复制链接
克隆策略

    {"description":"实验创建于2021/10/29","graph":{"edges":[{"to_node_id":"-192:features","from_node_id":"-152:data"}],"nodes":[{"node_id":"-152","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"f1 = close_0 / close_5","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-152"}],"output_ports":[{"name":"data","node_id":"-152"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-192","module_id":"BigQuantSpace.factorlens.factorlens-v2","parameters":[{"name":"title","value":"因子分析: {factor_name}","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2019-12-31","type":"Literal","bound_global_parameter":null},{"name":"rebalance_period","value":22,"type":"Literal","bound_global_parameter":null},{"name":"delay_rebalance_days","value":0,"type":"Literal","bound_global_parameter":null},{"name":"rebalance_price","value":"close_0","type":"Literal","bound_global_parameter":null},{"name":"stock_pool","value":"全市场","type":"Literal","bound_global_parameter":null},{"name":"quantile_count","value":5,"type":"Literal","bound_global_parameter":null},{"name":"commission_rate","value":0.0016,"type":"Literal","bound_global_parameter":null},{"name":"returns_calculation_method","value":"累乘","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"无","type":"Literal","bound_global_parameter":null},{"name":"drop_new_stocks","value":60,"type":"Literal","bound_global_parameter":null},{"name":"drop_price_limit_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_st_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_suspended_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"normalization","value":"True","type":"Literal","bound_global_parameter":null},{"name":"neutralization","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22displayValue%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22displayValue%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22displayValue%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"factor_coverage","value":0.5,"type":"Literal","bound_global_parameter":null},{"name":"user_data_merge","value":"left","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features","node_id":"-192"},{"name":"user_factor_data","node_id":"-192"}],"output_ports":[{"name":"data","node_id":"-192"},{"name":"save_data","node_id":"-192"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-152' Position='349,298,200,200'/><node_position Node='-192' Position='348,398,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [2]:
    # 本代码由可视化策略环境自动生成 2021年12月20日 10:39
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    m2 = M.input_features.v1(
        features='f1 = close_0 / close_5'
    )
    
    m3 = M.factorlens.v2(
        features=m2.data,
        title='因子分析: {factor_name}',
        start_date='2019-01-01',
        end_date='2019-12-31',
        rebalance_period=22,
        delay_rebalance_days=0,
        rebalance_price='close_0',
        stock_pool='全市场',
        quantile_count=5,
        commission_rate=0.0016,
        returns_calculation_method='累乘',
        benchmark='无',
        drop_new_stocks=60,
        drop_price_limit_stocks=True,
        drop_st_stocks=True,
        drop_suspended_stocks=True,
        normalization=True,
        neutralization=['行业', '市值'],
        metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
        factor_coverage=0.5,
        user_data_merge='left'
    )
    

    因子分析: f1

    { "type": "factor-track", "data": { "exprs": ["close_0 / close_5"], "options": {"BacktestInterval": ["2019-01-01", "2019-12-31"], "Benchmark": "none", "StockPool": "all", "UserDataMerge": "left", "DropSTStocks": 1, "DropPriceLimitStocks": 1, "DropNewStocks": 60, "DropSuspendedStocks": 1, "QuantileCount": 5, "CommissionRates": 0.0016, "Normalization": 1, "Neutralization": "industry,size", "DelayRebalanceDays": 0, "RebalancePeriod": 22, "RebalancePeriodsReturns": 0, "RebalancePrice": "close_0", "ReturnsCalculationMethod": "cumprod", "FactorCoverage": 0.5, "_HASH": "ffb2f8fbecb7cab66758ee29aeedc167"} } }

    因子表现概览

      累计收益 近1年收益 近3月收益 近1月收益 近1周收益 昨日收益 最大回撤 盈亏比 胜率 夏普比率 收益波动率
    最小分位 9.83% 9.83% -6.70% 5.65% 1.59% 0.24% 26.82% 0.78 0.58 0.38 23.45%
    最大分位 -14.77% -14.77% -6.57% 4.99% 2.02% 0.32% 34.10% 0.72 0.56 -0.77 22.72%
    多空组合 13.56% 13.56% -0.06% 0.32% -0.21% -0.04% 1.12% 1.34 0.64 3.31 2.94%

    基本特征分析

    IC分析

    -0.10

    0.10

    -0.95

    100.00%

    买入信号重合分析

    因子估值分析

    因子拥挤度分析

    因子值最小的20只股票 (2019-12-30)

    股票名称 股票代码 因子值
    正川股份 603976.SHA 0.8378
    苏奥传感 300507.SZA 0.8429
    共达电声 002655.SZA 0.8588
    泉峰汽车 603982.SHA 0.8608
    香雪制药 300147.SZA 0.8656
    飞亚达A 000026.SZA 0.8692
    金一文化 002721.SZA 0.8708
    大胜达 603687.SHA 0.8729
    新华联 000620.SZA 0.8780
    钢研纳克 300797.SZA 0.8835
    田中精机 300461.SZA 0.8869
    金宇车城 000803.SZA 0.8914
    佳禾智能 300793.SZA 0.8982
    宁波富邦 600768.SHA 0.9011
    南华仪器 300417.SZA 0.9066
    中牧股份 600195.SHA 0.9076
    光启技术 002625.SZA 0.9078
    新劲刚 300629.SZA 0.9108
    熊猫金控 600599.SHA 0.9121
    弘讯科技 603015.SHA 0.9124

    因子值最大的20只股票 (2019-12-30)

    股票名称 股票代码 因子值
    天齐锂业 002466.SZA 1.2177
    当升科技 300073.SZA 1.2220
    西部矿业 601168.SHA 1.2252
    鼎胜新材 603876.SHA 1.2258
    世运电路 603920.SHA 1.2346
    中微公司 688012.SHA 1.2374
    海能实业 300787.SZA 1.2438
    惠发食品 603536.SHA 1.2464
    朗博科技 603655.SHA 1.2486
    嘉元科技 688388.SHA 1.2522
    中广天择 603721.SHA 1.2560
    拓维信息 002261.SZA 1.2644
    中昌数据 600242.SHA 1.2687
    华扬联众 603825.SHA 1.2906
    融捷股份 002192.SZA 1.3010
    值得买 300785.SZA 1.3412
    中国宝安 000009.SZA 1.3455
    星期六 002291.SZA 1.3464
    容大感光 300576.SZA 1.3601
    一汽夏利 000927.SZA 1.4422
    In [9]:
    m3.data.read()
    
    Out[9]:
    {'data': {'factors': [{'meta': {'name': 'f1', 'expr': 'close_0 / close_5'},
        'results': [{'options': {'BacktestInterval': ('2019-01-01', '2019-12-31'),
           'Benchmark': 'none',
           'StockPool': 'all',
           'UserDataMerge': 'left',
           'DropSTStocks': 1,
           'DropPriceLimitStocks': 1,
           'DropNewStocks': 60,
           'DropSuspendedStocks': 1,
           'QuantileCount': 5,
           'CommissionRates': 0.0016,
           'Normalization': 1,
           'Neutralization': 'industry,size',
           'DelayRebalanceDays': 0,
           'RebalancePeriod': 22,
           'RebalancePeriodsReturns': 0,
           'RebalancePrice': 'close_0',
           'ReturnsCalculationMethod': 'cumprod',
           'FactorCoverage': 0.5,
           '_HASH': 'ffb2f8fbecb7cab66758ee29aeedc167'},
          'data': {'QuantileReturns':                  l_0       l_1       l_2       l_3       l_4  l_top_bottom  \
           date                                                                         
           2019-01-02  1.000000  1.000000  1.000000  1.000000  1.000000      1.000000   
           2019-01-03  1.000000  1.000000  1.000000  1.000000  1.000000      1.000000   
           2019-01-04  1.000000  1.000000  1.000000  1.000000  1.000000      1.000000   
           2019-01-07  1.000000  1.000000  1.000000  1.000000  1.000000      1.000000   
           2019-01-08  1.000000  1.000000  1.000000  1.000000  1.000000      1.000000   
           ...              ...       ...       ...       ...       ...           ...   
           2019-12-24  1.096263  1.097157  1.060061  1.009450  0.849066      1.136629   
           2019-12-25  1.101949  1.099249  1.061260  1.010261  0.851011      1.138275   
           2019-12-26  1.108675  1.108218  1.070639  1.017928  0.857029      1.137725   
           2019-12-27  1.095666  1.099077  1.061900  1.010809  0.849595      1.135984   
           2019-12-30  1.098329  1.106829  1.069310  1.017440  0.852282      1.135568   
           
                       daily_returns_top_bottom  
           date                                  
           2019-01-02                  1.000000  
           2019-01-03                  1.000000  
           2019-01-04                  1.000000  
           2019-01-07                  1.000000  
           2019-01-08                  1.000000  
           ...                              ...  
           2019-12-24                  0.998819  
           2019-12-25                  1.001448  
           2019-12-26                  0.999516  
           2019-12-27                  0.998470  
           2019-12-30                  0.999634  
           
           [243 rows x 7 columns],
           'IC': {'ic':                   ic
            date                
            2019-01-31 -0.337568
            2019-03-11 -0.225622
            2019-04-11 -0.077741
            2019-05-16 -0.072051
            2019-06-18 -0.097859
            2019-07-18 -0.046011
            2019-08-19 -0.034111
            2019-09-19  0.026264
            2019-10-28 -0.063618
            2019-11-27 -0.070290,
            'ic_lag':                  0
            ic_lag_0 -0.099861
            ic_lag_1  0.310902
            ic_lag_2  0.027553
            ic_lag_3  0.006139
            ic_lag_4 -0.028160},
           'BasicDescription': {'raw_hist':        value     count
            0   0.593624  0.000001
            1   0.603914  0.000002
            2   0.614205  0.000004
            3   0.624495  0.000010
            4   0.634786  0.000002
            ..       ...       ...
            95  1.571216  0.000004
            96  1.581506  0.000005
            97  1.591797  0.000002
            98  1.602087  0.000001
            99  1.612378  0.000010
            
            [100 rows x 2 columns],
            'adjusted_hist':        value     count
            0  -3.299482  0.000080
            1  -3.229666  0.000027
            2  -3.159850  0.000134
            3  -3.090035  0.000080
            4  -3.020219  0.000053
            ..       ...       ...
            95  3.333020  0.000053
            96  3.402836  0.000000
            97  3.472651  0.000000
            98  3.542467  0.000027
            99  3.612283  0.000027
            
            [100 rows x 2 columns]},
           'Industry':                        count          mean       std       min       25%  \
           industry_sw_level1_0                                                       
           农林牧渔                   859.0  5.299085e-18  1.041058 -3.089111 -0.692724   
           采掘                     634.0  7.085114e-16  0.726753 -2.774871 -0.373643   
           化工                    3413.0 -7.754971e-17  0.965782 -2.984282 -0.563646   
           钢铁                     341.0  9.572011e-17  0.668575 -2.236050 -0.375702   
           有色金属                  1168.0  6.206983e-16  0.961642 -2.847360 -0.568636   
           电子                    2479.0 -2.173198e-16  1.081787 -3.369298 -0.695930   
           汽车                    1776.0 -5.201670e-16  0.942985 -2.742349 -0.529770   
           家用电器                   605.0 -9.340554e-17  0.931704 -2.823624 -0.589745   
           食品饮料                   949.0  8.407980e-16  0.945586 -3.354893 -0.526497   
           纺织服装                   889.0 -2.937283e-16  0.883494 -2.599660 -0.476408   
           轻工制造                  1330.0  8.598176e-16  1.007456 -2.769389 -0.620119   
           医药生物                  3151.0  5.611013e-16  0.952067 -3.175993 -0.597039   
           公用事业                  1639.0  4.747067e-16  0.824101 -2.684810 -0.456260   
           交通运输                  1198.0  8.307211e-16  0.766808 -2.791207 -0.419843   
           房地产                   1330.0 -4.191301e-16  0.853211 -2.816743 -0.489521   
           商业贸易                   990.0 -4.872225e-16  0.842572 -2.704723 -0.453677   
           休闲服务                   356.0  6.673813e-17  0.789559 -2.246834 -0.486674   
           银行                     346.0  7.299877e-18  0.487315 -1.552284 -0.261345   
           非银金融                   799.0 -8.009168e-16  0.834146 -2.778120 -0.427390   
           综合                     336.0 -1.203733e-15  0.923664 -2.496763 -0.562317   
           建筑材料                   715.0 -1.386614e-16  0.881031 -2.583108 -0.528304   
           建筑装饰                  1314.0  3.450648e-16  0.839108 -3.199193 -0.454517   
           电气设备                  1933.0  6.209610e-16  0.959583 -2.681879 -0.529751   
           机械设备                  3468.0  2.352981e-17  0.950268 -2.779458 -0.533660   
           国防军工                   639.0  1.111960e-16  0.838681 -2.819804 -0.488896   
           计算机                   2104.0  7.873666e-16  0.981459 -3.285284 -0.606826   
           传媒                    1594.0 -5.195899e-17  1.031422 -2.890402 -0.623784   
           通信                    1036.0  2.175437e-16  0.981990 -2.947698 -0.621919   
           
                                      50%       75%       max  
           industry_sw_level1_0                                
           农林牧渔                 -0.045557  0.615630  2.817311  
           采掘                   -0.059999  0.334236  2.583984  
           化工                   -0.070522  0.508554  3.217629  
           钢铁                   -0.074875  0.327545  2.953668  
           有色金属                 -0.097281  0.483394  2.770264  
           电子                   -0.048739  0.670134  3.058109  
           汽车                   -0.051169  0.486529  3.296136  
           家用电器                 -0.092671  0.544616  2.781016  
           食品饮料                 -0.027851  0.522843  3.094515  
           纺织服装                 -0.020511  0.450520  2.695958  
           轻工制造                 -0.090222  0.570445  2.758277  
           医药生物                 -0.062378  0.517459  3.163162  
           公用事业                 -0.024072  0.425704  2.804037  
           交通运输                 -0.046064  0.349042  2.844967  
           房地产                  -0.058602  0.389307  3.126024  
           商业贸易                 -0.033409  0.408083  3.090762  
           休闲服务                 -0.069682  0.410126  2.637128  
           银行                   -0.005153  0.231876  1.887899  
           非银金融                 -0.054218  0.385263  3.125409  
           综合                   -0.085656  0.522950  2.666752  
           建筑材料                 -0.075640  0.430773  2.636158  
           建筑装饰                 -0.037555  0.402666  2.654618  
           电气设备                 -0.032864  0.463239  3.234359  
           机械设备                 -0.050543  0.523926  2.645623  
           国防军工                 -0.054008  0.406388  3.293146  
           计算机                  -0.049522  0.565674  3.612283  
           传媒                   -0.055024  0.554108  3.509895  
           通信                   -0.068790  0.551758  3.041874  ,
           'RebalanceOverlap':     l_top_9  l_bottom_9
           1  0.215728    0.200826
           2  0.213191    0.192870
           3  0.208488    0.181252
           4  0.206132    0.200472
           5  0.189340    0.195698
           6  0.203739    0.207136
           7  0.180953    0.185324
           8  0.196307    0.209248
           9  0.160714    0.216867,
           'PBRatio':                  l_0       l_1       l_2       l_3       l_4  top_bottom
           date                                                                    
           2019-01-02       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-03       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-04       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-07       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-08       NaN       NaN       NaN       NaN       NaN         NaN
           ...              ...       ...       ...       ...       ...         ...
           2019-12-24  3.474602  2.399033  1.941807  1.715654  2.076399    1.673379
           2019-12-25  3.472503  2.417343  1.950534  1.726948  2.066894    1.680059
           2019-12-26  3.528486  2.444673  1.958608  1.747903  2.103412    1.677505
           2019-12-27  3.511545  2.428725  1.960276  1.723444  2.092321    1.678301
           2019-12-30  2.729804  2.036063  1.848392  2.063911  2.973262    0.918118
           
           [243 rows x 6 columns],
           'Turnover':                  l_0       l_1       l_2       l_3       l_4  top_bottom
           date                                                                    
           2019-01-02       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-03       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-04       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-07       NaN       NaN       NaN       NaN       NaN         NaN
           2019-01-08       NaN       NaN       NaN       NaN       NaN         NaN
           ...              ...       ...       ...       ...       ...         ...
           2019-12-24  3.261087  1.931779  1.677159  1.259774  2.035743    1.601915
           2019-12-25  3.693612  2.109147  1.848662  1.467054  2.072664    1.782061
           2019-12-26  3.715582  2.173771  1.769293  1.477609  2.241547    1.657597
           2019-12-27  4.399734  2.644192  2.219546  1.712981  2.565770    1.714781
           2019-12-30  2.802923  1.806053  1.570662  1.859277  3.910803    0.716713
           
           [243 rows x 6 columns],
           'Stocks': {'date': '2019-12-30',
            'top':     RAW_FACTOR  instrument  name
            0     0.837799  603976.SHA  正川股份
            1     0.842891  300507.SZA  苏奥传感
            2     0.858786  002655.SZA  共达电声
            3     0.860783  603982.SHA  泉峰汽车
            4     0.865591  300147.SZA  香雪制药
            5     0.869218  000026.SZA  飞亚达A
            6     0.870769  002721.SZA  金一文化
            7     0.872948  603687.SHA   大胜达
            8     0.877996  000620.SZA   新华联
            9     0.883507  300797.SZA  钢研纳克
            10    0.886894  300461.SZA  田中精机
            11    0.891374  000803.SZA  金宇车城
            12    0.898226  300793.SZA  佳禾智能
            13    0.901146  600768.SHA  宁波富邦
            14    0.906552  300417.SZA  南华仪器
            15    0.907570  600195.SHA  中牧股份
            16    0.907843  002625.SZA  光启技术
            17    0.910818  300629.SZA   新劲刚
            18    0.912127  600599.SHA  熊猫金控
            19    0.912356  603015.SHA  弘讯科技,
            'bottom':       RAW_FACTOR  instrument  name
            3480    1.217704  002466.SZA  天齐锂业
            3481    1.221977  300073.SZA  当升科技
            3482    1.225225  601168.SHA  西部矿业
            3483    1.225756  603876.SHA  鼎胜新材
            3484    1.234599  603920.SHA  世运电路
            3485    1.237369  688012.SHA  中微公司
            3486    1.243761  300787.SZA  海能实业
            3487    1.246393  603536.SHA  惠发食品
            3488    1.248643  603655.SHA  朗博科技
            3489    1.252197  688388.SHA  嘉元科技
            3490    1.256036  603721.SHA  中广天择
            3491    1.264402  002261.SZA  拓维信息
            3492    1.268702  600242.SHA  中昌数据
            3493    1.290574  603825.SHA  华扬联众
            3494    1.301003  002192.SZA  融捷股份
            3495    1.341153  300785.SZA   值得买
            3496    1.345494  000009.SZA  中国宝安
            3497    1.346385  002291.SZA   星期六
            3498    1.360058  300576.SZA  容大感光
            3499    1.442211  000927.SZA  一汽夏利},
           'MarketCap':       count      mean       std       min       25%       50%       75%  \
           市值                                                                        
           超小型  7481.0 -0.016437  0.871042 -3.351373 -0.484065 -0.037299  0.439112   
           小型   7479.0 -0.006994  0.943480 -3.218604 -0.545132 -0.054232  0.468847   
           中型   7478.0  0.028054  0.993276 -3.354893 -0.558189 -0.037789  0.555411   
           大型   7479.0  0.014412  0.960749 -3.285284 -0.567480 -0.047910  0.516001   
           超大型  7474.0 -0.019040  0.895119 -3.369298 -0.545225 -0.083586  0.435336   
           
                     max  
           市值             
           超小型  3.509895  
           小型   3.612283  
           中型   3.163162  
           大型   3.141850  
           超大型  3.293146  ,
           'FactorValue':              date  instrument    FACTOR  RAW_FACTOR
           21     2019-01-31  000001.SZA  0.301301    1.055133
           43     2019-03-11  000001.SZA -0.220005    0.948422
           65     2019-04-11  000001.SZA  0.178394    1.007440
           87     2019-05-16  000001.SZA  1.150445    1.056743
           109    2019-06-18  000001.SZA  0.551716    1.011858
           ...           ...         ...       ...         ...
           829912 2019-12-27  688369.SHA -0.229541    0.976210
           829914 2019-09-19  688388.SHA -0.664331    0.958403
           829936 2019-10-28  688388.SHA  2.607323    1.119210
           829958 2019-11-27  688388.SHA  0.519278    1.019155
           829980 2019-12-27  688388.SHA  1.015489    1.123649
           
           [37391 rows x 4 columns]},
          'summary': {'QuantileReturns': {'summary_l_0': {'returns_total': 0.09832943613607559,
             'returns_1': 0.002430948460924176,
             'returns_5': 0.015935374205716446,
             'returns_22': 0.05645133804139135,
             'returns_66': -0.06695827658489939,
             'returns_255': 0.09832943613607559,
             'max_drawdown': 0.2681974331867978,
             'returns_volatility': 0.234537849846437,
             'sharpe_ratio': 0.3827667397711029,
             'win_ratio': 0.5843621399176955,
             'profit_loss_ratio': 0.7845910760514714},
            'summary_l_4': {'returns_total': -0.14771767375489286,
             'returns_1': 0.003163007188468958,
             'returns_5': 0.020235723519856252,
             'returns_22': 0.0498904572090777,
             'returns_66': -0.06566003621715599,
             'returns_255': -0.14771767375489286,
             'max_drawdown': 0.34101594426514464,
             'returns_volatility': 0.22724948850065224,
             'sharpe_ratio': -0.7693536259931057,
             'win_ratio': 0.5555555555555556,
             'profit_loss_ratio': 0.715131079791214},
            'summary_l_top_bottom': {'returns_total': 0.13556799771125605,
             'returns_1': -0.00036602936377228,
             'returns_5': -0.002113553883232422,
             'returns_22': 0.0031938887926596937,
             'returns_66': -0.0006235499386134657,
             'returns_255': 0.13556799771125605,
             'max_drawdown': 0.011180612482162709,
             'returns_volatility': 0.0293583116045862,
             'sharpe_ratio': 3.314368154797105,
             'win_ratio': 0.6419753086419753,
             'profit_loss_ratio': 1.344824734984519}},
           'IC': {'ic_mean': -0.09986078886039713,
            'ic_std': 0.10483072669470798,
            'ir': -0.9525908291298553,
            'ic_significance_ratio': 1.0},
           'BasicDescription': None,
           'Industry': None,
           'RebalanceOverlap': None,
           'PBRatio': {'spread': 0.9181176424026489},
           'Turnover': {'congestion_ratio': 0.7167128324508667},
           'Stocks': None,
           'MarketCap': None,
           'FactorValue': None}}]}],
      'merged': {'data': {}}},
     'title': '因子分析: {factor_name}'}