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 len(res) == 0:
return data
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 [12, 13, 14]], axis=1)
df.columns = rv.NEW_STOCKS_COLS
df['code'] = df['code'].map(lambda x : str(x).zfill(6))
df['xcode'] = df['xcode'].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 _get_cashflow_data(year, quarter, pageNo, dataArr,
retry_count=3, pause=0.001):
ct._write_console()
for _ in range(retry_count):
time.sleep(pause)
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:
pass
raise IOError(ct.NETWORK_URL_ERROR_MSG)
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=False)[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 _get_debtpaying_data(year, quarter, pageNo, dataArr):
ct._write_console()
try:
request = Request(ct.DEBTPAYING_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1]))
text = urlopen(request, timeout=ct.HTTP_TIMEOUT).read()
text = text.decode('GBK')
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.DEBTPAYING_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_debtpaying_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _get_forecast_data(year, quarter, pageNo, dataArr):
url = ct.FORECAST_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]//span/a/text()')[0]
name = trs.xpath('td[2]/span/a/text()')[0]
type = trs.xpath('td[3]/a/text()')
type = type[0] if len(type)>0 else trs.xpath('td[3]/text()')[0]
report_date = trs.xpath('td[4]/text()')[0]
pre_eps = trs.xpath('td[7]/text()')[0]
pre_eps = '0' if pre_eps == '--' else pre_eps
range = trs.xpath('td[8]/text()')[0]
dataArr.append([code,name,type,report_date,pre_eps,range])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+',nextPage[0])[0]
return _get_forecast_data(year,quarter,pageNo,dataArr)
else:
return dataArr
except:
pass
def _inst_tops(last=5, 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[2], ct.PAGES["fd"], last, 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 = df.drop([2, 3], axis=1)
df.columns = rv.LHB_JGZZ_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_tops(last, pageNo, retry_count, pause, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _get_detail(tag, retry_count=3, pause=10):
dfc = pd.DataFrame()
p = 0
num_limit = 100
while(True):
p = p+1
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'],
p,tag))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
except _network_error_classes:
pass
else:
break
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]
dfc = pd.concat([dfc, df])
if df.shape[0] < num_limit:
return dfc
def _dist_cotent(year, pageNo, retry_count, pause):
url = rv.DP_163_URL%(ct.P_TYPE['http'], ct.DOMAINS['163'],
ct.PAGES['163dp'], year, pageNo)
for _ in range(retry_count):
time.sleep(pause)
try:
if pageNo > 0:
ct._write_console()
html = lxml.html.parse(url)
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))
if pageNo == 0:
page = html.xpath('//div[@class=\"mod_pages\"]/a')
asr = page[len(page)-2]
pages = asr.xpath('text()')
except _network_error_classes:
pass
else:
if pageNo == 0:
return df, pages[0]
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
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.DataFrame(columns=ct.TODAY_TICK_COLUMNS)
df = pd.read_html(StringIO(sarr), infer_types=False)[0]
df.columns = list(ct.TODAY_TICK_COLUMNS)
df['Pchange'] = df['Pchange'].map(lambda x : x.replace('%',''))
df.insert(0,'Date',tdate)
#cols = ct.TODAY_TICK_COLUMNS.insert(0,'Date')
df = df[cols]
except _network_error_classes:
pass
else:
return df
raise IOError("获取失败，请检查网络" )
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 _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_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:
pass
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))
if pageNo == 0:
page = html.xpath('//div[@class="mod_pages"]/a')
asr = page[len(page) - 2]
pages = asr.xpath("text()")
except _network_error_classes:
pass
else:
if pageNo == 0:
return df, pages[0]
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
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 _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_report_data(year, quarter, pageNo, dataArr, orderby):
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], orderby))
# 默认排序抓取的信息有重复和遗漏,增加排序功能参数orderby
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,orderby)
else:
return dataArr
except Exception as e:
print(e)
def _get_debtpaying_data(year, quarter, pageNo, dataArr):
url = ct.DEBTPAYING_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]/a/text()')[0]
name = trs.xpath('td[2]/a/text()')[0]
currentratio = trs.xpath('td[3]/text()')[0]
currentratio = '0' if currentratio == '--' else currentratio
quickratio = trs.xpath('td[4]/text()')[0]
quickratio = '0' if quickratio == '--' else quickratio
cashratio = trs.xpath('td[5]/text()')[0]
cashratio = '0' if cashratio == '--' else cashratio
icratio = trs.xpath('td[6]/text()')[0]
icratio = '0' if icratio == '--' else icratio
sheqratio = trs.xpath('td[7]/text()')[0]
sheqratio = '0' if sheqratio == '--' else sheqratio
adratio = trs.xpath('td[8]/text()')[0]
adratio = '0' if adratio == '--' else adratio
dataArr.append([code, name, currentratio, quickratio, cashratio,
icratio, sheqratio, adratio])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_debtpaying_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except:
pass
def _get_cashflow_data(year, quarter, pageNo, dataArr):
url = ct.CASHFLOW_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]/a/text()')[0]
name = trs.xpath('td[2]/a/text()')[0]
cf_sales = trs.xpath('td[3]/text()')[0]
cf_sales = '0' if cf_sales == '--' else cf_sales
rateofreturn = trs.xpath('td[4]/text()')[0]
rateofreturn = '0' if rateofreturn == '--' else rateofreturn
cf_nm = trs.xpath('td[5]/text()')[0]
cf_nm = '0' if cf_nm == '--' else cf_nm
cf_liabilities = trs.xpath('td[6]/text()')[0]
cf_liabilities = '0' if cf_liabilities == '--' else cf_liabilities
cashflowratio = trs.xpath('td[7]/text()')[0]
cashflowratio = '0' if cashflowratio == '--' else cashflowratio
dataArr.append([code, name, cf_sales, rateofreturn, cf_nm,
cf_liabilities, cashflowratio])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_cashflow_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except:
pass
def _get_operation_data(year, quarter, pageNo, dataArr):
url = ct.OPERATION_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]/a/text()')[0]
name = trs.xpath('td[2]/a/text()')[0]
arturnover = trs.xpath('td[3]/text()')[0]
arturnover = '0' if arturnover == '--' else arturnover
arturndays = trs.xpath('td[4]/text()')[0]
arturndays = '0' if arturndays == '--' else arturndays
inventory_turnover = trs.xpath('td[5]/text()')[0]
inventory_turnover = '0' if inventory_turnover == '--' else inventory_turnover
inventory_days = trs.xpath('td[6]/text()')[0]
inventory_days = '0' if inventory_days == '--' else inventory_days
currentasset_turnover = trs.xpath('td[7]/text()')[0]
currentasset_turnover = '0' if currentasset_turnover == '--' else currentasset_turnover
currentasset_days = trs.xpath('td[8]/text()')[0]
currentasset_days = '0' if currentasset_days == '--' else currentasset_days
dataArr.append([code, name, arturnover, arturndays, inventory_turnover,
inventory_days, currentasset_turnover, currentasset_days])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_growth_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except:
pass
def _get_growth_data(year, quarter, pageNo, dataArr):
url = ct.GROWTH_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]/a/text()')[0]
name = trs.xpath('td[2]/a/text()')[0]
mbrg = trs.xpath('td[3]/text()')[0]
mbrg = '0' if mbrg == '--' else mbrg
nprg = trs.xpath('td[4]/text()')[0]
nprg = '0' if nprg == '--' else nprg
nav = trs.xpath('td[5]/text()')[0]
nav = '0' if nav == '--' else nav
targ = trs.xpath('td[6]/text()')[0]
targ = '0' if targ == '--' else targ
epsg = trs.xpath('td[7]/text()')[0]
epsg = '0' if epsg == '--' else epsg
seg = trs.xpath('td[8]/text()')[0]
seg = '0' if seg == '--' else seg
dataArr.append([code, name, mbrg, nprg, nav, targ, epsg, seg])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_growth_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except:
pass
def _get_profit_data(year, quarter, pageNo, dataArr):
url = ct.PROFIT_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]/a/text()')[0]
name = trs.xpath('td[2]/a/text()')[0]
roe = trs.xpath('td[3]/text()')[0]
roe = '0' if roe == '--' else roe
net_profit_ratio = trs.xpath('td[4]/text()')[0]
net_profit_ratio = '0' if net_profit_ratio == '--' else net_profit_ratio
gross_profit_rate = trs.xpath('td[5]/text()')[0]
gross_profit_rate = '0' if gross_profit_rate == '--' else gross_profit_rate
net_profits = trs.xpath('td[6]/text()')[0]
net_profits = '0' if net_profits == '--' else net_profits
eps = trs.xpath('td[7]/text()')[0]
eps = '0' if eps == '--' else eps
business_income = trs.xpath('td[8]/text()')[0]
business_income = '0' if business_income == '--' else business_income
bips = trs.xpath('td[9]/text()')[0]
bips = '0' if bips == '--' else bips
dataArr.append([code, name, roe, net_profit_ratio, gross_profit_rate,
net_profits, eps, business_income, bips])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_profit_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except:
pass
def _get_report_data(year, quarter, pageNo, dataArr):
url = ct.REPORT_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['fd'],
year, quarter, pageNo, ct.PAGE_NUM[1])
ct._write_console()
try:
html = lxml.html.parse(url)
xtrs = html.xpath("//table[@class=\"list_table\"]/tr")
for trs in xtrs:
code = trs.xpath('td[1]//span/a/text()')[0]
name = trs.xpath('td[2]/span/a/text()')[0]
eps = trs.xpath('td[3]/text()')[0] #每股收益(元)
eps_yoy = trs.xpath('td[4]/text()')[0] #每股收益同比(%)
bvps = trs.xpath('td[5]/text()')[0] #每股净资产(元)
bvps = '0' if bvps == '--' else bvps
roe = trs.xpath('td[6]/text()')[0] #净资产收益率(%)
roe = '0' if roe == '--' else roe
epcf = trs.xpath('td[7]/text()')[0] #每股现金流量(元)
epcf = '0' if epcf == '--' else epcf
net_profits = trs.xpath('td[8]/text()')[0] #净利润(万元)
profits_yoy = trs.xpath('td[9]/text()')[0] #净利润同比(%)
distrib = trs.xpath('td[10]/text()')[0] #分配方案
report_date = trs.xpath('td[11]/text()')[0] #发布日期
dataArr.append([code, name, eps, eps_yoy, bvps, roe,
epcf, net_profits, profits_yoy, distrib,
report_date])
nextPage = html.xpath('//div[@class=\"pages\"]/a[last()]/@onclick') #获取下一页
if len(nextPage)>0:
pageNo = re.findall(r'\d+', nextPage[0])[0]
return _get_report_data(year, quarter, pageNo, dataArr)
else:
return dataArr
except:
pass
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 _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 _network_error_classes:
pass
else:
return data
raise IOError(ct.NETWORK_URL_ERROR_MSG)
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 _network_error_classes:
pass
else:
if pageNo == 0:
return df, int(lines["pagecount"])
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
url = rv.MAR_SH_MX_URL%(ct.P_TYPE['http'], ct.DOMAINS['sseq'],
ct.PAGES['qmd'], _random(5), date,
symbol, start, end, tail,
_random())
ref = rv.MAR_SH_HZ_REF_URL%(ct.P_TYPE['http'], ct.DOMAINS['sse'])
clt = Client(url, 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]))
df = df.set_index('opDate')
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 _network_error_classes:
pass
else:
return data
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 _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:
pass
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 _broker_tops_detail(last=5, 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[1],
ct.PAGES['fd'], last, pageNo))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@id=\"dataTable\"]/tr/td/a")
urls = [a.attrib['href'] for a in res]
if len(urls) <= 0:
return dataArr
for url in urls:
ct._write_console()
time.sleep(pause)
request2 = Request(url)
text2 = urlopen(request2, timeout=10).read()
html2 = lxml.html.parse(StringIO(text2.decode('GBK')))
res2 = html2.xpath("//table[@id=\"dataTable\"]/tr")
broker = html2.xpath("//div[@class=\"page_config\"]")[0].text_content().strip().split(':')[1].strip()
ct._write_msg(broker + "\r\n")
if ct.PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res2]
else:
sarr = [etree.tostring(node) for node in res2]
sarr = ''.join(sarr)
sarr = '<table>%s</table>'%sarr
df = pd.read_html(sarr)[0]
df.columns = rv.LHB_YYMX_COLS
df['broker'] = pd.Series([broker]*df.index.size, index=list(df.index))
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 _broker_tops_detail(last, pageNo, retry_count, pause, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def get_h_data(code,
start=None,
end=None,
autype='qfq',
retry_count=3,
pause=0.001):
'''
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
return
-------
DataFrame
date 交易日期 (index)
open 开盘价
high 最高价
close 收盘价
low 最低价
volumn 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(
ct.HIST_FQ_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'], code, qt[0], qt[1]),
retry_count, pause)
if len(qs) > 1:
for d in range(1, len(qs)):
qt = qs[d]
ct._write_console()
url = ct.HIST_FQ_URL % (ct.P_TYPE['http'], ct.DOMAINS['vsf'], code,
qt[0], qt[1])
df = _parse_fq_data(url, retry_count, pause)
data = data.append(df, ignore_index=True)
data = data.drop_duplicates('date')
if start is not None:
data = data[data.date >= start]
if end is not None:
data = data[data.date <= end]
if autype == 'hfq':
data = data.drop('factor', axis=1)
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['factor']
data = data.drop('factor', axis=1)
if autype == 'qfq':
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df[df.date >= start]
df = df[df.date <= end]
df = pd.merge(data, df)
df = df.sort('date', ascending=False)
frow = df.head(1)
rate = float(frow['close']) / float(frow['factor'])
df['close_temp'] = df['close']
df['close'] = rate * df['factor']
for label in ['open', 'high', 'low']:
df[label] = df[label] * (df['close'] / df['close_temp'])
df[label] = df[label].map(ct.FORMAT)
df = df.drop(['factor', 'close_temp'], axis=1)
df['close'] = df['close'].map(ct.FORMAT)
df = df.set_index('date')
df = df.sort_index(ascending=False)
df = df.astype(float)
return df
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending=False)
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
'''
print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")
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 pd.DataFrame()
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 = False)
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 = False)
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('date', ascending = False)
firstDate = data.head(1)['date']
frow = df[df.date == firstDate[0]]
rt = get_realtime_quotes(code)
if rt is None:
return pd.DataFrame()
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 = False)
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 = False)
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):
'''
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
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 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)
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=False)
return data
if autype == 'hfq':
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 = False)
return data
else:
if autype == 'qfq':
data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort('date', ascending=False)
frow = df.head(1)
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 = False)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['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=False)
data = data.astype(float)
return data
