def get_xsg(f):
"""
获取限售股解禁
"""
for year in range(2010, datetime.today().year + 1):
for month in range(1, 13):
if month in range(1, 10):
dset_name = str(year) + "0" + str(month)
else:
dset_name = str(year) + str(month)
if f.get(dset_name) is not None:
count.inc_by_index(conf.HDF5_COUNT_PASS)
continue
try:
df = ts.xsg_data(year=year,
month=month,
pause=conf.REQUEST_BLANK)
df = df.drop("name", axis=1)
df = df.sort_values(by=[conf.HDF5_SHARE_DATE_INDEX])
tool.create_df_dataset(f, dset_name, df)
console.write_exec()
count.inc_by_index(conf.HDF5_COUNT_GET)
except Exception as er:
print(str(er))
time.sleep(conf.REQUEST_BLANK)
return
def get_xsg_monthly(self):
try:
df = ts.xsg_data()
df.drop_duplicates(inplace=True)
date = np.array(df['date'])
code = np.array(df['code'])
count = np.array(df['count'])
ratio = np.array(df['ratio'])
arrays = [date, code]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['date', 'code'])
s = pd.Series(count, index=index)
f = s.to_frame()
f.rename(index=str, columns={0: "count"}, inplace=True)
f['ratio'] = ratio
f.to_sql('stock_xsg_data',
mysqlEngine,
if_exists='append',
index=True,
index_label=['date', 'code'],
dtype={
'code': mysql.NVARCHAR(6),
'date': mysql.DATE
})
"""
mh = MysqlHandle()
DFToSQLOneByOne(mh.session,f,'Report')
"""
except Exception, e:
print e
def banshare(request, year, month):
# code name date count ratio
# 0 002032 苏泊尔 2019-08-30 8.29 0.01
# today = datetime.date.today()
# year = today.strftime("%Y")
# month = today.strftime("%m")
month_total = stock_ban.objects.filter(month=month).count()
if month_total == 0:
df = ts.xsg_data(year=year, month=month)
id_list = df.index.tolist()
for id in id_list:
code1 = df.loc[id, 'code']
name1 = df.loc[id, 'name']
date1 = df.loc[id, 'date']
count1 = df.loc[id, 'count']
ratio1 = df.loc[id, 'ratio']
sb = stock_ban(code=code1,
name=name1,
date=date1,
count=count1,
ratio=ratio1,
month=month)
sb.save()
return HttpResponse("{} banshare Done".format(month))
else:
return HttpResponse("{} have synced".format(month))
def restrict_share(self):
data = ts.xsg_data()
ratio = list(data.ratio)
restrict_code = list(data.code)
for index in range(len(ratio)):
if ratio[index] > 5 and restrict_code[index] in self.valid_code:
self.restrict_code.append(restrict_code[index])
self.valid_code.remove(restrict_code[index])
def xsg_data():
year=[i+2010 for i in range(7)]
DTS=[]
for y in year:
preDTS=ts.xsg_data(year=y)
DTS.append(preDTS)
DTS=pd.concat(DTS)
DTS.to_csv('D:\\ts\\tzck\\xsg_data.csv',encoding='gbk')
def get_stock_xsgjj(nd, yf):
"""
获取限售股解禁
"""
try:
res = ts.xsg_data(nd, yf)
return res
except:
return None
def download_xsg_data(self):
"""
"""
#print('downloading the xsg_data info...')
try:
data_2017_1 = ts.xsg_data(2017, 1)
filename = 'xsg_data_2017_1'
data_2017_1.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download xsg_data_2017_1 failed')
try:
data_2017_2 = ts.xsg_data(2017, 2)
filename = 'xsg_data_2017_2'
data_2017_2.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download xsg_data_2017_2 failed')
try:
data_2017_3 = ts.xsg_data(2017, 3)
filename = 'xsg_data_2017_3'
data_2017_3.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download xsg_data_2017_3 failed')
try:
data_2017_4 = ts.xsg_data(2017, 4)
filename = 'xsg_data_2017_4'
data_2017_4.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download xsg_data_2017_4 failed')
data = data_2017_3.append(data_2017_2).append(data_2017_1)
data.drop_duplicates(subset=['code'], keep='first', inplace=True)
filename = 'xsg_data'
data.to_excel(self.working_folder + filename + '.xlsx', encoding='GBK')
return
def getvrdata(isinit='no'):
mylogger = getmylogger()
curday = datetime.date.today()
print(curday)
curyear = curday.year
curmonth = curday.month
quarter = (curmonth - 1) // 3 + 1
# prdict = {'profitdivi': '分配预案', 'forecast': '业绩预告', 'xsg': '限售股解禁', 'fundholdings': '基金持股', 'newstocks': '新股'}
prdict = {'fundholdings': '基金持股'}
for dv in prdict:
vrtbname = dv
vrinfo = prdict[vrtbname]
try:
if dv == "profitdivi":
df = ts.profit_data(year=curyear, top=100)
if isinit == 'no' and df is not None:
df = df[df['report_date'] >= str(curday)]
elif dv == "forecast":
df = ts.forecast_data(curyear, quarter)
if isinit == 'no' and df is not None:
# print(df[df['report_date'].isin(['2019-12-28'])])
df = df[df['report_date'] >= str(curday)]
elif dv == "xsg":
df = ts.xsg_data()
if isinit == 'no' and df is not None:
curym = datetime.date.today().strftime("%Y-%m")
df = df[df['date'].str.contains(curym)]
elif dv == "fundholdings":
if quarter == 1:
quarter = 4
curyear -= 1
else:
quarter -= 1
df = ts.fund_holdings(curyear, quarter)
if isinit == 'no' and df is not None:
lastyq = str(curyear) + '-' + str(quarter)
df = df[df['date'].str.contains(lastyq)]
if df is not None:
newcolumns = ['nums', 'count', 'nlast', 'name', 'amount', 'date', 'ratio', 'code', 'clast']
df.columns = newcolumns
elif dv == "newstocks":
df = ts.new_stocks()
if isinit == 'no' and df is not None:
df = df[df['ipo_date'] >= str(curday)]
else:
mylogger.info("没有执行命令。")
if df is not None:
tosql(df, vrtbname, "append", vrinfo, mylogger)
else:
mylogger.info("没有%s数据。" % vrinfo)
except Exception:
tracelog = traceback.format_exc()
mylogger.info("获取数据异常。")
mylogger.info(tracelog)
def get_ban_shares(self, context):
curr_year = context.now.year
curr_month = context.now.month
jj_range = [
((curr_year * 12 + curr_month + i - 1) / 12, curr_year * 12 +
curr_month + i - (curr_year * 12 + curr_month + i - 1) / 12 * 12)
for i in range(-1, 1)
] #range 可指定解禁股的时间范围，单位为月
df_jj = functools.reduce(
lambda x, y: pd.concat([x, y], axis=0),
[ts.xsg_data(year=y, month=m) for (y, m) in jj_range])
return df_jj.code.values
def lifted(engine, year, month):
# tsu.to_sql(engine, 'invest_lifted', plist=[year, month])
tbl = "invest_lifted"
tsl.log(tbl + " start...")
try:
df = ts.xsg_data(year, month)
df = df.set_index('code', drop='true')
df.to_sql(tbl, engine, if_exists='append')
tsl.log(tbl + " done")
except BaseException, e:
print
print e
tsl.log(tbl + " error")
def xsg_data(request):
try:
year = request.POST.get('year', '') # 必填
month = request.POST.get('month', '') # 必填
data = ts.xsg_data(year, month)
res = {
'columns': ['code', 'name', 'date', 'count', 'ratio'],
'data': json.loads(json.dumps(data.values, cls=DateEncoder))
}
except (BaseException):
return HttpResponse(BaseException)
else:
return HttpResponse(json.dumps(res), content_type="application/json")
def updateXsgData(con, year: int, month: int):
import share.model.dao.reference.XsgData as Model
dateString = "{year:04d}-{month:02d}".format(year=year, month=month)
logging.debug("Updating month XsgData of {}".format(dateString))
df = ts.xsg_data(year=year, month=month, retry_count=16)
res = []
for _, row in df.iterrows():
obj = Model.rowToORM(row, year=year, month=month)
if obj is not None:
res.append(obj)
Base.metadata.create_all(con.engine)
con.save_all(res)
return
def get_restricted_stock(year=None,
month=None,
retry_count=RETRY_COUNT,
pause=PAUSE):
"""
获取限售股解禁数据
Args
year:年份,默认为当前年
month:解禁月份，默认为当前月
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
Returns:
字典的列表
"""
year = util.get_year() if year is None else year
month = util.get_month() if month is None else month
logger.info(
'Begin get restricted stock data, the year is: %s, month is: %s' %
(year, month))
try:
data_df = ts.xsg_data(year=year,
month=month,
retry_count=retry_count,
pause=pause)
except Exception as e:
logging.exception(
'Error get restricted stock data, the year is: %s, month is: %s' %
(year, month))
return None
else:
data_dicts = []
if data_df is not None and not data_df.empty:
data_dicts = [{
'code': row[0],
'name': row[1],
'year': year,
'month': month,
'date': row[2],
'count': row[3],
'ratio': row[4],
'insert_date': today_line
} for row in data_df.values]
logger.info(
'Success get restricted stock data, the year is: %s, month is: %s'
% (year, month))
else:
logger.warn(
'Empty get restricted stock data, the year is: %s, month is: %s'
% (year, month))
return data_dicts
def get_data_date(year,month):
cmd='''delete from %s where `year`=%s and `month`=%s '''%(table_name,year,month)
print cmd
run_mysql_cmd(cmd=cmd,conn=conn) #删除指定年和月的数据
try:
rs=ts.xsg_data(year=year,month=month)
rs['year']=year
rs['month']=month
rs=rs.drop_duplicates() #去除重复的数据，没想到还有重复的，心塞塞，这个api不咋地啊，挖地兔
pd.DataFrame.to_sql(rs, table_name, con=conn , flavor='mysql', if_exists='append',index=False)
return rs
except:
print("this year has no records")
return None
def dump_xsjj(year, month):
df = ts.xsg_data(year=year, month=month)
if df is None:
return
list = []
for index, row in df.iterrows():
xsjj_data = xsjj_model(code=row['code'],
name=row['name'],
date=row['date'])
xsjj_data.count = row['count']
xsjj_data.ratio = float(row['ratio'])
list.append(xsjj_data)
save_list(list)
def reference(year_to_start=2010):
def profit_data(year):
profit_data = ts.profit_data(year=year, top=100)
profit_data.sort('shares', ascending=False)
if profit_data is not None:
profit_data.to_sql('reference_profit_data',
engine,
flavor='mysql',
if_exists='append')
def forecast_data(year):
for i in range(4):
predts = ts.forecast_data(year, i + 1)
if predts is not None:
predts.to_sql('reference_forecast_data',
engine,
flavor='mysql',
if_exists='append')
def fund_holdings(year):
for i in range(4):
predts = ts.fund_holdings(year, i + 1)
if predts is not None:
predts.to_sql('reference_fund_holdings',
engine,
flavor='mysql',
if_exists='append')
starttime = datetime.datetime.today()
year = [
year_to_start + i for i in range(starttime.year - year_to_start + 1)
]
thread_pool.map(profit_data, year)
thread_pool.map(forecast_data, year)
thread_pool.map(fund_holdings, year)
thread_pool.join()
xsg_data = ts.xsg_data()
if xsg_data is not None:
xsg_data.to_sql('reference_xsg_data',
engine,
flavor='mysql',
if_exists='replace')
new_stocks = ts.new_stocks()
if new_stocks is not None:
new_stocks.to_sql('reference_new_stocks',
engine,
flavor='mysql',
if_exists='replace')
def setXsgData(self,year=None,month=None, number = 1, isSave = False,tableName = REFERENCE_XSG_DATA):
[year, month] = getYearMonth(year, month)
for i in range(0,number):
try:
df = ts.xsg_data(year, month)
if month<10:
df['date'] =str(year) +'0'+ str(month)
else:
df['date'] = str(year) + str(month)
if isSave is True:
df.to_sql(tableName,self.engine_sql, if_exists='append')
month = month - 1
if month < 1:
month = 12
year = year - 1
except IOError,e:
print e
def lift_ban_stock(self, interval=6):
'''
取即将解禁的股票，默认取半年的
'''
raw_data = None
start_month = int(datetime.strftime(datetime.now(), '%m'))
start_year = int(datetime.strftime(datetime.now(), '%Y'))
for month in xrange(start_month, start_month + interval):
foot = month / 13
month = month % 12
if month == 0:
month = 12
print 'get {0} {1} 解禁数据'.format(start_year + foot, month)
data = ts.xsg_data(year=(start_year + foot), month=month)
raw_data = pd.concat([raw_data, data], axis=0)
raw_data.index = raw_data['code']
return raw_data
def get_ref_xsg(year=None, month=None):
"""
投资参考 系列
以月的形式返回限售股解禁情况，通过了解解禁股本的大小，判断股票上行的压力。
可通过设定年份和月份参数获取不同时段的数据
"""
df = ts.xsg_data(year, month)
print(df)
if df is not None:
if len(df) > 0:
df.insert(0, 'year', year)
df.insert(1, 'month', month)
res = df.to_sql(invest_stock_ref_xsg, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取业绩预告数据: {0} {1}: {2}'.format(year, month, msg) + '\n')
else:
print('获取业绩预告数据: {0} {1}: {2}'.format(year, month, 'None') + '\n')
def job_2():
try:
print("I'm working......投资参考数据")
# 分配预案
profit_data = ts.profit_data(year, top=1000)
data = pd.DataFrame(profit_data)
data.to_sql('profit_data',engine,index=True,if_exists='replace')
print("分配预案......done")
# 业绩预告
forecast_data = ts.forecast_data(year,1)
data = pd.DataFrame(forecast_data)
data.to_sql('forecast_data',engine,index=True,if_exists='replace')
print("业绩预告......done")
# 限售股解禁
xsg_data = ts.xsg_data()
data = pd.DataFrame(xsg_data)
data.to_sql('xsg_data',engine,index=True,if_exists='replace')
print("限售股解禁......done")
# 基金持股
fund_holdings = ts.fund_holdings(year, 1)
data = pd.DataFrame(fund_holdings)
data.to_sql('fund_holdings',engine,index=True,if_exists='replace')
print("基金持股......done")
# 新股数据
new_stocks = ts.new_stocks()
data = pd.DataFrame(new_stocks)
data.to_sql('new_stocks',engine,index=True,if_exists='replace')
print("新股数据......done")
# 融资融券（沪市）
sh_margins = ts.sh_margins()
data = pd.DataFrame(sh_margins)
data.to_sql('sh_margins',engine,index=True,if_exists='replace')
print("融资融券（沪市）......done")
# 融资融券（深市）
sz_margins = ts.sz_margins()
data = pd.DataFrame(sz_margins)
data.to_sql('sz_margins',engine,index=True,if_exists='replace')
print("融资融券（深市）......done")
except Exception as e:
print(e)
def lift_ban_stock(self, interval=6):
'''
取即将解禁的股票，默认取半年的
'''
raw_data = None
start_month = int(datetime.strftime(datetime.now(), '%m'))
start_year = int(datetime.strftime(datetime.now(), '%Y'))
for month in xrange(start_month, start_month+interval):
foot = month / 13
month = month%12
if month == 0:
month = 12
print 'get {0} {1} 解禁数据'.format(start_year+foot, month)
data = ts.xsg_data(year=(start_year+foot),month=month)
raw_data = pd.concat([raw_data, data], axis=0)
raw_data.index = raw_data['code']
return raw_data
def update_restrict_stock_data( self, extend_years = 3 ):
if not os.path.exists( self.__restrict_stock_file_name ):
list_date_quarter = Utils.parse_date_to_ymd( self.__date )
df_restrict_stock_data = pd.DataFrame()
for year in range( 2010, int( list_date_quarter[0] ) + extend_years + 1 ):
for month in range( 1, 13 ):
try:
df_tmp = ts.xsg_data( year, month )
except:
ERROR( 'exception occurs when update restrict stock data of year {0} month {1}'.format( year, month ) )
else:
df_restrict_stock_data = df_restrict_stock_data.append( df_tmp )
LOG( 'update restrict stock data of year {0} month {1}'.format( year, month ) )
if year == int( list_date_quarter[0] ) + extend_years and month == int( list_date_quarter[1] ):
break
Utils.save_data( df_restrict_stock_data, self.__restrict_stock_file_name, 'restrict stock' )
def getXsg(cursor):
for i in range(1992, 2017 + 1):
for j in range(1, 12 + 1):
try:
print(i, j)
df = ts.xsg_data(year=i, month=j)
# 处理缺失值
df = df.stack().replace('--', '0').unstack()
print(df)
dfLen = len(df)
# print(dfLen)
uuidList = [] # 添加uuid
yearList = [] # 添加年份
monthList = [] # 添加月份
for l in range(0, dfLen):
uuidList.append(uuid.uuid1())
yearList.append(str(i))
monthList.append(str(j))
df['uuid'] = uuidList
df['year'] = yearList
df['month'] = monthList
for k in range(0, dfLen):
df2 = df[k:k + 1]
print(df2)
cursor.execute(
"insert into stock_xsg(uuid, code, name, lift_date, count, ratio, year, month) "
"values(:uuid, :code, :name, to_date(:lift_date, 'yyyy-MM-dd'), :count, :ratio, :year, :month)",
(str(list(df2['uuid'])[0]), str(list(df2['code'])[0]),
str(list(df2['name'])[0]), str(list(
df2['date'])[0]), round(float(df2['count']), 4),
round(float(
df2['ratio']), 4), str(list(
df2['year'])[0]), str(list(df2['month'])[0])))
cursor.execute("commit")
except Exception:
pass
def dump_xsjj(year, month):
logging.info("开始获取数据 %s %s", year, month)
df = ts.xsg_data(year=year, month=month)
logging.info("获取数据完成 %s %s", year, month)
if df is None:
return
list = []
for index, row in df.iterrows():
code = row['code']
name = row['name']
date = row['date']
xsjj_data = get_xsjj_by_code_date(code, date)
if xsjj_data is not None:
logging.info("%s %s %s 数据已dump", code, name, date)
xsjj_data = xsjj_model(code=code, name=name, date=date)
xsjj_data.count = row['count']
xsjj_data.ratio = float(row['ratio'])
list.append(xsjj_data)
session.add_all(list)
def restricted_stcok(self, durting=None):
"""
检查为未来一年的限售股信息
Parameters
----------
durting 默认值为 12 往后查询多少个月
Returns
-------
"""
if durting is None:
durting = 6
else:
pass
_month = int(self.month)
_year = int(self.year)
rsd = pandas.DataFrame() #rsd => Restricted Stock Datas
for i in tqdm.tqdm(range(0, durting)):
if _month > 12:
_year += 1
_month = 1
else:
pass
try:
rs_m = ts.xsg_data(year=_year, month=_month)
print(_month, _year)
rsd = rsd.append(rs_m, ignore_index=True)
except:
print('this month data error')
pass
_month += 1
_path = '../report/restricted_stock_%s.csv' % (self.year)
return rsd
def update_unblocked(self, during=12):
self.during = during
logger.info(get_time)
logger.info('update unblocked stock %s months' % self.during)
month = int(time.strftime("%m", time.localtime()))
year = int(time.strftime("%Y", time.localtime()))
df = pd.DataFrame()
for i in range(0, self.during):
if month > 12:
year += 1
month = 1
logger.debug('search the %s-%s datas' % (year, month))
try:
temp = ts.xsg_data(year=year, month=month)
df = df.append(temp, ignore_index=True)
except:
logger.error('this month data error')
pass
month += 1
#df=df.reset_index()
logger.info('downloading_completed')
return df
def xsg_data(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.xsg_data()
self.base.batchwri(self.df, 'xianshougujiejin', self.financial_data)
def get_classified_stock(self, year=None, month=None):
df = ts.xsg_data(year, month)
filename = '{}-{}-classified_stock.xls'.format(year, month)
self.save_to_excel(df, filename)
import tushare as ts # 获取分配预案数据 df = ts.profit_data(top=60) # 业绩预告 # 按年度、季度获取业绩预告数据，接口提供从1998年以后每年的业绩预告数据，需指定年度、季度两个参数。 fd = ts.forecast_data(2014, 2) print(fd) # 限售股解禁 # 以月的形式返回限售股解禁情况，通过了解解禁股本的大小，判断股票上行的压力。可通过设定年份和月份参数获取不同时段的数据。 xsg = ts.xsg_data() # 基金持股 # 获取每个季度基金持有上市公司股票的数据。 fh = ts.fund_holdings(2014, 4) print(fh) # 新股数据 ns = ts.new_stocks() print(ns) # 融资融券（沪市） # 沪市的融资融券数据从上海证券交易所网站直接获取，提供了有记录以来的全部汇总和明细数据。根据上交所网站提示：数据根据券商申报的数据汇总，由券商保证数据的真实、完整、准确。 sm = ts.sh_margins(start='2015-01-01', end='2015-04-19') print(sm) # 融资融券（深市） ts.sz_margins(start='2015-01-01', end='2015-04-19')
import tushare as ts
import pymongo
import json
# 600547:山东黄金
# 600362:江西铜业
# 600312:平高电气
# 600499：科达洁能
# 603993：洛阳钼业
db = "InvestInfos"
coll = "XSG_jiejin"
year = 2017
month = 12
conn = pymongo.MongoClient('127.0.0.1', port=27017)
df = ts.xsg_data()
# index data columns(X columns)
dicIndex = json.loads(df.to_json(orient='split'))
for i, ind in enumerate(dicIndex['index']):
jsonstr = {
'_id': dicIndex['data'][i][2] + "-" + dicIndex['data'][i][0],
'year': year,
'month': month,
dicIndex['columns'][0]: dicIndex['data'][i][0],
dicIndex['columns'][1]: dicIndex['data'][i][1],
dicIndex['columns'][2]: dicIndex['data'][i][2],
dicIndex['columns'][3]: dicIndex['data'][i][3],
dicIndex['columns'][4]: dicIndex['data'][i][4]
}
try:
conn[db][coll].insert(jsonstr)
def XsgJj():
df=ts.xsg_data()
df['ratio']=df['ratio'].astype(float)
df=df.sort_values('ratio',ascending=False)
return [u"限售股解禁：",df[df.ratio>10]]
def get_classified_stock(self,year=None,month=None):
df=ts.xsg_data(year,month)
filename='{}-{}-classified_stock.xls'.format(year,month)
self.save_to_excel(df,filename)
import tushare as ts
import rqalpha as rq
if __name__ == '__main__':
stock_list = ts.get_stock_basics()
stock_list.loc[:, 'name_3'] = stock_list.apply(lambda x: x['name'][0:3],
axis=1)
ST_list = stock_list[stock_list.name_3 == '*ST']
ST_list.drop(['name_3'], inplace=True, axis=1, errors='ignore')
ST_list.loc[:, '5日涨幅'] = 0
ST_list.loc[:, '10日涨幅'] = 0
ST_list.loc[:, '20日涨幅'] = 0
ST_list.loc[:, '最新价格'] = 0
ST_list.loc[:, '最新价格'] = 0
ST_list.loc[:, '限售股解禁'] = "N"
xsg1 = ts.xsg_data(month='02') #限售股解禁
xsg2 = ts.xsg_data(month='03') #限售股解禁
xsg3 = ts.xsg_data(month='04') #限售股解禁
for stk in ST_list.index:
# 20日涨幅
hist = ts.get_hist_data(stk, start='2017-02-08',
end='2017-03-07').sort_index(ascending=False)
if len(hist) > 0:
start_price = hist.tail(1).iloc[0, 0]
end_price = hist.head(1).iloc[0, 0]
percentage = 100 * (end_price - start_price) / start_price
ST_list.loc[stk, '20日涨幅'] = percentage
# 最新价格
ST_list.loc[stk, '最新价格'] = end_price
holding=stock.values
len(holding)
first=holding[0][0]
sec=holding[1][0]
he=first+sec
ho=re.split('\s+',he)
import pandas as pd
import tushare as ts
stock=ts.get_stock_basics()
s=stock.name
code=stock.index
#解禁股,st股，亏损股
m1=ts.xsg_data(month=11,year=2017)
m2=ts.xsg_data(month=12,year=2017)
m3=ts.xsg_data(month=1,year=2018)
m4=ts.xsg_data(month=2,year=2018)
st=ts.get_st_classified()
nianbao=ts.get_report_data(2016,4)
kui=nianbao.code[nianbao.eps<=0]
hei=pd.concat([m1.code,m2.code,m3.code,st.code,kui],axis=0)
hei.to_excel('hei.xls')
hq.loc[:,:28]=hq.loc[:,:28].astype('float64')
ts.get_concept_classified?? #显示源码
#m3=ts.xsg_data(month=1)
#st股
#实时5+1day
m=ts.get_k_data('399101', ktype='5')
#coding=utf-8
'''
Created on 2015年6月4日
@author: Administrator
'''
import tushare as ts
# 分配预案
#每到季报、年报公布的时段，就经常会有上市公司利润分配预案发布，而一些高送转高分红的股票往往会成为市场炒作的热点。
df = ts.profit_data(top=60)
df.sort('shares',ascending=False)
df[df.shares>=10]#选择每10股送转在10以上的
# 业绩预告
ts.forecast_data(2014,2)#获取2014年中报的业绩预告数据
# 限售股解禁
# 以月的形式返回限售股解禁情况，通过了解解禁股本的大小，判断股票上行的压力。可通过设定年份和月份参数获取不同时段的数据。
ts.xsg_data()
# 新股数据
ts.new_stocks()
# 融资融券
ts.sh_margins(start='2015-01-01', end='2015-04-19')
ts.sz_margins(start='2015-01-01', end='2015-04-19')
#如果不设symbol参数或者开始和结束日期时段设置过长，数据获取可能会比较慢，建议分段分步获取，比如一年为一个周期
ts.sh_margin_details(start='2015-01-01', end='2015-04-19', symbol='601989')
ts.sz_margin_details('2015-04-20')
