def get_yjyg():
df = ts.forecast_data(2018, 2)
df.to_sql('yjyg',
engine,
if_exists='replace',
index=False,
index_label='code')
def getForecast(cursor):
for i in range(1992, 2017+1):
for j in range(1, 4+1):
try:
print(i, j)
df = ts.forecast_data(i, 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
quarterList = [] # 添加季度
for l in range(0, dfLen):
uuidList.append(uuid.uuid1())
yearList.append(str(i))
quarterList.append(str(j))
df['uuid'] = uuidList
df['year'] = yearList
df['quarter'] = quarterList
for k in range(0, dfLen):
df2 = df[k:k+1]
cursor.execute("insert into stock_forecast(uuid, code, name, type, report_date, pre_eps, range, year, quarter) "
"values(:uuid, :code, :name, :type, to_date(:report_date, 'yyyy-MM-dd'), :pre_eps, :range, :year, :quarter)",
(str(list(df2['uuid'])[0]), str(list(df2['code'])[0]), str(list(df2['name'])[0]), str(list(df2['type'])[0]),
str(list(df2['report_date'])[0]), round(float(df2['pre_eps']), 4), str(list(df2['range'])[0]),
str(list(df2['year'])[0]), str(list(df2['quarter'])[0])) )
cursor.execute("commit")
except Exception:
pass
def _fetch_forecast():
for year in range(2004, 2018):
for quarter in range(1, 5):
print(year, ' year ', 'quarter ', quarter)
fcast = ts.forecast_data(year, quarter)
fcast.rename(columns={'report_date': 'date'}, inplace=True)
forecast.insert(fcast.to_dict('record'))
def forecast_data(self, year, quarter):
tsdata = ts.forecast_data(
year=year,
quarter=quarter,
)
jsdata = To_Json(tsdata)
return jsdata
def __init__(self):
self.__yearRange = 5 #5年 从2012到2016
self.__yearBegin = datetime.now().year-self.__yearRange
#self.__getYearReportOnline()
self.getThingsEveryday()
#print(yearbegin)
self.__pdYearReport = [pd.read_excel('D:/zzz/joinquant/data4stock/' + \
str(self.__yearBegin+i) + 'y.xls','Report',na_values=['NA']) \
for i in range(self.__yearRange)]
self.__pdYearProfit = [pd.read_excel('D:/zzz/joinquant/data4stock/' + \
str(self.__yearBegin+i) + 'Profit.xls', sheet_name='Profit',na_values=['NA'])\
for i in range(self.__yearRange)]
self.__stocksL = set(self.__pdYearReport[3].code.values)
self.__stocksNow = set(self.__pdYearReport[4].code.values) # 出了年报的股票
self.__stocksNotNow = set(self.__stocksL - self.__stocksNow)
#self.__stockNotNew = self.__stockBasics[(self.__stockBasics.timeToMarket < 20160101) &
# (self.__stockBasics.timeToMarket != 0)].index.values.astype(int)
#self.__stockNotNew
#print(self.__stockNotNew.index.values.astype(int)) #每天更新一次
pdYPL = self.__pdYearProfit[3]
pdYRL = self.__pdYearReport[3]
self.__pdYearReportNear = self.__pdYearReport[4].append(pdYRL[pdYRL.code.isin(self.__stocksNotNow)])
self.__pdYearProfitNear = self.__pdYearProfit[4].append(pdYPL[pdYPL.code.isin(self.__stocksNotNow)])
self.__minute_update = -1
self.__forecastLastYear = ts.forecast_data(datetime.now().year-1, 4)
self.__forecastLastYear['code'] = self.__forecastLastYear['code'].astype(int)
#print(self.__pdYearReportNear[self.__pdYearReportNear.code==600522])
#print(self.__pdYearReportNear[self.__pdYearReportNear.code == 600067])
#pdtest.to_excel('D:/zzz/joinquant/data4stock/' + str('test') + 'Profit.xls', sheet_name='Profit')
print('\n')
def save_forecast_data():
forecast_cols = ['股票代码', '预告类型', '预告日期', '变动范围']
fd = ts.forecast_data(forcast_year, forcast_season)
del fd['name']
del fd['pre_eps']
#fd.columns = forecast_cols;
fd.to_csv("forecast_%s.csv" % dateStr, encoding="utf-8", index=False)
def __init__(self):
self.__yearRange = 5 #5年 从2012到2016
self.__yearBegin = datetime.now().year - self.__yearRange
#self.__getYearReportOnline()
self.__flagUpdateReport = False
flag = input('Update report now(Y/N):')
if flag == 'Y' or flag == 'y':
self.__flagUpdateReport = True
self.getThingsEveryday()
#print(yearbegin)
self.__pdYearReport = [pd.read_excel('./' + \
str(self.__yearBegin+i) + 'y.xls','Report',na_values=['NA']) \
for i in range(self.__yearRange)]
self.__pdYearProfit = [pd.read_excel('./' + \
str(self.__yearBegin+i) + 'Profit.xls', sheet_name='Profit',na_values=['NA'])\
for i in range(self.__yearRange)]
self.__pdYearGrowth = [pd.read_excel('./' + \
str(self.__yearBegin+i) + 'Growth.xls','Growth',na_values=['NA']) \
for i in range(self.__yearRange)]
self.__stocksL = set(self.__pdYearGrowth[3].code.values)
self.__stocksNow = set(self.__pdYearGrowth[4].code.values) # 出了年报的股票
self.__stocksNotNow = set(self.__stocksL - self.__stocksNow)
#self.__stockNotNew = self.__stockBasics[(self.__stockBasics.timeToMarket < 20160101) &
# (self.__stockBasics.timeToMarket != 0)].index.values.astype(int)
#self.__stockNotNew
#print(self.__stockNotNew.index.values.astype(int)) #每天更新一次
pdYPL = self.__pdYearProfit[3]
pdYRL = self.__pdYearReport[3]
self.__pdYearReportNear = self.__pdYearReport[4].append(
pdYRL[pdYRL.code.isin(self.__stocksNotNow)])
self.__pdYearProfitNear = self.__pdYearProfit[4].append(
pdYPL[pdYPL.code.isin(self.__stocksNotNow)])
self.__minute_update = -1
self.__forecastLastYear = ts.forecast_data(datetime.now().year - 1, 4)
self.__forecastLastYear['code'] = self.__forecastLastYear[
'code'].astype(int)
#print(self.__pdYearReportNear[self.__pdYearReportNear.code==600522])
#print(self.__pdYearReportNear[self.__pdYearReportNear.code == 600067])
#pdtest.to_excel('D:/zzz/joinquant/data4stock/' + str('test') + 'Profit.xls', sheet_name='Profit')
print('\n')
for i in range(self.__yearRange):
self.__pdYearReport[i].rename(
columns={'profits_yoy': 'yoy' + str(i)}, inplace=True)
self.pdYear4Report = self.__pdYearReport[0].copy()
for i in range(1, self.__yearRange - 1):
self.pdYear4Report = self.pdYear4Report.merge(
self.__pdYearReport[i], on='code')
# 4年成长能力
for i in range(self.__yearRange): #5年 从2012到2016 0-4
self.__pdYearGrowth[i].rename(columns={'nprg': 'yoy' + str(i)},
inplace=True)
self.pdYear4Growth = self.__pdYearGrowth[0].copy()
for i in range(1, self.__yearRange - 1):
self.pdYear4Growth = self.pdYear4Growth.merge(
self.__pdYearGrowth[i], on='code')
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 forecast_info(year, month):
'''
业绩预告
'''
try:
return ts.forecast_data(year, month)
except:
print('error, month=[1,4], please check your parameter')
def get_stock_forecast(nd, jd):
"""
获取业绩预告
"""
try:
res = ts.forecast_data(nd, jd)
return res
except:
return None
def download_forecast_data(self, year=2017, quarter=1):
"""
"""
#print('downloading the forecast_data info...')
try:
data_2017_1 = ts.forecast_data(2017, 1)
filename = 'forecast_data_2017_1'
data_2017_1.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download forecast_data_2017_1 failed')
try:
data_2017_2 = ts.forecast_data(2017, 2)
filename = 'forecast_data_2017_2'
data_2017_2.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download forecast_data_2017_2 failed')
try:
data_2017_3 = ts.forecast_data(2017, 3)
filename = 'forecast_data_2017_3'
data_2017_3.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download forecast_data_2017_3 failed')
try:
data_2017_4 = ts.forecast_data(2017, 4)
filename = 'forecast_data_2017_4'
data_2017_4.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
except:
print('download forecast_data_2017_4 failed')
data = data_2017_4.append(data_2017_3).append(data_2017_2).append(
data_2017_1)
data.drop_duplicates(subset=['code'], keep='first', inplace=True)
filename = 'forecast_data'
data.to_excel(self.working_folder + filename + '.xlsx', encoding='GBK')
return
def download_forecast(year, quarter):
try:
df = ts.forecast_data(year, quarter)
df.set_index('code', inplace=True)
utils.df2csv(df, _forecast_file_path(year, quarter))
except Exception as e:
logger.error(
'download_forecast(year[{}], quarter[{}]) failed.{}'.format(
year, quarter, error.exception_string(e)))
raise error.ServerException(error.SERVER_ERR_DOWNLOAD_FAILED,
error.exception_string(e))
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 year_forecast_data(year):
DTS=[]
for i in range(4):
preDTS=ts.forecast_data(year,i+1)
DTS.append(preDTS)
try:
DTS = pd.concat(DTS)
except ValueError:
DTS = None
return DTS
def __init__(self):
TestArr = [["601366","利群股份","预升","2018-04-16","0.1700","30.33"],
["000018","神州长城","预增","2018-04-16","0.0583","140%~170%"],
["000626","远大控股","预亏","2018-04-16","0.1602","0"],
["000430" ,'张家界',"预亏","2018-04-14","0.0330",'-241.78%~-237.06%'],
["300107" ,'张家界2',"预亏","2018-04-19","0.0320",'-30%']]
if(Test):
self.forecastDF = DataFrame(TestArr, columns=["code", "name","type","report_date", "pre_eps","range"])
else:
self.forecastDF = ts.forecast_data(2018,1)
self.currentPrice = None
def updatepredict():
predictdatalist=ts.forecast_data(2014,1)
predictdata=pd.DataFrame(predictdatalist)
conn= ms.connect(host='localhost',port = 3306,user='******', passwd='123456',db ='investment',charset="utf8")
cur = conn.cursor()
values=[]
for index,row in predictdata.iterrows():
values.append((row['code'],row['name'],row['type'],row['report_date'],row['pre_eps'],row['range']))
cur.executemany("insert into predict20141 (code,name,type,report_date,pre_eps,ran) values(%s,%s,%s,%s,%s,%s)",values)
conn.commit()
cur.close()
conn.close()
def test_forecast(self):
# code, 代码
# name, 名称
# type, 业绩变动类型【预增、预亏等】
# report_date, 发布日期
# pre_eps, 上年同期每股收益
# range, 业绩变动范围
self.setUp()
# 获取2016年中报的业绩预告数据
df = ts.forecast_data(2017, 1)
insert_string = df.to_json(orient='records')
items = json.loads(insert_string)
self.mongo.db.forecast.insert(items)
def updateForecastDate(con, year: int, quarter: int):
import share.model.dao.reference.ForecastData as Model
logging.debug("Updating day updateForecastDate of {}Q{}".format(
year, quarter))
df = ts.forecast_data(year, quarter)
res = []
for _, row in df.iterrows():
obj = Model.rowToORM(row, year=year, quarter=quarter)
if obj is not None:
res.append(obj)
Base.metadata.create_all(con.engine)
con.save_all(res)
return
def setForecastData(self,year=None,quarter=None, number = 1, isSave = False,tableName = REFERENCE_FORECAST_DATA):
[year, quarter] = getYearQuarter(year, quarter)
for i in range(0,number):
try:
df = ts.forecast_data(year, quarter)
df['date'] =str(year) + str(quarter)
if isSave is True:
df.to_sql(tableName,self.engine_sql, if_exists='append')
quarter = quarter - 1
if quarter < 1:
quarter = 4
year = year - 1
except IOError,e:
print e
def forecast_history(engine, year, quarter):
tbl = "invest_forecast_history"
tsl.log(tbl + " start...")
try:
df = ts.forecast_data(year, quarter)
df['year'] = year
df['quarter'] = quarter
df = df.set_index('code', drop='true')
df.to_sql(tbl, engine, if_exists='append')
print
tsl.log(tbl + " done")
except BaseException, e:
print
print e
tsl.log(tbl + " error")
def forecast_data(request):
try:
year = request.POST.get('year', '') #必填
quarter = request.POST.get('quarter', '') #必填
data = ts.forecast_data(int(year), int(quarter))
res = {
'columns':
['code', 'name', 'type', 'report_date', 'pre_eps', 'range'],
'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 get_data_date(year, quarter):
cmd = '''delete from %s where `year`='%s' and quarter='%s' ''' % (
table_name, year, quarter)
print cmd
run_mysql_cmd(cmd=cmd, conn=conn) #先删除指定年和指定季度的数据
rs = ts.forecast_data(year=int(year), quarter=int(quarter))
rs['year'] = year
rs['quarter'] = quarter
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
def forecast(engine, year, quarter):
tbl = "invest_forecast"
tsl.log(tbl + " start...")
try:
df = ts.forecast_data(year, quarter)
if len(df) != 0:
df['year'] = year
df['quarter'] = quarter
df = df.reset_index()
df.to_sql(tbl, engine, if_exists='replace')
print
tsl.log(tbl + " done")
except BaseException, e:
print
print e
tsl.log(tbl + " error")
def getForecast(cursor):
for i in range(1992, 2017 + 1):
for j in range(1, 4 + 1):
try:
print(i, j)
df = ts.forecast_data(i, j)
# 处理缺失值
df = df.stack().replace('--', '0').unstack()
#print(df.ix[129])
#print(df)
#print(df.ix[131])
##处理'None'
# df=df.replace('None',0)
dfLen = len(df)
# print(dfLen)
uuidList = [] # 添加uuid
yearList = [] # 添加年份
quarterList = [] # 添加季度
for l in range(0, dfLen):
uuidList.append(uuid.uuid1())
yearList.append(str(i))
quarterList.append(str(j))
df['uuid'] = uuidList
df['year'] = yearList
df['quarter'] = quarterList
#print("xxxxx")
for k in range(0, dfLen):
df2 = df[k:k + 1]
sql = (
"insert into stock_forecast(uuid, code, name, type, report_date, pre_eps, `range`, year, quarter) "
"value ('%s', '%s', '%s', '%s', '%s','%.4f', '%s','%s', '%s')"
%
(str(list(df2['uuid'])[0]), str(list(df2['code'])[0]),
str(list(df2['name'])[0]), str(list(df2['type'])[0]),
str(list(df2['report_date'])[0]),
round(float(df2['pre_eps']), 4),
str(list(df2['range'])[0]), str(list(
df2['year'])[0]), str(list(df2['quarter'])[0])))
print(sql)
cursor.execute(sql)
cursor.execute("commit")
except Exception as e:
print(e)
pass
def update_forcast_quarter_report_data( self ):
if not os.path.exists( self.__forcast_quarter_report_file_name ):
list_date_quarter = Utils.parse_date_to_ymd( self.__date )
df_forcast_quarter_report_data = pd.DataFrame()
for year in range( 2003, int( list_date_quarter[0] ) + 1 ):
for quarter in range( 1, 5 ):
try:
df_tmp = ts.forecast_data( year, quarter )
except:
ERROR( 'exception occurs when update forcast quarter report data of year {0} quarter {1}'.format( year, quarter ) )
else:
df_forcast_quarter_report_data = df_forcast_quarter_report_data.append( df_tmp )
LOG( 'update forcast quarter report data of year {0} quarter {1}'.format( year, quarter ) )
if year == int( list_date_quarter[0] ) and quarter == list_date_quarter[3]:
break
Utils.save_data( df_forcast_quarter_report_data, self.__forcast_quarter_report_file_name, 'forcast quarter report' )
def get_ref_forecast_data(year, quarter):
"""
投资参考 系列
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
"""
df = ts.forecast_data(year, quarter)
print(df)
if df is not None:
if len(df) > 0:
df.insert(0, 'year', year)
df.insert(1, 'quarter', quarter)
res = df.to_sql(invest_stock_ref_forecast, 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, quarter, msg) + '\n')
else:
print('获取业绩预告数据: {0} {1}: {2}'.format(year, quarter, '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 get_performance_forecast(year, quarter):
"""得到业绩预告
Args:
year: 年份, YYYY格式数字
quarter: 季度, 只能是1, 2, 3, 4的数字
Returns:
data_dicts: 字段的列表, return None if have exception, return empty if no data
"""
_check_input(year, quarter)
logger.info(
'Begin get performance forecast data, the year is: %s, quarter is: %s'
% (year, quarter))
try:
data_df = ts.forecast_data(year=year, quarter=quarter)
except Exception as e:
logging.exception(
'Error get performance forecast data, the year is: %s, quarter is: %s'
% (year, quarter))
return None
else:
data_dicts = []
if data_df is None or data_df.empty:
logger.warn(
'Empty get performance forecast data, the year is: %s, quarter is: %s'
% (year, quarter))
else:
data_dicts = [{
'code': row[0],
'name': row[1],
'year': year,
'quarter': quarter,
'type': row[2],
'report_date': row[3],
'pre_eps': row[4],
'range': row[5],
'insert_date': today_line
} for row in data_df.values]
logger.info(
'Success get performance forecast data, the year is: %s, quarter is: %s'
% (year, quarter))
return data_dicts
def forecast(self):
today = datetime.date.today()
year = int(today.strftime('%Y'))
month = int(today.strftime('%m'))
if month > 3:
quarter = month / 3
else:
quarter = 1 if month == 3 else 4
data = ts.forecast_data(year, quarter)
code_list = list(data.code)
type_list = list(data.type)
for code in self.valid_code:
if code in code_list:
index = code_list.index(code)
if type_list[index] in [
'预降'.decode('utf-8'), '预减'.decode('utf-8'),
'预亏'.decode('utf-8')
]:
self.valid_code.remove(code)
self.expect_loss_code.append(code)
def updatepredict():
predictdatalist = ts.forecast_data(2014, 1)
predictdata = pd.DataFrame(predictdatalist)
conn = ms.connect(host='localhost',
port=3306,
user='******',
passwd='123456',
db='investment',
charset="utf8")
cur = conn.cursor()
values = []
for index, row in predictdata.iterrows():
values.append((row['code'], row['name'], row['type'],
row['report_date'], row['pre_eps'], row['range']))
cur.executemany(
"insert into predict20141 (code,name,type,report_date,pre_eps,ran) values(%s,%s,%s,%s,%s,%s)",
values)
conn.commit()
cur.close()
conn.close()
def fetchByYearQuarter(self, mongo, func):
years = range(self.begin_year, self.end_year_notinclude)
quarters = range(1, 1 + self.quarter)
for year in years:
for quarter in quarters:
print(str(year) + " " + str(quarter))
if (func == 'forecast_data'):
df = ts.forecast_data(year, quarter)
elif (func == 'fund_holdings'):
df = ts.fund_holdings(year, quarter)
else:
print(func)
df = {}
tmpJson = json.loads(df.to_json(orient='records'))
for i in range(len(tmpJson)):
tmpJson[i][u'year'] = int(year)
tmpJson[i][u'quarter'] = int(quarter)
coll = mongo.reference[func]
coll2 = mongo.reference[str(func) + '_' + str(year)]
coll2.insert(tmpJson)
coll.insert(tmpJson)
def ProfitExp():
year=dt.date.today().year
month=dt.date.today().month
if month>9:
season=4
elif month>6:
season=3
elif month>3:
season=2
else:
season=1
if season==1:
year=year-1
season=4
else:
season=season-1
dp=ts.forecast_data(year,season)
dp=dp.sort_values('range',ascending=False)
return [u"\n近期预披露靠前的结果：",dp[:30]]
def main():
code = []
time = []
profit1 = []
profit2 = []
df = ts.forecast_data(2016, 2)
for i in range(len(df)):
if df['pre_eps'][i] > 0.1:
code.append(df['code'][i])
time.append(df['report_date'][i])
for i in range(len(code)):
start_time = datetime.datetime.strptime(time[i], '%Y-%m-%d')
end_time1 = start_time + datetime.timedelta(days=30)
end_time2 = start_time + datetime.timedelta(days=60)
endstr1 = end_time1.strftime('%Y-%m-%d')
endstr2 = end_time2.strftime('%Y-%m-%d')
data = ts.get_k_data(start=time[i],
end=endstr1,
ktype='D',
autype='qfq',
code=code[i])
try:
profit1.append(data['close'][data.index[-1]] /
data['close'][data.index[0]] - 1)
except:
profit1.append(np.nan)
data = ts.get_k_data(start=time[i],
end=endstr2,
ktype='D',
autype='qfq',
code=code[i])
try:
profit2.append(data['close'][data.index[-1]] /
data['close'][data.index[0]] - 1)
except:
profit2.append(np.nan)
output = {'code': code, 'profit1': profit1, 'profit2': profit2}
a = pd.DataFrame(output)
a.to_csv('D:/pre_profit.csv')
def check_forecast(self, upgrade=False):
"""
检查该季度内是否发布业绩预告，放在report里面
Parameters
----------
self.code : stock code
self.year : 年份
self.quarter : 季度
Returns
-------
if not search forecast , return None.
code ：
name ：
type : 预升
report_date :
pre_eps :
range:
"""
_path = '../report/forecast_%s-%s.csv' % (self.year, self.quarter)
if os.path.exists(_path) and (upgrade == False):
all_forecast = pandas.read_csv(_path, encoding='gbk', header=0)
all_forecast.code = all_forecast.code.map(
lambda x: str(x).zfill(6))
#all_forecast=all_forecast.set_index(['code'])
else:
print('there is not files,watting download %s' % self.year)
all_forecast = ts.forecast_data(int(self.year), int(self.quarter))
all_forecast.to_csv(_path, encoding='gbk')
if self.code in all_forecast.code.values:
stock_forecast = all_forecast[all_forecast['code'] == self.code]
return stock_forecast #print (stock_forecast)
else:
print('< %s has not forecast >' % self.code)
return None
def baseAPI():
# df=ts.get_hist_data('002524',start='2017-01-01',end='2017-04-24')
# 这个函数只能获取近3年的数据
# 目前这个版本是从最新开始 【0】
# print(df)
# print(df['close'].sum())
# stock_info=ts.get_stock_basics()
# print(stock_info)
# stock_info.to_csv('2.csv',encoding='gbk')
# n_df=pd.read_csv('2.csv',encoding='gbk')
# n_df.to_excel('2.xls',encoding='gbk')
# print(n_df)
# 这样子居然搞定了。
# dx_1=ts.get_hist_data('603111',start='2017-01-28',end='2017-04-22')
# print(dx_1)
# print(len(dx_1))
# ts.get_sz50s()
# print(dx_1['close'][0])
'''
print(stock_info.dtypes)
cols=stock_info.columns
for col in cols:
if stock_info[col].dtype == 'O':
print("O in " ,col)
del stock_info[col]
print(stock_info)
stock_info.to_excel('new.xls')
'''
# 编码出错
# stock_info.to_excel('base.xls',encoding='gb2312')
'''
data=stock_info.ix['300141']['timeToMarket']
print(data)
print(type(data))
data=str(data)
print(type(data))
print(data[1:4])
print(data[4:6])
print(data)
date_format=data[0:4]+'-'+data[4:6]+'-'+data[6:8]
print(date_format)
delta=60*7/5
day0=datetime.date(datetime.date.today().year,datetime.date.today().month,datetime.date.today().day)
day30=day0-datetime.timedelta(delta)
print(day30)
day30=day30.strftime("%Y-%m-%d")
day0=day0.strftime("%Y-%m-%d")
'''
# df1=ts.new_stocks()
# print(df1)
# df2=ts.new_stocks(2)
# print(df2)
# df3=ts.new_stocks(3)
# print(df3)
# sz_index=ts.get_k_data('399001',index=True,start='2017-01-10',end='2017-04-28')
'''
sz_index=ts.get_k_data('300141')
print(sz_index)
print(sz_index.ix[sz_index['date']=='2014-05-06','high'].values[0])
'''
'''
df = ts.get_realtime_quotes(['600848', '000980', '000981']) #一次过返回3个数据
print(df)
'''
# bar 函数
# conn = ts.get_apis()
# df =ts.bar('000022',conn,start_date='2000-01-01',adj='qfq')
# print(df)
# print(df.dtypes)
df = ts.get_today_all()
print(df)
exit()
filename = datetime.datetime.now().strftime('%Y-%m-%d') + '.xls'
df.to_excel(filename)
forecast_filename = '2017-12.xls'
forecast_df = ts.forecast_data(2017, 4)
print(forecast_df)
forecast_df.to_excel(forecast_filename)
ts.stock_pledged()
#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')
def get_achievement(self):
fc = ts.forecast_data(2016, 4)
print fc
for code, stockRow in df.iterrows():
print("pre divi " + stockRow['code'])
print(type(stockRow['shares']))
spamwriter.writerow([stockRow['code'], stockRow['name'],
stockRow['year'], stockRow['report_date'],
stockRow["divi"],
stockRow["shares"]])
preDivi_csv.close()
preEarning_csv = open('PreEarningResult.csv', 'wb+')
spamwriter1 = csv.writer(preEarning_csv,dialect='excel')
spamwriter1.writerow(['stock', 'name', 'type', 'report_date', "pre_eps", "range"])
tmp = str('111')
print(tmp.decode('utf-8').encode('gb2312'))
df1 = ts.forecast_data(2016,3)
for code1, stockRow1 in df1.iterrows():
print("pre earning " + stockRow1['code'])
print("name type " + type(stockRow1['name']).__name__)
print("type type " + type(stockRow1['type']).__name__)
print("pre_eps type " + type(stockRow1['pre_eps']).__name__)
print(stockRow1['range'])
print("range type " + type(stockRow1['range']).__name__)
spamwriter1.writerow([stockRow1['code'], stockRow1['name'],
stockRow1['type'].encode('gb2312'), stockRow1['report_date'],
stockRow1["pre_eps"],
tmp.decode('utf-8').encode('gb2312')])
preEarning_csv.close()
def forcast_data(self,stock):
data = ts.forecast_data(2015,3)
print data[-100:]