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:
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=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_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'])
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