def getprofitreport():
profitreport = "./output/profit_%d_%d.csv"
outreport = "./output/profitcontinuous.csv"
year = 2015
quarter = 4
dflist = []
for i in range(3):
report = profitreport % (year, quarter)
if os.path.exists(report):
print("Get data from csv")
dfsingle = pd.read_csv(report, encoding="ANSI")
else:
print("Get data from internet")
try:
dfsingle = ts.get_report_data(year=year, quarter=quarter)
dfsingle["period"] = "%d_%d" % (year, quarter)
except Exception as e:
dfsingle = ts.get_report_data(year=year, quarter=quarter - 1)
dfsingle["period"] = "%d_%d" % (year, quarter - 1)
dfsingle.to_csv(report)
dflist.append(dfsingle)
year += 1
dfall = pd.concat(dflist)
dfposprofit = pd.DataFrame(dfall[(dfall["net_profits"] > 0)
& (dfall["profits_yoy"] > 0)])
result = dfposprofit.groupby(["code", "name"]).size().reset_index()
print(result.columns)
result.columns = ["code", "name", "poscontinue"]
print(result)
result = pd.DataFrame(result[(result["poscontinue"] >= 3)],
columns=["code", "name"]).drop_duplicates()
print(result)
result.to_csv(outreport, index=False)
def main():
year=2004
season=1
while year<2017:
while season<5:
try:#本地有记录
with open('report_'+str(year)+'_'+str(season)+'.pkl', "rb") as f:
report = pickle.load(f)
except:#本地没有存过
with open('report_' + str(year) + '_' + str(season) + '.pkl', "wb") as f:
report_1st = ts.get_report_data(year, season) # 获取业绩报表
report_2nd = ts.get_report_data(year, season) # 获取业绩报表
report_3rd = ts.get_report_data(year, season) # 获取业绩报表
#把三次合并:
report_all3 = pd.concat((report_1st,report_2nd,report_3rd),axis=0,join='outer')
#report_all3.to_excel(r'D:\work_python\DeepData\tushare_download\201701_01_all3.xlsx')
report =report_all3.drop_duplicates()
#report.to_excel(r'D:\work_python\DeepData\tushare_download\201701_01_quchong.xlsx')
pickle.dump(report, f)
#report =report.drop_duplicates()
#report.to_excel(r'D:\work_python\DeepData\tushare_download\201701_01_quchong.xlsx')
engine = create_engine(
'mysql+pymysql://' + DATABASS_USER_NAME + ':' + DATABASS_PASSWORD + '@127.0.0.1/'+DATABASS_NAME+'?charset=utf8')
#存入数据库
report.to_sql('report_'+str(year)+'_'+str(season),engine,if_exists='replace')
season=season+1
season =1
year += 1
def save_report_data():
"""
tushare: save_report_data
"""
report = ts.get_report_data(2017,2)
report.to_csv(CSV_REPORT_DATA_S2)
report = ts.get_report_data(2017,1)
report.to_csv(CSV_REPORT_DATA_S1)
def get_report_data(self, year, loops):
for i in range(1,loops):
print(i)
for j in year:
print(year)
try:
ts.get_report_data(j, 4).to_csv('report_data_%d.csv'%j, encoding='utf-8')
print(j)
year.remove(j)
except: pass
def find2017():
r2017 = ts.get_report_data(2017,3).set_index("code")
r2016 = ts.get_report_data(2016,4).set_index("code")
l = []
l1= []
for i in r2017.index:
if i not in r2016.index:
continue
rate = (r2017.net_profits[i] - r2016.net_profits[i]) / r2016.net_profits[i]
try:
rate = float(rate)
except:
rate = rate.values[0]
if rate > 0.25:
# l.append((rate,i))
l1.append(i)
print(l1)
r2015 = ts.get_report_data(2015, 4).set_index("code")
l2 = []
for i in l1:
if i not in r2015.index:
continue
rate = (r2016.net_profits[i] - r2015.net_profits[i]) / r2015.net_profits[i]
try:
rate = float(rate)
except:
rate = rate.values[0]
if rate > 0.3:
# ll.append((l1[i],rate, i))
l2.append(i)
r2014 = ts.get_report_data(2014, 4).set_index("code")
print(l2)
l3 = []
for i in l2:
if i not in r2014.index:
continue
rate = (r2015.net_profits[i] - r2014.net_profits[i]) / r2014.net_profits[i]
try:
rate = float(rate)
except:
rate = rate.values[0]
if rate > 0.3:
# ll.append((l1[i],rate, i))
l3.append(i)
print(l3)
return l3
def basic_information():
ts.get_cashflow_data(2017, 1).to_sql('cash_flow',
engine,
if_exists='append')
ts.get_debtpaying_data(2017, 1).to_sql('debtpaying',
engine,
if_exists='append')
ts.get_growth_data(2017, 1).to_sql('growth', engine, if_exists='append')
ts.get_operation_data(2017, 1).to_sql('operation',
engine,
if_exists='append')
ts.get_profit_data(2017, 1).to_sql('profit', engine, if_exists='append')
ts.get_report_data(2017, 2).to_sql('report', engine, if_exists='append')
print('basic information over ....')
def _fetch_finance():
for year in range(2004, 2018):
set_year = lambda x: str(year) + '-' + x
for quarter in range(1, 5):
print(year, ' year ', 'quarter ', quarter)
rep = ts.get_report_data(
year, quarter)[['code', 'eps', 'bvps', 'epcf', 'report_date']]
pro = ts.get_profit_data(year, quarter)[[
'code', 'roe', 'net_profit_ratio', 'gross_profit_rate',
'net_profits', 'business_income', 'bips'
]]
ope = ts.get_operation_data(year, quarter)[[
'code', 'arturnover', 'arturndays', 'inventory_turnover',
'currentasset_turnover', 'currentasset_days'
]]
gro = ts.get_growth_data(
year, quarter)[['code', 'mbrg', 'nprg', 'nav', 'epsg', 'seg']]
deb = ts.get_debtpaying_data(year, quarter)[[
'code', 'currentratio', 'quickratio', 'cashratio', 'icratio',
'sheqratio', 'adratio'
]]
cas = ts.get_cashflow_data(year, quarter)[[
'code', 'cf_sales', 'rateofreturn', 'cf_nm', 'cf_liabilities',
'cashflowratio'
]]
rep.rename(columns={'report_date': 'date'}, inplace=True)
rep['date'] = rep['date'].apply(set_year)
rep = rep.merge(pro, on='code', how='left')
rep = rep.merge(ope, on='code', how='left')
rep = rep.merge(gro, on='code', how='left')
rep = rep.merge(deb, on='code', how='left')
rep = rep.merge(cas, on='code', how='left')
finance.insert(rep.to_dict('record'))
print(year, quarter)
def valuation_factor(year):
report = ts.get_report_data(year,4)
report = report.sort_values(by = 'code',axis = 0,ascending = True)
report = report.reset_index(drop = True)
report.to_csv("/home/yirui/Desktop/Quant/Report/%s.csv"%year, mode="w")
profit = ts.get_profit_data(year, 4)
profit = profit.sort_values(by='code', axis=0, ascending=True)
profit = profit.reset_index(drop=True)
profit.to_csv("/home/yirui/Desktop/Quant/Profit/%s.csv"%year,mode= "w")
operation = ts.get_operation_data(year,4)
operation = operation.sort_values(by='code', axis=0, ascending=True)
operation = operation.reset_index(drop=True)
operation.to_csv("/home/yirui/Desktop/Quant/Operation/%s.csv" % year, mode="w")
growth = ts.get_growth_data(year,4)
growth = growth.sort_values(by='code', axis=0, ascending=True)
growth = growth.reset_index(drop=True)
growth.to_csv("/home/yirui/Desktop/Quant/Growth/%s.csv" % year, mode="w")
debtpaying = ts.get_debtpaying_data(year,4)
debtpaying = debtpaying.sort_values(by='code', axis=0, ascending=True)
debtpaying = debtpaying.reset_index(drop=True)
debtpaying.to_csv("/home/yirui/Desktop/Quant/Debtpaying/%s.csv" % year, mode="w")
cashflow = ts.get_cashflow_data(year,4)
cashflow = cashflow.sort_values(by='code', axis=0, ascending=True)
cashflow = cashflow.reset_index(drop=True)
cashflow.to_csv("/home/yirui/Desktop/Quant/Cashflow/%s.csv" % year, mode="w")
def getData():
#获取财务数据保存到本地，修改tushare源码使其仅包括金融类股票
for year in range(2010,2011):
for season in range(3,5):
df = ts.get_report_data(year,season)
filename = str(year) + '-' + str(season)
df.to_csv(filename,sep=',', encoding='utf-8')
def get_stock_report_manual(year, season):
frame = ts.get_report_data(year, season)
table_name = 'stock_report_' + str(year) + 's' + str(season)
db = MySQLdb.connect(host='localhost',
port=3306,
user='******',
passwd='123456',
db='stock_ts',
charset='utf8')
cursor = db.cursor()
createDBSql = 'create table if not exists ' + table_name + '(code varchar(10), name varchar(16), eps text, eps_yoy text, bvps text, roe text, epcf text, net_profits text, profits_yoy text, distrib text, report_date text)'
cursor.execute(createDBSql)
for i in range(0, len(frame)):
prefix = 'insert into ' + table_name + '(code, name, eps, eps_yoy, bvps, roe, epcf, net_profits, profits_yoy, distrib, report_date) values(\'%s\', \'%s\', \'%s\', \'%s\', \'%s\', \'%s\', \'%s\', \'%s\', \'%s\', \'%s\', \'%s\')'
sql = prefix % (
frame['code'][i], str(frame['name'][i]), float(frame['eps'][i]),
float(frame['eps_yoy'][i]), float(frame['bvps'][i]),
float(frame['roe'][i]), float(frame['epcf'][i]),
float(frame['net_profits'][i]), float(frame['profits_yoy'][i]),
str(frame['distrib'][i]), str(frame['report_date'][i]))
#print(sql)
cursor.execute(sql)
db.commit()
print(frame['name'][i])
db.close()
def get_report_data(self, year, quarter):
tsdata = ts.get_report_data(
year=year,
quarter=quarter,
)
jsdata = To_Json(tsdata)
return jsdata
def get_stock_report(year, season, engine): frame = ts.get_report_data(year, season) table_name = 'stock_report_' + str(year) + 's' + str(season) if useDB == True: frame.to_sql(table_name, engine) else: frame.to_csv(table_name + '.csv')
def fImportReportData(year="", quarter=""):
df = ts.get_report_data(year=year, quarter=quarter)
df['YEAR'] = year
df['QUARTER'] = quarter
#print(df)
engine = create_engine('oracle://c##tushare:didierg160@myoracle')
df.to_sql('tb_stock_report_data', engine, if_exists='append')
def export(exportType, datePicker):
sb = None
[year, quarter] = getYearQuarter(datePicker)
if exportType == "report":
name = "业绩报表"
sb = ts.get_report_data(year, quarter)
elif exportType == "profit":
name = "盈利能力报表"
sb = ts.get_profit_data(year, quarter)
elif exportType == "operation":
name = "营运能力报表"
sb = ts.get_operation_data(year, quarter)
elif exportType == "growth":
name = "成长能力报表"
sb = ts.get_growth_data(year, quarter)
elif exportType == "debtpaying":
name = "偿债能力报表"
sb = ts.get_debtpaying_data(year, quarter)
elif exportType == "cashflow":
name = "现金流量报表"
sb = ts.get_cashflow_data(year, quarter)
filename = quote(name + str(year) + "Q" + str(quarter) + ".xlsx")
filepath = os.path.join(basedir, app.config['UPLOAD_FOLDER'], filename)
sb.to_excel(filepath)
rtn = send_file(filepath, as_attachment=True)
rtn.headers['Content-Disposition'] += "; filename*=utf-8''%s" % (filename)
return rtn
def get_basic_datas(
data_kind): #datakind 为debtpaying,growth,operation,profit,report
client = pymongo.MongoClient('localhost', 27017)
table_stock = client['stock']
sheet = table_stock[data_kind]
for year in range(STARTYEAR, ENDYEAR + 1):
try:
for season in range(1, 5):
print('getting ' + datakind + ' data at year:' + str(year) +
" season:" + str(season))
if data_kind == 'debtpaying':
tf = ts.get_debtpaying_data(year, season)
elif data_kind == 'growth':
tf = ts.get_growth_data(year, season)
elif data_kind == 'operation':
tf = ts.get_operation_data(year, season)
elif data_kind == 'profit':
tf = ts.get_profit_data(year, season)
elif data_kind == 'report':
tf = ts.get_report_data(year, season)
else:
print('Not available data type of data_kind!')
return
jsonres = json.loads(tf.to_json(orient='records'))
for j in jsonres:
sheet.insert_one(j)
except: #数据缺失，tushare接口会报网络错误
print('the year: ' + str(year) + ' lost data will begin next year')
continue
def update_basics():
basics = ts.get_stock_basics()
f = os.path.join(DATA_DIR, 'basics.h5')
basics.to_hdf(f, 'basics')
length = 4 * 5
year, season = last_report_season()
for i in range(length):
f = os.path.join(DATA_DIR, 'basics-{0}-{1}.h5'.format(year, season))
if os.path.exists(f):
continue
report = ts.get_report_data(year, season)
report.to_hdf(f, 'report')
profit = ts.get_profit_data(year, season)
profit.to_hdf(f, 'profit')
operation = ts.get_operation_data(year, season)
operation.to_hdf(f, 'operation')
growth = ts.get_growth_data(year, season)
growth.to_hdf(f, 'growth')
debtpaying = ts.get_debtpaying_data(year, season)
debtpaying.to_hdf(f, 'debtpaying')
cashflow = ts.get_cashflow_data(year, season)
cashflow.to_hdf(f, 'cashflow')
season -= 1
if season == 0:
season = 4
year -= 1
def report_data():
#获取2014年第3季度的业绩报表数据
#季度 :1、2、3、4，只能输入这4个季度
quarter = 1
# 获取当前的年和月
nowYear = int(time.strftime('%Y', time.localtime(time.time())))
nowMonth = int(time.strftime('%m', time.localtime(time.time())))
if nowMonth <= 3:
# 上一年的第四季度
nowYear -= 1
quarter = 4
elif nowMonth <= 6:
# 第一季度
quarter = 1
elif nowMonth <= 9:
# 第一季度
quarter = 2
elif nowMonth <= 12:
# 第一季度
quarter = 3
rd = ts.get_report_data(nowYear, quarter)
rd.to_csv('report_data.csv')
def stat_stock_report(tmp_datetime, max_year=11):
"""
以后每年7月份取一下上年的年报即可，历史数据不必再取
经验: 19年4月份取18年的年报是不全的，所以延到7月取
"""
cur_year = int((tmp_datetime).strftime("%Y"))
# cur_year = 2005
i = cur_year - max_year
# i = 2001
MAX_RETRY_TIME = 3
retry_time = 0
while i < cur_year:
try:
data = ts.get_report_data(i, 4)
except IOError:
data = None
if not data is None and len(data) > 0:
print("\nyear done", i)
# data = data.drop_duplicates(subset="code", keep="last")
data.insert(0, "year", [i] * len(data))
data.head(n=1)
common.insert_db(data, "ts_stock_report", False, "`year`,`code`")
i += 1
retry_time = 0
else:
print("\nno data . stock_report year", i)
retry_time += 1
if retry_time > MAX_RETRY_TIME:
i += 1
retry_time = 0
time.sleep(5) # 停止5秒
def __init__(self,year=None,quarter=None,save=True,updateAll=False,beginyear=1990,beginquarter=1):
"""
获取季度的公司业绩报告
@@parm:year:年份
@@parm:quarter:季度
@@parm:updateAll是否全量更新历史所有数据
"""
now = dt.datetime.now()
if year is None or quarter is None:
year = now.year
quarter = math.ceil(now.month/3.0)
if not updateAll:
try:
profitData = ts.get_report_data(year,quarter)
profitData['datatime'] = now.strftime('%Y-%m-%d')
profitData['datatimestramp'] = now.strftime('%H:%M:%S')
profitData['year'] = year
profitData['quarter'] = quarter
indexlist = ['code','year','quarter']##数据库索引
tableName = 'companyRepor'
database(profitData,indexlist,tableName,save)
except :
traceback.print_exc()
else:
##生成季度时间序列
quarters = get_quarters((beginyear,beginquarter),(year,quarter))
for y,q in quarters:
companyRepor(y,q)##递归获取所有历史数据
def get_data(self, year, quarter):
data = ts.get_stock_basics()
data_roe = pd.DataFrame()
for i in range(4):
quarter += 1
data_roe = pd.concat((data_roe, ts.get_report_data(year, quarter)))
self.save_data(data, data_roe)
def stock_report(year, quarter, k_index):
"""
:param k_index: the k_index can be:
1: name
2: eps
3: eps_yoy
4: bvps
5: roe
6: epcf
7: net_profits
8: profits_yoy
9: distrib
10: report_data
:return: the data to be captured for k_index and for all code, code is the index of return result
"""
index_list = [
'name', 'eps', 'eps_yoy', 'bvps', 'roe', 'epcf', 'net_profits',
'profits_yoy', 'distrib', 'report_date'
]
if k_index not in index_list:
raise Exception('invalid k_index - the setting is not in the scope')
if year <= 0:
raise Exception('invalid year that should be larger than 0')
if quarter <= 0 and quarter > 4:
raise Exception('invalid quarter that we just 4 quarter in market')
data = ts.get_report_data(year, quarter)
new_data = data.set_index(['code'])
return new_data[k_index]
def call_report_v1(year, quarter):
'''
code,代码
name,名称
esp,每股收益
eps_yoy,每股收益同比(%)
bvps,每股净资产
roe,净资产收益率(%)
epcf,每股现金流量(元)
net_profits,净利润(万元)
profits_yoy,净利润同比(%)
distrib,分配方案
report_date,发布日期
'''
key = f'v1_report_{year}_{quarter}'
stores = pd.HDFStore(COMMEN_FILE_PATH)
if key not in stores:
df = ts.get_report_data(year, quarter)
if df.empty:
return df
stores[key] = df
if DEBUG:
print('STORE:', filePath)
else:
df = stores[key]
stores.close()
return df
def store_fund_data(quarter_list):
stock2year_path = os.path.join(LastFilePath, "stock_fundm_info")
for fun_year, fun_quarter in quarter_list:
#every dataframe you craw down all needs remove the duplicated row. Only need keep the first row of duplicates.
# stock2year_report is tushare:get_report_data (fundamental data).
stock2year_report = ts.get_report_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
# stock2year_prof is tushare.get_profit_data (fundamental data).
stock2year_prof = ts.get_profit_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
# stock2year_opera is tushare.get_operation_data (fundamental data).
stock2year_opera = ts.get_operation_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_grow is tushare.get_growth_data (fundamental data).
stock2year_grow = ts.get_growth_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_debt is tushare.get_debtpaying_data (fundamental data).
stock2year_debt = ts.get_debtpaying_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_cash is tushare.get_cashflow_data (fundamental data).
stock2year_cash = ts.get_cashflow_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_comb is to combine all the stock2year data of same year and quarter in a same stock code.
stock2year_list = [stock2year_report,stock2year_prof,stock2year_opera,stock2year_grow, \
stock2year_debt,stock2year_cash]
for every_fund_element in stock2year_list:
every_fund_element = every_fund_element.set_index('code')
#use pandas concat to combine all the dataframe along columns.
total_fund = pd.concat(stock2year_list, axis=1)
HeadName = fun_year + "/" + fun_quarter + "_" + "fundamt_info"
CsvName = os.path.join(stock2year_path, "{}.csv".format(HeadName))
total_fund.to_csv(CsvName)
def getThingsEveryday(self):
yearEnd = datetime.now().year - 1
if self.__flagUpdateReport:
pdGrowthLastYear = ts.get_growth_data(yearEnd, 4)
pdGrowthLastYear.to_excel('./' + \
str(yearEnd) + 'Growth.xls', sheet_name='Growth')
pdProfitLastYear = ts.get_profit_data(yearEnd, 4)
pdProfitLastYear.to_excel('./' + \
str(yearEnd) + 'Profit.xls', sheet_name='Profit')
pdReportLastYear = ts.get_report_data(yearEnd, 4)
pdReportLastYear.to_excel('./' + \
str(yearEnd) + 'y.xls', sheet_name='Report')
self.__stockBasics = ts.get_stock_basics() #获得昨天pe
self.__stockBasics['code'] = self.__stockBasics.index.astype(int)
self.__stockBasics.sort_index(inplace=True)
self.__stockTodayAll = ts.get_today_all() #获得昨收
self.__stockTodayAll['code'] = self.__stockTodayAll['code'].astype(int)
self.__pdForwardEps = pd.merge(self.__stockBasics,
self.__stockTodayAll,
on='code')
self.__pdForwardEps['feps'] = self.__pdForwardEps[
'settlement'] / self.__pdForwardEps['pe']
print('\n')
def stat_all(tmp_datetime):
# 返回 31 天前的数据，做上个季度数据统计。
tmp_datetime_1month = tmp_datetime + datetime.timedelta(days=-31)
year = int((tmp_datetime_1month).strftime("%Y"))
quarter = int(pd.Timestamp(tmp_datetime_1month).quarter) # 获得上个季度的数据。
print("############ year %d, quarter %d", year, quarter)
# 业绩报告（主表）
data = ts.get_report_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
global db
# 插入数据库。
db.insert_db(data, "ts_report_data", True, "`quarter`,`code`")
# 盈利能力
data = ts.get_profit_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_profit_data", True, "`quarter`,`code`")
# 营运能力
data = ts.get_operation_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_operation_data", True, "`quarter`,`code`")
# 成长能力
data = ts.get_growth_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_growth_data", True, "`quarter`,`code`")
# 偿债能力
data = ts.get_debtpaying_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_debtpaying_data", True, "`quarter`,`code`")
# 现金流量
data = ts.get_cashflow_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_cashflow_data", True, "`quarter`,`code`")
def _load_report(self,year,season):
#从本地或者网上取得report
#year: int
#season: int,1,2,3,4
report_date=None
eps_yoy,roe,profits_yoy=0.0,0.0,0.0
try:#本地有记录
with open('report_'+str(year)+'_'+str(season)+'.pkl', "rb") as f:
report = pickle.load(f)
except:#本地没有存过
with open('report_' + str(year) + '_' + str(season) + '.pkl', "wb") as f:
report = ts.get_report_data(year, season) # 获取业绩报表
pickle.dump(report, f)
for i in range(report.index.size):
if(report.iloc[i,0]==self.ticker):
#公布报告的日期：
if(season==4):#如果是4季报，发布年份是下一年
report_year=year+1
else:
report_year=year
report_date = date(report_year,int(report.iloc[i,10][0:2]),int(report.iloc[i,10][3:5]))#先看看报告日期.形如'06-16'
eps_yoy = report.iloc[i,3]/100
roe = report.iloc[i,5]/100
profits_yoy = report.iloc[i,8]/100
break
return report_date,[eps_yoy,roe,profits_yoy]
def main():
print("pd version:%s" %pd.__version__)
print("tushare version:%s" %ts.__version__)
'''
data = ts.get_today_all()
data['Profit yield'] = None
for i in range(0, len(data.index)):
if data.at[i, 'per'] == 0: #可能停牌的股票
continue
#if 'ST' in data.at[i, 'name']:
# continue
data.at[i, 'Profit yield'] = 1 / data.at[i, 'per']
data.to_csv("./get_today_all.csv")
'''
databasic = ts.get_stock_basics()
#databasic.sort_values(by = 'pb').to_csv("./get_today_all.csv")
dta8_1 = ts.get_report_data(2018, 1)
dtaROE = databasic['pe'] / dta8_1['roe']
print(dtaROE)
def call_report_v1(year, quarter):
'''
code,代码
name,名称
esp,每股收益
eps_yoy,每股收益同比(%)
bvps,每股净资产
roe,净资产收益率(%)
epcf,每股现金流量(元)
net_profits,净利润(万元)
profits_yoy,净利润同比(%)
distrib,分配方案
report_date,发布日期
'''
filePath = COMMEN_FILE_PATH + f'v1_report_{year}_{quarter}.csv'
if not os.path.exists(filePath):
df = ts.get_report_data(year, quarter)
if df.empty:
return df
df.to_csv(filePath)
if DEBUG:
print('STORE:', filePath)
else:
df = pd.read_csv(filePath)
return df
def get_temp_data(year, quarter):
df1 = ts.get_report_data(year, quarter)
#print (1)
df1 = df1.merge(ts.get_profit_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (2)
df1 = df1.merge(ts.get_operation_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (3, "n", df1)
df1 = df1.merge(ts.get_growth_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (4)
print(df1)
df1 = df1.merge(ts.get_debtpaying_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (5)
print(df1)
df1 = df1.merge(ts.get_cashflow_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (6)
(row, col) = df1.shape
for i in range(0, row):
df1.iloc[i, 0] = str(df1.iloc[i, 0])
return df1
def download_ACH_Q(year, quarter, df): #按照季度获取信息
Data = df
try:
achievement = ts.get_report_data(year, quarter)
achievement = pd.DataFrame(achievement)
except:
pass
achievement = achievement.set_index('code')
achievement = achievement.sort_index()
for title_name in achievement.columns:
print(title_name)
for code_ in achievement.index[1:]:
if achievement.at['%s' % code_, '%s' % title_name] != NaN:
try:
#print(achievement.at['%s'%code_,'%s'title_name])
Data.ix['%s' % code_,
'%s-%s-%s' %
(title_name, year,
quarter)] = achievement.at['%s' % code_,
'%s' % title_name]
except:
buf = achievement.at['%s' % code_, '%s' % title_name]
Data.ix['%s' % code_,
'%s-%s-%s' % (title_name, year, quarter)] = buf[0]
else:
pass
#print (code_,title_name)
#print (achievement.head(9))
return Data
def value_factor(end_year, season):
'''
计算价值因子
'''
#价值因子：每股收益与价格比率、每股经营现金流与价格比率、每股净资产与价格比率、股息收益率
df_report_new = ts.get_report_data(
end_year, season)[["name", "code", "eps", "epcf", "bvps"]]
df_new_price = ts.get_today_all()[["name", "code", "settlement"]]
file = "2005_2011.csv" if end_year in range(2005,
2012) else "2012_2018.csv"
df_interest = pd.read_csv(file, dtype={'code':
str})[["code",
str(end_year)]]
df_interest.columns = ["code", "interest_rate"]
data = pd.merge(df_report_new, df_new_price, how='inner')
data = pd.merge(data, df_interest, how='inner')
# 去掉ST股
data = data[data.name.map(lambda x: "ST" not in x)]
data["eps_rate"] = data["eps"] / data["settlement"]
data["epcf_rate"] = data["epcf"] / data["settlement"]
data["bvps_rate"] = data["bvps"] / data["settlement"]
#print("value_factor dataframe length: " + str(len(data)))
return data[[
"name", "code", "eps_rate", "epcf_rate", "bvps_rate", "interest_rate"
]].drop_duplicates().fillna(0.0)
def get_report_data(year, season):
if not available(year, season):
return None
print("get_report_data")
save(ts.get_report_data(year, season), "basics/report_data", year, season)
print("get_profit_data")
save(ts.get_profit_data(year, season), "basics/profit_data", year, season)
filename = "operation_data"
print("get_operation_data")
save(ts.get_operation_data(year, season), "basics/operation_data", year,
season)
filename = "growth_data"
print("get_growth_data")
save(ts.get_growth_data(year, season), "basics/growth_data", year, season)
filename = "get_debtpaying_data"
print("get_debtpaying_data")
save(ts.get_debtpaying_data(year, season), "basics/debtpaying_data", year,
season)
filename = "get_debtpaying_data"
print("get_cashflow_data")
save(ts.get_cashflow_data(year, season), "basics/cashflow_data", year,
season)
def divident_rate(self):
stock = ts.get_hist_data(self.id)
df = dividend_rate.get_bonus_table(self)
df_dividend = df[['年度', '派息', '登记日']]
# print(df_dividend)
stock_close_price = stock["close"]
sIndex = stock_close_price.index.tolist()
# 获取登记日
regis = df_dividend['登记日'].tolist()
# print(sIndex)
# print(regis)
close_price = []
diVi = []
aPe = []
bonus = []
div_year = []
for i in regis:
if i != "--" and i in sIndex:
cprice = stock_close_price.loc[i]
close_price.append(cprice)
aDiv = df_dividend[df_dividend['登记日'] == i]['派息'].tolist()[0]
year = df_dividend[df_dividend['登记日'] == i]['年度'].values #获得年份
div_year.append(year[0])
#此处的bonus暂时通过ts获得，以后可以直接搜索本地数据库
profit_table = ts.get_report_data(year[0], 4) #获取年度eps
print('')
target_eps = profit_table[profit_table['code'] ==
self.id]['eps'].values
eps = target_eps[0].item() #numpy.float64 -> float
per_bonus = round(float(aDiv) / 10 / eps * 100, 2)
# per_bonus = 1 #测试时开启
bonus.append(per_bonus)
diVi.append(float(aDiv) / 10) #10股派息转1股派息
div_ratio = []
for i, j in zip(diVi, close_price):
adivr = float(i) / float(j) * 100
div_ratio.append(round(adivr, 2))
aPe.append(round(100 / adivr, 2))
reDf = pd.DataFrame(
{
"cash_div": diVi, #每股派现方案
"div_ratio(%)": div_ratio, #股息率
'ape': aPe, #真实市盈率
'bonus_ratio(%)': bonus #分红率
},
index=div_year)
# 统计输出
print(self.id + '分红情况统计如下：')
avg_bonus = round(sum(bonus) / len(bonus), 2)
print('1.平均分红率:', avg_bonus, '%')
avg_div = round(sum(div_ratio) / len(div_ratio), 2)
print('2.平均股息率:', avg_div, '%')
print('3.详细列表如下所示')
return reDf
def get_report_data(year, quarter):
try:
df = ts.get_report_data(year, quarter)
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('report_data', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def sync_report_data(): ''' sync report data ''' year = datetime.datetime.now().year month = datetime.datetime.now().month seaon = month/3 if month<3: year = year - 1 seaon = 4 monthstr = '%s%s'%(year,seaon) DataFrameToMongo(ts.get_report_data(year, seaon), MongoClient(mongourl)['stoinfo']['report_data'], ['code'], monthstr)
def getQuerterReport(year):
for q in [1, 2, 3, 4]:
getDownloaded()
if not isDownloaded(reportByYear(year, q)):
try:
print('Report of ' + str(year) + ' and quarter is ' + str(q))
report_df = ts.get_report_data(year, q)
report_df.to_csv(path + reportByYear(year, q))
time.sleep(5)
except Exception:
logError('Report: ' + str(year) + '-' + str(q) +
' there is problem, will skip it. ')
def collect_report_data(year, term):
try:
report_data_path = os.path.join(BASE_FOLDER, 'report')
if not os.path.isdir(report_data_path):
os.makedirs(report_data_path)
path = os.path.join(report_data_path, '{}-{}.csv'.format(year, term))
if not os.path.exists(path):
df = ts.get_report_data(year, term)
df.to_csv(path)
return report_data_path
except Exception as ex:
print("error occurred in retrieving report data: ", ex)
return None
def profit(self):
df_2016=ts.get_report_data(2016,4)
#第四季度就是年报
#df= df.sort_values('profits_yoy',ascending=False)
#df.to_excel('profit.xls')
df_2015=ts.get_report_data(2015,4)
df_2016.to_excel('2016_report.xls')
df_2015.to_excel('2015_report.xls')
code_2015_lost=df_2015[df_2015['net_profits']<0]['code'].values
code_2016_lost=df_2016[df_2016['net_profits']<0]['code'].values
print code_2015_lost
print code_2016_lost
two_year_lost=[]
#two_year_lost_name=[]
for i in code_2015_lost:
if i in code_2016_lost:
print i,
#name=self.base[self.base['code']==i].values[0]
two_year_lost.append(i)
self.saveList(two_year_lost,'st_dangours.csv')
def get_basic():
hsdq = stock_info.ix['300141']
print(hsdq)
report = ts.get_report_data(2014, 1)
print(report)
# hsdq=stock_info.ix['300141']
# print(hsdq)
# report=ts.get_report_data(2014,1)
# print(report)
print('*' * 20)
df = ts.get_today_all()
zrkj = df[df['code'] == '300333']
print(type(zrkj))
print(type(zrkj['code']))
print(zrkj['name'].values[0])
def updatereport():
reportdatalist=ts.get_report_data(2014,4)
reportdata=pd.DataFrame(reportdatalist)
conn= ms.connect(host='localhost',port = 3306,user='******', passwd='123456',db ='investment',charset="utf8")
cur = conn.cursor()
values=[]
for index,row in reportdata.iterrows():
if math.isnan(row['eps']):
eps=0
else:
eps=row['eps']
if math.isnan(row['eps_yoy']):
eps_yoy=0
else:
eps_yoy=row['eps_yoy']
if math.isnan(row['bvps']):
bvps=0
else:
bvps=row['bvps']
if math.isnan(row['epcf']):
epcf=0
else:
epcf=row['epcf']
if math.isnan(row['roe']):
roe=0
else:
roe=row['roe']
if math.isnan(row['net_profits']):
net_profits=0
else:
net_profits=row['net_profits']
if math.isnan(row['profits_yoy']):
profits_yoy=0
else:
profits_yoy=row['profits_yoy']
values.append((row['code'],row['name'],eps,eps_yoy,bvps,roe,epcf,net_profits,profits_yoy,'2015-'+row['report_date']))
cur.executemany('insert into report20144 (code,name,eps,eps_yoy,bvps,roe,epcf,net_profis,profis_yoy,report_date) values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',values)
conn.commit()
cur.close()
conn.close()
def update_basics():
basics = ts.get_stock_basics()
f = os.path.join(base_dir, 'basics.h5')
basics.to_hdf(f, 'basics')
today = datetime.date.today()
current_year = today.year
current_season = today.month / 3
if current_season == 0:
current_year -= 1
current_season = 4
length = 4 * 5
year = current_year
season = current_season
for i in range(length):
f = os.path.join(base_dir, 'basics-{0}-{1}.h5'.format(year, season))
if os.path.exists(f):
continue
print(f)
report = ts.get_report_data(year, season)
report.to_hdf(f, 'report')
profit = ts.get_profit_data(year, season)
profit.to_hdf(f, 'profit')
operation = ts.get_operation_data(year, season)
operation.to_hdf(f, 'operation')
growth = ts.get_growth_data(year, season)
growth.to_hdf(f, 'growth')
debtpaying = ts.get_debtpaying_data(year, season)
debtpaying.to_hdf(f, 'debtpaying')
cashflow = ts.get_cashflow_data(year, season)
cashflow.to_hdf(f, 'cashflow')
season -= 1
if season == 0:
season = 4
year -= 1
def finance_report(year=2018, quarter=2):
latest_equity = Equity.objects().order_by('-date').first()
# print(latest_equity)
date = latest_equity.date
df = ts.get_report_data(year, quarter)
print(df)
data = df.to_dict('index')
print(data)
print(len(data.items()))
from mongoengine.queryset.visitor import Q
for index, value in sorted(data.items()):
code = value['code']
name = value['name']
roe = value['roe']
eps = value['eps']
report_date = value['report_date']
# print('code:{} roe:{}'.format(code, roe))
FinanceReport.objects(code=code, year=year, quarter=quarter).update_one(code=code, name=name,
year=year, quarter=quarter,
report_date=report_date,
roe=roe, eps=eps, upsert=True)
def store_data(self):
# 预测
# year_2016=ts.forecast_data(2016, 4)
# self.save_to_excel(year_2016,'2016-profit.xls')
# year_2017=ts.forecast_data(2017, 4)
# self.save_to_excel(year_2017,'2017-profit.xls')
# 盈利能力
# profit_2016=ts.get_profit_data(2016,4)
# profit_2017=ts.get_profit_data(2017,3)
# self.save_to_excel(profit_2016, '2016-profit.xls')
# self.save_to_excel(profit_2017, '2017-3rdprofit.xls')
# 股票基本信息
# basic=ts.get_stock_basics()
# basic.to_csv('temp.xls',encoding='gbk')
# df=pd.read_csv('temp.xls',encoding='gbk',dtype={'code':str})
# # print df
# self.save_to_excel(df,'Markets.xls')
# 基本面 每股净资产<1
df=ts.get_report_data(2017, 3)
self.save_to_excel(df,'2017-3rd-report.xls')
"""
Created on Wed Oct 07 09:21:05 2015
@author: Fuqian
"""
from sqlalchemy import create_engine
import tushare as ts
#import pymongo
import pandas as pd
df_base = ts.get_stock_basics()
df_report_1503 = ts.get_report_data(2015,3)
df_profit_1503 = ts.get_profit_data(2015,3)
df_growth_1503 = ts.get_growth_data(2015,3)
#detail_daily={}
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
for row_index, row in df_base.iterrows():
try:
f = open('qfq_err', 'a')
f_d = open('detailDay_err','a')
#coding:utf-8
from sqlalchemy import create_engine
import tushare as ts
# define engine
engine = create_engine('mysql://*****:*****@127.0.0.1/tushare?charset=utf8')
df = ts.get_report_data(2014,4)
df = df.assign(quater=20144)
df.to_sql('report_data',engine, if_exists='append')
df = ts.get_report_data(2015,4)
df = df.assign(quater=20154)
df.to_sql('report_data',engine, if_exists='append')
df = ts.get_report_data(2016,4)
df = df.assign(quater=20164)
df.to_sql('report_data',engine, if_exists='append')
def pick_data(self, max_num_threads = 20, pause = 0):
"""
pick all necessary data from local database and from internet for loaded stocks. This function will take a while.
"""
logging.info('getting basics from tushare')
self._init_stock_objs()
# self.data_manager.drop_stock()
# self.stocks = {key: self.stocks[key] for key in ['600233', '600130']}
logging.info('totally there are %d listed companies' % len(self.stocks))
logging.info('get indexes from tushare')
self._get_indexes()
# self._pick_hist_data_and_save(self.stocks, False, self.indexes['000001'].hist_start_date, max_num_threads)
logging.info('getting last stock trading data')
df = ts.get_today_all()
self._extract_from_dataframe(df,
ignore=('changepercent', 'open', 'high', 'low', 'settlement', 'volume', 'turnoverratio', 'amount'),
remap={'trade': 'price', 'per': 'pe'})
# calculate the report quarter
report_year, report_quarter = ts.get_last_report_period()
logging.info('getting last report (%d quarter %d) from tushare' % (report_year, report_quarter))
df = ts.get_report_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last profit data from tushare')
df = ts.get_profit_data(report_year, report_quarter)
self._extract_from_dataframe(df, ignore=('net_profits', 'roe', 'eps'))
logging.info('getting last operation data from tushare')
df = ts.get_operation_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last growth data from tushare')
df = ts.get_growth_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last debtpaying data from tushare')
df = ts.get_debtpaying_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last cashflow data from tushare')
df = ts.get_cashflow_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting history trading data from tushare')
start_from = self.indexes['000001'].hist_start_date
data_full = self._pick_hist_data_and_save(self.stocks, False, start_from, max_num_threads, pause) # anything that pulling data must before here
self._remove_unavailable_stocks()
'''
# calculate qianfuquan data
# deprecated due to precision issue
for code, stock in self.stocks.items():
for i in range(1, len(stock.hist_data.index)-1):
b = stock.hist_data.at[stock.hist_data.index[i], 'close']
a = stock.hist_data.at[stock.hist_data.index[i+1], 'close']
p = stock.hist_data.at[stock.hist_data.index[i+1], 'p_change'] / 100.0
q = (p*a+a)/b
if q > 1.1:
print('%s chuq-uan %s: %s %s %s, 1/%s' % (stock, stock.hist_data.index[i], b, a, p, q))
'''
return data_full
import tushare as ts
import marshal, pickle
year = 2015
print(report_data)
report_data = ts.get_report_data(year, 4)
f = file('report_data', 'w')
pickle.dump(report_data, f)
f.close()
profit_data = ts.get_profit_data(year, 4)
f = file('profit_data', 'w')
pickle.dump(profit_data, f)
f.close()
growth_data = ts.get_growth_data(year, 4)
f = file('growth_data', 'w')
pickle.dump(growth_data, f)
f.close()
# -*- coding: utf8 -*-
import tushare as ts
print(ts.__version__)
report = ts.get_report_data(2016, 1)
report['year'] = 2016
report['quarter'] = 1
for y in range(2005, 2016):
for q in range(1, 5):
print(y, q)
r = ts.get_report_data(y, q)
r['year'] = y
r['quarter'] = q
report = report.append(r, ignore_index=True)
report.to_csv('report05Q4-16Q1.csv')
#coding=utf-8 import tushare as ts # 获取沪深上市公司基本情况 df = ts.get_stock_basics() date = df.ix['600848']['timeToMarket']#上市日期YYYYMMDD #获取2014年第3季度的业绩报表数据 ts.get_report_data(2014,3) #获取2014年第3季度的盈利能力数据 ts.get_profit_data(2014,3) #获取2014年第3季度的营运能力数据 ts.get_operation_data(2014,3) #获取2014年第3季度的成长能力数据 ts.get_growth_data(2014,3) #获取2014年第3季度的偿债能力数据 ts.get_debtpaying_data(2014,3) #获取2014年第3季度的现金流量数据 ts.get_cashflow_data(2014,3)
def download_report_info(file_path, year, quarter):
report_data = ts.get_report_data(year, quarter)
if report_data is not None:
report_data.to_csv(file_path + 'report_' + str(year) + '_' + str(quarter) + '.csv', encoding='utf-8')
`eps` double NOT NULL DEFAULT '0' COMMENT '每股收益',\
`eps_yoy` double NOT NULL DEFAULT '0' COMMENT '每股收益同比(%)',\
`bvps` double NOT NULL DEFAULT '0' COMMENT '每股净资产',\
`roe` double NOT NULL DEFAULT '0' COMMENT '净资产收益率(%)',\
`epcf` double NOT NULL DEFAULT '0' COMMENT '每股现金流量(元)',\
`net_profits` double NOT NULL DEFAULT '0' COMMENT '净利润(万元)',\
`profits_yoy` double NOT NULL DEFAULT '0' COMMENT '净利润同比(%)',\
`distrib` char(30) NOT NULL DEFAULT '' COMMENT '分配方案',\
`report_date` char(10) NOT NULL DEFAULT '' COMMENT '发布日期',\
PRIMARY KEY (`id`),\
UNIQUE KEY `code` (`code`)\
) ENGINE=MyISAM DEFAULT CHARSET=utf8 COMMENT='业绩报告（主表）';"
cursor.execute(sql)
try:
df = ts.get_report_data(year, season)
if df is not None:
for id in df.index:
temp = df.ix[id]
code = temp['code']
# 以下先判断temp['']是否为NAN
if temp['eps'] != temp['eps']:
eps = 0.00
else:
eps = temp['eps']
if temp['eps_yoy'] != temp['eps_yoy']:
eps_yoy = 0.00
else:
eps_yoy = temp['eps_yoy']
# v_ma10:10日均量 # v_ma20:20日均量 # turnover:换手率[注：指数无此项] 4/4 raw_data_profit = ts.get_profit_data() # code,代码 # name,名称 # roe,净资产收益率(%) 7 # net_profit_ratio,净利率(%) # gross_profit_rate,毛利率(%) # net_profits,净利润(万元) # eps,每股收益 # business_income,营业收入(百万元) # bips,每股主营业务收入(元) raw_data_report = ts.get_report_data() # code,代码 # name,名称 # eps,每股收益 # eps_yoy,每股收益同比(%) # bvps,每股净资产 # roe,净资产收益率(%) # epcf,每股现金流量(元) # net_profits,净利润(万元) # profits_yoy,净利润同比(%) # distrib,分配方案 # report_date,发布日期 raw_data_growth = ts.get_growth_data()
import tushare as ts import sys df = ts.get_report_data(int(sys.argv[1]), int(sys.argv[2])) df.to_csv(sys.argv[3], encoding="utf8")
####################
# Financial Report #
####################
# FinancialReport: EPS, EPS_YOY, ROE, net_profits, profits_yoy
# ProfitData: ROE, net_profit_ratio, gross_profit_rate, EPS, bips (business income per share)
# GrowthData: mbrg (main business rate growth), nprg (net profit),
# nav, targ (total asset), epsg, seg (shareholder's eqty)
# DebtPayingData: currentratio, quickratio, cashratio, icratio (interest coverage)
# TODO Data is available quarterly
# TODO Compare data for FinancialReport and ProfitData
FinancialData = ts.get_report_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
FinancialData = FinancialData.set_index('code')
FinancialData = FinancialData.drop(['name', 'bvps', 'distrib', 'epcf', 'report_date'], axis = 1)
FinancialData.to_csv('./ASHR/DATA/FinancialData_2015_1.csv', index = True)
ProfitData = ts.get_profit_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
ProfitData = ProfitData.set_index('code')
ProfitData = ProfitData.drop(['name', 'business_income', 'net_profits'], axis = 1)
ProfitData.to_csv('./ASHR/DATA/ProfitData_2015_1.csv', index = True)
GrowthData = ts.get_growth_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
GrowthData = GrowthData.set_index('code')
GrowthData = GrowthData.drop(['name'], axis = 1)
GrowthData.to_csv('./ASHR/DATA/GrowthData_2015_1.csv', index = True)
DebtPayingData = ts.get_debtpaying_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
df = ts.get_stock_basics()
df.to_sql('basic_info',engine, if_exists='replace')
# import report data
#code,代码
#name,名称
#esp,每股收益
#eps_yoy,每股收益同比(%)
#bvps,每股净资产
#roe,净资产收益率(%)
#epcf,每股现金流量(元)
#net_profits,净利润(万元)
#profits_yoy,净利润同比(%)
#distrib,分配方案
#report_date,发布日期
df = ts.get_report_data(lastYear,lastSeason)
df = df.assign(quater=lastQuater)
df.to_sql('report_data',engine, if_exists='replace')
df = ts.get_report_data(currentYear,currentSeason)
df = df.assign(quater=currentQuater)
df.to_sql('report_data',engine, if_exists='append')
# import profit data
#code,代码
#name,名称
#roe,净资产收益率(%)
#net_profit_ratio,净利率(%)
#gross_profit_rate,毛利率(%)
#net_profits,净利润(万元)
#esp,每股收益
#business_income,营业收入(百万元)
