++

generated_alpha/2025-12-12/154831.txt

@@ -0,0 +1,225 @@
+multiply(group_mean(ts_delta(close, 5), 1, bucket(rank(volume), range="0,3,0.4")), ts_corr(volume, returns, 10))
+
+if_else(ts_rank(ts_std_dev(returns, 20), 120) > 0.7, ts_delta(close, 5), ts_delta(close, 30))
+
+multiply(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), 1, industry), ts_corr(group_rank(returns, industry), returns, 10))
+
+multiply(reverse(ts_rank(divide(volume, ts_mean(volume, 20)), 10)), ts_mean(returns, 30))
+
+multiply(if_else(rank(volume) > 0.7, ts_delta(close, 10), ts_delta(close, 30)), ts_av_diff(returns, 5))
+
+if_else(ts_std_dev(returns, 60) > 0.02, ts_delta(close, 10), group_mean(ts_delta(close, 60), 1, bucket(rank(volume), range="0,3,0.4")))
+
+multiply(ts_zscore(volume, 20), ts_decay_linear(ts_delta(close, 5), 10))
+
+multiply(group_mean(ts_delta(close, 20), 1, industry), ts_rank(group_mean(returns, 1, industry), 30))
+
+if_else(ts_rank(ts_mean(abs(returns), 30), 60) > 0.5, ts_delta(ts_delta(close, 5), 10), ts_delta(close, 20))
+
+multiply(ts_corr(ts_delta(close, 10), ts_delta(volume, 10), 20), if_else(bucket(rank(volume), range="0,3,0.4") == 0, 1, reverse(1)))
+
+group_mean(ts_sum(returns, 10), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_rank(ts_std_dev(returns, 10), 30) > 0.8, ts_delta(close, 5) / ts_std_dev(returns, 10), ts_delta(close, 20) / ts_std_dev(returns, 30))
+
+multiply(ts_corr(group_mean(returns, 1, sector), returns, 30), ts_regression(close, ts_step(1), 60, 0, 1))
+
+multiply(reverse(ts_rank(divide(volume, ts_mean(volume, 60)), 20)), ts_corr(returns, ts_delta(close, 5), 10))
+
+if_else(ts_std_dev(returns, 20) > ts_mean(ts_std_dev(returns, 120), 20), ts_delta(close, 10), group_mean(ts_delta(close, 30), 1, industry))
+
+multiply(ts_mean(returns, 10), if_else(rank(ts_std_dev(returns, 20)) > 0.7, reverse(1), 1))
+
+multiply(group_mean(ts_rank(returns, 5), 1, bucket(rank(volume), range="0,3,0.4")), ts_decay_linear(ts_delta(close, 1), 10))
+
+if_else(ts_rank(ts_mean(returns, 20), 60) > 0.5, ts_delta(close, 10), reverse(ts_delta(close, 10)))
+
+multiply(ts_corr(ts_delta(close, 5), ts_delta(high, 5), 10), if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_zscore(close, 20), 0))
+
+group_mean(ts_delta(close, 5) / ts_std_dev(returns, 20), 1, industry)
+
+if_else(ts_std_dev(returns, 30) > 0.015, ts_mean(returns, 5), ts_mean(returns, 20))
+
+multiply(ts_rank(volume, 30), ts_regression(close, ts_step(1), 10, 0, 1))
+
+multiply(group_zscore(ts_delta(close, 10), industry), ts_corr(returns, group_mean(returns, 1, industry), 30))
+
+if_else(ts_rank(divide(volume, ts_mean(volume, 30)), 60) > 0.7, reverse(ts_delta(close, 5)), ts_delta(close, 20))
+
+multiply(ts_av_diff(returns, 5), if_else(rank(volume) > 0.8, 1, reverse(1)))
+
+group_mean(ts_delta(close, 20) * sign(ts_delta(volume, 20)), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_mean(abs(returns), 10) > 0.02, ts_delta(close, 5), group_mean(ts_delta(close, 30), 1, industry))
+
+multiply(ts_corr(ts_delta(close, 10), ts_delta(vwap, 10), 15), ts_rank(ts_mean(returns, 10), 20))
+
+multiply(reverse(ts_rank(ts_std_dev(returns, 10), 30)), ts_sum(returns, 10))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_delta(close, 10) / ts_std_dev(returns, 10), ts_delta(close, 30) / ts_std_dev(returns, 30))
+
+multiply(group_scale(ts_delta(close, 5), industry), ts_corr(returns, ts_delta(volume, 5), 10))
+
+if_else(ts_rank(ts_mean(returns, 5), 20) > 0.7, ts_delta(close, 5), reverse(ts_delta(close, 5)))
+
+multiply(ts_mean(ts_delta(close, 1), 10), if_else(rank(ts_zscore(volume, 30)) > 0.5, 1, reverse(1)))
+
+group_mean(ts_delta(close, 30) * ts_corr(close, volume, 30), 1, industry)
+
+if_else(ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 120), 60), ts_delta(close, 10), ts_delta(close, 60))
+
+multiply(ts_rank(ts_delta(close, 5), 20), if_else(bucket(rank(volume), range="0,3,0.4") == 2, reverse(1), 1))
+
+multiply(group_neutralize(ts_delta(close, 20), industry), ts_zscore(volume, 20))
+
+if_else(ts_rank(divide(volume, ts_mean(volume, 90)), 120) > 0.6, ts_delta(close, 10), group_mean(ts_delta(close, 20), 1, bucket(rank(volume), range="0,3,0.4")))
+
+multiply(ts_decay_linear(returns, 10), ts_corr(returns, group_mean(returns, 1, sector), 30))
+
+multiply(reverse(ts_rank(ts_delta(volume, 5), 15)), ts_regression(close, ts_step(1), 30, 0, 1))
+
+if_else(ts_mean(abs(returns), 20) > 0.01, group_mean(ts_delta(close, 5), 1, industry), group_mean(ts_delta(close, 20), 1, industry))
+
+multiply(ts_scale(ts_delta(close, 10), 20), if_else(rank(volume) > 0.7, ts_zscore(returns, 10), 0))
+
+group_mean(ts_sum(if_else(returns > 0, returns, 0), 10), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns, 30) > 0.025, ts_delta(close, 5) / ts_std_dev(returns, 5), ts_delta(close, 30) / ts_std_dev(returns, 30))
+
+multiply(ts_corr(group_rank(close, industry), ts_rank(volume, 20), 20), ts_delta(close, 10))
+
+multiply(ts_rank(ts_mean(returns, 30), 60), if_else(bucket(rank(volume), range="0,3,0.4") == 0, 1, reverse(1)))
+
+group_mean(ts_delta(close, 5) * ts_delta(volume, 5), 1, industry)
+
+if_else(ts_rank(ts_std_dev(returns, 10), 40) > 0.8, reverse(ts_delta(close, 5)), ts_delta(close, 20))
+
+multiply(ts_zscore(ts_delta(close, 1), 10), ts_corr(returns, ts_delta(volume, 1), 15))
+
+multiply(reverse(ts_rank(divide(volume, ts_mean(volume, 40)), 30)), ts_mean(ts_delta(close, 1), 20))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_delta(close, 10), ts_delta(close, 30) / ts_std_dev(returns, 30))
+
+multiply(group_scale(ts_regression(close, ts_step(1), 20, 0, 1), industry), ts_rank(ts_mean(volume, 10), 20))
+
+if_else(ts_mean(returns, 10) > 0, ts_delta(close, 10), reverse(ts_delta(close, 10)))
+
+multiply(ts_mean(returns, 5), if_else(rank(ts_std_dev(returns, 20)) > 0.6, 1, reverse(1)))
+
+group_mean(ts_delta(close, 15) / ts_mean(abs(returns), 15), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns, 50) > 0.02, group_mean(ts_delta(close, 5), 1, industry), ts_delta(close, 30))
+
+multiply(ts_corr(ts_delta(close, 20), ts_delta(ts_mean(volume, 5), 20), 30), ts_av_diff(returns, 5))
+
+multiply(reverse(ts_rank(ts_zscore(volume, 20), 40)), ts_sum(ts_delta(close, 1), 10))
+
+if_else(ts_rank(ts_mean(returns, 15), 30) > 0.5, ts_delta(close, 10), group_mean(ts_delta(close, 20), 1, industry))
+
+multiply(if_else(rank(volume) > 0.5, ts_zscore(close, 20), reverse(ts_zscore(close, 20))), ts_decay_linear(returns, 15))
+
+group_mean(ts_delta(close, 10) * ts_corr(close, volume, 10), 1, industry)
+
+if_else(ts_mean(abs(returns), 25) > 0.015, ts_delta(close, 5) / ts_std_dev(returns, 5), ts_delta(close, 25) / ts_std_dev(returns, 25))
+
+multiply(ts_rank(ts_delta(close, 3), 10), ts_corr(returns, group_mean(returns, 1, sector), 20))
+
+multiply(ts_scale(ts_mean(returns, 10), 15), if_else(bucket(rank(volume), range="0,3,0.4") == 1, 1, reverse(1)))
+
+group_mean(ts_sum(if_else(ts_delta(close, 1) > 0, 1, reverse(1)), 10), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns, 70) > ts_mean(ts_std_dev(returns, 140), 70), ts_delta(close, 10), ts_delta(close, 40))
+
+multiply(ts_corr(group_rank(returns, industry), ts_rank(ts_delta(volume, 5), 10), 15), ts_regression(close, ts_step(1), 25, 0, 1))
+
+multiply(reverse(ts_rank(divide(ts_delta(volume, 1), ts_mean(volume, 20)), 25)), ts_mean(ts_delta(close, 2), 15))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_delta(close, 15) / ts_mean(abs(returns), 15), ts_delta(close, 30) / ts_mean(abs(returns), 30))
+
+multiply(group_zscore(ts_delta(close, 5), sector), ts_corr(returns, ts_delta(volume, 10), 20))
+
+if_else(ts_mean(returns, 20) > 0, group_mean(ts_delta(close, 10), 1, industry), reverse(group_mean(ts_delta(close, 10), 1, industry)))
+
+multiply(ts_zscore(ts_delta(close, 1), 20), if_else(rank(ts_std_dev(returns, 10)) > 0.7, 1, reverse(1)))
+
+group_mean(ts_delta(close, 8) * sign(ts_delta(volume, 8)), 1, industry)
+
+if_else(ts_std_dev(returns, 35) > 0.018, ts_delta(close, 8), group_mean(ts_delta(close, 35), 1, bucket(rank(volume), range="0,3,0.4")))
+
+multiply(ts_corr(ts_mean(returns, 5), ts_mean(volume, 5), 15), ts_rank(ts_delta(close, 5), 10))
+
+multiply(ts_rank(ts_mean(volume, 30), 60), ts_av_diff(close, 10))
+
+if_else(ts_rank(divide(volume, ts_mean(volume, 50)), 100) > 0.65, reverse(ts_delta(close, 10)), ts_delta(close, 25))
+
+multiply(if_else(rank(ts_zscore(close, 30)) > 0.5, ts_delta(close, 10), reverse(ts_delta(close, 10))), ts_decay_linear(returns, 20))
+
+group_mean(ts_delta(close, 12) / ts_std_dev(returns, 12), 1, industry)
+
+if_else(ts_mean(abs(returns), 30) > 0.012, ts_delta(close, 10), ts_delta(close, 30) / ts_std_dev(returns, 30))
+
+multiply(ts_corr(group_mean(ts_delta(close, 1), 1, sector), returns, 40), ts_regression(close, ts_step(1), 15, 0, 1))
+
+multiply(reverse(ts_rank(ts_delta(volume, 10), 20)), ts_sum(ts_delta(close, 1) * sign(volume), 15))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_delta(close, 20) * ts_corr(close, volume, 20), ts_delta(close, 40))
+
+multiply(group_scale(ts_mean(returns, 8), industry), ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 10))
+
+if_else(ts_rank(ts_mean(returns, 12), 24) > 0.5, group_mean(ts_delta(close, 12), 1, industry), reverse(group_mean(ts_delta(close, 12), 1, industry)))
+
+multiply(ts_zscore(volume, 15), ts_delta(ts_delta(close, 5), 10))
+
+group_mean(ts_sum(if_else(ts_delta(close, 1) > ts_mean(returns, 5), 1, reverse(1)), 8), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns, 80) > 0.022, ts_delta(close, 10) / ts_std_dev(returns, 10), group_mean(ts_delta(close, 40), 1, industry))
+
+multiply(ts_corr(ts_rank(close, 10), ts_rank(volume, 10), 20), ts_delta(close, 15))
+
+multiply(ts_scale(ts_delta(close, 6), 12), if_else(bucket(rank(volume), range="0,3,0.4") == 2, reverse(1), 1))
+
+group_mean(ts_delta(close, 18) * ts_zscore(volume, 18), 1, industry)
+
+if_else(ts_mean(abs(returns), 40) > 0.01, reverse(ts_delta(close, 10)), ts_delta(close, 30))
+
+multiply(ts_rank(ts_delta(close, 4), 8), ts_corr(returns, group_zscore(volume, 10), 15))
+
+multiply(reverse(ts_rank(divide(volume, ts_mean(volume, 25)), 50)), ts_mean(ts_delta(close, 3), 12))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 1, ts_delta(close, 15) / ts_mean(abs(returns), 15), ts_delta(close, 30) / ts_std_dev(returns, 30))
+
+multiply(group_neutralize(ts_regression(close, ts_step(1), 18, 0, 1), sector), ts_rank(ts_mean(volume, 15), 30))
+
+if_else(ts_mean(returns, 25) > 0, ts_delta(close, 15), reverse(ts_delta(close, 15)))
+
+multiply(ts_mean(returns, 6), if_else(rank(ts_std_dev(returns, 15)) > 0.55, 1, reverse(1)))
+
+group_mean(ts_delta(close, 9) / ts_mean(abs(returns), 9), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns, 45) > ts_mean(ts_std_dev(returns, 90), 45), ts_delta(close, 9), group_mean(ts_delta(close, 45), 1, industry))
+
+multiply(ts_corr(group_rank(ts_delta(close, 1), industry), ts_zscore(volume, 20), 25), ts_av_diff(close, 5))
+
+multiply(ts_rank(ts_mean(volume, 20), 40), ts_sum(ts_delta(close, 1) * if_else(returns > 0, 1, reverse(1)), 10))
+
+if_else(ts_rank(divide(volume, ts_mean(volume, 35)), 70) > 0.6, ts_delta(close, 12), group_mean(ts_delta(close, 24), 1, bucket(rank(volume), range="0,3,0.4")))
+
+multiply(if_else(rank(ts_zscore(close, 25)) > 0.6, ts_delta(close, 8), ts_delta(close, 20)), ts_decay_linear(returns, 15))
+
+group_mean(ts_delta(close, 14) * ts_corr(close, ts_mean(volume, 5), 14), 1, industry)
+
+if_else(ts_mean(abs(returns), 22) > 0.014, ts_delta(close, 7) / ts_std_dev(returns, 7), ts_delta(close, 22) / ts_std_dev(returns, 22))
+
+multiply(ts_corr(group_mean(ts_delta(close, 2), 1, sector), ts_delta(volume, 2), 30), ts_regression(close, ts_step(1), 22, 0, 1))
+
+multiply(reverse(ts_rank(ts_delta(volume, 8), 18)), ts_mean(ts_delta(close, 2) * sign(ts_delta(volume, 2)), 14))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_delta(close, 25) * ts_rank(volume, 25), ts_delta(close, 50))
+
+multiply(group_scale(ts_mean(returns, 9), sector), ts_corr(ts_delta(close, 8), ts_delta(ts_mean(volume, 3), 8), 12))
+
+if_else(ts_rank(ts_mean(returns, 18), 36) > 0.52, group_mean(ts_delta(close, 18), 1, industry), reverse(group_mean(ts_delta(close, 18), 1, industry)))
+
+multiply(ts_zscore(ts_delta(close, 1), 15), ts_corr(returns, ts_zscore(volume, 12), 18))
+
+group_mean(ts_sum(if_else(ts_delta(close, 1) > group_mean(returns, 1, industry), 1, reverse(1)), 12), 1, bucket(rank(volume), range="0,3,0.4"))

generated_alpha/2025-12-12/155502.txt

@@ -0,0 +1,205 @@
+ts_delta(close, 5) * group_mean(ts_delta(close, 20), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_rank(ts_std_dev(returns,20), 60) > 0.7, ts_delta(close,5), ts_delta(close,60))
+
+ts_corr(ts_delta(close,5), ts_delta(volume,5), 30) * ts_rank(returns, 20)
+
+group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+ts_decay_linear(returns, 20) * if_else(ts_std_dev(returns,20) > 0.015, 1, 0.5)
+
+multiply(ts_delta(close,10), group_rank(returns, industry)) * ts_zscore(volume, 30)
+
+ts_delta(ts_delta(close,5), 5) / ts_std_dev(returns,20) * if_else(ts_rank(volume, 30) > 0.7, 1.2, 0.8)
+
+group_neutralize(ts_delta(close,20), industry) * ts_corr(group_mean(returns,1,sector), group_mean(returns,1,industry), 60)
+
+if_else(ts_rank(ts_mean(returns,20), 60) > 0.5, ts_delta(close,10), reverse(ts_delta(close,10))) * ts_std_dev(group_rank(returns, industry), 20)
+
+multiply(ts_mean(returns,30), group_scale(ts_regression(close, ts_step(1), 60, 0, 1), industry)) * ts_zscore(volume, 20)
+
+ts_delta(close,20) * group_mean(ts_regression(close, ts_step(1), 20, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,30)) * group_rank(returns, sector)
+
+ts_corr(ts_delta(close,10), ts_delta(volume,10), 20) * ts_mean(rank(returns), 30)
+
+group_neutralize(ts_decay_linear(returns, 60), industry) * ts_rank(ts_delta(close,60), 120)
+
+multiply(ts_regression(close, ts_step(1), 30, 0, 1), group_rank(returns, industry)) * ts_zscore(volume, 60)
+
+ts_delta(close,30) * if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, ts_delta(close,5), ts_delta(close,60))
+
+group_mean(ts_delta(close,20), 1, bucket(rank(volume), range="0,3,0.4")) * ts_corr(group_mean(returns,1,sector), group_mean(returns,1,industry), 30)
+
+ts_decay_linear(returns, 30) * group_scale(ts_delta(close,10), industry) * ts_std_dev(group_rank(returns, industry), 20)
+
+if_else(ts_rank(ts_mean(returns,10), 30) > 0.5, ts_delta(close,10), reverse(ts_delta(close,10))) * group_rank(returns, sector)
+
+multiply(ts_mean(returns,20), group_neutralize(ts_regression(close, ts_step(1), 60, 0, 1), industry)) * ts_zscore(volume, 30)
+
+ts_delta(close,60) * group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,30) > 0.018, ts_delta(close,5), ts_delta(close,20)) * group_rank(returns, industry)
+
+ts_corr(ts_delta(close,5), ts_delta(volume,5), 60) * ts_mean(rank(returns), 20)
+
+group_neutralize(ts_delta(close,30), sector) * ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), 60)
+
+multiply(ts_regression(close, ts_step(1), 20, 0, 1), group_scale(returns, industry)) * ts_zscore(volume, 20)
+
+ts_delta(ts_delta(close,10), 5) / ts_std_dev(returns,30) * if_else(ts_rank(volume, 60) > 0.7, 1.1, 0.9)
+
+group_mean(ts_decay_linear(returns, 20), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(returns, 30)
+
+if_else(ts_rank(ts_std_dev(returns,30), 90) > 0.7, ts_delta(close,10), ts_delta(close,60)) * group_rank(returns, sector)
+
+ts_mean(returns,60) * group_neutralize(ts_regression(close, ts_step(1), 30, 0, 1), industry) * ts_zscore(volume, 60)
+
+multiply(ts_delta(close,20), group_rank(returns, industry)) * ts_corr(ts_delta(close,5), ts_delta(volume,5), 30)
+
+ts_delta(close,10) * group_mean(ts_regression(close, ts_step(1), 60, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,60) > 0.016, ts_delta(close,5), ts_delta(close,30)) * group_rank(returns, industry)
+
+ts_corr(ts_delta(close,20), ts_delta(volume,20), 20) * ts_mean(rank(returns), 60)
+
+group_neutralize(ts_decay_linear(returns, 30), sector) * ts_corr(group_mean(returns,1,sector), group_mean(returns,1,industry), 30)
+
+multiply(ts_regression(close, ts_step(1), 60, 0, 1), group_neutralize(returns, industry)) * ts_zscore(volume, 20)
+
+ts_delta(close,20) * if_else(ts_rank(ts_mean(returns,30), 90) > 0.5, ts_delta(close,5), reverse(ts_delta(close,5)))
+
+group_mean(ts_delta(close,10), 1, bucket(rank(volume), range="0,3,0.4")) * ts_std_dev(group_rank(returns, industry), 30)
+
+ts_decay_linear(returns, 60) * group_scale(ts_delta(close,20), sector) * ts_zscore(volume, 30)
+
+if_else(ts_rank(ts_std_dev(returns,20), 60) > 0.7, ts_delta(close,10), ts_delta(close,60)) * group_rank(returns, sector)
+
+multiply(ts_mean(returns,30), group_neutralize(ts_regression(close, ts_step(1), 20, 0, 1), industry)) * ts_zscore(volume, 20)
+
+ts_delta(close,30) * group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20)) * group_scale(returns, industry)
+
+ts_corr(ts_delta(close,10), ts_delta(volume,10), 60) * ts_mean(rank(returns), 20)
+
+group_neutralize(ts_delta(close,60), industry) * ts_corr(group_mean(returns,1,sector), group_mean(returns,1,industry), 60)
+
+multiply(ts_regression(close, ts_step(1), 30, 0, 1), group_rank(returns, industry)) * ts_zscore(volume, 60)
+
+ts_delta(ts_delta(close,20), 5) / ts_std_dev(returns,20) * if_else(ts_rank(volume, 30) > 0.7, 1.3, 0.7)
+
+group_mean(ts_decay_linear(returns, 60), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(returns, 20)
+
+if_else(ts_rank(ts_mean(returns,20), 60) > 0.5, ts_delta(close,10), reverse(ts_delta(close,10))) * group_rank(returns, sector)
+
+multiply(ts_mean(returns,20), group_neutralize(ts_regression(close, ts_step(1), 60, 0, 1), industry)) * ts_zscore(volume, 30)
+
+ts_delta(close,10) * group_mean(ts_regression(close, ts_step(1), 20, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,30) > 0.018, ts_delta(close,5), ts_delta(close,60)) * group_rank(returns, industry)
+
+ts_corr(ts_delta(close,5), ts_delta(volume,5), 30) * ts_mean(rank(returns), 60)
+
+group_neutralize(ts_decay_linear(returns, 20), sector) * ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), 30)
+
+multiply(ts_regression(close, ts_step(1), 60, 0, 1), group_scale(returns, sector)) * ts_zscore(volume, 20)
+
+ts_delta(close,60) * if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, ts_delta(close,5), ts_delta(close,30))
+
+group_mean(ts_delta(close,30), 1, bucket(rank(volume), range="0,3,0.4")) * ts_std_dev(group_rank(returns, industry), 20)
+
+ts_decay_linear(returns, 30) * group_neutralize(ts_delta(close,10), industry) * ts_zscore(volume, 60)
+
+if_else(ts_rank(ts_mean(returns,10), 30) > 0.5, ts_delta(close,5), reverse(ts_delta(close,5))) * group_rank(returns, sector)
+
+multiply(ts_mean(returns,60), group_neutralize(ts_regression(close, ts_step(1), 30, 0, 1), sector)) * ts_zscore(volume, 20)
+
+ts_delta(close,20) * group_mean(ts_regression(close, ts_step(1), 60, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,60) > 0.015, ts_delta(close,5), ts_delta(close,20)) * group_scale(returns, industry)
+
+ts_corr(ts_delta(close,20), ts_delta(volume,20), 30) * ts_mean(rank(returns), 30)
+
+group_neutralize(ts_delta(close,30), industry) * ts_corr(group_mean(returns,1,sector), group_mean(returns,1,industry), 60)
+
+multiply(ts_regression(close, ts_step(1), 20, 0, 1), group_rank(returns, sector)) * ts_zscore(volume, 30)
+
+ts_delta(ts_delta(close,5), 5) / ts_std_dev(returns,60) * if_else(ts_rank(volume, 60) > 0.7, 1.2, 0.8)
+
+group_mean(ts_decay_linear(returns, 30), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(returns, 60)
+
+if_else(ts_rank(ts_std_dev(returns,20), 60) > 0.7, ts_delta(close,10), ts_delta(close,60)) * group_rank(returns, sector)
+
+multiply(ts_mean(returns,20), group_neutralize(ts_regression(close, ts_step(1), 60, 0, 1), sector)) * ts_zscore(volume, 60)
+
+ts_delta(close,30) * group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20)) * group_neutralize(returns, industry)
+
+ts_corr(ts_delta(close,10), ts_delta(volume,10), 60) * ts_mean(rank(returns), 20)
+
+group_neutralize(ts_decay_linear(returns, 60), sector) * ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), 30)
+
+multiply(ts_regression(close, ts_step(1), 30, 0, 1), group_scale(returns, industry)) * ts_zscore(volume, 20)
+
+ts_delta(close,10) * if_else(ts_rank(ts_mean(returns,30), 90) > 0.5, ts_delta(close,5), reverse(ts_delta(close,5)))
+
+group_mean(ts_delta(close,20), 1, bucket(rank(volume), range="0,3,0.4")) * ts_std_dev(group_rank(returns, sector), 20)
+
+ts_decay_linear(returns, 20) * group_neutralize(ts_delta(close,30), industry) * ts_zscore(volume, 30)
+
+if_else(ts_rank(ts_std_dev(returns,30), 90) > 0.7, ts_delta(close,5), ts_delta(close,60)) * group_rank(returns, industry)
+
+multiply(ts_mean(returns,60), group_neutralize(ts_regression(close, ts_step(1), 20, 0, 1), industry)) * ts_zscore(volume, 20)
+
+ts_delta(close,60) * group_mean(ts_regression(close, ts_step(1), 60, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,60) > 0.016, ts_delta(close,5), ts_delta(close,30)) * group_scale(returns, sector)
+
+ts_corr(ts_delta(close,5), ts_delta(volume,5), 20) * ts_mean(rank(returns), 60)
+
+group_neutralize(ts_delta(close,20), sector) * ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), 60)
+
+multiply(ts_regression(close, ts_step(1), 60, 0, 1), group_rank(returns, industry)) * ts_zscore(volume, 60)
+
+ts_delta(ts_delta(close,10), 5) / ts_std_dev(returns,30) * if_else(ts_rank(volume, 30) > 0.7, 1.1, 0.9)
+
+group_mean(ts_decay_linear(returns, 60), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(returns, 30)
+
+if_else(ts_rank(ts_mean(returns,20), 60) > 0.5, ts_delta(close,10), reverse(ts_delta(close,10))) * group_rank(returns, sector)
+
+multiply(ts_mean(returns,30), group_neutralize(ts_regression(close, ts_step(1), 30, 0, 1), sector)) * ts_zscore(volume, 30)
+
+ts_delta(close,20) * group_mean(ts_regression(close, ts_step(1), 20, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,60)) * group_neutralize(returns, sector)
+
+ts_corr(ts_delta(close,20), ts_delta(volume,20), 60) * ts_mean(rank(returns), 20)
+
+group_neutralize(ts_decay_linear(returns, 30), industry) * ts_corr(group_mean(returns,1,sector), group_mean(returns,1,industry), 30)
+
+multiply(ts_regression(close, ts_step(1), 20, 0, 1), group_scale(returns, industry)) * ts_zscore(volume, 20)
+
+ts_delta(close,30) * if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, ts_delta(close,5), ts_delta(close,20))
+
+group_mean(ts_delta(close,10), 1, bucket(rank(volume), range="0,3,0.4")) * ts_std_dev(group_rank(returns, industry), 30)
+
+ts_decay_linear(returns, 60) * group_neutralize(ts_delta(close,20), sector) * ts_zscore(volume, 60)
+
+if_else(ts_rank(ts_mean(returns,10), 30) > 0.5, ts_delta(close,5), reverse(ts_delta(close,5))) * group_rank(returns, industry)
+
+multiply(ts_mean(returns,20), group_neutralize(ts_regression(close, ts_step(1), 60, 0, 1), industry)) * ts_zscore(volume, 30)
+
+ts_delta(close,60) * group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_std_dev(returns,30) > 0.018, ts_delta(close,5), ts_delta(close,30)) * group_scale(returns, industry)
+
+ts_corr(ts_delta(close,5), ts_delta(volume,5), 30) * ts_mean(rank(returns), 60)
+
+group_neutralize(ts_delta(close,10), sector) * ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), 60)
+
+multiply(ts_regression(close, ts_step(1), 60, 0, 1), group_rank(returns, sector)) * ts_zscore(volume, 20)
+
+ts_delta(ts_delta(close,20),

generated_alpha/2025-12-12/155552.txt

@@ -0,0 +1,240 @@
+
+ts_delta(close, 5) - ts_mean(ts_delta(close, 5), 20)
+
+group_mean(ts_delta(close, 10), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_rank(ts_std_dev(returns, 20), 60) > 0.7, ts_delta(close, 5), ts_delta(close, 30))
+
+ts_regression(close, ts_step(1), 30, 0, 1) * rank(volume)
+
+multiply(group_rank(ts_delta(close, 10), industry), ts_zscore(volume, 20))
+
+ts_decay_linear(ts_delta(close, 1), 20) * ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 30)
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_delta(close, 20), 0)
+
+divide(ts_delta(close, 10), ts_std_dev(returns, 30))
+
+ts_mean(ts_delta(close, 5) / ts_std_dev(returns, 20), 60)
+
+group_mean(ts_mean(returns, 10), 1, bucket(rank(volume), range="0,3,0.4")) - ts_mean(returns, 20)
+
+reverse(ts_rank(volume / ts_mean(volume, 20), 10)) * ts_delta(close, 20)
+
+ts_corr(group_mean(returns, 1, industry), ts_delta(close, 10), 60) * ts_delta(close, 20)
+
+ts_delta(ts_mean(returns, 5), 20) * rank(volume)
+
+if_else(ts_std_dev(returns, 20) > 0.015, ts_delta(close, 5), ts_regression(close, ts_step(1), 60, 0, 1))
+
+group_mean(ts_delta(close, 20) / ts_std_dev(returns, 60), 1, bucket(rank(volume), range="0,3,0.4"))
+
+multiply(ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 30), group_rank(returns, industry))
+
+ts_regression(close, ts_step(1), 20, 0, 1) - ts_mean(ts_regression(close, ts_step(1), 20, 0, 1), 120)
+
+if_else(rank(volume) > 0.8, ts_delta(close, 10), ts_delta(close, 30) * -1)
+
+ts_mean(ts_rank(returns, 20), 30) * ts_zscore(volume, 10)
+
+group_mean(ts_zscore(ts_delta(close, 10), 60), 1, bucket(rank(volume), range="0,3,0.4"))
+
+multiply(ts_rank(ts_std_dev(returns, 30), 120), ts_delta(close, 10) * -1)
+
+divide(ts_delta(ts_mean(returns, 20), 5), ts_std_dev(returns, 60))
+
+if_else(ts_rank(ts_corr(ts_delta(close,5), ts_delta(volume,5), 30), 60) > 0.6, ts_delta(close, 20), ts_delta(close, 60))
+
+group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, bucket(rank(volume), range="0,3,0.4")) * rank(ts_std_dev(returns, 60))
+
+multiply(ts_delta(close, 5), rank(group_mean(returns, 1, industry)))
+
+ts_mean(ts_delta(close, 10), 30) - ts_decay_linear(returns, 20)
+
+if_else(ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 120), ts_zscore(close, 10), ts_zscore(close, 60))
+
+group_rank(ts_corr(ts_delta(close,5), ts_delta(volume,5), 30), industry) * rank(volume)
+
+divide(ts_regression(close, ts_step(1), 60, 0, 1), ts_std_dev(returns, 30))
+
+multiply(ts_mean(returns, 10), if_else(rank(volume) > 0.7, 1, -1))
+
+ts_decay_linear(volume, 20) * ts_delta(close, 30)
+
+if_else(ts_rank(ts_mean(returns, 20), 60) > 0.5, ts_mean(returns, 10), ts_mean(returns, 60) * -1)
+
+group_mean(ts_delta(close, 5) - ts_delay(close, 10), 1, bucket(rank(volume), range="0,3,0.4"))
+
+multiply(ts_zscore(returns, 20), group_rank(returns, sector))
+
+ts_delta(close, 20) / ts_mean(ts_std_dev(returns, 20), 60)
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 1, group_mean(ts_regression(close, ts_step(1), 30, 0, 1), 1, industry), 0)
+
+ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 60) - ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 20)
+
+multiply(ts_rank(volume, 20), ts_delta(close, 5))
+
+group_zscore(ts_mean(returns, 10), 1, bucket(rank(volume), range="0,3,0.4"))
+
+if_else(ts_rank(ts_std_dev(returns, 20), 240) > 0.8, reverse(ts_delta(close, 5)), ts_delta(close, 30))
+
+ts_regression(close, ts_step(1), 20, 0, 1) * ts_zscore(volume, 60)
+
+group_mean(ts_delta(close, 10), 1, industry) * rank(volume)
+
+multiply(ts_decay_linear(returns, 10), ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 30))
+
+divide(ts_mean(ts_delta(close, 5), 20), ts_mean(abs(ts_delta(close, 5)), 20))
+
+if_else(rank(volume) > 0.6, ts_regression(close, ts_step(1), 20, 0, 1), ts_zscore(close, 60))
+
+group_rank(ts_mean(returns, 30), industry) * ts_std_dev(volume, 20)
+
+multiply(ts_rank(ts_std_dev(returns, 60), 120), group_rank(ts_mean(returns, 10), industry))
+
+ts_mean(ts_delta(close, 10), 60) / ts_std_dev(close, 60)
+
+if_else(ts_corr(close, volume, 20) > 0.1, ts_delta(close, 20), ts_regression(close, ts_step(1), 60, 0, 1))
+
+group_mean(ts_zscore(returns, 30), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(volume, 60)
+
+reverse(ts_rank(volume, 30)) * ts_mean(returns, 20)
+
+ts_corr(ts_delta(close, 10), ts_delta(volume, 10), 60) * ts_delta(close, 30)
+
+if_else(ts_std_dev(returns, 20) > 0.025, ts_mean(returns, 5), ts_mean(returns, 30))
+
+group_mean(ts_regression(close, ts_step(1), 60, 0, 1), 1, bucket(rank(volume), range="0,3,0.4")) - ts_mean(ts_regression(close, ts_step(1), 60, 0, 1), 240)
+
+multiply(ts_rank(returns, 20), group_rank(volume, industry))
+
+divide(ts_mean(returns, 10), ts_std_dev(returns, 60))
+
+if_else(rank(ts_mean(volume, 20)) > 0.5, ts_delta(close, 5), ts_delta(close, 60) * -1)
+
+ts_regression(close, ts_step(1), 30, 0, 1) * rank(ts_std_dev(returns, 30))
+
+group_mean(ts_zscore(returns, 10), 1, industry) * ts_zscore(volume, 30)
+
+multiply(reverse(ts_rank(returns, 30)), group_mean(ts_delta(close, 20), 1, sector))
+
+ts_delta(close, 60) - ts_delay(close, 20)
+
+if_else(ts_rank(ts_corr(close, volume, 30), 120) > 0.6, ts_corr(close, volume, 5), ts_corr(close, volume, 60))
+
+group_mean(ts_mean(returns, 20), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(close, 60)
+
+divide(ts_std_dev(returns, 60), ts_std_dev(returns, 20))
+
+multiply(ts_rank(ts_mean(returns, 60), 120), group_rank(volume, sector))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 2, ts_mean(returns, 10), ts_mean(returns, 60))
+
+ts_regression(close, ts_step(1), 10, 0, 1) + ts_regression(close, ts_step(1), 30, 0, 1) - ts_regression(close, ts_step(1), 120, 0, 1)
+
+group_zscore(ts_regression(close, ts_step(1), 20, 0, 1), 1, industry)
+
+multiply(rank(ts_mean(volume, 60)), ts_zscore(ts_delta(close, 20), 60))
+
+if_else(ts_mean(abs(returns), 10) > ts_mean(abs(returns), 60), reverse(ts_delta(close, 10)), ts_delta(close, 30))
+
+group_mean(ts_decay_linear(returns, 20), 1, bucket(rank(volume), range="0,3,0.4")) - ts_mean(ts_decay_linear(returns, 20), 60)
+
+multiply(rank(ts_std_dev(returns, 30)), group_rank(ts_delta(close, 10), industry))
+
+divide(ts_delay(close, 5), ts_delay(close, 20)) - 1
+
+if_else(ts_rank(volume, 10) > 0.9, ts_zscore(close, 5), ts_zscore(close, 60))
+
+group_mean(ts_corr(ts_delta(close,5), ts_delta(volume,5), 20), 1, industry) * rank(volume)
+
+multiply(ts_regression(close, ts_step(1), 60, 0, 1), reverse(ts_rank(volume, 60)))
+
+ts_std_dev(returns, 20) / ts_std_dev(ts_mean(volume, 20), 60)
+
+if_else(group_rank(volume, sector) > 0.7, ts_mean(returns, 20), ts_mean(returns, 120))
+
+group_mean(ts_delta(close, 30), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(ts_mean(volume, 120), 240)
+
+multiply(ts_rank(returns, 10) - ts_rank(returns, 60), group_rank(returns, industry))
+
+divide(ts_sum(returns, 20), ts_sum(abs(returns), 20))
+
+if_else(ts_rank(ts_std_dev(returns, 30), 120) > 0.7, ts_regression(close, ts_step(1), 10, 0, 1), ts_delta(close, 60))
+
+group_mean(ts_zscore(returns, 20), 1, industry) * ts_rank(volume, 120)
+
+reverse(ts_rank(ts_mean(volume, 60))) * ts_zscore(close, 30)
+
+multiply(ts_corr(ts_delta(close, 10), ts_delta(volume, 10), 20), group_rank(ts_mean(returns, 10), 1, bucket(rank(volume), range="0,3,0.4")))
+
+divide(ts_mean(returns, 10), ts_mean(volume, 120))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0, ts_zscore(returns, 20), ts_zscore(returns, 120) * -1)
+
+group_mean(ts_regression(close, ts_step(1), 60, 0, 1), 1, sector) * rank(volume)
+
+multiply(rank(ts_zscore(returns, 30)), group_rank(close, industry))
+
+ts_mean(ts_delta(close, 5), 20) - ts_mean(ts_delta(close, 20), 60)
+
+if_else(ts_corr(close, volume, 60) > ts_corr(close, volume, 120), ts_mean(returns, 10), ts_mean(returns, 60))
+
+group_mean(ts_rank(returns, 30), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(ts_std_dev(returns, 60), 120)
+
+multiply(reverse(rank(ts_zscore(volume, 30))), ts_regression(close, ts_step(1), 30, 0, 1))
+
+divide(ts_delay(close, 10) - ts_delay(close, 60), ts_delay(close, 60))
+
+if_else(ts_rank(ts_zscore(volume, 10), 120) > 0.8, ts_delta(close, 5), ts_delta(close, 120))
+
+group_mean(ts_mean(returns, 30), 1, industry) * rank(ts_mean(volume, 60), 120)
+
+multiply(ts_std_dev(returns, 30) / ts_std_dev(returns, 120), group_rank(ts_delta(close, 20), sector))
+
+ts_decay_linear(ts_delta(close, 1), 30) - ts_decay_linear(ts_delta(close, 1), 10)
+
+if_else(rank(volume) > 0.9, ts_zscore(close, 5), ts_zscore(close, 120) * -1)
+
+group_rank(ts_std_dev(returns, 60), industry) * rank(volume)
+
+multiply(ts_corr(ts_delta(close, 20), ts_delta(volume, 20), 60), group_mean(ts_zscore(returns, 20), 1, bucket(rank(volume), range="0,3,0.4")))
+
+divide(ts_sum(returns, 10) - ts_sum(returns, 60), ts_sum(abs(returns), 60))
+
+if_else(ts_mean(ts_zscore(volume, 20), 60) > 0, ts_delta(close, 10), ts_regression(close, ts_step(1), 120, 0, 1))
+
+group_mean(ts_regression(close, ts_step(1), 20, 0, 1), 1, industry) * group_rank(volume, industry)
+
+multiply(ts_rank(returns, 30) - ts_rank(returns, 120), rank(volume))
+
+ts_regression(close, ts_step(1), 60, 0, 1) - ts_mean(ts_regression(close, ts_step(1), 60, 0, 1), 240)
+
+if_else(group_rank(returns, sector) > 0.6, ts_mean(returns, 10), ts_mean(returns, 120))
+
+group_mean(ts_zscore(ts_delta(close, 10), 60), 1, bucket(rank(volume), range="0,3,0.4")) * ts_rank(ts_std_dev(returns, 30), 60)
+
+multiply(reverse(rank(ts_mean(returns, 30))), group_rank(ts_mean(returns, 60), industry))
+
+divide(ts_mean(returns, 20), ts_zscore(returns, 60))
+
+if_else(bucket(rank(volume), range="0,3,0.4") == 0 and ts_rank(close, 20) > 0.8, ts_delta(close, 5), ts_delta(close, 120) * -1)
+
+group_mean(ts_corr(ts_delta(close,5), ts_delta(volume,5), 60), 1, industry) * rank(ts_mean(volume, 30), 120)
+
+multiply(ts_rank(ts_std_dev(returns, 30), 120), group_rank(close, sector))
+
+ts_mean(ts_zscore(returns, 20), 60) - ts_mean(ts_zscore(returns, 20), 120)
+
+if_else(rank(volume) > 0.8, ts_regression(close, ts_step(1), 30, 0, 1), ts_regression(close, ts_step(1), 120, 0, 1) * -1)
+
+group_mean(ts_delta(close, 20), 1, bucket(rank(volume), range="0,3,0.4")) - ts_mean(ts_delta(close, 20), 240)
+
+multiply(rank(ts_corr(close, volume, 30)), group_rank(ts_std_dev(returns, 60), industry))
+
+divide(ts_rank(close, 60), ts_rank(volume, 60))
+
+if_else(ts_mean(volume, 20) > ts_delay(ts_mean(volume, 20), 20), ts_delta(close, 10), reverse(ts_delta(close, 10)))
+
+group_rank(ts_regression(close, ts_step(1), 60, 0, 1), sector) * rank(volume)

generated_alpha/2025-12-12/161043.txt

@@ -0,0 +1,165 @@
+
+group_mean(ts_delta(close,5),1,industry)
+group_mean(ts_delta(close,10),1,industry)
+group_mean(ts_delta(close,20),1,industry)
+group_mean(ts_delta(close,30),1,industry)
+group_mean(ts_delta(close,60),1,industry)
+group_mean(ts_regression(close,ts_step(1),20,0,1),1,industry)
+group_mean(ts_regression(close,ts_step(1),30,0,1),1,industry)
+group_mean(ts_regression(close,ts_step(1),60,0,1),1,industry)
+group_mean(ts_delta(ts_delta(close,5),10),1,industry)
+group_mean(ts_delta(ts_delta(close,10),20),1,industry)
+group_mean(ts_mean(ts_delta(close,1),20),1,industry)
+group_mean(ts_mean(ts_delta(close,1),30),1,industry)
+group_mean(ts_decay_linear(ts_delta(close,1),20),1,industry)
+group_mean(ts_decay_linear(ts_delta(close,1),30),1,industry)
+group_mean(ts_corr(ts_delta(close,5),ts_delta(volume,5),20),1,industry)
+group_mean(ts_corr(ts_delta(close,10),ts_delta(volume,5),20),1,industry)
+group_mean(if_else(rank(volume)>0.7,ts_delta(close,20),ts_delta(close,5)),1,industry)
+group_mean(if_else(rank(volume)>0.8,ts_delta(close,30),ts_delta(close,10)),1,industry)
+group_mean(group_mean(ts_delta(close,20),1,bucket(rank(volume),range="0,3,0.4")),1,industry)
+group_mean(group_mean(ts_delta(close,30),1,bucket(rank(volume),range="0,1,0.2")),1,industry)
+group_mean(ts_std_dev(group_rank(ts_delta(close,1),industry),20),1,industry)
+group_mean(ts_std_dev(group_rank(ts_delta(close,1),industry),30),1,industry)
+group_mean(ts_mean(rank(ts_delta(close,1)),20),1,industry)
+group_mean(ts_mean(rank(ts_delta(close,1)),30),1,industry)
+group_mean(ts_delta(close,5)/ts_std_dev(ts_delta(close,1),20),1,industry)
+group_mean(ts_delta(close,10)/ts_std_dev(ts_delta(close,1),20),1,industry)
+group_mean(if_else(ts_std_dev(ts_delta(close,1),20)>0.02,ts_delta(close,5),ts_delta(close,20)),1,industry)
+group_mean(if_else(ts_std_dev(ts_delta(close,1),20)>0.03,ts_delta(close,10),ts_delta(close,30)),1,industry)
+group_mean(if_else(ts_std_dev(ts_delta(close,1),20)>0.025,ts_delta(close,5),ts_delta(close,60)),1,industry)
+group_mean(group_mean(ts_delta(close,d1),1,bucket(rank(volume),range="0,3,0.4")),1,industry)
+group_mean(multiply(ts_delta(close,5),if_else(ts_std_dev(ts_delta(close,1),20)>0.02,1.5,1)),1,industry)
+group_mean(multiply(ts_delta(close,10),if_else(ts_std_dev(ts_delta(close,1),20)>0.015,1.2,0.8)),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_std_dev(ts_delta(close,1),60),120)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_std_dev(ts_delta(close,1),60),120)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_std_dev(ts_delta(close,1),60),120)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_std_dev(ts_delta(close,1),60),120)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),20)>0.001,1.2,0.8),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),20)>0.001,1.2,0.8),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),20)>0.001,1.2,0.8),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),20)>0.001,1.2,0.8),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),20)<-0.001,0.8,1.2),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),20)<-0.001,0.8,1.2),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),20)<-0.001,0.8,1.2),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),20)<-0.001,0.8,1.2),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),20)>0.025,1.5,1),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),20)>0.025,1.5,1),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),20)>0.025,1.5,1),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),20)>0.025,1.5,1),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),20)<0.015,0.8,1.2),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),20)<0.015,0.8,1.2),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),20)<0.015,0.8,1.2),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),20)<0.015,0.8,1.2),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_delta(close,1),20)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_delta(close,1),20)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_delta(close,1),20)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_delta(close,1),20)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_delta(close,1),20)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_delta(close,1),20)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_delta(close,1),20)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_delta(close,1),20)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_delta(close,1),30)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_delta(close,1),30)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_delta(close,1),30)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_delta(close,1),30)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_delta(close,1),30)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_delta(close,1),30)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_delta(close,1),30)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_delta(close,1),30)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_delta(close,1),60)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_delta(close,1),60)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_delta(close,1),60)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_delta(close,1),60)>0.7,1.3,0.7),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_rank(ts_delta(close,1),60)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_rank(ts_delta(close,1),60)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_rank(ts_delta(close,1),60)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_rank(ts_delta(close,1),60)<0.3,0.7,1.3),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),5)>0.002,1.5,1),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),5)>0.002,1.5,1),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),5)>0.002,1.5,1),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),5)>0.002,1.5,1),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),5)<-0.002,0.5,1.5),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),5)<-0.002,0.5,1.5),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),5)<-0.002,0.5,1.5),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),5)<-0.002,0.5,1.5),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),10)>0.001,1.3,0.7),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),10)>0.001,1.3,0.7),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),10)>0.001,1.3,0.7),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),10)>0.001,1.3,0.7),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),10)<-0.001,0.7,1.3),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),10)<-0.001,0.7,1.3),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),10)<-0.001,0.7,1.3),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),10)<-0.001,0.7,1.3),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),15)>0.0015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),15)>0.0015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),15)>0.0015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),15)>0.0015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_mean(ts_delta(close,1),15)<-0.0015,0.6,1.4),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_mean(ts_delta(close,1),15)<-0.0015,0.6,1.4),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_mean(ts_delta(close,1),15)<-0.0015,0.6,1.4),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_mean(ts_delta(close,1),15)<-0.0015,0.6,1.4),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),5)>0.02,1.5,1),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),5)>0.02,1.5,1),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),5)>0.02,1.5,1),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),5)>0.02,1.5,1),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),5)<0.01,0.8,1.3),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),5)<0.01,0.8,1.3),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),5)<0.01,0.8,1.3),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),5)<0.01,0.8,1.3),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),10)>0.015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),10)>0.015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),10)>0.015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),10)>0.015,1.4,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),10)<0.01,0.7,1.4),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),10)<0.01,0.7,1.4),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),10)<0.01,0.7,1.4),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),10)<0.01,0.7,1.4),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),15)>0.012,1.3,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),15)>0.012,1.3,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),15)>0.012,1.3,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),15)>0.012,1.3,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),15)<0.008,0.8,1.3),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),15)<0.008,0.8,1.3),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),15)<0.008,0.8,1.3),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),15)<0.008,0.8,1.3),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),20)>0.01,1.2,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),20)>0.01,1.2,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),20)>0.01,1.2,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),20)>0.01,1.2,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),20)<0.007,0.8,1.2),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),20)<0.007,0.8,1.2),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),20)<0.007,0.8,1.2),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),20)<0.007,0.8,1.2),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),25)>0.009,1.2,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),25)>0.009,1.2,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),25)>0.009,1.2,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),25)>0.009,1.2,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),25)<0.006,0.8,1.2),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),25)<0.006,0.8,1.2),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),25)<0.006,0.8,1.2),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),25)<0.006,0.8,1.2),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),30)>0.008,1.1,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),30)>0.008,1.1,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),30)>0.008,1.1,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),30)>0.008,1.1,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),30)<0.006,0.8,1.1),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),30)<0.006,0.8,1.1),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),30)<0.006,0.8,1.1),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),30)<0.006,0.8,1.1),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),60)>0.006,1.1,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),60)>0.006,1.1,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),60)>0.006,1.1,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),60)>0.006,1.1,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),60)<0.004,0.8,1.1),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),60)<0.004,0.8,1.1),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),60)<0.004,0.8,1.1),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),60)<0.004,0.8,1.1),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),120)>0.004,1.1,0.9),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),120)>0.004,1.1,0.9),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),120)>0.004,1.1,0.9),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),120)>0.004,1.1,0.9),1,industry)
+group_mean(ts_delta(close,5)*if_else(ts_std_dev(ts_delta(close,1),120)<0.003,0.8,1.1),1,industry)
+group_mean(ts_delta(close,10)*if_else(ts_std_dev(ts_delta(close,1),120)<0.003,0.8,1.1),1,industry)
+group_mean(ts_delta(close,20)*if_else(ts_std_dev(ts_delta(close,1),120)<0.003,0.8,1.1),1,industry)
+group_mean(ts_delta(close,30)*if_else(ts_std_dev(ts_delta(close,1),120)<0.003,0.8,1.1),1,industry)

manual_prompt/manual_prompt_20251212140534.txt

@@ -0,0 +1,898 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成 10 个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+目标：识别价格趋势的强度、速度和持续性
+可用的具体构建方法：
+1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
+2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
+3. 动量加速度：ts_delta(ts_delta(close, d1), d2) [避免嵌套ts_regression]
+4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
+5. 动量衰减：ts_decay_linear(returns, d)
+6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
+建议组合：使用不同d参数创建短期/中期/长期动量
+维度2：横截面领导力（CL）
+目标：识别行业内的龙头股和相对强度
+具体构建方法：
+1. 龙头股筛选：if_else(rank(volume) > 0.7, 龙头值, 其他值) [使用volume代替market_cap]
+2. 龙头组合：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4")) [使用volume排序]
+3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
+4. 相对排名稳定性：ts_mean(rank(returns), d)
+维度3：市场状态适应性（MS）
+目标：根据波动率、趋势状态调整参数
+具体构建方法：
+1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
+2. 状态条件选择：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
+3. 参数动态化：if_else(ts_std_dev(returns,20) > 阈值, 5, 20) [作为d参数]
+4. 趋势状态识别：ts_rank(ts_mean(returns,20), 60) > 0.5
+基本结构：
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+=== 关键语法规则（必须遵守） ===
+1. 数据字段规范：
+   - 可使用字段：close, volume, returns
+   - ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
+   - ✅ 正确：使用volume作为规模代理，close作为价格
+   - returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
+2. ts_regression使用规范：
+   - 避免深度嵌套ts_regression，特别是作为其他函数的参数
+   - ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
+   - ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
+   - 替代方案：用ts_delta组合计算动量变化
+3. if_else使用规范：
+   - 条件必须是简单布尔表达式
+   - 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
+   - 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
+4. bucket函数使用规范：
+   - bucket()返回分组ID，可用于条件判断
+   - ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
+   - ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
+   - 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
+=== 关键语法规则结束 ===
+*=====*
+注意事项:
+1. 避免过度复杂的嵌套（建议不超过3层）
+2. 每个表达式应有明确的经济逻辑
+3. 考虑实际交易可行性（避免未来函数）
+4. 包含风险控制元素（如波动率调整）
+5. 只能使用可用的数据字段：close, volume, returns等
+*=====*
+参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
+行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现"行业"逻辑。
+构建框架指导（请按此逻辑创造新因子）：
+维度融合模板（选择至少2个）：
+A. 领导力动量 = 时序动量 × 横截面调整
+   逻辑：大成交量股票的动量更强
+   结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
+B. 状态自适应动量 = 条件选择动量
+   逻辑：高波动用短期动量，低波动用长期动量
+   结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
+C. 行业传导因子 = 领先行业动量 × 相关性强度
+   逻辑：与强势行业相关性高的行业未来表现好
+   结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
+D. 情绪反转 = 过度交易信号 × 基础趋势
+   逻辑：过度交易时反转，趋势延续时跟随
+   结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
+关键组件库（可自由组合）：
+1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
+2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
+3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
+4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
+5. 相关性类：ts_corr({x},{y},{d})
+6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
+参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
+*=====*
+输出格式：
+输出必须是且仅是纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
+===================== !!! 重点(输出方式) !!! =====================
+现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+=================================================================
+重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子：
+
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+Description: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符
+特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false)
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
+Operator: divide(x, y)
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false)
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false)
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1
+Operator: ts_arg_min(x, d)
+Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default.
+Operator: ts_rank(x, d, constant = 0)
+Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
+Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
+Description: Returns various parameters related to regression function
+Operator: ts_scale(x, d, constant = 0)
+Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
+Operator: ts_std_dev(x, d)
+Description: Returns standard deviation of x for the past d days
+Operator: ts_step(1)
+Description: Returns days' counter
+Operator: ts_sum(x, d)
+Description: Sum values of x for the past d days.
+Operator: ts_zscore(x, d)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
+Operator: normalize(x, useStd = false, limit = 0.0)
+Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
+Operator: quantile(x, driver = gaussian, sigma = 1.0)
+Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector
+Operator: rank(x, rate=2)
+Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0
+Operator: scale(x, scale=1, longscale=1, shortscale=1)
+Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
+Operator: winsorize(x, std=4)
+Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
+Operator: zscore(x)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
+Operator: vec_avg(x)
+Description: Taking mean of the vector field x
+Operator: vec_sum(x)
+Description: Sum of vector field x
+Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
+Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
+Operator: trade_when(x, y, z)
+Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition
+Operator: group_backfill(x, group, d, std = 4.0)
+Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
+Operator: group_mean(x, weight, group)
+Description: All elements in group equals to the mean
+Operator: group_neutralize(x, group)
+Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
+Operator: group_rank(x, group)
+Description: Each elements in a group is assigned the corresponding rank in this group
+Operator: group_scale(x, group)
+Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
+Operator: group_zscore(x, group)
+Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
+========================= 操作符结束 =======================================
+
+========================= 数据字段开始 =======================================注意: DataField: 后面的是数据字段, DataFieldDescription: 此字段后面的是数据字段对应的描述或使用说明, DataFieldDescription字段后面的内容是使用说明, 不是数据字段
+
+DataField: pcr_vol_120
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
+DataField: put_breakeven_270
+DataFieldDescription: Price at which a stock's put options with expiration 270 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_720
+DataFieldDescription: Price at which a stock's call options with expiration 720 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_1080
+DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_120
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 120 days in the future.
+DataField: pcr_vol_1080
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.
+DataField: put_breakeven_720
+DataFieldDescription: Price at which a stock's put options with expiration 720 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_20
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 20 days in the future.
+DataField: put_breakeven_30
+DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_1080
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
+DataField: forward_price_150
+DataFieldDescription: Forward price at 150 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: call_breakeven_1080
+DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_360
+DataFieldDescription: Price at which a stock's put options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_180
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
+DataField: forward_price_1080
+DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: call_breakeven_120
+DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_20
+DataFieldDescription: Price at which a stock's put options with expiration 20 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_90
+DataFieldDescription: Price at which a stock's call options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_20
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
+DataField: call_breakeven_360
+DataFieldDescription: Price at which a stock's call options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_60
+DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_720
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.
+DataField: forward_price_270
+DataFieldDescription: Forward price at 270 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: call_breakeven_150
+DataFieldDescription: Price at which a stock's call options with expiration 150 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_360
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 360 days in the future.
+DataField: pcr_oi_all
+DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
+DataField: forward_price_180
+DataFieldDescription: Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.
+DataField: pcr_vol_90
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 90 days in the future.
+DataField: put_breakeven_180
+DataFieldDescription: Price at which a stock's put options with expiration 180 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: fnd6_pncepsq
+DataFieldDescription: Core Pension Adjustment Basic EPS Effect
+DataField: fnd6_newa1v1300_cshfd
+DataFieldDescription: Common Shares Used to Calc Earnings Per Share - Fully Diluted
+DataField: fnd6_newqeventv110_glpq
+DataFieldDescription: Gain/Loss Pretax
+DataField: fnd6_newqeventv110_txdbaq
+DataFieldDescription: Deferred Tax Asset - Long Term
+DataField: fnd6_newqv1300_esopnrq
+DataFieldDescription: Preferred ESOP Obligation - Non-Redeemable
+DataField: fnd6_newa2v1300_xoptd
+DataFieldDescription: Implied Option EPS Diluted
+DataField: fnd6_eventv110_dd1q
+DataFieldDescription: Long Term Debt Due in 1 Year
+DataField: fnd6_newqeventv110_drltq
+DataFieldDescription: Deferred Revenue - Long-term
+DataField: fnd6_newqeventv110_rcaq
+DataFieldDescription: Restructuring Cost After-tax
+DataField: fnd6_newa1v1300_csho
+DataFieldDescription: Common Shares Outstanding
+DataField: fnd6_np
+DataFieldDescription: Notes Payable - Short-Term Borrowings
+DataField: fnd6_npq
+DataFieldDescription: Notes Payable
+DataField: fnd6_drlt
+DataFieldDescription: Deferred Revenue - Long-term
+DataField: fnd6_newqv1300_loxdrq
+DataFieldDescription: Liabilities - Other - Excluding Deferred Revenue
+DataField: fnd6_cptnewqv1300_dlttq
+DataFieldDescription: Long-Term Debt - Total
+DataField: fnd6_newqeventv110_seqoq
+DataFieldDescription: Other Stockholders' Equity Adjustments
+DataField: fnd6_newqeventv110_pncpeps12
+DataFieldDescription: Core Pension Adjustment 12MM Basic EPS Effect Preliminary
+DataField: fnd6_newqv1300_spceepsp12
+DataFieldDescription: S&P Core 12MM EPS - Basic - Preliminary
+DataField: fnd6_newqeventv110_chq
+DataFieldDescription: Cash
+DataField: fnd6_cptnewqeventv110_apq
+DataFieldDescription: Accounts Payable/Creditors - Trade
+DataField: fnd6_newqeventv110_esopnrq
+DataFieldDescription: Preferred ESOP Obligation - Non-Redeemable
+DataField: fnd6_newa2v1300_rect
+DataFieldDescription: Receivables - Total
+DataField: fnd6_cisecgl
+DataFieldDescription: Comp Inc - Securities Gains/Losses
+DataField: fnd6_cptnewqeventv110_oeps12
+DataFieldDescription: Earnings Per Share from Operations - 12 Months Moving
+DataField: fnd6_newqeventv110_pncpdq
+DataFieldDescription: Core Pension Adjustment Diluted EPS Effect Preliminary
+DataField: fnd6_newqeventv110_cipenq
+DataFieldDescription: Comp Inc - Minimum Pension Adj
+DataField: fnd6_ranks
+DataFieldDescription: Ranking
+DataField: fnd6_cptnewqv1300_apq
+DataFieldDescription: Accounts Payable/Creditors - Trade
+DataField: fnd6_newa1v1300_epspx
+DataFieldDescription: Earnings Per Share (Basic) - Excluding Extraordinary Items
+DataField: fnd6_newqeventv110_cheq
+DataFieldDescription: Cash and Short-Term Investments
+DataField: scl12_alltype_buzzvec
+DataFieldDescription: sentiment volume
+DataField: scl12_alltype_sentvec
+DataFieldDescription: sentiment
+DataField: scl12_alltype_typevec
+DataFieldDescription: instrument type index
+DataField: scl12_buzz
+DataFieldDescription: relative sentiment volume
+DataField: scl12_buzz_fast_d1
+DataFieldDescription: relative sentiment volume
+DataField: scl12_buzzvec
+DataFieldDescription: sentiment volume
+DataField: scl12_sentiment
+DataFieldDescription: sentiment
+DataField: scl12_sentiment_fast_d1
+DataFieldDescription: sentiment
+DataField: scl12_sentvec
+DataFieldDescription: sentiment
+DataField: scl12_typevec
+DataFieldDescription: instrument type index
+DataField: snt_buzz
+DataFieldDescription: negative relative sentiment volume, fill nan with 0
+DataField: snt_buzz_bfl
+DataFieldDescription: negative relative sentiment volume, fill nan with 1
+DataField: snt_buzz_bfl_fast_d1
+DataFieldDescription: negative relative sentiment volume, fill nan with 1
+DataField: snt_buzz_fast_d1
+DataFieldDescription: negative relative sentiment volume, fill nan with 0
+DataField: snt_buzz_ret
+DataFieldDescription: negative return of relative sentiment volume
+DataField: snt_buzz_ret_fast_d1
+DataFieldDescription: negative return of relative sentiment volume
+DataField: snt_value
+DataFieldDescription: negative sentiment, fill nan with 0
+DataField: snt_value_fast_d1
+DataFieldDescription: negative sentiment, fill nan with 0
+DataField: analyst_revision_rank_derivative
+DataFieldDescription: Change in ranking for analyst revisions and momentum compared to previous period.
+DataField: cashflow_efficiency_rank_derivative
+DataFieldDescription: Change in ranking for cash flow generation and profitability compared to previous period.
+DataField: composite_factor_score_derivative
+DataFieldDescription: Change in overall composite factor score from the prior period.
+DataField: earnings_certainty_rank_derivative
+DataFieldDescription: Change in ranking for earnings sustainability and certainty compared to previous period.
+DataField: fscore_bfl_growth
+DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
+DataField: fscore_bfl_momentum
+DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
+DataField: fscore_bfl_profitability
+DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
+DataField: fscore_bfl_quality
+DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
+DataField: fscore_bfl_surface
+DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
+DataField: fscore_bfl_surface_accel
+DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
+DataField: fscore_bfl_total
+DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
+DataField: fscore_bfl_value
+DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
+DataField: fscore_growth
+DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
+DataField: fscore_momentum
+DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
+DataField: fscore_profitability
+DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
+DataField: fscore_quality
+DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
+DataField: fscore_surface
+DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
+DataField: fscore_surface_accel
+DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
+DataField: fscore_total
+DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
+DataField: fscore_value
+DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
+DataField: growth_potential_rank_derivative
+DataFieldDescription: Change in ranking for medium-term growth potential compared to previous period.
+DataField: multi_factor_acceleration_score_derivative
+DataFieldDescription: Change in the acceleration of multi-factor score compared to previous period.
+DataField: multi_factor_static_score_derivative
+DataFieldDescription: Change in static multi-factor score compared to previous period.
+DataField: relative_valuation_rank_derivative
+DataFieldDescription: Change in ranking for valuation metrics compared to previous period.
+DataField: snt_social_value
+DataFieldDescription: Z score of sentiment
+DataField: snt_social_volume
+DataFieldDescription: Normalized tweet volume
+DataField: beta_last_30_days_spy
+DataFieldDescription: Beta to SPY in 30 Days
+DataField: beta_last_360_days_spy
+DataFieldDescription: Beta to SPY in 360 Days
+DataField: beta_last_60_days_spy
+DataFieldDescription: Beta to SPY in 60 Days
+DataField: beta_last_90_days_spy
+DataFieldDescription: Beta to SPY in 90 Days
+DataField: correlation_last_30_days_spy
+DataFieldDescription: Correlation to SPY in 30 Days
+DataField: correlation_last_360_days_spy
+DataFieldDescription: Correlation to SPY in 360 Days
+DataField: correlation_last_60_days_spy
+DataFieldDescription: Correlation to SPY in 60 Days
+DataField: correlation_last_90_days_spy
+DataFieldDescription: Correlation to SPY in 90 Days
+DataField: systematic_risk_last_30_days
+DataFieldDescription: Systematic Risk Last 30 Days
+DataField: systematic_risk_last_360_days
+DataFieldDescription: Systematic Risk Last 360 Days
+DataField: systematic_risk_last_60_days
+DataFieldDescription: Systematic Risk Last 60 Days
+DataField: systematic_risk_last_90_days
+DataFieldDescription: Systematic Risk Last 90 Days
+DataField: unsystematic_risk_last_30_days
+DataFieldDescription: Unsystematic Risk Last 30 Days - Relative to SPY
+DataField: unsystematic_risk_last_360_days
+DataFieldDescription: Unsystematic Risk Last 360 Days - Relative to SPY
+DataField: unsystematic_risk_last_60_days
+DataFieldDescription: Unsystematic Risk Last 60 Days - Relative to SPY
+DataField: unsystematic_risk_last_90_days
+DataFieldDescription: Unsystematic Risk Last 90 Days - Relative to SPY
+DataField: anl4_qf_az_eps
+DataFieldDescription: EPS - aggregation on estimations, 50th percentile
+DataField: est_netprofit_adj
+DataFieldDescription: Adjusted net income - Mean of estimations
+DataField: anl4_fsdtlestmtsafv4_item
+DataFieldDescription: Financial item
+DataField: anl4_guiqfv4_est
+DataFieldDescription: Estimation value
+DataField: anl4_totgw_high
+DataFieldDescription: Total Goodwill - The highest estimation
+DataField: anl4_gric_std
+DataFieldDescription: Gross income - std of estimations
+DataField: anl4_qfd1_az_cfps_median
+DataFieldDescription: Cash Flow Per Share - Median value among forecasts
+DataField: max_book_value_per_share_guidance
+DataFieldDescription: Book value per share - Maximum value among forecasts
+DataField: min_share_count_guidance
+DataFieldDescription: Minimum guidance for shares on an annual basis
+DataField: anl4_cuo1actualqfv110_actual
+DataFieldDescription: Announced financial data
+DataField: anl4_afv4_maxguidance
+DataFieldDescription: Max guidance value
+DataField: anl4_dez1basicqfv4v104_est
+DataFieldDescription: Estimation value
+DataField: cash_flow_financing_max_guidance
+DataFieldDescription: Cash Flow From Financing - Maximum guidance value provided annually
+DataField: anl4_qfv4_maxguidance
+DataFieldDescription: Max guidance value
+DataField: operating_profit_before_depr_amort
+DataFieldDescription: EBITDA value - Annual
+DataField: min_total_assets_guidance_2
+DataFieldDescription: Minimum guidance value for Total Assets on an annual basis
+DataField: anl4_qfd1_az_cfps_number
+DataFieldDescription: Cash Flow Per Share - number of estimations
+DataField: min_basic_shares_guidance
+DataFieldDescription: Shares Basic - Minimum guidance value
+DataField: anl4_bvps_value
+DataFieldDescription: Book value per share - announced financial value
+DataField: min_tangible_book_value_per_share_guidance
+DataFieldDescription: Tangible Book Value per Share - minimum guidance value
+DataField: previous_recommendation_value
+DataFieldDescription: The previous estimation of financial item for recommendation
+DataField: anl4_fsguidanceqfv4_minguidance
+DataFieldDescription: Min guidance value
+DataField: anl4_dez1safv4_est
+DataFieldDescription: Estimation value
+DataField: max_capital_expenditure_guidance
+DataFieldDescription: The maximum guidance value for Capital Expenditures on an annual basis.
+DataField: minimum_guidance_value
+DataFieldDescription: Minimum guidance value for basic annual financials
+DataField: anl4_rd_exp_number
+DataFieldDescription: Research and Development Expense - Number of Estimations
+DataField: anl4_dez1basicqfv4_est
+DataFieldDescription: Estimation value
+DataField: anl4_ebitda_low
+DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - The lowest estimation
+DataField: est_ptpr
+DataFieldDescription: Reported pretax income - mean of estimations
+DataField: guidance_value_currency_code_qtr
+DataFieldDescription: Home currency of instrument
+DataField: pv13_r2_min2_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_index_cap
+DataFieldDescription: Company market capitalization
+DataField: pv13_hierarchy_min51_f2_sector
+DataFieldDescription: grouping fields
+DataField: primary_sector_focused_company_count
+DataFieldDescription: Number of companies primarily focused in a given sector.
+DataField: pv13_hierarchy_min50_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_reportperiodend
+DataFieldDescription: Stated end date for the report
+DataField: pv13_hierarchy_min20_3k_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min22_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min5_f3g2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_sector
+DataFieldDescription: grouping fields
+DataField: rel_num_all
+DataFieldDescription: number of the companies whose product overlapped with the instrument
+DataField: pv13_hierarchy_min10_industry_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min30_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_f3_sector
+DataFieldDescription: grouping fields
+DataField: pv13_r2_min20_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min10_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_reveremap
+DataFieldDescription: Mapping data
+DataField: pv13_hierarchy_min2_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f1_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_top3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min20_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_3k_all_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_r2_min20_1000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_di_6l
+DataFieldDescription: grouping fields
+DataField: rel_num_cust
+DataFieldDescription: number of the instrument's customers
+DataField: pv13_revere_term_sector_total
+DataFieldDescription: Number of terminal sectors for the company
+DataField: pv13_com_page_rank
+DataFieldDescription: the PageRank of competitors
+DataField: pv13_h_min52_3000_sector
+DataFieldDescription: grouping fields
+DataField: implied_volatility_mean_skew_90
+DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
+DataField: implied_volatility_call_30
+DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
+DataField: implied_volatility_put_10
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
+DataField: implied_volatility_call_180
+DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
+DataField: parkinson_volatility_150
+DataFieldDescription: Parkinson model's historical volatility over 150 days
+DataField: implied_volatility_put_90
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
+DataField: implied_volatility_put_30
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
+DataField: implied_volatility_call_1080
+DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
+DataField: implied_volatility_call_360
+DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
+DataField: implied_volatility_put_360
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
+DataField: implied_volatility_mean_skew_60
+DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
+DataField: implied_volatility_mean_360
+DataFieldDescription: At-the-money option-implied volatility mean for 360 days
+DataField: implied_volatility_put_150
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
+DataField: parkinson_volatility_10
+DataFieldDescription: Parkinson model's historical volatility over 2 weeks
+DataField: implied_volatility_mean_10
+DataFieldDescription: At-the-money option-implied volatility mean for 10 days
+DataField: implied_volatility_mean_20
+DataFieldDescription: At-the-money option-implied volatility mean for 20 days
+DataField: implied_volatility_mean_skew_30
+DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
+DataField: implied_volatility_call_10
+DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
+DataField: implied_volatility_put_120
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
+DataField: implied_volatility_put_270
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
+DataField: implied_volatility_mean_skew_720
+DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
+DataField: implied_volatility_mean_90
+DataFieldDescription: At-the-money option-implied volatility mean for 90 days
+DataField: implied_volatility_call_720
+DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
+DataField: implied_volatility_mean_1080
+DataFieldDescription: At-the-money option-implied volatility mean for 3 years
+DataField: implied_volatility_mean_150
+DataFieldDescription: At-the-money option-implied volatility mean for 150 days
+DataField: implied_volatility_mean_180
+DataFieldDescription: At-the-money option-implied volatility mean for 180 days
+DataField: implied_volatility_call_60
+DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
+DataField: implied_volatility_mean_skew_270
+DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
+DataField: implied_volatility_mean_skew_150
+DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
+DataField: historical_volatility_120
+DataFieldDescription: Close-to-close Historical volatility over 120 days
+DataField: nws12_mainz_5s
+DataFieldDescription: Number of minutes that elapsed before price went down 5 percentage points
+DataField: nws12_mainz_maxup
+DataFieldDescription: Percent change from the price at the time of the news to the after the news high
+DataField: nws12_mainz_result2
+DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
+DataField: news_mins_4_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
+DataField: nws12_afterhsz_tonlast
+DataFieldDescription: Price at the time of news
+DataField: nws12_afterhsz_range
+DataFieldDescription: Session High Price - Session Low Price) / Session Low Price.
+DataField: news_mins_10_pct_dn
+DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
+DataField: nws12_prez_range
+DataFieldDescription: Session High Price - Session Low Price) / Session Low Price.
+DataField: nws12_allz_reportsess
+DataFieldDescription: Index of Session on which the spreadsheet is reporting
+DataField: news_pct_90min
+DataFieldDescription: The percent change in price in the first 90 minutes following the news release
+DataField: news_eod_low
+DataFieldDescription: Lowest price reached between the time of news and the end of the session
+DataField: nws12_afterhsz_1s
+DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
+DataField: nws12_afterhsz_2s
+DataFieldDescription: Number of minutes that elapsed before price went down 2 percentage points
+DataField: news_max_dn_amt
+DataFieldDescription: The price at the time of the news minus the after the news low
+DataField: nws12_prez_peratio
+DataFieldDescription: Reported price to earnings ratio for the calendar day of the session
+DataField: nws12_afterhsz_1l
+DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage points
+DataField: news_range_stddev
+DataFieldDescription: (RangeAmt - AvgRange) / RangeStdDev, where AvgRange is the average of the daily range, and RangeStdDev is one standard deviation for the daily range, both for 30 calendar days
+DataField: news_mins_3_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
+DataField: nws12_mainz_reportsess
+DataFieldDescription: Index of Session on which the spreadsheet is reporting
+DataField: news_ton_high
+DataFieldDescription: Highest price reached during the session before the time of news
+DataField: news_mins_7_5_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
+DataField: nws12_prez_57p
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: nws12_mainz_rangeamt
+DataFieldDescription: Session High Price - Session Low Price
+DataField: nws12_afterhsz_01l
+DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
+DataField: nws12_afterhsz_120_min
+DataFieldDescription: The percent change in price in the first 120 minutes following the news release
+DataField: nws12_mainz_allvwap
+DataFieldDescription: Volume weighted average price of all sessions
+DataField: nws12_mainz_prev_vol
+DataFieldDescription: Previous day's session volume
+DataField: nws12_afterhsz_newssess
+DataFieldDescription: Index of the session in which the news was reported
+DataField: nws12_prez_tonlow
+DataFieldDescription: Lowest price reached during the session before the time of the news
+DataField: nws12_mainz_highexcstddev
+DataFieldDescription: (EODHigh - TONLast)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
+DataField: top1000
+DataFieldDescription: 20140630
+DataField: top200
+DataFieldDescription: 20140630
+DataField: top3000
+DataFieldDescription: 20140630
+DataField: top500
+DataFieldDescription: 20140630
+DataField: topsp500
+DataFieldDescription: 20140630
+DataField: rp_ess_dividends
+DataFieldDescription: Event sentiment score of dividends news
+DataField: rp_ess_technical
+DataFieldDescription: Event sentiment score based on technical analysis
+DataField: rp_ess_legal
+DataFieldDescription: Event sentiment score of legal news
+DataField: rp_css_product
+DataFieldDescription: Composite sentiment score of product and service-related news
+DataField: rp_nip_labor
+DataFieldDescription: News impact projection of labor issues news
+DataField: rp_css_marketing
+DataFieldDescription: Composite sentiment score of marketing news
+DataField: rp_css_legal
+DataFieldDescription: Composite sentiment score of legal news
+DataField: rp_nip_product
+DataFieldDescription: News impact projection of product and service-related news
+DataField: rp_ess_insider
+DataFieldDescription: Event sentiment score of insider trading news
+DataField: rp_nip_ratings
+DataFieldDescription: News impact projection of analyst ratings-related news
+DataField: rp_nip_mna
+DataFieldDescription: News impact projection of mergers and acquisitions-related news
+DataField: rp_ess_labor
+DataFieldDescription: Event sentiment score of labor issues news
+DataField: rp_css_credit
+DataFieldDescription: Composite sentiment score of credit news
+DataField: rp_nip_inverstor
+DataFieldDescription: News impact projection of investor relations news
+DataField: rp_ess_earnings
+DataFieldDescription: Event sentiment score of earnings news
+DataField: rp_css_dividends
+DataFieldDescription: Composite sentiment score of dividends news
+DataField: rp_ess_product
+DataFieldDescription: Event sentiment score of product and service-related news
+DataField: rp_nip_equity
+DataFieldDescription: News impact projection of equity action news
+DataField: nws18_bee
+DataFieldDescription: News sentiment specializing in growth of earnings
+DataField: rp_ess_ratings
+DataFieldDescription: Event sentiment score of analyst ratings-related news
+DataField: rp_nip_partner
+DataFieldDescription: News impact projection of partnership news
+DataField: rp_ess_mna
+DataFieldDescription: Event sentiment score of mergers and acquisitions-related news
+DataField: rp_ess_price
+DataFieldDescription: Event sentiment score of stock price news
+DataField: rp_css_equity
+DataFieldDescription: Composite sentiment score of equity action news
+DataField: rp_nip_technical
+DataFieldDescription: News impact projection based on technical analysis
+DataField: rp_ess_credit_ratings
+DataFieldDescription: Event sentiment score of credit ratings news
+DataField: nws18_relevance
+DataFieldDescription: Relevance of news to the company
+DataField: rp_nip_business
+DataFieldDescription: News impact projection of business-related news
+DataField: rp_nip_marketing
+DataFieldDescription: News impact projection of marketing news
+DataField: rp_nip_credit
+DataFieldDescription: News impact projection of credit news
+DataField: fn_comp_options_grants_weighted_avg_a
+DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options that were terminated.
+DataField: fnd2_asdm
+DataFieldDescription: Assets, Domestic
+DataField: fn_op_lease_min_pay_due_in_4y_a
+DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_dbplanartonplas
+DataFieldDescription: Defined Benefit Plan, Benefits Paid, Plan Assets
+DataField: fn_comp_non_opt_vested_a
+DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that vested during the reporting period.
+DataField: fn_def_tax_assets_liab_net_q
+DataFieldDescription: Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.
+DataField: fn_comp_options_exercises_weighted_avg_q
+DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
+DataField: fnd2_q_flintasamt1expythree
+DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 3rd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fn_repayments_of_lines_of_credit_q
+DataFieldDescription: Amount of cash outflow for payment of an obligation from a lender, including but not limited to, letter of credit, standby letter of credit and revolving credit arrangements.
+DataField: fn_intangible_assets_accum_amort_q
+DataFieldDescription: Accumulated amount of amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
+DataField: fn_comp_non_opt_nonvested_number_q
+DataFieldDescription: The number of non-vested equity-based payment instruments, excluding stock (or unit) options, that validly exist and are outstanding as of the balance sheet date.
+DataField: fn_comp_options_out_number_a
+DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
+DataField: fnd2_dfdfritxexp
+DataFieldDescription: Income Tax Expense, Deferred - Foreign
+DataField: fnd2_dbplanchgbnfolintcst
+DataFieldDescription: Defined Benefit Plan Change In Benefit Obligation Interest Cost
+DataField: fn_prepaid_expense_q
+DataFieldDescription: Carrying amount for an unclassified balance sheet date of expenditures made in advance of when the economic benefit of the cost will be realized, and which will be expensed in future periods with the passage of time or when a triggering event occurs. For a classified balance sheet, represents the noncurrent portion of prepaid expenses (the current portion has a separate concept).
+DataField: fn_comp_options_grants_fair_value_a
+DataFieldDescription: Annual Share-Based Compensation Arrangement by Share-Based Payment Award Options Grants in Period Weighted Average Grant Date Fair Value
+DataField: fn_liab_fair_val_l1_a
+DataFieldDescription: Liabilities Fair Value, Recurring, Level 1
+DataField: fn_op_lease_min_pay_due_a
+DataFieldDescription: Amount of required minimum rental payments for leases having an initial or remaining non-cancelable letter-terms in excess of 1 year.
+DataField: fn_comp_options_forfeitures_and_expirations_a
+DataFieldDescription: For presentations that combine terminations, the number of shares under options that were canceled during the reporting period as a result of occurrence of a terminating event specified in contractual agreements pertaining to the stock option plan or that expired.
+DataField: fn_comp_options_exercisable_number_a
+DataFieldDescription: The number of shares into which fully or partially vested stock options outstanding as of the balance sheet date can be currently converted under the option plan.
+DataField: fn_incremental_shares_attributable_to_share_based_payment_q
+DataFieldDescription: Additional shares included in the calculation of diluted EPS as a result of the potentially dilutive effect of share-based payment arrangements using the treasury stock method.
+DataField: fnd2_dbplanbnfpaid
+DataFieldDescription: The amount of payments made for which participants are entitled under a pension plan, including pension benefits, death benefits, and benefits due on termination of employment. Also includes payments made under a postretirement benefit plan, including prescription drug benefits, health care benefits, life insurance benefits, and legal, educational and advisory services. This item represents a periodic decrease to the plan obligations and a decrease to plan assets.
+DataField: fn_payments_for_repurchase_of_common_stock_q
+DataFieldDescription: Value reported on Cash Flow Statement. May include shares repurchased as part of a buyback plan, as well as shares purchased for employee compensation, etc.
+DataField: fnd2_a_ltrmdmrepoplinythree
+DataFieldDescription: Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing in the 3rd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fn_comp_options_out_number_q
+DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
+DataField: fn_derivative_notional_amount_q
+DataFieldDescription: Nominal or face amount used to calculate payments on the derivative liability.
+DataField: fn_line_of_credit_facility_max_borrowing_capacity_a
+DataFieldDescription: Maximum borrowing capacity under the credit facility without consideration of any current restrictions on the amount that could be borrowed or the amounts currently outstanding under the facility.
+DataField: fn_oth_income_loss_available_for_sale_securities_adj_of_tax_a
+DataFieldDescription: Amount after tax and reclassification adjustments, of appreciation (loss) in value of unsold available-for-sale securities. Excludes amounts related to other than temporary impairment (OTTI) loss.
+DataField: fnd2_a_acmopclcchngcfectnt
+DataFieldDescription: Accumulated change, net of tax, in accumulated gains and losses from derivative instruments designated and qualifying as the effective portion of cash flow hedges. Includes an entity's share of an equity investee's Increase or Decrease in deferred hedging gains or losses.
+DataField: fnd2_a_sbcpnargmsptawervl
+DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
+DataField: adv20
+DataFieldDescription: Average daily volume in past 20 days
+DataField: cap
+DataFieldDescription: Daily market capitalization (in millions)
+DataField: close
+DataFieldDescription: Daily close price
+DataField: country
+DataFieldDescription: Country grouping
+DataField: currency
+DataFieldDescription: Currency
+DataField: cusip
+DataFieldDescription: CUSIP Value
+DataField: dividend
+DataFieldDescription: Dividend
+DataField: exchange
+DataFieldDescription: Exchange grouping
+DataField: high
+DataFieldDescription: Daily high price
+DataField: industry
+DataFieldDescription: Industry grouping
+DataField: isin
+DataFieldDescription: ISIN Value
+DataField: low
+DataFieldDescription: Daily low price
+DataField: market
+DataFieldDescription: Market grouping
+DataField: open
+DataFieldDescription: Daily open price
+DataField: returns
+DataFieldDescription: Daily returns
+DataField: sector
+DataFieldDescription: Sector grouping
+DataField: sedol
+DataFieldDescription: Sedol
+DataField: sharesout
+DataFieldDescription: Daily outstanding shares (in millions)
+DataField: split
+DataFieldDescription: Stock split ratio
+DataField: subindustry
+DataFieldDescription: Subindustry grouping
+DataField: ticker
+DataFieldDescription: Ticker
+DataField: volume
+DataFieldDescription: Daily volume
+DataField: vwap
+DataFieldDescription: Daily volume weighted average price
+========================= 数据字段结束 =======================================
+

manual_prompt/manual_prompt_20251212150134.txt

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+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成 100 个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+目标：识别价格趋势的强度、速度和持续性
+可用的具体构建方法：
+1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
+2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
+3. 动量加速度：ts_delta(ts_delta(close, d1), d2) [避免嵌套ts_regression]
+4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
+5. 动量衰减：ts_decay_linear(returns, d)
+6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
+建议组合：使用不同d参数创建短期/中期/长期动量
+维度2：横截面领导力（CL）
+目标：识别行业内的龙头股和相对强度
+具体构建方法：
+1. 龙头股筛选：if_else(rank(volume) > 0.7, 龙头值, 其他值) [使用volume代替market_cap]
+2. 龙头组合：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4")) [使用volume排序]
+3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
+4. 相对排名稳定性：ts_mean(rank(returns), d)
+维度3：市场状态适应性（MS）
+目标：根据波动率、趋势状态调整参数
+具体构建方法：
+1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
+2. 状态条件选择：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
+3. 参数动态化：if_else(ts_std_dev(returns,20) > 阈值, 5, 20) [作为d参数]
+4. 趋势状态识别：ts_rank(ts_mean(returns,20), 60) > 0.5
+基本结构：
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+=== 关键语法规则（必须遵守） ===
+1. 数据字段规范：
+   - 可使用字段：close, volume, returns
+   - ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
+   - ✅ 正确：使用volume作为规模代理，close作为价格
+   - returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
+2. ts_regression使用规范：
+   - 避免深度嵌套ts_regression，特别是作为其他函数的参数
+   - ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
+   - ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
+   - 替代方案：用ts_delta组合计算动量变化
+3. if_else使用规范：
+   - 条件必须是简单布尔表达式
+   - 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
+   - 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
+4. bucket函数使用规范：
+   - bucket()返回分组ID，可用于条件判断
+   - ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
+   - ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
+   - 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
+=== 关键语法规则结束 ===
+*=====*
+注意事项:
+1. 避免过度复杂的嵌套（建议不超过3层）
+2. 每个表达式应有明确的经济逻辑
+3. 考虑实际交易可行性（避免未来函数）
+4. 包含风险控制元素（如波动率调整）
+5. 只能使用可用的数据字段：close, volume, returns等
+*=====*
+参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
+行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现"行业"逻辑。
+构建框架指导（请按此逻辑创造新因子）：
+维度融合模板（选择至少2个）：
+A. 领导力动量 = 时序动量 × 横截面调整
+   逻辑：大成交量股票的动量更强
+   结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
+B. 状态自适应动量 = 条件选择动量
+   逻辑：高波动用短期动量，低波动用长期动量
+   结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
+C. 行业传导因子 = 领先行业动量 × 相关性强度
+   逻辑：与强势行业相关性高的行业未来表现好
+   结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
+D. 情绪反转 = 过度交易信号 × 基础趋势
+   逻辑：过度交易时反转，趋势延续时跟随
+   结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
+关键组件库（可自由组合）：
+1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
+2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
+3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
+4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
+5. 相关性类：ts_corr({x},{y},{d})
+6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
+参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
+*=====*
+输出格式：
+输出必须是且仅是纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
+===================== !!! 重点(输出方式) !!! =====================
+现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+=================================================================
+重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子：
+
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+Description: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符
+特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false)
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
+Operator: divide(x, y)
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false)
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false)
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1
+Operator: ts_arg_min(x, d)
+Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default.
+Operator: ts_rank(x, d, constant = 0)
+Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
+Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
+Description: Returns various parameters related to regression function
+Operator: ts_scale(x, d, constant = 0)
+Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
+Operator: ts_std_dev(x, d)
+Description: Returns standard deviation of x for the past d days
+Operator: ts_step(1)
+Description: Returns days' counter
+Operator: ts_sum(x, d)
+Description: Sum values of x for the past d days.
+Operator: ts_zscore(x, d)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
+Operator: normalize(x, useStd = false, limit = 0.0)
+Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
+Operator: quantile(x, driver = gaussian, sigma = 1.0)
+Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector
+Operator: rank(x, rate=2)
+Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0
+Operator: scale(x, scale=1, longscale=1, shortscale=1)
+Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
+Operator: winsorize(x, std=4)
+Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
+Operator: zscore(x)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
+Operator: vec_avg(x)
+Description: Taking mean of the vector field x
+Operator: vec_sum(x)
+Description: Sum of vector field x
+Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
+Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
+Operator: trade_when(x, y, z)
+Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition
+Operator: group_backfill(x, group, d, std = 4.0)
+Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
+Operator: group_mean(x, weight, group)
+Description: All elements in group equals to the mean
+Operator: group_neutralize(x, group)
+Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
+Operator: group_rank(x, group)
+Description: Each elements in a group is assigned the corresponding rank in this group
+Operator: group_scale(x, group)
+Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
+Operator: group_zscore(x, group)
+Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
+========================= 操作符结束 =======================================
+
+========================= 数据字段开始 =======================================注意: DataField: 后面的是数据字段, DataFieldDescription: 此字段后面的是数据字段对应的描述或使用说明, DataFieldDescription字段后面的内容是使用说明, 不是数据字段
+
+DataField: forward_price_180
+DataFieldDescription: Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.
+DataField: option_breakeven_150
+DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: option_breakeven_360
+DataFieldDescription: Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_120
+DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_270
+DataFieldDescription: Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_20
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
+DataField: call_breakeven_60
+DataFieldDescription: Price at which a stock's call options with expiration 60 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_720
+DataFieldDescription: Price at which a stock's options with expiration 720 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_20
+DataFieldDescription: Price at which a stock's call options with expiration 20 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_20
+DataFieldDescription: Price at which a stock's put options with expiration 20 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_90
+DataFieldDescription: Price at which a stock's call options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_1080
+DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_vol_10
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
+DataField: option_breakeven_10
+DataFieldDescription: Price at which a stock's options with expiration 10 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_270
+DataFieldDescription: Price at which a stock's put options with expiration 270 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_all
+DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
+DataField: call_breakeven_30
+DataFieldDescription: Price at which a stock's call options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_720
+DataFieldDescription: Price at which a stock's put options with expiration 720 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_1080
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
+DataField: pcr_vol_180
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
+DataField: pcr_vol_720
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 720 days in the future.
+DataField: pcr_vol_120
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
+DataField: pcr_oi_720
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.
+DataField: pcr_vol_270
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 270 days in the future.
+DataField: pcr_vol_30
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.
+DataField: pcr_oi_90
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
+DataField: forward_price_270
+DataFieldDescription: Forward price at 270 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_60
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 60 days in the future.
+DataField: pcr_vol_90
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 90 days in the future.
+DataField: fnd6_sales
+DataFieldDescription: Net Sales
+DataField: operating_expense
+DataFieldDescription: Operating Expense - Total
+DataField: fnd6_cik
+DataFieldDescription: nonimportant technical code
+DataField: fnd6_ivaco
+DataFieldDescription: Investing Activities - Other
+DataField: fnd6_newa1v1300_bkvlps
+DataFieldDescription: Book Value Per Share
+DataField: fnd6_optlifeq
+DataFieldDescription: Life of Options - Assumption (# yrs)
+DataField: fnd6_invfg
+DataFieldDescription: Inventories - Finished Goods
+DataField: fnd6_mfmq_ibcomq
+DataFieldDescription: Income Before Extraordinary Items - Available for Common
+DataField: fnd6_txtubsoflimit
+DataFieldDescription: Lapse of Statute of Limitations
+DataField: fnd6_fyrc
+DataFieldDescription: Unimportant technical code, please ignore for research purposes
+DataField: fnd6_itcb
+DataFieldDescription: Investment Tax Credit (Balance Sheet)
+DataField: fnd6_eventv110_aqdq
+DataFieldDescription: Acquisition/Merger Diluted EPS Effect
+DataField: fnd6_prclq
+DataFieldDescription: Price Low - Quarter
+DataField: fnd6_newqeventv110_glcea12
+DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) After-tax 12MM
+DataField: fnd6_newqv1300_acomincq
+DataFieldDescription: Accumulated Other Comprehensive Income (Loss)
+DataField: fnd6_eventv110_pncd12
+DataFieldDescription: Core Pension Adjustment Diluted EPS Effect 12MM
+DataField: fnd6_newa2v1300_recch
+DataFieldDescription: Accounts Receivable - Decrease (Increase)
+DataField: fnd6_newqeventv110_xoptdqp
+DataFieldDescription: Implied Option EPS Diluted Preliminary
+DataField: fnd6_newqeventv110_ibadjq
+DataFieldDescription: Income Before Extraordinary Items - Adjusted for Common Stock Equivalents
+DataField: fnd6_newqv1300_wcapq
+DataFieldDescription: Working Capital (Balance Sheet)
+DataField: fnd6_newqeventv110_prshoq
+DataFieldDescription: Redeem Pfd Shares Outs (000)
+DataField: fnd6_mkvalt
+DataFieldDescription: Market Value - Total
+DataField: fnd6_newqeventv110_seta12
+DataFieldDescription: Settlement (Litigation/Insurance) After Tax - 12mm
+DataField: fnd6_cptnewqv1300_saleq
+DataFieldDescription: Sales/Turnover (Net)
+DataField: fnd6_newqeventv110_prcpd12
+DataFieldDescription: Core Post-Retirement Adjustment 12MM Diluted EPS Effect Preliminary
+DataField: fnd6_newqeventv110_gdwlq
+DataFieldDescription: Goodwill (net)
+DataField: fnd6_newa2v1300_tstkn
+DataFieldDescription: Treasury Stock - Number of Common Shares
+DataField: fnd6_newqeventv110_ivstq
+DataFieldDescription: Short-Term Investments - Total
+DataField: fnd6_cptmfmq_opepsq
+DataFieldDescription: Earnings Per Share from Operations
+DataField: fnd6_newqv1300_ltmibq
+DataFieldDescription: Liabilities - Total and Noncontrolling Interest
+DataField: scl12_alltype_buzzvec
+DataFieldDescription: sentiment volume
+DataField: scl12_alltype_sentvec
+DataFieldDescription: sentiment
+DataField: scl12_alltype_typevec
+DataFieldDescription: instrument type index
+DataField: scl12_buzz
+DataFieldDescription: relative sentiment volume
+DataField: scl12_buzz_fast_d1
+DataFieldDescription: relative sentiment volume
+DataField: scl12_buzzvec
+DataFieldDescription: sentiment volume
+DataField: scl12_sentiment
+DataFieldDescription: sentiment
+DataField: scl12_sentiment_fast_d1
+DataFieldDescription: sentiment
+DataField: scl12_sentvec
+DataFieldDescription: sentiment
+DataField: scl12_typevec
+DataFieldDescription: instrument type index
+DataField: snt_buzz
+DataFieldDescription: negative relative sentiment volume, fill nan with 0
+DataField: snt_buzz_bfl
+DataFieldDescription: negative relative sentiment volume, fill nan with 1
+DataField: snt_buzz_bfl_fast_d1
+DataFieldDescription: negative relative sentiment volume, fill nan with 1
+DataField: snt_buzz_fast_d1
+DataFieldDescription: negative relative sentiment volume, fill nan with 0
+DataField: snt_buzz_ret
+DataFieldDescription: negative return of relative sentiment volume
+DataField: snt_buzz_ret_fast_d1
+DataFieldDescription: negative return of relative sentiment volume
+DataField: snt_value
+DataFieldDescription: negative sentiment, fill nan with 0
+DataField: snt_value_fast_d1
+DataFieldDescription: negative sentiment, fill nan with 0
+DataField: analyst_revision_rank_derivative
+DataFieldDescription: Change in ranking for analyst revisions and momentum compared to previous period.
+DataField: cashflow_efficiency_rank_derivative
+DataFieldDescription: Change in ranking for cash flow generation and profitability compared to previous period.
+DataField: composite_factor_score_derivative
+DataFieldDescription: Change in overall composite factor score from the prior period.
+DataField: earnings_certainty_rank_derivative
+DataFieldDescription: Change in ranking for earnings sustainability and certainty compared to previous period.
+DataField: fscore_bfl_growth
+DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
+DataField: fscore_bfl_momentum
+DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
+DataField: fscore_bfl_profitability
+DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
+DataField: fscore_bfl_quality
+DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
+DataField: fscore_bfl_surface
+DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
+DataField: fscore_bfl_surface_accel
+DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
+DataField: fscore_bfl_total
+DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
+DataField: fscore_bfl_value
+DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
+DataField: fscore_growth
+DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
+DataField: fscore_momentum
+DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
+DataField: fscore_profitability
+DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
+DataField: fscore_quality
+DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
+DataField: fscore_surface
+DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
+DataField: fscore_surface_accel
+DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
+DataField: fscore_total
+DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
+DataField: fscore_value
+DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
+DataField: growth_potential_rank_derivative
+DataFieldDescription: Change in ranking for medium-term growth potential compared to previous period.
+DataField: multi_factor_acceleration_score_derivative
+DataFieldDescription: Change in the acceleration of multi-factor score compared to previous period.
+DataField: multi_factor_static_score_derivative
+DataFieldDescription: Change in static multi-factor score compared to previous period.
+DataField: relative_valuation_rank_derivative
+DataFieldDescription: Change in ranking for valuation metrics compared to previous period.
+DataField: snt_social_value
+DataFieldDescription: Z score of sentiment
+DataField: snt_social_volume
+DataFieldDescription: Normalized tweet volume
+DataField: beta_last_30_days_spy
+DataFieldDescription: Beta to SPY in 30 Days
+DataField: beta_last_360_days_spy
+DataFieldDescription: Beta to SPY in 360 Days
+DataField: beta_last_60_days_spy
+DataFieldDescription: Beta to SPY in 60 Days
+DataField: beta_last_90_days_spy
+DataFieldDescription: Beta to SPY in 90 Days
+DataField: correlation_last_30_days_spy
+DataFieldDescription: Correlation to SPY in 30 Days
+DataField: correlation_last_360_days_spy
+DataFieldDescription: Correlation to SPY in 360 Days
+DataField: correlation_last_60_days_spy
+DataFieldDescription: Correlation to SPY in 60 Days
+DataField: correlation_last_90_days_spy
+DataFieldDescription: Correlation to SPY in 90 Days
+DataField: systematic_risk_last_30_days
+DataFieldDescription: Systematic Risk Last 30 Days
+DataField: systematic_risk_last_360_days
+DataFieldDescription: Systematic Risk Last 360 Days
+DataField: systematic_risk_last_60_days
+DataFieldDescription: Systematic Risk Last 60 Days
+DataField: systematic_risk_last_90_days
+DataFieldDescription: Systematic Risk Last 90 Days
+DataField: unsystematic_risk_last_30_days
+DataFieldDescription: Unsystematic Risk Last 30 Days - Relative to SPY
+DataField: unsystematic_risk_last_360_days
+DataFieldDescription: Unsystematic Risk Last 360 Days - Relative to SPY
+DataField: unsystematic_risk_last_60_days
+DataFieldDescription: Unsystematic Risk Last 60 Days - Relative to SPY
+DataField: unsystematic_risk_last_90_days
+DataFieldDescription: Unsystematic Risk Last 90 Days - Relative to SPY
+DataField: anl4_fsguidanceqfv4_item
+DataFieldDescription: Financial item
+DataField: anl4_fsguidanceqfv4_minguidance
+DataFieldDescription: Min guidance value
+DataField: anl4_basicdetailrec_ratingvalue
+DataFieldDescription: Score on the given instrument
+DataField: anl4_eaz2lltv110_prevval
+DataFieldDescription: The previous estimation of financial item
+DataField: anl4_flag_erbfintax
+DataFieldDescription: Earnings before interest and taxes - forecast type (revision/new/...)
+DataField: selling_general_admin_expense
+DataFieldDescription: Selling, General & Administrative Expense Value
+DataField: sales_guidance_value
+DataFieldDescription: Sales - Guidance value for the annual period
+DataField: min_pretax_profit_guidance
+DataFieldDescription: Minimum guidance value for Pretax income
+DataField: anl4_qfd1_az_div_number
+DataFieldDescription: Dividend per share - number of estimations
+DataField: anl4_cfi_number
+DataFieldDescription: Cash Flow From Investing - number of estimations
+DataField: anl4_ads1detailafv110_prevval
+DataFieldDescription: The Previous Estimation of Financial Item
+DataField: anl4_adxqfv110_numest
+DataFieldDescription: The number of forecasts counted in aggregation
+DataField: est_epsa
+DataFieldDescription: Earnings per share adjusted by excluding extraordinary items and stock option expenses - average of estimations
+DataField: anl4_dez1basicafv4_est
+DataFieldDescription: Estimation value
+DataField: previous_recommendation_value
+DataFieldDescription: The previous estimation of financial item for recommendation
+DataField: anl4_netprofit_high
+DataFieldDescription: Net Profit - The highest estimation
+DataField: anl4_ebitda_high
+DataFieldDescription: Earnings before interest, taxes, depreciation, and amortization - the highest estimation
+DataField: anl4_qf_az_div_median
+DataFieldDescription: Dividend per share - median of estimations
+DataField: anl4_qfv4_cfps_mean
+DataFieldDescription: Cash Flow Per Share - average of estimations
+DataField: anl4_cfo_number
+DataFieldDescription: Cash Flow From Operations - number of estimations
+DataField: shareholders_equity_min_guidance
+DataFieldDescription: Minimum guidance value for Share Equity
+DataField: anl4_qfv4_eps_number
+DataFieldDescription: Earnings per share - number of estimations
+DataField: max_free_cashflow_guidance
+DataFieldDescription: The maximum guidance value for Free Cash Flow.
+DataField: anl4_baz1v110_estvalue
+DataFieldDescription: Estimation value
+DataField: free_cash_flow_per_share
+DataFieldDescription: Free cash flow per share - actual financial value for the annual period
+DataField: net_debt_min_guidance_qtr
+DataFieldDescription: Minimum guidance value for Net Debt
+DataField: min_research_development_expense_guidance_2
+DataFieldDescription: Minimum guidance value for Research & Development Expense on an annual basis
+DataField: min_adjusted_funds_from_operations_guidance
+DataFieldDescription: Funds from operation - minimum guidance value
+DataField: estimate_value_currency_code_detail_qtr
+DataFieldDescription: Home currency of instrument
+DataField: pretax_income_reported
+DataFieldDescription: Reported Pretax income - actual value for the annual fiscal period
+DataField: pv13_2l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_new_4l_scibr
+DataFieldDescription: grouping fields
+DataField: rel_ret_comp
+DataFieldDescription: Averaged one-day return of the competing companies
+DataField: pv13_revere_parent
+DataFieldDescription: Code of parent sector
+DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_term_sector_total
+DataFieldDescription: Number of terminal sectors for the company
+DataField: pv13_di_6l
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f1_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_top3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_industry_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f4_sector
+DataFieldDescription: grouping fields
+DataField: pv13_ompetitorgraphrank_hub_rank
+DataFieldDescription: the HITS hub score of competitors
+DataField: pv13_hierarchy_min20_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_pureplay_only_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_pureplay_only_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_f3_sector
+DataFieldDescription: grouping fields
+DataField: rel_num_cust
+DataFieldDescription: number of the instrument's customers
+DataField: pv13_h_min2_focused_sector
+DataFieldDescription: Grouping fields for top 200
+DataField: pv13_hierarchy_sector
+DataFieldDescription: grouping fields
+DataField: pv13_new_3l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min25_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min20_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_3k_all_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_focused_pureplay_sector
+DataFieldDescription: grouping fields
+DataField: rel_ret_cust
+DataFieldDescription: averaged one-day-return of the instrument's customers
+DataField: pv13_hierarchy_min100_corr21_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min2_3000_sector
+DataFieldDescription: grouping fields
+DataField: rel_ret_part
+DataFieldDescription: Averaged one-day return of the instrument's partners
+DataField: pv13_new_1l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min5_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: implied_volatility_mean_skew_60
+DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
+DataField: parkinson_volatility_90
+DataFieldDescription: Parkinson model's historical volatility over 90 days
+DataField: implied_volatility_mean_20
+DataFieldDescription: At-the-money option-implied volatility mean for 20 days
+DataField: implied_volatility_put_90
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
+DataField: implied_volatility_call_270
+DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
+DataField: implied_volatility_call_20
+DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
+DataField: implied_volatility_mean_skew_720
+DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
+DataField: implied_volatility_put_180
+DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
+DataField: parkinson_volatility_60
+DataFieldDescription: Parkinson model's historical volatility over 60 days
+DataField: implied_volatility_call_60
+DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
+DataField: implied_volatility_call_150
+DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
+DataField: implied_volatility_mean_30
+DataFieldDescription: At-the-money option-implied volatility mean for 30 days
+DataField: historical_volatility_30
+DataFieldDescription: Close-to-close Historical volatility over 30 days
+DataField: implied_volatility_call_720
+DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
+DataField: implied_volatility_mean_skew_10
+DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
+DataField: implied_volatility_mean_1080
+DataFieldDescription: At-the-money option-implied volatility mean for 3 years
+DataField: implied_volatility_mean_270
+DataFieldDescription: At-the-money option-implied volatility mean for 270 days
+DataField: implied_volatility_mean_skew_30
+DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
+DataField: historical_volatility_90
+DataFieldDescription: Close-to-close Historical volatility over 90 days
+DataField: historical_volatility_60
+DataFieldDescription: Close-to-close Historical volatility over 60 days
+DataField: implied_volatility_mean_skew_120
+DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
+DataField: implied_volatility_put_720
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
+DataField: implied_volatility_put_60
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
+DataField: implied_volatility_put_20
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 20 days
+DataField: implied_volatility_mean_skew_20
+DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
+DataField: parkinson_volatility_150
+DataFieldDescription: Parkinson model's historical volatility over 150 days
+DataField: historical_volatility_150
+DataFieldDescription: Close-to-close Historical volatility over 150 days
+DataField: parkinson_volatility_120
+DataFieldDescription: Parkinson model's historical volatility over 120 days
+DataField: implied_volatility_call_120
+DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
+DataField: implied_volatility_put_1080
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
+DataField: nws12_mainz_57p
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: nws12_prez_prevwap
+DataFieldDescription: Pre-session volume weighted average price
+DataField: nws12_mainz_02s
+DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
+DataField: nws12_afterhsz_1l
+DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage points
+DataField: nws12_afterhsz_sl
+DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS = 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
+DataField: nws12_allz_result2
+DataFieldDescription: Percent change between the price at the time of the news release and the price at the close of the session
+DataField: nws12_mainz_02p
+DataFieldDescription: The minimum of L or S above for 20-minute bucket
+DataField: nws12_mainz_maxupamt
+DataFieldDescription: The after-the-news high minus the price at the time of the news
+DataField: nws12_afterhsz_57l
+DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
+DataField: news_eod_low
+DataFieldDescription: Lowest price reached between the time of news and the end of the session
+DataField: nws12_afterhsz_volstddev
+DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
+DataField: news_mins_20_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
+DataField: news_mins_4_chg
+DataFieldDescription: The minimum of L or S above for 4-minute bucket
+DataField: nws12_prez_mktcap
+DataFieldDescription: Reported market capitalization for the calendar day of the session
+DataField: nws12_mainz_3l
+DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
+DataField: nws12_mainz_result1
+DataFieldDescription: Percent change between the price at the time of the news release and the price at the close of the session
+DataField: nws12_afterhsz_rangestddev
+DataFieldDescription: (RangeAmt - AvgRange) / RangeStdDev, where AvgRange is the average of the daily range, and RangeStdDev is one standard deviation for the daily range, both for 30 calendar days
+DataField: nws12_mainz_allvwap
+DataFieldDescription: Volume weighted average price of all sessions
+DataField: nws12_prez_4p
+DataFieldDescription: The minimum of L or S above for 4-minute bucket
+DataField: nws12_mainz_01l
+DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
+DataField: nws12_prez_curr_vol
+DataFieldDescription: Current day's session volume
+DataField: news_curr_vol
+DataFieldDescription: Current day's session volume
+DataField: nws12_mainz_5p
+DataFieldDescription: The minimum of L or S above for 5-minute bucket
+DataField: nws12_prez_maxdown
+DataFieldDescription: Percent change from the price at the time of the news to the after the news low
+DataField: news_mins_5_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 5 percentage points
+DataField: nws12_afterhsz_01s
+DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
+DataField: nws12_prez_result_vs_index
+DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
+DataField: nws12_afterhsz_1p
+DataFieldDescription: The minimum of L or S above for 1-minute bucket
+DataField: nws12_prez_open_vol
+DataFieldDescription: Main open volume
+DataField: news_ton_last
+DataFieldDescription: Price at the time of news
+DataField: top1000
+DataFieldDescription: 20140630
+DataField: top200
+DataFieldDescription: 20140630
+DataField: top3000
+DataFieldDescription: 20140630
+DataField: top500
+DataFieldDescription: 20140630
+DataField: topsp500
+DataFieldDescription: 20140630
+DataField: rp_ess_product
+DataFieldDescription: Event sentiment score of product and service-related news
+DataField: nws18_ber
+DataFieldDescription: News sentiment specializing in earnings result
+DataField: rp_ess_assets
+DataFieldDescription: Event sentiment score of assets news
+DataField: rp_ess_insider
+DataFieldDescription: Event sentiment score of insider trading news
+DataField: rp_nip_equity
+DataFieldDescription: News impact projection of equity action news
+DataField: rp_ess_equity
+DataFieldDescription: Event sentiment score of equity action news
+DataField: rp_css_ratings
+DataFieldDescription: Composite sentiment score of analyst ratings-related news
+DataField: rp_ess_labor
+DataFieldDescription: Event sentiment score of labor issues news
+DataField: nws18_bee
+DataFieldDescription: News sentiment specializing in growth of earnings
+DataField: rp_nip_society
+DataFieldDescription: News impact projection of society-related news
+DataField: nws18_nip
+DataFieldDescription: Degree of impact of the news
+DataField: rp_nip_assets
+DataFieldDescription: News impact projection of assets news
+DataField: rp_ess_business
+DataFieldDescription: Event sentiment score of business-related news
+DataField: rp_css_marketing
+DataFieldDescription: Composite sentiment score of marketing news
+DataField: rp_ess_ratings
+DataFieldDescription: Event sentiment score of analyst ratings-related news
+DataField: rp_css_labor
+DataFieldDescription: Composite sentiment score of labor issues news
+DataField: nws18_qcm
+DataFieldDescription: News sentiment of relevant news with high confidence
+DataField: rp_ess_credit_ratings
+DataFieldDescription: Event sentiment score of credit ratings news
+DataField: rp_css_partner
+DataFieldDescription: Composite sentiment score of partnership news
+DataField: nws18_ghc_lna
+DataFieldDescription: Change in analyst recommendation
+DataField: rp_css_ptg
+DataFieldDescription: Composite sentiment score of price target news
+DataField: rp_ess_society
+DataFieldDescription: Event sentiment score of society-related news
+DataField: nws18_sse
+DataFieldDescription: Sentiment of phrases impacting the company
+DataField: rp_nip_credit
+DataFieldDescription: News impact projection of credit news
+DataField: rp_nip_technical
+DataFieldDescription: News impact projection based on technical analysis
+DataField: rp_css_equity
+DataFieldDescription: Composite sentiment score of equity action news
+DataField: rp_css_credit_ratings
+DataFieldDescription: Composite sentiment score of credit ratings news
+DataField: rp_ess_ptg
+DataFieldDescription: Event sentiment score of price target news
+DataField: rp_nip_mna
+DataFieldDescription: News impact projection of mergers and acquisitions-related news
+DataField: rp_css_inverstor
+DataFieldDescription: Composite sentiment score of investor relations news
+DataField: fnd2_dbplanartonplas
+DataFieldDescription: Defined Benefit Plan, Benefits Paid, Plan Assets
+DataField: fn_repayments_of_debt_q
+DataFieldDescription: The cash outflow during the period from the repayment of aggregate short-term and long-term debt. Excludes payment of capital lease obligations.
+DataField: fn_op_lease_rent_exp_a
+DataFieldDescription: Rental expense for the reporting period incurred under operating leases, including minimum and any contingent rent expense, net of related sublease income.
+DataField: fnd2_q_seniornotes
+DataFieldDescription: Including the current and noncurrent portions, carrying value as of the balance sheet date of Notes with the highest claim on the assets of the issuer in case of bankruptcy or liquidation (with maturities initially due after 1 year or beyond the operating cycle if longer). Senior note holders are paid off in full before any payments are made to junior note holders.
+DataField: fnd2_itxreexftfedstyitxrt
+DataFieldDescription: Income tax amount computed at the federal tax rate, before any adjustments
+DataField: fn_op_lease_min_pay_due_in_5y_a
+DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fn_line_of_credit_facility_amount_out_a
+DataFieldDescription: Amount borrowed under the credit facility as of the balance sheet date.
+DataField: fnd2_a_inventoryrawmaterials
+DataFieldDescription: Amount before valuation and LIFO reserves of raw materials expected to be sold, or consumed within 1 year or operating cycle, if longer.
+DataField: fn_proceeds_from_lt_debt_q
+DataFieldDescription: Proceeds From Issuance Of Debt, Long Term
+DataField: fnd2_dbplanbnfpaid_ast
+DataFieldDescription: The amount of payments made for which participants are entitled under a pension plan, including pension benefits, death benefits, and benefits due on termination of employment. Also includes payments made under a postretirement benefit plan, including prescription drug benefits, health care benefits, life insurance benefits, and legal, educational and advisory services. This item represents a periodic decrease to the plan obligations and a decrease to plan assets.
+DataField: fn_comp_options_out_number_a
+DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
+DataField: fnd2_a_allfdbflaccrwriteoffs
+DataFieldDescription: Amount of recoveries of receivables doubtful of collection that were previously charged off.
+DataField: fn_income_from_equity_investments_a
+DataFieldDescription: Income From Equity Method Investments
+DataField: fn_accum_oth_income_loss_fx_adj_net_of_tax_q
+DataFieldDescription: Accumulated adjustment, net of tax, that results from the process of translating subsidiary financial statements and foreign equity investments into the reporting currency from the functional currency of the reporting entity, net of reclassification of realized foreign currency translation gains or losses.
+DataField: fn_business_combination_purchase_price_a
+DataFieldDescription: Business Combination, Purchase Price
+DataField: fnd2_currfedtxexp
+DataFieldDescription: Income Tax Expense, Current - Federal
+DataField: fn_assets_fair_val_a
+DataFieldDescription: Asset Fair Value, Recurring, Total
+DataField: fnd2_dfdfritxexp
+DataFieldDescription: Income Tax Expense, Deferred - Foreign
+DataField: fn_income_taxes_paid_q
+DataFieldDescription: The amount of cash paid during the current period to foreign, federal, state, and local authorities as taxes on income.
+DataField: fn_employee_related_liab_q
+DataFieldDescription: Total of the carrying values as of the balance sheet date of obligations incurred through that date and payable for obligations related to services received from employees, such as accrued salaries and bonuses, payroll taxes and fringe benefits. For classified balance sheets, used to reflect the current portion of the liabilities (due within 1 year or within the normal operating cycle if longer); for unclassified balance sheets, used to reflect the total liabilities (regardless of due date).
+DataField: fnd2_a_sbcpnatqsttotnsvdptfv
+DataFieldDescription: Fair value of share-based awards for which the grantee gained the right by satisfying service and performance requirements, to receive or retain shares or units, other instruments, or cash.
+DataField: fn_accum_oth_income_loss_net_of_tax_q
+DataFieldDescription: Accumulated change in equity from transactions and other events and circumstances from non-owner sources, net of tax effect, at period end. Excludes Net Income (Loss), and accumulated changes in equity from transactions resulting from investments by owners and distributions to owners. Includes foreign currency translation items, certain pension adjustments, unrealized gains and losses on certain investments in debt and equity securities, other than temporary impairment (OTTI) losses related to factors other than credit losses on available-for-sale and held-to-maturity debt securities that an entity does not intend to sell and it is not more likely than not that the entity will be required to sell before recovery of the amortized cost basis, as well as changes in the fair value of derivatives related to the effective portion of a designated cash flow hedge.
+DataField: fn_debt_instrument_interest_rate_stated_percentage_a
+DataFieldDescription: Stated percentage of interest rate on debt
+DataField: fnd2_a_consinprogressg
+DataFieldDescription: Amount of structure or a modification to a structure under construction. Includes recently completed structures or modifications to structures that have not been placed into service.
+DataField: fnd2_a_flintasamt1expytwo
+DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date
+DataField: fn_eff_income_tax_rate_continuing_operations_q
+DataFieldDescription: Percentage of current income tax expense (benefit) and deferred income tax expense (benefit) pertaining to continuing operations.
+DataField: fnd2_q_lineofcrfcyrmbrgcap
+DataFieldDescription: Amount of borrowing capacity currently available under the credit facility (current borrowing capacity less the amount of borrowings outstanding).
+DataField: fn_oth_comp_forfeitures_fair_value_a
+DataFieldDescription: Annual Share Based Compensation Equity Instruments Other Than Options Forfeitures Weighted Average Grant Date Fair Value
+DataField: fn_comp_options_exercises_weighted_avg_a
+DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
+DataField: fnd2_a_lhdiprtsg
+DataFieldDescription: Amount before accumulated depreciation of additions or improvements to assets held under a lease arrangement.
+DataField: adv20
+DataFieldDescription: Average daily volume in past 20 days
+DataField: cap
+DataFieldDescription: Daily market capitalization (in millions)
+DataField: close
+DataFieldDescription: Daily close price
+DataField: country
+DataFieldDescription: Country grouping
+DataField: currency
+DataFieldDescription: Currency
+DataField: cusip
+DataFieldDescription: CUSIP Value
+DataField: dividend
+DataFieldDescription: Dividend
+DataField: exchange
+DataFieldDescription: Exchange grouping
+DataField: high
+DataFieldDescription: Daily high price
+DataField: industry
+DataFieldDescription: Industry grouping
+DataField: isin
+DataFieldDescription: ISIN Value
+DataField: low
+DataFieldDescription: Daily low price
+DataField: market
+DataFieldDescription: Market grouping
+DataField: open
+DataFieldDescription: Daily open price
+DataField: returns
+DataFieldDescription: Daily returns
+DataField: sector
+DataFieldDescription: Sector grouping
+DataField: sedol
+DataFieldDescription: Sedol
+DataField: sharesout
+DataFieldDescription: Daily outstanding shares (in millions)
+DataField: split
+DataFieldDescription: Stock split ratio
+DataField: subindustry
+DataFieldDescription: Subindustry grouping
+DataField: ticker
+DataFieldDescription: Ticker
+DataField: volume
+DataFieldDescription: Daily volume
+DataField: vwap
+DataFieldDescription: Daily volume weighted average price
+========================= 数据字段结束 =======================================
+

manual_prompt/manual_prompt_20251212154430.txt

@@ -0,0 +1,898 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成 100 个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+目标：识别价格趋势的强度、速度和持续性
+可用的具体构建方法：
+1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
+2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
+3. 动量加速度：ts_delta(ts_delta(close, d1), d2) [避免嵌套ts_regression]
+4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
+5. 动量衰减：ts_decay_linear(returns, d)
+6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
+建议组合：使用不同d参数创建短期/中期/长期动量
+维度2：横截面领导力（CL）
+目标：识别行业内的龙头股和相对强度
+具体构建方法：
+1. 龙头股筛选：if_else(rank(volume) > 0.7, 龙头值, 其他值) [使用volume代替market_cap]
+2. 龙头组合：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4")) [使用volume排序]
+3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
+4. 相对排名稳定性：ts_mean(rank(returns), d)
+维度3：市场状态适应性（MS）
+目标：根据波动率、趋势状态调整参数
+具体构建方法：
+1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
+2. 状态条件选择：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
+3. 参数动态化：if_else(ts_std_dev(returns,20) > 阈值, 5, 20) [作为d参数]
+4. 趋势状态识别：ts_rank(ts_mean(returns,20), 60) > 0.5
+基本结构：
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+=== 关键语法规则（必须遵守） ===
+1. 数据字段规范：
+   - 可使用字段：close, volume, returns
+   - ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
+   - ✅ 正确：使用volume作为规模代理，close作为价格
+   - returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
+2. ts_regression使用规范：
+   - 避免深度嵌套ts_regression，特别是作为其他函数的参数
+   - ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
+   - ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
+   - 替代方案：用ts_delta组合计算动量变化
+3. if_else使用规范：
+   - 条件必须是简单布尔表达式
+   - 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
+   - 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
+4. bucket函数使用规范：
+   - bucket()返回分组ID，可用于条件判断
+   - ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
+   - ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
+   - 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
+=== 关键语法规则结束 ===
+*=====*
+注意事项:
+1. 避免过度复杂的嵌套（建议不超过3层）
+2. 每个表达式应有明确的经济逻辑
+3. 考虑实际交易可行性（避免未来函数）
+4. 包含风险控制元素（如波动率调整）
+5. 只能使用可用的数据字段：close, volume, returns等
+*=====*
+参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
+行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现"行业"逻辑。
+构建框架指导（请按此逻辑创造新因子）：
+维度融合模板（选择至少2个）：
+A. 领导力动量 = 时序动量 × 横截面调整
+   逻辑：大成交量股票的动量更强
+   结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
+B. 状态自适应动量 = 条件选择动量
+   逻辑：高波动用短期动量，低波动用长期动量
+   结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
+C. 行业传导因子 = 领先行业动量 × 相关性强度
+   逻辑：与强势行业相关性高的行业未来表现好
+   结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
+D. 情绪反转 = 过度交易信号 × 基础趋势
+   逻辑：过度交易时反转，趋势延续时跟随
+   结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
+关键组件库（可自由组合）：
+1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
+2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
+3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
+4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
+5. 相关性类：ts_corr({x},{y},{d})
+6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
+参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
+*=====*
+输出格式：
+输出必须是且仅是纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
+===================== !!! 重点(输出方式) !!! =====================
+现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+=================================================================
+重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子：
+
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+Description: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符
+特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false)
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
+Operator: divide(x, y)
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false)
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false)
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1
+Operator: ts_arg_min(x, d)
+Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default.
+Operator: ts_rank(x, d, constant = 0)
+Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
+Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
+Description: Returns various parameters related to regression function
+Operator: ts_scale(x, d, constant = 0)
+Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
+Operator: ts_std_dev(x, d)
+Description: Returns standard deviation of x for the past d days
+Operator: ts_step(1)
+Description: Returns days' counter
+Operator: ts_sum(x, d)
+Description: Sum values of x for the past d days.
+Operator: ts_zscore(x, d)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
+Operator: normalize(x, useStd = false, limit = 0.0)
+Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
+Operator: quantile(x, driver = gaussian, sigma = 1.0)
+Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector
+Operator: rank(x, rate=2)
+Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0
+Operator: scale(x, scale=1, longscale=1, shortscale=1)
+Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
+Operator: winsorize(x, std=4)
+Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
+Operator: zscore(x)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
+Operator: vec_avg(x)
+Description: Taking mean of the vector field x
+Operator: vec_sum(x)
+Description: Sum of vector field x
+Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
+Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
+Operator: trade_when(x, y, z)
+Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition
+Operator: group_backfill(x, group, d, std = 4.0)
+Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
+Operator: group_mean(x, weight, group)
+Description: All elements in group equals to the mean
+Operator: group_neutralize(x, group)
+Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
+Operator: group_rank(x, group)
+Description: Each elements in a group is assigned the corresponding rank in this group
+Operator: group_scale(x, group)
+Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
+Operator: group_zscore(x, group)
+Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
+========================= 操作符结束 =======================================
+
+========================= 数据字段开始 =======================================注意: DataField: 后面的是数据字段, DataFieldDescription: 此字段后面的是数据字段对应的描述或使用说明, DataFieldDescription字段后面的内容是使用说明, 不是数据字段
+
+DataField: forward_price_10
+DataFieldDescription: Forward price at 10 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: option_breakeven_360
+DataFieldDescription: Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_150
+DataFieldDescription: Price at which a stock's call options with expiration 150 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_30
+DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_150
+DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_60
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 60 days in the future.
+DataField: pcr_vol_30
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.
+DataField: pcr_oi_150
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.
+DataField: pcr_oi_720
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.
+DataField: pcr_vol_150
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
+DataField: pcr_vol_10
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
+DataField: option_breakeven_120
+DataFieldDescription: Price at which a stock's options with expiration 120 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_90
+DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_10
+DataFieldDescription: Price at which a stock's options with expiration 10 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_90
+DataFieldDescription: Price at which a stock's put options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_1080
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
+DataField: put_breakeven_1080
+DataFieldDescription: Price at which a stock's put options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_720
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 720 days in the future.
+DataField: call_breakeven_360
+DataFieldDescription: Price at which a stock's call options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_20
+DataFieldDescription: Price at which a stock's put options with expiration 20 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_150
+DataFieldDescription: Forward price at 150 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_vol_180
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
+DataField: forward_price_1080
+DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_360
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 360 days in the future.
+DataField: call_breakeven_1080
+DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_120
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
+DataField: forward_price_720
+DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_10
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
+DataField: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_vol_all
+DataFieldDescription: Ratio of put volume to call volume for all maturities on stock's options.
+DataField: fnd6_rea
+DataFieldDescription: Retained Earnings - Restatement
+DataField: fnd6_newa2v1300_ppegt
+DataFieldDescription: Property, Plant and Equipment - Total (Gross)
+DataField: fnd6_newqv1300_mibtq
+DataFieldDescription: Noncontrolling Interests - Total - Balance Sheet - Quarterly
+DataField: ebit
+DataFieldDescription: Earnings Before Interest and Taxes
+DataField: fnd6_newqeventv110_ppegtq
+DataFieldDescription: Property, Plant and Equipment - Total (Gross) - Quarterly
+DataField: fnd6_newqeventv110_gdwlipq
+DataFieldDescription: Impairment of Goodwill Pretax
+DataField: fnd6_newqeventv110_spceepsq
+DataFieldDescription: S&P Core Earnings EPS Basic
+DataField: fnd6_newqeventv110_glcedq
+DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS
+DataField: fnd6_newqeventv110_spceeps12
+DataFieldDescription: S&P Core Earnings EPS Basic 12MM
+DataField: fnd6_txdfed
+DataFieldDescription: Deferred Taxes - Federal
+DataField: fnd6_fatb
+DataFieldDescription: Plant, Property and Equipment at Cost - Buildings
+DataField: fnd6_newa1v1300_dp
+DataFieldDescription: Depreciation and Amortization
+DataField: fnd6_newa2v1300_prsho
+DataFieldDescription: Redeem Pfd Shares Outs (000)
+DataField: fnd6_newqv1300_aol2q
+DataFieldDescription: Assets Level 2 (Observable)
+DataField: fnd6_mfma1_dpc
+DataFieldDescription: Depreciation and Amortization (Cash Flow)
+DataField: fnd6_ptis
+DataFieldDescription: Pretax Income
+DataField: fnd6_cptnewqv1300_ceqq
+DataFieldDescription: Common/Ordinary Equity - Total
+DataField: fnd6_newqv1300_cogsq
+DataFieldDescription: Cost of Goods Sold
+DataField: fnd6_newa1v1300_dltt
+DataFieldDescription: Long-Term Debt - Total
+DataField: fnd6_newqv1300_invrmq
+DataFieldDescription: Inventory - Raw Materials
+DataField: fnd6_newqeventv110_pncq
+DataFieldDescription: Core Pension Adjustment
+DataField: fnd6_txtubtxtr
+DataFieldDescription: Impact on Effective Tax Rate
+DataField: fnd6_newa1v1300_dcom
+DataFieldDescription: Deferred Compensation
+DataField: fnd6_newa1v1300_ebit
+DataFieldDescription: Earnings Before Interest and Taxes
+DataField: fnd6_dd5
+DataFieldDescription: Debt Due in 5th Year
+DataField: fnd6_newqv1300_cshfdq
+DataFieldDescription: Common Shares for Diluted EPS
+DataField: fnd6_newa1v1300_dv
+DataFieldDescription: Cash Dividends (Cash Flow)
+DataField: cash
+DataFieldDescription: Cash
+DataField: fnd6_newqeventv110_seteps12
+DataFieldDescription: Settlement (Litigation/Insurance) Basic EPS Effect 12MM
+DataField: fnd6_mfma2_opeps
+DataFieldDescription: Earnings Per Share from Operations
+DataField: scl12_alltype_buzzvec
+DataFieldDescription: sentiment volume
+DataField: scl12_alltype_sentvec
+DataFieldDescription: sentiment
+DataField: scl12_alltype_typevec
+DataFieldDescription: instrument type index
+DataField: scl12_buzz
+DataFieldDescription: relative sentiment volume
+DataField: scl12_buzz_fast_d1
+DataFieldDescription: relative sentiment volume
+DataField: scl12_buzzvec
+DataFieldDescription: sentiment volume
+DataField: scl12_sentiment
+DataFieldDescription: sentiment
+DataField: scl12_sentiment_fast_d1
+DataFieldDescription: sentiment
+DataField: scl12_sentvec
+DataFieldDescription: sentiment
+DataField: scl12_typevec
+DataFieldDescription: instrument type index
+DataField: snt_buzz
+DataFieldDescription: negative relative sentiment volume, fill nan with 0
+DataField: snt_buzz_bfl
+DataFieldDescription: negative relative sentiment volume, fill nan with 1
+DataField: snt_buzz_bfl_fast_d1
+DataFieldDescription: negative relative sentiment volume, fill nan with 1
+DataField: snt_buzz_fast_d1
+DataFieldDescription: negative relative sentiment volume, fill nan with 0
+DataField: snt_buzz_ret
+DataFieldDescription: negative return of relative sentiment volume
+DataField: snt_buzz_ret_fast_d1
+DataFieldDescription: negative return of relative sentiment volume
+DataField: snt_value
+DataFieldDescription: negative sentiment, fill nan with 0
+DataField: snt_value_fast_d1
+DataFieldDescription: negative sentiment, fill nan with 0
+DataField: analyst_revision_rank_derivative
+DataFieldDescription: Change in ranking for analyst revisions and momentum compared to previous period.
+DataField: cashflow_efficiency_rank_derivative
+DataFieldDescription: Change in ranking for cash flow generation and profitability compared to previous period.
+DataField: composite_factor_score_derivative
+DataFieldDescription: Change in overall composite factor score from the prior period.
+DataField: earnings_certainty_rank_derivative
+DataFieldDescription: Change in ranking for earnings sustainability and certainty compared to previous period.
+DataField: fscore_bfl_growth
+DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
+DataField: fscore_bfl_momentum
+DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
+DataField: fscore_bfl_profitability
+DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
+DataField: fscore_bfl_quality
+DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
+DataField: fscore_bfl_surface
+DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
+DataField: fscore_bfl_surface_accel
+DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
+DataField: fscore_bfl_total
+DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
+DataField: fscore_bfl_value
+DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
+DataField: fscore_growth
+DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
+DataField: fscore_momentum
+DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
+DataField: fscore_profitability
+DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
+DataField: fscore_quality
+DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
+DataField: fscore_surface
+DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
+DataField: fscore_surface_accel
+DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
+DataField: fscore_total
+DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
+DataField: fscore_value
+DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
+DataField: growth_potential_rank_derivative
+DataFieldDescription: Change in ranking for medium-term growth potential compared to previous period.
+DataField: multi_factor_acceleration_score_derivative
+DataFieldDescription: Change in the acceleration of multi-factor score compared to previous period.
+DataField: multi_factor_static_score_derivative
+DataFieldDescription: Change in static multi-factor score compared to previous period.
+DataField: relative_valuation_rank_derivative
+DataFieldDescription: Change in ranking for valuation metrics compared to previous period.
+DataField: snt_social_value
+DataFieldDescription: Z score of sentiment
+DataField: snt_social_volume
+DataFieldDescription: Normalized tweet volume
+DataField: beta_last_30_days_spy
+DataFieldDescription: Beta to SPY in 30 Days
+DataField: beta_last_360_days_spy
+DataFieldDescription: Beta to SPY in 360 Days
+DataField: beta_last_60_days_spy
+DataFieldDescription: Beta to SPY in 60 Days
+DataField: beta_last_90_days_spy
+DataFieldDescription: Beta to SPY in 90 Days
+DataField: correlation_last_30_days_spy
+DataFieldDescription: Correlation to SPY in 30 Days
+DataField: correlation_last_360_days_spy
+DataFieldDescription: Correlation to SPY in 360 Days
+DataField: correlation_last_60_days_spy
+DataFieldDescription: Correlation to SPY in 60 Days
+DataField: correlation_last_90_days_spy
+DataFieldDescription: Correlation to SPY in 90 Days
+DataField: systematic_risk_last_30_days
+DataFieldDescription: Systematic Risk Last 30 Days
+DataField: systematic_risk_last_360_days
+DataFieldDescription: Systematic Risk Last 360 Days
+DataField: systematic_risk_last_60_days
+DataFieldDescription: Systematic Risk Last 60 Days
+DataField: systematic_risk_last_90_days
+DataFieldDescription: Systematic Risk Last 90 Days
+DataField: unsystematic_risk_last_30_days
+DataFieldDescription: Unsystematic Risk Last 30 Days - Relative to SPY
+DataField: unsystematic_risk_last_360_days
+DataFieldDescription: Unsystematic Risk Last 360 Days - Relative to SPY
+DataField: unsystematic_risk_last_60_days
+DataFieldDescription: Unsystematic Risk Last 60 Days - Relative to SPY
+DataField: unsystematic_risk_last_90_days
+DataFieldDescription: Unsystematic Risk Last 90 Days - Relative to SPY
+DataField: eps_adjusted_min_guidance_value
+DataFieldDescription: The minimum guidance value for adjusted earnings per share excluding extraordinary items and stock option expenses on an annual basis.
+DataField: anl4_fsguidanceafv4_minguidance
+DataFieldDescription: Min guidance value
+DataField: anl4_afv4_eps_high
+DataFieldDescription: Earnings per share - The highest estimation
+DataField: anl4_basicdetailqfv110_prevval
+DataFieldDescription: The previous estimation of financial item
+DataField: anl4_basicconltv110_high
+DataFieldDescription: The highest estimation
+DataField: dividend_previous_estimate_value
+DataFieldDescription: The previous estimation of dividend
+DataField: anl4_bac1conafv110_item
+DataFieldDescription: Financial item
+DataField: anl4_fsguidanceafv4_maxguidance
+DataFieldDescription: Maximum guidance value
+DataField: anl4_eaz2lafv110_prevval
+DataFieldDescription: The previous estimation of financial item
+DataField: anl4_fsdetailltv4v104_item
+DataFieldDescription: Financial item
+DataField: selling_general_admin_expense_reported_value
+DataFieldDescription: Selling, General & Administrative Expense value
+DataField: max_free_cashflow_guidance
+DataFieldDescription: The maximum guidance value for Free Cash Flow.
+DataField: anl4_cfo_value
+DataFieldDescription: Cash Flow From Operations - announced financial value
+DataField: anl4_fsdtlestmtbscv104_item
+DataFieldDescription: Financial item
+DataField: min_total_goodwill_guidance
+DataFieldDescription: Total Goodwill - The lowest guidance value
+DataField: anl4_qfd1_az_wol_spfc
+DataFieldDescription: Cash Flow Per Share - The lowest estimation
+DataField: eps_reported_min_guidance_qtr
+DataFieldDescription: Reported Earnings Per Share - Minimum guidance value
+DataField: anl4_gric_value
+DataFieldDescription: Gross income- announced financial value
+DataField: anl4_detailltv4_est
+DataFieldDescription: Long term estimation value
+DataField: max_pretax_profit_guidance
+DataFieldDescription: The maximum guidance value for Pretax income on an annual basis.
+DataField: anl4_fsguidancebasicqfv4_item
+DataFieldDescription: Financial item
+DataField: anl4_afv4_div_std
+DataFieldDescription: Dividend per share - standard deviation of estimations
+DataField: cashflow_per_share_median_value
+DataFieldDescription: Cash Flow Per Share - Median value among forecasts
+DataField: anl4_dei3lafv110_item
+DataFieldDescription: Financial item
+DataField: anl4_ady_high
+DataFieldDescription: The highest estimation
+DataField: anl4_epsa_flag
+DataFieldDescription: Earnings per share adjusted by excluding extraordinary items and stock option expenses - forecast type (revision/new/...)
+DataField: max_share_buyback_guidance
+DataFieldDescription: Maximum guidance value for Shares Basic - Annual
+DataField: anl4_netdebt_flag
+DataFieldDescription: Net debt - forecast type (revision/new/...)
+DataField: anl4_qfd1_az_cfps_median
+DataFieldDescription: Cash Flow Per Share - Median value among forecasts
+DataField: anl4_qfv4_minguidance
+DataFieldDescription: Min guidance value
+DataField: rel_ret_comp
+DataFieldDescription: Averaged one-day return of the competing companies
+DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min40_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min54_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_focused_pureplay_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchys32_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f1_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_3l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min52_2k_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_top3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min52_sector
+DataFieldDescription: grouping fields
+DataField: pv13_2l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min25_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min5_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_company_total
+DataFieldDescription: Total number of companies in the sector
+DataField: pv13_rha2_min2_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_term_sector_total
+DataFieldDescription: Number of terminal sectors for the company
+DataField: pv13_hierarchy23_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_custretsig_retsig
+DataFieldDescription: Sign of customer return
+DataField: pv13_hierarchy_min20_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_term
+DataFieldDescription: Indicates when a sector is the terminal sector (i.e., no sub-sectors)
+DataField: pv13_hierarchy_min100_corr21_sector
+DataFieldDescription: grouping fields
+DataField: rel_ret_all
+DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
+DataField: pv13_h_min2_focused_pureplay_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f4_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_focused_pureplay_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min30_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min5_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min2_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f4_513_sector
+DataFieldDescription: grouping fields
+DataField: implied_volatility_mean_skew_720
+DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
+DataField: implied_volatility_mean_skew_20
+DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
+DataField: implied_volatility_mean_720
+DataFieldDescription: At-the-money option-implied volatility mean for 720 days
+DataField: implied_volatility_call_1080
+DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
+DataField: implied_volatility_call_30
+DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
+DataField: implied_volatility_call_10
+DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
+DataField: implied_volatility_call_720
+DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
+DataField: parkinson_volatility_120
+DataFieldDescription: Parkinson model's historical volatility over 120 days
+DataField: implied_volatility_put_120
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
+DataField: historical_volatility_20
+DataFieldDescription: Close-to-close Historical volatility over 20 days
+DataField: parkinson_volatility_60
+DataFieldDescription: Parkinson model's historical volatility over 60 days
+DataField: implied_volatility_call_150
+DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
+DataField: implied_volatility_put_270
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
+DataField: implied_volatility_mean_120
+DataFieldDescription: At-the-money option-implied volatility mean for 120 days
+DataField: implied_volatility_mean_10
+DataFieldDescription: At-the-money option-implied volatility mean for 10 days
+DataField: implied_volatility_put_90
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
+DataField: implied_volatility_mean_270
+DataFieldDescription: At-the-money option-implied volatility mean for 270 days
+DataField: implied_volatility_mean_skew_1080
+DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
+DataField: parkinson_volatility_30
+DataFieldDescription: Parkinson model's historical volatility over 30 days
+DataField: implied_volatility_call_120
+DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
+DataField: historical_volatility_150
+DataFieldDescription: Close-to-close Historical volatility over 150 days
+DataField: implied_volatility_mean_skew_120
+DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
+DataField: parkinson_volatility_10
+DataFieldDescription: Parkinson model's historical volatility over 2 weeks
+DataField: implied_volatility_mean_skew_150
+DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
+DataField: parkinson_volatility_90
+DataFieldDescription: Parkinson model's historical volatility over 90 days
+DataField: implied_volatility_mean_skew_360
+DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
+DataField: historical_volatility_180
+DataFieldDescription: Close-to-close Historical volatility over 180 days
+DataField: implied_volatility_call_180
+DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
+DataField: implied_volatility_put_360
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
+DataField: implied_volatility_mean_360
+DataFieldDescription: At-the-money option-implied volatility mean for 360 days
+DataField: news_indx_perf
+DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
+DataField: nws12_afterhsz_3p
+DataFieldDescription: The minimum of L or S above for 3-minute bucket
+DataField: news_prev_day_ret
+DataFieldDescription: Percent change between the previous day's open and close
+DataField: nws12_mainz_01s
+DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
+DataField: nws12_prez_57s
+DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
+DataField: nws12_afterhsz_120_min
+DataFieldDescription: The percent change in price in the first 120 minutes following the news release
+DataField: nws12_afterhsz_41rta
+DataFieldDescription: 14-day Average True Range
+DataField: nws12_afterhsz_mov_vol
+DataFieldDescription: 30-day moving average session volume
+DataField: nws12_allz_newssess
+DataFieldDescription: Index of session in which the news was reported
+DataField: nws12_mainz_2l
+DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
+DataField: news_eps_actual
+DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
+DataField: news_mins_3_chg
+DataFieldDescription: The minimum of L or S above for 3-minute bucket
+DataField: news_mins_7_5_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
+DataField: nws12_prez_maxdnamt
+DataFieldDescription: The price at the time of the news minus the after the news low
+DataField: nws12_afterhsz_maxdnamt
+DataFieldDescription: The price at the time of the news minus the after the news low
+DataField: nws12_afterhsz_02l
+DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
+DataField: nws12_prez_dayopen
+DataFieldDescription: Price at the session open
+DataField: news_mins_5_chg
+DataFieldDescription: The minimum of L or S above for 5-minute bucket
+DataField: nws12_mainz_30_min
+DataFieldDescription: The percent change in price in the first 30 minutes following the news release
+DataField: nws12_mainz_peratio
+DataFieldDescription: Reported price-to-earnings ratio for the calendar day of the session
+DataField: news_pct_10min
+DataFieldDescription: The percent change in price in the first 10 minutes following the news release
+DataField: news_mins_5_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 5 percentage points
+DataField: nws12_mainz_newrecord
+DataFieldDescription: Tracks whether the news is first instance or a duplicate
+DataField: nws12_prez_eodlow
+DataFieldDescription: Lowest price reached between the time of news and the end of the session.
+DataField: nws12_mainz_prevday
+DataFieldDescription: Percent change between the previous day's open and close
+DataField: nws12_prez_02p
+DataFieldDescription: The minimum of L or S above for 20-minute bucket
+DataField: nws12_afterhsz_div_y
+DataFieldDescription: Annual yield
+DataField: nws12_afterhsz_lowexcstddev
+DataFieldDescription: (TONLast - EODLow) / StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
+DataField: nws12_afterhsz_1s
+DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
+DataField: news_mins_4_chg
+DataFieldDescription: The minimum of L or S above for 4-minute bucket
+DataField: top1000
+DataFieldDescription: 20140630
+DataField: top200
+DataFieldDescription: 20140630
+DataField: top3000
+DataFieldDescription: 20140630
+DataField: top500
+DataFieldDescription: 20140630
+DataField: topsp500
+DataFieldDescription: 20140630
+DataField: rp_nip_assets
+DataFieldDescription: News impact projection of assets news
+DataField: rp_ess_technical
+DataFieldDescription: Event sentiment score based on technical analysis
+DataField: nws18_event_relevance
+DataFieldDescription: Relevance of the event to the story
+DataField: rp_ess_insider
+DataFieldDescription: Event sentiment score of insider trading news
+DataField: rp_nip_society
+DataFieldDescription: News impact projection of society-related news
+DataField: nws18_bam
+DataFieldDescription: News sentiment specializing in mergers and acquisitions
+DataField: rp_nip_marketing
+DataFieldDescription: News impact projection of marketing news
+DataField: nws18_sse
+DataFieldDescription: Sentiment of phrases impacting the company
+DataField: rp_nip_product
+DataFieldDescription: News impact projection of product and service-related news
+DataField: nws18_event_similarity_days
+DataFieldDescription: Days since a similar event was detected
+DataField: nws18_relevance
+DataFieldDescription: Relevance of news to the company
+DataField: rp_ess_credit
+DataFieldDescription: Event sentiment score of credit news
+DataField: nws18_qep
+DataFieldDescription: News sentiment based on positive and negative words on global equity
+DataField: nws18_ssc
+DataFieldDescription: Sentiment of the news calculated using multiple techniques
+DataField: rp_ess_earnings
+DataFieldDescription: Event sentiment score of earnings news
+DataField: rp_ess_equity
+DataFieldDescription: Event sentiment score of equity action news
+DataField: rp_ess_society
+DataFieldDescription: Event sentiment score of society-related news
+DataField: rp_nip_inverstor
+DataFieldDescription: News impact projection of investor relations news
+DataField: rp_ess_price
+DataFieldDescription: Event sentiment score of stock price news
+DataField: rp_ess_ptg
+DataFieldDescription: Event sentiment score of price target news
+DataField: rp_css_partner
+DataFieldDescription: Composite sentiment score of partnership news
+DataField: rp_nip_partner
+DataFieldDescription: News impact projection of partnership news
+DataField: rp_nip_credit
+DataFieldDescription: News impact projection of credit news
+DataField: rp_css_earnings
+DataFieldDescription: Composite sentiment score of earnings news
+DataField: rp_ess_dividends
+DataFieldDescription: Event sentiment score of dividends news
+DataField: nws18_acb
+DataFieldDescription: News sentiment specializing in corporate action announcements
+DataField: rp_nip_equity
+DataFieldDescription: News impact projection of equity action news
+DataField: nws18_nip
+DataFieldDescription: Degree of impact of the news
+DataField: rp_nip_labor
+DataFieldDescription: News impact projection of labor issues news
+DataField: rp_css_business
+DataFieldDescription: Composite sentiment score of business-related news
+DataField: fn_avg_diluted_sharesout_adj_a
+DataFieldDescription: The sum of dilutive potential common shares or units used in the calculation of the diluted per-share or per-unit computation.
+DataField: fn_comp_non_opt_forfeited_q
+DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that were forfeited during the reporting period.
+DataField: fn_proceeds_from_stock_options_exercised_q
+DataFieldDescription: The cash inflow associated with the amount received from holders exercising their stock options. This item inherently excludes any excess tax benefit, which the entity may have realized and reported separately.
+DataField: fn_profit_loss_q
+DataFieldDescription: The consolidated profit or loss for the period, net of income taxes, including the portion attributable to the noncontrolling interest.
+DataField: fnd2_a_sbcpnargmsawpfipwerpr
+DataFieldDescription: Weighted average price of options that were either forfeited or expired.
+DataField: fnd2_a_sbcpnargmpmwggil
+DataFieldDescription: Amount by which the current fair value of the underlying stock exceeds the exercise price of fully vested and expected to vest options outstanding.
+DataField: fn_finite_lived_intangible_assets_net_q
+DataFieldDescription: Finite Lived Intangible Assets, Net
+DataField: fnd2_dbplanepdfbnfpnext12m
+DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_a_sbcpnargmsptawervl
+DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
+DataField: fn_finite_lived_intangible_assets_gross_q
+DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
+DataField: fn_comp_non_opt_vested_q
+DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that vested during the reporting period.
+DataField: fnd2_dfdtxastxdfdexprssaccrs
+DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible temporary differences from reserves and accruals.
+DataField: fnd2_a_stkrpeprogramardamt
+DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
+DataField: fnd2_a_curritxexp
+DataFieldDescription: Income Tax Expense, Current
+DataField: fn_comp_non_opt_nonvested_number_a
+DataFieldDescription: The number of non-vested equity-based payment instruments, excluding stock (or unit) options, that validly exist and are outstanding as of the balance sheet date.
+DataField: fn_comp_options_out_number_q
+DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
+DataField: fnd2_a_flintasamt1expyfour
+DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_q_atdlsecexfcepsastkos
+DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
+DataField: fn_accum_depr_depletion_and_amortization_ppne_a
+DataFieldDescription: Amount of accumulated depreciation, depletion and amortization for physical assets used in the normal conduct of business to produce goods and services.
+DataField: fn_accum_depr_depletion_and_amortization_ppne_q
+DataFieldDescription: Amount of accumulated depreciation, depletion and amortization for physical assets used in the normal conduct of business to produce goods and services.
+DataField: fn_finite_lived_intangible_assets_gross_a
+DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
+DataField: fnd2_a_gwllimrml
+DataFieldDescription: Amount of loss from the write-down of an asset representing the future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized.
+DataField: fn_repayments_of_lt_debt_a
+DataFieldDescription: The cash outflow for debt initially having maturity due after 1 year or beyond the normal operating cycle, if longer.
+DataField: fn_comp_not_rec_a
+DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
+DataField: fn_income_taxes_paid_q
+DataFieldDescription: The amount of cash paid during the current period to foreign, federal, state, and local authorities as taxes on income.
+DataField: fn_comp_options_out_intrinsic_value_a
+DataFieldDescription: The intrinsic value of a stock option is the amount by which the market value of the underlying stock exceeds the exercise price of the option.
+DataField: fn_income_tax_expense_q
+DataFieldDescription: Income Tax Expense (Benefit)
+DataField: fnd2_a_atdlsecexfcepsastkos
+DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
+DataField: fnd2_a_flintasacmamtzcsrld
+DataFieldDescription: Finite Lived Intangible Assets Accumulated Amortization, Customer Related
+DataField: fnd2_a_ltrmdmrepoplinnext12m
+DataFieldDescription: Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: adv20
+DataFieldDescription: Average daily volume in past 20 days
+DataField: cap
+DataFieldDescription: Daily market capitalization (in millions)
+DataField: close
+DataFieldDescription: Daily close price
+DataField: country
+DataFieldDescription: Country grouping
+DataField: currency
+DataFieldDescription: Currency
+DataField: cusip
+DataFieldDescription: CUSIP Value
+DataField: dividend
+DataFieldDescription: Dividend
+DataField: exchange
+DataFieldDescription: Exchange grouping
+DataField: high
+DataFieldDescription: Daily high price
+DataField: industry
+DataFieldDescription: Industry grouping
+DataField: isin
+DataFieldDescription: ISIN Value
+DataField: low
+DataFieldDescription: Daily low price
+DataField: market
+DataFieldDescription: Market grouping
+DataField: open
+DataFieldDescription: Daily open price
+DataField: returns
+DataFieldDescription: Daily returns
+DataField: sector
+DataFieldDescription: Sector grouping
+DataField: sedol
+DataFieldDescription: Sedol
+DataField: sharesout
+DataFieldDescription: Daily outstanding shares (in millions)
+DataField: split
+DataFieldDescription: Stock split ratio
+DataField: subindustry
+DataFieldDescription: Subindustry grouping
+DataField: ticker
+DataFieldDescription: Ticker
+DataField: volume
+DataFieldDescription: Daily volume
+DataField: vwap
+DataFieldDescription: Daily volume weighted average price
+========================= 数据字段结束 =======================================
+

prepare_prompt/alpha_prompt.txt

@@ -1,99 +1,126 @@
 任务指令
-你是一个WorldQuant WebSim因子工程师。你的任务是生成 10 个用于行业轮动策略的复合型Alpha因子表达式。
+你是一个WorldQuant WebSim因子工程师。你的任务是生成 100 个用于行业轮动策略的复合型Alpha因子表达式。
 核心规则
 设计维度框架
 维度1：时间序列动量（TM）
-核心概念：捕捉行业价格的趋势、动量和形态变化
-设计思路：
-动量的变化率、加速度或平滑度构建
-动量衰减或增强模式识别
-价格与成交量关系的时序分析
+目标：识别价格趋势的强度、速度和持续性
+可用的具体构建方法：
+1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
+2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
+3. 动量加速度：ts_delta(ts_delta(close, d1), d2) [避免嵌套ts_regression]
+4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
+5. 动量衰减：ts_decay_linear(returns, d)
+6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
+建议组合：使用不同d参数创建短期/中期/长期动量
+
 维度2：横截面领导力（CL）
-核心概念：识别行业内部的分化、龙头效应和相对强度
-bucket（用于龙头股筛选）
-设计思路：
-行业内部龙头股与平均表现的差异
-行业成分股的离散度分析
-相对排名的变化和稳定性
+目标：识别行业内的龙头股和相对强度
+具体构建方法：
+1. 龙头股筛选：if_else(rank(volume) > 0.7, 龙头值, 其他值) [使用volume代替market_cap]
+2. 龙头组合：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4")) [使用volume排序]
+3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
+4. 相对排名稳定性：ts_mean(rank(returns), d)
+
 维度3：市场状态适应性（MS）
-核心概念：根据市场环境动态调整因子逻辑
-设计思路：
-波动率调整的动量指标
-不同市场状态（高/低波动）使用不同的回顾期
-条件逻辑下的参数动态调整
-维度4：行业间联动（IS）
-多序列相关性分析
-设计思路：
-领先-滞后行业的相关性分析
-行业间动量传导效应
-板块轮动的早期信号识别
-维度5：交易行为情绪（TS）
-核心概念：基于交易行为和情绪指标的反转信号
-设计思路：
-超买超卖状态识别
-交易拥挤度指标
-情绪极端值后的均值回归
-复合因子设计原则
-强制要求：
-每个表达式必须融合至少两个设计维度
-必须使用提供的操作符列表中的函数
-因子应具有经济逻辑解释性
-推荐组合模式：
-TM + CL：时序动量 + 横截面领导力
-示例：行业动量加速度 × 龙头股相对强度
-TM + MS：时序动量 + 状态适应性
-示例：波动率调整后的动量指标
-CL + IS：横截面 + 行业间联动
-示例：龙头股表现与相关行业的领先滞后关系
-MS + TS：状态适应 + 交易情绪
-示例：不同市场状态下的反转信号
-IS + TS：行业联动 + 交易情绪
-示例：行业间相关性变化与交易拥挤度
-参数化建议：
-使用不同的时间窗口组合（短/中/长周期）
-尝试不同的权重分配方式
-考虑非线性变换（log, power, sqrt）
-使用条件逻辑增强鲁棒性
-表达式构建指南
+目标：根据波动率、趋势状态调整参数
+具体构建方法：
+1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
+2. 状态条件选择：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
+3. 参数动态化：if_else(ts_std_dev(returns,20) > 阈值, 5, 20) [作为d参数]
+4. 趋势状态识别：ts_rank(ts_mean(returns,20), 60) > 0.5
+
 基本结构：
-text
 复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
-操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
-操作符使用策略：
-算术运算：abs(x), add(x, y, filter = false), densify(x), divide(x, y), inverse(x), max(x, y, ..), min(x, y ..), multiply(x ,y, ... , filter=false), power(x, y), reverse(x), sign(x), signed_power(x, y), sqrt(x), subtract(x, y, filter=false)
-条件逻辑：and(input1, input2), if_else(input1, input2, input 3), input1 < input2, input1 <= input2, input1 == input2, input1 > input2, input1 >= input2, input1!= input2, is_nan(input), not(x), or(input1, input2)
-时间序列操作：days_from_last_change(x), hump(x, hump = 0.01), kth_element(x, d, k), last_diff_value(x, d), ts_arg_max(x, d), ts_arg_min(x, d), ts_av_diff(x, d), ts_backfill(x,lookback = d, k=1, ignore="NAN"), ts_corr(x, y, d), ts_count_nans(x ,d), ts_covariance(y, x, d), ts_decay_linear(x, d, dense = false), ts_delay(x, d), ts_delta(x, d), ts_mean(x, d), ts_product(x, d), "ts_quantile(x,d, driver=""gaussian"" )", ts_rank(x, d, constant = 0), ts_regression(y, x, d, lag = 0, rettype = 0), ts_scale(x, d, constant = 0), ts_std_dev(x, d), ts_step(1), ts_sum(x, d), ts_zscore(x, d)
-横截面操作: normalize(x, useStd = false, limit = 0.0), quantile(x, driver = gaussian, sigma = 1.0), rank(x, rate=2), scale(x, scale=1, longscale=1, shortscale=1), winsorize(x, std=4), zscore(x)
-向量操作符：vec_avg(x), vec_sum(x)
-转换操作符: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10"), trade_when(x, y, z)
-聚合操作符: group_backfill(x, group, d, std = 4.0), group_mean(x, weight, group), group_neutralize(x, group), group_rank(x, group), group_scale(x, group), group_zscore(x, group), subtract(x, y, filter=false), multiply(x ,y, ... , filter=false), divide(x, y), add(x, y, filter = false)
+
+=== 关键语法规则（必须遵守） ===
+1. 数据字段规范：
+   - 可使用字段：close, volume, returns
+   - ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
+   - ✅ 正确：使用volume作为规模代理，close作为价格
+   - returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
+
+2. ts_regression使用规范：
+   - 避免深度嵌套ts_regression，特别是作为其他函数的参数
+   - ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
+   - ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
+   - 替代方案：用ts_delta组合计算动量变化
+
+3. if_else使用规范：
+   - 条件必须是简单布尔表达式
+   - 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
+   - 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
+
+4. bucket函数使用规范：
+   - bucket()返回分组ID，可用于条件判断
+   - ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
+   - ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
+   - 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
+=== 关键语法规则结束 ===
+
 *=====*
 注意事项:
-避免过度复杂的嵌套
-使用经济直觉验证逻辑合理性
-考虑实际交易可行性
-包含风险控制元素（如波动率调整）
+1. 避免过度复杂的嵌套（建议不超过3层）
+2. 每个表达式应有明确的经济逻辑
+3. 考虑实际交易可行性（避免未来函数）
+4. 包含风险控制元素（如波动率调整）
+5. 只能使用可用的数据字段：close, volume, returns等
 *=====*
+
 参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
-行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现“行业”逻辑。
+行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现"行业"逻辑。
+
+构建框架指导（请按此逻辑创造新因子）：
+
+维度融合模板（选择至少2个）：
+
+A. 领导力动量 = 时序动量 × 横截面调整
+   逻辑：大成交量股票的动量更强
+   结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
+
+B. 状态自适应动量 = 条件选择动量
+   逻辑：高波动用短期动量，低波动用长期动量
+   结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
+
+C. 行业传导因子 = 领先行业动量 × 相关性强度
+   逻辑：与强势行业相关性高的行业未来表现好
+   结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
+
+D. 情绪反转 = 过度交易信号 × 基础趋势
+   逻辑：过度交易时反转，趋势延续时跟随
+   结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
+
+关键组件库（可自由组合）：
+1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
+2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
+3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
+4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
+5. 相关性类：ts_corr({x},{y},{d})
+6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
+
+参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
+
+*=====*
+
 输出格式：
 输出必须是且仅是纯文本。
 每一行是一个完整、独立、语法正确的WebSim表达式。
 严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
 
-示例思维（仅供理解，不输出）
-一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为(此为示例, 读取操作符的使用说明, 并结合上述的维度方案, 组合并创新因子)：
-multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
-这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
+===================== !!! 重点(输出方式) !!! =====================
 现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
-**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+
 表达式
+
 表达式
+
 表达式
+
 ...
+
 表达式
-请提供具体的WQ表达式。
+=================================================================
+
 重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
-以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
 
-以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子：

prompt_set_ref/alpha_prompt002.txt

@@ -0,0 +1,122 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成 10 个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+
+维度1：时间序列动量（TM）
+目标：识别价格趋势的强度、速度和持续性
+可用的具体构建方法：
+1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
+2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
+3. 动量加速度：ts_delta(ts_regression(close, ts_step(1), d1, 0, 1), d2)
+4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
+5. 动量衰减：ts_decay_linear(returns, d)
+6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
+建议组合：使用不同d参数创建短期/中期/长期动量
+
+维度2：横截面领导力（CL）
+目标：识别行业内的龙头股和相对强度
+具体构建方法：
+1. 龙头股筛选：bucket(rank(market_cap), range="0,1,0.2") [第一组为龙头]
+2. 龙头vs平均：group_mean(x, 1, bucket(rank(cap),range="0,1,0.2")) - group_mean(x, 1, 所有股票)
+3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
+4. 相对排名稳定性：ts_mean(rank(returns), d)
+
+维度3：市场状态适应性（MS）
+目标：根据波动率、趋势状态调整参数
+具体构建方法：
+1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
+2. 状态条件选择：if_else(ts_mean(returns,60)>0, 短期动量, 长期动量)
+3. 参数动态化：if_else(ts_std_dev(returns,20)>X, 5, 20) [作为d参数]
+4. 趋势状态识别：ts_mean(ts_delta(close,5), 20) > 0
+
+基本结构：
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+*=====*
+注意事项:
+避免过度复杂的嵌套
+使用经济直觉验证逻辑合理性
+考虑实际交易可行性
+包含风险控制元素（如波动率调整）
+*=====*
+参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
+行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现“行业”逻辑。
+输出格式：
+输出必须是且仅是纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
+
+*=====*
+构建框架指导（请按此逻辑创造新因子）：
+
+维度融合模板（选择至少2个）：
+
+A. 领导力动量 = 时序动量 × 横截面调整
+   逻辑：强势股的动量更强
+   结构：group_mean(ts_momentum(close, d1), 1, bucket(rank(cap), range="0,3,0.4"))
+
+B. 状态自适应动量 = 条件选择动量
+   逻辑：高波动用短期动量，低波动用长期动量
+   结构：if_else(ts_std_dev(ret,20)>X, ts_delta(close,5), ts_delta(close,20))
+
+C. 行业传导因子 = 领先行业动量 × 相关性强度
+   逻辑：与强势行业相关性高的行业未来表现好
+   结构：ts_corr(group_mean(ret,1,ind_i), group_mean(ret,1,ind_j), d) × ts_delta(close,d2)
+
+D. 情绪反转 = 过度交易信号 × 基础趋势
+   逻辑：过度交易时反转，趋势延续时跟随
+   结构：reverse(ts_rank(volume/ts_mean(volume,20), 10)) × ts_regression(close,ts_step(1),20,0,1)
+
+关键组件库（可自由组合）：
+1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
+2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
+3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
+4. 横截面类：bucket(rank({metric}), n), rank({x, y, z, ...})
+5. 相关性类：ts_corr({x},{y},{d})
+6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
+
+参数池：d ∈ [5,10,20,30,60,120], n ∈ [3,5,10]
+*=====*
+
+===================== !!! 重点(输出方式) !!! =====================
+现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+
+表达式
+
+表达式
+
+...
+
+表达式
+=================================================================
+
+=== 关键语法规则（必须遵守） ===
+1. if_else使用规范：
+   - 条件必须是布尔值：如 ts_mean(x,20) > 0
+   - 不能嵌套复杂条件：避免 if_else(A > B, X, Y) 其中A和B都是序列
+   - 建议：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
+
+2. bucket函数使用规范：
+   - bucket()返回分组ID，不能与数字直接比较
+   - 正确用法：作为group_mean的group参数
+   - 错误用法：bucket(rank(cap), [0,0.7,1.0]) == 1 ❌
+   - 替代方案：用条件筛选代替
+     正确：if_else(rank(cap) > 0.7, 龙头因子, 非龙头因子)
+
+3. 操作符输入数量：
+   - multiply, add等需要至少2个输入，但每个输入必须是单个表达式
+   - 避免：multiply(A, if_else(B, C, D)) 可能导致参数数量解析错误
+   - 建议：先计算中间变量或使用更简单结构
+
+4. 类型兼容性：
+   - 比较操作符（==, >, <）两边必须是相同类型
+   - 分组类型不能与数值类型直接运算
+=== 关键语法规则结束 ===
+
+重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
+操作符使用策略：
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。