def _get_report_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.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:
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 _parsing_dayprice_json(types=None, page=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'], types, page))
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:
text = text.decode('UTF8')
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_stock_hq_list(pageNo, dataArr):
ct._write_console()
try:
#param:["hq","hs_a","{sort}",{asc},{page},{num}]
hq_list_param = '["hq","hs_a","",0,%d,%d]'%(pageNo,ct.OPEN_API_PAGE_NUM)
request = Request(ct.SINA_OPEN_API_URL%(quote(hq_list_param,',[]')))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
js = json.loads(text.strip())
if js is None:
return dataArr
df = pd.DataFrame(js[0]['items'], columns=js[0]['fields'])
dataArr = dataArr.append(df, ignore_index=True)
if int(js[0]['count']) > pageNo * ct.OPEN_API_PAGE_NUM :
pageNo = pageNo+1
return _get_stock_hq_list(pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
def _get_growth_data(year, quarter,pageNo,dataArr):
ct._write_console()
try:
#param:["cnl","行业","地域","概念","年","季度","{sort}",{asc},{page},{num}]
list_param = '["cnl","","","","%d","%d","",0,%d,%d]'%(year, quarter,pageNo,ct.OPEN_API_PAGE_NUM)
request = Request(ct.SINA_OPEN_API_URL%(quote(list_param,',[]')))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
js = json.loads(text.strip())
if js is None:
return dataArr
df = pd.DataFrame(js[0]['items'],columns=ct.GROWTH_COLS)
dataArr = dataArr.append(df, ignore_index=True)
if int(js[0]['count']) > pageNo * ct.OPEN_API_PAGE_NUM :
pageNo = pageNo+1
return _get_growth_data(year, quarter,pageNo, dataArr)
else:
return dataArr
except Exception as e:
print(e)
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 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
