Alpha101

数据简介

复现 alpha 101 因子

公式说明

• Alpha#1: (rank(Ts_ArgMax(SignedPower(((returns < 0) ? stddev(returns, 20) : close), 2.), 5)) - 0.5)
• Alpha#2: (-1 * correlation(rank(delta(log(volume), 2)), rank(((close - open) / open)), 6))
• Alpha#3: (-1 * correlation(rank(open), rank(volume), 10))
• Alpha#4: (-1 * Ts_Rank(rank(low), 9))
• Alpha#5: (rank((open - (sum(vwap, 10) / 10))) * (-1 * abs(rank((close - vwap)))))
• Alpha#6: (-1 * correlation(open, volume, 10))
• Alpha#7: ((adv20 < volume) ? ((-1 * ts_rank(abs(delta(close, 7)), 60)) * sign(delta(close, 7))) : (-1 * 1))
• Alpha#8: (-1 * rank(((sum(open, 5) * sum(returns, 5)) - delay((sum(open, 5) * sum(returns, 5)), 10))))
• Alpha#9: ((0 < ts_min(delta(close, 1), 5)) ? delta(close, 1) : ((ts_max(delta(close, 1), 5) < 0) ? delta(close, 1) : (-1 * delta(close, 1))))
• Alpha#10: rank(((0 < ts_min(delta(close, 1), 4)) ? delta(close, 1) : ((ts_max(delta(close, 1), 4) < 0) ? delta(close, 1) : (-1 * delta(close, 1)))))
• Alpha#11: ((rank(ts_max((vwap - close), 3)) + rank(ts_min((vwap - close), 3))) * rank(delta(volume, 3)))
• Alpha#12: (sign(delta(volume, 1)) * (-1 * delta(close, 1)))
• Alpha#13: (-1 * rank(covariance(rank(close), rank(volume), 5)))
• Alpha#14: ((-1 * rank(delta(returns, 3))) * correlation(open, volume, 10))
• Alpha#15: (-1 * sum(rank(correlation(rank(high), rank(volume), 3)), 3))
• Alpha#16: (-1 * rank(covariance(rank(high), rank(volume), 5)))
• Alpha#17: (((-1 * rank(ts_rank(close, 10))) * rank(delta(delta(close, 1), 1))) * rank(ts_rank((volume / adv20), 5)))
• Alpha#18: (-1 * rank(((stddev(abs((close - open)), 5) + (close - open)) + correlation(close, open, 10))))
• Alpha#19: ((-1 * sign(((close - delay(close, 7)) + delta(close, 7)))) * (1 + rank((1 + sum(returns, 250)))))
• Alpha#20: (((-1 * rank((open - delay(high, 1)))) * rank((open - delay(close, 1)))) * rank((open - delay(low, 1))))
• Alpha#21: ((((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))))
• Alpha#22: (-1 * (delta(correlation(high, volume, 5), 5) * rank(stddev(close, 20))))
• Alpha#23: (((sum(high, 20) / 20) < high) ? (-1 * delta(high, 2)) : 0)
• Alpha#24: ((((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 * delt(close, 3)))
• Alpha#25: rank(((((-1 * returns) * adv20) * vwap) * (high - close)))
• Alpha#26: (-1 * ts_max(correlation(ts_rank(volume, 5), ts_rank(high, 5), 5), 3))
• Alpha#27: ((0.5 < rank((sum(correlation(rank(volume), rank(vwap), 6), 2) / 2.0))) ? (-1 * 1) : 1)
• Alpha#28: scale(((correlation(adv20, low, 5) + ((high + low) / 2)) - close))
• Alpha#29: (min(product(rank(rank(scale(log(sum(ts_min(rank(rank((-1 * rank(delta((close - 1), 5))))), 2), 1))))), 1), 5) + ts_rank(delay((-1 * returns), 6), 5))
• Alpha#30: (((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))
• Alpha#31: ((rank(rank(rank(decay_linear((-1 * rank(rank(delta(close, 10)))), 10)))) + rank((-1 * delta(close, 3)))) + sign(scale(correlation(adv20, low, 12))))
• Alpha#32: (scale(((sum(close, 7) / 7) - close)) + (20 * scale(correlation(vwap, delay(close, 5), 230))))
• Alpha#33: rank((-1 * ((1 - (open / close))^1)))
• Alpha#34: rank(((1 - rank((stddev(returns, 2) / stddev(returns, 5)))) + (1 - rank(delta(close, 1)))))
• Alpha#35: ((Ts_Rank(volume, 32) * (1 - Ts_Rank(((close + high) - low), 16))) * (1 - Ts_Rank(returns, 32)))
• Alpha#36: (((((2.21 * rank(correlation((close - open), delay(volume, 1), 15))) + (0.7 * rank((open - close)))) + (0.73 * rank(Ts_Rank(delay((-1 * returns), 6), 5)))) + rank(abs(correlation(vwap, adv20, 6)))) + (0.6 * rank((((sum(close, 200) / 200) - open) * (close - open)))))
• Alpha#37: (rank(correlation(delay((open - close), 1), close, 200)) + rank((open - close)))
• Alpha#38: ((-1 * rank(Ts_Rank(close, 10))) * rank((close / open)))
• Alpha#39: ((-1 * rank((delta(close, 7) * (1 - rank(decay_linear((volume / adv20), 9)))))) * (1 + rank(sum(returns, 250))))
• Alpha#40: ((-1 * rank(stddev(high, 10))) * correlation(high, volume, 10))
• Alpha#41: (((high * low)^0.5) - vwap)
• Alpha#42: (rank((vwap - close)) / rank((vwap + close)))
• Alpha#43: (ts_rank((volume / adv20), 20) * ts_rank((-1 * delta(close, 7)), 8))
• Alpha#44: (-1 * correlation(high, rank(volume), 5))
• Alpha#45: (-1 * ((rank((sum(delay(close, 5), 20) / 20)) * correlation(close, volume, 2)) * rank(correlation(sum(close, 5), sum(close, 20), 2))))
• Alpha#46: ((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)))))
• Alpha#47: ((((rank((1 / close)) * volume) / adv20) * ((high * rank((high - close))) / (sum(high, 5) / 5))) - rank((vwap - delay(vwap, 5))))
• Alpha#48: (indneutralize(((correlation(delta(close, 1), delta(delay(close, 1), 1), 250) * delta(close, 1)) / close), IndClass.subindustry) / sum(((delta(close, 1) / delay(close, 1))^2), 250))
• Alpha#49: (((((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)) < (-1 * 0.1)) ? 1 : ((-1 * 1) * (close - delay(close, 1))))
• Alpha#50: (-1 * ts_max(rank(correlation(rank(volume), rank(vwap), 5)), 5))
• Alpha#51: (((((delay(close, 20) - delay(close, 10)) / 10) - ((delay(close, 10) - close) / 10)) < (-1 * 0.05)) ? 1 : ((-1 * 1) * (close - delay(close, 1))))
• Alpha#52: ((((-1 * ts_min(low, 5)) + delay(ts_min(low, 5), 5)) * rank(((sum(returns, 240) - sum(returns, 20)) / 220))) * ts_rank(volume, 5))
• Alpha#53: (-1 * delta((((close - low) - (high - close)) / (close - low)), 9))
• Alpha#54: ((-1 * ((low - close) * (open^5))) / ((low - high) * (close^5)))
• Alpha#55: (-1 * correlation(rank(((close - ts_min(low, 12)) / (ts_max(high, 12) - ts_min(low, 12)))), rank(volume), 6))
• Alpha#56: (0 - (1 * (rank((sum(returns, 10) / sum(sum(returns, 2), 3))) * rank((returns * cap)))))
• Alpha#57: (0 - (1 * ((close - vwap) / decay_linear(rank(ts_argmax(close, 30)), 2))))
• Alpha#58: (-1 * Ts_Rank(decay_linear(correlation(IndNeutralize(vwap, IndClass.sector), volume, 3.92795), 7.89291), 5.50322))
• Alpha#59: (-1 * Ts_Rank(decay_linear(correlation(IndNeutralize(((vwap * 0.728317) + (vwap * (1 - 0.728317))), IndClass.industry), volume, 4.25197), 16.2289), 8.19648))
• Alpha#60: (0 - (1 * ((2 * scale(rank(((((close - low) - (high - close)) / (high - low)) * volume)))) - scale(rank(ts_argmax(close, 10))))))
• Alpha#61: (rank((vwap - ts_min(vwap, 16.1219))) < rank(correlation(vwap, adv180, 17.9282)))
• Alpha#62: ((rank(correlation(vwap, sum(adv20, 22.4101), 9.91009)) < rank(((rank(open) + rank(open)) < (rank(((high + low) / 2)) + rank(high))))) * -1)
• Alpha#63: ((rank(decay_linear(delta(IndNeutralize(close, IndClass.industry), 2.25164), 8.22237)) - rank(decay_linear(correlation(((vwap * 0.318108) + (open * (1 - 0.318108))), sum(adv180, 37.2467), 13.557), 12.2883))) * -1)
• Alpha#64: ((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)
• Alpha#65: ((rank(correlation(((open * 0.00817205) + (vwap * (1 - 0.00817205))), sum(adv60, 8.6911), 6.40374)) < rank((open - ts_min(open, 13.635)))) * -1)
• Alpha#66: ((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)
• Alpha#67: ((rank((high - ts_min(high, 2.14593)))^rank(correlation(IndNeutralize(vwap, IndClass.sector), IndNeutralize(adv20, IndClass.subindustry), 6.02936))) * -1)
• Alpha#68: ((Ts_Rank(correlation(rank(high), rank(adv15), 8.91644), 13.9333) < rank(delta(((close * 0.518371) + (low * (1 - 0.518371))), 1.06157))) * -1)
• Alpha#69: ((rank(ts_max(delta(IndNeutralize(vwap, IndClass.industry), 2.72412), 4.79344))^Ts_Rank(correlation(((close * 0.490655) + (vwap * (1 - 0.490655))), adv20, 4.92416), 9.0615)) * -1)
• Alpha#70: ((rank(delta(vwap, 1.29456))^Ts_Rank(correlation(IndNeutralize(close, IndClass.industry), adv50, 17.8256), 17.9171)) * -1)
• Alpha#71: max(Ts_Rank(decay_linear(correlation(Ts_Rank(close, 3.43976), Ts_Rank(adv180, 12.0647), 18.0175), 4.20501), 15.6948), Ts_Rank(decay_linear((rank(((low + open) - (vwap + vwap)))^2), 16.4662), 4.4388))
• Alpha#72: (rank(decay_linear(correlation(((high + low) / 2), adv40, 8.93345), 10.1519)) / rank(decay_linear(correlation(Ts_Rank(vwap, 3.72469), Ts_Rank(volume, 18.5188), 6.86671), 2.95011)))
• Alpha#73: (max(rank(decay_linear(delta(vwap, 4.72775), 2.91864)), Ts_Rank(decay_linear(((delta(((open * 0.147155) + (low * (1 - 0.147155))), 2.03608) / ((open * 0.147155) + (low * (1 - 0.147155)))) * -1), 3.33829), 16.7411)) * -1)
• Alpha#74: ((rank(correlation(close, sum(adv30, 37.4843), 15.1365)) < rank(correlation(rank(((high * 0.0261661) + (vwap * (1 - 0.0261661)))), rank(volume), 11.4791))) * -1)
• Alpha#75: (rank(correlation(vwap, volume, 4.24304)) < rank(correlation(rank(low), rank(adv50), 12.4413)))
• Alpha#76: (max(rank(decay_linear(delta(vwap, 1.24383), 11.8259)), Ts_Rank(decay_linear(Ts_Rank(correlation(IndNeutralize(low, IndClass.sector), adv81, 8.14941), 19.569), 17.1543), 19.383)) * -1)
• Alpha#77: min(rank(decay_linear(((((high + low) / 2) + high) - (vwap + high)), 20.0451)), rank(decay_linear(correlation(((high + low) / 2), adv40, 3.1614), 5.64125)))
• Alpha#78: (rank(correlation(sum(((low * 0.352233) + (vwap * (1 - 0.352233))), 19.7428), sum(adv40, 19.7428), 6.83313))^rank(correlation(rank(vwap), rank(volume), 5.77492)))
• Alpha#79: (rank(delta(IndNeutralize(((close * 0.60733) + (open * (1 - 0.60733))), IndClass.sector), 1.23438)) < rank(correlation(Ts_Rank(vwap, 3.60973), Ts_Rank(adv150, 9.18637), 14.6644)))
• Alpha#80: ((rank(Sign(delta(IndNeutralize(((open * 0.868128) + (high * (1 - 0.868128))), IndClass.industry), 4.04545)))^Ts_Rank(correlation(high, adv10, 5.11456), 5.53756)) * -1)
• Alpha#81: ((rank(Log(product(rank((rank(correlation(vwap, sum(adv10, 49.6054), 8.47743))^4)), 14.9655))) < rank(correlation(rank(vwap), rank(volume), 5.07914))) * -1)
• Alpha#82: (min(rank(decay_linear(delta(open, 1.46063), 14.8717)), Ts_Rank(decay_linear(correlation(IndNeutralize(volume, IndClass.sector), ((open * 0.634196) + (open * (1 - 0.634196))), 17.4842), 6.92131), 13.4283)) * -1)
• Alpha#83: ((rank(delay(((high - low) / (sum(close, 5) / 5)), 2)) * rank(rank(volume))) / (((high - low) / (sum(close, 5) / 5)) / (vwap - close)))
• Alpha#84: SignedPower(Ts_Rank((vwap - ts_max(vwap, 15.3217)), 20.7127), delta(close, 4.96796))
• Alpha#85: (rank(correlation(((high * 0.876703) + (close * (1 - 0.876703))), adv30, 9.61331))^rank(correlation(Ts_Rank(((high + low) / 2), 3.70596), Ts_Rank(volume, 10.1595), 7.11408)))
• Alpha#86: ((Ts_Rank(correlation(close, sum(adv20, 14.7444), 6.00049), 20.4195) < rank(((open + close) - (vwap + open)))) * -1)
• Alpha#87: (max(rank(decay_linear(delta(((close * 0.369701) + (vwap * (1 - 0.369701))), 1.91233), 2.65461)), Ts_Rank(decay_linear(abs(correlation(IndNeutralize(adv81, IndClass.industry), close, 13.4132)), 4.89768), 14.4535)) * -1)
• Alpha#88: min(rank(decay_linear(((rank(open) + rank(low)) - (rank(high) + rank(close))), 8.06882)), Ts_Rank(decay_linear(correlation(Ts_Rank(close, 8.44728), Ts_Rank(adv60, 20.6966), 8.01266), 6.65053), 2.61957))
• Alpha#89: (Ts_Rank(decay_linear(correlation(((low * 0.967285) + (low * (1 - 0.967285))), adv10, 6.94279), 5.51607), 3.79744) - Ts_Rank(decay_linear(delta(IndNeutralize(vwap, IndClass.industry), 3.48158), 10.1466), 15.3012))
• Alpha#90: ((rank((close - ts_max(close, 4.66719)))^Ts_Rank(correlation(IndNeutralize(adv40, IndClass.subindustry), low, 5.38375), 3.21856)) * -1)
• Alpha#91: ((Ts_Rank(decay_linear(decay_linear(correlation(IndNeutralize(close, IndClass.industry), volume, 9.74928), 16.398), 3.83219), 4.8667) - rank(decay_linear(correlation(vwap, adv30, 4.01303), 2.6809))) * -1)
• Alpha#92: min(Ts_Rank(decay_linear(((((high + low) / 2) + close) < (low + open)), 14.7221), 18.8683), Ts_Rank(decay_linear(correlation(rank(low), rank(adv30), 7.58555), 6.94024), 6.80584))
• Alpha#93: (Ts_Rank(decay_linear(correlation(IndNeutralize(vwap, IndClass.industry), adv81, 17.4193), 19.848), 7.54455) / rank(decay_linear(delta(((close * 0.524434) + (vwap * (1 - 0.524434))), 2.77377), 16.2664)))
• Alpha#94: ((rank((vwap - ts_min(vwap, 11.5783)))^Ts_Rank(correlation(Ts_Rank(vwap, 19.6462), Ts_Rank(adv60, 4.02992), 18.0926), 2.70756)) * -1)
• Alpha#95: (rank((open - ts_min(open, 12.4105))) < Ts_Rank((rank(correlation(sum(((high + low) / 2), 19.1351), sum(adv40, 19.1351), 12.8742))^5), 11.7584))
• Alpha#96: (max(Ts_Rank(decay_linear(correlation(rank(vwap), rank(volume), 3.83878), 4.16783), 8.38151), Ts_Rank(decay_linear(Ts_ArgMax(correlation(Ts_Rank(close, 7.45404), Ts_Rank(adv60, 4.13242), 3.65459), 12.6556), 14.0365), 13.4143)) * -1)
• Alpha#97: ((rank(decay_linear(delta(IndNeutralize(((low * 0.721001) + (vwap * (1 - 0.721001))), IndClass.industry), 3.3705), 20.4523)) - Ts_Rank(decay_linear(Ts_Rank(correlation(Ts_Rank(low, 7.87871), Ts_Rank(adv60, 17.255), 4.97547), 18.5925), 15.7152), 6.71659)) * -1)
• Alpha#98: (rank(decay_linear(correlation(vwap, sum(adv5, 26.4719), 4.58418), 7.18088)) - rank(decay_linear(Ts_Rank(Ts_ArgMin(correlation(rank(open), rank(adv15), 20.8187), 8.62571), 6.95668), 8.07206)))
• Alpha#99: ((rank(correlation(sum(((high + low) / 2), 19.8975), sum(adv60, 19.8975), 8.8136)) < rank(correlation(low, volume, 6.28259))) * -1)
• Alpha#100: (0 - (1 * (((1.5 * scale(indneutralize(indneutralize(rank(((((close - low) - (high - close)) / (high - low)) * volume)), IndClass.subindustry), IndClass.subindustry))) - scale(indneutralize((correlation(close, rank(adv20), 5) - rank(ts_argmin(close, 30))), IndClass.subindustry))) * (volume / adv20))))
• Alpha#101: (close - open) / ((high - low) + .001)

操作函数定义

(Below “{ }” stands for a placeholder. All expressions are case insensitive.)

• abs(x), log(x), sign(x) = standard definitions; same for the operators “+”, “-”, “*”, “/”, “>”, “<”, “==”, “||”, “x ? y : z”
• rank(x) = cross-sectional rank
• delay(x, d) = value of x d days ago
• correlation(x, y, d) = time-serial correlation of x and y for the past d days
• covariance(x, y, d) = time-serial covariance of x and y for the past d days
• scale(x, a) = rescaled x such that sum(abs(x)) = a (the default is a = 1)
• delta(x, d) = today’s value of x minus the value of x d days ago
• signedpower(x, a) = x^a
• decay_linear(x, d) = weighted moving average over the past d days with linearly decaying
• weights d, d – 1, …, 1 (rescaled to sum up to 1)
• indneutralize(x, g) = x cross-sectionally neutralized against groups g (subindustries, industries,
• sectors, etc.), i.e., x is cross-sectionally demeaned within each group g
• ts_{O}(x, d) = operator O applied across the time-series for the past d days; non-integer number of days d is converted to floor(d)
• ts_min(x, d) = time-series min over the past d days
• ts_max(x, d) = time-series max over the past d days
• ts_argmax(x, d) = which day ts_max(x, d) occurred on
• ts_argmin(x, d) = which day ts_min(x, d) occurred on
• ts_rank(x, d) = time-series rank in the past d days
• min(x, d) = ts_min(x, d)
• max(x, d) = ts_max(x, d)
• sum(x, d) = time-series sum over the past d days
• product(x, d) = time-series product over the past d days
• stddev(x, d) = moving time-series standard deviation over the past d days

输入数据定义

• returns = daily close-to-close returns
• open, close, high, low, volume = standard definitions for daily price and volume data
• vwap = daily volume-weighted average price
• cap = market cap
• adv{d} = average daily dollar volume for the past d days
• IndClass = a generic placeholder for a binary industry classification such as GICS, BICS, NAICS, SIC, etc., in indneutralize(x, IndClass.level), where level = sector, industry, subindustry, etc. Multiple IndClass in the same alpha need not correspond to the same industry classification.

示例数据

展示000001.SZ最新一日的数据前5行和后5行

{{sample_data}}