def write_new_stocks():
"""
从Tushare取每日新股数据，因Tushare数据限制，最多取到2016-04-26
:return: None
"""
ts.new_stocks(pause=0.1).to_csv(
os.path.abspath("{}/{}.csv".format(other_dir, 'newstock')))
def ipo_rank():
new_stk_df=ts.new_stocks()
# print('info',new_stk_df.info())
# new_stk_df.dropna(inplace=True,axis=0)
# print('lens after drop ',len(new_stk_df))
# new_stk_df['issue_date']=new_stk_df['issue_date'].astype('datetime64[ns]')
new_stk_df['issue_date']=pd.to_datetime(new_stk_df['issue_date'])
# new_stk_df['issue_date']=pd.to_datetime(new_stk_df['issue_date'])
# new_stk_df=new_stk_df.set_index('issue_date')
# print(new_stk_df2.info())
# print(new_stk_df2)
# print(new_stk_df2.truncate(after=date))
# print(new_stk_df2['2017-11-01':])
# print(len(new_stk_df))
# print(new_stk_df.head())
# print(new_stk_df.head())
# print(new_stk_df.info())
df_2017 = new_stk_df[new_stk_df['issue_date']>'2017-01-01']
# df_2017.to_excel('2017_new_stock.xls')
# print(new_stk_df.ix['2017-01-01':,:])
pro=[]
# print(df_2017.iloc[0]['issue_date'])
# '''
for i in range(len(df_2017)):
s=df_2017.iloc[i]
p=profit(s['code'],s['issue_date'].strftime('%Y-%m-%d'),'2017-12-29')
pro.append(p)
df_2017['raise']=pro
df_2017.to_excel('2017newstockprofit.xls')
def new_stocks(request):
try:
data = ts.new_stocks()
stocksData = np.array(data.values)
for i in range(len(stocksData)):
for x in range(len(stocksData[i])):
if isinstance(stocksData[i][x], float):
if math.isnan(stocksData[i][x]):
stocksData[i][x] = 0
res = {
"columns": [
'code', 'name', 'ipo_date', 'issue_date', 'amount', 'markets',
'price', 'pe', 'limit', 'funds', 'ballot'
],
"data":
json.loads(json.dumps(stocksData, cls=DateEncoder))
}
except (BaseException):
return HttpResponse(BaseException)
else:
return HttpResponse(json.dumps(res), content_type="application/json")
def ipo_rank():
new_stk_df = ts.new_stocks()
# print('info',new_stk_df.info())
# new_stk_df.dropna(inplace=True,axis=0)
# print('lens after drop ',len(new_stk_df))
# new_stk_df['issue_date']=new_stk_df['issue_date'].astype('datetime64[ns]')
new_stk_df['issue_date'] = pd.to_datetime(new_stk_df['issue_date'])
# new_stk_df['issue_date']=pd.to_datetime(new_stk_df['issue_date'])
# new_stk_df=new_stk_df.set_index('issue_date')
# print(new_stk_df2.info())
# print(new_stk_df2)
# print(new_stk_df2.truncate(after=date))
# print(new_stk_df2['2017-11-01':])
# print(len(new_stk_df))
# print(new_stk_df.head())
# print(new_stk_df.head())
# print(new_stk_df.info())
df_2017 = new_stk_df[new_stk_df['issue_date'] > '2017-01-01']
# df_2017.to_excel('2017_new_stock.xls')
# print(new_stk_df.ix['2017-01-01':,:])
pro = []
# print(df_2017.iloc[0]['issue_date'])
# '''
for i in range(len(df_2017)):
s = df_2017.iloc[i]
p = profit(s['code'], s['issue_date'].strftime('%Y-%m-%d'),
'2017-12-29')
pro.append(p)
df_2017['raise'] = pro
df_2017.to_excel('2017newstockprofit.xls')
def new_stocks():
df = ts.new_stocks()
df.to_sql('new_stocks',
db_utils.engine,
if_exists='append',
index=False,
index_label='code')
def get_new_stocks(retry_count=RETRY_COUNT, pause=PAUSE):
logger.info('Begin get new stocks.')
try:
data_df = ts.new_stocks(retry_count, pause)
except Exception:
logger.exception('Error get new stocks.')
return None
else:
data_dicts = []
if data_df is None or data_df.empty:
logger.warn('Empty get get new stocks.')
else:
data_dicts = [{
'code': row[0],
'xcode': row[1],
'name': row[2],
'ipo_date': row[3],
'issue_date': row[4],
'amount': row[5],
'markets': row[6],
'price': row[7],
'pe': row[8],
'limit': row[9],
'funds': row[10],
'ballot': row[11],
'insert_date': today_line
} for row in data_df.values]
logger.info('Success get get new stocks.')
return data_dicts
def setNewStocks(self, isSave = False,tableName = REFERENCE_NEW_STOCKS):
try:
df = ts.new_stocks()
if isSave is True:
df.to_sql(tableName,self.engine_sql, if_exists='append')
except IOError,e:
print e
def __init__(self):
self.ipo = ts.new_stocks()
# 日期转化
self.ipo['ipo_date'] = self.ipo['ipo_date'].astype('datetime64')
self.start = self.ipo['ipo_date'].iloc[-1]
self.end = self.ipo['ipo_date'].values[0]
def get_new_stock_info():
df = ts.new_stocks()
# print(df)
if df is not None:
res = df.to_sql(all_new_stock_table, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取新股数据: ' + msg + '\n')
else:
print('获取新股数据: ' + 'None' + '\n')
def __init__(self):
self.ipo=ts.new_stocks()
#print ipo.info()
#日期转化
self.ipo['ipo_date']=self.ipo['ipo_date'].astype('datetime64')
#print ipo.info()
self.start=self.ipo['ipo_date'].values[-1]
self.end=self.ipo['ipo_date'].values[0]
print type(self.end)
def __init__(self, init=True):
"""
初始化，筛除688,300，ST，退市股，新股，低价股，下跌股
"""
if init:
self.data = ts.get_today_all() #今日复盘
self.updateNMC(self.data)
# data = ts.get_day_all(date='2021-02-18') #历史复盘
filt = self.data['code'].str.contains('^(?!688|605|300|301|8|43)')
self.data = self.data[filt]
filt = self.data['name'].str.contains('^(?!S|退市|\*ST|N)')
self.data = self.data[filt]
self.data = self.data.drop_duplicates()
data = self.data[self.data['trade'] >= 2]
data = data[data['changepercent'] > -3]
data = data.to_json(orient='records')
db.get_collection('today').remove()
base = db.get_collection('base').find()
industry = {i['code']: i['industry'] for i in base}
self.intersect = {}
for i in eval(data):
db.get_collection('today').insert(i)
res = db.get_collection('today').find()
for i in res:
try:
db.get_collection('today').update_many(
{'code': i['code']},
{'$set': {
'industry': industry[i['code']]
}})
except:
pass
# 剔除新股
try:
newStock = ts.new_stocks()
if not newStock.empty:
newStock = newStock[newStock.ipo_date > '2020-01-01']
for i in newStock['code'].tolist():
db.get_collection('today').remove({'code': i},
multi=True)
except Exception as e:
print('newStock Error', e)
# 剔除停牌
db.get_collection('today').remove({'open': 0})
# 龙虎榜
if not (10 < time.localtime().tm_hour < 18):
db.get_collection('topList').remove()
longhu = ts.top_list()
longhu = longhu.to_json(orient='index', )
longhu = json.loads(longhu)
for k, v in longhu.items():
db.get_collection('topList').insert(v)
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_new_stock_list():
stock_list = []
top_stocks_info = ts.new_stocks().head(STOCK_NUM)
stocks_info = top_stocks_info[[IPO_CODE_HEAD,IPO_DATE_HEAD,IPO_PRICE_HEAD]]
for i in xrange(STOCK_NUM):
stock_date = stocks_info.at[i, IPO_DATE_HEAD]
if pd.to_datetime(stock_date).strftime('%Y-%m-%d') == datetime.datetime.now().strftime('%Y-%m-%d'):
stock_code = stocks_info.at[i, IPO_CODE_HEAD]
stock_price = stocks_info.at[i, IPO_PRICE_HEAD]
stock_list.append(Stock(stock_code, name = "", price = stock_price))
return stock_list
def get_ipo(f, reset_flag=False):
"""
获取ipo数据
"""
df = ts.new_stocks(pause=conf.REQUEST_BLANK)
df = df.drop("name", axis=1)
df = df.sort_values(by=["ipo_date"])
if reset_flag is False:
tool.merge_df_dataset(f, conf.HDF5_FUNDAMENTAL_IPO, df)
else:
tool.create_df_dataset(f, conf.HDF5_FUNDAMENTAL_IPO, df)
return
def get_stock_codes(markets=['zz500s'],ipo_date=None):
'''
markets: list
e.g.['sme','gem','st','hs300s','sz50s','zz500s','general'];
ipo_date: str
e.g.'2015-03-30'
'''
code_list=[]
if 'sme' in markets:#中小板
path='/Users/xuegeng/StockCodes/sme.csv'
sr1=pd.read_csv(path,names=['n','code'])
codes=list(sr1['code'])
code_list.extend(codes)
if 'gem' in markets:#创业板
path='/Users/xuegeng/StockCodes/gem.csv'
sr1=pd.read_csv(path,names=['n','code'])
codes=list(sr1['code'])
code_list.extend(codes)
if 'st' in markets:#风险板
path='/Users/xuegeng/StockCodes/st.csv'
sr1=pd.read_csv(path,names=['n','code'])
codes=list(sr1['code'])
code_list.extend(codes)
if 'hs300s' in markets:#沪深300
path='/Users/xuegeng/StockCodes/hs300s.csv'
sr1=pd.read_csv(path,names=['n','code'])
codes=list(sr1['code'])
code_list.extend(codes)
if 'sz50s' in markets:#上证50
path='/Users/xuegeng/StockCodes/sz50s.csv'
sr1=pd.read_csv(path,names=['n','code'])
codes=list(sr1['code'])
code_list.extend(codes)
if 'zz500s' in markets:#中证500
path='/Users/xuegeng/StockCodes/zz500s.csv'
sr1=pd.read_csv(path,names=['n','code'])
codes=list(sr1['code'])
code_list.extend(codes)
if ipo_date:
new_stock_df=ts.new_stocks()
new_stock_df=new_stock_df[new_stock_df['ipo_date']>ipo_date]
new_stock_codes=list(new_stock_df.code)
#得到输入时间之后发行的股票
code_list=list(set(code_list))
desired_codes=list(set(code_list)-set(new_stock_codes))
#剔除新股
desired_codes=list(set(code_list))
return desired_codes
def load_data():
#下载公司基本信息，包括股票代码、pe、市盈率等数据
try:
rs = ts.new_stocks()
pd.DataFrame.to_sql(rs,
table_name,
con=conn,
flavor='mysql',
if_exists='replace',
index=True)
except Exception as e:
print(e.message)
print("加载新股数据出错")
def new_stock(engine):
tbl = "invest_new_stock"
tsl.log(tbl + " start...")
try:
df = ts.new_stocks()
df['date'] = datetime.date.today()
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 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 new_stock():
"""
新股数据
retry_count：当网络异常后重试次数，默认为3
pause:重试时停顿秒数，默认为0
code：股票代码 name：股票名称
ipo_date:上网发行日期 issue_date:上市日期
amount:发行数量(万股) markets:上网发行数量(万股)
price:发行价格(元) pe:发行市盈率
limit:个人申购上限(万股) funds：募集资金(亿元)
ballot:网上中签率(%)
"""
df = ts.new_stocks()
print(df.head(10))
print(df)
print(datetime.datetime.today())
def get_stock_codes(markets=['zz500s'], ipo_date=None):
'''
markets: list
e.g.['sme','gem','st','hs300s','sz50s','zz500s','general'];
ipo_date: str
e.g.'2015-03-30'
'''
code_list = []
if 'sme' in markets: #中小板
df = ts.get_sme_classified()
codes = list(df.code)
code_list.extend(codes)
if 'gem' in markets: #创业板
df = ts.get_gem_classified()
codes = list(df.code)
code_list.extend(codes)
if 'st' in markets: #风险板
df = ts.get_st_classified()
codes = list(df.code)
code_list.extend(codes)
if 'hs300s' in markets: #沪深300
df = ts.get_hs300s()
codes = list(df.code)
code_list.extend(codes)
if 'sz50s' in markets: #上证50
df = ts.get_sz50s()
codes = list(df.code)
code_list.extend(codes)
if 'zz500s' in markets: #中证500
df = ts.get_zz500s()
codes = list(df.code)
code_list.extend(codes)
if ipo_date:
new_stock_df = ts.new_stocks()
new_stock_df = new_stock_df[new_stock_df['ipo_date'] > ipo_date]
new_stock_codes = list(new_stock_df.code)
#得到输入时间之后发行的股票
code_list = list(set(code_list))
desired_codes = list(set(code_list) - set(new_stock_codes))
#剔除新股
desired_codes = list(set(code_list))
return desired_codes
def download_new_stocks(self):
"""
"""
#print('downloading the new_stocks info...')
total = 0
new_stocks = ts.new_stocks()
if new_stocks is not None:
filename = 'new_stocks'
new_stocks.to_excel(self.working_folder + filename + '.xlsx',
encoding='GBK')
else:
warning_code = pd.DataFrame({
'return code': ['for all stocks'],
'description': ['new_stocks download failed']
})
self.warning_list = self.warning_list.append(warning_code)
return
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_stock_info(stock_pandas):
concept = ts.get_concept_classified()
stock_basics = ts.get_stock_basics()
new_stocks = ts.new_stocks()
stock_info = pd.merge(stock_pandas, stock_basics, how="left", on="name")
stock_info = pd.merge(stock_info, concept, how="left", on="code")
stock_info = stock_info.rename(columns={
"name_x": "stock_name",
"c_name": "concept"
})
return stock_info[[
"code", "stock_name", "changepercent", "concept", "industry", "area",
"timeToMarket", "per", "pb_x", "nmc", "turnoverratio", "amount", "rev",
"profit", "gpr", "npr", "holders"
]]
def __init__(self,init=True):
"""
初始化，筛除688,300，ST，退市股，新股，低价股，下跌股
"""
if init:
self.data = ts.get_today_all() #今日复盘
# data = ts.get_day_all(date='2021-02-18') #历史复盘
filt = self.data['code'].str.contains('^(?!688|605|300)')
self.data = self.data[filt]
filt = self.data['name'].str.contains('^(?!S|退市|\*ST)')
self.data = self.data[filt]
self.data = self.data.drop_duplicates()
data = self.data[self.data['trade']>=2]
data = data[data['changepercent']>0]
data = data.to_json(orient='records')
db.get_collection('today').remove()
base = db.get_collection('base').find()
industry = {i['code']: i['industry'] for i in base}
self.intersect = {}
for i in eval(data):
db.get_collection('today').insert(i)
res = db.get_collection('today').find()
for i in res:
try:
db.get_collection('today').update_many({'code':i['code']},{'$set':{'industry':industry[i['code']]}})
except :
pass
# 剔除新股
try:
newStock = ts.new_stocks()
if not newStock.empty:
for i in newStock['code'].tolist():
db.get_collection('today').remove({'code':i},multi=True)
except Exception as e:
print('newStock Error',e)
# 剔除停牌
db.get_collection('today').remove({'open': 0})
def crawl_new_stocks(self):
df_stocks = ts.new_stocks()
update_list = []
for index in df_stocks.index:
raw = dict(df_stocks.loc[index])
doc = {
'code': raw['code'],
'ipo_date': raw['issue_date'],
'price': raw['price']
}
update_list.append(
UpdateOne({'code': doc['code']}, {'$set': doc}, upsert=True))
if len(update_list) > 0:
update_db = collection_dict['NEW_STOCK_COLLECTION'][1].bulk_write(
update_list, ordered=False)
print(
'update new stocks, --> [collection: %s] inserted：%4d, modified：%4d'
% (collection_dict['NEW_STOCK_COLLECTION'][0],
update_db.upserted_count, update_db.modified_count),
flush=True)
import tushare as ts
from sqlalchemy import create_engine
engine = create_engine(
'mysql+pymysql://root:[email protected]/packageing?charset=utf8')
df = ts.new_stocks()
#df.to_sql('new_stocks',engine)
df.to_sql('new_stocks', engine, if_exists='append')
holdings = ts.fund_holdings(2015, 1)
#holdings.to_sql('fund_holdings',engine)
holdings.to_sql('fund_holdings', engine, if_exists='append')
def fill_high_limit_low_limit(begin_date, end_date):
"""
为daily数据集填充涨停价和跌停价
:param begin_date: 开始日期
:param end_date: 结束日期
"""
# 从tushare获取新股数据
df_new_stocks = ts.new_stocks()
print(df_new_stocks)
code_ipo_price_dict = dict()
code_ipo_date_set = set()
for index in df_new_stocks.index:
ipo_price = df_new_stocks.loc[index]['price']
code = df_new_stocks.loc[index]['code']
ipo_date = df_new_stocks.loc[index]['ipo_date']
code_ipo_price_dict[code + '_' + ipo_date] = ipo_price
code_ipo_date_set.add(code + '_' + ipo_date)
all_codes = get_all_codes()
basic_cursor = DB_CONN['basic'].find(
{'date': {
'$gte': begin_date,
'$lte': end_date
}},
projection={
'code': True,
'date': True,
'name': True,
'_id': False
},
batch_size=1000)
code_date_basic_dict = dict([(x['code'] + '_' + x['date'], x['name'])
for x in basic_cursor])
code_date_key_sets = set(code_date_basic_dict.keys())
print(code_date_basic_dict)
for code in all_codes:
daily_cursor = DB_CONN['daily'].find(
{
'code': code,
'date': {
'$lte': end_date,
'$gte': begin_date
},
'index': False
},
sort=[('date', ASCENDING)],
projection={
'date': True,
'pre_close': True
})
update_requests = []
for daily in daily_cursor:
date = daily['date']
code_date_key = code + '_' + daily['date']
try:
high_limit = -1
low_limit = -1
pre_close = daily['pre_close']
if code_date_key in code_ipo_date_set:
high_limit = round(
code_ipo_price_dict[code_date_key] * 1.44, 2)
low_limit = round(
code_ipo_price_dict[code_date_key] * 0.64, 2)
elif code_date_key in code_date_key_sets and code_date_basic_dict[code_date_key][0:2]\
in ['ST', '*S'] and pre_close > 0:
high_limit = round(pre_close * 1.04, 2)
low_limit = round(pre_close * 0.95, 2)
elif pre_close > 0:
high_limit = round(pre_close * 1.10, 2)
low_limit = round(pre_close * 0.9, 2)
print(
'pre_close: %6.2f, high_limit: %6.2f, low_limit: %6.2f'
% (pre_close, high_limit, low_limit),
flush=True)
if high_limit > 0 and low_limit > 0:
update_requests.append(
UpdateOne(
{
'code': code,
'date': date,
'index': False
}, {
'$set': {
'high_limit': high_limit,
'low_limit': low_limit
}
}))
except:
print('填充涨跌停时发生错误，股票代码：%s，日期：%s' % (code, date),
flush=True)
if len(update_requests) > 0:
update_result = DB_CONN['daily'].bulk_write(update_requests,
ordered=False)
print('填充涨跌停，股票：%s，更新：%4d条' %
(code, update_result.modified_count),
flush=True)
def getNewStock():
df = ts.new_stocks()
df.to_excel('new_stock.xls', 'gbk')
def Get_new_stocks(self):
# 获取新股数据
df = ts.new_stocks()
#df = df.sort_values(by=['shares'], ascending=False)
print(df)
def new_stocks(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.new_stocks()
self.base.batchwri(self.df, 'xingushuju', self.financial_data)
def dump_ipo_data():
df = ts.new_stocks()
folder = os.path.dirname(__file__)
filepath = os.path.join(folder, "ipodata")
df.to_csv(filepath, encoding="utf-8")
def new_stocks():
DTS=ts.new_stocks()
DTS.to_csv('new_stocks.csv',encoding='gbk')
ts.get_hist_data('600848'，ktype='M') #获取月k线数据
ts.get_hist_data('600848'，ktype='5') #获取5分钟k线数据
ts.get_hist_data('600848'，ktype='15') #获取15分钟k线数据
ts.get_hist_data('600848'，ktype='30') #获取30分钟k线数据
ts.get_hist_data('600848'，ktype='60') #获取60分钟k线数据
ts.get_hist_data('sh'）#获取上证指数k线数据，其它参数与个股一致，下同
ts.get_hist_data('sz'）#获取深圳成指k线数据
ts.get_hist_data('hs300'）#获取沪深300指数k线数据
ts.get_hist_data('sz50'）#获取上证50指数k线数据
ts.get_hist_data('zxb'）#获取中小板指数k线数据
ts.get_hist_data('cyb'）#获取创业板指数k线数据
ts.get_today_all()#当天实时数据
df = ts.get_index()#大盘指数
ts.new_stocks()#新股数据
#基本面数据
ts.get_stock_basics()
#获取2015年第1季度的业绩报表数据
ts.get_report_data(2015,1)
#获取2015年第1季度的盈利能力数据
ts.get_profit_data(2015,1)
#获取2015年第1季度的营运能力数据
ts.get_operation_data(2015,1)
#获取2015年第1季度的成长能力数据
ts.get_growth_data(2015,1)
#获取2015年第1季度的偿债能力数据
ts.get_debtpaying_data(2015,1)
#获取2015年第1季度的现金流量数据
ts.get_cashflow_data(2015,1)
#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')
