涨停股票封单统计

来源：https://uqer.io/community/share/559e49d5f9f06c6dd1e17ed5

import pandas as pd

today = Date.todaysDate()
cal = Calendar('China.SSE')
yesterday = cal.advanceDate(today, '-1B',  BizDayConvention.Following).strftime('%Y%m%d')
allSecList = DataAPI.EquGet(equTypeCD = 'A', field = ['secID'])['secID'].tolist()
precls = {}
uplimit, dnlimit = [], []


for i in range(0, len(allSecList), 200):
    sub = allSecList[i:min(len(allSecList), i+200)]
    df_precls = DataAPI.MktEqudGet(secID = sub, beginDate = yesterday, endDate = yesterday, field = 'secID,closePrice')
    df_lasprx = DataAPI.MktTickRTSnapshotGet(securityID = sub, field = 'shortNM,lastPrice,bidBook,askBook')
    for j in range(len(df_precls)):
        precls[df_precls.at[j,'secID']] = df_precls.at[j,'closePrice']
    for j in range(len(df_lasprx)):
        if df_lasprx.at[j,'lastPrice'] > 0:
            sec = df_lasprx.at[j,'ticker']+'.'+df_lasprx.at[j,'exchangeCD']
            if df_lasprx.at[j,'bidBook_volume1'] == 0 and df_lasprx.at[j,'askBook_volume1'] > 0:
                bang = df_lasprx.at[j,'askBook_volume1'] * df_lasprx.at[j,'askBook_price1']
                dnlimit.append([df_lasprx.at[j,'shortNM'], sec, precls[sec], df_lasprx.at[j,'lastPrice'],df_lasprx.at[j,'askBook_volume1'], bang/10000])
            if df_lasprx.at[j,'askBook_volume1'] == 0 and df_lasprx.at[j,'bidBook_volume1'] > 0:
                bang = df_lasprx.at[j,'bidBook_volume1'] * df_lasprx.at[j,'bidBook_price1']
                uplimit.append([df_lasprx.at[j,'shortNM'], sec, precls[sec], df_lasprx.at[j,'lastPrice'],df_lasprx.at[j,'bidBook_volume1'], bang/10000])

name, sec, precls, latprx, bangvol, bang = zip(*uplimit)
df_uplimit = pd.DataFrame({'简称': name, '代码': sec, '前收': precls, '最新价': latprx,'封单量': bangvol, '封单金额（万）': bang}).sort(columns='封单金额（万）').reset_index()
df_uplimit = df_uplimit.loc[:, ['简称', '代码','前收','最新价','封单量','封单金额（万）']]
print '涨停股票数量：', len(uplimit) , '个， 收盘封单总金额：', df_uplimit.sum()['封单金额（万）']/10000 , '亿'
df_uplimit

涨停股票数量： 1280 个， 收盘封单总金额： 644.57280353 亿

1280 rows × 6 columns

nfList = DataAPI.EquGet(equTypeCD = 'A', field = ['secID','nonrestfloatA'])
nf = {}
for j in range(len(nfList)):
    nf[nfList.at[j,'secID']] = nfList.at[j,'nonrestfloatA']

name, sec, precls, latprx, bangvol, bang = zip(*uplimit)
nflist = []
nfrate = []
for j in range(len(sec)):
   nflist.append(nf[sec[j]])
   if nf[sec[j]] > 0:
       nfrate.append(bangvol[j]*1000/nf[sec[j]])
   else:
       nfrate.append(0)

print "按照封单跟流通股本比例排序"
df_uplimit1 = pd.DataFrame({'简称': name, '代码': sec, '前收': precls, '最新价': latprx,'封单量': bangvol, '封单金额（万）': bang, '总流通股本': tuple(nflist), '封单总股本比(千分之n)': tuple(nfrate)}).sort(columns='封单总股本比(千分之n)',ascending=False).reset_index()
df_uplimit1 = df_uplimit1.loc[:, ['简称', '代码','前收','最新价','封单量','封单金额（万）','总流通股本','封单总股本比(千分之n)']]
df_uplimit1

按照封单跟流通股本比例排序

1280 rows × 8 columns