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量化交易吧 /  量化平台 帖子：3365818 新帖：25

前十大股东中的负alpha

专门亏损发表于：5 月 10 日 04：34回复(1)

昨天看到了lei_shirley老师的融资余额占比研究，受了些启发。谈一谈前十大股东里涵盖的信息。

庄家拉升股价由于资金有限，一般需要外部资金支持，往往会通过信托或者资管产品配资（庄家购买劣后级）来实现控盘。这只是往往，并不是绝对。所以，当在前十大股东看到“资管”和“信托”这类词的时候（我的关键词设置了'资管产品','信托','资产管理计划','集合'四个，也许还有疏漏。），我往往是打退堂鼓的，不希望给别人抬轿子，托盘子。再加上现在去杠杆的势头，这些配资托起来的盘子一旦跌破平仓线，卖盘会如洪水猛兽一般袭来。

我统计了沪深3000余只股票前十大股东带有“资管”和“信托”的占有比例，从大到小排序。计算自今年年初至上周四的回报（上周五和本周一实属特殊）。回报分为3组，第一组Head是排名前300只股票的平均回报，也就是“资管”和“信托”占比前300多的。第二组Tail是排名后300只股票的平均回报，也就是“资管”和“信托”占比最少的300个。第三组All是全部股票的平均回报。

数据长这个样子：
1.png

同时，我按"融资比例"从大到小排序，按上述规则计算了回报。

最后，将上述两样加总排序，计算分析，得到如下结果：
2.png

正如我们计划看到的，那些前十大股东中“资管”和“信托”占比多的，在今年下跌的幅度比总体和占比少的要更加显著。
为什么尾巴tail和全部all的结果那么近似，大概是因为尾巴都是0，all中也大多是0吧。

其中编写的get_ratio_table函数，我觉得比较有用。除了查“资管“和”信托”外，你也可以查查国家队的持股。
比如说：“中国证券金融”
3.png
结果：
4.png

话说上周末释放众多利好，也许这些担心平仓的负alpha股票未来更容易反弹吧。

后记：
我个人比较感兴趣的是查查场外期权一级交易商那几家券商。

import pandas as pd
from jqdata import *
import string

def get_top10_shareholder(stock):
    q = query(finance.STK_SHAREHOLDER_FLOATING_TOP10.code,finance.STK_SHAREHOLDER_FLOATING_TOP10.pub_date,finance.STK_SHAREHOLDER_FLOATING_TOP10.shareholder_name,finance.STK_SHAREHOLDER_FLOATING_TOP10.share_ratio).\
    filter(finance.STK_SHAREHOLDER_FLOATING_TOP10.code==stock)
    df = finance.run_query(q).sort_values('pub_date',ascending=False)[0:10].sort_values('share_ratio',ascending=False)
    df.index=range(min(len(df),10))
    return df

def get_ratio(share_info,word_list):
    ratio={}
    #weight_list=[]
    for word in word_list:
        weight=0
        for i,shareholder in enumerate(list(share_info['shareholder_name'].values)):
            if shareholder.find(word)!=-1:
                weight=weight+share_info.iloc[i,3]
        ratio[word]=weight
        #weight_list.append(weight)
    return ratio

def get_ratio_table(word_list,stock_list):
    print('一共有',len(stock_list),'股票，你就等吧')
    result=[]
    count=0
    for stock in stock_list:
        share_info=get_top10_shareholder(stock)
        if share_info.empty:
            continue
        ratio_dict=get_ratio(share_info,word_list)
        ratio_df=pd.DataFrame(ratio_dict,index=[stock])
        result.append(ratio_df)
        count=count+1
        print(count,end=',')
    df=pd.concat(result,axis=0)
    df['关键词合计']=sum(df,axis=1)
    return df

def get_mtss_table(stock_list,date):
    print('\n取融资融券数据，再等会儿~')
    df_mtss=get_mtss(stock_list, start_date=date, end_date=date,fields=['sec_code','fin_value'])
    df_mtss.index=df_mtss['sec_code'].values
    del df_mtss['sec_code']

    stock_list=list(df_mtss.index)
    q = query(valuation.code,valuation.circulating_market_cap).filter(valuation.code.in_(stock_list))
    df_mktcap = get_fundamentals(q)
    df_mktcap.index=df_mktcap['code'].values
    del df_mktcap['code']

    df=pd.concat([df_mtss,df_mktcap],axis=1)
    df['融资比例']=df['fin_value']/df['circulating_market_cap']/10**8*100
    del df['fin_value']
    del df['circulating_market_cap'] 
    return df

def get_my_table(word_list,stock_list,date,sortby='总合计'):
    df_ratio=get_ratio_table(word_list,stock_list)
    df_mtss=get_mtss_table(stock_list,date)
    df=pd.concat([df_ratio,df_mtss],axis=1).fillna(0)
    df['总合计']=df['关键词合计']+df['融资比例']
    name=[get_security_info(i).display_name for i in df.index]
    df['股票名称']=name
    df=df[['股票名称']+list(df.columns)[:-1]]
    df=round(df,2)
    df=df.sort_values(sortby,ascending=False)
    print('\n完成')
    return df

#输入
word_list=['资管产品','信托','资产管理计划','集合']
stock_list=list(get_all_securities(types=['stock']).index)
date='2018-10-19'    #最近一个交易日
sortby='关键词合计'

#结果
df=get_my_table(word_list,stock_list,date,sortby)

一共有 3628 股票，你就等吧
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,1221,12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取融资融券数据，再等会儿~

完成

import pickle
pkl_file = open('dfmtss.pkl', 'wb')
pickle.dump(df,pkl_file,0)

import pickle
pkl_file = open('dfmtss.pkl', 'rb')
df = pickle.load(pkl_file)

df
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
股票名称 信托 资产管理计划 资管产品 集合 关键词合计 融资比例 总合计
000040.XSHE 东旭蓝天 35.95 3.52 0.0 12.54 52.01 0.58 52.60
600515.XSHG 海航基础 25.30 0.00 0.0 25.30 50.59 3.38 53.97
000979.XSHE 中弘股份 0.00 26.16 0.0 14.67 40.83 0.00 40.83
603399.XSHG 吉翔股份 19.30 0.76 0.0 19.30 39.35 0.00 39.35
600432.XSHG 退市吉恩 0.00 37.07 0.0 0.00 37.07 0.00 37.07
600811.XSHG 东方集团 11.38 15.41 0.0 7.47 34.26 6.75 41.01
603077.XSHG 和邦生物 17.32 0.00 0.0 15.71 33.03 0.00 33.03
000793.XSHE 华闻传媒 19.59 6.65 0.0 6.13 32.37 13.41 45.78
002676.XSHE 顺威股份 0.00 30.71 0.0 0.00 30.71 0.00 30.71
603003.XSHG 龙宇燃油 12.26 3.29 0.0 14.85 30.40 0.00 30.40
000683.XSHE 远兴能源 15.07 0.00 0.0 15.07 30.14 0.00 30.14
600410.XSHG 华胜天成 13.44 3.73 0.0 11.98 29.15 13.72 42.87
000504.XSHE 南华生物 28.80 0.00 0.0 0.00 28.80 0.00 28.80
002085.XSHE 万丰奥威 14.25 0.00 0.0 14.25 28.51 1.25 29.76
600882.XSHG 广泽股份 14.45 0.00 0.0 12.41 26.86 0.00 26.86
601928.XSHG 凤凰传媒 26.72 0.00 0.0 0.00 26.72 2.16 28.88
002630.XSHE 华西能源 17.24 0.00 0.0 9.43 26.68 0.00 26.68
002736.XSHE 国信证券 25.15 0.68 0.0 0.00 25.83 0.60 26.42
000576.XSHE 广东甘化 12.64 0.00 0.0 12.64 25.29 0.00 25.29
002240.XSHE 威华股份 11.99 1.21 0.0 11.99 25.18 0.00 25.18
600978.XSHG 宜华生活 11.33 1.18 0.0 12.51 25.02 11.86 36.88
600177.XSHG 雅戈尔 12.33 0.00 0.0 12.33 24.66 3.53 28.19
000566.XSHE 海南海药 20.17 0.00 0.0 3.78 23.96 0.00 23.96
002042.XSHE 华孚时尚 16.59 2.00 0.0 4.07 22.65 2.81 25.46
000939.XSHE *ST凯迪 12.22 2.71 0.0 7.63 22.56 0.00 22.56
002193.XSHE 如意集团 15.85 0.00 0.0 6.66 22.51 0.00 22.51
002712.XSHE 思美传媒 11.17 0.00 0.0 11.17 22.35 0.00 22.35
002199.XSHE 东晶电子 22.20 0.00 0.0 0.00 22.20 0.00 22.20
002313.XSHE 日海智能 11.06 0.00 0.0 11.06 22.12 13.01 35.14
002519.XSHE 银河电子 11.04 0.00 0.0 11.04 22.09 0.00 22.09
... ... ... ... ... ... ... ... ...
300189.XSHE 神农基因 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300191.XSHE 潜能恒信 0.00 0.00 0.0 0.00 0.00 9.19 9.19
300192.XSHE 科斯伍德 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300193.XSHE 佳士科技 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300194.XSHE 福安药业 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300198.XSHE 纳川股份 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300199.XSHE 翰宇药业 0.00 0.00 0.0 0.00 0.00 8.74 8.74
300200.XSHE 高盟新材 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300169.XSHE 天晟新材 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300167.XSHE 迪威迅 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300166.XSHE 东方国信 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300163.XSHE 先锋新材 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300139.XSHE 晓程科技 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300140.XSHE 中环装备 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300141.XSHE 和顺电气 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300144.XSHE 宋城演艺 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300146.XSHE 汤臣倍健 0.00 0.00 0.0 0.00 0.00 2.52 2.52
300147.XSHE 香雪制药 0.00 0.00 0.0 0.00 0.00 9.78 9.78
300149.XSHE 量子生物 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300150.XSHE 世纪瑞尔 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300151.XSHE 昌红科技 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300152.XSHE 科融环境 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300154.XSHE 瑞凌股份 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300155.XSHE 安居宝 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300156.XSHE 神雾环保 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300157.XSHE 恒泰艾普 0.00 0.00 0.0 0.00 0.00 23.77 23.77
300158.XSHE 振东制药 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300161.XSHE 华中数控 0.00 0.00 0.0 0.00 0.00 0.00 0.00
300162.XSHE 雷曼股份 0.00 0.00 0.0 0.00 0.00 0.00 0.00
603999.XSHG 读者传媒 0.00 0.00 0.0 0.00 0.00 0.00 0.00

3604 rows × 8 columns

# 按关键词
N=300
start_date='2018-01-02'
end_date='2018-10-18'
df=df.sort_values('关键词合计',ascending=False)
securities_list=list(df.index[0:N])
S0=get_price(securities_list,start_date=start_date,end_date=start_date,fields='close',fq='pre')
ST=get_price(securities_list,start_date=end_date,end_date=end_date,fields='close',fq='pre')
RetHead=(ST['close'].iloc[0,:]/S0['close'].iloc[0,:]-1).mean()

securities_list=list(df.index)
S0=get_price(securities_list,start_date=start_date,end_date=start_date,fields='close',fq='pre')
ST=get_price(securities_list,start_date=end_date,end_date=end_date,fields='close',fq='pre')
RetAll=(ST['close'].iloc[0,:]/S0['close'].iloc[0,:]-1).mean()

securities_list=list(df.index[-N:])
S0=get_price(securities_list,start_date=start_date,end_date=start_date,fields='close',fq='pre')
ST=get_price(securities_list,start_date=end_date,end_date=end_date,fields='close',fq='pre')
RetTail=(ST['close'].iloc[0,:]/S0['close'].iloc[0,:]-1).mean()

word_result=[RetHead,RetTail,RetAll]

# 按两融
df=df.sort_values('融资比例',ascending=False)
securities_list=list(df.index[:N])
S=get_price(securities_list,start_date='2018-01-02',end_date='2018-10-18',fields='close',fq='pre')
RetHead=(S['close'].iloc[-1,:]/S['close'].iloc[0,:]-1).mean()
securities_list=list(df.index[-N:])
S=get_price(securities_list,start_date='2018-01-02',end_date='2018-10-18',fields='close',fq='pre')
RetTail=(S['close'].iloc[-1,:]/S['close'].iloc[0,:]-1).mean()
securities_list=list(df.index)
S=get_price(securities_list,start_date='2018-01-02',end_date='2018-10-18',fields='close',fq='pre')
RetAll=(S['close'].iloc[-1,:]/S['close'].iloc[0,:]-1).mean()

mtss_result=[RetHead,RetTail,RetAll]

# 按总合计
df=df.sort_values('总合计',ascending=False)
securities_list=list(df.index[:N])
S=get_price(securities_list,start_date='2018-01-02',end_date='2018-10-18',fields='close',fq='pre')
RetHead=(S['close'].iloc[-1,:]/S['close'].iloc[0,:]-1).mean()
securities_list=list(df.index[-N:])
S=get_price(securities_list,start_date='2018-01-02',end_date='2018-10-18',fields='close',fq='pre')
RetTail=(S['close'].iloc[-1,:]/S['close'].iloc[0,:]-1).mean()
securities_list=list(df.index)
S=get_price(securities_list,start_date='2018-01-02',end_date='2018-10-18',fields='close',fq='pre')
RetAll=(S['close'].iloc[-1,:]/S['close'].iloc[0,:]-1).mean()

total_result=[RetHead,RetTail,RetAll]

result=[word_result,mtss_result,total_result]

import pandas as pd
result_df=pd.DataFrame(result,index=['关键词','融资比例','总合计'],columns=['Head','Tail','All'])
result_df
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
Head Tail All
关键词 -0.412412 -0.354977 -0.367917
融资比例 -0.442642 -0.366021 -0.367917
总合计 -0.424130 -0.386676 -0.367917 
# 查查国家队
word_list=['中国证券金融']
stock_list=get_index_stocks('000300.XSHG')
df=get_ratio_table(word_list,stock_list).sort_values('关键词合计',ascending=False)
# 方便起见
name=[get_security_info(i).display_name for i in df.index]
df['股票名称']=name

一共有 300 股票，你就等吧
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,
df
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
中国证券金融 关键词合计 股票名称
600177.XSHG 5.009 5.009 雅戈尔
600637.XSHG 4.991 4.991 东方明珠
600089.XSHG 4.904 4.904 特变电工
600585.XSHG 4.901 4.901 海螺水泥
600271.XSHG 4.901 4.901 航天信息
601618.XSHG 4.900 4.900 中国中冶
600170.XSHG 4.900 4.900 上海建工
600153.XSHG 4.900 4.900 建发股份
600066.XSHG 4.900 4.900 宇通客车
600837.XSHG 4.900 4.900 海通证券
601788.XSHG 4.900 4.900 光大证券
601766.XSHG 4.900 4.900 中国中车
600886.XSHG 4.900 4.900 国投电力
600109.XSHG 4.900 4.900 国金证券
601669.XSHG 4.900 4.900 中国电建
600958.XSHG 4.900 4.900 东方证券
600415.XSHG 4.900 4.900 小商品城
601607.XSHG 4.900 4.900 上海医药
600795.XSHG 4.900 4.900 国电电力
600023.XSHG 4.900 4.900 浙能电力
601390.XSHG 4.900 4.900 中国中铁
601377.XSHG 4.900 4.900 兴业证券
601006.XSHG 4.900 4.900 大秦铁路
601216.XSHG 4.900 4.900 君正集团
601211.XSHG 4.900 4.900 国泰君安
601198.XSHG 4.900 4.900 东兴证券
601009.XSHG 4.900 4.900 南京银行
601186.XSHG 4.900 4.900 中国铁建
601169.XSHG 4.900 4.900 北京银行
600068.XSHG 4.900 4.900 葛洲坝
... ... ... ...
600739.XSHG 0.000 0.000 辽宁成大
002925.XSHE 0.000 0.000 盈趣科技
002456.XSHE 0.000 0.000 欧菲科技
002460.XSHE 0.000 0.000 赣锋锂业
002468.XSHE 0.000 0.000 申通快递
600352.XSHG 0.000 0.000 浙江龙盛
601108.XSHG 0.000 0.000 财通证券
002493.XSHE 0.000 0.000 荣盛石化
002500.XSHE 0.000 0.000 山西证券
002508.XSHE 0.000 0.000 老板电器
002555.XSHE 0.000 0.000 三七互娱
601012.XSHG 0.000 0.000 隆基股份
002558.XSHE 0.000 0.000 巨人网络
002572.XSHE 0.000 0.000 索菲亚
002594.XSHE 0.000 0.000 比亚迪
002601.XSHE 0.000 0.000 龙蟒佰利
002602.XSHE 0.000 0.000 世纪华通
002608.XSHE 0.000 0.000 江苏国信
600926.XSHG 0.000 0.000 杭州银行
600909.XSHG 0.000 0.000 华安证券
002624.XSHE 0.000 0.000 完美世界
002625.XSHE 0.000 0.000 光启技术
002673.XSHE 0.000 0.000 西部证券
600867.XSHG 0.000 0.000 通化东宝
002714.XSHE 0.000 0.000 牧原股份
600820.XSHG 0.000 0.000 隧道股份
600809.XSHG 0.000 0.000 山西汾酒
002739.XSHE 0.000 0.000 万达电影
002797.XSHE 0.000 0.000 第一创业
603993.XSHG 0.000 0.000 洛阳钼业

300 rows × 3 columns

       

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