def _parsing_dayprice_json(pageNum=1):
"""
处理当日行情分页数据，格式为json
Parameters
------
pageNum:页码
return
-------
DataFrame 当日所有股票交易数据(DataFrame)
"""
ct._write_console()
request = Request(ct.SINA_DAY_PRICE_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['jv'], pageNum))
text = urlopen(request, timeout=10).read()
if text == 'null':
return None
reg = re.compile(r'\,(.*?)\:')
text = reg.sub(r',"\1":', text.decode('gbk') if ct.PY3 else text)
text = text.replace('"{symbol', '{"symbol')
text = text.replace('{symbol', '{"symbol"')
if ct.PY3:
jstr = json.dumps(text)
else:
jstr = json.dumps(text, encoding='GBK')
js = json.loads(jstr)
df = pd.DataFrame(pd.read_json(js, dtype={'code':object}),
columns=ct.DAY_TRADING_COLUMNS)
df = df.drop('symbol', axis=1)
# df = df.ix[df.volume > 0]
return df
def _get_cashflow_data(year, quarter, pageNo, dataArr):
ct._write_console()
try:
request = Request(ct.CASHFLOW_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1]))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
text = text.replace('--', '')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@class=\"list_table\"]/tr")
if ct.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]
df.columns = ct.CASHFLOW_COLS
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick')
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_cashflow_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _get_forecast_data(year, quarter, pageNo, dataArr):
ct._write_console()
try:
html = lxml.html.parse(ct.FORECAST_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['fd'], year, quarter, pageNo,
ct.PAGE_NUM[1]))
res = html.xpath("//table[@class=\"list_table\"]/tr")
if ct.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 = sarr.replace('--', '0')
sarr = '<table>%s</table>'%sarr
df = pd.read_html(sarr)[0]
df = df.drop([4, 5, 8], axis=1)
df.columns = ct.FORECAST_COLS
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick')
if len(nextPage)>0:
pageNo = re.findall(r'\d+',nextPage[0])[0]
return _get_forecast_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _get_report_data(year, quarter, pageNo, dataArr):
ct._write_console()
try:
request = Request(
ct.REPORT_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1]))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
text = text.replace('--', '')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@class=\"list_table\"]/tr")
if ct.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]
df = df.drop(11, axis=1)
df.columns = ct.REPORT_COLS
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick')
if len(nextPage) > 0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_report_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _newstocks(data, pageNo, retry_count, pause):
for _ in range(retry_count):
time.sleep(pause)
ct._write_console()
try:
html = lxml.html.parse(rv.NEW_STOCKS_URL%(ct.P_TYPE['http'],ct.DOMAINS['vsf'],
ct.PAGES['newstock'], pageNo))
res = html.xpath('//table[@id=\"NewStockTable\"]/tr')
if ct.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 = sarr.replace('<font color="red">*</font>', '')
sarr = '<table>%s</table>'%sarr
df = pd.read_html(StringIO(sarr), skiprows=[0, 1])[0]
df = df.drop([df.columns[idx] for idx in [1, 12, 13, 14]], axis=1)
df.columns = rv.NEW_STOCKS_COLS
df['code'] = df['code'].map(lambda x : str(x).zfill(6))
res = html.xpath('//table[@class=\"table2\"]/tr[1]/td[1]/a/text()')
tag = '下一页' if ct.PY3 else unicode('下一页', 'utf-8')
hasNext = True if tag in res else False
data = data.append(df, ignore_index=True)
pageNo += 1
if hasNext:
data = _newstocks(data, pageNo, retry_count, pause)
except Exception as ex:
print(ex)
else:
return data
def _inst_detail(pageNo=1, retry_count=3, pause=0.001, dataArr=pd.DataFrame()):
ct._write_console()
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(rv.LHB_SINA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'],
rv.LHB_KINDS[3], ct.PAGES['fd'], '', pageNo))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@id=\"dataTable\"]/tr")
if ct.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]
df.columns = rv.LHB_JGMX_COLS
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick')
if len(nextPage) > 0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _inst_detail(pageNo, retry_count, pause, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _today_ticks(symbol, tdate, pageNo, retry_count, pause):
ct._write_console()
for _ in range(retry_count):
time.sleep(pause)
try:
html = lxml.html.parse(ct.TODAY_TICKS_URL % (ct.P_TYPE['http'],
ct.DOMAINS['vsf'], ct.PAGES['t_ticks'],
symbol, tdate, pageNo
))
res = html.xpath('//table[@id=\"datatbl\"]/tbody/tr')
if ct.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
sarr = sarr.replace('--', '0')
df = pd.read_html(StringIO(sarr), parse_dates=True)[0]
df.columns = ct.TODAY_TICK_COLUMNS
df['pchange'] = df['pchange'].map(lambda x : x.replace('%', ''))
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _newcbonds(pageNo, retry_count, pause):
for _ in range(retry_count):
time.sleep(pause)
if pageNo != 1:
ct._write_console()
try:
html = lxml.html.parse(
rv.NEW_CBONDS_URL %
(ct.P_TYPE['http'], ct.DOMAINS['sstar'], pageNo))
res = html.xpath('//table/tr')
if len(res) == 0:
return None
if ct.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(StringIO(sarr), skiprows=[0])
if len(df) < 1:
return None
df = df[0]
df = df.drop([df.columns[14], df.columns[15]], axis=1)
df.columns = rv.NEW_CBONDS_COLS
df['scode'] = df['scode'].map(lambda x: str(x).zfill(6))
df['xcode'] = df['xcode'].map(lambda x: str(x).zfill(6))
except Exception as ex:
print(ex)
else:
return df
def _inst_detail(pageNo=1, retry_count=3, pause=0.001, dataArr=pd.DataFrame()):
ct._write_console()
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(rv.LHB_SINA_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], rv.LHB_KINDS[3],
ct.PAGES['fd'], '', pageNo))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@id=\"dataTable\"]/tr")
if ct.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]
df.columns = rv.LHB_JGMX_COLS
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick')
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _inst_detail(pageNo, retry_count, pause, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _get_forecast_data(year, quarter, pageNo, dataArr):
ct._write_console()
try:
gparser = etree.HTMLParser(encoding='GBK')
html = lxml.html.parse(
ct.FORECAST_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1]),
parser=gparser)
res = html.xpath("//table[@class=\"list_table\"]/tr")
if ct.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 = sarr.replace('--', '0')
sarr = '<table>%s</table>' % sarr
df = pd.read_html(sarr)[0]
df = df.drop([4, 5, 8], axis=1)
df.columns = ct.FORECAST_COLS
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick')
if len(nextPage) > 0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_forecast_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _parsing_dayprice_json(pageNum=1):
"""
处理当日行情分页数据，格式为json
Parameters
------
pageNum:页码
return
-------
DataFrame 当日所有股票交易数据(DataFrame)
"""
ct._write_console()
request = Request(
ct.SINA_DAY_PRICE_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['jv'], pageNum))
text = urlopen(request, timeout=10).read()
if text == 'null':
return None
reg = re.compile(r'\,(.*?)\:')
text = reg.sub(r',"\1":', text.decode('gbk') if ct.PY3 else text)
text = text.replace('"{symbol', '{"symbol')
text = text.replace('{symbol', '{"symbol"')
if ct.PY3:
jstr = json.dumps(text)
else:
jstr = json.dumps(text, encoding='GBK')
js = json.loads(jstr)
df = pd.DataFrame(pd.read_json(js, dtype={'code': object}),
columns=ct.DAY_TRADING_COLUMNS)
df = df.drop('symbol', axis=1)
# df = df.ix[df.volume > 0]
return df
def _today_ticks(symbol, tdate, pageNo, retry_count, pause):
ct._write_console()
for _ in range(retry_count):
time.sleep(pause)
try:
html = lxml.html.parse(
ct.TODAY_TICKS_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['t_ticks'],
symbol, tdate, pageNo))
res = html.xpath('//table[@id=\"datatbl\"]/tbody/tr')
if ct.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
sarr = sarr.replace('--', '0')
df = pd.read_html(StringIO(sarr), parse_dates=True)[0]
df.columns = ct.TODAY_TICK_COLUMNS
df['pchange'] = df['pchange'].map(lambda x: x.replace('%', ''))
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _sh_mx(data,
date='',
start='',
end='',
symbol='',
pageNo='',
beginPage='',
endPage='',
retry_count=3,
pause=0.001):
for _ in range(retry_count):
time.sleep(pause)
ct._write_console()
try:
tail = '&pageHelp.pageNo=%s&pageHelp.beginPage=%s&pageHelp.endPage=%s' % (
pageNo, beginPage, endPage)
if pageNo == '':
pageNo = 6
tail = ''
else:
pageNo += 5
beginPage = pageNo
endPage = pageNo + 4
ref = rv.MAR_SH_HZ_REF_URL % (ct.P_TYPE['http'], ct.DOMAINS['sse'])
clt = Client(
rv.MAR_SH_MX_URL %
(ct.P_TYPE['http'], ct.DOMAINS['sseq'], ct.PAGES['qmd'],
_random(5), date, symbol, start, end, tail, _random()),
ref=ref,
cookie=rv.MAR_SH_COOKIESTR)
lines = clt.gvalue()
lines = lines.decode('utf-8') if ct.PY3 else lines
lines = lines[19:-1]
lines = json.loads(lines)
pagecount = int(lines['pageHelp'].get('pageCount'))
datapage = int(pagecount / 5 +
1 if pagecount % 5 > 0 else pagecount / 5)
if pagecount == 0:
return data
if pageNo == 6:
ct._write_tips(lines['pageHelp'].get('total'))
df = pd.DataFrame(lines['result'], columns=rv.MAR_SH_MX_COLS)
df['opDate'] = df['opDate'].map(lambda x: '%s-%s-%s' %
(x[0:4], x[4:6], x[6:8]))
data = data.append(df, ignore_index=True)
if beginPage < datapage * 5:
data = _sh_mx(data,
start=start,
end=end,
pageNo=pageNo,
beginPage=beginPage,
endPage=endPage,
retry_count=retry_count,
pause=pause)
except Exception as e:
print(e)
else:
return data
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _sh_mx(data, date='', start='', end='',
symbol='',
pageNo='', beginPage='',
endPage='',
retry_count=3, pause=0.001):
for _ in range(retry_count):
time.sleep(pause)
ct._write_console()
try:
tail = '&pageHelp.pageNo=%s&pageHelp.beginPage=%s&pageHelp.endPage=%s'%(pageNo,
beginPage, endPage)
if pageNo == '':
pageNo = 6
tail = ''
else:
pageNo += 5
beginPage = pageNo
endPage = pageNo + 4
ref = rv.MAR_SH_HZ_REF_URL%(ct.P_TYPE['http'], ct.DOMAINS['sse'])
clt = Client(rv.MAR_SH_MX_URL%(ct.P_TYPE['http'], ct.DOMAINS['sseq'],
ct.PAGES['qmd'], _random(5), date,
symbol, start, end, tail,
_random()), ref=ref, cookie=rv.MAR_SH_COOKIESTR)
lines = clt.gvalue()
lines = lines.decode('utf-8') if ct.PY3 else lines
lines = lines[19:-1]
lines = json.loads(lines)
pagecount = int(lines['pageHelp'].get('pageCount'))
datapage = int(pagecount/5+1 if pagecount%5>0 else pagecount/5)
if pagecount == 0:
return data
if pageNo == 6:
ct._write_tips(lines['pageHelp'].get('total'))
df = pd.DataFrame(lines['result'], columns=rv.MAR_SH_MX_COLS)
df['opDate'] = df['opDate'].map(lambda x: '%s-%s-%s'%(x[0:4], x[4:6], x[6:8]))
data = data.append(df, ignore_index=True)
if beginPage < datapage*5:
data = _sh_mx(data, start=start, end=end, pageNo=pageNo,
beginPage=beginPage, endPage=endPage,
retry_count=retry_count, pause=pause)
except Exception as e:
print(e)
else:
return data
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _sz_hz(date='', retry_count=3, pause=0.001):
for _ in range(retry_count):
time.sleep(pause)
ct._write_console()
try:
request = Request(rv.MAR_SZ_HZ_URL%(ct.P_TYPE['http'], ct.DOMAINS['szse'],
ct.PAGES['szsefc'], date))
lines = urlopen(request, timeout = 10).read()
if len(lines) <= 200:
return pd.DataFrame()
df = pd.read_html(lines, skiprows=[0])[0]
df.columns = rv.MAR_SZ_HZ_COLS
df['opDate'] = date
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _day_cinema(date=None, pNo=1, retry_count=3, pause=0.001):
ct._write_console()
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(
ct.BOXOFFICE_CBD %
(ct.P_TYPE['http'], ct.DOMAINS['mbox'], ct.BOX, pNo, date))
lines = urlopen(request, timeout=10).read()
if len(lines) < 15: #no data
return None
except Exception as e:
print(e)
else:
js = json.loads(lines.decode('utf-8') if ct.PY3 else lines)
df = pd.DataFrame(js['data1'])
df = df.drop(['CinemaID'], axis=1)
return df
def _holding_cotent(start, end, pageNo, retry_count, pause):
for _ in range(retry_count):
time.sleep(pause)
if pageNo > 0:
ct._write_console()
try:
request = Request(
rv.FUND_HOLDS_URL %
(ct.P_TYPE['http'], ct.DOMAINS['163'], ct.PAGES['163fh'],
ct.PAGES['163fh'], pageNo, start, end, _random(5)))
lines = urlopen(request, timeout=10).read()
lines = lines.decode('utf-8') if ct.PY3 else lines
lines = lines.replace('--', '0')
lines = json.loads(lines)
data = lines['list']
df = pd.DataFrame(data)
df = df.drop([
'CODE', 'ESYMBOL', 'EXCHANGE', 'NAME', 'RN', 'SHANGQIGUSHU',
'SHANGQISHIZHI', 'SHANGQISHULIANG'
],
axis=1)
for col in ['GUSHU', 'GUSHUBIJIAO', 'SHIZHI', 'SCSTC27']:
df[col] = df[col].astype(float)
df['SCSTC27'] = df['SCSTC27'] * 100
df['GUSHU'] = df['GUSHU'] / 10000
df['GUSHUBIJIAO'] = df['GUSHUBIJIAO'] / 10000
df['SHIZHI'] = df['SHIZHI'] / 10000
df['GUSHU'] = df['GUSHU'].map(ct.FORMAT)
df['GUSHUBIJIAO'] = df['GUSHUBIJIAO'].map(ct.FORMAT)
df['SHIZHI'] = df['SHIZHI'].map(ct.FORMAT)
df['SCSTC27'] = df['SCSTC27'].map(ct.FORMAT)
df.columns = rv.FUND_HOLDS_COLS
df = df[[
'code', 'name', 'date', 'nums', 'nlast', 'count', 'clast',
'amount', 'ratio'
]]
except Exception as e:
print(e)
else:
if pageNo == 0:
return df, int(lines['pagecount'])
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _profit_divis(pageNo, dataArr, nextPage):
ct._write_console()
html = lxml.html.parse('%sdata.cfi.cn/%s' % (ct.P_TYPE['http'], nextPage))
res = html.xpath("//table[@class=\"table_data\"]/tr")
if ct.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 = sarr.replace('--', '0')
sarr = '<table>%s</table>' % sarr
df = pd.read_html(sarr, skiprows=[0])[0]
dataArr = dataArr.append(df, ignore_index=True)
nextPage = html.xpath('//div[@id=\"content\"]/div[2]/a[last()]/@href')[0]
np = nextPage.split('&')[2].split('=')[1]
if pageNo < int(np):
return _profit_divis(int(np), dataArr, nextPage)
else:
return dataArr
def _holding_cotent(start, end, pageNo, retry_count, pause):
for _ in range(retry_count):
time.sleep(pause)
if pageNo>0:
ct._write_console()
try:
request = Request(rv.FUND_HOLDS_URL%(ct.P_TYPE['http'], ct.DOMAINS['163'],
ct.PAGES['163fh'], ct.PAGES['163fh'],
pageNo, start, end, _random(5)))
lines = urlopen(request, timeout = 10).read()
lines = lines.decode('utf-8') if ct.PY3 else lines
lines = lines.replace('--', '0')
lines = json.loads(lines)
data = lines['list']
df = pd.DataFrame(data)
df = df.drop(['CODE', 'ESYMBOL', 'EXCHANGE', 'NAME', 'RN', 'SHANGQIGUSHU',
'SHANGQISHIZHI', 'SHANGQISHULIANG'], axis=1)
for col in ['GUSHU', 'GUSHUBIJIAO', 'SHIZHI', 'SCSTC27']:
df[col] = df[col].astype(float)
df['SCSTC27'] = df['SCSTC27']*100
df['GUSHU'] = df['GUSHU']/10000
df['GUSHUBIJIAO'] = df['GUSHUBIJIAO']/10000
df['SHIZHI'] = df['SHIZHI']/10000
df['GUSHU'] = df['GUSHU'].map(ct.FORMAT)
df['GUSHUBIJIAO'] = df['GUSHUBIJIAO'].map(ct.FORMAT)
df['SHIZHI'] = df['SHIZHI'].map(ct.FORMAT)
df['SCSTC27'] = df['SCSTC27'].map(ct.FORMAT)
df.columns = rv.FUND_HOLDS_COLS
df = df[['code', 'name', 'date', 'nums', 'nlast', 'count',
'clast', 'amount', 'ratio']]
except Exception as e:
print(e)
else:
if pageNo == 0:
return df, int(lines['pagecount'])
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _get_detail(tag, retry_count=3, pause=0.001):
for _ in range(retry_count):
time.sleep(pause)
try:
ct._write_console()
request = Request(
ct.SINA_DATA_DETAIL_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['jv'], tag))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
except _network_error_classes:
pass
else:
reg = re.compile(r'\,(.*?)\:')
text = reg.sub(r',"\1":', text)
text = text.replace('"{symbol', '{"symbol')
text = text.replace('{symbol', '{"symbol"')
jstr = json.dumps(text)
js = json.loads(jstr)
df = pd.DataFrame(pd.read_json(js, dtype={'code': object}),
columns=ct.THE_FIELDS)
df = df[ct.FOR_CLASSIFY_B_COLS]
return df
def _dist_cotent(year, pageNo, retry_count, pause):
for _ in range(retry_count):
time.sleep(pause)
try:
if pageNo > 0:
ct._write_console()
html = lxml.html.parse(rv.DP_163_URL %
(ct.P_TYPE['http'], ct.DOMAINS['163'],
ct.PAGES['163dp'], year, pageNo))
res = html.xpath('//div[@class=\"fn_rp_list\"]/table')
if ct.PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = ''.join(sarr)
df = pd.read_html(sarr, skiprows=[0])[0]
df = df.drop(df.columns[0], axis=1)
df.columns = rv.DP_163_COLS
df['divi'] = df['plan'].map(_fun_divi)
df['shares'] = df['plan'].map(_fun_into)
df = df.drop('plan', axis=1)
df['code'] = df['code'].astype(object)
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
pages = []
if pageNo == 0:
page = html.xpath('//div[@class=\"mod_pages\"]/a')
if len(page) > 1:
asr = page[len(page) - 2]
pages = asr.xpath('text()')
except Exception as e:
print(e)
else:
if pageNo == 0:
return df, pages[0] if len(pages) > 0 else 0
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _dist_cotent(year, pageNo, retry_count, pause):
for _ in range(retry_count):
time.sleep(pause)
try:
if pageNo > 0:
ct._write_console()
html = lxml.html.parse(rv.DP_163_URL%(ct.P_TYPE['http'], ct.DOMAINS['163'],
ct.PAGES['163dp'], year, pageNo))
res = html.xpath('//div[@class=\"fn_rp_list\"]/table')
if ct.PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = ''.join(sarr)
df = pd.read_html(sarr, skiprows=[0])[0]
df = df.drop(df.columns[0], axis=1)
df.columns = rv.DP_163_COLS
df['divi'] = df['plan'].map(_fun_divi)
df['shares'] = df['plan'].map(_fun_into)
df = df.drop('plan', axis=1)
df['code'] = df['code'].astype(object)
df['code'] = df['code'].map(lambda x : str(x).zfill(6))
pages = []
if pageNo == 0:
page = html.xpath('//div[@class=\"mod_pages\"]/a')
if len(page)>1:
asr = page[len(page)-2]
pages = asr.xpath('text()')
except Exception as e:
print(e)
else:
if pageNo == 0:
return df, pages[0] if len(pages)>0 else 0
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_h_data(code,
start=None,
end=None,
autype='qfq',
index=False,
retry_count=3,
pause=0.001,
drop_factor=True):
'''
获取历史复权数据
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取当前日期
end:string
结束日期 format：YYYY-MM-DD 为空时取去年今日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
drop_factor : bool, 默认 True
是否移除复权因子，在分析过程中可能复权因子意义不大，但是如需要先储存到数据库之后再分析的话，有该项目会更加灵活
return
-------
DataFrame
date 交易日期 (index)
open 开盘价
high 最高价
close 收盘价
low 最低价
volume 成交量
amount 成交金额
'''
start = du.today_last_year() if start is None else start
end = du.today() if end is None else end
qs = du.get_quarts(start, end)
qt = qs[0]
ct._write_head()
data = _parse_fq_data(_get_index_url(index, code, qt), index, retry_count,
pause)
if data is None:
data = pd.DataFrame()
if len(qs) > 1:
for d in range(1, len(qs)):
qt = qs[d]
ct._write_console()
df = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
if df is None: # 可能df为空，退出循环
break
else:
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(
data[(data.date >= start) & (data.date <= end)]) == 0:
return None
data = data.drop_duplicates('date')
if index:
data = data[(data.date >= start) & (data.date <= end)]
data = data.set_index('date')
data = data.sort_index(ascending=True)
return data
if autype == 'hfq':
if drop_factor:
data = data.drop('factor', axis=1)
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending=True)
return data
else:
if autype == 'qfq':
if drop_factor:
data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort_values(by='date', ascending=True)
firstDate = data.head(1)['date']
frow = df[df.date == firstDate[0]]
rt = get_realtime_quotes(code)
if rt is None:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['factor']) / preClose
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'low', 'close']:
data[label] = data[label] / rate
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending=True)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['factor']
if drop_factor:
data = data.drop('factor', axis=1)
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending=True)
data = data.astype(float)
return data
def get_h_data(code, start=None, end=None, autype='qfq',
index=False, retry_count=3, pause=0.001, drop_factor=True):
'''
获取历史复权数据
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取当前日期
end:string
结束日期 format：YYYY-MM-DD 为空时取去年今日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
drop_factor : bool, 默认 True
是否移除复权因子，在分析过程中可能复权因子意义不大，但是如需要先储存到数据库之后再分析的话，有该项目会更加灵活
return
-------
DataFrame
date 交易日期 (index)
open 开盘价
high 最高价
close 收盘价
low 最低价
volume 成交量
amount 成交金额
'''
start = du.today_last_year() if start is None else start
end = du.today() if end is None else end
qs = du.get_quarts(start, end)
qt = qs[0]
ct._write_head()
data = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
if data is None:
data = pd.DataFrame()
if len(qs)>1:
for d in range(1, len(qs)):
qt = qs[d]
ct._write_console()
df = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
if df is None: # 可能df为空，退出循环
break
else:
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(data[(data.date>=start)&(data.date<=end)]) == 0:
return None
data = data.drop_duplicates('date')
if index:
data = data[(data.date>=start) & (data.date<=end)]
data = data.set_index('date')
data = data.sort_index(ascending=True)
return data
if autype == 'hfq':
if drop_factor:
data = data.drop('factor', axis=1)
data = data[(data.date>=start) & (data.date<=end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending = True)
return data
else:
if autype == 'qfq':
if drop_factor:
data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort_values(by='date', ascending=True)
firstDate = data.head(1)['date']
frow = df[df.date == firstDate[0]]
rt = get_realtime_quotes(code)
if rt is None:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['factor']) / preClose
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'low', 'close']:
data[label] = data[label] / rate
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending = True)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['factor']
if drop_factor:
data = data.drop('factor', axis=1)
data = data[(data.date>=start) & (data.date<=end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending = True)
data = data.astype(float)
return data
