def job_7():
try:
print("I'm working......龙虎榜数据")
# 每日龙虎榜列表
top_list = ts.top_list(today)
data = pd.DataFrame(top_list)
data.to_sql('top_list',engine,index=True,if_exists='replace')
print("每日龙虎榜列表......done")
# 个股上榜统计
cap_tops = ts.cap_tops()
data = pd.DataFrame(cap_tops)
data.to_sql('cap_tops',engine,index=True,if_exists='replace')
print("个股上榜统计......done")
# 营业部上榜统计
broker_tops = ts.broker_tops()
data = pd.DataFrame(broker_tops)
data.to_sql('broker_tops',engine,index=True,if_exists='replace')
print("营业部上榜统计......done")
# 机构席位追踪
inst_tops = ts.inst_tops()
data = pd.DataFrame(inst_tops)
data.to_sql('inst_tops',engine,index=True,if_exists='replace')
print("机构席位追踪......done")
# 机构成交明细
inst_detail = ts.inst_detail()
data = pd.DataFrame(inst_detail)
data.to_sql('inst_detail',engine,index=True,if_exists='replace')
print("机构成交明细......done")
except Exception as e:
print(e)
def store_top_data(self, trading_date=None):
"""龙虎榜数据: 龙虎榜数据接口提供历史龙虎榜上榜股票数据"""
trading_date = self.last_trading_date if trading_date is None else trading_date
# 每日龙虎榜列表
print('top_list...')
top_df = ts.top_list(self.stock_date_format(trading_date))
self.mysqlUtils.append_data(top_df, 'top_list')
# 个股上榜统计
print('cap_tops...')
cap_tops_df = ts.cap_tops()
cap_tops_df['date'] = trading_date
self.mysqlUtils.append_data(cap_tops_df, 'cap_tops')
# 营业部上榜统计
print('broker_tops...')
broker_tops_df = ts.broker_tops()
broker_tops_df['date'] = trading_date
self.mysqlUtils.append_data(broker_tops_df, 'broker_tops')
# 龙虎榜机构席位追踪
print('inst_tops...')
inst_tops_df = ts.inst_tops()
inst_tops_df['date'] = trading_date
self.mysqlUtils.append_data(inst_tops_df, 'inst_tops')
# 龙虎榜机构席位成交明细
print('inst_detail...')
inst_detail_df = ts.inst_detail()
self.mysqlUtils.append_data(inst_detail_df, 'inst_detail')
def cap_tops(days,retry_count,pause):
try:
df = ts.cap_tops(days,retry_count,pause)
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('cap_tops', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def get_cap_tops():
df = ts.cap_tops()
df.to_sql('cap_tops',
engine,
if_exists='append',
index=False,
index_label='code')
def getdragontigerdata():
curday = datetime.date.today()
curdate = curday.strftime('%Y%m%d')
print(curdate)
mylogger = getmylogger()
# 每日龙虎榜列表
df = ts.top_list(curday.strftime('%Y-%m-%d'))
if df is not None:
df['date'] = curdate
tosql(df, 'toplistdata', "append", "每日龙虎榜数据", mylogger)
else:
mylogger.info("没有每日龙虎榜数据。")
# 个股上榜统计
for i in [5, 10, 30, 60]:
df = ts.cap_tops(i)
logmsg = "个股上榜数据" + "%d日：" % i
if df is not None:
df['date'] = curdate
df['period'] = i
tosql(df, 'captops', "append", logmsg, mylogger)
else:
mylogger.info("没有" + logmsg)
# 营业部上榜统计
for i in [5, 10, 30, 60]:
df = ts.broker_tops(i)
logmsg = "营业部上榜数据" + "%d日：" % i
if df is not None:
df['date'] = curdate
df['period'] = i
tosql(df, 'brokertops', "append", logmsg, mylogger)
else:
mylogger.info("没有" + logmsg)
# 机构席位追踪
for i in [5, 10, 30, 60]:
df = ts.inst_tops(i)
logmsg = "机构席位追踪数据" + "%d日：" % i
if df is not None:
df['date'] = curdate
df['period'] = i
tosql(df, 'instops', "append", logmsg, mylogger)
else:
mylogger.info("没有" + logmsg)
# 机构成交明细
df = ts.inst_detail()
logmsg = "机构成交明细："
if df is not None:
df['date'] = curdate
tosql(df, 'instdetail', "append", logmsg, mylogger)
else:
mylogger.info("没有机构成交明细。")
def get_td_cap_list(days=5, retry=3, pause=0):
"""
description:n日内上榜的累积卖出和买入
params:
days: n
retry: 重试次数
pause: 重启间隔
"""
data = ts.cap_tops(days=days, retry_count=retry, pause=pause)
return data
def test():
ts.get_sz50s()
ts.get_hs300s()
ts.get_zz500s()
ts.realtime_boxoffice()
ts.get_latest_news()
ts.get_notices(tk)
ts.guba_sina()
ts.get_cpi()
ts.get_ppi()
ts.get_stock_basics()
ts.get_concept_classified()
ts.get_money_supply()
ts.get_gold_and_foreign_reserves()
ts.top_list() #每日龙虎榜列表
ts.cap_tops() #个股上榜统计
ts.broker_tops() #营业部上榜统计
ts.inst_tops() # 获取机构席位追踪统计数据
ts.inst_detail()
def countInList(days=1):
"""
获取所有10天内上龙虎榜
:param days:
:return:
"""
df = ts.cap_tops(days)
# df = df[df.loc[:, 'net'] > 0]
sortdf = df.sort_values(by='count', ascending=False)
# print(sortdf)
print(sortdf[sortdf.loc[:, 'name'] == '方大炭素'])
def capTops():
"""
获取近5、10、30、60日个股上榜统计数据,包括上榜次数、累积购买额、累积卖出额、净额、买入席位数和卖出席位数
"""
try:
day = int(input('近天数(5、10、30、60)：'))
except:
day = 10
print('默认10天')
cap = ts.cap_tops(days=day)
print('\n')
print(cap.sort_values(by=['bamount', 'count'], ascending=(False, False)))
def stk_cap_tops(days=5,table = 'stk_cap_tops_ma5'):
print("\n插入数据：" + table)
try:
conn = connMysql().createconn()
cur = conn.cursor()
sql = 'truncate table ' + table + ';'
cur.execute(sql)
cur.close()
conn.close()
df = ts.cap_tops(days=days)
df.to_sql(table,engine,if_exists='append')
except:
print("\n收集个股上榜统计失败")
def tops(engine, freq):
today = datetime.date.today()
tsl.log("tops_stock start...")
try:
df = ts.cap_tops(freq)
df['date'] = today
df['freq'] = freq
df = df.set_index('code', drop='true')
df.to_sql('tops_stock',engine,if_exists='append')
print
tsl.log("tops_stock done")
except BaseException, e:
print
print e
tsl.log("tops_stock error")
def get_top_inst_count(self, days=5):
"""
获取个股在近5、10、30、60日的龙虎榜上榜次数、累积购买额、累积卖出额、净额、买入席位数和卖出席位数。
获取机构的近5、10、30、60日累积买入次数
具体返回参数参考tushare.cap_tops和tushare.inst_tops
Parameters：
----------
days:5、10、30、60,表示统计周期
"""
top_count_df = ts.cap_tops(days=days, retry_count=5, pause=1)
with codecs.open(u"{}/cap_tops_count_{}_{}.csv".format(data_path, days, self.today), 'w+', "utf-8") as f:
top_count_df.to_csv(f, header=True, sep="\t", index=True)
inst_tops_df = ts.inst_tops(days=days, retry_count=5, pause=1)
with codecs.open(u"{}/inst_tops_count_{}_{}.csv".format(data_path, days, self.today), 'w+', "utf-8") as f:
top_count_df.to_csv(f, header=True, sep="\t", index=True)
def get_lbh_cap_tops(day):
"""
个股上榜统计
获取近5、10、30、60日个股上榜统计数据,包括上榜次数、
累积购买额、累积卖出额、净额、买入席位数和卖出席位数。
"""
df = ts.cap_tops(day)
print(df)
if df is not None:
res = df.to_sql(lbh_cap_tops, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取近 {0} 日个股上榜统计数据,包括上榜次数、累积购买额、累积卖出额、净额、'
'买入席位数和卖出席位数：{1}'.format(day, msg) + '\n')
else:
print('获取近 {0} 日个股上榜统计数据,包括上榜次数、累积购买额、累积卖出额、净额、'
'买入席位数和卖出席位数：{1}'.format(day, 'None') + '\n')
def getStockLHB(self,days =5):
'''
个股上榜统计
输入：
days：统计周期5、10、30和60日，默认为5日
输出:
code：代码
name:名称
count：上榜次数
bamount：累积购买额(万)
samount：累积卖出额(万)
net：净额(万)
bcount：买入席位数
scount：卖出席位数
'''
df =ts.cap_tops(days)#5
return df
def get_individual_statistics_tops(days=5, retry_count=RETRY_COUNT, pause=PAUSE):
"""个股上榜统计"""
logger.info('Begin get IndividualStatisticsTops. Days is: %s.' % days)
try:
data_df = ts.cap_tops(days, retry_count, pause)
except Exception as e:
logger.exception('Error get IndividualStatisticsTops. Days is: %s.' % days)
return None
else:
data_dicts = []
if data_df is None or data_df.empty:
logger.warn('Empty get IndividualStatisticsTops. Days is: %s.' % days)
else:
data_dicts = [{'code': row[0], 'name': row[1],
'count': row[2], 'bamount': row[3], 'samount': row[4],
'net': row[5], 'bcount': row[6], 'scount': row[7],
'days_type': days, 'insert_date': today_line}
for row in data_df.values]
logger.info('Success get IndividualStatisticsTops. Days is: %s.' % days)
return data_dicts
def top_type(top_type):
today = datetime.datetime.today().strftime('%Y-%m-%d')
if top_type == 'top_list':
top_list = ts.top_list(today)
if top_list is not None:
top_list.to_sql('top_list',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'cap_tops':
cap_tops = ts.cap_tops()
if cap_tops is not None:
cap_tops['date'] = today
cap_tops.to_sql('top_cap_tops',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'broker_tops':
broker_tops = ts.broker_tops()
if broker_tops is not None:
broker_tops['date'] = today
broker_tops.to_sql('top_broker_tops',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'inst_tops':
inst_tops = ts.inst_tops()
if inst_tops is not None:
inst_tops['date'] = today
inst_tops.to_sql('top_inst_tops',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'inst_detail':
inst_detail = ts.inst_detail()
if inst_detail is not None:
inst_detail.to_sql('top_inst_detail',
engine,
flavor='mysql',
if_exists='append')
days：统计周期5、10、30和60日，默认为5日
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
返回值说明：
code：代码
name:名称
count：上榜次数
bamount：累积购买额(万)
samount：累积卖出额(万)
net：净额(万)
bcount：买入席位数
scount：卖出席位数'''
print('个股上榜统计')
print(ts.cap_tops())
'''
获取营业部近5、10、30、60日上榜次数、累积买卖等情况。
参数说明：
days：统计周期5、10、30和60日，默认为5日
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
返回值说明：
broker：营业部名称
count：上榜次数
bamount：累积购买额(万)
bcount：买入席位数
samount：累积卖出额(万)
def cap_list(days=5, retry_count=5, pause=1):
df = ts.cap_tops(days=days, retry_count=retry_count, pause=pause)
return df
def toDB_cap_tops(date, days):
df = ts.cap_tops()
Add_col(df, date=date, days=days).to_sql('t_cap_tops',
c.ENGINE,
if_exists='append')
for idx in df.index:
temp = df.ix[idx]
sql = "insert into top_list(code,name,pchange,amount,buy,bratio,sell,sratio,reason,date) values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)"
param = (temp['code'],temp['name'],temp['pchange'],temp['amount'],temp['buy'],temp['bratio'],temp['sell'],temp['sratio'],temp['reason'],temp['date'])
cursor.execute(sql, param)
conn.commit()
except:
f=open("errors/"+today+".log",'a')
traceback.print_exc(file=f)
f.flush()
f.close()
# 个股上榜统计
try:
df = idx = temp = sql = param = None
df = ts.cap_tops()
if df is not None:
for idx in df.index:
temp = df.ix[idx]
sql = "insert into cap_tops(code,name,count,bamount,samount,net,bcount,scount) values(%s,%s,%s,%s,%s,%s,%s,%s)"
param = (temp['code'],temp['name'],temp['count'],temp['bamount'],temp['samount'],temp['net'],temp['bcount'],temp['scount'])
cursor.execute(sql, param)
conn.commit()
except:
f=open("errors/"+today+".log",'a')
traceback.print_exc(file=f)
f.flush()
f.close()
# 营业部上榜统计
try:
def TopTotal():
df=ts.cap_tops()
df=df.sort_values('count',ascending=False)
return [u"个股上榜统计：",df[:20]]
def getCapTops(self):
file_name = 'cap_tops.csv'
path = self.index + self.index_cap_tops_data + file_name
data = ts.cap_tops()
data.to_csv(path, encoding='utf-8')
print(file_name)
def recommend(request):
#基金
if not models.RecommendFund.objects.all():
r = requests.get('http://fund.eastmoney.com/trade/default.html')
encode_content = r.content.decode('gb2312')
soup = BeautifulSoup(encode_content, 'lxml')
name = soup.find_all('td', 'fname')
pattern1 = re.compile("<td>(\d\d\d\d\d\d)</td>")
code = re.findall(pattern1, encode_content)
rate = []
for item in code[0:25]:
r = requests.get('http://fund.eastmoney.com/pingzhongdata/' + item + '.js')
pattern3 = re.compile('var syl_1n="(.*?)"')
tmp = re.findall(pattern3, r.text)
#tmp[0] += '%'
rate.append(tmp[0])
for i in range(0, 25):
recF = models.RecommendFund()
recF.code = code[i]
recF.name = name[i].string
recF.annualrate = rate[i]
recF.save()
#股票
if not models.RecommendStock.objects.all():
rs = ts.cap_tops()
for i in range(0, 30):
stock_code=rs.code[i]
stockdata = requests.get('http://hq.sinajs.cn/list=sh' + stock_code)
stockdatasplit = stockdata.text.split(',')
if (len(stockdata.text) == 24):
stockdata = requests.get('http://hq.sinajs.cn/list=sz' + stock_code)
stockdatasplit = stockdata.text.split(',')
stock = models.Stock()
stock.code = stock_code
stock.name = stockdatasplit[0][21:]
stock.open = stockdatasplit[1]
stock.close = stockdatasplit[2]
if float(stock.close)==0:
continue
stock.high = stockdatasplit[4]
stock.low = stockdatasplit[5]
stock.price = stockdatasplit[3]
stock.currentrate = (float(stock.price) - float(stock.close)) / float(stock.close) * 100
else:
stock = models.Stock()
stock.code = stock_code
stock.name = stockdatasplit[0][21:]
stock.open = stockdatasplit[1]
stock.close = stockdatasplit[2]
if float(stock.close)==0:
continue
stock.high = stockdatasplit[4]
stock.low = stockdatasplit[5]
stock.price = stockdatasplit[3]
stock.currentrate = (float(stock.price) - float(stock.close)) / float(stock.close) * 100
w = round(stock.currentrate, 4)
if abs(w)>11:
continue
recS = models.RecommendStock()
recS.code = rs.code[i]
recS.name = rs.name[i]
recS.rate = w
recS.save()
recF = models.RecommendFund.objects.all()
recS = models.RecommendStock.objects.all()
return render(request, 'recommend.html', {'recF': recF, 'recS': recS})
def longhuban(self, date):
print(ts.top_list(date))
print(ts.cap_tops())
import pymongo
import json
# ts.get_cash_flow : get specialed Code Cash_flow in the History .
# 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 = "TopRanks"
coll = "CapTop"
date = '2017-12-22'
tops = 5
conn = pymongo.MongoClient('127.0.0.1', port=27017)
df = ts.cap_tops(tops)
# 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][0] + "-" + date + "-" + str(tops),
'Date': date,
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],
dicIndex['columns'][5]: dicIndex['data'][i][5],
dicIndex['columns'][6]: dicIndex['data'][i][6],
dicIndex['columns'][7]: dicIndex['data'][i][7]
}
import tushare as ts
import urllib.request
proxy = urllib.request.ProxyHandler({'http': 'http://10.144.1.10:8080'})
opener = urllib.request.build_opener(proxy)
urllib.request.install_opener(opener)
user_agent = 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'
headers = {'User-Agent': user_agent}
#df = ts.inst_tops()
#df = ts.top_list('2017-05-08')
df = ts.cap_tops()
df.to_csv('d:/temp/cap.csv')
print(df)
def Getgegushangbang():
df = ts.cap_tops()
df = df.to_json(force_ascii=False)
print(df)
return df
def GetStockCountOnBillBoardIn(days):
"""
Args:
days: days should be 5,10,30 or 60
"""
return ts.cap_tops(date)
# 行业分类
# df = ts.get_industry_classified()
# df.to_csv('F:/01/get_industry_classified.csv', encoding='utf_8_sig')
# 2.概念分类
# df = ts.get_concept_classified()
# df.to_csv('F:/01/get_concept_classified.csv', encoding='utf_8_sig')
# 1.存款利率
# print(ts.get_deposit_rate())
# 2.贷款利率
# print(ts.get_loan_rate())
# print(ts.top_list('2019-04-02'))
df = ts.cap_tops(10) # 默认为5日
df.to_csv('F:/01/cap_tops.csv', encoding='utf_8_sig')
# df.to_json('F:/01/cap_tops.json')
def capture_stock_data():
capture_date = datetime.datetime.now().strftime("%Y%m%d")
save_dir = "/home/dandelion/stock_data/" + capture_date
if not os.path.exists(save_dir):
os.mkdir(save_dir)
print("The save directory is created successfully!\n", save_dir)
print("The save directory is already exist!\n", save_dir)
# ======================Daily Command================================================================
# get the boxoffcie data of the last day and save as csvfile named as the capture command
ts.day_boxoffice().to_csv(
save_dir + "/" + capture_date + "_day_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("day_boxoffice data capture completed!")
# get the cinema data of the last day and save as csvfile named as the capture command
ts.day_cinema().to_csv(
save_dir + "/" + capture_date + "_day_cinema.csv",
header=True,
sep=",",
index=False,
)
print("day_cinema data capture completed!")
ts.month_boxoffice().to_csv(
save_dir + "/" + capture_date + "_month_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("month_boxoffice data capture completed!")
ts.realtime_boxoffice().to_csv(
save_dir + "/" + capture_date + "_realtime_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("realtime_boxoffice data capture completed!")
# get the stock data index of the last day and save as csvfile named as the capture command
ts.get_index().to_csv(
save_dir + "/" + capture_date + "_get_index.csv",
header=True,
sep=",",
index=False,
)
print("get_index data capture completed!")
# get the history cpi data and save as csvfile named as the capture command
ts.get_cpi().to_csv(
save_dir + "/" + capture_date + "_get_cpi.csv",
header=True,
sep=",",
index=False,
)
print("get_cpi data capture completed!")
# get the history gdp data by month and save as csvfile named as the capture command
ts.get_gdp_year().to_csv(
save_dir + "/" + capture_date + "_get_gdp_year.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_year data capture completed!")
# get today all stock data and save as csvfile named as the capture command
# ts.get_today_all().to_csv(save_dir+'/'+capture_date+'_get_today_all.csv',header=True,sep=',',index=False)
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.broker_tops().to_csv(
save_dir + "/" + capture_date + "_broker_tops.csv",
header=True,
sep=",",
index=False,
)
print("broker_tops data capture completed!")
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.cap_tops().to_csv(
save_dir + "/" + capture_date + "_cap_tops.csv",
header=True,
sep=",",
index=False,
)
print("cap_tops data capture completed!")
ts.get_area_classified().to_csv(
save_dir + "/" + capture_date + "_get_area_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_area_classified data capture completed!")
# ts.get_balance_sheet(code='').to_csv(save_dir+'/'+capture_date+'_get_balance_sheet.csv',header=True,sep=',',index=False)
# print('get_balance_sheet data capture completed!')
# ts.get_cash_flow(code='').to_csv(save_dir+'/'+capture_date+'_get_cash_flow.csv',header=True,sep=',',index=False)
# print('get_cash_flow data capture completed!')
ts.get_day_all().to_csv(
save_dir + "/" + capture_date + "_get_day_all.csv",
header=True,
sep=",",
index=False,
)
print("get_day_all data capture completed!")
ts.get_cashflow_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_cashflow_data.csv",
header=True,
sep=",",
index=False,
)
print("get_cashflow_data data capture completed!")
ts.get_concept_classified().to_csv(
save_dir + "/" + capture_date + "_get_concept_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_concept_classified data capture completed!")
ts.get_debtpaying_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_debtpaying_data.csv",
header=True,
sep=",",
index=False,
)
print("get_debtpaying_data data capture completed!")
ts.get_deposit_rate().to_csv(
save_dir + "/" + capture_date + "_get_deposit_rate.csv",
header=True,
sep=",",
index=False,
)
print("get_deposit_rate data capture completed!")
ts.get_gdp_contrib().to_csv(
save_dir + "/" + capture_date + "_get_gdp_contrib.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_for().to_csv(
save_dir + "/" + capture_date + "_get_gdp_for.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_pull().to_csv(
save_dir + "/" + capture_date + "_get_gdp_pull.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_quarter().to_csv(
save_dir + "/" + capture_date + "_get_gdp_quarter.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_ data capture completed!")
# ts.get_gdp_year().to_csv(save_dir+'/'+capture_date+'_get_gdp_year.csv',header=True,sep=',',index=False)
ts.get_gem_classified().to_csv(
save_dir + "/" + capture_date + "_get_gem_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_gold_and_foreign_reserves().to_csv(
save_dir + "/" + capture_date + "_get_gold_and_foreign_reserves.csv",
header=True,
sep=",",
index=False,
)
ts.get_growth_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_growth_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_industry_classified().to_csv(
save_dir + "/" + capture_date + "_get_industry_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_hs300s().to_csv(
save_dir + "/" + capture_date + "_get_hs300s.csv",
header=True,
sep=",",
index=False,
)
ts.get_sz50s().to_csv(
save_dir + "/" + capture_date + "_get_sz50s.csv",
header=True,
sep=",",
index=False,
)
ts.get_zz500s().to_csv(
save_dir + "/" + capture_date + "_get_zz500s.csv",
header=True,
sep=",",
index=False,
)
ts.get_operation_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_operation_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_stock_basics().to_csv(
save_dir + "/" + capture_date + "_get_stock_basics.csv",
header=True,
sep=",",
index=False,
)
ts.get_report_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_report_data.csv",
header=True,
sep=",",
index=False,
)
ts.inst_detail().to_csv(
save_dir + "/" + capture_date + "_inst_detail.csv",
header=True,
sep=",",
index=False,
)
ts.inst_tops().to_csv(
save_dir + "/" + capture_date + "_inst_tops.csv",
header=True,
sep=",",
index=False,
)
print("inst_tops data capture completed!")
ts.new_stocks().to_csv(
save_dir + "/" + capture_date + "_new_stocks.csv",
header=True,
sep=",",
index=False,
)
print("new_stocks data capture completed!")
ts.top_list().to_csv(
save_dir + "/" + capture_date + "_top_list.csv",
header=True,
sep=",",
index=False,
)
print("top_list data capture completed!")
def get_cap_tops(days=30):
return ts.cap_tops(days)
def GetCaptops(self):
datateee = ts.cap_tops()
self.SaveCSV(datateee, 'captops.csv')
#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_today_all()
df.to_sql('realtime_price',engine, if_exists='replace')
df = ts.cap_tops(60)
df.to_sql('cap_tops',engine, if_exists='replace')
#df=ts.inst_tops(10)
#df.to_sql('inst_tops',engine, if_exists='replace')
#df=ts.inst_detail(10)
#df.to_sql('inst_detail',engine, if_exists='replace')
def longhuban(self, date):
print(ts.top_list(date))
print(ts.cap_tops())
import tushare as ts
from sqlalchemy import *
token = ts.set_token('c723069dd4a25402d05ea6afad36da2937111adf012f8258abb5f7e05936e575')
engine = create_engine('mysql+pymysql://root:toor@localhost/pack?charset=utf8')
#df = ts.new_stocks()
#df.to_sql('new_stocks',engine,if_exists='append')
#holdings = ts.fund_holdings(2015,3)
#holdings.to_sql('fund_holdings',engine,if_exists='append')
#df = ts.get_industry_classified()
#df.to_sql('industry_classified',engine,if_exists='append')
#top = ts.top_list('2016-04-21') #每日龙虎榜列表
cap = ts.cap_tops() #个股上榜统计，
broker = ts.broker_tops() #营业部上榜统计
detail = ts.inst_detail() #机构成交明细
#top.to_sql('top_list',engine,if_exists = 'append')
cap.to_sql('cap_tops',engine,if_exists = 'append')
broker.to_sql('broker_tops',engine,if_exists = 'append')
detail.to_sql('inst_detail',engine,if_exists = 'append')
