聚宽提供了基础的数据，但如何可以直观的观测某天某个品种其会员持仓的龙虎榜数据呢，就像下图这样：
M1909合约，20190419的成交、多头持仓、空头持仓龙虎榜。
本研究基于聚宽提供的数据，重现了这一龙虎榜，为后续研究提供支撑，为主观交易提供了参考：
（以下截图仅为部分）
经比较可以看出，与东财网站上是一样的。
需要注意的是：
在计算净多头、净空头龙虎榜时，东财采用了只要是上榜会员直接相减的方法。
比如，华泰期货仅上了多头持仓龙虎榜，没有上空头持仓龙虎榜，但在计算净持仓时，东财把华泰的空头持仓当0处理，这是不合理的。
本算法在计算净多头持仓与净空头持仓时，只计算同时上多头持仓龙虎榜，以及上空头持仓龙虎榜的会员单位，所以在净多龙虎榜与净空龙虎榜方面与东财的不一样：
from jqdata import finance
def get_menber_vol_opid(day,code):
"""
输入日期、代码
输出9张龙虎榜数据
"""
import pandas as pd
#获取数据
q=query(finance.FUT_MEMBER_POSITION_RANK.day,
finance.FUT_MEMBER_POSITION_RANK.code,
finance.FUT_MEMBER_POSITION_RANK.rank_type,
finance.FUT_MEMBER_POSITION_RANK.rank,
finance.FUT_MEMBER_POSITION_RANK.member_name,
finance.FUT_MEMBER_POSITION_RANK.indicator,
finance.FUT_MEMBER_POSITION_RANK.indicator_increase).\
filter(finance.FUT_MEMBER_POSITION_RANK.code==code,
finance.FUT_MEMBER_POSITION_RANK.day==day)
df=finance.run_query(q)
#数据分离，得3张表
df1 = df[df['rank_type']=='成交量排名'][['rank','member_name','indicator','indicator_increase']]
df2 = df[df['rank_type']=='持买单量排名'][['rank','member_name','indicator','indicator_increase']]
df3 = df[df['rank_type']=='持卖单量排名'][['rank','member_name','indicator','indicator_increase']]
#-------------------------------------基础龙虎榜数据
#成交量龙虎榜
df1.columns = ['名次','会员简称','成交量','增减']
sum1a = sum(df1['成交量'])
sum1b = sum(df1['增减'])
df1 = df1.append(pd.DataFrame(['成交量','本日合计',sum1a,sum1b],index=['名次','会员简称','成交量','增减']).T,sort=False)
df1 = df1.reset_index(drop=True)
#多头持仓龙虎榜
df2.columns = ['名次','会员简称','多单量','增减']
sum2a = sum(df2['多单量'])
sum2b = sum(df2['增减'])
df2 = df2.append(pd.DataFrame(['多头','本日合计',sum2a,sum2b],index=['名次','会员简称','多单量','增减']).T,sort=False)
df2 = df2.reset_index(drop=True)
#空头持仓龙虎榜
df3.columns = ['名次','会员简称','空单量','增减']
sum3a = sum(df3['空单量'])
sum3b = sum(df3['增减'])
df3 = df3.append(pd.DataFrame(['空头','本日合计',sum3a,sum3b],index=['名次','会员简称','空单量','增减']).T,sort=False)
df3 = df3.reset_index(drop=True)
#-----------------------------------计算持仓龙虎榜
#持仓数据
temp1 = df2[['会员简称','多单量']].set_index('会员简称')
temp1.columns = ['持仓']
temp2 = df3[['会员简称','空单量']].set_index('会员简称')
temp2.columns = ['持仓']
opid = temp1.iloc[:-1,:]-temp2.iloc[:-1,:] #最后一行的本日合计不参与
#增减数据
temp3 = df2[['会员简称','增减']].set_index('会员简称')
temp4 = df3[['会员简称','增减']].set_index('会员简称')
opid_inc = temp3.iloc[:-1,:]-temp4.iloc[:-1,:]
#合并持仓、增减
#注意东方财富的结果不对（如果多头持仓龙虎榜中有，而空头持仓龙虎榜中没有，东财直接把多持仓龙虎榜中持仓当作净持仓）
opid_result = pd.concat([opid,opid_inc],axis=1).sort_values(by='持仓',ascending=False)
#---------净多头龙虎榜
df4 = opid_result[opid_result['持仓']>0]
temp1 = sum(df4['持仓'])
temp2 = sum(df4['增减'])
df4 = df4.reset_index()
df4['名次']=range(1,len(df4)+1)
df4 = df4[['名次','会员简称','持仓','增减']]
#插入空行
insert_row=pd.DataFrame([[' ',' ',' ',' ']],columns=['名次','会员简称','持仓','增减'])
for i in range(20-len(df4)):
df4=df4.append(insert_row,ignore_index=True)
df4 = df4.append(pd.DataFrame(['净多','本日合计',temp1,temp2],index=['名次','会员简称','持仓','增减']).T,sort=False)
df4 = df4.reset_index(drop=True)
df4.columns=['名次','会员简称','净多量','增减']
#---------净空头龙虎榜
df7 = opid_result[opid_result['持仓']<0]
df7 = 0-df7
df7 = df7.sort_values(by='持仓',ascending=False)
temp1 = sum(df7['持仓'])
temp2 = sum(df7['增减'])
df7 = df7.reset_index()
df7['名次']=range(1,len(df7)+1)
df7 = df7[['名次','会员简称','持仓','增减']]
#插入空行
insert_row=pd.DataFrame([[' ',' ',' ',' ']],columns=['名次','会员简称','持仓','增减'])
for i in range(20-len(df7)):
df7=df7.append(insert_row,ignore_index=True)
df7 = df7.append(pd.DataFrame(['净空','本日合计',temp1,temp2],index=['名次','会员简称','持仓','增减']).T,sort=False)
df7 = df7.reset_index(drop=True)
df7.columns=['名次','会员简称','净空量','增减']
#----------------多头增减仓龙虎榜
import warnings
warnings.filterwarnings('ignore')
#多头增仓龙虎榜
temp = df2.iloc[:-1,:].sort_values(by='增减',ascending=False)
df5 = temp[temp['增减']>0]
temp1 = sum(df5['多单量'])
temp2 = sum(df5['增减'])
df5.loc[:,'名次']=range(1,len(df5)+1)
#插入空行
insert_row=pd.DataFrame([[' ',' ',' ',' ']],columns=['名次','会员简称','多单量','增减'])
for i in range(20-len(df5)):
df5=df5.append(insert_row,ignore_index=True)
df5 = df5.append(pd.DataFrame(['多增','本日合计',temp1,temp2],index=['名次','会员简称','多单量','增减']).T,sort=False)
df5 = df5.reset_index(drop=True)
df5 = df5[['名次','会员简称','增减','多单量']]
#多头减仓龙虎榜
temp = df2.iloc[:-1,:].sort_values(by='增减')
df6 = temp[temp['增减']<0]
temp1 = sum(df6['多单量'])
temp2 = sum(df6['增减'])
df6.loc[:,'名次']=range(1,len(df6)+1)
#插入空行
insert_row=pd.DataFrame([[' ',' ',' ',' ']],columns=['名次','会员简称','多单量','增减'])
for i in range(20-len(df6)):
df6=df6.append(insert_row,ignore_index=True)
df6 = df6.append(pd.DataFrame(['多减','本日合计',temp1,temp2],index=['名次','会员简称','多单量','增减']).T,sort=False)
df6 = df6.reset_index(drop=True)
df6 = df6[['名次','会员简称','增减','多单量']]
#---------------空头增减仓龙虎榜
#空头增仓龙虎榜
temp = df3.iloc[:-1,:].sort_values(by='增减',ascending=False)
df8 = temp[temp['增减']>0]
temp1 = sum(df8['空单量'])
temp2 = sum(df8['增减'])
df8.loc[:,'名次']=range(1,len(df8)+1)
#插入空行
insert_row=pd.DataFrame([[' ',' ',' ',' ']],columns=['名次','会员简称','空单量','增减'])
for i in range(20-len(df8)):
df8=df8.append(insert_row,ignore_index=True)
df8 = df8.append(pd.DataFrame(['空增','本日合计',temp1,temp2],index=['名次','会员简称','空单量','增减']).T,sort=False)
df8 = df8.reset_index(drop=True)
df8 = df8[['名次','会员简称','增减','空单量']]
#空头减仓龙虎榜
temp = df3.iloc[:-1,:].sort_values(by='增减')
df9 = temp[temp['增减']<0]
temp1 = sum(df9['空单量'])
temp2 = sum(df9['增减'])
df9.loc[:,'名次']=range(1,len(df9)+1)
#插入空行
insert_row=pd.DataFrame([[' ',' ',' ',' ']],columns=['名次','会员简称','空单量','增减'])
for i in range(20-len(df9)):
df9=df9.append(insert_row,ignore_index=True)
df9 = df9.append(pd.DataFrame(['空减','本日合计',temp1,temp2],index=['名次','会员简称','空单量','增减']).T,sort=False)
df9 = df9.reset_index(drop=True)
df9 = df9[['名次','会员简称','增减','空单量']]
#-------------------------合并9个龙虎榜
all_data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9],axis=1)
columns1 = all_data.columns
all_data=pd.DataFrame(all_data.values,columns=[['成交量龙虎榜']*4+\
['多头持仓龙虎榜']*4+\
['空头持仓龙虎榜']*4+\
['净多头龙虎榜']*4+\
['多头增仓龙虎榜']*4+\
['多头减仓龙虎榜']*4+\
['净空头龙虎榜']*4+\
['空头增仓龙虎榜']*4+\
['空头减仓龙虎榜']*4,columns1])
filename = day+'日'+code+'会员成交持仓龙虎榜数据.xlsx'
all_data.to_excel(filename)
return all_data
def test1():
#大连品种
code = 'M1909.XDCE'
day = '2019-04-19'
all_data = get_menber_vol_opid(day,code)
return all_data
def test2():
code = 'RB1905.XSGE'
day = '2019-02-13'
all_data = get_menber_vol_opid(day,code)
return all_data
if __name__=='__main__':
all_data = test1()
#all_data2 = test2()
all_data
