def historyTicks(self, date=None, retry=3, pause=0.001):
"""
获取历史分笔明细数据
Parameters
------
date : string
日期 format：YYYY-MM-DD, 默认为前一个交易日
retry : 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 or list: [{'time':, 'price':, ...}, ...]
time：时间
price：成交价格
change：价格变动
volume：成交手
amount：成交金额(元)
type：买卖类型【买盘、卖盘、中性盘】
"""
self._data = pd.DataFrame()
symbol = Utility.symbol(self.__code)
date = Utility.lastTradeDate() if date is None else date
try:
self._writeHead()
page = 1
while (True):
# http://vip.stock.finance.sina.com.cn/quotes_service/view/vMS_tradehistory.php?symbol=sh600000&date=2018-12-26&page=1
# http://market.finance.sina.com.cn/transHis.php?date=2019-01-25&symbol=sh600000&page=1
url = cf.HISTORY_TICKS_URL % (date, symbol, page)
tick_data = self.__handleTicks(url, cf.HISTORY_TICK_COLUMNS,
retry, pause)
if tick_data is not None:
self._data = self._data.append(tick_data,
ignore_index=True)
page = page + 1
else:
break
except Exception as er:
print(str(er))
else:
return self._result()
raise IOError(cf.NETWORK_URL_ERROR_MSG)
def topList(self, date=None, retry=3, pause=0.001):
"""
获取每日龙虎榜列表
Parameters
--------
date:string
明细数据日期 format：YYYY-MM-DD 如果为空，返回最近一个交易日的数据
retry : 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 or List: [{'code':, 'name':, ...}, ...]
code：代码
name ：名称
pchange：涨跌幅
amount：龙虎榜成交额(万)
buy：买入额(万)
bratio：占总成交比例
sell：卖出额(万)
sratio ：占总成交比例
reason：上榜原因
unscramble: 解读
date ：日期
"""
self._data = pd.DataFrame()
if date is None:
if Utility.getHour() < 18:
date = Utility.lastTradeDate()
else:
date = Utility.getToday()
else:
if not Utility.isTradeDay(date):
return None
for _ in range(retry):
time.sleep(pause)
try:
# http://data.eastmoney.com/DataCenter_V3/stock2016/TradeDetail/pagesize=200,page=1,sortRule=-1,sortType=,startDate=2019-01-10,endDate=2019-01-10,gpfw=0,js=vardata_tab_1.html
request = self._session.get(cf.LHB_URL % (date, date),
timeout=10)
request.encoding = 'gbk'
text = request.text.split('_1=')[1]
dataDict = Utility.str2Dict(text)
self._data = pd.DataFrame(dataDict['data'],
columns=cf.LHB_TMP_COLS)
self._data.columns = cf.LHB_COLS
self._data['buy'] = self._data['buy'].astype(float)
self._data['sell'] = self._data['sell'].astype(float)
self._data['amount'] = self._data['amount'].astype(float)
self._data['Turnover'] = self._data['Turnover'].astype(float)
self._data[
'bratio'] = self._data['buy'] / self._data['Turnover']
self._data[
'sratio'] = self._data['sell'] / self._data['Turnover']
self._data['bratio'] = self._data['bratio'].map(cf.FORMAT)
self._data['sratio'] = self._data['sratio'].map(cf.FORMAT)
self._data['date'] = date
for col in ['amount', 'buy', 'sell']:
self._data[col] = self._data[col].astype(float)
self._data[col] = self._data[col] / 10000
self._data[col] = self._data[col].map(cf.FORMAT)
self._data = self._data.drop('Turnover', axis=1)
except:
pass
else:
return self._result()
raise IOError(cf.NETWORK_URL_ERROR_MSG)
