wpwpwpwpwpwpwpwpwp/Alpha-101-GTJA-191
You can’t perform that action at this time.
ram-ki/101_formulaic_alphas
You can’t perform that action at this time.
Releases: wpwpwpwpwpwpwpwpwp/Alpha-101-GTJA-191
Releases · wpwpwpwpwpwpwpwpwp/Alpha-101-GTJA-191
yli188/WorldQuant_alpha101_code
You can’t perform that action at this time.
There aren’t any releases here
You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.
You can’t perform that action at this time.
You can’t perform that action at this time.
You can’t perform that action at this time.
from scipy.stats import rankdata
from dateutil import parser
import numpy.linalg as la
from datetime import datetime
import scipy.stats as stats
import matplotlib.pyplot as plt
from WindPy import * # 万矿数据接口
stocks = list(data.index)
data_ = pd.DataFrame(ind.Data,index=['INDUSTRY'],columns=ind.Codes).T
data_med = pd.get_dummies(data_,columns=['INDUSTRY'])
beta_ols = la.inv(X.T.dot(X)).dot(X.T).dot(y)
residual = y - X.dot(beta_ols)
def IndNeutralize(vwap,ind):
vwap_ = vwap.fillna(value = 0)
ind = w.wss(stocks, "industry_citic","unit=1;tradeDate="+ind.Times[0].strftime("%Y%m%d")+";industryType=1")
for i in range(len(vwap_)):
vwap_.iloc[i] = neutral(vwap_.iloc[i],ind)
return df.rolling(window).sum()
return df.rolling(window).mean()
return df.rolling(window).std()
def correlation(x, y, window):
return x.rolling(window).corr(y)
def covariance(x, y, window):
return x.rolling(window).cov(y)
return df.rolling(window).apply(rolling_rank)
return df.rolling(window).apply(rolling_prod)
return df.rolling(window).min()
return df.rolling(window).max()
return df.rank(pct=True,axis=1)
return df.mul(k).div(np.abs(df).sum())
def ts_argmax(df, window):
return df.rolling(window).apply(np.argmax) + 1
def ts_argmin(df, window):
return df.rolling(window).apply(np.argmin) + 1
def decay_linear(df, period):
if df.isnull().values.any():
df.fillna(method='ffill', inplace=True)
df.fillna(method='bfill', inplace=True)
df.fillna(value=0, inplace=True)
na_lwma = np.zeros_like(df) # 生成与df大小相同的零数组
na_lwma[:period, :] = df.iloc[:period, :] # 赋前period项的值
na_series = df.as_matrix()
divisor = period * (period + 1) / 2
y = (np.arange(period) + 1) * 1.0 / divisor
for row in range(period - 1, df.shape[0]):
x = na_series[row - period + 1: row + 1, :]
na_lwma[row, :] = (np.dot(x.T, y))
return pd.DataFrame(na_lwma,index = df.index,columns = df.columns)
def alpha1(close,returns):
x[returns < 0] = stddev(returns,20)
alpha = rank(ts_argmax(x ** 2, 5))-0.5
return alpha.fillna(value = 0)
def alpha2(Open,close,volume):
r1 = rank(delta(np.log(volume), 2))
r2 = rank((close - Open) / Open)
alpha = -1 * correlation(r1,r2,6)
return alpha.fillna(value = 0)
alpha = -1 * correlation(r1,r2,10)
return alpha.replace([-np.inf, np.inf], 0).fillna(value = 0)
alpha = -1 * ts_rank(r,9)
return alpha.fillna(value = 0)
def alpha5(Open,vwap,close):
alpha = (rank((Open - (ts_sum(vwap, 10) / 10))) * (-1 * abs(rank((close - vwap)))))
return alpha.fillna(value = 0)
def alpha6(Open, volume):
alpha = -1 * correlation(Open, volume, 10)
return alpha.replace([-np.inf, np.inf], 0).fillna(value = 0)
def alpha7(volume,close):
alpha = -1 * ts_rank(abs(delta(close, 7)), 60) * np.sign(delta(close, 7))
alpha[adv20 >= volume] = -1
return alpha.fillna(value = 0)
def alpha8(Open,returns):
x1 = (ts_sum(Open, 5) * ts_sum(returns, 5))
x2 = delay((ts_sum(Open, 5) * ts_sum(returns, 5)), 10)
return alpha.fillna(value = 0)
delta_close = delta(close, 1)
x1 = ts_min(delta_close, 5) > 0
x2 = ts_max(delta_close, 5) < 0
alpha[x1 | x2] = delta_close
return alpha.fillna(value = 0)
delta_close = delta(close, 1)
x1 = ts_min(delta_close, 4) > 0
x2 = ts_max(delta_close, 4) < 0
return alpha.fillna(value = 0)
def alpha11(vwap,close,volume):
x1 = rank(ts_max((vwap - close), 3))
x2 = rank(ts_min((vwap - close), 3))
x3 = rank(delta(volume, 3))
return alpha.fillna(value = 0)
def alpha12(volume,close):
alpha = np.sign(delta(volume, 1)) * (-1 * delta(close, 1))
return alpha.fillna(value = 0)
def alpha13(volume,close):
alpha = -1 * rank(covariance(rank(close), rank(volume), 5))
return alpha.fillna(value = 0)
def alpha14(Open,volume,returns):
x1 = correlation(Open, volume, 10).replace([-np.inf, np.inf], 0).fillna(value=0)
x2 = -1 * rank(delta(returns, 3))
return alpha.fillna(value = 0)
def alpha15(high,volume):
x1 = correlation(rank(high), rank(volume), 3).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = -1 * ts_sum(rank(x1), 3)
return alpha.fillna(value = 0)
def alpha16(high,volume):
alpha = -1 * rank(covariance(rank(high), rank(volume), 5))
return alpha.fillna(value = 0)
def alpha17(volume,close):
x1 = rank(ts_rank(close, 10))
x2 = rank(delta(delta(close, 1), 1))
x3 = rank(ts_rank((volume / adv20), 5))
alpha = -1 * (x1 * x2 * x3)
return alpha.fillna(value = 0)
x = correlation(close, Open, 10).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = -1 * (rank((stddev(abs((close - Open)), 5) + (close - Open)) + x))
return alpha.fillna(value = 0)
def alpha19(close,returns):
x1 = (-1 * np.sign((close - delay(close, 7)) + delta(close, 7)))
x2 = (1 + rank(1 + ts_sum(returns, 250)))
return alpha.fillna(value = 0)
def alpha20(Open,high,close,low):
alpha = -1 * (rank(Open - delay(high, 1)) * rank(Open - delay(close, 1)) * rank(Open - delay(low, 1)))
return alpha.fillna(value = 0)
def alpha21(volume,close):
x1 = sma(close, 8) + stddev(close, 8) < sma(close, 2)
x2 = sma(close, 8) - stddev(close, 8) > sma(close, 2)
x3 = sma(volume, 20) / volume < 1
alpha = pd.DataFrame(np.ones_like(close), index = close.index,columns = close.columns)
alpha[x1 | x3] = -1 * alpha
def alpha22(high,volume,close):
x = correlation(high, volume, 5).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = -1 * delta(x, 5) * rank(stddev(close, 20))
return alpha.fillna(value = 0)
alpha = pd.DataFrame(np.zeros_like(close),index = close.index,columns = close.columns)
alpha[x] = -1 * delta(high, 2).fillna(value = 0)
x = delta(sma(close, 100), 100) / delay(close, 100) <= 0.05
alpha = -1 * delta(close, 3)
alpha[x] = -1 * (close - ts_min(close, 100))
return alpha.fillna(value = 0)
def alpha25(volume,returns,vwap,high,close):
alpha = rank((((-1 * returns) * adv20) * vwap) * (high - close))
return alpha.fillna(value = 0)
def alpha26(volume,high):
x = correlation(ts_rank(volume, 5), ts_rank(high, 5), 5).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = -1 * ts_max(x, 3)
return alpha.fillna(value = 0)
def alpha27(volume,vwap):
alpha = rank((sma(correlation(rank(volume), rank(vwap), 6), 2) / 2.0))
return alpha.fillna(value = 0)
def alpha28(volume,high,low,close):
x = correlation(adv20, low, 5).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = scale(((x + ((high + low) / 2)) - close))
return alpha.fillna(value = 0)
def alpha29(close,returns):
x1 = ts_min(rank(rank(scale(np.log(ts_sum(rank(rank(-1 * rank(delta((close - 1), 5)))), 2))))), 5)
x2 = ts_rank(delay((-1 * returns), 6), 5)
return alpha.fillna(value = 0)
def alpha30(close,volume):
delta_close = delta(close, 1)
x = np.sign(delta_close) + np.sign(delay(delta_close, 1)) + np.sign(delay(delta_close, 2))
alpha = ((1.0 - rank(x)) * ts_sum(volume, 5)) / ts_sum(volume, 20)
return alpha.fillna(value = 0)
def alpha31(close,low,volume):
x1 = rank(rank(rank(decay_linear((-1 * rank(rank(delta(close, 10)))), 10))))
x2 = rank((-1 * delta(close, 3)))
x3 = np.sign(scale(correlation(adv20, low, 12).replace([-np.inf, np.inf], 0).fillna(value=0)))
return alpha.fillna(value = 0)
x = correlation(vwap, delay(close, 5),230).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = scale(((sma(close, 7)) - close)) + 20 * scale(x)
return alpha.fillna(value = 0)
alpha = rank(-1 + (Open / close))
def alpha34(close,returns):
x = (stddev(returns, 2) / stddev(returns, 5)).fillna(value = 0)
alpha = rank(2 - rank(x) - rank(delta(close, 1)))
return alpha.fillna(value = 0)
def alpha35(volume,close,high,low,returns):
x2 = 1 - ts_rank(close + high - low, 16)
x3 = 1 - ts_rank(returns, 32)
alpha = (x1 * x2 * x3).fillna(value = 0)
def alpha36(Open,close,volume,returns,vwap):
x1 = 2.21 * rank(correlation((close - Open), delay(volume, 1), 15))
x2 = 0.7 * rank((Open- close))
x3 = 0.73 * rank(ts_rank(delay((-1 * returns), 6), 5))
x4 = rank(abs(correlation(vwap,adv20, 6)))
x5 = 0.6 * rank((sma(close, 200) - Open) * (close - Open))
alpha = x1 + x2 + x3 + x4 + x5
return alpha.fillna(value = 0)
alpha = rank(correlation(delay(Open - close, 1), close, 200)) + rank(Open - close)
return alpha.fillna(value = 0)
x = (close / Open).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = -1 * rank(ts_rank(Open, 10)) * rank(x)
return alpha.fillna(value = 0)
def alpha39(volume,close,returns):
x = -1 * rank(delta(close, 7)) * (1 - rank(decay_linear((volume / adv20), 9)))
alpha = x *(1 + rank(ts_sum(returns, 250)))
return alpha.fillna(value = 0)
def alpha40(high,volume):
alpha = -1 * rank(stddev(high, 10)) * correlation(high, volume, 10)
return alpha.fillna(value = 0)
def alpha41(high,low,vwap):
alpha = pow((high * low),0.5) - vwap
alpha = rank((vwap - close)) / rank((vwap + close))
def alpha43(volume,close):
alpha = ts_rank(volume / adv20, 20) * ts_rank((-1 * delta(close, 7)), 8)
return alpha.fillna(value = 0)
def alpha44(high,volume):
alpha = -1 *correlation(high, rank(volume), 5).replace([-np.inf, np.inf], 0).fillna(value=0)
def alpha45(close,volume):
x = correlation(close, volume, 2).replace([-np.inf, np.inf], 0).fillna(value=0)
alpha = -1 * (rank(sma(delay(close, 5), 20)) * x * rank(correlation(ts_sum(close, 5), ts_sum(close, 20), 2)))
return alpha.fillna(value = 0)
x = ((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)
alpha = (-1 * (close - delay(close, 1)))
return alpha.fillna(value = 0)
def alpha47(volume,close,high,vwap):
alpha = ((rank((1 / close)) * volume) / adv20) * ((high * rank((high - close))) / sma(high, 5)) - rank((vwap - delay(vwap, 5)))
return alpha.fillna(value = 0)
x = (((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10))
alpha = (-1 * delta(close,1))
return alpha.fillna(value = 0)
def alpha50(volume,vwap):
alpha = -1 * ts_max(rank(correlation(rank(volume), rank(vwap), 5)), 5)
return alpha.fillna(value = 0)
inner = (((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10))
alpha = (-1 * delta(close,1))
return alpha.fillna(value = 0)
def alpha52(returns,volume,low):
x = rank(((ts_sum(returns, 240) - ts_sum(returns, 20)) / 220))
alpha = -1 * delta(ts_min(low, 5), 5) * x * ts_rank(volume, 5)
return alpha.fillna(value = 0)
def alpha53(close,high,low):
alpha = -1 * delta((((close - low) - (high - close)) / (close - low).replace(0, 0.0001)), 9)
return alpha.fillna(value = 0)
def alpha54(Open,close,high,low):
x = (low - high).replace(0, -0.0001)
alpha = -1 * (low - close) * (Open ** 5) / (x * (close ** 5))
def alpha55(high,low,close,volume):
x = (close - ts_min(low, 12)) / (ts_max(high, 12) - ts_min(low, 12)).replace(0, 0.0001)
alpha = -1 * correlation(rank(x), rank(volume), 6).replace([-np.inf, np.inf], 0).fillna(value=0)
def alpha56(returns,cap):
alpha = 0 - (1 * (rank((sma(returns, 10) / sma(sma(returns, 2), 3))) * rank((returns * cap))))
return alpha.fillna(value = 0)
alpha = 0 - 1 * ((close - vwap) / decay_linear(rank(ts_argmax(close, 30)), 2))
return alpha.fillna(value = 0)
def alpha60(close,high,low,volume):
x = ((close - low) - (high - close)) * volume / (high - low).replace(0, 0.0001)
alpha = - ((2 * scale(rank(x))) - scale(rank(ts_argmax(close, 10))))
return alpha.fillna(value = 0)
def alpha61(volume,vwap):
adv180 = sma(volume, 180)
alpha = rank((vwap - ts_min(vwap, 16))) < rank(correlation(vwap, adv180, 18))
def alpha62(volume,high,low,Open,vwap):
x1 = rank(correlation(vwap, ts_sum(adv20, 22), 10))
x2 = rank(((rank(Open) + rank(Open)) < (rank(((high + low) / 2)) + rank(high))))
def alpha64(high,low,Open,volume,vwap):
adv120 = sma(volume, 120)
x1 = rank(correlation(ts_sum(((Open * 0.178404) + (low * (1 - 0.178404))), 13),ts_sum(adv120, 13), 17))
x2 = rank(delta(((((high + low) / 2) * 0.178404) + (vwap * (1 -0.178404))), 3.69741))
def alpha65(volume,vwap,Open):
x1 = rank(correlation(((Open * 0.00817205) + (vwap * (1 - 0.00817205))), ts_sum(adv60,9), 6))
x2 = rank((Open - ts_min(Open, 14)))
def alpha66(vwap,low,Open,high):
x1 = rank(decay_linear(delta(vwap, 4), 7))
x2 = (((low* 0.96633) + (low * (1 - 0.96633))) - vwap) / (Open - ((high + low) / 2))
alpha = (x1 + ts_rank(decay_linear(x2, 11), 7)) * -1
return alpha.fillna(value = 0)
def alpha68(volume,high,close,low):
x1 = ts_rank(correlation(rank(high), rank(adv15), 9), 14)
x2 = rank(delta(((close * 0.518371) + (low * (1 - 0.518371))), 1.06157))
def alpha71(volume,close,low,Open,vwap):
adv180 = sma(volume, 180)
x1 = ts_rank(decay_linear(correlation(ts_rank(close, 3), ts_rank(adv180,12), 18), 4), 16)
x2 = ts_rank(decay_linear((rank(((low + Open) - (vwap + vwap))).pow(2)), 16), 4)
return alpha.fillna(value = 0)
def alpha72(volume,high,low,vwap):
x1 = rank(decay_linear(correlation(((high + low) / 2), adv40, 9), 10))
x2 = rank(decay_linear(correlation(ts_rank(vwap, 4), ts_rank(volume, 19), 7), 3))
alpha = (x1 / x2.replace(0, 0.0001)).fillna(value = 0)
def alpha73(vwap,Open,low):
x1 = rank(decay_linear(delta(vwap, 5), 3))
x2 = delta(((Open * 0.147155) + (low * (1 - 0.147155))), 2) / ((Open *0.147155) + (low * (1 - 0.147155)))
x3 = ts_rank(decay_linear((x2 * -1), 3), 17)
return -1 * alpha.fillna(value = 0)
def alpha74(volume,close,high,vwap):
x1 = rank(correlation(close, ts_sum(adv30, 37), 15))
x2 = rank(correlation(rank(((high * 0.0261661) + (vwap * (1 - 0.0261661)))), rank(volume), 11))
def alpha75(volume,vwap,low):
alpha = rank(correlation(vwap, volume, 4)) < rank(correlation(rank(low), rank(adv50), 12))
def alpha77(volume,high,low,vwap):
x1 = rank(decay_linear(((((high + low) / 2) + high) - (vwap + high)), 20))
x2 = rank(decay_linear(correlation(((high + low) / 2), adv40, 3), 6))
return alpha.fillna(value = 0)
def alpha78(volume,low,vwap):
x1 = rank(correlation(ts_sum(((low * 0.352233) + (vwap * (1 - 0.352233))), 20), ts_sum(adv40, 20),7))
x2 = rank(correlation(rank(vwap), rank(volume), 6))
return alpha.fillna(value = 0)
def alpha81(volume,vwap):
x1 = rank(np.log(product(rank((rank(correlation(vwap, ts_sum(adv10, 50), 8)).pow(4))), 15)))
x2 = rank(correlation(rank(vwap), rank(volume), 5))
def alpha83(high,low,close,volume):
x = rank(delay(((high - low) / (ts_sum(close, 5) / 5)), 2)) * rank(rank(volume))
alpha = x / (((high - low) / (ts_sum(close, 5) / 5)) / (vwap - close))
return alpha.fillna(value = 0)
alpha = pow(ts_rank((vwap - ts_max(vwap, 15)), 21), delta(close,5))
return alpha.fillna(value = 0)
def alpha85(volume,high,close,low):
x1 = rank(correlation(((high * 0.876703) + (close * (1 - 0.876703))), adv30,10))
alpha = x1.pow(rank(correlation(ts_rank(((high + low) / 2), 4), ts_rank(volume, 10), 7)))
return alpha.fillna(value = 0)
def alpha86(volume,close,Open,vwap):
x1 = ts_rank(correlation(close, sma(adv20, 15), 6), 20)
x2 = rank(((Open+ close) - (vwap + Open)))
def alpha88(volume,Open,low,high,close):
x1 = rank(decay_linear(((rank(Open) + rank(low)) - (rank(high) + rank(close))),8))
x2 = ts_rank(decay_linear(correlation(ts_rank(close, 8), ts_rank(adv60,21), 8), 7), 3)
return alpha.fillna(value = 0)
def alpha92(volume,high,low,close,Open):
x1 = ts_rank(decay_linear(((((high + low) / 2) + close) < (low + Open)), 15), 19)
x2 = ts_rank(decay_linear(correlation(rank(low), rank(adv30), 8), 7),7)
return alpha.fillna(value = 0)
def alpha94(volume,vwap):
x = rank((vwap - ts_min(vwap, 12)))
alpha = x.pow(ts_rank(correlation(ts_rank(vwap,20), ts_rank(adv60, 4), 18), 3))* -1
return alpha.fillna(value = 0)
def alpha95(volume,high,low,Open):
x = ts_rank((rank(correlation(sma(((high + low)/ 2), 19), sma(adv40, 19), 13)).pow(5)), 12)
alpha = rank((Open - ts_min(Open, 12))) < x
return alpha.fillna(value = 0)
def alpha96(volume,vwap,close):
x1 = ts_rank(decay_linear(correlation(rank(vwap), rank(volume), 4),4), 8)
x2 = ts_rank(decay_linear(ts_argmax(correlation(ts_rank(close, 7),ts_rank(adv60, 4), 4), 13), 14), 13)
return alpha.fillna(value = 0)
def alpha98(volume,Open,vwap):
x1 = rank(decay_linear(correlation(vwap, sma(adv5, 26), 5), 7))
alpha = x1 - rank(decay_linear(ts_rank(ts_argmin(correlation(rank(Open), rank(adv15), 21), 9),7), 8))
return alpha.fillna(value = 0)
def alpha99(volume,high,low):
x1 = rank(correlation(ts_sum(((high + low) / 2), 20), ts_sum(adv60,20), 9))
x2 = rank(correlation(low, volume, 6))
def alpha101(close,Open,high,low):
alpha = (close - Open) / ((high - low) + 0.001)
r1 = (correlation(delta(close, 1), delta(delay(close, 1), 1), 250) * delta(close, 1)) / close
r2 = ts_sum((pow((delta(close, 1) / delay(close, 1)),2)), 250)
alpha = IndNeutralize(r1, ind) / r2
return alpha.fillna(value = 0)
def alpha58(vwap,volume,ind):
x = IndNeutralize(vwap, ind)
alpha = -1 * ts_rank(decay_linear(correlation(x, volume, 4), 8), 6)
return alpha.fillna(value = 0)
def alpha59(vwap,volume,ind):
x = IndNeutralize(((vwap * 0.728317) + (vwap * (1 - 0.728317))), ind)
alpha = -1 * ts_rank(decay_linear(correlation(x, volume, 4), 16), 8)
return alpha.fillna(value = 0)
def alpha63(volume,close,vwap,Open,ind):
adv180 = sma(volume, 180).fillna(value = 0)
r1 = rank(decay_linear(delta(IndNeutralize(close, ind), 2), 8))
r2 = rank(decay_linear(correlation(((vwap * 0.318108) + (Open * (1 - 0.318108))), ts_sum(adv180, 37), 14), 12))
return alpha.fillna(value = 0)
def alpha67(volume,vwap,high,ind):
r = rank(correlation(IndNeutralize(vwap, ind), IndNeutralize(adv20, ind), 6))
alpha = pow(rank(high - ts_min(high, 2)),r) * -1
return alpha.fillna(value = 0)
def alpha69(volume,vwap,ind,close):
r1 = rank(ts_max(delta(IndNeutralize(vwap, ind), 3), 5))
r2 = ts_rank(correlation(((close * 0.490655) + (vwap * (1 - 0.490655))), adv20, 5), 9)
return alpha.fillna(value = 0)
def alpha70(close,ind,vwap):
adv50 = sma(volume, 50).fillna(value = 0)
r = ts_rank(correlation(IndNeutralize(close, ind), adv50, 18), 18)
alpha = pow(rank(delta(vwap, 1)),r) * -1
return alpha.fillna(value = 0)
def alpha76(volume,vwap,low,ind):
adv81 = sma(volume, 81).fillna(value = 0)
r1 = rank(decay_linear(delta(vwap, 1), 12))
r2 = ts_rank(decay_linear(ts_rank(correlation(IndNeutralize(low, ind), adv81, 8), 20), 17), 19)
return alpha.fillna(value = 0)
def alpha79(volume,close,Open,ind,vwap):
adv150 = sma(volume, 150).fillna(value = 0)
r1 = rank(delta(IndNeutralize(((close * 0.60733) + (Open * (1 - 0.60733))), ind), 1))
r2 = rank(correlation(ts_rank(vwap, 4), ts_rank(adv150, 9), 15))
return alpha.fillna(value = 0)
def alpha80(Open,high,ind):
r1 = rank(np.sign(delta(IndNeutralize(((Open * 0.868128) + (high * (1 - 0.868128))), ind), 4)))
r2 = ts_rank(correlation(high, adv10, 5), 6)
return alpha.fillna(value = 0)
def alpha82(Open,volume,ind):
r1 = rank(decay_linear(delta(Open, 1), 15))
r2 = ts_rank(decay_linear(correlation(IndNeutralize(volume, ind), ((Open * 0.634196) + (Open * (1 - 0.634196))), 17), 7), 13)
return -1 * alpha.fillna(value = 0)
def alpha87(volume,close,vwap):
adv81 = sma(volume, 81).fillna(value = 0)
r1 = rank(decay_linear(delta(((close * 0.369701) + (vwap * (1 - 0.369701))), 2), 3))
r2 = ts_rank(decay_linear(abs(correlation(IndNeutralize(adv81, ind), close, 13)), 5), 14)
return -1 * alpha.fillna(value = 0)
def alpha89(low,vwap,ind):
r1 = ts_rank(decay_linear(correlation(((low * 0.967285) + (low * (1 - 0.967285))), adv10, 7), 6), 4)
r2 = ts_rank(decay_linear(delta(IndNeutralize(vwap, ind), 3), 10), 15)
return alpha.fillna(value = 0)
def alpha90(volume,close,ind,low):
adv40 = sma(volume, 40).fillna(value = 0)
r1 = rank((close - ts_max(close, 5)))
r2 = ts_rank(correlation(IndNeutralize(adv40, ind), low, 5), 3)
return alpha.fillna(value = 0)
def alpha91(close,ind,volume,vwap):
r1 = ts_rank(decay_linear(decay_linear(correlation(IndNeutralize(close, ind), volume, 10), 16), 4), 5)
r2 = rank(decay_linear(correlation(vwap, adv30, 4), 3))
return alpha.fillna(value = 0)
def alpha93(vwap,ind,volume,close):
adv81 = sma(volume, 81).fillna(value = 0)
r1 = ts_rank(decay_linear(correlation(IndNeutralize(vwap, ind), adv81, 17), 20), 8)
r2 = rank(decay_linear(delta(((close * 0.524434) + (vwap * (1 - 0.524434))), 3), 16))
return alpha.fillna(value = 0)
def alpha97(volume,low,vwap,ind):
adv60 = sma(volume, 60).fillna(value = 0)
r1 = rank(decay_linear(delta(IndNeutralize(((low * 0.721001) + (vwap * (1 - 0.721001))), ind), 3), 20))
r2 = ts_rank(decay_linear(ts_rank(correlation(ts_rank(low, 8), ts_rank(adv60, 17), 5), 19), 16), 7)
return alpha.fillna(value = 0)
def alpha100(volume,close,low,high,ind):
r1 = IndNeutralize(rank(((((close - low) - (high - close)) / (high - low)) * volume)), ind)
r2 = 1.5 * scale(IndNeutralize(r1, ind))
r3 = scale(IndNeutralize((correlation(close, rank(adv20), 5) - rank(ts_argmin(close, 30))), ind))
alpha = -1 * (r2 - r3) * (volume / adv20)
return alpha.fillna(value = 0)
You can’t perform that action at this time.
