def qqfinance_InSt(code):
"""获取某只股票的财务指标
Parameter：
code：上海、深圳交易所的股票代码
-------------------------------------
Return：
DataFrame：
单位均为元(万元或元）。
"""
url = 'http://stock.finance.qq.com/corp1/inst.php?zqdm={0}'.format(code)
html = requests.get(url, headers=hds())
h = lxml.html.parse(StringIO(html.text))
urls = h.xpath('//div[@class="right"]/table[2]/tr/td/a/@href')
AT = pd.DataFrame()
for url in urls:
html = requests.get(url, headers=hds())
soup = BeautifulSoup(html.text, 'lxml')
table = soup.find_all('table', attrs={'class', 'list'})
df = pd.read_html(str(table))[0]
df = df.dropna(how='all', axis=1)
df = df.T
df.columns = df.ix[0, :]
df = df.drop(0, axis=0)
AT = AT.append(df)
AT = AT.set_index('报表日期')
AT = AT.dropna(how='all', axis=1)
AT = AT.applymap(lambda x: _str2fl(x))
AT = AT.sort_index()
return AT
def get_holdernum_change_EM(year,qu):
"""reportdate='2017-06-30'，所有股票的股东户数
"""
if year is None:
reportdate=''
else:
reportdate=quarter[qu]%year
url='http://data.eastmoney.com/DataCenter_V3/gdhs/GetList.ashx?reportdate={0}&market=&changerate==&range==&pagesize=50000&page=1&sortRule=-1&sortType=NoticeDate'.format(reportdate)
r=requests.get(url,headers=hds())
text=r.text
data=json.loads(text)
tp=data['pages']
df=pd.DataFrame(data['data'])
if tp>1:
for i in range(2,tp+1):
url='http://data.eastmoney.com/DataCenter_V3/gdhs/GetList.ashx?reportdate={0}&market=&changerate==&range==&pagesize=50000&page={1}&sortRule=-1&sortType=NoticeDate'.format(reportdate,i)
r=requests.get(url,headers=hds())
text=r.text
data=json.loads(text)
df=df.append(pd.DataFrame(data['data']))
return df
def getlist(url, page=2):
r = requests.get(url, headers=hds())
txt = r.content.decode('utf8')
#print(txt)
html = lxml.html.parse(StringIO(txt))
lis = html.xpath('//div[@id="container"]/div[@class="sec_list"]/ul/li')
datasets = {}
for li in lis:
name = re.sub(r'\s*', '', li.xpath('a/text()')[0].strip())
name = name.replace('“', '').replace('”', '')
date = li.xpath('i/text()')[0]
name = date + '_' + name
#print(name)
href = 'http://www.court.gov.cn' + li.xpath('a/@href')[0]
#if name.endswith('指导性案例'):
datasets[name] = href
for i in range(2, page + 1):
urll = url + '?page=%s' % i
r = requests.get(urll, headers=hds())
txt = r.content.decode('utf8')
#print(txt)
html = lxml.html.parse(StringIO(txt))
lis = html.xpath('//div[@id="container"]/div[@class="sec_list"]/ul/li')
for li in lis:
name = re.sub(r'\s*', '', li.xpath('a/text()')[0].strip())
name = name.replace('“', '').replace('”', '')
date = li.xpath('i/text()')[0]
name = date + '_' + name
#print(name)
href = 'http://www.court.gov.cn' + li.xpath('a/@href')[0]
#if name.endswith('指导性案例'):
datasets[name] = href
return datasets
def get_holder_analys_EM(year,qu):
"""股东持股明细,reportdate='2017-06-30'
"""
reportdate=quarter[qu]%year
url="http://dcfm.eastmoney.com//em_mutisvcexpandinterface/api/js/get?type=NSHDDETAILLA&token=70f12f2f4f091e459a279469fe49eca5&cmd=&st=NDATE,SCODE,RANK&sr=1&p=1&ps=5000&js={1}&filter=(RDATE=%27^{0}^%27)".format(reportdate,'{%22pages%22:(tp),%22data%22:(x)}')
#print(url)
r=requests.get(url,headers=hds())
text=r.text
text=text.replace('pages:','"pages":').replace("data:",'"data":')
#print(text[:30])
data=json.loads(text)
df=pd.DataFrame(data['data'])
tp=data['pages']
if tp>1:
for i in range(2,tp+1):
url="http://dcfm.eastmoney.com//em_mutisvcexpandinterface/api/js/get?type=NSHDDETAILLA&token=70f12f2f4f091e459a279469fe49eca5&cmd=&st=NDATE,SCODE,RANK&sr=1&p={2}&ps=5000&js={1}&filter=(RDATE=%27^{0}^%27)".format(reportdate,'{%22pages%22:(tp),%22data%22:(x)}',i)
r=requests.get(url,headers=hds())
text=r.text
text=text.replace('pages:','"pages":').replace("data:",'"data":')
data=json.loads(text)
df=df.append(pd.DataFrame(data['data']))
df['NDATE']=df['NDATE'].map(lambda x:x[:10])
df['RDATE']=df['RDATE'].map(lambda x:x[:10])
df=df.replace('-',np.nan)
df=df.drop(['SHAREHDCODE','COMPANYCODE', 'SHARESTYPE', 'LTAG'],axis=1)
df=df.applymap(lambda x:wt._tofl(x))
df['SCODE']=df['SCODE'].map(lambda x:str(x).split('.')[0].zfill(6))
return df
def get_holders_num_qq(rpdate='2017-06-30'):
url = 'http://web.ifzq.gtimg.cn/fund/zcjj/zcjj/allzc?colum=3&order=desc&page=1&pagesize=50&bgrq={0}&_var=v_jjcg'.format(
rpdate)
r = requests.get(url, headers=hds())
text = r.text.split("jjcg=")[1]
data = json.loads(text)
df = pd.DataFrame(data['data']['data'])
tpage = data['data']['totalPages']
if tpage > 1:
tpage = tpage + 1
for i in range(2, tpage):
url = 'http://web.ifzq.gtimg.cn/fund/zcjj/zcjj/allzc?colum=3&order=desc&page={1}&pagesize=50&bgrq={0}&_var=v_jjcg'.format(
rpdate, i)
r = requests.get(url, headers=hds())
text = r.text.split("jjcg=")[1]
data = json.loads(text)
df = df.append(pd.DataFrame(data['data']['data']))
df.columns = [
'Total.Num(10K)', 'change(10K)', 'inst.Num', 'Curr.Rate%', 'code',
'name'
]
df['date'] = rpdate
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
return df
def get_search_inst_num_EM(code=None):
"""机构调研列表或个股机构调研列表
"""
if code is None:
url='http://data.eastmoney.com/DataCenter_V3/jgdy/gsjsdy.ashx?pagesize=5000&page=1'
else:
url='http://data.eastmoney.com/DataCenter_V3/jgdy/gsjsdy.ashx?pagesize=5000&page=1&code={0}'.format(code)
r=requests.get(url,headers=hds())
#print(url)
text=r.text
data=json.loads(text)
df=pd.DataFrame(data['data'])
tp=data['pages']
if tp>1:
for i in range(2,tp+1):
if code is None:
url='http://data.eastmoney.com/DataCenter_V3/jgdy/gsjsdy.ashx?pagesize=5000&page={0}'.format(i)
else:
url='http://data.eastmoney.com/DataCenter_V3/jgdy/gsjsdy.ashx?pagesize=5000&page={1}&code={0}'.format(code,i)
r=requests.get(url,headers=hds())
text=r.text
data=json.loads(text)
df=df.append(pd.DataFrame(data['data']))
if df.empty is False:
del df['CompanyCode']
#df=df.applymap(lambda x:wt._tofl(x))
#df['SCode']=df['SCode'].map(lambda x:str(x).zfill(6))
return df
def get_tick_today_EM(code, mtype=0):
if code[0] in ['6', '9']:
code = code + '1'
if code[0] in ['0', '2', '3']:
code = code + '2'
url = 'http://hqdigi2.eastmoney.com/EM_Quote2010NumericApplication/CompatiblePage.aspx?Type=OB&stk={0}&Reference=xml&limit={1}&page=1'.format(
code, mtype)
r = requests.get(url, headers=hds())
text = r.text.split('=')[1]
data = text.split('data:["')[1].replace('"]};', '').replace('","', '\n')
df = pd.read_csv(StringIO(data), header=None)
page = text.split('{pages:')[1].split(',data:')[0]
page = int(page)
if page > 1:
for i in range(2, page + 1):
url = 'http://hqdigi2.eastmoney.com/EM_Quote2010NumericApplication/CompatiblePage.aspx?Type=OB&stk={0}&Reference=xml&limit={1}&page={2}'.format(
code, mtype, i)
r = requests.get(url, headers=hds())
text = r.text.split('=')[1]
data = text.split('data:["')[1].replace('"]};',
'').replace('","', '\n')
df = df.append(pd.read_csv(StringIO(data), header=None))
df.columns = ['time', 'price', 'volume', 'type']
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_sharesholded_change_EM(code=None,mtype='all'):
"""股东对股票的增减持情况
-------------------------
mtype: all--所有的变动；jjc--减持；jzc--增持。
"""
if code is None:
code=''
url='http://data.eastmoney.com/DataCenter_V3/gdzjc.ashx?pagesize=5000&page=1¶m=&sortRule=-1&sortType=BDJZ&tabid={1}&code={0}&name='.format(code,mtype)
r=requests.get(url,headers=hds())
text=r.text.split(',data:["')[1].split('"] ,"url')[0]
text=text.replace('","','\n')
df=pd.read_csv(StringIO(text),header=None)
tp=r.text.split(",data:[")[0].split("pages:")[1]
tp=int(tp)
if tp>1:
for i in range(2,tp+1):
url='http://data.eastmoney.com/DataCenter_V3/gdzjc.ashx?pagesize=5000&page={1}¶m=&sortRule=-1&sortType=BDJZ&tabid=all&code={0}&name='.format(code,i)
r=requests.get(url,headers=hds())
text=r.text.split(',data:["')[1].split('"] ,"url')[0]
text=text.replace('","','\n')
df=df.append(pd.read_csv(StringIO(text),header=None))
#df=df.drop(16,axis=1)
df.columns=['code','name','price','chg','holder','type','chg_num','chg.cur.rate','source','hold_num','tt.rate','hold_cur_num','cur.rate','start','end','date','chg.tt.rate']
df=df.applymap(lambda x:wt._tofl(x))
df['code']=df['code'].map(lambda x:str(x).zfill(6))
df=df.reset_index(drop=True)
return df
def qqhyxw(code):
"""
获取股票所在行业新闻的标题
"""
url = 'http://stockhtm.finance.qq.com/sstock/ggcx/{0}.shtml'.format(code)
html = requests.get(url, headers=hds())
soup = BeautifulSoup(html.content, 'lxml')
dataset = []
url = soup.find_all('a', text='行业新闻')[0].get('href')
html = requests.get(url)
content = html.content.decode('gbk')
text = lxml.html.parse(StringIO(content))
_read_table(text, dataset)
for _ in range(11):
soup = BeautifulSoup(content, 'lxml')
try:
nexturl = soup.find('a', text='下一页').get('href')
html = requests.get(nexturl, headers=hds())
content = html.content.decode('gbk')
text = lxml.html.parse(StringIO(content))
_read_table(text, dataset)
except Exception as e:
print(e)
df = pd.DataFrame(dataset)
df.columns = ['name', 'href', 'datetime']
return df
def get_pepb_Sina():
dff = pd.DataFrame()
url = 'http://money.finance.sina.com.cn/d/api/openapi_proxy.php/?__s=[[%22hq%22,%22hs_a%22,%22%22,0,1,500]]&callback=FDC_DC.theTableData'
r = requests.get(url, headers=hds())
text = r.text.split('theTableData(')[1]
text = text.replace(')\n', '')
d = json.loads(text)
df = pd.DataFrame(d[0]['items'])
df.columns = d[0]['fields']
dff = dff.append(df)
pageNo = 2
while True:
url = 'http://money.finance.sina.com.cn/d/api/openapi_proxy.php/?__s=[[%22hq%22,%22hs_a%22,%22%22,0,{0},500]]&callback=FDC_DC.theTableData'.format(
pageNo)
#print(url)
r = requests.get(url, headers=hds())
text = r.text.split('theTableData(')[1]
text = text.replace(')\n', '')
d = json.loads(text)
if len(d[0]['items']) < 1:
print('Exit ....')
break
df = pd.DataFrame(d[0]['items'])
df.columns = d[0]['fields']
dff = dff.append(df)
pageNo = pageNo + 1
dff['date'] = d[0]['day']
dff = dff.drop(["symbol", "favor", "guba"], axis=1)
dff = dff.set_index('code')
return dff
def get_financeindex_all_THS(year, qt):
yqt = seasons[qt] % year
#print(yqt)
url = 'http://data.10jqka.com.cn/financial/yjgg/date/{0}/board/ALL/field/DECLAREDATE/order/desc/page/1/ajax/1/'.format(
yqt)
r = requests.get(url, timeout=10, headers=hds())
text = r.text
html = lxml.html.parse(StringIO(text))
res = html.xpath(
'//table[@class="m-table J-ajax-table J-canvas-table"]/tbody//tr')
if PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = ''.join(sarr)
sarr = '<table>%s</table>' % sarr
df = pd.read_html(sarr)[0]
pages = html.xpath('//div[@class="m-page J-ajax-page"]//span/text()')
pages = int(pages[0].split('/')[1])
#print('Total pages %s'%pages)
if pages > 1:
for i in range(2, pages + 1):
url = 'http://data.10jqka.com.cn/financial/yjgg/date/{0}/board/ALL/field/DECLAREDATE/order/desc/page/{1}/ajax/1/'.format(
yqt, i)
r = requests.get(url, timeout=10, headers=hds())
#print(url)
text = r.text
html = lxml.html.parse(StringIO(text))
res = html.xpath(
'//table[@class="m-table J-ajax-table J-canvas-table"]/tbody//tr'
)
if PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = ''.join(sarr)
sarr = '<table>%s</table>' % sarr
df = df.append(pd.read_html(sarr)[0])
#print(df.tail())
df = df.drop([0, 15], axis=1)
df.columns = [
'code', 'name', 'pdate', 'rev', 'rev_yoy', 'rev_hb', 'profit',
'profit_yoy', 'profit_hb', 'eps', 'nav', 'roe', 'cf_ps', 'margin'
]
df = df.applymap(lambda x: _str2fl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('code')
return df
def get_forcast_qq(code=None, year=2017, qu=3, mtype=0):
"""获取企业的业绩预告信息
code:股票代码6个字符
report：报告期,like 20170630
mtype: 类型,0-全部的业绩预告,1-预增的业绩预告,2-预减的业绩预告,
3-预盈的业绩预告,4-预亏的业绩预告,5-大幅上升的业绩预告,
6-大幅下降的业绩预告,7-扭亏的业绩预告,8-减亏的业绩预告,
9-无大变的业绩预告,10-不确定的业绩预告
"""
report = quarter[qu] % year
if code is None:
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm=&type={1}&bgq={0}&p=1&lmt=50&sort_name=ggrq&order=desc'.format(
report, mtype)
elif isinstance(code, str):
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm={0}&type={2}&bgq={1}&p=1&lmt=50&sort_name=ggrq&order=desc'.format(
code, report, mtype)
r = requests.get(url, headers=hds())
#print(url)
text = r.text.split("yjyg=")[1]
data = json.loads(text)
df = pd.DataFrame(data['data']['data'])
tpage = data['data']['totalPages']
if tpage > 1:
tpage = tpage + 1
for i in range(2, tpage):
if code is None:
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm=&type={1}&bgq={0}&p={2}&lmt=50&sort_name=ggrq&order=desc'.format(
report, mtype, i)
elif isinstance(code, str):
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm={0}&type={3}&bgq={1}&p={2}&lmt=50&sort_name=ggrq&order=desc'.format(
code, report, i, mtype)
r = requests.get(url, headers=hds())
text = r.text.split("yjyg=")[1]
data = json.loads(text)
df = df.append(pd.DataFrame(data['data']['data']))
try:
df.columns = [
'report.D', 'publish.D', 'code', 'name', 'eps_last', 'type',
'describe'
]
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
except:
pass
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_sharesGroupby_f10_EM(code, mtype='hy'):
"""
所属行业、概念、地域中的分析、收集同类股票信息
--------------------------
code:为股票代码，为6位数
"""
if code[0] in ['6', '9']:
code = 'SH' + code
if code[0] in ['0', '2', '3']:
code = 'SZ' + code
#hy
if mtype == 'hy':
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/StockRelationship/StockRelationshipAjax?code={0}&orderBy=1&isAsc=false'.format(
code)
#dy
if mtype == 'dy':
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/StockRelationship3/StockRelationshipAjax?code={0}&orderBy=1&isAsc=false'.format(
code)
#gn
if mtype == 'gn':
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/StockRelationship2/GetConceptList?code={0}&orderBy=1&isAsc=true&type=1'.format(
code)
r = requests.get(url, headers=hds())
data = json.loads(r.text)
dtt = pd.DataFrame(data["Result"])
dtt = dtt.set_index('glid')
print(dtt.loc[:, 'glmc'].tolist())
#for idd in dtt.index:
bkname = input("输入概念名称:")
idd = dtt[dtt['glmc'] == bkname].index[0]
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/StockRelationship2/GetSameConceptStockRankList?code={0}&orderBy=1&typeId={1}&isAsc=false'.format(
code, idd)
#print(url)
r = requests.get(url, headers=hds())
text = r.text
#print(text)
data = json.loads(text)
try:
df = pd.DataFrame(data["Result"]["stockRandList"])
except:
df = pd.DataFrame(data["Result"])
df = df.replace('--', np.nan)
df = df.dropna(how='all', axis=1)
return df
def finance_share_news(code):
"""
获取股票个股新闻的标题和内容
--------------------------------
Parameter:
code: 股票代码，String like，600026
Return:
title: 标题
url： 网址
datetime： 新闻发布时间
id： 识别号
code： 与相关股份的相应
"""
code = _set_code(code)
url = 'http://news2.gtimg.cn/lishinews.php?name=finance_news&symbol={0}&page=1'.format(
code)
#print(url)
r = requests.get(url, headers=hds())
content = r.text
pageN = re.findall('"total_page":\d+', content)
pageNo = int(re.findall('\d+', pageN[0])[0]) + 1
tem = []
for i in range(pageNo):
url = 'http://news2.gtimg.cn/lishinews.php?name=finance_news&symbol={0}&page={1}'.format(
code, i)
r = requests.get(url, headers=hds())
content = r.content.decode('unicode-escape') #decode('gb18030')
r = content.split(':{"data":[')[1][:-1]
r = r.split('}],')[0]
newslist = r.split('},{')
for news in newslist:
news = news.split(',')
tem1 = []
for n in news:
try:
#name=n.split('":"',1)[0]
value = n.split('":"', 1)[1]
value = value.replace('"', '')
#if '\\' in value:
value = value.replace('\\', '')
#print(u'%s'%value)
tem1.append(value)
except:
pass
tem.append(tem1)
df = pd.DataFrame(tem)
df.columns = ['title', 'url', 'datetime', 'id', 'code']
return df
def LawInterpretation(url='http://www.court.gov.cn/fabu-gengduo-16.html',
pgs=3):
uls = []
datasets = {}
uls.append(url)
if pgs > 1:
for p in range(2, pgs + 1):
url = 'http://www.court.gov.cn/fabu-gengduo-16.html?page=%s' % p
uls.append(url)
for url in uls:
r = requests.get(url, headers=hds())
#urls=[]
txt = r.content.decode('utf8')
html = lxml.html.parse(StringIO(txt))
lis = html.xpath('//div[@id="container"]/div[@class="sec_list"]/ul/li')
for li in lis:
name = re.sub(r'\s*', '', li.xpath('a/@title')[0].strip())
name = name.replace('“', '').replace('”',
'').replace(':', '：').replace(
' ', '')
#print(name)
tm = li.xpath('i/text()')[0]
href = 'http://www.court.gov.cn' + li.xpath('a/@href')[0]
#if name.startswith('指导案例'):
name = tm + '_' + name
datasets[name] = href
for tt, ul in datasets.items():
path = 'law/sikao/sifa/' + tt + '.txt'
if not os.path.exists(path):
rr = requests.get(ul, headers=hds())
soup = BeautifulSoup(rr.text, 'lxml')
txt = soup.find('div', attrs={'class', 'txt_txt'}).text
print("getting file %s" % path)
try:
f = open(path, 'w', encoding='utf8')
f.write(txt)
f.close()
except:
f = open(path, 'w', encoding='gbk')
f.write(txt)
f.close()
finally:
f.close()
time.sleep(0.1)
return
def get_drogan_tiger_qq(code=None, start=None, end=None):
"""查看龙虎榜的信息
------------------------
code : 查询股票的代码
start：开始查询的时间，20170101
end :截止查询的时间，20170630
"""
if (code is None) and (start is None) and (end is None):
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=0&c=&b=&e=&p=1&l=&ol=6&o=desc'
if (start is not None) and (end is not None):
if code is None:
code = ''
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=2&c={0}&b={1}&e={2}&p=1&l=&ol=6&o=desc'.format(
code, start, end)
else:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=1&c={0}&b={1}&e={2}&p=1&l=&ol=6&o=desc'.format(
code, start, end)
#print(url)
r = requests.get(url, headers=hds())
text = r.text
df = _text2pd(text)
tps = int(r.text.split(",_pages:")[1].split(",_num:")[0])
tpss = tps + 1
if tps > 1:
for i in range(2, tpss):
if (code is None) and (start is None) and (end is None):
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=0&c=&b=&e=&p={0}&l=&ol=6&o=desc'.format(
i)
if (start is not None) and (end is not None):
if code is None:
code = ''
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=2&c={0}&b={1}&e={2}&p={3}&l=&ol=6&o=desc'.format(
code, start, end, i)
else:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=1&c={0}&b={1}&e={2}&p={3}&l=&ol=6&o=desc'.format(
code, start, end, i)
r = requests.get(url, headers=hds())
text = r.text
df = df.append(_text2pd(text))
df = df.drop(4, axis=1)
df.columns = ['date', 'code', 'name', 'descrise', 'price', 'chg%']
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('date')
df = df.sort_index()
df = df.applymap(lambda x: wt._tofl(x))
return df
def hynewEM(self, code=None):
if code is not None:
self.setcode(code)
df=wd.get_cashf_alltoday_EM()[['name', 'price', 'Rank.T',\
'Chg%.T','industry', 'indu.ID']]
hyid = df.loc[self.__code, 'indu.ID'][3:6]
url = 'http://stock.eastmoney.com/hangye/hy{}.html'.format(hyid)
r = requests.get(url, headers=hds())
try:
text = r.content.decode('gbk')
except:
text = r.text
html = lxml.html.parse(StringIO(text))
dataset1 = []
hyinfo = html.xpath(
'//div[@class="americaleft mt10"]/div[1]//div[@class="deta"]/ul/li'
)
for hy in hyinfo:
text = hy.xpath('a/text()')[0]
print(text)
href = hy.xpath('a/@href')[0]
dataset1.append([text, href])
df = pd.DataFrame(dataset1, columns=['title', 'href'])
dataset = []
for url in df['href']:
r = requests.get(url, headers=hds())
#print(url)
try:
text = r.content.decode('utf8')
except:
text = r.text
html = lxml.html.parse(StringIO(text))
textc = html.xpath('//div[@id="ContentBody"]//text()')
dataset.append('\n'.join(textc))
data = [x.strip() for x in dataset]
text = '\n'.join(data)
text = text.replace('\r', '').replace(' ', '')
text1 = re.sub(r'\n{1,}', r'\n\n', text)
f = open('hynewsEM.txt', 'w')
f.write(text1)
f.close()
return text1
def HK_notice_qq(tick):
"""
获取某家上市公司的公告资讯
-------------------------------
tick: 五位数的香港上市公司代码，如00005
--
return ：
DataFrame
"""
tick = _set_code(tick)
page = 1
url = wt.notice_hk.format(tick, page)
#print(url)
r = requests.get(url, headers=hds())
text = r.content.decode('utf8')
#print(text)
text = text.split('notice=')[1]
data = json.loads(text)
df = pd.DataFrame(data['data']['data'])
df['time'] = df['time'].map(lambda x: x[:10])
try:
del df['symbols']
del df['type']
del df['url']
del df['src']
del df['id']
del df['summary']
except:
pass
df = df.set_index('time')
return df
def HK_news_qq(tick):
"""
获取某家上市公司的新闻资讯
-------------------------------
tick: 五位数的香港上市公司代码，如00005
--
return ：
DataFrame
"""
tick = _set_code(tick)
page = 1
url = wt.news_hk.format(tick, page)
r = requests.get(url, headers=hds())
text = r.content.decode('utf8')
#print(text)
text = text.split('news=')[1]
data = json.loads(text)
df = pd.DataFrame(data['data']['data'])
try:
del df['symbols']
del df['type']
del df['id']
except:
pass
return df
def get_cashfl_m163(code):
"""
quotes.money.163.com
获取股票的买卖现金流量
"""
df = pd.DataFrame()
url = 'http://quotes.money.163.com/trade/lszjlx_{0},0.html'.format(code)
while True:
try:
r = requests.get(url, headers=hds())
soup = BeautifulSoup(r.text, 'lxml')
tb = soup.find('table', attrs={'class': "table_bg001 border_box"})
df = df.append(pd.read_html(str(tb))[0])
df = df.drop(0, axis=0)
page = soup.find('a', text='下一页')
url = 'http://quotes.money.163.com' + page.get('href')
except:
break
df = df.set_index('日期')
df = df.sort_index()
df = df.applymap(lambda x: wc._tofl(x))
try:
df['股票代码'] = df['股票代码'].map(lambda x: x.replace("'", ''))
except:
pass
return df
def get_mk_data_m163(code):
"""
获取股票的历史交易数据
"""
df = pd.DataFrame()
ty = dt.datetime.today().year
#years=range(1998,ty+1)
years = range(ty - 3, ty + 1)
for year in years:
for season in [1, 2, 3, 4]:
url = 'http://quotes.money.163.com/trade/lsjysj_{2}.html?year={0}&season={1}'.format(
year, season, code)
try:
#print(url)
r = requests.get(url, headers=hds())
soup = BeautifulSoup(r.text, 'lxml')
tb = soup.find(
attrs={"class": "table_bg001 border_box limit_sale"})
df = df.append(pd.read_html(str(tb))[0])
#print(df.head())
except:
pass
df = df.drop(0, axis=0)
df = df.set_index('日期')
df = df.sort_index()
df = df.applymap(lambda x: wc._tofl(x))
df['股票代码'] = df['股票代码'].map(lambda x: x.replace("'", ''))
return df
def get_cashflhy_m163(code):
"""
同行业的涨跌幅前10只股票的资金流向情况
type:jc---表示流出
zc---表示流入
"""
Df = pd.DataFrame()
for mtype in ['zc', 'jc']:
url = "http://quotes.money.163.com/service/zjlx_table.html?symbol={0}&type={1}".format(
code, mtype)
r = requests.get(url, headers=hds())
text = r.text
df = pd.read_html(text)[0]
df = df.drop(0, axis=0)
Df = Df.append(df)
Df = Df.drop('排名', axis=1)
Df.columns = [
'name', 'price', 'chg', 'turnover', 'amount', 'out_amount', 'in_amout',
'net_amount'
]
Df = Df.reset_index(drop=True)
return Df
def get_cashf_sharehist_min_EM(code, day=today):
"""获得某只股票的资金流入流出情况，按1min中进行统计，
day为交易日的条数
bkname 为板块的名称
"""
if code[0] in ['6', '9']:
code = code + '1'
elif code[0] in ['0', '2', '3']:
code = code + '2'
url = 'http://ff.eastmoney.com/EM_CapitalFlowInterface/api/js?id={0}&type=ff&check=MLBMS&cb=var%20aff_data={1}&js={2}&rtntype=3&acces_token=1942f5da9b46b069953c873404aad4b5'.format(
code, day, '{(x)}')
r = requests.get(url, headers=hds())
pc = '=' + day
text = r.text.split(pc)[1]
#print(text)
data = json.loads(text)
dataset = []
for i in data['ya']:
dataset.append(i.split(','))
df = pd.DataFrame(dataset)
index = data['xa'].split(',')
index = index[:df.shape[0]]
df.index = index
df = df.replace('', np.nan)
df = df.dropna(how='all', axis=0)
df.columns = ['Main', 'Super', 'Big', 'Middle', 'Small']
df = df.applymap(lambda x: wt._tofl(x))
return df
def grow(year,quarter):
pn=1
DF=pd.DataFrame()
ws._write_head()
while True:
try:
ws._write_console()
url='http://vip.stock.finance.sina.com.cn/q/go.php/vFinanceAnalyze/kind/grow/index.phtml?s_i=&s_a=&s_c=&reportdate={0}&quarter={1}&p={2}'.format(year,quarter,pn)
r=requests.get(url,headers=hds())
r=r.content.decode('gbk')
html=BeautifulSoup(r,'lxml')
text=html.find(id='dataTable')
df=pd.read_html(str(text),header=0)[0]
if df.empty is True:
break
else:
pn = pn + 1
DF =DF.append(df)
except:
break
DF=DF.applymap(lambda x:np.where(x=='--',np.nan,x))
DF=DF.set_index('股票代码')
DF.index=DF.index.map(lambda x: str(x).split('.')[0].zfill(6))
DF['date']=str(year)+'_'+str(quarter).zfill(2)
name=list(DF.columns)
name.remove('股票名称')
name.remove('date')
for label in name:
DF[label]=DF[label].astype(float)
return DF
def get_text(url):
"""
获取网址对应的标题、和内容。
--------------
url：输入的网址
-----------------
return：
title:文章的标题
：相应的网址内容。
"""
html = requests.get(url, headers=hds())
con = lxml.html.parse(StringIO(html.text))
#ti=con.xpath("/h1/text()")
tt = con.xpath('//div[@id="Cnt-Main-Article-QQ"]/p')
textset = []
for t in tt:
try:
text = t.xpath('text()')[0]
text = text.strip()
textset.append(text)
except Exception as e:
#print(e)
pass
return '\n\n'.join(textset)
def get_cashf_sharehist_EM(code):
"""个股的历史资金流向,资金流入金额的单位均为万元
--------------------------------------
Main.I.net ：今日主力净流入净额
Main.I.R ：今日主力净流入净占比
"""
if code[0] in ['6', '9']:
code = code + '1'
if code[0] in ['0', '2', '3']:
code = code + '2'
url = 'http://ff.eastmoney.com//EM_CapitalFlowInterface/api/js?type=hff&rtntype=2&check=TMLBMSPROCR&acces_token=1942f5da9b46b069953c873404aad4b5&id={0}'.format(
code)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
df.columns = [
'date', 'Main.I.net', 'Main.I.R', 'Su.I.net', 'Su.I.R', 'Big.I.net',
'Big.I.R', 'Mid.I.net', 'Mid.I.R', 'Sm.I.net', 'Sm.I.R', 'price',
'Chg%'
]
df = df.applymap(lambda x: wt._tofl(x))
df = df.set_index('date')
return df
def get_shares_GroupbyClassify_EM(concept):
fp='webdata/puse/eastmpy/classified.pkl'
f=open(fp,'rb')
classify=pickle.load(f)
f.close()
if concept in classify.keys():
url='http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.{0}1&sty=FCOIATA&sortType=(ChangePercent)&sortRule=-1&page=1&pageSize=1000&js={1}&token=7bc05d0d4c3c22ef9fca8c2a912d779c'.format(classify[concept],'{%22rank%22:[(x)],%22pages%22:(pc),%22total%22:(tot)}')
r=requests.get(url,headers=hds())
dtext=json.loads(r.text)
dd='\n'.join(dtext['rank'])
df=pd.read_csv(StringIO(dd),header=None)
df=df.drop([0,13,14,15,16,18,17,19,20],axis=1)
df.columns=['code','name','close','chg','chgp%','zhengfu%','volume','amount','pre_close','open','high','low','chgp_in_5m%','liangbi','turnover%','PE','listed_date']
df['code']=df['code'].map(lambda x:str(x).zfill(6))
df=df.set_index('code')
df=df.replace('-',np.nan)
for label in df.columns:
try:
df[label]=df[label].astype(float)
except:
df[label]=df[label].map(lambda x: wt._tofl(x))
return df
else:
print("%s not in classify......"%concept)
print(classify.keys())
sys.exit()
def get_financeindex_f9_EM(code, mtype='zhzb'):
"""
code:公司股票代码
mtype:类型，有zhzb:按报告期,djzb按单季报告
"""
if code[0] in ['6', '9']:
code = code + '01'
elif code[0] in ['0', '2', '3']:
code = code + '02'
url = f9url[mtype].format(code)
print(url)
r = requests.get(url, headers=hds())
text = r.content.decode('utf8')
soup = BeautifulSoup(text, 'lxml')
tbs = soup.find_all('table')
df = pd.read_html(str(tbs[0]))[0]
df.columns = df.iloc[0, :].tolist()
df = df.drop(0, axis=0)
df = df.set_index('报告期日期')
#df=df.applymap(lambda x:wt._tofl(x))
df = df.dropna(how='all', axis=0)
df = df.T
df = df.sort_index()
return df
def get_dadan(code, opt=4):
"""
默认是以400手
Parameters:
code: 上海和深圳的股票代码
opt: 1-9:的数字代表成交量，分别为100手，
200手,300,400,500,800,1000,1500,2000手
10-13:代表成交额大于等于 100万元、200、500、1000万元
Return:
time: 股票成交的时间
price: 股票成交的价格（元）
volumn 股票成交的股数（手，即100股的整数倍）
amount: 股票成交金额（万元）
type: 买（B）或卖（S)
"""
code = _set_code(code)
url = 'http://stock.finance.qq.com/sstock/list/view/dadan.php?t=js&c={0}&max=800000&p=1&opt={1}&o=0'.format(
code, opt)
r = requests.get(url, headers=hds())
r = r.text.split(",'")[1]
r = r.split("']")[0]
r = r.split('^')
dataset = []
for data in r:
#print(data)
data = data.split('~')
dataset.append(data)
df = pd.DataFrame(dataset)
df = df.drop(0, axis=1)
df.columns = ['time', 'price', 'volume', 'amount', 'type']
for label in ['price', 'volume', 'amount']:
df[label] = df[label].astype(float)
return df
def get_cashf_concepthist_EM(bkname):
"""板块的历史资金流向,资金流入金额的单位均为万元
--------------------------------------
Main.I.net ：今日主力净流入净额
Main.I.R ：今日主力净流入净占比
"""
try:
df = pd.read_pickle('output/eastmsource/bk.pkl')
except:
df = pd.read_csv('output/eastmsource/bk.csv', encoding='gbk')
idd = df[df['name'] == bkname]['id'].tolist()[0]
#idd=df.loc[bkname,'id']
url = 'http://ff.eastmoney.com//EM_CapitalFlowInterface/api/js?type=hff&rtntype=2&js={1}&cb=var%20aff_data=&check=TMLBMSPROCR&acces_token=1942f5da9b46b069953c873404aad4b5&id={0}1&_=1502340432743'.format(
idd, '{%22data%22:(x)}')
r = requests.get(url, headers=hds())
text = r.text.split('{"data":["')[1].replace('(["', '').replace('"])', '')
text = text.replace('"]}', '')
text = text.replace('","', '\n')
#print(text)
df = pd.read_csv(StringIO(text), header=None)
df.columns = [
'date', 'Main.I.net', 'Main.I.R', 'Su.I.net', 'Su.I.R', 'Big.I.net',
'Big.I.R', 'Mid.I.net', 'Mid.I.R', 'Sm.I.net', 'Sm.I.R', 'price',
'Chg%'
]
df = df.applymap(lambda x: wt._tofl(x))
df = df.set_index('date')
return df
