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因子学习-在研究中进行因子分析

特朗普对头发表于：5 月 10 日 14：52回复(1)

IC 代表了预测值和实现值之间的相关性， 通常用以评价预测能力。 取值在-1到1之间， 绝对值越大， 表示预测能力越好。
请问：IC一般为多少就可以说因子好？大家一般算的IC是多少？

# 第一分位数的因子值最小， 第五分位数的因子值最大。
# IC 代表了预测值和实现值之间的相关性， 通常用以评价预测能力。 取值在-1到1之间， 绝对值越大， 表示预测能力越好。

#导入需要的数据库
import pandas as pd
import numpy as np
import jqfactor

#获取因子值，人气指标 AR
factor_data = jqfactor.get_factor_values(securities=get_index_stocks('000300.XSHG'), factors=['AR'],
                  start_date='2018-01-01', end_date='2018-06-01')['AR']
factor_data.tail()
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
000001.XSHE 000002.XSHE 000063.XSHE 000069.XSHE 000100.XSHE 000157.XSHE 000166.XSHE 000333.XSHE 000338.XSHE 000402.XSHE ... 603156.XSHG 603160.XSHG 603259.XSHG 603260.XSHG 603288.XSHG 603799.XSHG 603833.XSHG 603858.XSHG 603986.XSHG 603993.XSHG
2018-05-28 58.268607 58.065768 NaN 97.532674 79.116466 101.818182 94.736842 94.866644 88.156883 90.923521 ... 123.916599 144.339344 NaN 154.095176 168.508966 118.879634 152.718918 178.920118 103.665834 106.386861
2018-05-29 53.583907 54.172044 NaN 91.765782 76.954733 96.491228 87.857143 87.864186 89.953870 100.015411 ... 133.697895 151.917033 NaN 154.437680 186.666667 115.240252 133.765621 146.149218 96.871487 103.184713
2018-05-30 52.793372 50.331210 NaN 101.853858 81.896552 93.244653 87.412587 89.810888 96.141392 103.015873 ... 161.622857 114.779602 35.845472 173.471539 184.560976 103.286312 141.276698 143.680188 88.635935 85.416667
2018-05-31 46.630521 53.546811 NaN 104.229064 86.206897 100.051308 99.253731 99.762018 112.195820 109.870044 ... 172.804469 116.610169 57.595993 176.256192 187.121951 98.341953 141.716329 159.222702 92.438549 88.254756
2018-06-01 42.239868 52.077854 NaN 95.878871 77.872340 96.872710 99.275362 91.388279 106.007368 105.135266 ... 154.842690 102.605339 88.228393 145.009074 181.777933 93.620245 116.351805 144.857143 87.643634 81.899351

5 rows × 300 columns

#使用获取的因子值进行单因子分析
far = jqfactor.analyze_factor(factor=factor_data, start_date='2018-01-01', end_date='2018-06-01', 
                              weight_method='mktcap', industry='jq_l1', 
                              quantiles=5, periods=(1,5,22),max_loss=0.2)

# 去除 nan/inf，整理后的因子值、forward_return 和分位数
far.clean_factor_data.head()
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
period_1 period_5 period_22 factor group factor_quantile weight
date asset
2018-01-02 000001.XSHE -0.027010 -0.045253 0.024090 96.729027 金融指数 2 0.000067
000002.XSHE -0.007064 0.100737 0.151721 155.987377 房地产指数 5 0.000100
000063.XSHE 0.026865 0.017350 -0.216056 94.053043 信息技术指数 2 0.000040
000069.XSHE 0.005627 0.067407 0.131450 131.010592 可选消费指数 4 0.000021
000100.XSHE 0.020997 0.030621 -0.130359 93.563218 可选消费指数 2 0.000015 
# 信息比率
far.ic.tail()
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
period_1 period_5 period_22
date
2018-05-28 -0.161237 -0.032813 0.085490
2018-05-29 0.207990 0.161555 0.207288
2018-05-30 0.328195 0.147335 0.214697
2018-05-31 -0.422589 -0.167478 0.039210
2018-06-01 0.003620 0.144754 0.114503 
# 分行业信息比率
far.ic_by_group.tail()
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
period_1 period_5 period_22
group
日常消费指数 -0.022225 -0.012118 -0.026843
材料指数 -0.022160 -0.033475 -0.102178
电信服务指数 NaN NaN NaN
能源指数 -0.061081 -0.125773 -0.211658
金融指数 0.002846 -0.049626 -0.079116 
# 换手率
far.quantile_turnover

{'period_1':                    1         2         3         4         5
 date                                                        
 2018-01-02       NaN       NaN       NaN       NaN       NaN
 2018-01-03  0.172414  0.344828  0.368421  0.344828  0.172414
 2018-01-04  0.120690  0.293103  0.385965  0.344828  0.137931
 2018-01-05  0.155172  0.310345  0.456140  0.448276  0.172414
 2018-01-08  0.241379  0.482759  0.517241  0.362069  0.120690
 2018-01-09  0.172414  0.379310  0.344828  0.293103  0.155172
 2018-01-10  0.206897  0.448276  0.473684  0.396552  0.137931
 2018-01-11  0.155172  0.448276  0.508772  0.396552  0.189655
 2018-01-12  0.137931  0.293103  0.350877  0.258621  0.103448
 2018-01-15  0.172414  0.362069  0.491228  0.482759  0.224138
 2018-01-16  0.086207  0.258621  0.333333  0.224138  0.068966
 2018-01-17  0.137931  0.379310  0.526316  0.482759  0.224138
 2018-01-18  0.086207  0.275862  0.350877  0.293103  0.120690
 2018-01-19  0.086207  0.241379  0.333333  0.241379  0.086207
 2018-01-22  0.137931  0.280702  0.310345  0.210526  0.086207
 2018-01-23  0.086207  0.210526  0.206897  0.140351  0.051724
 2018-01-24  0.137931  0.310345  0.280702  0.258621  0.120690
 2018-01-25  0.086207  0.275862  0.333333  0.241379  0.103448
 2018-01-26  0.103448  0.241379  0.245614  0.155172  0.051724
 2018-01-29  0.120690  0.293103  0.280702  0.206897  0.103448
 2018-01-30  0.155172  0.241379  0.175439  0.137931  0.051724
 2018-01-31  0.086207  0.258621  0.362069  0.258621  0.086207
 2018-02-01  0.120690  0.362069  0.362069  0.275862  0.155172
 2018-02-02  0.137931  0.310345  0.293103  0.241379  0.120690
 2018-02-05  0.155172  0.275862  0.275862  0.310345  0.155172
 2018-02-06  0.120690  0.275862  0.275862  0.224138  0.086207
 2018-02-07  0.118644  0.258621  0.241379  0.241379  0.155172
 2018-02-08  0.120690  0.258621  0.327586  0.344828  0.206897
 2018-02-09  0.086207  0.293103  0.396552  0.362069  0.189655
 2018-02-12  0.086207  0.310345  0.396552  0.275862  0.086207
 ...              ...       ...       ...       ...       ...
 2018-04-19  0.135593  0.275862  0.344828  0.310345  0.103448
 2018-04-20  0.152542  0.310345  0.310345  0.258621  0.103448
 2018-04-23  0.101695  0.310345  0.362069  0.258621  0.137931
 2018-04-24  0.118644  0.258621  0.258621  0.224138  0.120690
 2018-04-25  0.172414  0.344828  0.293103  0.275862  0.137931
 2018-04-26  0.118644  0.275862  0.327586  0.293103  0.137931
 2018-04-27  0.118644  0.293103  0.344828  0.293103  0.120690
 2018-05-02  0.152542  0.310345  0.379310  0.362069  0.152542
 2018-05-03  0.084746  0.206897  0.310345  0.293103  0.101695
 2018-05-04  0.166667  0.315789  0.379310  0.379310  0.169492
 2018-05-07  0.203390  0.465517  0.431034  0.310345  0.118644
 2018-05-08  0.152542  0.327586  0.379310  0.310345  0.118644
 2018-05-09  0.220339  0.396552  0.327586  0.310345  0.118644
 2018-05-10  0.118644  0.275862  0.413793  0.431034  0.169492
 2018-05-11  0.152542  0.362069  0.396552  0.327586  0.135593
 2018-05-14  0.152542  0.344828  0.413793  0.396552  0.169492
 2018-05-15  0.152542  0.327586  0.379310  0.396552  0.186441
 2018-05-16  0.152542  0.396552  0.413793  0.362069  0.186441
 2018-05-17  0.084746  0.241379  0.310345  0.327586  0.169492
 2018-05-18  0.203390  0.379310  0.379310  0.396552  0.203390
 2018-05-21  0.186441  0.310345  0.372881  0.396552  0.152542
 2018-05-22  0.101695  0.258621  0.271186  0.327586  0.186441
 2018-05-23  0.101695  0.275862  0.372881  0.396552  0.169492
 2018-05-24  0.101695  0.275862  0.362069  0.344828  0.152542
 2018-05-25  0.135593  0.293103  0.396552  0.413793  0.186441
 2018-05-28  0.186441  0.413793  0.344828  0.379310  0.220339
 2018-05-29  0.186441  0.396552  0.379310  0.362069  0.186441
 2018-05-30  0.118644  0.258621  0.305085  0.275862  0.118644
 2018-05-31  0.169492  0.379310  0.372881  0.206897  0.084746
 2018-06-01  0.203390  0.275862  0.271186  0.310345  0.135593
 
 [100 rows x 5 columns],
 'period_22':                    1         2         3         4         5
 date                                                        
 2018-01-02       NaN       NaN       NaN       NaN       NaN
 2018-01-03       NaN       NaN       NaN       NaN       NaN
 2018-01-04       NaN       NaN       NaN       NaN       NaN
 2018-01-05       NaN       NaN       NaN       NaN       NaN
 2018-01-08       NaN       NaN       NaN       NaN       NaN
 2018-01-09       NaN       NaN       NaN       NaN       NaN
 2018-01-10       NaN       NaN       NaN       NaN       NaN
 2018-01-11       NaN       NaN       NaN       NaN       NaN
 2018-01-12       NaN       NaN       NaN       NaN       NaN
 2018-01-15       NaN       NaN       NaN       NaN       NaN
 2018-01-16       NaN       NaN       NaN       NaN       NaN
 2018-01-17       NaN       NaN       NaN       NaN       NaN
 2018-01-18       NaN       NaN       NaN       NaN       NaN
 2018-01-19       NaN       NaN       NaN       NaN       NaN
 2018-01-22       NaN       NaN       NaN       NaN       NaN
 2018-01-23       NaN       NaN       NaN       NaN       NaN
 2018-01-24       NaN       NaN       NaN       NaN       NaN
 2018-01-25       NaN       NaN       NaN       NaN       NaN
 2018-01-26       NaN       NaN       NaN       NaN       NaN
 2018-01-29       NaN       NaN       NaN       NaN       NaN
 2018-01-30       NaN       NaN       NaN       NaN       NaN
 2018-01-31       NaN       NaN       NaN       NaN       NaN
 2018-02-01  0.810345  0.810345  0.793103  0.810345  0.758621
 2018-02-02  0.844828  0.758621  0.810345  0.827586  0.741379
 2018-02-05  0.827586  0.741379  0.844828  0.810345  0.706897
 2018-02-06  0.793103  0.810345  0.862069  0.879310  0.724138
 2018-02-07  0.711864  0.741379  0.810345  0.844828  0.775862
 2018-02-08  0.706897  0.827586  0.896552  0.879310  0.775862
 2018-02-09  0.672414  0.724138  0.827586  0.913793  0.741379
 2018-02-12  0.741379  0.810345  0.844828  0.844828  0.706897
 ...              ...       ...       ...       ...       ...
 2018-04-19  0.711864  0.758621  0.879310  0.775862  0.637931
 2018-04-20  0.677966  0.775862  0.827586  0.741379  0.637931
 2018-04-23  0.745763  0.758621  0.810345  0.672414  0.603448
 2018-04-24  0.728814  0.724138  0.896552  0.741379  0.620690
 2018-04-25  0.655172  0.689655  0.844828  0.689655  0.620690
 2018-04-26  0.711864  0.741379  0.827586  0.706897  0.603448
 2018-04-27  0.694915  0.706897  0.724138  0.793103  0.620690
 2018-05-02  0.644068  0.706897  0.724138  0.741379  0.559322
 2018-05-03  0.711864  0.724138  0.844828  0.827586  0.593220
 2018-05-04  0.766667  0.684211  0.896552  0.827586  0.661017
 2018-05-07  0.762712  0.655172  0.862069  0.844828  0.677966
 2018-05-08  0.847458  0.758621  0.879310  0.844828  0.661017
 2018-05-09  0.813559  0.724138  0.879310  0.741379  0.627119
 2018-05-10  0.779661  0.741379  0.913793  0.793103  0.610169
 2018-05-11  0.762712  0.758621  0.862069  0.793103  0.661017
 2018-05-14  0.728814  0.758621  0.862069  0.741379  0.694915
 2018-05-15  0.694915  0.741379  0.758621  0.775862  0.661017
 2018-05-16  0.728814  0.758621  0.810345  0.758621  0.661017
 2018-05-17  0.728814  0.775862  0.758621  0.741379  0.694915
 2018-05-18  0.694915  0.758621  0.758621  0.810345  0.694915
 2018-05-21  0.711864  0.793103  0.813559  0.793103  0.711864
 2018-05-22  0.677966  0.775862  0.830508  0.810345  0.762712
 2018-05-23  0.728814  0.793103  0.779661  0.844828  0.779661
 2018-05-24  0.711864  0.862069  0.810345  0.827586  0.745763
 2018-05-25  0.711864  0.758621  0.741379  0.775862  0.779661
 2018-05-28  0.830508  0.810345  0.775862  0.793103  0.813559
 2018-05-29  0.762712  0.810345  0.758621  0.827586  0.847458
 2018-05-30  0.762712  0.724138  0.813559  0.810345  0.830508
 2018-05-31  0.830508  0.844828  0.881356  0.810345  0.762712
 2018-06-01  0.694915  0.827586  0.813559  0.844828  0.728814
 
 [100 rows x 5 columns],
 'period_5':                    1         2         3         4         5
 date                                                        
 2018-01-02       NaN       NaN       NaN       NaN       NaN
 2018-01-03       NaN       NaN       NaN       NaN       NaN
 2018-01-04       NaN       NaN       NaN       NaN       NaN
 2018-01-05       NaN       NaN       NaN       NaN       NaN
 2018-01-08       NaN       NaN       NaN       NaN       NaN
 2018-01-09  0.310345  0.551724  0.586207  0.517241  0.293103
 2018-01-10  0.379310  0.689655  0.649123  0.568966  0.362069
 2018-01-11  0.293103  0.517241  0.526316  0.568966  0.327586
 2018-01-12  0.310345  0.568966  0.578947  0.586207  0.293103
 2018-01-15  0.310345  0.603448  0.614035  0.586207  0.362069
 2018-01-16  0.327586  0.620690  0.631579  0.603448  0.327586
 2018-01-17  0.327586  0.568966  0.631579  0.551724  0.379310
 2018-01-18  0.500000  0.655172  0.666667  0.689655  0.413793
 2018-01-19  0.465517  0.655172  0.754386  0.724138  0.431034
 2018-01-22  0.431034  0.684211  0.724138  0.701754  0.379310
 2018-01-23  0.327586  0.578947  0.775862  0.684211  0.362069
 2018-01-24  0.258621  0.517241  0.736842  0.586207  0.275862
 2018-01-25  0.258621  0.603448  0.649123  0.482759  0.258621
 2018-01-26  0.258621  0.534483  0.578947  0.465517  0.224138
 2018-01-29  0.327586  0.551724  0.561404  0.482759  0.206897
 2018-01-30  0.275862  0.620690  0.596491  0.551724  0.224138
 2018-01-31  0.293103  0.568966  0.603448  0.431034  0.172414
 2018-02-01  0.224138  0.586207  0.637931  0.431034  0.189655
 2018-02-02  0.275862  0.568966  0.620690  0.500000  0.275862
 2018-02-05  0.327586  0.500000  0.637931  0.551724  0.275862
 2018-02-06  0.293103  0.534483  0.689655  0.568966  0.327586
 2018-02-07  0.271186  0.551724  0.620690  0.586207  0.379310
 2018-02-08  0.310345  0.448276  0.568966  0.586207  0.293103
 2018-02-09  0.258621  0.465517  0.517241  0.517241  0.275862
 2018-02-12  0.275862  0.586207  0.603448  0.620690  0.362069
 ...              ...       ...       ...       ...       ...
 2018-04-19  0.322034  0.431034  0.517241  0.465517  0.224138
 2018-04-20  0.305085  0.500000  0.500000  0.482759  0.241379
 2018-04-23  0.372881  0.689655  0.637931  0.534483  0.258621
 2018-04-24  0.322034  0.568966  0.586207  0.517241  0.224138
 2018-04-25  0.224138  0.517241  0.551724  0.517241  0.258621
 2018-04-26  0.237288  0.586207  0.620690  0.603448  0.310345
 2018-04-27  0.254237  0.551724  0.586207  0.534483  0.293103
 2018-05-02  0.220339  0.431034  0.500000  0.551724  0.271186
 2018-05-03  0.288136  0.465517  0.534483  0.534483  0.288136
 2018-05-04  0.350000  0.561404  0.620690  0.500000  0.305085
 2018-05-07  0.355932  0.637931  0.689655  0.551724  0.355932
 2018-05-08  0.372881  0.655172  0.758621  0.603448  0.305085
 2018-05-09  0.406780  0.637931  0.706897  0.689655  0.338983
 2018-05-10  0.355932  0.637931  0.672414  0.637931  0.338983
 2018-05-11  0.338983  0.655172  0.706897  0.672414  0.389831
 2018-05-14  0.355932  0.655172  0.741379  0.603448  0.355932
 2018-05-15  0.338983  0.603448  0.724138  0.586207  0.355932
 2018-05-16  0.355932  0.586207  0.603448  0.586207  0.406780
 2018-05-17  0.338983  0.603448  0.689655  0.586207  0.305085
 2018-05-18  0.338983  0.672414  0.689655  0.689655  0.305085
 2018-05-21  0.406780  0.655172  0.728814  0.724138  0.338983
 2018-05-22  0.322034  0.568966  0.694915  0.603448  0.305085
 2018-05-23  0.254237  0.482759  0.644068  0.655172  0.355932
 2018-05-24  0.305085  0.603448  0.620690  0.568966  0.322034
 2018-05-25  0.322034  0.603448  0.551724  0.603448  0.355932
 2018-05-28  0.372881  0.655172  0.620690  0.655172  0.457627
 2018-05-29  0.355932  0.672414  0.655172  0.672414  0.389831
 2018-05-30  0.338983  0.603448  0.661017  0.620690  0.423729
 2018-05-31  0.440678  0.620690  0.627119  0.620690  0.389831
 2018-06-01  0.406780  0.620690  0.525424  0.586207  0.355932
 
 [100 rows x 5 columns]}
# 计算按分位数分组加权因子收益和标准差
mean, std = far.calc_mean_return_by_quantile(by_date=False, by_group=False, demeaned=False, group_adjust=False)

# 按分位数分组加权因子收益和标准差
std
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
period_1 period_5 period_22
factor_quantile
1 0.000242 0.000527 0.000977
2 0.000249 0.000566 0.001003
3 0.000269 0.000610 0.001142
4 0.000293 0.000687 0.001322
5 0.000352 0.000845 0.001231 
# 计算因子的 alpha 和 beta
far.calc_factor_alpha_beta(demeaned=True, group_adjust=False)
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
period_1 period_5 period_22
Ann. alpha -0.063672 0.000702 -0.151840
beta 0.118797 0.105225 -0.218097 
# 计算每日因子信息比率（IC值）
far.calc_factor_information_coefficient(group_adjust=False, by_group=False, method='rank').tail()
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period_1 period_5 period_22
date
2018-05-28 -0.161237 -0.032813 0.085490
2018-05-29 0.207990 0.161555 0.207288
2018-05-30 0.328195 0.147335 0.214697
2018-05-31 -0.422589 -0.167478 0.039210
2018-06-01 0.003620 0.144754 0.114503 
# 打印因子收益表
far.plot_returns_table(demeaned=False, group_adjust=False)

收益分析
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period_1 period_5 period_22
Ann. alpha 0.015 0.051 -0.036
beta 1.075 1.096 0.922
Mean Period Wise Return Top Quantile (bps) -4.312 -10.159 -26.112
Mean Period Wise Return Bottom Quantile (bps) -9.562 -6.599 -8.540
Mean Period Wise Spread (bps) 1.466 -3.194 -10.786 
# 打印换手率表
far.plot_turnover_table()

换手率分析
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period_1 period_22 period_5
Quantile 1 Mean Turnover 0.140 0.746 0.332
Quantile 2 Mean Turnover 0.319 0.784 0.596
Quantile 3 Mean Turnover 0.354 0.814 0.637
Quantile 4 Mean Turnover 0.313 0.795 0.594
Quantile 5 Mean Turnover 0.139 0.730 0.324
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period_1 period_5 period_22
Mean Factor Rank Autocorrelation 0.965 0.823 0.15 
# 打印信息比率（IC）相关表
far.plot_information_table(group_adjust=False, method='rank')

IC 分析
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period_1 period_5 period_22
IC Mean -0.009 -0.013 -0.084
IC Std. 0.211 0.190 0.201
Risk-Adjusted IC -0.044 -0.069 -0.416
t-stat(IC) -0.440 -0.693 -4.156
p-value(IC) 0.661 0.490 0.000
IC Skew 0.020 -0.704 -1.058
IC Kurtosis -0.127 0.654 0.367 
# 打印个分位数统计表
far.plot_quantile_statistics_table()

分位数统计
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min max mean std count count %
factor_quantile
1 0.000000 97.107614 69.203011 10.838712 5841 20.119179
2 67.355082 119.153058 90.173125 9.049117 5794 19.957289
3 85.153872 144.742528 107.740956 12.184891 5779 19.905621
4 100.366300 195.807050 129.107820 17.908045 5795 19.960733
5 116.948403 383950.000000 250.004126 5053.798296 5823 20.057178 
# 画信息比率(IC)时间序列图
far.plot_ic_ts(group_adjust=False, method='rank')

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# 画信息比率分布直方图
far.plot_ic_hist(group_adjust=False, method='rank')

/opt/conda/lib/python3.5/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
/opt/conda/lib/python3.5/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "

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# 画信息比率 qq 图

from scipy.stats import norm

far.plot_ic_qq(group_adjust=False, method='rank', theoretical_dist=norm)

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# 画信息比率 qq 图
from scipy.stats import t

far.plot_ic_qq(group_adjust=False, method='normal', theoretical_dist=t)

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# 画各分位数平均收益图
far.plot_quantile_returns_bar(by_group=False, demeaned=False, group_adjust=False)

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# 画最高分位减最低分位收益图
far.plot_mean_quantile_returns_spread_time_series(demeaned=False, group_adjust=False, bandwidth=1)

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# 画按行业分组信息比率(IC)图
far.plot_ic_by_group(group_adjust=False, method='rank')

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# 画因子自相关图
far.plot_factor_auto_correlation(rank=True)

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# 画最高最低分位换手率图
far.plot_top_bottom_quantile_turnover(periods=(1, 3, 9))

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# 画月度信息比率(IC)图
far.plot_monthly_ic_heatmap(group_adjust=False)

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# 画按因子值加权多空组合每日累积收益图
far.plot_cumulative_returns(period=1, demeaned=False, group_adjust=False)

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# 画做多最高分位做空最低分位多空组合每日累计收益图
far.plot_cumulative_returns_by_quantile(period=(1, 3, 9), demeaned=False, group_adjust=False)

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# 因子预测能力平均累计收益图
far.plot_quantile_average_cumulative_return(by_quantile=False, std_bar=False)

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