def todayTicks(self, retry=3, pause=0.001):
"""
获取当日分笔明细数据
Parameters
------
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：当前价格
pchange:涨跌幅
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()
if self.__code is None or len(self.__code)!=6 :
return None
if not Utility.isTradeDay():
return None
# 不到交易时间
openTime = time.mktime(time.strptime(Utility.getToday() + ' 09:25:00', '%Y-%m-%d %H:%M:%S'))
now = time.time()
if now < openTime:
return None
symbol = Utility.symbol(self.__code)
date = Utility.getToday()
for _ in range(retry):
time.sleep(pause)
try:
# http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_Transactions.getAllPageTime?date=2018-12-26&symbol=sh600000
request = self._session.get( cf.TODAY_TICKS_PAGE_URL % (date, symbol), timeout=10 )
request.encoding = 'gbk'
text = request.text[1:-1]
data_dict = Utility.str2Dict(text)
pages = len(data_dict['detailPages'])
self._writeHead()
for pNo in range(1, pages+1):
# http://vip.stock.finance.sina.com.cn/quotes_service/view/vMS_tradedetail.php?symbol=sh600000&date=2018-12-26&page=1
url = cf.TODAY_TICKS_URL % (symbol, date, pNo)
self._data = self._data.append(self.__handleTicks(url, cf.TODAY_TICK_COLUMNS, retry, pause), ignore_index=True)
except Exception as e:
print(str(e))
else:
return self._result()
raise IOError(cf.NETWORK_URL_ERROR_MSG)
def lpr(self, startDate, endDate=None):
"""
获取贷款基础利率
Parameters
------
startDate:开始日期(格式：YYYY-MM-DD)
endDate:结束日期(格式：YYYY-MM-DD，为None时默认取当前日期)
Return
------
DataFrame or List: [{'date':, '1Y':, ...}, ...]
date:日期
1Y:1年贷款基础利率
"""
self._data = pd.DataFrame()
if endDate is None:
endDate = Utility.getToday()
request = self._session.get(cf.LPR_URL % (startDate, endDate))
dataDict = json.loads(request.text)
dataList = []
for row in dataDict['records']:
data = {'date': row['showDateCN'], '1Y': float(row['1Y'])}
dataList.append(data)
self._data = pd.DataFrame(dataList, columns=cf.LPR_COLS)
return self._result()
def xrxd(self, date='', retry=3, pause=0.001):
"""
获取股票除权除息信息
Parameters
------
date: string
format：YYYY-MM-DD
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
------
Dict or None
nd, 对应年份
fh_sh, 分红数额
djr, 股权登记日
cqr, 除权日
FHcontent, 除权除息信息
"""
data = None
if not date:
date = Utility.getToday()
symbol = Utility.symbol(self.__code)
for _ in range(retry):
time.sleep(pause)
url = cf.HISTORY_URL % ('fq', 'qfq', symbol, 'day', date, date,
'qfq', Utility.random(17))
try:
request = self._session.get(url, timeout=10)
pattern = re.compile(r'({"nd".+?})')
result = re.search(pattern, request.text)
if result:
data = eval(result.group(1))
except Exception as e:
print(str(e))
return data
raise IOError(cf.NETWORK_URL_ERROR_MSG)
def history(self,
start='',
end='',
ktype='D',
autype='qfq',
index=False,
retry=3,
pause=0.001):
"""
获取股票交易历史数据
---------
Parameters:
start:string
开始日期 format：YYYY-MM-DD 为空时取上市首日
end:string
结束日期 format：YYYY-MM-DD 为空时取最近一个交易日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
ktype：string
数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D
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: [{'date':, 'open':, ...}, ...]
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
code 股票代码
"""
self._data = pd.DataFrame()
url = ''
dataflag = ''
symbol = cf.INDEX_SYMBOL[self.__code] if index else Utility.symbol(
self.__code)
autype = '' if autype is None else autype
if (start is not None) & (start != ''):
end = Utility.getToday() if end is None or end == '' else end
if ktype.upper() in cf.K_LABELS:
fq = autype if autype is not None else ''
if self.__code[:1] in ('1', '5') or index:
fq = ''
kline = '' if autype is None else 'fq'
if ((start is None or start == '') or
(end is None or end == '')) or (start == end):
# http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq¶m=sh600000,day,,,640,qfq&r=0.73327461855388564
urls = [
cf.HISTORY_URL %
(kline, fq, symbol, cf.TT_K_TYPE[ktype.upper()], start,
end, fq, Utility.random(17))
]
else:
years = Utility.ttDates(start, end)
urls = []
for year in years:
startdate = str(year) + '-01-01'
enddate = str(year + 1) + '-12-31'
# http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq2008¶m=sh600000,day,2008-01-01,2009-12-31,640,qfq&r=0.73327461855388564
url = cf.HISTORY_URL % (kline, fq + str(year), symbol,
cf.TT_K_TYPE[ktype.upper()],
startdate, enddate, fq,
Utility.random(17))
urls.append(url)
dataflag = '%s%s' % (fq, cf.TT_K_TYPE[ktype.upper()]) # qfqday
elif ktype in cf.K_MIN_LABELS:
# http://ifzq.gtimg.cn/appstock/app/kline/mkline?param=sh600000,m30,,640&_var=m30_today&r=0.5537154641907898
urls = [
cf.HISTORY_MIN_URL % (symbol, ktype, ktype, Utility.random(16))
]
dataflag = 'm%s' % ktype # m30
else:
raise TypeError('ktype input error.')
for url in urls:
self._data = self._data.append(self.__handleHistory(
url, dataflag, symbol, index, ktype, retry, pause),
ignore_index=True)
if ktype not in cf.K_MIN_LABELS:
if ((start is not None) & (start != '')) & ((end is not None) &
(end != '')):
self._data = self._data[(self._data.date >= start)
& (self._data.date <= end)]
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)