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_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 get_holder_change_EM(code):
url = 'http://data.eastmoney.com/DataCenter_V3/gdhs/GetDetial.ashx?code={0}&js=var%20wDmaHnNC&pagesize=5000&page=1&sortRule=-1&sortType=EndDate¶m=&rt=50019973'.format(
code)
r = requests.get(url, headers=hds())
text = r.text.split("wDmaHnNC=")[1]
data = json.loads(text)
df = pd.DataFrame(data['data'])
#df['NoticeDate']=df['NoticeDate'].map(lambda x:x[:10])
df['EndDate'] = df['EndDate'].map(lambda x: x[:10])
df = df.set_index('EndDate')
df = df.sort_index()
try:
del df['NoticeDate']
del df['CapitalStockChange']
del df['CapitalStockChangeEvent']
except:
pass
df = df.applymap(lambda x: wt._tofl(x))
return df
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_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 _settle(url):
r = requests.get(url, headers=hds())
text = r.text
data = json.loads(text)
try:
data = json.loads(data)
except:
pass
df = pd.DataFrame(data)
name = list(df.columns)[:4]
name.extend(list(df.iloc[0, 4:]))
df.columns = name
df = df.drop(0, axis=0)
del df['Indent']
del df['IsBold']
del df['IsShowChart']
df = df.set_index("IndexName")
df = df.dropna(how='all', axis=0)
#df=df.dropna(how='all',axis=0)
df = df.applymap(lambda x: wt._tofl(x))
df = df.T
df = df.sort_index()
df = df.replace('-', np.nan)
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_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
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_Future_info_EM(mtype='sqs'):
"""
核心题材
--------------------------
mtype:为股票代码，为6位数
"""
url = future[mtype]
df = _basic(url)
df = df.applymap(lambda x: wt._tofl(x))
df = df.replace('-', np.nan)
return df
def get_seniorM_holder_EM():
"""董监高及相关人员持股变动明细
"""
url='http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx?type=GG&sty=GGMX&p=1&ps=5000'
#print(url)
r=requests.get(url,headers=hds())
text=r.text.replace('(["','').replace('"])','')
text=text.replace('","','\n')
df=pd.read_csv(StringIO(text),header=None)
df=df.drop([4,11,15],axis=1)
df.columns=['chg','senior','code','holder','date','chg_num','num','trade_price','name','relation','reason','amount','title']
df=df.applymap(lambda x:wt._tofl(x))
df['code']=df['code'].map(lambda x:str(x).zfill(6))
#df=df.set_index('code')
#df=df.replace('-',np.nan)
return df
def HK_dividend_EM(code):
url = wt.div_url.format(code, wt.yearl)
#df=_settle(url)
r = requests.get(url, headers=hds())
text = r.text
data = json.loads(text)
df = pd.DataFrame(data)
df.columns = df.iloc[0, :].tolist()
df = df.set_index('除权日')
df = df.applymap(lambda x: wt._tofl(x))
df = df.T
df = df.sort_index()
df = df.replace('-', np.nan)
df = df.drop('除权日', axis=1)
df = df.dropna(how='all', axis=1)
#print(data)
return df
def HK_trade_data_EM(code):
"""
查询香港股票的每日交易统计数据
http://hkf10.eastmoney.com/html_HKStock/index.html?securitycode=00005&name=dailyData
--------------------------
code:为香港的股票代码，为5位数
"""
url = 'http://hkf10.eastmoney.com/F9HKStock/GetStockDailyMarketDataList.do?securityCode={0}.HK&yearList=2017,2016,2015,2014,2013&dateSearchType=1&rotate=1&seperate=0&order=desc&cashType=0&exchangeValue=1&customSelect=0&CurrencySelect=0'.format(
code)
#print(url)
r = requests.get(url, headers=hds())
text = r.text
data = json.loads(text)
df = pd.DataFrame(data)
df = df.set_index('TRADEDATE')
df = df.sort_index()
df.index.name = 'date'
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_Future_comparetoGlobal():
url = 'http://hq2data.eastmoney.com/qhbj/index.aspx?param=RmbLatestPrice&sort=0'
r = requests.get(url, headers=hds())
content = r.text
content = content.split('futures:["')[1].replace('"]}',
'').replace('","', '\n')
df = pd.read_csv(StringIO(content), header=None)
df = df.drop([2, 3, 4, 7, 8, 15, 16, 17], axis=1)
df.columns = [
'name', 'Exchange', 'M_name', 'G_name', 'M_price', 'M_chg', 'G_price',
'G_chg', 'ToRMB', 'spread'
]
del df['Exchange']
df = df.set_index('name')
df = df.applymap(lambda x: wt._tofl(x))
df = df.replace('-', np.nan)
return df
def get_classified_index_EM(concept,mtype='day',fq='fq'):
"""
获取行业\概念\地域版块的指数：
-------------------------------
concept:行业\概念\地域 名词
fq:不复权，None;qfq:前复权;hfq:后复权
mtype:day,日线；week，周线；month,月线；m5：5分钟线；m15：15分钟线；m30：30分钟
线；m60：60分钟线
"""
fqv={'fq':'','qfq':'authorityType=fa','hfq':'authorityType=ba'}
kline={'day':'K','month':'mk','week':'wk','m5':'m5k','m15':'m15k','m30':'m30k','m60':'m60k'}
fp='webdata/puse/eastmpy/classified.pkl'
f=open(fp,'rb')
classify=pickle.load(f)
f.close()
if concept in classify.keys():
if fq =='fq':
url='http://pdfm2.eastmoney.com/EM_UBG_PDTI_Fast/api/js?id=%s1&TYPE=%s&js=(x)&rtntype=5'%(classify[concept],kline[mtype])
else:
url='http://pdfm2.eastmoney.com/EM_UBG_PDTI_Fast/api/js?id=%s1&TYPE=%s&js=(x)&rtntype=5&%s'%(classify[concept],kline[mtype],fqv[fq])
r=requests.get(url,headers=hds())
dtext=json.loads(r.text)
dd='\n'.join(dtext['data'])
df=pd.read_csv(StringIO(dd),header=None)
df.columns=['date','open','close','high','low','volume','amount','turnover']
df=df.set_index('date')
df.index=pd.to_datetime(df.index)
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 HK_HSG_EM():
hurl = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.BK07071&sty=FCOIATA&sortType=C&sortRule=-1&page=1&pageSize=20000&js=var%20quote_123%3d{%22rank%22:[(x)],%22pages%22:(pc)}&token=7bc05d0d4c3c22ef9fca8c2a912d779c&jsName=quote_123&_g=0.785481323255226'
surl = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.BK08041&sty=FCOIATA&sortType=C&sortRule=-1&page=1&pageSize=20000&js=var%20quote_123%3d{%22rank%22:[(x)],%22pages%22:(pc)}&token=7bc05d0d4c3c22ef9fca8c2a912d779c&jsName=quote_123&_g=0.03943358431570232'
uls = [surl, hurl]
df = pd.DataFrame()
for url in uls:
#print(url)
df = df.append(_hk_ggt(url))
df = df.drop([0, 13, 14, 15, 16, 17, 18, 19, 20], axis=1)
df.columns = [
'code', 'name', 'close', 'chg', 'chg%', 'apmature', 'volume', 'amount',
'pre.close', 'open', 'high', 'low', 'chg_5m', 'LB', 'turnover', 'PE'
]
df = df.drop_duplicates()
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
#df=df.sort_values(by='chg%',ascending=False)
return df
def cashfl_concept_hist(self, bkname):
"""核心题材
"""
idd = self.bk[self.bk['name'] == bkname]['id'].tolist()[0]
#print(idd)
url = 'http://data.eastmoney.com/bkzj/{0}.html'.format(idd)
self.driver.get(url)
waitForLoad(self.driver)
self.driver.find_element_by_xpath('//ul[@id="s1-tab"]/li[2]').click()
text = self.driver.find_element_by_xpath(
'//table[@id="tb_lishi"]').text
text = text.replace(' ', ',')
text = text.split('\n')
dataset = []
for i in text[2:]:
dataset.append(i)
df = pd.read_csv(StringIO('\n'.join(dataset)), header=None)
df = df.applymap(lambda x: wt._tofl(x))
#df=df.applymap(lambda x:wt._tofl(x))
return df
def get_finance_index_EM(year, qu):
yq = quarter[qu] % year
url = 'http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx?type=SR&sty=YJBB&fd=%s&st=13&sr=-1&p=1&ps=5000000' % yq
#print(url)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop([14, 15, 18], axis=1)
df.columns = [
'code', 'name', 'eps', 'eps_d', 'rev', 'rev_yoy', 'rev_hb', 'profit',
'profit_yoy', 'profit_hb', 'nav_ps', 'roe', 'cf_ps', 'margin',
'pulish', 'report'
]
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('code')
df = df.replace('-', np.nan)
return df
def get_bkindex_EM(name, mtype='D'):
mtp = {
'd': 'k',
'wk': 'wk',
'mk': 'mk',
'5min': 'm5k',
'15min': 'm15k',
'30min': 'm30k',
'60min': 'm60k'
}
mtype = mtype.lower()
timestamp = int(time.time() * 1000)
names = wt.bkurlsets.keys()
flag = True
if name not in names:
while flag:
name = input("Input BK name:")
if name in names:
flag = False
bkid = os.path.splitext(os.path.basename(urlsets[name]))[0] + '1'
url = 'http://pdfm2.eastmoney.com/EM_UBG_PDTI_Fast/api/js?id=%s&TYPE=%s&js=(x)&rtntype=5&isCR=false&fsData%s' % (
bkid, mtp[mtype], timestamp)
r = requests.get(url)
text = r.text
#print(text[:300])
data = json.loads(text)
df = pd.read_csv(StringIO('\n'.join(data['data'])), header=None)
df.columns = [
'date', 'open', 'close', 'high', 'low', 'volume', 'amount', 'amplitude'
]
df = df.set_index('date')
df.index = pd.to_datetime(df.index)
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
def get_forcast_EM(year, qu):
yq = quarter[qu] % year
url = 'http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx?type=SR&sty=YJYG&fd=%s&st=4&sr=-1&p=1&ps=500000' % yq
#print(url)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
#df=df.drop([14,15,18],axis=1)
df.columns = [
'code', 'name', 'describe', 'change_period', 'type', 'prof_l', 'ttype',
'pulish', 'date'
]
df = df.replace('-', np.nan)
df['cc'] = df['change_period'].map(lambda x: ss.findall(str(x)))
df['Down'] = np.nan
df['Up'] = np.nan
for i in range(df.shape[0]):
if len(df.iloc[i, df.columns.get_loc('cc')]) == 2:
df.iloc[i, df.columns.get_loc('Down')] = df.iloc[
i, df.columns.get_loc('cc')][0]
df.iloc[i, df.columns.get_loc('Up')] = df.iloc[
i, df.columns.get_loc('cc')][1]
elif len(df.iloc[i, df.columns.get_loc('cc')]) == 1:
df.iloc[i, df.columns.get_loc('Down')] = df.iloc[
i, df.columns.get_loc('cc')][0]
df.iloc[i, df.columns.get_loc('Up')] = df.iloc[
i, df.columns.get_loc('cc')][0]
del df['cc']
df = df[[
'code', 'name', 'change_period', 'Down', 'Up', 'type', 'prof_l',
'ttype', 'describe', 'pulish', 'date'
]]
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('code')
return df
def HK_GGT_EM():
"""
获取港股通的股票数据，即通过国内交易系统可以购买香港市场的股票
"""
urlh = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.mk0144&sty=CTF&sortType=C&sortRule=-1&page=1&pageSize=20000&js=var%20quote_123%3d{%22rank%22:[(x)],%22pages%22:(pc)}&token=44c9d251add88e27b65ed86506f6e5da&jsName=quote_123&_g=0.5358432093635201'
urls = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.mk0146&sty=CTF&sortType=C&sortRule=-1&page=1&pageSize=20000&js=var%20quote_123%3d{%22rank%22:[(x)],%22pages%22:(pc)}&token=44c9d251add88e27b65ed86506f6e5da&jsName=quote_123&_g=0.15627210377715528'
uls = [urls, urlh]
df = pd.DataFrame()
for url in uls:
#print(url)
df = df.append(_hk_ggt(url))
df = df.drop([0, 6, 7], axis=1)
df.columns = [
'code', 'name', 'close', 'chg', 'chg%', 'unk', 'volume', 'amount',
'open', 'pre.close', 'high', 'low', 'turnover', 'PE'
]
df = df.drop_duplicates()
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(5))
#df=df.sort_values(by='chg%',ascending=False)
return df
def get_STI_EM():
"""
钢铁指数
--------------------------
"""
url = 'http://dcfm.eastmoney.com//EM_MutiSvcExpandInterface/api/js/get?type=QHHQBLDH&token=70f12f2f4f091e459a279469fe49eca5&filter=(ID=%27EMI00662545%27)&st=DATADATE&sr=-1&p=1&ps=2000&js=var%20CENlUZQH={pages:(tp),data:(x)}&rt=50063561'
r = requests.get(url, headers=hds())
text = r.text
text = text.replace('pages', '"pages"').replace('data:[', '"data":[')
text = text.split("=")[1]
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=QHHQBLDH&token=70f12f2f4f091e459a279469fe49eca5&filter=(ID=%27EMI00662545%27)&st=DATADATE&sr=-1&p={0}&ps=2000&js=var%20CENlUZQH={1}&rt=50063561'.format(
i, '{pages:(tp),data:(x)}')
r = requests.get(url, headers=hds())
text = r.text
text = text.replace('pages',
'"pages"').replace('data:[', '"data":[')
text = text.split("=")[1]
data = json.loads(text)
df = df.append(pd.DataFrame(data['data']))
df.columns = ['date', 'code', 'price', 'change', 'chg']
df = df[['date', 'price', 'change', 'chg']]
df = df.set_index('date')
df = df.sort_index()
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_cashf_conceptshares_EM(bkname, day=''):
"""获取同行业的股票涨跌信息
"""
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']
#print(idd)
if len(day) == 0:
day = 'BalFlowMain'
elif len(day) > 0:
day = 'BalFlowMainNet' + day
url = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?cmd=C.{0}1&type=ct&st=({1})&sr=-1&p=1&ps=5000&token=894050c76af8597a853f5b408b759f5d&sty=DCFFITA'.format(
idd, day)
#print(url)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop(0, axis=1)
df = df.applymap(lambda x: wt._tofl(x))
try:
df.columns = [
'code', 'name', 'price', 'chg', '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', 'date'
]
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df['date'] = df['date'].map(lambda x: x[:10])
except:
pass
df = df.replace('-', np.nan)
return df
def get_share_holdernum_change_EM(code):
"""个股的股东户数历史详情
"""
url='http://data.eastmoney.com/DataCenter_V3/gdhs/GetDetial.ashx?code={0}&pagesize=500&page=1&sortRule=-1&sortType=EndDate¶m=&rt=50055484'.format(code)
print(url)
r=requests.get(url,headers=hds())
text=r.text
print(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/GetDetial.ashx?code={0}&pagesize=500&page={1}&sortRule=-1&sortType=EndDate¶m=&rt=50055484'.format(code,i)
r=requests.get(url,headers=hds())
text=r.text
data=json.loads(text)
df=df.append(pd.DataFrame(data['data']))
df=df.applymap(lambda x:wt._tofl(x))
for label in [ 'EndDate', 'NoticeDate', 'PreviousEndDate']:
df[label]=df[label].map(lambda x:x[:10])
return df
def get_cashf_concept_EM(mtype='hy', day=''):
"""获取板块的现金流入情况
mtype:hy--行业板块；gn--概念板块；dy--地域板块
day： 分为当日的数据，5日的数据，10日的数据
===========================================
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
"""
day = 'BalFlowMain' + day
if mtype == 'gn':
url = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?cmd=C._BKGN&type=ct&st=({0})&sr=-1&p=1&ps=5000&token=894050c76af8597a853f5b408b759f5d&sty=DCFFITABK&rt=50054963'.format(
day)
if mtype == 'hy':
url = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?cmd=C._BKHY&type=ct&st=({0})&sr=-1&p=1&ps=50000&token=894050c76af8597a853f5b408b759f5d&sty=DCFFITABK&rt=50054967'.format(
day)
if mtype == 'dy':
url = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?cmd=C._BKDY&type=ct&st=({0})&sr=-1&p=1&ps=50&token=894050c76af8597a853f5b408b759f5d&sty=DCFFITABK'.format(
day)
#print(url)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop([0], axis=1)
df.columns = [
'id', 'name', 'Chg%', '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',
'Sec_name', 'code'
]
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(7))
df['code'] = df['code'].map(lambda x: x[:6])
return df
def get_cashf_conceptcenter_EM(bkname):
"""获取同行业股票行情中心的涨跌信息
"""
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://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.{0}1&sty=FCOIATA&sortType=C&sortRule=-1&page=1&pageSize=2000&token=7bc05d0d4c3c22ef9fca8c2a912d779c'.format(
idd)
#print(url)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop([0, 13, 14, 15, 16, 17, 18, 19, 20], axis=1)
df = df.applymap(lambda x: wt._tofl(x))
try:
df.columns = [
'code', 'name', 'price', 'chg', 'chg%', 'ZF', 'volume', 'amount',
'pre.close', 'open', 'high', 'low', 'chg.5m', 'LR', 'turnover',
'PE'
]
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
except:
pass
df = df.replace('-', np.nan)
df = df.dropna(how='all', axis=1)
return df
def get_cashf_alltoday_EM():
"""主力资金流入排名
Main.R.T:主力净占比,
Rank.T ：今日排名
Chg%.T ：今日涨跌
"""
url = 'http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx/JS.aspx?type=ct&st=(FFRank)&sr=1&p=1&ps=50000&token=894050c76af8597a853f5b408b759f5d&cmd=C._AB&sty=DCFFITAM'
#print(url)
r = requests.get(url, headers=hds())
text = r.text.replace('(["', '').replace('"])', '')
text = text.replace('","', '\n')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop([0], axis=1)
df.columns = [
'code', 'name', 'price', 'Main.R.T', 'Rank.T', 'Chg%.T', 'Main.R.5',
'Rank.5', 'Chg%.5', 'Main.R.10', 'Rank.10', 'Chg%.10', 'industry',
'indu.ID', 'date'
]
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('code')
#df=df.replace('-',np.nan)
return df
