v110)
v1 = v10 * v11
out[:] = v0 + v1
# (rank(correlation(delay((open - close), 1), close, 200)) + rank((open - close)))
class Alpha37(CustomFactor):
inputs = [USEquityPricing.close, USEquityPricing.open]
window_length = 200
def compute(self, today, assets, out, close, open):
v000 = np.empty((200, out.shape[0]))
for i0 in range(1, 201):
v00000 = open[-1]
v00001 = close[-1]
v0000 = v00000 - v00001
v000[-i0] = v0000 # delay
v001 = np.empty((200, out.shape[0]))
for i0 in range(1, 201):
v001[-i0] = close[-i0]
v00 = pd.DataFrame(v000).rolling(window=200).cov(pd.DataFrame(v001)).tail(1).as_matrix()[-1]
v0 = stats.rankdata(v00)
v100 = open[-1]
v101 = close[-1]
v10 = v100 - v101
v1 = stats.rankdata(v10)
out[:] = v0 + v1
# ((-1 * rank(Ts_Rank(close, 10))) * rank((close / open)))
class Alpha38(CustomFactor):
inputs = [USEquityPricing.close, USEquityPricing.open]
window_length = 10
def compute(self, today, assets, out, close, open):
v00 = np.full(out.shape[0], -1.0)
v0100 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v0100[-i0] = close[-i0]
v010 = pd.DataFrame(v0100).rank().tail(1).as_matrix()[-1]
v01 = stats.rankdata(v010)
v0 = v00 * v01
v100 = close[-1]
v101 = open[-1]
v10 = v100 / v101
v1 = stats.rankdata(v10)
out[:] = v0 * v1
# ((-1 * rank((delta(close, 7) * (1 - rank(decay_linear((volume / adv20), 9)))))) * (1 + rank(sum(returns, 250))))
class Alpha39(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, Returns(window_length=2), adv20_in]
window_length = 250
def compute(self, today, assets, out, volume, close, returns, adv20):
v00 = np.full(out.shape[0], -1.0)
v01000 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v01000[-i0] = close[-i0]
v0100 = v01000[-1] - v01000[-8]
v01010 = np.full(out.shape[0], 1.0)
v0101100 = np.empty((9, out.shape[0]))
for i0 in range(1, 10):
v01011000 = volume[-i0]
v01011001 = adv20[-i0]
v0101100[-i0] = v01011000 / v01011001
v010110 = (v0101100 * (np.arange(1.0, 10, 1.0)/45)[:, np.newaxis]).sum(axis=0) # decay_linear
v01011 = stats.rankdata(v010110)
v0101 = v01010 - v01011
v010 = v0100 * v0101
v01 = stats.rankdata(v010)
v0 = v00 * v01
v10 = np.full(out.shape[0], 1.0)
v1100 = np.empty((250, out.shape[0]))
for i0 in range(1, 251):
v1100[-i0] = returns[-i0]
v110 = v1100.sum(axis=0)
v11 = stats.rankdata(v110)
v1 = v10 + v11
out[:] = v0 * v1
# ((-1 * rank(stddev(high, 10))) * correlation(high, volume, 10))
class Alpha40(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.volume]
window_length = 10
def compute(self, today, assets, out, high, volume):
v00 = np.full(out.shape[0], -1.0)
v0100 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v0100[-i0] = high[-i0]
v010 = np.std(v0100, axis=0)
v01 = stats.rankdata(v010)
v0 = v00 * v01
v10 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v10[-i0] = high[-i0]
v11 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v11[-i0] = volume[-i0]
v1 = pd.DataFrame(v10).rolling(window=10).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (((high * low)^0.5) - vwap)
class Alpha41(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.low, vwap_in]
window_length = 2
def compute(self, today, assets, out, high, low, vwap):
v000 = high[-1]
v001 = low[-1]
v00 = v000 * v001
v01 = np.full(out.shape[0], 0.5)
v0 = np.power(v00, v01)
v1 = vwap[-1]
out[:] = v0 - v1
# (rank((vwap - close)) / rank((vwap + close)))
class Alpha42(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.close, vwap_in]
window_length = 2
def compute(self, today, assets, out, close, vwap):
v000 = vwap[-1]
v001 = close[-1]
v00 = v000 - v001
v0 = stats.rankdata(v00)
v100 = vwap[-1]
v101 = close[-1]
v10 = v100 + v101
v1 = stats.rankdata(v10)
out[:] = v0 / v1
# (ts_rank((volume / adv20), 20) * ts_rank((-1 * delta(close, 7)), 8))
class Alpha43(CustomFactor):
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, adv20_in]
window_length = 20
def compute(self, today, assets, out, volume, close, adv20):
v00 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v000 = volume[-i0]
v001 = adv20[-i0]
v00[-i0] = v000 / v001
v0 = pd.DataFrame(v00).rank().tail(1).as_matrix()[-1]
v10 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v100 = np.full(out.shape[0], -1.0)
v1010 = np.empty((8, out.shape[0]))
for i1 in range(1, 9):
v1010[-i1] = close[-i1]
v101 = v1010[-1] - v1010[-8]
v10[-i0] = v100 * v101
v1 = pd.DataFrame(v10).rank().tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (-1 * correlation(high, rank(volume), 5))
class Alpha44(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.volume]
window_length = 5
def compute(self, today, assets, out, high, volume):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v10[-i0] = high[-i0]
v11 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v110 = volume[-i0]
v11[-i0] = stats.rankdata(v110)
v1 = pd.DataFrame(v10).rolling(window=5).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (-1 * ((rank((sum(delay(close, 5), 20) / 20)) * correlation(close, volume, 2)) * rank(correlation(sum(close, 5), sum(close, 20), 2))))
class Alpha45(CustomFactor):
inputs = [USEquityPricing.volume, USEquityPricing.close]
window_length = 20
def compute(self, today, assets, out, volume, close):
v0 = np.full(out.shape[0], -1.0)
v100000 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v1000000 = close[-1]
v100000[-i0] = v1000000 # delay
v10000 = v100000.sum(axis=0)
v10001 = np.full(out.shape[0], 20.0)
v1000 = v10000 / v10001
v100 = stats.rankdata(v1000)
v1010 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v1010[-i0] = close[-i0]
v1011 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v1011[-i0] = volume[-i0]
v101 = pd.DataFrame(v1010).rolling(window=2).cov(pd.DataFrame(v1011)).tail(1).as_matrix()[-1]
v10 = v100 * v101
v1100 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v11000 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v11000[-i1] = close[-i1]
v1100[-i0] = v11000.sum(axis=0)
v1101 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v11010 = np.empty((20, out.shape[0]))
for i1 in range(1, 21):
v11010[-i1] = close[-i1]
v1101[-i0] = v11010.sum(axis=0)
v110 = pd.DataFrame(v1100).rolling(window=2).cov(pd.DataFrame(v1101)).tail(1).as_matrix()[-1]
v11 = stats.rankdata(v110)
v1 = v10 * v11
out[:] = v0 * v1
# ((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)))))
class Alpha46(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 21
def compute(self, today, assets, out, close):
v00 = np.full(out.shape[0], 0.25)
v010000 = close[-1]
v01000 = v010000 # delay
v010010 = close[-1]
v01001 = v010010 # delay
v0100 = v01000 - v01001
v0101 = np.full(out.shape[0], 10.0)
v010 = v0100 / v0101
v011000 = close[-1]
v01100 = v011000 # delay
v01101 = close[-1]
v0110 = v01100 - v01101
v0111 = np.full(out.shape[0], 10.0)
v011 = v0110 / v0111
v01 = v010 - v011
v0 = v00 < v01
v10 = np.full(out.shape[0], -1.0)
v11 = np.full(out.shape[0], 1.0)
v1 = v10 * v11
v2000000 = close[-1]
v200000 = v2000000 # delay
v2000010 = close[-1]
v200001 = v2000010 # delay
v20000 = v200000 - v200001
v20001 = np.full(out.shape[0], 10.0)
v2000 = v20000 / v20001
v2001000 = close[-1]
v200100 = v2001000 # delay
v200101 = close[-1]
v20010 = v200100 - v200101
v20011 = np.full(out.shape[0], 10.0)
v2001 = v20010 / v20011
v200 = v2000 - v2001
v201 = np.full(out.shape[0], 0.0)
v20 = v200 < v201
v21 = np.full(out.shape[0], 1.0)
v2200 = np.full(out.shape[0], -1.0)
v2201 = np.full(out.shape[0], 1.0)
v220 = v2200 * v2201
v2210 = close[-1]
v22110 = close[-1]
v2211 = v22110 # delay
v221 = v2210 - v2211
v22 = v220 * v221
v2lgcl = np.empty(out.shape[0])
v2lgcl[v20] = 1
v2lgcl[~v20] = v22[~v20]
v2 = v2lgcl
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = v1[v0]
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# ((((rank((1 / close)) * volume) / adv20) * ((high * rank((high - close))) / (sum(high, 5) / 5))) - rank((vwap - delay(vwap, 5))))
class Alpha47(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.volume, USEquityPricing.close, USEquityPricing.high, adv20_in, vwap_in]
window_length = 6
def compute(self, today, assets, out, volume, close, high, adv20, vwap):
v000000 = np.full(out.shape[0], 1.0)
v000001 = close[-1]
v00000 = v000000 / v000001
v0000 = stats.rankdata(v00000)
v0001 = volume[-1]
v000 = v0000 * v0001
v001 = adv20[-1]
v00 = v000 / v001
v0100 = high[-1]
v010100 = high[-1]
v010101 = close[-1]
v01010 = v010100 - v010101
v0101 = stats.rankdata(v01010)
v010 = v0100 * v0101
v01100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v01100[-i0] = high[-i0]
v0110 = v01100.sum(axis=0)
v0111 = np.full(out.shape[0], 5.0)
v011 = v0110 / v0111
v01 = v010 / v011
v0 = v00 * v01
v100 = vwap[-1]
v1010 = vwap[-1]
v101 = v1010 # delay
v10 = v100 - v101
v1 = stats.rankdata(v10)
out[:] = v0 - v1
# (((((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)) < (-1 * 0.1)) ? 1 : ((-1 * 1) * (close - delay(close, 1))))
class Alpha49(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 21
def compute(self, today, assets, out, close):
v000000 = close[-1]
v00000 = v000000 # delay
v000010 = close[-1]
v00001 = v000010 # delay
v0000 = v00000 - v00001
v0001 = np.full(out.shape[0], 10.0)
v000 = v0000 / v0001
v001000 = close[-1]
v00100 = v001000 # delay
v00101 = close[-1]
v0010 = v00100 - v00101
v0011 = np.full(out.shape[0], 10.0)
v001 = v0010 / v0011
v00 = v000 - v001
v010 = np.full(out.shape[0], -1.0)
v011 = np.full(out.shape[0], 0.1)
v01 = v010 * v011
v0 = v00 < v01
v1 = np.full(out.shape[0], 1.0)
v200 = np.full(out.shape[0], -1.0)
v201 = np.full(out.shape[0], 1.0)
v20 = v200 * v201
v210 = close[-1]
v2110 = close[-1]
v211 = v2110 # delay
v21 = v210 - v211
v2 = v20 * v21
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = 1
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# (-1 * ts_max(rank(correlation(rank(volume), rank(vwap), 5)), 5))
class Alpha50(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.volume, vwap_in]
window_length = 5
def compute(self, today, assets, out, volume, vwap):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v1000 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v10000 = volume[-i1]
v1000[-i1] = stats.rankdata(v10000)
v1001 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v10010 = vwap[-i1]
v1001[-i1] = stats.rankdata(v10010)
v100 = pd.DataFrame(v1000).rolling(window=5).cov(pd.DataFrame(v1001)).tail(1).as_matrix()[-1]
v10[-i0] = stats.rankdata(v100)
v1 = np.max(v10, axis=0)
out[:] = v0 * v1
# (((((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)) < (-1 * 0.05)) ? 1 : ((-1 * 1) * (close - delay(close, 1))))
class Alpha51(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 21
def compute(self, today, assets, out, close):
v000000 = close[-1]
v00000 = v000000 # delay
v000010 = close[-1]
v00001 = v000010 # delay
v0000 = v00000 - v00001
v0001 = np.full(out.shape[0], 10.0)
v000 = v0000 / v0001
v001000 = close[-1]
v00100 = v001000 # delay
v00101 = close[-1]
v0010 = v00100 - v00101
v0011 = np.full(out.shape[0], 10.0)
v001 = v0010 / v0011
v00 = v000 - v001
v010 = np.full(out.shape[0], -1.0)
v011 = np.full(out.shape[0], 0.05)
v01 = v010 * v011
v0 = v00 < v01
v1 = np.full(out.shape[0], 1.0)
v200 = np.full(out.shape[0], -1.0)
v201 = np.full(out.shape[0], 1.0)
v20 = v200 * v201
v210 = close[-1]
v2110 = close[-1]
v211 = v2110 # delay
v21 = v210 - v211
v2 = v20 * v21
vlgcl = np.empty(out.shape[0])
vlgcl[v0] = 1
vlgcl[~v0] = v2[~v0]
out[:] = vlgcl
# ((((-1 * ts_min(low, 5)) + delay(ts_min(low, 5), 5)) * rank(((sum(returns, 240) - sum(returns, 20)) / 220))) * ts_rank(volume, 5))
class Alpha52(CustomFactor):
inputs = [USEquityPricing.volume, Returns(window_length=2), USEquityPricing.low]
window_length = 240
def compute(self, today, assets, out, volume, returns, low):
v0000 = np.full(out.shape[0], -1.0)
v00010 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v00010[-i0] = low[-i0]
v0001 = np.min(v00010, axis=0)
v000 = v0000 * v0001
v00100 = np.empty((5, out.shape[0]))
for i0 in range(6, 11):
v00100[5-i0] = low[-i0]
v0010 = np.min(v00100, axis=0)
v001 = v0010 # delay
v00 = v000 + v001
v010000 = np.empty((240, out.shape[0]))
for i0 in range(1, 241):
v010000[-i0] = returns[-i0]
v01000 = v010000.sum(axis=0)
v010010 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v010010[-i0] = returns[-i0]
v01001 = v010010.sum(axis=0)
v0100 = v01000 - v01001
v0101 = np.full(out.shape[0], 220.0)
v010 = v0100 / v0101
v01 = stats.rankdata(v010)
v0 = v00 * v01
v10 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v10[-i0] = volume[-i0]
v1 = pd.DataFrame(v10).rank().tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (-1 * delta((((close - low) - (high - close)) / (close - low)), 9))
class Alpha53(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.low]
window_length = 10
def compute(self, today, assets, out, high, close, low):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v10000 = close[-i0]
v10001 = low[-i0]
v1000 = v10000 - v10001
v10010 = high[-i0]
v10011 = close[-i0]
v1001 = v10010 - v10011
v100 = v1000 - v1001
v1010 = close[-i0]
v1011 = low[-i0]
v101 = v1010 - v1011
v10[-i0] = v100 / v101
v1 = v10[-1] - v10[-10]
out[:] = v0 * v1
# ((-1 * ((low - close) * (open^5))) / ((low - high) * (close^5)))
class Alpha54(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.open, USEquityPricing.low]
window_length = 2
def compute(self, today, assets, out, high, close, open, low):
v00 = np.full(out.shape[0], -1.0)
v0100 = low[-1]
v0101 = close[-1]
v010 = v0100 - v0101
v0110 = open[-1]
v0111 = np.full(out.shape[0], 5.0)
v011 = np.power(v0110, v0111)
v01 = v010 * v011
v0 = v00 * v01
v100 = low[-1]
v101 = high[-1]
v10 = v100 - v101
v110 = close[-1]
v111 = np.full(out.shape[0], 5.0)
v11 = np.power(v110, v111)
v1 = v10 * v11
out[:] = v0 / v1
# (-1 * correlation(rank(((close - ts_min(low, 12)) / (ts_max(high, 12) - ts_min(low, 12)))), rank(volume), 6))
class Alpha55(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.low, USEquityPricing.volume]
window_length = 12
def compute(self, today, assets, out, high, close, low, volume):
v0 = np.full(out.shape[0], -1.0)
v10 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v10000 = close[-i0]
v100010 = np.empty((12, out.shape[0]))
for i1 in range(1, 13):
v100010[-i1] = low[-i1]
v10001 = np.min(v100010, axis=0)
v1000 = v10000 - v10001
v100100 = np.empty((12, out.shape[0]))
for i1 in range(1, 13):
v100100[-i1] = high[-i1]
v10010 = np.max(v100100, axis=0)
v100110 = np.empty((12, out.shape[0]))
for i1 in range(1, 13):
v100110[-i1] = low[-i1]
v10011 = np.min(v100110, axis=0)
v1001 = v10010 - v10011
v100 = v1000 / v1001
v10[-i0] = stats.rankdata(v100)
v11 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v110 = volume[-i0]
v11[-i0] = stats.rankdata(v110)
v1 = pd.DataFrame(v10).rolling(window=6).cov(pd.DataFrame(v11)).tail(1).as_matrix()[-1]
out[:] = v0 * v1
# (0 - (1 * (rank((sum(returns, 10) / sum(sum(returns, 2), 3))) * rank((returns * cap)))))
class Alpha56(CustomFactor):
inputs = [Returns(window_length=2)]
window_length = 10
def compute(self, today, assets, out, returns):
v0 = np.full(out.shape[0], 0.0)
v10 = np.full(out.shape[0], 1.0)
v110000 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v110000[-i0] = returns[-i0]
v11000 = v110000.sum(axis=0)
v110010 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v1100100 = np.empty((2, out.shape[0]))
for i1 in range(1, 3):
v1100100[-i1] = returns[-i1]
v110010[-i0] = v1100100.sum(axis=0)
v11001 = v110010.sum(axis=0)
v1100 = v11000 / v11001
v110 = stats.rankdata(v1100)
v11100 = returns[-1]
v11101 = cap[-1]
v1110 = v11100 * v11101
v111 = stats.rankdata(v1110)
v11 = v110 * v111
v1 = v10 * v11
out[:] = v0 - v1
# (0 - (1 * ((close - vwap) / decay_linear(rank(ts_argmax(close, 30)), 2))))
class Alpha57(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.close, vwap_in]
window_length = 30
def compute(self, today, assets, out, close, vwap):
v0 = np.full(out.shape[0], 0.0)
v10 = np.full(out.shape[0], 1.0)
v1100 = close[-1]
v1101 = vwap[-1]
v110 = v1100 - v1101
v1110 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v111000 = np.empty((30, out.shape[0]))
for i1 in range(1, 31):
v111000[-i1] = close[-i1]
v11100 = np.argmax(v111000, axis=0)
v1110[-i0] = stats.rankdata(v11100)
v111 = (v1110 * (np.arange(1.0, 3, 1.0)/3)[:, np.newaxis]).sum(axis=0) # decay_linear
v11 = v110 / v111
v1 = v10 * v11
out[:] = v0 - v1
# (0 - (1 * ((2 * scale(rank(((((close - low) - (high - close)) / (high - low)) * volume)))) - scale(rank(ts_argmax(close, 10))))))
class Alpha60(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.low, USEquityPricing.volume]
window_length = 10
def compute(self, today, assets, out, high, close, low, volume):
v0 = np.full(out.shape[0], 0.0)
v10 = np.full(out.shape[0], 1.0)
v1100 = np.full(out.shape[0], 2.0)
v1101000000 = close[-1]
v1101000001 = low[-1]
v110100000 = v1101000000 - v1101000001
v1101000010 = high[-1]
v1101000011 = close[-1]
v110100001 = v1101000010 - v1101000011
v11010000 = v110100000 - v110100001
v110100010 = high[-1]
v110100011 = low[-1]
v11010001 = v110100010 - v110100011
v1101000 = v11010000 / v11010001
v1101001 = volume[-1]
v110100 = v1101000 * v1101001
v11010 = stats.rankdata(v110100)
v1101 = v11010/np.abs(v11010).sum()
v110 = v1100 * v1101
v111000 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v111000[-i0] = close[-i0]
v11100 = np.argmax(v111000, axis=0)
v1110 = stats.rankdata(v11100)
v111 = v1110/np.abs(v1110).sum()
v11 = v110 - v111
v1 = v10 * v11
out[:] = v0 - v1
# (rank((vwap - ts_min(vwap, 16.1219))) < rank(correlation(vwap, adv180, 17.9282)))
class Alpha61(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv180_in = AverageDollarVolume(window_length=180)
adv180_in.window_safe = True
inputs = [adv180_in, vwap_in]
window_length = 17
def compute(self, today, assets, out, adv180, vwap):
v000 = vwap[-1]
v0010 = np.empty((16, out.shape[0]))
for i0 in range(1, 17):
v0010[-i0] = vwap[-i0]
v001 = np.min(v0010, axis=0)
v00 = v000 - v001
v0 = stats.rankdata(v00)
v100 = np.empty((18, out.shape[0]))
for i0 in range(1, 19):
v100[-i0] = vwap[-i0]
v101 = np.empty((18, out.shape[0]))
for i0 in range(1, 19):
v101[-i0] = adv180[-i0]
v10 = pd.DataFrame(v100).rolling(window=18).cov(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = v0 < v1
# ((rank(correlation(vwap, sum(adv20, 22.4101), 9.91009)) < rank(((rank(open) + rank(open)) < (rank(((high + low) / 2)) + rank(high))))) * -1)
class Alpha62(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.open, adv20_in, USEquityPricing.low, vwap_in]
window_length = 22
def compute(self, today, assets, out, high, open, adv20, low, vwap):
v0000 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v0000[-i0] = vwap[-i0]
v0001 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v00010 = np.empty((22, out.shape[0]))
for i1 in range(1, 23):
v00010[-i1] = adv20[-i1]
v0001[-i0] = v00010.sum(axis=0)
v000 = pd.DataFrame(v0000).rolling(window=10).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = stats.rankdata(v000)
v010000 = open[-1]
v01000 = stats.rankdata(v010000)
v010010 = open[-1]
v01001 = stats.rankdata(v010010)
v0100 = v01000 + v01001
v01010000 = high[-1]
v01010001 = low[-1]
v0101000 = v01010000 + v01010001
v0101001 = np.full(out.shape[0], 2.0)
v010100 = v0101000 / v0101001
v01010 = stats.rankdata(v010100)
v010110 = high[-1]
v01011 = stats.rankdata(v010110)
v0101 = v01010 + v01011
v010 = v0100 < v0101
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((rank(correlation(sum(((open * 0.178404) + (low * (1 - 0.178404))), 12.7054), sum(adv120, 12.7054), 16.6208)) < rank(delta(((((high + low) / 2) * 0.178404) + (vwap * (1 - 0.178404))), 3.69741))) * -1)
class Alpha64(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv120_in = AverageDollarVolume(window_length=120)
adv120_in.window_safe = True
inputs = [adv120_in, USEquityPricing.high, USEquityPricing.open, USEquityPricing.low, vwap_in]
window_length = 16
def compute(self, today, assets, out, adv120, high, open, low, vwap):
v0000 = np.empty((17, out.shape[0]))
for i0 in range(1, 18):
v00000 = np.empty((13, out.shape[0]))
for i1 in range(1, 14):
v0000000 = open[-i1]
v0000001 = np.full(out.shape[0], 0.178404)
v000000 = v0000000 * v0000001
v0000010 = low[-i1]
v00000110 = np.full(out.shape[0], 1.0)
v00000111 = np.full(out.shape[0], 0.178404)
v0000011 = v00000110 - v00000111
v000001 = v0000010 * v0000011
v00000[-i1] = v000000 + v000001
v0000[-i0] = v00000.sum(axis=0)
v0001 = np.empty((17, out.shape[0]))
for i0 in range(1, 18):
v00010 = np.empty((13, out.shape[0]))
for i1 in range(1, 14):
v00010[-i1] = adv120[-i1]
v0001[-i0] = v00010.sum(axis=0)
v000 = pd.DataFrame(v0000).rolling(window=17).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = stats.rankdata(v000)
v0100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v01000000 = high[-i0]
v01000001 = low[-i0]
v0100000 = v01000000 + v01000001
v0100001 = np.full(out.shape[0], 2.0)
v010000 = v0100000 / v0100001
v010001 = np.full(out.shape[0], 0.178404)
v01000 = v010000 * v010001
v010010 = vwap[-i0]
v0100110 = np.full(out.shape[0], 1.0)
v0100111 = np.full(out.shape[0], 0.178404)
v010011 = v0100110 - v0100111
v01001 = v010010 * v010011
v0100[-i0] = v01000 + v01001
v010 = v0100[-1] - v0100[-5]
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((rank(correlation(((open * 0.00817205) + (vwap * (1 - 0.00817205))), sum(adv60, 8.6911), 6.40374)) < rank((open - ts_min(open, 13.635)))) * -1)
class Alpha65(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv60_in = AverageDollarVolume(window_length=60)
adv60_in.window_safe = True
inputs = [adv60_in, USEquityPricing.open, vwap_in]
window_length = 13
def compute(self, today, assets, out, adv60, open, vwap):
v0000 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v000000 = open[-i0]
v000001 = np.full(out.shape[0], 0.00817205)
v00000 = v000000 * v000001
v000010 = vwap[-i0]
v0000110 = np.full(out.shape[0], 1.0)
v0000111 = np.full(out.shape[0], 0.00817205)
v000011 = v0000110 - v0000111
v00001 = v000010 * v000011
v0000[-i0] = v00000 + v00001
v0001 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v00010 = np.empty((9, out.shape[0]))
for i1 in range(1, 10):
v00010[-i1] = adv60[-i1]
v0001[-i0] = v00010.sum(axis=0)
v000 = pd.DataFrame(v0000).rolling(window=6).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = stats.rankdata(v000)
v0100 = open[-1]
v01010 = np.empty((14, out.shape[0]))
for i0 in range(1, 15):
v01010[-i0] = open[-i0]
v0101 = np.min(v01010, axis=0)
v010 = v0100 - v0101
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((rank(decay_linear(delta(vwap, 3.51013), 7.23052)) + Ts_Rank(decay_linear(((((low * 0.96633) + (low * (1 - 0.96633))) - vwap) / (open - ((high + low) / 2))), 11.4157), 6.72611)) * -1)
class Alpha66(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.open, USEquityPricing.low, vwap_in]
window_length = 11
def compute(self, today, assets, out, high, open, low, vwap):
v0000 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v00000 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v00000[-i1] = vwap[-i1]
v0000[-i0] = v00000[-1] - v00000[-5]
v000 = (v0000 * (np.arange(1.0, 8, 1.0)/28)[:, np.newaxis]).sum(axis=0) # decay_linear
v00 = stats.rankdata(v000)
v010 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v0100 = np.empty((11, out.shape[0]))
for i1 in range(1, 12):
v01000000 = low[-i1]
v01000001 = np.full(out.shape[0], 0.96633)
v0100000 = v01000000 * v01000001
v01000010 = low[-i1]
v010000110 = np.full(out.shape[0], 1.0)
v010000111 = np.full(out.shape[0], 0.96633)
v01000011 = v010000110 - v010000111
v0100001 = v01000010 * v01000011
v010000 = v0100000 + v0100001
v010001 = vwap[-i1]
v01000 = v010000 - v010001
v010010 = open[-i1]
v01001100 = high[-i1]
v01001101 = low[-i1]
v0100110 = v01001100 + v01001101
v0100111 = np.full(out.shape[0], 2.0)
v010011 = v0100110 / v0100111
v01001 = v010010 - v010011
v0100[-i1] = v01000 / v01001
v010[-i0] = (v0100 * (np.arange(1.0, 12, 1.0)/66)[:, np.newaxis]).sum(axis=0) # decay_linear
v01 = pd.DataFrame(v010).rank().tail(1).as_matrix()[-1]
v0 = v00 + v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((Ts_Rank(correlation(rank(high), rank(adv15), 8.91644), 13.9333) < rank(delta(((close * 0.518371) + (low * (1 - 0.518371))), 1.06157))) * -1)
class Alpha68(CustomFactor):
adv15_in = AverageDollarVolume(window_length=15)
adv15_in.window_safe = True
inputs = [USEquityPricing.high, adv15_in, USEquityPricing.low, USEquityPricing.close]
window_length = 13
def compute(self, today, assets, out, high, adv15, low, close):
v000 = np.empty((14, out.shape[0]))
for i0 in range(1, 15):
v0000 = np.empty((9, out.shape[0]))
for i1 in range(1, 10):
v00000 = high[-i1]
v0000[-i1] = stats.rankdata(v00000)
v0001 = np.empty((9, out.shape[0]))
for i1 in range(1, 10):
v00010 = adv15[-i1]
v0001[-i1] = stats.rankdata(v00010)
v000[-i0] = pd.DataFrame(v0000).rolling(window=9).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = pd.DataFrame(v000).rank().tail(1).as_matrix()[-1]
v0100 = np.empty((2, out.shape[0]))
for i0 in range(1, 3):
v010000 = close[-i0]
v010001 = np.full(out.shape[0], 0.518371)
v01000 = v010000 * v010001
v010010 = low[-i0]
v0100110 = np.full(out.shape[0], 1.0)
v0100111 = np.full(out.shape[0], 0.518371)
v010011 = v0100110 - v0100111
v01001 = v010010 * v010011
v0100[-i0] = v01000 + v01001
v010 = v0100[-1] - v0100[-2]
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# (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)))
class Alpha72(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv40_in = AverageDollarVolume(window_length=40)
adv40_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.volume, adv40_in, USEquityPricing.low, vwap_in]
window_length = 18
def compute(self, today, assets, out, high, volume, adv40, low, vwap):
v000 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v0000 = np.empty((9, out.shape[0]))
for i1 in range(1, 10):
v000000 = high[-i1]
v000001 = low[-i1]
v00000 = v000000 + v000001
v00001 = np.full(out.shape[0], 2.0)
v0000[-i1] = v00000 / v00001
v0001 = np.empty((9, out.shape[0]))
for i1 in range(1, 10):
v0001[-i1] = adv40[-i1]
v000[-i0] = pd.DataFrame(v0000).rolling(window=9).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = (v000 * (np.arange(1.0, 11, 1.0)/55)[:, np.newaxis]).sum(axis=0) # decay_linear
v0 = stats.rankdata(v00)
v100 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v1000 = np.empty((7, out.shape[0]))
for i1 in range(1, 8):
v10000 = np.empty((4, out.shape[0]))
for i2 in range(1, 5):
v10000[-i2] = vwap[-i2]
v1000[-i1] = pd.DataFrame(v10000).rank().tail(1).as_matrix()[-1]
v1001 = np.empty((7, out.shape[0]))
for i1 in range(1, 8):
v10010 = np.empty((19, out.shape[0]))
for i2 in range(1, 20):
v10010[-i2] = volume[-i2]
v1001[-i1] = pd.DataFrame(v10010).rank().tail(1).as_matrix()[-1]
v100[-i0] = pd.DataFrame(v1000).rolling(window=7).cov(pd.DataFrame(v1001)).tail(1).as_matrix()[-1]
v10 = (v100 * (np.arange(1.0, 4, 1.0)/6)[:, np.newaxis]).sum(axis=0) # decay_linear
v1 = stats.rankdata(v10)
out[:] = v0 / v1
# ((rank(correlation(close, sum(adv30, 37.4843), 15.1365)) < rank(correlation(rank(((high * 0.0261661) + (vwap * (1 - 0.0261661)))), rank(volume), 11.4791))) * -1)
class Alpha74(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv30_in = AverageDollarVolume(window_length=30)
adv30_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.close, adv30_in, vwap_in, USEquityPricing.volume]
window_length = 37
def compute(self, today, assets, out, high, close, adv30, vwap, volume):
v0000 = np.empty((15, out.shape[0]))
for i0 in range(1, 16):
v0000[-i0] = close[-i0]
v0001 = np.empty((15, out.shape[0]))
for i0 in range(1, 16):
v00010 = np.empty((37, out.shape[0]))
for i1 in range(1, 38):
v00010[-i1] = adv30[-i1]
v0001[-i0] = v00010.sum(axis=0)
v000 = pd.DataFrame(v0000).rolling(window=15).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = stats.rankdata(v000)
v0100 = np.empty((11, out.shape[0]))
for i0 in range(1, 12):
v0100000 = high[-i0]
v0100001 = np.full(out.shape[0], 0.0261661)
v010000 = v0100000 * v0100001
v0100010 = vwap[-i0]
v01000110 = np.full(out.shape[0], 1.0)
v01000111 = np.full(out.shape[0], 0.0261661)
v0100011 = v01000110 - v01000111
v010001 = v0100010 * v0100011
v01000 = v010000 + v010001
v0100[-i0] = stats.rankdata(v01000)
v0101 = np.empty((11, out.shape[0]))
for i0 in range(1, 12):
v01010 = volume[-i0]
v0101[-i0] = stats.rankdata(v01010)
v010 = pd.DataFrame(v0100).rolling(window=11).cov(pd.DataFrame(v0101)).tail(1).as_matrix()[-1]
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# (rank(correlation(vwap, volume, 4.24304)) < rank(correlation(rank(low), rank(adv50), 12.4413)))
class Alpha75(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv50_in = AverageDollarVolume(window_length=50)
adv50_in.window_safe = True
inputs = [USEquityPricing.volume, adv50_in, USEquityPricing.low, vwap_in]
window_length = 12
def compute(self, today, assets, out, volume, adv50, low, vwap):
v000 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v000[-i0] = vwap[-i0]
v001 = np.empty((4, out.shape[0]))
for i0 in range(1, 5):
v001[-i0] = volume[-i0]
v00 = pd.DataFrame(v000).rolling(window=4).cov(pd.DataFrame(v001)).tail(1).as_matrix()[-1]
v0 = stats.rankdata(v00)
v100 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v1000 = low[-i0]
v100[-i0] = stats.rankdata(v1000)
v101 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v1010 = adv50[-i0]
v101[-i0] = stats.rankdata(v1010)
v10 = pd.DataFrame(v100).rolling(window=12).cov(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = v0 < v1
# (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)))
class Alpha78(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv40_in = AverageDollarVolume(window_length=40)
adv40_in.window_safe = True
inputs = [USEquityPricing.volume, adv40_in, USEquityPricing.low, vwap_in]
window_length = 19
def compute(self, today, assets, out, volume, adv40, low, vwap):
v000 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v0000 = np.empty((20, out.shape[0]))
for i1 in range(1, 21):
v000000 = low[-i1]
v000001 = np.full(out.shape[0], 0.352233)
v00000 = v000000 * v000001
v000010 = vwap[-i1]
v0000110 = np.full(out.shape[0], 1.0)
v0000111 = np.full(out.shape[0], 0.352233)
v000011 = v0000110 - v0000111
v00001 = v000010 * v000011
v0000[-i1] = v00000 + v00001
v000[-i0] = v0000.sum(axis=0)
v001 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v0010 = np.empty((20, out.shape[0]))
for i1 in range(1, 21):
v0010[-i1] = adv40[-i1]
v001[-i0] = v0010.sum(axis=0)
v00 = pd.DataFrame(v000).rolling(window=7).cov(pd.DataFrame(v001)).tail(1).as_matrix()[-1]
v0 = stats.rankdata(v00)
v100 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v1000 = vwap[-i0]
v100[-i0] = stats.rankdata(v1000)
v101 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v1010 = volume[-i0]
v101[-i0] = stats.rankdata(v1010)
v10 = pd.DataFrame(v100).rolling(window=6).cov(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = np.power(v0, v1)
# ((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)
class Alpha81(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv10_in = AverageDollarVolume(window_length=10)
adv10_in.window_safe = True
inputs = [USEquityPricing.volume, adv10_in, vwap_in]
window_length = 49
def compute(self, today, assets, out, volume, adv10, vwap):
v00000 = np.empty((15, out.shape[0]))
for i0 in range(1, 16):
v000000000 = np.empty((8, out.shape[0]))
for i1 in range(1, 9):
v000000000[-i1] = vwap[-i1]
v000000001 = np.empty((8, out.shape[0]))
for i1 in range(1, 9):
v0000000010 = np.empty((50, out.shape[0]))
for i2 in range(1, 51):
v0000000010[-i2] = adv10[-i2]
v000000001[-i1] = v0000000010.sum(axis=0)
v00000000 = pd.DataFrame(v000000000).rolling(window=8).cov(pd.DataFrame(v000000001)).tail(1).as_matrix()[-1]
v0000000 = stats.rankdata(v00000000)
v0000001 = np.full(out.shape[0], 4.0)
v000000 = np.power(v0000000, v0000001)
v00000[-i0] = stats.rankdata(v000000)
v0000 = np.prod(v00000, axis=0)
v000 = np.log(v0000)
v00 = stats.rankdata(v000)
v0100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v01000 = vwap[-i0]
v0100[-i0] = stats.rankdata(v01000)
v0101 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v01010 = volume[-i0]
v0101[-i0] = stats.rankdata(v01010)
v010 = pd.DataFrame(v0100).rolling(window=5).cov(pd.DataFrame(v0101)).tail(1).as_matrix()[-1]
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((rank(delay(((high - low) / (sum(close, 5) / 5)), 2)) * rank(rank(volume))) / (((high - low) / (sum(close, 5) / 5)) / (vwap - close)))
class Alpha83(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.low, vwap_in, USEquityPricing.volume]
window_length = 7
def compute(self, today, assets, out, high, close, low, vwap, volume):
v000000 = high[-1]
v000001 = low[-1]
v00000 = v000000 - v000001
v0000100 = np.empty((5, out.shape[0]))
for i0 in range(3, 8):
v0000100[2-i0] = close[-i0]
v000010 = v0000100.sum(axis=0)
v000011 = np.full(out.shape[0], 5.0)
v00001 = v000010 / v000011
v0000 = v00000 / v00001
v000 = v0000 # delay
v00 = stats.rankdata(v000)
v0100 = volume[-1]
v010 = stats.rankdata(v0100)
v01 = stats.rankdata(v010)
v0 = v00 * v01
v1000 = high[-1]
v1001 = low[-1]
v100 = v1000 - v1001
v10100 = np.empty((5, out.shape[0]))
for i0 in range(1, 6):
v10100[-i0] = close[-i0]
v1010 = v10100.sum(axis=0)
v1011 = np.full(out.shape[0], 5.0)
v101 = v1010 / v1011
v10 = v100 / v101
v110 = vwap[-1]
v111 = close[-1]
v11 = v110 - v111
v1 = v10 / v11
out[:] = v0 / v1
# SignedPower(Ts_Rank((vwap - ts_max(vwap, 15.3217)), 20.7127), delta(close, 4.96796))
class Alpha84(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
inputs = [USEquityPricing.close, vwap_in]
window_length = 20
def compute(self, today, assets, out, close, vwap):
v00 = np.empty((21, out.shape[0]))
for i0 in range(1, 22):
v000 = vwap[-i0]
v0010 = np.empty((15, out.shape[0]))
for i1 in range(1, 16):
v0010[-i1] = vwap[-i1]
v001 = np.max(v0010, axis=0)
v00[-i0] = v000 - v001
v0 = pd.DataFrame(v00).rank().tail(1).as_matrix()[-1]
v10 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v10[-i0] = close[-i0]
v1 = v10[-1] - v10[-6]
out[:] = np.power(v0, v1)
# (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)))
class Alpha85(CustomFactor):
adv30_in = AverageDollarVolume(window_length=30)
adv30_in.window_safe = True
inputs = [USEquityPricing.high, USEquityPricing.close, adv30_in, USEquityPricing.low, USEquityPricing.volume]
window_length = 10
def compute(self, today, assets, out, high, close, adv30, low, volume):
v000 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v00000 = high[-i0]
v00001 = np.full(out.shape[0], 0.876703)
v0000 = v00000 * v00001
v00010 = close[-i0]
v000110 = np.full(out.shape[0], 1.0)
v000111 = np.full(out.shape[0], 0.876703)
v00011 = v000110 - v000111
v0001 = v00010 * v00011
v000[-i0] = v0000 + v0001
v001 = np.empty((10, out.shape[0]))
for i0 in range(1, 11):
v001[-i0] = adv30[-i0]
v00 = pd.DataFrame(v000).rolling(window=10).cov(pd.DataFrame(v001)).tail(1).as_matrix()[-1]
v0 = stats.rankdata(v00)
v100 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v1000 = np.empty((4, out.shape[0]))
for i1 in range(1, 5):
v100000 = high[-i1]
v100001 = low[-i1]
v10000 = v100000 + v100001
v10001 = np.full(out.shape[0], 2.0)
v1000[-i1] = v10000 / v10001
v100[-i0] = pd.DataFrame(v1000).rank().tail(1).as_matrix()[-1]
v101 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v1010 = np.empty((10, out.shape[0]))
for i1 in range(1, 11):
v1010[-i1] = volume[-i1]
v101[-i0] = pd.DataFrame(v1010).rank().tail(1).as_matrix()[-1]
v10 = pd.DataFrame(v100).rolling(window=7).cov(pd.DataFrame(v101)).tail(1).as_matrix()[-1]
v1 = stats.rankdata(v10)
out[:] = np.power(v0, v1)
# ((Ts_Rank(correlation(close, sum(adv20, 14.7444), 6.00049), 20.4195) < rank(((open + close) - (vwap + open)))) * -1)
class Alpha86(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv20_in = AverageDollarVolume(window_length=20)
adv20_in.window_safe = True
inputs = [USEquityPricing.close, adv20_in, vwap_in, USEquityPricing.open]
window_length = 20
def compute(self, today, assets, out, close, adv20, vwap, open):
v000 = np.empty((20, out.shape[0]))
for i0 in range(1, 21):
v0000 = np.empty((6, out.shape[0]))
for i1 in range(1, 7):
v0000[-i1] = close[-i1]
v0001 = np.empty((6, out.shape[0]))
for i1 in range(1, 7):
v00010 = np.empty((15, out.shape[0]))
for i2 in range(1, 16):
v00010[-i2] = adv20[-i2]
v0001[-i1] = v00010.sum(axis=0)
v000[-i0] = pd.DataFrame(v0000).rolling(window=6).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = pd.DataFrame(v000).rank().tail(1).as_matrix()[-1]
v01000 = open[-1]
v01001 = close[-1]
v0100 = v01000 + v01001
v01010 = vwap[-1]
v01011 = open[-1]
v0101 = v01010 + v01011
v010 = v0100 - v0101
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((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)
class Alpha94(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv60_in = AverageDollarVolume(window_length=60)
adv60_in.window_safe = True
inputs = [adv60_in, vwap_in]
window_length = 19
def compute(self, today, assets, out, adv60, vwap):
v0000 = vwap[-1]
v00010 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v00010[-i0] = vwap[-i0]
v0001 = np.min(v00010, axis=0)
v000 = v0000 - v0001
v00 = stats.rankdata(v000)
v010 = np.empty((3, out.shape[0]))
for i0 in range(1, 4):
v0100 = np.empty((18, out.shape[0]))
for i1 in range(1, 19):
v01000 = np.empty((20, out.shape[0]))
for i2 in range(1, 21):
v01000[-i2] = vwap[-i2]
v0100[-i1] = pd.DataFrame(v01000).rank().tail(1).as_matrix()[-1]
v0101 = np.empty((18, out.shape[0]))
for i1 in range(1, 19):
v01010 = np.empty((4, out.shape[0]))
for i2 in range(1, 5):
v01010[-i2] = adv60[-i2]
v0101[-i1] = pd.DataFrame(v01010).rank().tail(1).as_matrix()[-1]
v010[-i0] = pd.DataFrame(v0100).rolling(window=18).cov(pd.DataFrame(v0101)).tail(1).as_matrix()[-1]
v01 = pd.DataFrame(v010).rank().tail(1).as_matrix()[-1]
v0 = np.power(v00, v01)
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# (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))
class Alpha95(CustomFactor):
adv40_in = AverageDollarVolume(window_length=40)
adv40_in.window_safe = True
inputs = [USEquityPricing.high, adv40_in, USEquityPricing.open, USEquityPricing.low]
window_length = 19
def compute(self, today, assets, out, high, adv40, open, low):
v000 = open[-1]
v0010 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v0010[-i0] = open[-i0]
v001 = np.min(v0010, axis=0)
v00 = v000 - v001
v0 = stats.rankdata(v00)
v10 = np.empty((12, out.shape[0]))
for i0 in range(1, 13):
v10000 = np.empty((13, out.shape[0]))
for i1 in range(1, 14):
v100000 = np.empty((19, out.shape[0]))
for i2 in range(1, 20):
v10000000 = high[-i2]
v10000001 = low[-i2]
v1000000 = v10000000 + v10000001
v1000001 = np.full(out.shape[0], 2.0)
v100000[-i2] = v1000000 / v1000001
v10000[-i1] = v100000.sum(axis=0)
v10001 = np.empty((13, out.shape[0]))
for i1 in range(1, 14):
v100010 = np.empty((19, out.shape[0]))
for i2 in range(1, 20):
v100010[-i2] = adv40[-i2]
v10001[-i1] = v100010.sum(axis=0)
v1000 = pd.DataFrame(v10000).rolling(window=13).cov(pd.DataFrame(v10001)).tail(1).as_matrix()[-1]
v100 = stats.rankdata(v1000)
v101 = np.full(out.shape[0], 5.0)
v10[-i0] = np.power(v100, v101)
v1 = pd.DataFrame(v10).rank().tail(1).as_matrix()[-1]
out[:] = v0 < v1
# (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)))
class Alpha98(CustomFactor):
vwap_in = VWAP(window_length=2)
vwap_in.window_safe = True
adv5_in = AverageDollarVolume(window_length=5)
adv5_in.window_safe = True
adv15_in = AverageDollarVolume(window_length=15)
adv15_in.window_safe = True
inputs = [adv5_in, adv15_in, USEquityPricing.open, vwap_in]
window_length = 26
def compute(self, today, assets, out, adv5, adv15, open, vwap):
v000 = np.empty((7, out.shape[0]))
for i0 in range(1, 8):
v0000 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v0000[-i1] = vwap[-i1]
v0001 = np.empty((5, out.shape[0]))
for i1 in range(1, 6):
v00010 = np.empty((26, out.shape[0]))
for i2 in range(1, 27):
v00010[-i2] = adv5[-i2]
v0001[-i1] = v00010.sum(axis=0)
v000[-i0] = pd.DataFrame(v0000).rolling(window=5).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = (v000 * (np.arange(1.0, 8, 1.0)/28)[:, np.newaxis]).sum(axis=0) # decay_linear
v0 = stats.rankdata(v00)
v100 = np.empty((8, out.shape[0]))
for i0 in range(1, 9):
v1000 = np.empty((7, out.shape[0]))
for i1 in range(1, 8):
v10000 = np.empty((9, out.shape[0]))
for i2 in range(1, 10):
v100000 = np.empty((21, out.shape[0]))
for i3 in range(1, 22):
v1000000 = open[-i3]
v100000[-i3] = stats.rankdata(v1000000)
v100001 = np.empty((21, out.shape[0]))
for i3 in range(1, 22):
v1000010 = adv15[-i3]
v100001[-i3] = stats.rankdata(v1000010)
v10000[-i2] = pd.DataFrame(v100000).rolling(window=21).cov(pd.DataFrame(v100001)).tail(1).as_matrix()[-1]
v1000[-i1] = np.argmin(v10000, axis=0)
v100[-i0] = pd.DataFrame(v1000).rank().tail(1).as_matrix()[-1]
v10 = (v100 * (np.arange(1.0, 9, 1.0)/36)[:, np.newaxis]).sum(axis=0) # decay_linear
v1 = stats.rankdata(v10)
out[:] = v0 - v1
# ((rank(correlation(sum(((high + low) / 2), 19.8975), sum(adv60, 19.8975), 8.8136)) < rank(correlation(low, volume, 6.28259))) * -1)
class Alpha99(CustomFactor):
adv60_in = AverageDollarVolume(window_length=60)
adv60_in.window_safe = True
inputs = [USEquityPricing.high, adv60_in, USEquityPricing.low, USEquityPricing.volume]
window_length = 19
def compute(self, today, assets, out, high, adv60, low, volume):
v0000 = np.empty((9, out.shape[0]))
for i0 in range(1, 10):
v00000 = np.empty((20, out.shape[0]))
for i1 in range(1, 21):
v0000000 = high[-i1]
v0000001 = low[-i1]
v000000 = v0000000 + v0000001
v000001 = np.full(out.shape[0], 2.0)
v00000[-i1] = v000000 / v000001
v0000[-i0] = v00000.sum(axis=0)
v0001 = np.empty((9, out.shape[0]))
for i0 in range(1, 10):
v00010 = np.empty((20, out.shape[0]))
for i1 in range(1, 21):
v00010[-i1] = adv60[-i1]
v0001[-i0] = v00010.sum(axis=0)
v000 = pd.DataFrame(v0000).rolling(window=9).cov(pd.DataFrame(v0001)).tail(1).as_matrix()[-1]
v00 = stats.rankdata(v000)
v0100 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v0100[-i0] = low[-i0]
v0101 = np.empty((6, out.shape[0]))
for i0 in range(1, 7):
v0101[-i0] = volume[-i0]
v010 = pd.DataFrame(v0100).rolling(window=6).cov(pd.DataFrame(v0101)).tail(1).as_matrix()[-1]
v01 = stats.rankdata(v010)
v0 = v00 < v01
v1 = np.full(out.shape[0], -1.0)
out[:] = v0 * v1
# ((close - open) / ((high - low) + 0.001))
class Alpha101(CustomFactor):
inputs = [USEquityPricing.high, USEquityPricing.close, USEquityPricing.open, USEquityPricing.low]
window_length = 2
def compute(self, today, assets, out, high, close, open, low):
v00 = close[-1]
v01 = open[-1]
v0 = v00 - v01
v100 = high[-1]
v101 = low[-1]
v10 = v100 - v101
v11 = np.full(out.shape[0], 0.001)
v1 = v10 + v11
out[:] = v0 / v1
all_factors = {
'Alpha1' : Alpha1,
'Alpha2' : Alpha2,
'Alpha3' : Alpha3,
'Alpha4' : Alpha4,
'Alpha5' : Alpha5,
'Alpha6' : Alpha6,
'Alpha7' : Alpha7,
'Alpha8' : Alpha8,
'Alpha9' : Alpha9,
'Alpha10' : Alpha10,
'Alpha11' : Alpha11,
'Alpha12' : Alpha12,
'Alpha13' : Alpha13,
'Alpha14' : Alpha14,
'Alpha15' : Alpha15,
'Alpha16' : Alpha16,
'Alpha17' : Alpha17,
'Alpha18' : Alpha18,
'Alpha19' : Alpha19,
'Alpha20' : Alpha20,
'Alpha21' : Alpha21,
'Alpha22' : Alpha22,
'Alpha23' : Alpha23,
'Alpha24' : Alpha24,
'Alpha25' : Alpha25,
'Alpha26' : Alpha26,
'Alpha27' : Alpha27,
'Alpha28' : Alpha28,
'Alpha29' : Alpha29,
'Alpha30' : Alpha30,
'Alpha31' : Alpha31,
'Alpha32' : Alpha32,
'Alpha33' : Alpha33,
'Alpha34' : Alpha34,
'Alpha35' : Alpha35,
'Alpha36' : Alpha36,
'Alpha37' : Alpha37,
'Alpha38' : Alpha38,
'Alpha39' : Alpha39,
'Alpha40' : Alpha40,
'Alpha41' : Alpha41,
'Alpha42' : Alpha42,
'Alpha43' : Alpha43,
'Alpha44' : Alpha44,
'Alpha45' : Alpha45,
'Alpha46' : Alpha46,
'Alpha47' : Alpha47,
'Alpha49' : Alpha49,
'Alpha50' : Alpha50,
'Alpha51' : Alpha51,
'Alpha52' : Alpha52,
'Alpha53' : Alpha53,
'Alpha54' : Alpha54,
'Alpha55' : Alpha55,
'Alpha56' : Alpha56,
'Alpha57' : Alpha57,
'Alpha60' : Alpha60,
'Alpha61' : Alpha61,
'Alpha62' : Alpha62,
'Alpha64' : Alpha64,
'Alpha65' : Alpha65,
'Alpha66' : Alpha66,
'Alpha68' : Alpha68,
'Alpha72' : Alpha72,
'Alpha74' : Alpha74,
'Alpha75' : Alpha75,
'Alpha78' : Alpha78,
'Alpha81' : Alpha81,
'Alpha83' : Alpha83,
'Alpha84' : Alpha84,
'Alpha85' : Alpha85,
'Alpha86' : Alpha86,
'Alpha94' : Alpha94,
'Alpha95' : Alpha95,
'Alpha98' : Alpha98,
'Alpha99' : Alpha99,
'Alpha101' : Alpha101
}
return all_factors
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