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_BCISCF_m163(code, bic='xjllb', mytype='report'):
"""
code:股票代码
bic:zcfzb（资产负债表）,lrb（利润表）,xjllb（现金流量表）三个参数
mytype:report, year,两个参数
"""
url = wc.bic_url[mytype].format(code, bic)
#print(url)
r = requests.get(url, stream=True)
text = r.text.replace('\r\n', '\n')
#f=open('bic_%s_%s_%s.csv'%(code,bic,mytype),'w')
#f.write(text)
#f.close()
df = pd.read_csv(StringIO(text), header=0)
df = df.dropna(how='all', axis=1)
#print(df.head())
name = list(df.columns)
name = [s.strip() for s in name]
df.columns = name
df = df.set_index('报告日期')
df = df.T
df = df.applymap(lambda x: np.where(x == '--', np.nan, wc._tofl(x)))
return df
def get_finance_summary_m163(code, mytype='report'):
"""
"""
url = wc.finace_summary_url[mytype].format(code)
r = requests.get(url, stream=True)
text = r.text.replace('\r\n', '\n')
#f=open('financesummary_%s_%s.csv'%(code,mytype),'w')
#f.write(text)
#f.close()
df = pd.read_csv(StringIO(text), header=0)
df = df.dropna(how='all', axis=1)
df = df.set_index('报告期')
df = df.T
df = df.applymap(lambda x: np.where(x == '--', np.nan, wc._tofl(x)))
return df
def get_trading_data_m163(code,
start='20100101',
end=dt.datetime.today().strftime('%Y%m%d')):
"""
code:股票代码
start:开始读取的日期,like 20100101.
end: 截止的日期，like 20170620
"""
if code[0] in ['0', '2']:
url = wc.trading['sz'].format(code, start, end)
elif code[0] in ['6', '9']:
url = wc.trading['sh'].format(code, start, end)
elif code[0] in ['3']:
url = wc.trading['gme'].format(code, start, end)
#print(url)
r = requests.get(url, stream=True, headers=hds())
text = r.content.decode('gbk').replace('\r\n', '\n')
#print(text)
#f=open('trading_%s_%s_%s.csv'%(code,start,end),'w')
#f.write(text)
#f.close()
df = pd.read_csv(StringIO(text), header=0)
df = df.dropna(how='all', axis=1)
#print(df.head())
name = list(df.columns)
name = [s.strip() for s in name]
df.columns = name
try:
df = df.set_index('日期')
#del df['股票代码']
#df=df.T
df = df.sort_index()
df = df.applymap(lambda x: np.where(x == '--', np.nan, wc._tofl(x)))
df['股票代码'] = df['股票代码'].map(lambda x: x.replace("'", ''))
except:
pass
return df