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：
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：
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”
结果：
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 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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
| 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
| 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
| 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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