def get_a_quote_by_tdx(code_list):
"""
获取A股市场行情，该接口只获取A股市场行情
其他的行情使用拓展行情接口
code_list：数字或者字符串格式的list，调用def standard_tdx_code(code_list)来格式化市场信息及code
"""
if len(code_list) == 0:
return pd.DataFrame(columns=stock_tdx_columns)
# 一次最多获取50只股票的实时行情，如果code_list 多于50只，则50只股票取一次行情，然后进行拼接
tdx_api = TdxHq_API()
if len(code_list) >= 50:
tmp = div_list(code_list, 50)
df_tmp = pd.DataFrame(columns=stock_tdx_columns)
if tdx_api.connect('119.147.212.81', 7709):
for tmp_list in tmp:
tmp_list = standard_tdx_code(tmp_list)
tdx_data = tdx_api.to_df(tdx_api.get_security_quotes(tmp_list))
df_tmp = pd.concat([df_tmp, tdx_data])
df_tmp = df_tmp[stock_tdx_columns]
else:
tmp = standard_tdx_code(code_list)
if tdx_api.connect('119.147.212.81', 7709):
tdx_data = tdx_api.to_df(tdx_api.get_security_quotes(tmp))
df_tmp = tdx_data[stock_tdx_columns]
tdx_api.disconnect()
return process_tdx_price(df_tmp)
def quote_TDX(ds_tuple):
api = TdxHq_API()
if api.connect('119.147.212.81', 7709):
MainCodeData = api.get_security_quotes([(1, code)])
n = ceil(len(ds_tuple) / 80)
for i in range(n):
if i == 0:
quot = api.get_security_quotes(ds_tuple[i * 80:(i + 1) *
80]) #每次最多请求80个数据？
else:
quot_add = api.get_security_quotes(
ds_tuple[i * 80:(i + 1) * 80])
quot = quot + quot_add
api.disconnect()
return quot, MainCodeData
def original_quotes(code, best_ip):
api = TdxHq_API()
api.connect(best_ip)
now = datetime.now()
data = [
api.get_security_quotes(code[80 * pos:80 * (pos + 1)])
for pos in range(int(len(code) / 80) + 1)
]
return (datetime.now() - now).total_seconds()
def request_data(stklist, parse_one_result, ip, port):
"""请求失败将抛出异常"""
api = TdxHq_API()
hku_check(api.connect(ip, port),
'Failed connect tdx ({}:{})!'.format(ip, port))
quotes_list = api.get_security_quotes(stklist)
result = [parse_one_result(q)
for q in quotes_list] if quotes_list is not None else []
return [r for r in result if r is not None]
def original_quotes():
api = TdxHq_API()
api.connect(best_ip)
now = datetime.now()
#data=api.get_security_quotes((get_market(x), x))
#print(data)
data = [api.get_security_quotes([(get_market(x), x) for x in code[80 * pos:80 * (pos + 1)]]) for pos in range(int(len(code) / 80) + 1)]
return data[0:-1]
def __hq_tick(self, code):
api = TdxHq_API()
ip, port = self.__hq.get()
with api.connect(ip, port):
df = api.get_security_quotes(code)
if df is not None:
df = api.to_df(df)
api.disconnect()
self.__hq.put((ip, port))
return df
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=best_ip, port=7709):
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(__select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'code', 'open', 'high', 'low', 'price']]
return data.set_index('code', drop=False, inplace=False)
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=best_ip, port=7709):
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(__select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'code', 'open', 'high', 'low', 'price']]
return data.set_index('code', drop=False, inplace=False)
def get_up_count(webname='ths'):
try:
api = TdxHq_API()
with api.connect('119.147.212.81', 7709):
data = api.get_security_quotes([(1, '880005')])
this_count = data[0]['price']
return int(this_count)
except Exception as e:
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
'(KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36',
}
if webname == 'ths':
try:
#从同花顺取值
url = 'http://q.10jqka.com.cn/api.php?t=indexflash&'
html = requests.get(url, headers=headers)
jsoninfo = json.loads(html.text)
this_count = jsoninfo['zdfb_data']['znum']
return this_count
except Exception as e:
#从东方财富取值
url = 'http://push2.eastmoney.com/api/qt/ulist.np/get?fid=f3&pi=0&pz=20&po=1&ut=bd1d9ddb04089700cf9c27f6f7426281&fltt=2&fields=f104&np=1&secids=1.000001,0.399001'
html = requests.get(url, headers=headers)
jsoninfo = json.loads(html.text)
this_count = jsoninfo['data']['diff'][0]['f104'] + jsoninfo[
'data']['diff'][1]['f104'] - 30
return this_count
if webname == 'dfcf':
try:
#从东方财富取值
url = 'http://push2.eastmoney.com/api/qt/ulist.np/get?fid=f3&pi=0&pz=20&po=1&ut=bd1d9ddb04089700cf9c27f6f7426281&fltt=2&fields=f104&np=1&secids=1.000001,0.399001'
html = requests.get(url, headers=headers)
jsoninfo = json.loads(html.text)
this_count = jsoninfo['data']['diff'][0]['f104'] + jsoninfo[
'data']['diff'][1]['f104'] - 30
return this_count
except Exception as e:
#从同花顺取值
url = 'http://q.10jqka.com.cn/api.php?t=indexflash&'
html = requests.get(url, headers=headers)
jsoninfo = json.loads(html.text)
this_count = jsoninfo['zdfb_data']['znum']
return this_count
def QA_fetch_depth_market_data(code=['000001', '000002'], ip=best_ip['stock'], port=7709):
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(_select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'active1', 'active2', 'last_close', 'code', 'open', 'high', 'low', 'price', 'cur_vol',
's_vol', 'b_vol', 'vol', 'ask1', 'ask_vol1', 'bid1', 'bid_vol1', 'ask2', 'ask_vol2',
'bid2', 'bid_vol2', 'ask3', 'ask_vol3', 'bid3', 'bid_vol3', 'ask4',
'ask_vol4', 'bid4', 'bid_vol4', 'ask5', 'ask_vol5', 'bid5', 'bid_vol5']]
return data.set_index(['datetime', 'code'], drop=False, inplace=False)
def get_quote_by_tdx(code_list):
'''
使用通达信接口获取股票实时行情
:param code_list: 股票代码list
:return: df
'''
tdx_code = stander_stock_code(code_list, 'tdx')
if not tdx_code:
return pd.DataFrame()
api = TdxHq_API()
with api.connect('119.147.212.81', 7709):
data = api.to_df(api.get_security_quotes(tdx_code))
data = data[stock_tdx_columns]
return data
def get_quote_by_tdx2(code_list, isbTDX):
'''
使用通达信接口获取股票实时行情
该方法带有市场信息，主要是对于一些ETF或者lof行情，不能用0开头还是6开头来判断是上海市场还是深圳市场
:param code_list: 股票代码list,带市场信息
:param isbTDX:true,带有通达信标准获取信息行情的内容
:return: df
'''
if not isbTDX:
return pd.DataFrame()
api = TdxHq_API()
with api.connect('119.147.212.81', 7709):
data = api.to_df(api.get_security_quotes(code_list))
if data.empty:
return pd.DataFrame()
data = data[stock_tdx_columns]
return data
def test_hq():
import pprint
api = TdxHq_API(multithread=False)
with api.connect(time_out=30):
for i in range(100):
stocks = api.get_security_quotes([(0, "000001"), (1, "600993")])
#print(stocks)
#print(type(stocks))
print(stocks[1].get('code'), stocks[1].get('price'),
stocks[1].get('bid1'), stocks[1].get('bid_vol1'),
stocks[1].get('ask1'), stocks[1].get('ask_vol1'))
#data = api.get_transaction_data(TDXParams.MARKET_SH, '601818', 0, 100)
#pprint.pprint(data)
#print('time:{},price:{},vol:{},buyorsell:{}'.format(data[0].get('time'), data[0].get('price'), data[0].get('vol'), data[0].get('buyorsell')))
time.sleep(1.5)
class PYTDXService():
"""pytdx数据服务类"""
def __init__(self):
"""Constructor"""
self.connected = False # 数据服务连接状态
self.hq_api = None # 行情API
def connect_api(self):
"""连接API"""
# 连接增强行情API并检查连接情况
try:
if not self.connected:
host = SETTINGS["TDX_HOST"]
port = SETTINGS["TDX_PORT"]
self.hq_api = TdxHq_API()
self.hq_api.connect(host, port)
self.connected = True
return True
except Exception:
raise ConnectionError("pytdx连接错误")
def get_realtime_data(self, symbol: str):
"""获取股票实时数据"""
try:
symbols = self.generate_symbols(symbol)
df = self.hq_api.to_df(self.hq_api.get_security_quotes(symbols))
return df
except Exception:
raise ValueError("股票数据获取失败")
@staticmethod
def generate_symbols(symbol: str):
"""组装symbols数据，pytdx接收的是以市场代码和标的代码组成的元祖的list"""
new_symbols = []
code, exchange = symbol.split('.')
new_symbols.append((exchange_map[exchange], code))
return new_symbols
def close(self):
"""数据服务关闭"""
self.connected = False
self.hq_api.disconnect()
class tdxApi:
def __init__(self):
self.ipset = [(v[1], v[2]) for v in hq_hosts]
random.shuffle(self.ipset)
self.ippool = AvailableIPPool(TdxHq_API, self.ipset)
self.primary_ip, _ = self.ippool.sync_get_top_n(2)
self.api = TdxHq_API(multithread=True, heartbeat=True, auto_retry=True)
def connect(self):
self.api.connect(self.primary_ip[0], self.primary_ip[1])
def release(self):
self.api.disconnect()
def getQuotes(self, stock_list):
datas = self.api.get_security_quotes(stock_list)
if datas is None:
return None
else:
return self.api.to_df(datas)
def getList(self, market, index):
df = self.api.to_df(self.api.get_security_list(market, index))
return df
def getCount(self, market=0):
return self.api.get_security_count(market)
def getMinQuotes(self, market, stock_code):
datas = self.api.get_minute_time_data(market, stock_code)
if datas is None:
return None
return self.api.to_df(datas)
def getMinQuotesHis(self, market, stock_code, date):
datas = self.api.get_history_minute_time_data(market, stock_code, date)
if datas is None:
return None
return self.api.to_df(datas)
def QA_fetch_depth_market_data(code=['000001', '000002'], ip=None, port=None):
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(_select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'active1', 'active2', 'last_close', 'code', 'open', 'high', 'low', 'price', 'cur_vol',
's_vol', 'b_vol', 'vol', 'ask1', 'ask_vol1', 'bid1', 'bid_vol1', 'ask2', 'ask_vol2',
'bid2', 'bid_vol2', 'ask3', 'ask_vol3', 'bid3', 'bid_vol3', 'ask4',
'ask_vol4', 'bid4', 'bid_vol4', 'ask5', 'ask_vol5', 'bid5', 'bid_vol5']]
return data.set_index(['datetime', 'code'], drop=False, inplace=False)
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=None, port=None):
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(_select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'active1', 'active2', 'last_close', 'code', 'open', 'high', 'low', 'price', 'cur_vol',
's_vol', 'b_vol', 'vol', 'ask1', 'ask_vol1', 'bid1', 'bid_vol1', 'ask2', 'ask_vol2',
'bid2', 'bid_vol2', 'ask3', 'ask_vol3', 'bid3', 'bid_vol3', 'ask4',
'ask_vol4', 'bid4', 'bid_vol4', 'ask5', 'ask_vol5', 'bid5', 'bid_vol5']]
return data.set_index('code', drop=False, inplace=False)
def test_all_functions(multithread, heartbeat, auto_retry, raise_exception):
api = TdxHq_API(multithread=multithread, heartbeat=heartbeat,
auto_retry=auto_retry, raise_exception=raise_exception)
with api.connect(time_out=30):
log.info("获取股票行情")
stocks = api.get_security_quotes([(0, "000001"), (1, "600300")])
assert stocks is not None
assert type(stocks) is list
# 方法2
stocks = api.get_security_quotes(0, "000001")
assert stocks is not None
assert type(stocks) is list
# 方法3
stocks = api.get_security_quotes((0, "000001"))
assert stocks is not None
assert type(stocks) is list
log.info("获取k线")
data = api.get_security_bars(9, 0, '000001', 4, 3)
assert data is not None
assert type(data) is list
assert len(data) == 3
log.info("获取 深市 股票数量")
assert api.get_security_count(0) > 0
log.info("获取股票列表")
stocks = api.get_security_list(1, 0)
assert stocks is not None
assert type(stocks) is list
assert len(stocks) > 0
log.info("获取指数k线")
data = api.get_index_bars(9, 1, '000001', 1, 2)
assert data is not None
assert type(data) is list
assert len(data) == 2
log.info("查询分时行情")
data = api.get_minute_time_data(TDXParams.MARKET_SH, '600300')
assert data is not None
log.info("查询历史分时行情")
data = api.get_history_minute_time_data(
TDXParams.MARKET_SH, '600300', 20161209)
assert data is not None
assert type(data) is list
assert len(data) > 0
log.info("查询分时成交")
data = api.get_transaction_data(TDXParams.MARKET_SZ, '000001', 0, 30)
assert data is not None
assert type(data) is list
log.info("查询历史分时成交")
data = api.get_history_transaction_data(
TDXParams.MARKET_SZ, '000001', 0, 10, 20170209)
assert data is not None
assert type(data) is list
assert len(data) == 10
log.info("查询公司信息目录")
data = api.get_company_info_category(TDXParams.MARKET_SZ, '000001')
assert data is not None
assert type(data) is list
assert len(data) > 0
start = data[0]['start']
length = data[0]['length']
log.info("读取公司信息-最新提示")
data = api.get_company_info_content(
0, '000001', '000001.txt', start, length)
assert data is not None
assert len(data) > 0
log.info("读取除权除息信息")
data = api.get_xdxr_info(1, '600300')
assert data is not None
assert type(data) is list
assert len(data) > 0
log.info("读取财务信息")
data = api.get_finance_info(0, '000001')
assert data is not None
assert type(data) is OrderedDict
assert len(data) > 0
log.info("日线级别k线获取函数")
data = api.get_k_data('000001', '2017-07-01', '2017-07-10')
assert type(data) is pd.DataFrame
assert len(data) == 6
log.info("获取板块信息")
data = api.get_and_parse_block_info(TDXParams.BLOCK_FG)
assert data is not None
assert type(data) is list
assert len(data) > 0
class TBStockData:
__serverList = []
_bestIP = []
__bestIPFile = ''
__tdx = None
_lastBaseHistList = pd.DataFrame()
_xdxrData = None
def __init__(self, autoIP = False):
self.__serverList = hq_hosts
self.__bestIPFile = os.path.dirname(os.path.realpath(__file__)) + '/best.ip'
if autoIP:
self.getBestIP()
else:
if os.path.exists(self.__bestIPFile):
with open(self.__bestIPFile, 'r') as f:
data = f.read()
self._bestIP = json.loads(data)
def ping(self, ip, port):
api = TdxHq_API()
time1 = datetime.datetime.now()
try:
with api.connect(ip, int(port)):
if len(api.get_security_list(0, 1)) > 800:
return datetime.datetime.now() - time1
else:
return datetime.timedelta(9, 9, 0)
except:
return datetime.timedelta(9, 9, 0)
def getBestIP(self):
pingTimeList = [self.ping(x[1], x[2]) for x in self.__serverList]
self._bestIP = self.__serverList[pingTimeList.index(min(pingTimeList))]
with open(self.__bestIPFile, 'w') as f:
f.write(json.dumps(self._bestIP))
def showAllIP(self):
for item in self.__serverList:
print item[0],'\t', item[1], '\t', item[2]
def _connect(self):
if self.__tdx is None:
if not self._bestIP:
self.getBestIP()
#self.__tdx = TdxHq_API(heartbeat=True, auto_retry=True)
self.__tdx = TdxHq_API(auto_retry=True)
self.__tdx.connect(self._bestIP[1], int(self._bestIP[2]))
#计算量比
def _setVolRaito(self, row):
date = row.name
histList = self._lastBaseHistList[:date]
if len(histList) < 6:
return np.nan
return round((histList['vol'].values[-1] / 240) / (histList[-6:-1]['vol'].sum() / 1200), 3)
#计算各种指标
def getData(self, df = pd.DataFrame(), indexs=['turnover', 'vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']):
indexs = [x.lower() for x in indexs]
histList = pd.DataFrame()
if not df.empty:
histList = df.copy()
elif not self._lastBaseHistList.empty:
histList = self._lastBaseHistList.copy()
if histList.empty:
return None
dayKStatus = False
try:
if int(time.mktime(time.strptime(str(histList.index[-1]), "%Y-%m-%d %X"))) - int(time.mktime(time.strptime(str(histList.index[-2]), "%Y-%m-%d %X"))) > 43200:
#日线以上行情
dayKStatus = True
except:
dayKStatus = True
#计算涨幅
histList['p_change'] = histList['close'].pct_change().round(5) * 100
#量比
histList['vol_ratio'] = histList.apply(self._setVolRaito, axis=1)
#振幅
histList['amp'] = ((histList['high'] - histList['low']) / histList.shift()['close'] * 100).round(3)
#计算换手率
if self._xdxrData is None:
xdxrData = self.getXdxr(str(histList['code'].values[0]))
else:
xdxrData = self._xdxrData
info = xdxrData[xdxrData['liquidity_after'] > 0][['liquidity_after', 'shares_after']]
if dayKStatus:
startDate = str(histList.index[0])[0:10]
endDate = str(histList.index[-1])[0:10]
info1 = info[info.index <= startDate][-1:]
info = info1.append(info[info.index >= startDate]).drop_duplicates()
info = info.reindex(pd.date_range(info1.index[-1], endDate))
info = info.resample('1D').last().fillna(method='pad')[startDate:endDate]
#info['date'] = info.index
#info['date'] = info['date'].dt.strftime('%Y-%m-%d')
#info = info.set_index('date')
circulate = info['liquidity_after'] * 10000
capital = info['shares_after'] * 10000
else:
circulate = info['liquidity_after'].values[-1] * 10000
capital = info['shares_after'].values[-1] * 10000
#histList['circulate'] = (circulate / 10000 / 10000).round(4)
if 'turnover' in indexs and dayKStatus:
histList['turnover'] = (histList['vol'] * 100 / circulate).round(5) * 100
histList['turnover5'] = talib.MA(histList['turnover'].values, timeperiod=5).round(3)
#stockstats转换,主要是用来计算KDJ等相关指标
#用talib计算KDJ时会与现有软件偏差大
ss = StockDataFrame.retype(histList[['high','low','open','close']])
#MACD计算
if 'macd' in indexs:
difList, deaList, macdList = talib.MACD(histList['close'].values, fastperiod=12, slowperiod=26, signalperiod=9)
macdList = macdList * 2
histList['macd_dif'] = difList.round(3)
histList['macd_dea'] = deaList.round(3)
histList['macd_value'] = macdList.round(3)
histList['macd_value_ma'] = 0
try:
histList['macd_value_ma'] = talib.MA(histList['macd_value'].values, timeperiod=5).round(3)
except:
pass
histList['macd_cross_status'] = 0
macdPosList = histList['macd_dif'] > histList['macd_dea']
histList.loc[macdPosList[(macdPosList == True) & (macdPosList.shift() == False)].index, 'macd_cross_status'] = 1
histList.loc[macdPosList[(macdPosList == False) & (macdPosList.shift() == True)].index, 'macd_cross_status'] = -1
#histList[['macd_cross_status']] = histList[['macd_cross_status']].fillna(method='pad')
#KDJ计算
if 'kdj' in indexs:
histList['kdj_k'] = ss['kdjk'].round(3)
histList['kdj_d'] = ss['kdjd'].round(3)
histList['kdj_j'] = ss['kdjj'].round(3)
histList['kdj_cross_status'] = 0
kdjPosList = histList['kdj_k'] >= histList['kdj_d']
histList.loc[kdjPosList[(kdjPosList == True) & (kdjPosList.shift() == False)].index, 'kdj_cross_status'] = 1
histList.loc[kdjPosList[(kdjPosList == False) & (kdjPosList.shift() == True)].index, 'kdj_cross_status'] = -1
#histList[['kdj_cross_status']] = histList[['kdj_cross_status']].fillna(method='pad')
#CCI计算
if 'cci' in indexs:
histList['cci'] = ss['cci'].round(3)
#ma相关计算
if 'ma' in indexs:
histList['ma5'] = talib.MA(histList['close'].values, timeperiod=5).round(3)
histList['ma10'] = talib.MA(histList['close'].values, timeperiod=10).round(3)
histList['ma20'] = talib.MA(histList['close'].values, timeperiod=20).round(3)
histList['ma30'] = talib.MA(histList['close'].values, timeperiod=30).round(3)
histList['ma60'] = talib.MA(histList['close'].values, timeperiod=60).round(3)
histList['ma240'] = talib.MA(histList['close'].values, timeperiod=240).round(3)
histList[['ma5', 'ma10', 'ma20', 'ma30', 'ma60', 'ma240']] = histList[['ma5', 'ma10', 'ma20', 'ma30', 'ma60', 'ma240']].fillna(0)
#成交量计算
if 'vol' in indexs:
histList['vol5'] = talib.MA(histList['vol'].values, timeperiod=5).round(3)
histList['vol10'] = talib.MA(histList['vol'].values, timeperiod=10).round(3)
histList['vol20'] = talib.MA(histList['vol'].values, timeperiod=20).round(3)
histList['vol_zoom'] = (histList['vol'] / histList['vol5'] * 1.0).round(3)
histList['vol5_vol10_cross_status'] = 0
volumePosList = histList['vol5'] >= histList['vol10']
histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'vol5_vol10_cross_status'] = 1
histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'vol5_vol10_cross_status'] = -1
del volumePosList
histList['vol5_vol20_cross_status'] = 0
volumePosList = histList['vol5'] >= histList['vol20']
histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'vol5_vol20_cross_status'] = 1
histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'vol5_vol20_cross_status'] = -1
del volumePosList
histList['vol10_vol20_cross_status'] = 0
volumePosList = histList['vol10'] >= histList['vol20']
histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'vol10_vol20_cross_status'] = 1
histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'vol10_vol20_cross_status'] = -1
#histList[['vol5_vol10_cross_status', 'vol5_vol20_cross_status', 'vol10_vol20_cross_status']] = histList[['vol5_vol10_cross_status', 'vol5_vol20_cross_status', 'vol10_vol20_cross_status']].fillna(method='pad')
#bbi计算
if 'bbi' in indexs:
ma3 = talib.MA(histList['close'].values, timeperiod=3)
ma6 = talib.MA(histList['close'].values, timeperiod=6)
ma12 = talib.MA(histList['close'].values, timeperiod=12)
ma24 = talib.MA(histList['close'].values, timeperiod=24)
histList['bbi'] = (ma3 + ma6 + ma12 + ma24) / 4
histList['bbi'] = histList['bbi'].round(3)
#SAR计算
if 'sar' in indexs:
sarList = talib.SAR(histList['high'].values, histList['low'].values, acceleration=0.04, maximum=0.2)
histList['sar'] = sarList.round(3)
histList['sar_cross_status'] = 0
sarPosList = histList['close'] >= histList['sar']
histList.loc[sarPosList[(sarPosList == True) & (sarPosList.shift() == False)].index, 'sar_cross_status'] = 1
histList.loc[sarPosList[(sarPosList == False) & (sarPosList.shift() == True)].index, 'sar_cross_status'] = -1
#计算TRIX
if 'trix' in indexs:
histList['trix'] = np.nan
histList['trma'] = np.nan
histList['trix_diff'] = np.nan
try:
trix = talib.TRIX(histList['close'].values, 12)
trma = talib.MA(trix, timeperiod=20)
histList['trix'] = trix.round(3)
histList['trma'] = trma.round(3)
histList['trix_diff'] = histList['trix'] - histList['trma']
histList['trix_cross_status'] = 0
trixPosList = histList['trix'] >= histList['trma']
histList.loc[trixPosList[(trixPosList == True) & (trixPosList.shift() == False)].index, 'trix_cross_status'] = 1
histList.loc[trixPosList[(trixPosList == False) & (trixPosList.shift() == True)].index, 'trix_cross_status'] = -1
#histList[['trix_cross_status']] = histList[['trix_cross_status']].fillna(method='pad')
except:
pass
if 'cyc' in indexs:
avePrice = histList['amount'] / (histList['vol'] * 100)
histList['cyc5'] = talib.MA(avePrice.values, timeperiod=5).round(3)
histList['cyc13'] = talib.MA(avePrice.values, timeperiod=13).round(3)
histList['cyc34'] = talib.MA(avePrice.values, timeperiod=34).round(3)
#histList['cycx'] = talib.EMA(histList['close'].values, timeperiod=histList['vol'].values * 100 / circulate).round(3)
histList['cyc5_cyc13_cross_status'] = 0
volumePosList = histList['cyc5'] >= histList['cyc13']
histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'cyc5_cyc13_cross_status'] = 1
histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'cyc5_cyc13_cross_status'] = -1
del volumePosList
histList['cyc13_cyc34_cross_status'] = 0
volumePosList = histList['cyc13'] >= histList['cyc34']
histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'cyc13_cyc34_cross_status'] = 1
histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'cyc13_cyc34_cross_status'] = -1
del volumePosList
if 'boll' in indexs:
up, mid, low = talib.BBANDS(
histList['close'].values,
timeperiod=20,
# number of non-biased standard deviations from the mean
nbdevup=2,
nbdevdn=2,
# Moving average type: simple moving average here
matype=0)
histList['boll_up'] = up.round(3)
histList['boll_mid'] = mid.round(3)
histList['boll_low'] = low.round(3)
return histList
#整理开始,结束时间,并计算相差天数
def _getDate(self, start, end):
if not end:
end = time.strftime('%Y-%m-%d',time.localtime())
if not start:
t = int(time.mktime(time.strptime(str(end), '%Y-%m-%d'))) - 86400 * 800
start = str(time.strftime('%Y-%m-%d',time.localtime(t)))
startTimestamp = int(time.mktime(time.strptime(str(start), '%Y-%m-%d')))
endTimestamp = int(time.mktime(time.strptime(str(end), '%Y-%m-%d')))
diffDayNum = int((time.time() - startTimestamp) / 86400)
if diffDayNum <= 0:
diffDayNum = 1
return start, end, diffDayNum
#得到市场代码
def getMarketCode(self, code):
code = str(code)
if code[0] in ['5', '6', '9'] or code[:3] in ["009", "126", "110", "201", "202", "203", "204"]:
return 1
return 0
#时间整理
def _dateStamp(self, date):
datestr = str(date)[0:10]
date = time.mktime(time.strptime(datestr, '%Y-%m-%d'))
return date
#整理时间
def _timeStamp(self, _time):
if len(str(_time)) == 10:
# yyyy-mm-dd格式
return time.mktime(time.strptime(_time, '%Y-%m-%d'))
elif len(str(_time)) == 16:
# yyyy-mm-dd hh:mm格式
return time.mktime(time.strptime(_time, '%Y-%m-%d %H:%M'))
else:
timestr = str(_time)[0:19]
return time.mktime(time.strptime(timestr, '%Y-%m-%d %H:%M:%S'))
#得到除权信息
def getXdxr(self, code):
self._connect()
category = {
'1': '除权除息', '2': '送配股上市', '3': '非流通股上市', '4': '未知股本变动', '5': '股本变化',
'6': '增发新股', '7': '股份回购', '8': '增发新股上市', '9': '转配股上市', '10': '可转债上市',
'11': '扩缩股', '12': '非流通股缩股', '13': '送认购权证', '14': '送认沽权证'}
data = self.__tdx.to_df(self.__tdx.get_xdxr_info(self.getMarketCode(code), code))
if len(data) >= 1:
data = data\
.assign(date=pd.to_datetime(data[['year', 'month', 'day']], format='%Y-%m-%d'))\
.drop(['year', 'month', 'day'], axis=1)\
.assign(category_meaning=data['category'].apply(lambda x: category[str(x)]))\
.assign(code=str(code))\
.rename(index=str, columns={'panhouliutong': 'liquidity_after',
'panqianliutong': 'liquidity_before', 'houzongguben': 'shares_after',
'qianzongguben': 'shares_before'})\
.set_index('date', drop=False, inplace=False)
xdxrData = data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
#xdxrData = xdxrData.set_index('date')
self._xdxrData = xdxrData
return xdxrData
else:
return None
#得到股本
def getGuben(self, code):
self._connect()
if self._xdxrData is None:
xdxrData = self.getXdxr(code)
else:
xdxrData = self._xdxrData
info = xdxrData[xdxrData['liquidity_after'] > 0][['liquidity_after', 'shares_after']]
circulate = info['liquidity_after'].values[-1] * 10000
capital = info['shares_after'].values[-1] * 10000
return capital,circulate
#按天得到标准数据
'''
ktype = D(天)/W(周)/M(月)/Q(季)/Y(年)
autype = bfq(不复权)/hfq(后复权)/qfq(前复权)
'''
def getDays(self, code, ktype = 'D', start = '', end = '', autype = 'qfq', indexs = ['turnover', 'vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']):
startDate, endDate, diffDayNum = self._getDate(start, end)
self._connect()
ktypeCode = 9
if ktype.lower() == 'd':
ktypeCode = 9
elif ktype.lower() == 'w':
ktypeCode = 5
elif ktype.lower() == 'm':
ktypeCode = 6
elif ktype.lower() == 'q':
ktypeCode = 10
elif ktype.lower() == 'y':
ktypeCode = 11
histList = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars(ktypeCode, self.getMarketCode(code), code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0)
if histList.empty:
return None
histList = histList[histList['open'] != 0]
histList = histList[histList['vol'] > 1]
if not autype or autype == 'bfq':
histList = histList.assign(date=histList['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10])))
histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp'], axis=1)
histList = histList.set_index('date')
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
histList['p_change'] = histList['close'].pct_change().round(5) * 100
if indexs:
return self.getData(indexs=indexs)
else:
return histList
elif autype == 'qfq':
bfqData = histList.assign(date=pd.to_datetime(histList['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\
.assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10])))
bfqData = bfqData.set_index('date')
bfqData = bfqData.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
xdxrData = self.getXdxr(code)
if xdxrData is not None:
info = xdxrData[xdxrData['category'] == 1]
bfqData['if_trade'] = True
data = pd.concat([bfqData, info[['category']]
[bfqData.index[0]:]], axis=1)
#data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfqData.index[0]:]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
histList = data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category', 'preclose', 'date_stamp', 'adj'], axis=1)
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
histList['p_change'] = histList['close'].pct_change().round(5) * 100
if indexs:
return self.getData(indexs=indexs)
else:
return histList
else:
bfqData['preclose'] = bfqData['close'].shift(1)
bfqData['adj'] = 1
histList = bfqData.drop(['preclose', 'date_stamp', 'adj'], axis=1)
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
if indexs:
return self.getData(indexs=indexs)
else:
return histList
elif autype == 'hfq':
xdxrData = self.getXdxr(code)
info = xdxrData[xdxrData['category'] == 1]
bfqData = histList.assign(date=histList['datetime'].apply(lambda x: x[0:10])).assign(code=str(code))\
.assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10])))
bfqData = bfqData.set_index('date')
bfqData = bfqData.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
bfqData['if_trade'] = True
data = pd.concat([bfqData, info[['category']]
[bfqData.index[0]:]], axis=1)
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfqData.index[0]:]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
histList = data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category', 'preclose', 'date_stamp', 'adj'], axis=1)
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
histList['p_change'] = histList['close'].pct_change().round(5) * 100
if indexs:
return self.getData(indexs=indexs)
else:
return histList
#按分钟得到标准数据
'''
ktype = 1/5/15/30/60 分钟
'''
def getMins(self, code, ktype = 1, start = '', end = '', indexs=['vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']):
startDate, endDate, diffDayNum = self._getDate(start, end)
self._connect()
ktypeCode = 8
if int(ktype) == 1:
ktypeCode = 8
diffDayNum = 240 * diffDayNum
elif int(ktype) == 5:
ktypeCode = 0
diffDayNum = 48 * diffDayNum
elif int(ktype) == 15:
ktypeCode = 1
diffDayNum = 16 * diffDayNum
elif int(ktype) == 30:
ktypeCode = 2
diffDayNum = 8 * diffDayNum
elif int(ktype) == 60:
ktypeCode = 3
diffDayNum = 4 * diffDayNum
if diffDayNum > 20800:
diffDayNum = 20800
histList = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars(ktypeCode, self.getMarketCode(
str(code)), str(code), (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0)
if histList.empty:
return None
histList = histList\
.assign(datetime=pd.to_datetime(histList['datetime']), code=str(code))\
.assign(date=histList['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(x)))\
.assign(time_stamp=histList['datetime'].apply(lambda x: self._timeStamp(x)))
histList['date'] = histList['datetime']
histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp', 'time_stamp'], axis=1)
histList = histList.set_index('date')
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
histList['p_change'] = histList['close'].pct_change().round(5) * 100
histList['vol'] = histList['vol'] / 100.0
if indexs:
return self.getData(indexs=indexs)
else:
return histList
#按天得到指数日k线
'''
ktype = D(天)/W(周)/M(月)/Q(季)/Y(年)
'''
def getIndexDays(self, code, ktype = 'D', start = '', end = '', indexs=['turnover', 'vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']):
startDate, endDate, diffDayNum = self._getDate(start, end)
self._connect()
ktypeCode = 9
if ktype.lower() == 'd':
ktypeCode = 9
elif ktype.lower() == 'w':
ktypeCode = 5
elif ktype.lower() == 'm':
ktypeCode = 6
elif ktype.lower() == 'q':
ktypeCode = 10
elif ktype.lower() == 'y':
ktypeCode = 11
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars(
ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0)
else:
data = pd.concat([self.__tdx.to_df(self.__tdx.get_index_bars(
ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0)
histList = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10])))\
.assign(code=code)
if histList.empty:
return None
histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp', 'up_count', 'down_count'], axis=1)
histList = histList.set_index('date')
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
histList['p_change'] = histList['close'].pct_change().round(5) * 100
if indexs:
return self.getData(indexs=indexs)
else:
return histList
#按分钟得到标准数据
'''
ktype = 1/5/15/30/60 分钟
'''
def getIndexMins(self, code, ktype = 1, start = '', end = '', indexs=['vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']):
startDate, endDate, diffDayNum = self._getDate(start, end)
self._connect()
ktypeCode = 8
if int(ktype) == 1:
ktypeCode = 8
diffDayNum = 240 * diffDayNum
elif int(ktype) == 5:
ktypeCode = 0
diffDayNum = 48 * diffDayNum
elif int(ktype) == 15:
ktypeCode = 1
diffDayNum = 16 * diffDayNum
elif int(ktype) == 30:
ktypeCode = 2
diffDayNum = 8 * diffDayNum
elif int(ktype) == 60:
ktypeCode = 3
diffDayNum = 4 * diffDayNum
if diffDayNum > 20800:
diffDayNum = 20800
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars(
ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0)
else:
data = pd.concat([self.__tdx.to_df(self.__tdx.get_index_bars(
ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0)
histList = data.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: self._dateStamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: self._timeStamp(x)))
if histList.empty:
return None
histList['date'] = histList['datetime']
histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp', 'time_stamp', 'up_count', 'down_count'], axis=1)
histList = histList.set_index('date')
histList = histList[startDate:endDate]
self._lastBaseHistList = histList
histList['p_change'] = histList['close'].pct_change().round(5) * 100
if indexs:
return self.getData(indexs=indexs)
else:
return histList
#实时逐笔
'''
0买 1卖 2中性
'''
def getRealtimeTransaction(self, code):
self._connect()
try:
data = pd.concat([self.__tdx.to_df(self.__tdx.get_transaction_data(
self.getMarketCode(str(code)), code, (2 - i) * 2000, 2000)) for i in range(3)], axis=0)
if 'value' in data.columns:
data = data.drop(['value'], axis=1)
data = data.dropna()
day = datetime.date.today()
histList = data.assign(date=str(day)).assign(datetime=pd.to_datetime(data['time'].apply(lambda x: str(day) + ' ' + str(x))))\
.assign(code=str(code)).assign(order=range(len(data.index)))
histList['money'] = histList['price'] * histList['vol'] * 100
histList['type'] = histList['buyorsell']
histList['type'].replace([0,1,2], ['B','S','N'], inplace = True)
histList = histList.drop(['order', 'buyorsell'], axis=1).reset_index()
return histList
except:
return None
#历史逐笔
'''
0买 1卖 2中性
'''
def getHistoryTransaction(self, code, date):
self._connect()
try:
data = pd.concat([self.__tdx.to_df(self.__tdx.get_history_transaction_data(
self.getMarketCode(str(code)), code, (2 - i) * 2000, 2000, int(str(date).replace('-', '')))) for i in range(3)], axis=0)
if 'value' in data.columns:
data = data.drop(['value'], axis=1)
data = data.dropna()
#day = datetime.date.today()
day = date
histList = data.assign(date=str(day)).assign(datetime=pd.to_datetime(data['time'].apply(lambda x: str(day) + ' ' + str(x))))\
.assign(code=str(code)).assign(order=range(len(data.index)))
histList['money'] = histList['price'] * histList['vol'] * 100
histList['type'] = histList['buyorsell']
histList['type'].replace([0,1,2], ['B','S','N'], inplace = True)
histList = histList.drop(['order', 'buyorsell'], axis=1).reset_index()
return histList
except:
return None
#实时分时数据
def getRealtimeMinuteTime(self, code):
self._connect()
date = str(time.strftime('%Y-%m-%d',time.localtime()))
morningData = pd.date_range(start=str(date) + ' 09:31', end=str(date) + ' 11:30', freq = 'min')
morningDF = pd.DataFrame(index=morningData)
afternoonData = pd.date_range(start=str(date) + ' 13:01',end=str(date) + ' 15:00', freq = 'min')
afternoonDF = pd.DataFrame(index=afternoonData)
timeData = morningDF.append(afternoonDF)
histList = self.__tdx.to_df(self.__tdx.get_minute_time_data(
self.getMarketCode(str(code)), code))
#非标准均价计算
money = histList['price'] * histList['vol'] * 100
histList['money'] = money.round(2)
totalMoney = money.cumsum()
totalVol = histList['vol'].cumsum()
histList['ave'] = totalMoney / (totalVol * 100)
histList['ave'] = histList['ave'].round(3)
histList['datetime'] = timeData.index[0:len(histList)]
histList['date'] = histList['datetime'].apply(lambda x: x.strftime('%Y-%m-%d'))
histList['time'] = histList['datetime'].apply(lambda x: x.strftime('%H:%M'))
histList = histList.reset_index()
return histList
#历史分时数据
def getHistoryMinuteTime(self, code, date):
self._connect()
morningData = pd.date_range(start=str(date) + ' 09:31', end=str(date) + ' 11:30', freq = 'min')
morningDF = pd.DataFrame(index=morningData)
afternoonData = pd.date_range(start=str(date) + ' 13:01',end=str(date) + ' 15:00', freq = 'min')
afternoonDF = pd.DataFrame(index=afternoonData)
timeData = morningDF.append(afternoonDF)
histList = self.__tdx.to_df(self.__tdx.get_history_minute_time_data(
self.getMarketCode(str(code)), code, int(str(date).replace('-', ''))))
#非标准均价计算
money = histList['price'] * histList['vol'] * 100
histList['money'] = money.round(2)
totalMoney = money.cumsum()
totalVol = histList['vol'].cumsum()
histList['ave'] = totalMoney / (totalVol * 100)
histList['ave'] = histList['ave'].round(3)
histList['datetime'] = timeData.index[0:len(histList)]
histList['date'] = histList['datetime'].apply(lambda x: x.strftime('%Y-%m-%d'))
histList['time'] = histList['datetime'].apply(lambda x: x.strftime('%H:%M'))
histList = histList.reset_index()
return histList
#实时报价(五档行情)
'''
market => 市场
active1 => 活跃度
price => 现价
last_close => 昨收
open => 开盘
high => 最高
low => 最低
reversed_bytes0 => 保留
reversed_bytes1 => 保留
vol => 总量
cur_vol => 现量
amount => 总金额
s_vol => 内盘
b_vol => 外盘
reversed_bytes2 => 保留
reversed_bytes3 => 保留
bid1 => 买一价
ask1 => 卖一价
bid_vol1 => 买一量
ask_vol1 => 卖一量
bid2 => 买二价
ask2 => 卖二价
bid_vol2 => 买二量
ask_vol2 => 卖二量
bid3 => 买三价
ask3 => 卖三价
bid_vol3 => 买三量
ask_vol3 => 卖三量
bid4 => 买四价
ask4 => 卖四价
bid_vol4 => 买四量
ask_vol4 => 卖四量
bid5 => 买五价
ask5 => 卖五价
bid_vol5 => 买五量
ask_vol5 => 卖五量
reversed_bytes4 => 保留
reversed_bytes5 => 保留
reversed_bytes6 => 保留
reversed_bytes7 => 保留
reversed_bytes8 => 保留
reversed_bytes9 => 涨速
active2 => 活跃度
'''
def getRealtimeQuotes(self, codeList):
self._connect()
itemList = []
for item in codeList:
itemList.append((self.getMarketCode(item), item))
histList = self.__tdx.to_df(self.__tdx.get_security_quotes(itemList))
histList = histList.set_index('code')
return histList
#计算指定日期成交量细节
def getVolAnalysis(self, code, date):
self._connect()
if str(time.strftime('%Y-%m-%d',time.localtime())) == str(date):
if int(time.strftime('%H%M',time.localtime())) > 1600:
volList = self.getHistoryTransaction(code, date)
else:
volList = self.getRealtimeTransaction(code)
else:
volList = self.getHistoryTransaction(code, date)
if volList is None:
return None
guben,circulate = self.getGuben(code)
if not self._lastBaseHistList.empty:
histList = self._lastBaseHistList.copy()
else:
histList = self.getDays(code, end=date, indexs=[])
#涨停单数量
limitVol = round(histList[-5:]['vol'].mean() * 0.0618)
#超大单,先转成市值,再转回成手数
superVol = float(circulate) * float(histList['close'].values[-1]) * 0.000618 / float(histList['close'].values[-1]) / 100
#大单
bigVol = round(superVol * 0.518)
#中单
middleVol = round(superVol * 0.382)
#小单
smallVol = round(superVol * 0.191)
#买单统计
buyVolList = volList[volList['type'] == 'B']
totalBuyVolNum = buyVolList['vol'].sum()
mainBuyVolNum = buyVolList[buyVolList['vol'] >= bigVol]['vol'].sum()
limitBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] >= limitVol)]['vol'].sum() / limitVol)
superBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < limitVol) & (buyVolList['vol'] >= superVol)]['vol'].sum() / superVol)
bigBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < superVol) & (buyVolList['vol'] >= bigVol)]['vol'].sum() / bigVol)
middleBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < bigVol) & (buyVolList['vol'] >= middleVol)]['vol'].sum() / middleVol)
smallBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < middleVol) & (buyVolList['vol'] >= smallVol)]['vol'].sum() / smallVol)
microBuyVolNum = len(buyVolList[(buyVolList['vol'] < smallVol)])
#print limitBuyVolNum,superBuyVolNum,bigBuyVolNum,middleBuyVolNum,smallBuyVolNum,microBuyVolNum
#卖单统计
sellVolList = volList[volList['type'] == 'S']
totalSellVolNum = sellVolList['vol'].sum()
mainSellVolNum = sellVolList[sellVolList['vol'] >= bigVol]['vol'].sum()
limitSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] >= limitVol)]['vol'].sum() / limitVol)
superSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < limitVol) & (sellVolList['vol'] >= superVol)]['vol'].sum() / superVol)
bigSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < superVol) & (sellVolList['vol'] >= bigVol)]['vol'].sum() / bigVol)
middleSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < bigVol) & (sellVolList['vol'] >= middleVol)]['vol'].sum() / middleVol)
smallSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < middleVol) & (sellVolList['vol'] >= smallVol)]['vol'].sum() / smallVol)
microSellVolNum = len(sellVolList[(sellVolList['vol'] < smallVol)])
#print limitSellVolNum,superSellVolNum,bigSellVolNum,middleSellVolNum,smallSellVolNum,microSellVolNum
#计算吸筹线
#主力标准吸筹金额
mainBaseMoney = round(histList['close'].values[-1] * circulate * 0.001 / 10000 / 10000, 4)
#主力强力吸筹金额
mainBigMoney = round(histList['close'].values[-1] * circulate * 0.003 / 10000 / 10000, 4)
#资金统计
totalMoney = round(volList['money'].sum() / 10000 / 10000, 4)
totalBuyMoney = round(buyVolList['money'].sum() / 10000 / 10000, 4)
totalSellMoney = round(sellVolList['money'].sum() / 10000 / 10000, 4)
totalAbsMoney = round(totalBuyMoney - totalSellMoney, 3)
mainMoney = round(volList[volList['vol'] >= bigVol]['money'].sum() / 10000 / 10000, 4)
mainBuyMoney = round(buyVolList[buyVolList['vol'] >= bigVol]['money'].sum() / 10000 / 10000, 4)
mainSellMoney = round(sellVolList[sellVolList['vol'] >= bigVol]['money'].sum() / 10000 / 10000, 4)
mainAbsMoney = round(mainBuyMoney - mainSellMoney, 3)
mainRate = 0
try:
mainRate = round((mainBuyMoney + mainSellMoney) / totalMoney * 100, 2)
except:
pass
mainBuyRate = 0
try:
mainBuyRate = round(mainBuyMoney / (mainBuyMoney + mainSellMoney) * 100, 2)
except:
pass
#print totalAbsMoney,mainAbsMoney,totalMoney,totalBuyMoney,totalSellMoney,mainBuyMoney,mainSellMoney,mainRate,mainBuyRate
#成交笔数
volNum = len(volList)
#平均每笔交易价格
aveTradePrice = round(totalMoney / volNum * 10000 * 10000, 2)
#平均每股买价格
avePerShareBuyPrice = 0
try:
avePerShareBuyPrice = round(totalBuyMoney * 10000 * 10000 / (totalBuyVolNum * 100), 3)
except:
pass
#主力平均每股买价格
mainAvePerShareBuyPrice = 0
try:
mainAvePerShareBuyPrice = round(mainBuyMoney * 10000 * 10000 / (mainBuyVolNum * 100), 3)
except:
pass
#平均每股卖价格
avePerShareSellPrice = 0
try:
avePerShareSellPrice = round(totalSellMoney * 10000 * 10000 / (totalSellVolNum * 100), 3)
except:
pass
#主力平均每股卖价格
mainAvePerShareSellPrice = 0
try:
mainAvePerShareSellPrice = round(mainSellMoney * 10000 * 10000 / (mainSellVolNum * 100), 3)
except:
pass
#print totalMoney,volNum,aveVolPrice * 10000 * 10000
statData = {}
statData['limit_buy_vol_num'] = limitBuyVolNum
statData['super_buy_vol_num'] = superBuyVolNum
statData['big_buy_vol_num'] = bigBuyVolNum
statData['middle_buy_vol_num'] = middleBuyVolNum
statData['small_buy_vol_num'] = smallBuyVolNum
statData['micro_buy_vol_num'] = microBuyVolNum
statData['limit_sell_vol_num'] = limitSellVolNum
statData['super_sell_vol_num'] = superSellVolNum
statData['big_sell_vol_num'] = bigSellVolNum
statData['middle_sell_vol_num'] = middleSellVolNum
statData['small_sell_vol_num'] = smallSellVolNum
statData['micro_sell_vol_num'] = microSellVolNum
statData['total_abs_money'] = totalAbsMoney
statData['main_abs_money'] = mainAbsMoney
statData['total_money'] = totalMoney
statData['total_buy_money'] = totalBuyMoney
statData['total_sell_money'] = totalSellMoney
statData['main_money'] = mainMoney
statData['main_buy_money'] = mainBuyMoney
statData['main_sell_money'] = mainSellMoney
statData['main_rate'] = mainRate
statData['main_buy_rate'] = mainBuyRate
statData['trade_num'] = volNum
statData['vol_num'] = volList['vol'].sum()
statData['ave_trade_price'] = aveTradePrice
statData['main_base_money'] = mainBaseMoney
statData['main_big_money'] = mainBigMoney
statData['ave_per_share_buy_price'] = avePerShareBuyPrice
statData['ave_per_share_sell_price'] = avePerShareSellPrice
statData['main_ave_per_share_buy_price'] = mainAvePerShareBuyPrice
statData['main_ave_per_share_sell_price'] = mainAvePerShareSellPrice
statData['circulate_money'] = round(circulate * histList['close'].values[-1] / 10000 / 10000, 4)
return statData
#输出ebk文件
def outputEbk(self, stockList, ebkPath = ''):
if len(ebkPath) <= 0:
ebkPath = os.getcwd() + '/' + sys.argv[0][0:-3] + '.' + str(time.strftime('%Y%m%d',time.localtime())) + '.ebk'
if not isinstance(stockList,list):
return False
fp = open(ebkPath, "a")
fp.write('\r\n') #ebk第一行为空行
for code in stockList:
if self.getMarketCode(code) == 1:
fp.write('1' + code)
else:
fp.write('0' + code)
fp.write('\r\n')
fp.close()
return True
#输出sel文件
def outputSel(self, stockList, selPath = ''):
import struct
if len(selPath) <= 0:
selPath = os.getcwd() + '/' + sys.argv[0][0:-3] + '.' + str(time.strftime('%Y%m%d',time.localtime())) + '.sel'
if not isinstance(stockList,list):
return False
stocks = []
for code in stockList:
if self.getMarketCode(code) == 1:
stocks.append('\x07\x11' + code)
else:
stocks.append('\x07\x21' + code)
with open(selPath, 'ab') as fp:
data = struct.pack('H', len(stocks)).decode() + ''.join(stocks)
fp.write(data.encode())
return True
#ebk to sel
def ebk2sel(self, ebkPath):
import struct
if not os.path.exists(ebkPath):
return False
selPath = ebkPath.replace('.ebk', '.sel')
stocks = []
with open(ebkPath, 'r') as ebkfp:
for code in ebkfp:
code = code.strip()
if len(code) > 0:
if self.getMarketCode(code[1:]) == 1:
stocks.append('\x07\x11' + code[1:])
else:
stocks.append('\x07\x21' + code[1:])
with open(selPath, 'wb') as selfp:
data = struct.pack('H', len(stocks)).decode() + ''.join(stocks)
selfp.write(data.encode())
return True
#sel to ebk
def sel2ebk(self, selPath):
import struct
if not os.path.exists(selPath):
return False
ebkPath = selPath.replace('.sel', '.ebk')
with open(selPath, 'rb') as selfp:
ebkfp = open(ebkPath, "a")
cnt = struct.unpack('<H', selfp.read(2))[0]
for _ in range(cnt):
data = selfp.readline(8).decode()
exch = '1' if data[1] == '\x11' else '0'
code = exch + data[2:]
ebkfp.write(code + '\r\n')
ebkfp.close()
return True
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect('119.147.212.81', 7709):
df = api.to_df(api.get_security_quotes([(0, '000001'), (1, '600300')]))
print(df.to_json())
data = api.get_minute_time_data(1, '600300')
print(data)
list_df = api.to_df(api.get_security_list(0, 300))
print(list_df.to_json())
class MongoDB(object):
def __init__(
self,
ip="stock_mongo", #mongo db 数据库docker 容器名
port=27017,
user_name=None,
pwd=None,
authdb=None):
self.__server_ip = ip
self.__server_port = port
self.__user_name = user_name
self.__pwd = pwd
self.__authdb = authdb
self.client = None
self.trade_day = True
self.TDX_IP_SETS = STOCK_IP_SETS
self.api = TdxHq_API(heartbeat=True)
self.today = None
self.accounts = []
self.db = "stock_mock" #数据库
self.account_collection = "account" #保存各个账户的当前资金信息
self.account_his_collection = "account_history" #保存每个账户的历史净值信息
self.prefix = "holdlist_"
self.accounts = []
self.stocks = []
def connect(self):
'''建立数据库的连接
'''
_db_session = MongoClient(self.__server_ip, self.__server_port)
if self.__user_name:
eval("_db_session.{}".format(self.__authdb)).authenticate(
self.__user_name, self.__pwd)
self.client = _db_session
def connect_market(self):
for ip in self.TDX_IP_SETS:
try:
if self.api.connect(ip, 7709):
return
except:
pass
def disconnect(self):
'''断开数据库连接
'''
self.client.close()
return True
def _dbclient(self, db):
'''返回某个特定数据库的对象
'''
return eval("self.client.{}".format(db))
def handle_ex_right(self):
'''处理持仓股票除权价格和数量
'''
func = lambda x: 0 if not x else x
today = datetime.datetime.today().date().day
year = datetime.datetime.today().date().year
month = datetime.datetime.today().date().month
for stock in self.stocks:
mk = self._select_market_code(stock)
cqcx = self.api.get_xdxr_info(mk, stock)[::-1]
dct = {"fenhong": 0, 'peigu': 0, 'peigujia': 0, "songzhuangu": 0}
iscq = False
for i in cqcx:
if i["day"] != today or i["month"] != month or i[
"year"] != year:
break
else:
iscq = True
dct["fenhong"] += func(i["fenhong"])
dct["peigu"] += func(i["peigu"])
dct["peigujia"] += func(i["peigujia"])
dct["songzhuangu"] += func(i["songzhuangu"])
if iscq: #发生除权除息
rst = self.api.get_security_bars(4, mk, stock, 0, 2)
if rst[0]["day"] != today or i["month"] != month or i[
"year"] != year:
close = rst[0]["close"]
else:
close = rst[1]["close"]
preclose = (close * 10 - dct["fenhong"] +
dct["peigu"] * dct['peigujia']) / (
10 + dct['peigu'] + dct['songzhuangu'])
rate = close / preclose
logger.info("除权除息：{}，rate：{}".format(stock, rate))
for account in self.accounts:
filt = {"code": stock, "cx_date": {"$ne": self.today}}
dt = {
"$mul": {
"cost": 1 / rate,
"number": rate
},
"$set": {
"cx_date": self.today
}
}
self._dbclient(self.db)[self.prefix + account].update_one(
filt, dt)
def set_accounts(self):
self.accounts = [
i["account"]
for i in self._dbclient(self.db)[self.account_collection].find()
]
def set_stocks(self):
rst = []
for account in self.accounts:
rst.extend([
i["code"]
for i in self._dbclient(self.db)[self.prefix + account].find(
{"number": {
"$gt": 0
}}, {
"_id": 0,
"code": 1
})
])
self.stocks = set(rst)
def initial(self):
'''每天初始化状态，连接行情数据源，更新除权信息
'''
self.today = datetime.datetime.today().date().strftime('%Y-%m-%d')
df = ts.trade_cal()
self.trade_day = df[(
df["calendarDate"] == self.today)].isOpen.values[0]
if self.trade_day: #交易日，连接数据库，连接行情源，处理除权除息
self.connect()
self.connect_market()
self.set_accounts()
self.set_stocks()
self.handle_ex_right()
logger.info("initial finished")
def _select_market_code(self, code):
code = str(code)
if code[0] in ['5', '6', '9'] or code[:3] in [
"009", "126", "110", "201", "202", "203", "204"
]:
return 1
return 0
def updateaccount(self, account="test"):
'''更新账户净值，添加一条净值记录
'''
hold_collection = self.prefix + account
tm = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
plimit = 80
filt = {'number': {'$gt': 0}}
stocks = [(self._select_market_code(i["code"]), i["code"])
for i in self._dbclient(self.db)[hold_collection].find(filt)]
rst = {}
for i in range(math.ceil(len(stocks) / plimit)):
ss = stocks[i * plimit:(i + 1) * plimit]
for item in self.api.get_security_quotes(ss):
rst[item["code"]] = item["price"]
if len(rst) > 0:
bulk = self._dbclient(
self.db)[hold_collection].initialize_ordered_bulk_op()
for code, price in rst.items():
d = {"price": price, "update_datetime": tm}
bulk.find({"code": code}).upsert().update({"$set": d})
bulk.execute()
holdvalue = self._dbclient(self.db)[hold_collection].aggregate([{
"$group": {
"_id": None,
"total": {
"$sum": {
"$multiply": ["$price", "$number"]
}
}
}
}])
try:
holdvalue = [i for i in holdvalue][0]["total"]
except:
holdvalue = 0
rst = self._dbclient(self.db)[self.account_collection].find(
{"account": account}, {"_id": 0})[0]
rst["total"] = rst["rest"] + holdvalue
rst["hold"] = holdvalue
#更新账户当前值
self._dbclient(self.db)[self.account_collection].update_one(
{"account": account}, {"$set": rst})
#更新账户历史记录值
dtm = datetime.datetime.now()
minute = dtm.minute
if minute >= 50:
dtm = dtm.replace(hour=dtm.hour + 1, minute=0)
else:
minute = int(minute / 10) * 10 + 10
dtm = dtm.replace(minute=minute)
self._dbclient(self.db)[self.account_his_collection].update_one(
{
"account": account,
"date": dtm.strftime("%Y-%m-%d %H:%M")
}, {"$set": rst},
upsert=True)
def run(self):
if not self.trade_day: #交易日，更新账户信息
return
for account in self.accounts:
self.updateaccount(account)
def single_client_benchmark(ip):
def _log(msg):
click.echo("HQ_BENCHMARK: [{:15s}] {} ".format(ip, datetime.datetime.now()) + msg)
def _grouped_list(stocks):
return [stocks[i:i + GET_QUOTES_PER_GROUPS] for i in range(0, len(stocks), GET_QUOTES_PER_GROUPS)]
_log("start benchmark")
total_time = connecting_time = get_security_count_time = get_security_list_time = get_security_quotes_time = num = 0
start_time = time.time()
last_time = start_time
try:
api = TdxHq_API(multithread=True)
port = 7709
if ":" in ip:
ip, port = ip.split(':')
port = int(port)
with api.connect(ip=ip, port=port):
_log("connected")
cur_time = time.time()
connecting_time = cur_time - last_time
last_time = cur_time
_log("connecting time is {}".format(connecting_time))
num = api.get_security_count(0)
_log("all shenzhen market stock count is {}".format(num))
cur_time = time.time()
get_security_count_time = cur_time - last_time
last_time = cur_time
_log("get_security_count_time is {}".format(get_security_count_time))
all = []
for i in range((num // 1000) + 1):
offset = i * 1000
section = api.get_security_list(0, offset)
all = all + section
cur_time = time.time()
get_security_list_time = cur_time - last_time
last_time = cur_time
_log("get_security_list_time is {}".format(get_security_list_time))
codes = [one['code'] for one in all]
results = []
for stocks in _grouped_list(codes):
req_list = [(0, code) for code in stocks]
one_results = api.get_security_quotes(req_list)
results = results + one_results
cur_time = time.time()
get_security_quotes_time = cur_time - last_time
last_time = cur_time
_log("get_security_quotes_time is {}".format(get_security_quotes_time))
total_time = last_time - start_time
_log("total_time is {}".format(total_time))
_log("end benchmark")
except Exception as e:
_log("hit exception " + str(e))
return {
"ip": ip,
"total_time": total_time,
"connecting_time": connecting_time,
"get_security_count_time": get_security_count_time,
"get_security_list_time": get_security_list_time,
"get_security_quotes_time": get_security_quotes_time,
"security_count": num
}
class TDX_GW(object):
__lc_min_bar_reader = None
__daily_bar_reader = None
__tdx_api = None
__connected_ip = '119.147.212.81'
__connected_port = 7709
def __init__(self):
self.__lc_min_bar_reader = lc_min_bar_reader.TdxLCMinBarReader()
self.__daily_bar_reader = TdxDailyBarReader()
self.__block_reader = block_reader.BlockReader()
self.__tdx_api = TdxHq_API()
self.__cfg_provider = ConfigProvider.ConfigProvider()
def get_local_stock_bars(self, file_path: str, stock_date_type: StockDataType):
if stock_date_type == StockDataType.ONE_MIN or \
stock_date_type == StockDataType.FIVE_MINS:
# start = time.time()
# df = self.__lc_min_bar_reader.get_df(file_path)
data = self.__lc_min_bar_reader.parse_data_by_file(file_path)
df = pd.DataFrame(data=data)
# df = df['date', 'open', 'high', 'low', 'close', 'amount', 'volume']
# print(f"TDX get 1min bar time spent: {(time.time() - start) * 1000} ms")
return df[['date', 'open', 'high', 'low', 'close', 'amount', 'volume']]
elif stock_date_type == StockDataType.DAILY:
data = self.__daily_bar_reader.get_df(file_path).reset_index()
data['date'] = data['date'].dt.strftime('%Y-%m-%d')
return data[['date', 'open', 'high', 'low', 'close', 'amount', 'volume']]
else:
raise UnimplementedException
def get_local_stock_bars_raw_data(self, file_path: str, stock_date_type: StockDataType):
if stock_date_type == StockDataType.ONE_MIN or \
stock_date_type == StockDataType.FIVE_MINS:
return self.__lc_min_bar_reader.parse_data_by_file(file_path)
elif stock_date_type == StockDataType.DAILY:
return self.__daily_bar_reader.parse_data_by_file(file_path)
else:
raise UnimplementedException
def get_local_block(self):
file_path = self.__cfg_provider.get_tdx_block_directory_path()
return self.__block_reader.get_df(file_path, BlockReader_TYPE_GROUP)
def get_realtime_stock_1min_bars(self, market: str, stock_id: str):
with self.__tdx_api.connect(self.__connected_ip, self.__connected_port):
market_code = self.__get_market_code(market)
df = self.__tdx_api.to_df(
self.__tdx_api.get_security_bars(8, market_code, stock_id, 0, 10)) # 返回DataFrame
return df
def get_realtime_stocks_quotes(self, stock_ids: []):
stock_list = []
for id in stock_ids:
stock_list.append((com_utils.get_stock_market(id), id))
with self.__tdx_api.connect(self.__connected_ip, self.__connected_port):
return self.__tdx_api.get_security_quotes(stock_list)
def get_history_minute_time_data(self, market: str, stock_id: str, date: int):
with self.__tdx_api.connect(self.__connected_ip, self.__connected_port):
market_code = self.__get_market_code(market)
df = self.__tdx_api.to_df(
self.__tdx_api.get_history_minute_time_data(market_code, stock_id, date))
return df
def get_xdxr_info(self, market: str, stock_id: str):
with self.__tdx_api.connect(self.__connected_ip, self.__connected_port):
market_code = self.__get_market_code(market)
df = self.__tdx_api.to_df(
self.__tdx_api.get_xdxr_info(market_code, stock_id))
return df
def test(self):
with self.__tdx_api.connect(self.__connected_ip, self.__connected_port):
return self.__tdx_api.get_history_minute_time_data(0, '000001', '2019-05-05')
def __get_market_code(self, market: str):
if market == cfg.MARKET.SHANGHAI:
return 1
elif market == cfg.MARKET.SHENZHEN:
return 0
else:
raise UnimplementedException
def get_all_stock():
df = ts.get_stock_basics()
df['stock'] = df.index
df = df.sort_values(by=['stock'], ascending=True)
df['stock'] = df.stock.map(lambda x:
(1 if str(x).startswith('60') else 0, x))
return df['stock'].tolist()
if __name__ == '__main__':
stocks = get_all_stock()
api = TdxHq_API()
if api.connect('119.147.212.81', 7709):
data = api.get_security_quotes(stocks[0:200])
print(data)
api.disconnect()
# ips = [(v[1], v[2]) for v in hq_hosts]
# # 获取5个随机ip作为ip池
# random.shuffle(ips)
# ips5 = ips[:5]
#
# ## IP 池对象
# ippool = AvailableIPPool(TdxHq_API, ips5)
#
# ## 选出M, H
# primary_ip, hot_backup_ip = ippool.sync_get_top_n(2)
#
# print("make pool api")
("bid4", self._cal_price(price, bid4)),
("ask4", self._cal_price(price, ask4)),
("bid_vol4", bid_vol4),
("ask_vol4", ask_vol4),
("bid5", self._cal_price(price, bid5)),
("ask5", self._cal_price(price, ask5)),
("bid_vol5", bid_vol5),
("ask_vol5", ask_vol5),
("reversed_bytes4", reversed_bytes4),
("reversed_bytes5", reversed_bytes5),
("reversed_bytes6", reversed_bytes6),
("reversed_bytes7", reversed_bytes7),
("reversed_bytes8", reversed_bytes8),
("reversed_bytes9", reversed_bytes9 / 100.0), # 涨速
("active2", active2)
])
stocks.append(one_stock)
return stocks
def _cal_price(self, base_p, diff):
return float(base_p + diff) / 100
if __name__ == '__main__':
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect():
#print(api.to_df(api.get_security_quotes([(0, '102672'), (0, '002672')])))
print(
api.to_df(api.get_security_quotes([(0, '101612'), (0, '002672')])))
class Engine:
def __init__(self, *args, **kwargs):
if kwargs.pop('best_ip', False):
self.ip = self.best_ip
else:
self.ip = '14.17.75.71'
self.ip = kwargs.pop('ip', '14.17.75.71')
self.thread_num = kwargs.pop('thread_num', 1)
if not PY2 and self.thread_num != 1:
self.use_concurrent = True
else:
self.use_concurrent = False
self.api = TdxHq_API(args, kwargs)
if self.use_concurrent:
self.apis = [
TdxHq_API(args, kwargs) for i in range(self.thread_num)
]
self.executor = ThreadPoolExecutor(self.thread_num)
def connect(self):
self.api.connect(self.ip)
if self.use_concurrent:
for api in self.apis:
api.connect(self.ip)
return self
def __enter__(self):
return self
def exit(self):
self.api.disconnect()
if self.use_concurrent:
for api in self.apis:
api.disconnect()
def __exit__(self, exc_type, exc_val, exc_tb):
self.api.disconnect()
if self.use_concurrent:
for api in self.apis:
api.disconnect()
def quotes(self, code):
code = [code] if not isinstance(code, list) else code
code = self.security_list[self.security_list.code.isin(
code)].index.tolist()
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 * (pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
# data = data[['code', 'open', 'high', 'low', 'price']]
# data['datetime'] = datetime.datetime.now()
# return data.set_index('code', drop=False, inplace=False)
def stock_quotes(self):
code = self.stock_list.index.tolist()
if self.use_concurrent:
res = {
self.executor.submit(self.apis[pos % self.thread_num].get_security_quotes,
code[80 * pos:80 * (pos + 1)]) \
for pos in range(int(len(code) / 80) + 1)}
return pd.concat([self.api.to_df(dic.result()) for dic in res])
else:
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 *
(pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
@lazyval
def security_list(self):
return pd.concat([
pd.concat([
self.api.to_df(self.api.get_security_list(
j, i * 1000)).assign(sse=0 if j == 0 else 1).set_index(
['sse', 'code'], drop=False)
for i in range(int(self.api.get_security_count(j) / 1000) + 1)
],
axis=0) for j in range(2)
],
axis=0)
@lazyval
def stock_list(self):
aa = map(stock_filter, self.security_list.index.tolist())
return self.security_list[list(aa)]
@lazyval
def best_ip(self):
return select_best_ip()
@lazyval
def concept(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_GN))
@lazyval
def index(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_SZ))
@lazyval
def fengge(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_FG))
@lazyval
def customer_block(self):
return CustomerBlockReader().get_df(CUSTOMER_BLOCK_PATH)
@lazyval
def gbbq(self):
df = GbbqReader().get_df(GBBQ_PATH).query('category == 1')
df['datetime'] = pd.to_datetime(df['datetime'], format='%Y%m%d')
return df
def get_security_type(self, code):
if code in self.security_list.code.values:
return self.security_list[self.security_list.code ==
code]['sse'].as_matrix()[0]
else:
raise SecurityNotExists()
def get_security_bars(self, code, freq, index=False):
if index:
exchange = self.get_security_type(code)
func = self.api.get_index_bars
else:
exchange = get_stock_type(code)
func = self.api.get_security_bars
df = pd.DataFrame()
if freq in ['1d', 'day']:
freq = 9
elif freq in ['1m', 'min']:
freq = 8
else:
raise Exception("1d and 1m frequency supported only")
res = []
start = 0
while True:
data = func(freq, exchange, code, start, 800)
if not data:
break
res = data + res
start += 800
df = self.api.to_df(res).drop(
['year', 'month', 'day', 'hour', 'minute'], axis=1)
df['datetime'] = pd.to_datetime(df.datetime)
df['code'] = code
return df.set_index('datetime')
def _get_transaction(self, code, date):
res = []
start = 0
while True:
data = self.api.get_history_transaction_data(
get_stock_type(code), code, start, 2000, date)
if not data:
break
start += 2000
res = data + res
if len(res) == 0:
return pd.DataFrame()
df = self.api.to_df(res).assign(date=date)
df.index = pd.to_datetime(str(date) + " " + df["time"])
df['code'] = code
return df.drop("time", axis=1)
def time_and_price(self, code):
start = 0
res = []
exchange = self.get_security_type(code)
while True:
data = self.api.get_transaction_data(exchange, code, start, 2000)
if not data:
break
res = data + res
start += 2000
df = self.api.to_df(res)
df.time = pd.to_datetime(
str(pd.to_datetime('today').date()) + " " + df['time'])
df.loc[0, 'time'] = df.time[1]
return df.set_index('time')
@classmethod
def minute_bars_from_transaction(cls, transaction, freq):
if transaction.empty:
return pd.DataFrame()
data = transaction['price'].resample(freq,
label='right',
closed='left').ohlc()
data['volume'] = transaction['vol'].resample(freq,
label='right',
closed='left').sum()
data['code'] = transaction['code'][0]
return fillna(data)
def get_k_data(self, code, start, end, freq):
if isinstance(start, str) or isinstance(end, str):
start = pd.Timestamp(start)
end = pd.Timestamp(end)
sessions = pd.date_range(start, end)
trade_days = map(int, sessions.strftime("%Y%m%d"))
if freq == '1m':
freq = '1 min'
if freq == '1d':
freq = '24 H'
res = []
for trade_day in trade_days:
df = Engine.minute_bars_from_transaction(
self._get_transaction(code, trade_day), freq)
if df.empty:
continue
res.append(df)
if len(res) != 0:
return pd.concat(res)
return pd.DataFrame()
class StdQuotes(object):
"""股票市场实时行情"""
bestip = ('47.103.48.45', 7709)
def __init__(self, **kwargs):
try:
default = settings.get('SERVER').get('HQ')[0]
self.bestip = config.get('BESTIP').get('HQ', default)
except ValueError:
self.config = None
self.client = TdxHq_API(**kwargs)
def traffic(self):
with self.client.connect(*self.bestip):
return self.client.get_traffic_stats()
def quotes(self, symbol=[]):
'''
获取实时日行情数据
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
logger.debug(type(logger))
if type(symbol) is str:
symbol = [symbol]
with self.client.connect(*self.bestip):
symbol = get_stock_markets(symbol)
result = self.client.get_security_quotes(symbol)
return to_data(result)
def bars(self, symbol='000001', frequency='9', start='0', offset='100'):
'''
获取实时日K线数据
:param symbol: 股票代码
:param frequency: 数据类别
:param market: 证券市场
:param start: 开始位置
:param offset: 每次获取条数
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_security_bars(int(frequency), int(market),
str(symbol), int(start),
int(offset))
return to_data(result)
def stock_count(self, market=MARKET_SH):
'''
获取市场股票数量
:param market: 股票市场代码 sh 上海， sz 深圳
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_security_count(market=market)
return result
def stocks(self, market=MARKET_SH):
'''
获取股票列表
:param market:
:return:
'''
with self.client.connect(*self.bestip):
counts = self.client.get_security_count(market=market)
stocks = None
for start in tqdm(range(0, counts, 1000)):
result = self.client.get_security_list(market=market,
start=start)
stocks = pandas.concat(
[stocks, to_data(result)],
ignore_index=True) if start > 1 else to_data(result)
return stocks
def index_bars(self,
symbol='000001',
frequency='9',
start='0',
offset='100'):
'''
获取指数k线
:param symbol:
:param frequency:
:param start:
:param offset:
:return:
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_index_bars(frequency=frequency,
market=market,
code=symbol,
start=start,
count=offset)
return to_data(result)
def minute(self, symbol=''):
'''
获取实时分时数据
:param market: 证券市场
:param symbol: 股票代码
:return: pd.DataFrame
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_minute_time_data(market=market,
code=symbol)
return to_data(result)
def minutes(self, symbol='', date='20191023'):
'''
分时历史数据
:param market:
:param symbol:
:param date:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_history_minute_time_data(market=market,
code=symbol,
date=date)
return to_data(result)
def transaction(self, symbol='', start=0, offset=10):
'''
查询分笔成交
:param market: 市场代码
:param symbol: 股票代码
:param start: 起始位置
:param offset: 请求数量
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_transaction_data(int(market), symbol,
int(start), int(offset))
return to_data(result)
def transactions(self, symbol='', start=0, offset=10, date='20170209'):
'''
查询历史分笔成交
参数：市场代码， 股票代码，起始位置，日期 数量 如： 0,000001,0,10,20170209
:param symbol: 股票代码
:param start: 起始位置
:param offset: 数量
:param date: 日期
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol, string=False)
result = self.client.get_history_transaction_data(market=market,
code=symbol,
start=start,
count=offset,
date=date)
return to_data(result)
def F10C(self, symbol=''):
'''
查询公司信息目录
:param market: 市场代码
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_company_info_category(int(market), symbol)
return result
def F10(self, symbol='', name=''):
'''
读取公司信息详情
:param name: 公司 F10 标题
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = {}
market = get_stock_market(symbol, string=False)
frequency = self.client.get_company_info_category(
int(market), symbol)
if name:
for x in frequency:
if x['name'] == name:
return self.client.get_company_info_content(
market=market,
code=symbol,
filename=x['filename'],
start=x['start'],
length=x['length'])
for x in frequency:
result[x['name']] = self.client.get_company_info_content(
market=market,
code=symbol,
filename=x['filename'],
start=x['start'],
length=x['length'])
else:
pass
return result
def xdxr(self, symbol=''):
'''
读取除权除息信息
:param market: 市场代码
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_xdxr_info(int(market), symbol)
return to_data(result)
def finance(self, symbol='000001'):
'''
读取财务信息
:param symbol:
:return:
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_finance_info(market=market, code=symbol)
return to_data(result)
def k(self, symbol='', begin=None, end=None):
'''
读取k线信息
:param symbol:
:param begin: 开始日期
:param end: 截止日期
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_k_data(symbol, begin, end)
return to_data(result)
def index(self,
symbol='000001',
market=MARKET_SH,
frequency='9',
start=1,
offset=2):
'''
获取指数k线
K线种类:
- 0 5分钟K线
- 1 15分钟K线
- 2 30分钟K线
- 3 1小时K线
- 4 日K线
- 5 周K线
- 6 月K线
- 7 1分钟
- 8 1分钟K线
- 9 日K线
- 10 季K线
- 11 年K线
:param symbol: 股票代码
:param frequency: 数据类别
:param market: 证券市场
:param start: 开始位置
:param offset: 每次获取条数
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_index_bars(int(frequency), int(market),
str(symbol), int(start),
int(offset))
return to_data(result)
def block(self, tofile="block.dat"):
'''
获取证券板块信息
:param tofile:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_and_parse_block_info(tofile)
return to_data(result)
class PytdxApi:
"""TDX数据服务类"""
def __init__(self):
"""Constructor"""
self.connect_status: bool = False
self.login_status: bool = False
self.hq_api = None # 行情API
self.conc_code_num = 50 # 并发获取行情的股票个数
# 行情订阅
self.active = False
self.run_subscribe = Thread(target=self.get_realtime_data)
self.symbols = list()
self.symbols_split = list()
def connect_api(self, host: str = "", port: int = 0):
"""连接行情api"""
# 连接行情API并检查连接情况
try:
if not self.connect_status:
self.hq_api = TdxHq_API()
self.hq_api.connect(host, port)
self.connect_status = True
self.login_status = True
self.subscribe_start()
except Exception as e:
return e
def get_realtime_quotes(self, quotes_list: list):
"""获取实时行情数据"""
data = self.hq_api.get_security_quotes(quotes_list)
return data
def get_realtime_data(self):
"""获取实时行情切片"""
try:
while self.active:
if not self.symbols_split:
sleep(1)
continue
data = list()
for symbols in self.symbols_split:
d = self.get_realtime_quotes(symbols)
data.extend(d)
self.on_tick_data(data)
sleep(2)
except:
error = dict()
error["error_id"] = "pytdx"
error["error_msg"] = "行情订阅失败"
self.on_error(error)
def get_transaction_count(self, market: int) -> int:
"""
查询市场标的数量
"""
return self.hq_api.get_security_count(market)
def get_transaction_list(self, market: int, start: int) -> list:
"""查询市场标的列表"""
return self.hq_api.get_security_list(market, start)
def subscribe_start(self):
"""启动行情订阅"""
self.active = True
self.run_subscribe.start()
def subscribe(self, symbol: Any):
"""订阅行情数据"""
if isinstance(symbol, tuple):
if symbol not in self.symbols:
self.symbols.append(symbol)
elif isinstance(symbol, list):
for s in symbol:
if s not in self.symbols:
self.symbols.append(s)
else:
error = dict()
error["error_id"] = "pytdx"
error["error_msg"] = f"订阅标的代码格式不正确{symbol}"
self.on_error(error)
return
symbol_split = self.get_code_split()
self.symbols_split = copy(symbol_split)
def subscribe_close(self):
"""关闭订阅"""
if self.active:
self.active = False
self.run_subscribe.join()
def get_transaction_info(self):
"""获取所有合约信息"""
for exchange in list(exchange_map.values()):
count = self.get_transaction_count(exchange)
for c in range(0, count, 1000):
symbols = self.get_transaction_list(exchange, c)
for symbol in symbols:
symbol["exchange"] = exchange
if symbol["code"][:2] in ["60", "30", "688", "00"]:
symbol["product"] = 3
else:
symbol["product"] = 2
self.on_contract_info(symbol, False)
self.on_contract_info({"exchange": exchange}, True)
def get_all_stock(self):
"""获取所有股票数据"""
stocks = list()
for exchange in list(exchange_map.values()):
count = self.get_transaction_count(exchange)
for s in range(0, count, 1000):
d = self.get_transaction_list(exchange, s)
stocks.extend(d)
l = len(stocks) - 1
for i, stock in enumerate(stocks):
if stock["code"][:1] in ["60", "30", "688", "00"]:
if i == l:
self.on_contract_info(stock, True)
else:
self.on_contract_info(stock, False)
def on_contract_info(self, data: dict, last: bool) -> None:
""""""
pass
def on_tick_data(self, data):
"""订阅数据处理"""
pass
def on_error(self, err):
"""接口错误处理"""
pass
@staticmethod
def generate_symbols(symbols: list):
"""组装symbols数据，pytdx接收的是以市场代码和标的代码组成的元祖的list"""
new_symbols = []
for symbol in symbols:
code, exchange = symbol.split(".")
new_symbol = (exchange_map[exchange], code)
new_symbols.append(new_symbol)
return new_symbols
@staticmethod
def get_fast_ip():
"""获取最快IP"""
host = "210.51.39.201"
port = 7709
return host, port
@staticmethod
def check_symbol(symbol: str):
"""检查标的格式"""
if symbol:
code, market = symbol.split(".")
market = exchange_map.get(market)
return code, market
else:
return False
def get_code_split(self):
"""获得切割好的股票代码段"""
code_split_list = []
for i in range(0, len(self.symbols) + 1, self.conc_code_num):
code_split = self.symbols[i : i + self.conc_code_num]
code_split_list.append(code_split)
return code_split_list
def exit(self):
"""数据服务关闭"""
# 关闭订阅
self.subscribe_close()
# 关闭接口
self.login_status = False
self.connect_status = False
self.hq_api.disconnect()
self.hq_api = None
])
stocks.append(one_stock)
return stocks
def _cal_price(self, base_p, diff):
return float(base_p + diff) / 100
def _format_time(self, time_stamp):
"""
format time from reversed_bytes0
by using method from https://github.com/rainx/pytdx/issues/187
"""
time = time_stamp[:-6] + ':'
if int(time_stamp[-6:-4]) < 60:
time += '%s:' % time_stamp[-6:-4]
time += '%06.3f' % (int(time_stamp[-4:]) * 60 / 10000.0)
else:
time += '%02d:' % (int(time_stamp[-6:]) * 60 / 1000000)
time += '%06.3f' % (
(int(time_stamp[-6:]) * 60 % 1000000) * 60 / 1000000.0)
return time
if __name__ == '__main__':
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect('182.131.7.211', 7709):
#print(api.to_df(api.get_security_quotes([(0, '102672'), (0, '002672')])))
print(
api.to_df(api.get_security_quotes([(0, '000677'), (0, '002672')])))
##pip install pytdx
from pytdx.hq import TdxHq_API
api = TdxHq_API()
api.connect('59.173.18.140', 7709)
print("获取股票行情")
data = api.get_k_data('000002', '2005-07-01', '2017-07-10')
data2 = api.get_xdxr_info(1, '600300')
print(data2)
print("获取股票行情")
stocks = api.get_security_quotes([(0, "000002"), (1, "600300")])
print(stocks)
print("获取k线")
data = api.get_security_bars(9, 0, '000001', 4, 3)
print(data)
print("获取 深市 股票数量")
print(api.get_security_count(0))
print("获取股票列表")
stocks = api.get_security_list(1, 255)
print(stocks)
print("获取指数k线")
data = api.get_index_bars(9, 1, '000001', 1, 2)
print(data)
print("查询分时行情")
data = api.get_minute_time_data(1, '600300')
class TdxData:
def __init__(self):
self.api = TdxHq_API(heartbeat=True)
self.dbUtil = DBUtil.getInstance()
def get_security_quotes(self, code, type):
return self.api.get_security_quotes([(type, code)])
# 支持板块及个股
def days(self, code, type, bk=False, all=False, day=5):
category = int(config.getByKey('TDX_CATEGORY'))
try:
with self.api.connect(TDX_IP, TDX_PORT):
data = []
if all:
if bk:
for i in range(10):
data += self.api.get_index_bars(
category, type, code, (9 - i) * 800, 800)
else:
for i in range(10):
data += self.api.get_security_bars(
category, type, code, (9 - i) * 800, 800)
if len(data) > 0:
df = self.api.to_df(data).drop([
'amount', 'year', 'month', 'day', 'hour', 'minute'
],
axis=1)
df['trade_date'] = df['datetime'].apply(
lambda x: x[0:10].replace('-', ''))
df = df.drop(['datetime'], axis=1)
df = df.sort_values(by=['trade_date'],
axis=0,
ascending=False)
return df
else:
return self.api.to_df(data)
else:
if bk:
data = self.api.get_index_bars(category, type, code, 0,
day) # 返回DataFrame
else:
data = self.api.get_security_bars(
category, type, code, 0, day)
if len(data) > 0:
df = self.api.to_df(data).drop([
'amount', 'year', 'month', 'day', 'hour', 'minute'
],
axis=1)
df['trade_date'] = df['datetime'].apply(
lambda x: x[0:10].replace('-', ''))
df = df.drop(['datetime'], axis=1)
df = df.sort_values(by=['trade_date'],
axis=0,
ascending=False)
return df
else:
return self.api.to_df(data)
except Exception as e:
logging.info("暂不支持类型,代码：%s:%s" % (code, e))
return self.api.to_df([])
# F10 查询公司信息目录
def get_company_info_category(self, code, type):
with self.api.connect(TDX_IP, TDX_PORT):
df = pd.DataFrame(self.api.get_company_info_category(type, code))
df['txt'] = None
return df
return []
def get_company_info_content(self, code, type, df):
with self.api.connect(TDX_IP, TDX_PORT):
return self.api.get_company_info_content(type, code,
df['filename'].values[0],
df['start'].values[0],
df['length'].values[0])
return ""
# 查询财务数据
def get_finance_info(self, code, type):
with self.api.connect(TDX_IP, TDX_PORT):
return self.api.get_finance_info(type, code)
return ''
# 每年更新一次，板块个股关系
def updateBk(self):
with self.api.connect(TDX_IP, TDX_PORT):
"""
# 获取股票所属板块信息
# 板块相关参数
BLOCK_SZ = "block_zs.dat"
BLOCK_FG = "block_fg.dat"
BLOCK_GN = "block_gn.dat"
BLOCK_DEFAULT = "block.dat"
"""
bk_zs = self.api.to_df(
self.api.get_and_parse_block_info("block_zs.dat")) #指数板块
bk_fg = self.api.to_df(
self.api.get_and_parse_block_info("block_fg.dat")) #风格板块
bk_gn = self.api.to_df(
self.api.get_and_parse_block_info("block_gn.dat")) #概念板块
bk_default = self.api.to_df(
self.api.get_and_parse_block_info("block.dat")) # 默认
self.dbUtil.to_sql(bk_gn,
'stock_bk_gn',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_fg,
'stock_bk_fg',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_zs,
'stock_bk_zs',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_default,
'stock_bk_default',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
# 获取股票列表
tmp1 = self.api.to_df(self.api.get_security_list(0, 0)) # 深圳
tmp1['type'] = 0
tmp2 = self.api.to_df(self.api.get_security_list(1, 0)) # 上海
tmp2['type'] = 1
tmp = tmp1.append(tmp2)
self.dbUtil.to_sql(tmp,
'stock_bk',
if_exists='replace',
index=False,
dtype=STOCK_BK)
def updateGD(self, code, type):
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/ShareholderResearch/ShareholderResearchAjax?code=%s%s' % (
type.lower(), code)
html = urllib.request.urlopen(url).read()
# 将字符串转换成字典
data = json.loads(html.decode('utf-8'))
# gdrs 股东人数,sdgd 十大股东 ，sdltgd 十大流通股东
df_gdrs = pd.DataFrame(data['gdrs'])
df_gdrs['code'] = code
try:
db_df_gdrs = self.dbUtil.read_sql(
"select * from stock_gdrs where code ='%s'" % code)
# 数据合并
df_gdrs = df_gdrs.append(db_df_gdrs).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_gdrs,
'stock_gdrs',
if_exists='append',
index=False,
dtype=STOCK_GDRS_DTYPE)
sdgd = []
for i in range(len(data['sdgd'])):
sdgd += data['sdgd'][i]['sdgd']
df_sdgd = pd.DataFrame(sdgd)
df_sdgd['code'] = code
try:
db_df_sdgd = self.dbUtil.read_sql(
"select * from stock_sdgd where code ='%s'" % code)
df_sdgd = df_sdgd.append(db_df_sdgd).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_sdgd,
'stock_sdgd',
if_exists='append',
index=False,
dtype=STOCK_SDGD_DTYPE)
sdltgd = []
for i in range(len(data['sdltgd'])):
sdltgd += data['sdltgd'][i]['sdltgd']
df_sdltgd = pd.DataFrame(sdltgd)
df_sdltgd['code'] = code
# 获取后与数据库中的数据进行merge,首次表不存在，会抛异常
try:
db_df_sdltgd = self.dbUtil.read_sql(
"select * from stock_sdltgd where code ='%s'" % code)
df_sdltgd = df_sdltgd.append(db_df_sdltgd).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_sdltgd,
'stock_sdltgd',
if_exists='append',
index=False,
dtype=STOCK_SDGD_DTYPE)
# 没季度更新一次
def updateGDs(self):
codes = self.dbUtil.read_sql("select ts_code from stock_basic")
tmp = codes['ts_code'].str.split('.', expand=True)
for index, row in tmp.iterrows():
try:
self.updateGD(row[0], row[1])
logging.info('%s更新结束,当前索引%s' % (row[0], index))
except Exception as e:
logging.info('%s更新失败,当前索引%s' % (row[0], index))
# 分红
# 分红地址http://data.eastmoney.com/yjfp/201812.html
def updateFh(self, rq):
url = 'http://data.eastmoney.com/DataCenter_V3/yjfp/getlist.ashx?filter=(ReportingPeriod=^%s^)' % rq
html = requests.get(url)
# 将字符串转换成字典
data = json.loads(html.text)['data']
if len(data) == 0:
return 0
df = pd.DataFrame(data)
df['ReportingPeriod'] = df['ReportingPeriod'].apply(lambda x: x[0:10])
# 首次需要将df_fh制空，因为表还不存在
if self.dbUtil.is_exist("stock_fh"):
db_fh = self.dbUtil.read_sql(
"select * from stock_fh where ReportingPeriod = '%s'" %
df['ReportingPeriod'][0])
if db_fh.empty: # 不存在当前日期的分红信息，进行拼接
self.dbUtil.to_sql(df,
'stock_fh',
if_exists='append',
index=False)
return 1
else:
pass
else:
self.dbUtil.to_sql(df, 'stock_fh', if_exists='append', index=False)
return 1
# 更新历年分红
def updateFhYears(self):
now = int(time.strftime("%Y", time.localtime())) + 1
lastYear = int(self._getFhMaxYear())
for i in range(lastYear, now): #初始化时开启
type = self.updateFh('%s-06-30' % i)
logging.info('%s-06-30%s' % (i, '成功' if type == 1 else '失败'))
self.updateFh('%s-12-31' % i)
logging.info('%s-12-31%s' % (i, '成功' if type == 1 else '失败'))
def _getFhMaxYear(self):
if self.dbUtil.is_exist('stock_fh'):
try:
df = self.dbUtil.read_sql(
'select substr(max(t.ReportingPeriod ),0,5) year from stock_fh t'
)
return df['year'][0].values
except Exception as e:
pass
return 1991
@classmethod
def getInstance(cls):
if not hasattr(TdxData, "_instance"):
TdxData._instance = TdxData()
return TdxData._instance
class PYTDXService():
"""pytdx数据服务类"""
def __init__(self):
"""Constructor"""
self.connected = False # 数据服务连接状态
self.hq_api = None # 行情API
def connect_api(self):
"""连接API"""
# 连接增强行情API并检查连接情况
try:
if not self.connected:
host = SETTINGS["TDX_HOST"]
port = SETTINGS["TDX_PORT"]
self.hq_api = TdxHq_API()
self.hq_api.connect(host, port)
self.connected = True
return True
except Exception:
raise ConnectionError("pytdx连接错误")
def get_realtime_data(self, symbol: str):
"""获取股票实时数据"""
try:
symbols = self.generate_symbols(symbol)
df = self.hq_api.to_df(self.hq_api.get_security_quotes(symbols))
return df
except Exception:
raise ValueError("股票数据获取失败")
def get_history_transaction_data(self, symbol, date):
"""
查询历史分笔数据
get_history_transaction_data(TDXParams.MARKET_SZ, '000001', 0, 10, 20170209)
参数：市场代码, 股票代码, 起始位置, 数量, 日期
输出[time, price, vol, buyorsell(0:buy, 1:sell, 2:平)]
"""
# 获得标的
code, market = self.check_symbol(symbol)
# 设置参数
check_date = int(date)
count = 2000
data_list = []
position = [6000, 4000, 2000, 0]
for start in position:
data = self.hq_api.to_df(
self.hq_api.get_history_transaction_data(
market, code, start, count, check_date))
data_list.append(data)
df = pd.concat(data_list)
df.drop_duplicates(inplace=True)
return df
@staticmethod
def generate_symbols(symbol: str):
"""组装symbols数据，pytdx接收的是以市场代码和标的代码组成的元祖的list"""
new_symbols = []
code, exchange = symbol.split('.')
new_symbols.append((exchange_map[exchange], code))
return new_symbols
@staticmethod
def check_symbol(symbol: str):
"""检查标的格式"""
if symbol:
code, market = symbol.split('.')
market = exchange_map.get(market)
return code, market
else:
return False
def close(self):
"""数据服务关闭"""
self.connected = False
self.hq_api.disconnect()
class Engine:
concurrent_thread_count = 50
def __init__(self, *args, **kwargs):
if 'ip' in kwargs:
self.ip = kwargs.pop('ip')
else:
if kwargs.pop('best_ip', False):
self.ip = self.best_ip
else:
self.ip = '14.17.75.71'
if 'concurrent_thread_count' in kwargs:
self.concurrent_thread_count = kwargs.pop(
'concurrent_thread_count', 50)
self.thread_num = kwargs.pop('thread_num', 1)
self.api = TdxHq_API(args, kwargs, raise_exception=True)
def connect(self):
self.api.connect(self.ip)
return self
def __enter__(self):
return self
def exit(self):
self.api.disconnect()
def __exit__(self, exc_type, exc_val, exc_tb):
self.api.disconnect()
def quotes(self, code):
code = [code] if not isinstance(code, list) else code
code = self.security_list[self.security_list.code.isin(
code)].index.tolist()
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 * (pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
# data = data[['code', 'open', 'high', 'low', 'price']]
# data['datetime'] = datetime.datetime.now()
# return data.set_index('code', drop=False, inplace=False)
def stock_quotes(self):
code = self.stock_list.index.tolist()
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 * (pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
@lazyval
def security_list(self):
return pd.concat([
pd.concat([
self.api.to_df(self.api.get_security_list(
j, i * 1000)).assign(sse=0 if j == 0 else 1).set_index(
['sse', 'code'], drop=False)
for i in range(int(self.api.get_security_count(j) / 1000) + 1)
],
axis=0) for j in range(2)
],
axis=0)
@lazyval
def stock_list(self):
aa = map(stock_filter, self.security_list.index.tolist())
return self.security_list[list(aa)]
@lazyval
def best_ip(self):
return select_best_ip()
@lazyval
def concept(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_GN))
@lazyval
def index(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_SZ))
@lazyval
def fengge(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_FG))
@lazyval
def block(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_DEFAULT))
@lazyval
def customer_block(self):
return CustomerBlockReader().get_df(CUSTOMER_BLOCK_PATH)
def xdxr(self, code):
df = self.api.to_df(
self.api.get_xdxr_info(self.get_security_type(code), code))
if df.empty:
return df
df['datetime'] = pd.to_datetime((df.year * 10000 + df.month * 100 +
df.day).apply(lambda x: str(x)))
return df.drop(['year', 'month', 'day'], axis=1).set_index('datetime')
@lazyval
def gbbq(self):
df = GbbqReader().get_df(GBBQ_PATH).query('category == 1')
df['datetime'] = pd.to_datetime(df['datetime'], format='%Y%m%d')
return df
def get_security_type(self, code):
if code in self.security_list.code.values:
return self.security_list[self.security_list.code ==
code]['sse'].as_matrix()[0]
else:
raise SecurityNotExists()
@retry(3)
def get_security_bars(self, code, freq, start=None, end=None, index=False):
if index:
exchange = self.get_security_type(code)
func = self.api.get_index_bars
else:
exchange = get_stock_type(code)
func = self.api.get_security_bars
if start:
start = start.tz_localize(None)
if end:
end = end.tz_localize(None)
if freq in ['1d', 'day']:
freq = 9
elif freq in ['1m', 'min']:
freq = 8
else:
raise Exception("1d and 1m frequency supported only")
res = []
pos = 0
while True:
data = func(freq, exchange, code, pos, 800)
if not data:
break
res = data + res
pos += 800
if start and pd.to_datetime(data[0]['datetime']) < start:
break
try:
df = self.api.to_df(res).drop(
['year', 'month', 'day', 'hour', 'minute'], axis=1)
df['datetime'] = pd.to_datetime(df.datetime)
df.set_index('datetime', inplace=True)
if freq == 9:
df.index = df.index.normalize()
except ValueError: # 未上市股票，无数据
logger.warning("no k line data for {}".format(code))
# return pd.DataFrame({
# 'amount': [0],
# 'close': [0],
# 'open': [0],
# 'high': [0],
# 'low': [0],
# 'vol': [0],
# 'code': code
# },
# index=[start]
# )
return pd.DataFrame()
close = [df.close.values[-1]]
if start:
df = df.loc[lambda df: start <= df.index]
if end:
df = df.loc[lambda df: df.index.normalize() <= end]
if df.empty:
# return pd.DataFrame({
# 'amount': [0],
# 'close': close,
# 'open': close,
# 'high': close,
# 'low': close,
# 'vol': [0],
# 'code': code
# },
# index=[start]
# )
return df
else:
if int(df['vol'][-1]) <= 0 and end == df.index[-1] and len(
df) == 1: # 成交量为0，当天返回的是没开盘的数据
return pd.DataFrame()
df['code'] = code
return df
def _get_transaction(self, code, date):
res = []
start = 0
while True:
data = self.api.get_history_transaction_data(
get_stock_type(code), code, start, 2000, date)
if not data:
break
start += 2000
res = data + res
if len(res) == 0:
return pd.DataFrame()
df = self.api.to_df(res).assign(date=date)
df.loc[0, 'time'] = df.time[1]
df.index = pd.to_datetime(str(date) + " " + df["time"])
df['code'] = code
return df.drop("time", axis=1)
def time_and_price(self, code):
start = 0
res = []
exchange = self.get_security_type(code)
while True:
data = self.api.get_transaction_data(exchange, code, start, 2000)
if not data:
break
res = data + res
start += 2000
df = self.api.to_df(res)
df.time = pd.to_datetime(
str(pd.to_datetime('today').date()) + " " + df['time'])
df.loc[0, 'time'] = df.time[1]
return df.set_index('time')
@classmethod
def minute_bars_from_transaction(cls, transaction, freq):
if transaction.empty:
return pd.DataFrame()
mask = transaction.index < transaction.index[0].normalize(
) + pd.Timedelta('12 H')
def resample(transaction):
if transaction.empty:
return pd.DataFrame()
data = transaction['price'].resample(freq,
label='right',
closed='left').ohlc()
data['volume'] = transaction['vol'].resample(freq,
label='right',
closed='left').sum()
data['code'] = transaction['code'][0]
return data
morning = resample(transaction[mask])
afternoon = resample(transaction[~mask])
if morning.empty and afternoon.empty:
return pd.DataFrame()
if not afternoon.empty:
morning.index.values[-1] = afternoon.index[0] - pd.Timedelta(
'1 min')
df = pd.concat([morning, afternoon])
return fillna(df)
def _get_k_data(self, code, freq, sessions):
trade_days = map(int, sessions.strftime("%Y%m%d"))
if freq == '1m':
freq = '1 min'
if freq == '1d':
freq = '24 H'
res = []
concurrent_count = self.concurrent_thread_count
jobs = []
for trade_day in trade_days:
# df = Engine.minute_bars_from_transaction(self._get_transaction(code, trade_day), freq)
reqevent = gevent.spawn(Engine.minute_bars_from_transaction,
self._get_transaction(code, trade_day),
freq)
jobs.append(reqevent)
if len(jobs) >= concurrent_count:
gevent.joinall(jobs, timeout=30)
for j in jobs:
if j.value is not None and not j.value.empty:
res.append(j.value)
jobs.clear()
gevent.joinall(jobs, timeout=30)
for j in jobs:
if j.value is not None and not j.value.empty:
res.append(j.value)
jobs.clear()
if len(res) != 0:
return pd.concat(res)
return pd.DataFrame()
def get_k_data(self, code, start, end, freq, check=True):
if isinstance(start, str) or isinstance(end, str):
start = pd.Timestamp(start)
end = pd.Timestamp(end)
if check:
daily_bars = self.get_security_bars(code, '1d', start, end)
if daily_bars is None or daily_bars.empty:
return daily_bars
sessions = daily_bars.index
else:
sessions = pd.bdate_range(start,
end,
weekmask='Mon Tue Wed Thu Fri')
df = self._get_k_data(code, freq, sessions)
def check_df(freq, df, daily_bars):
if freq == '1m':
need_check = pd.DataFrame({
'open':
df['open'].resample('1D').first(),
'high':
df['high'].resample('1D').max(),
'low':
df['low'].resample('1D').min(),
'close':
df['close'].resample('1D').last(),
'volume':
df['volume'].resample('1D').sum()
}).dropna()
else:
need_check = df
if daily_bars.shape[0] != need_check.shape[0]:
logger.warning("{} merged {}, expected {}".format(
code, need_check.shape[0], daily_bars.shape[0]))
need_check = fillna(
need_check.reindex(daily_bars.index, copy=False))
diff = daily_bars[['open',
'close']] == need_check[['open', 'close']]
res = (diff.open) & (diff.close)
sessions = res[res == False].index
return sessions
if not df.empty:
if check:
sessions = check_df(freq, df, daily_bars)
if sessions.shape[0] != 0:
logger.info(
"fixing data for {}-{} with sessions: {}".format(
code, freq, sessions))
fix = self._get_k_data(code, freq, sessions)
df.loc[fix.index] = fix
return df
return df
