def api(retry_count=3):
for _ in range(retry_count):
try:
api = TdxHq_API(heartbeat=True)
api.connect(ct._get_server(), ct.T_PORT)
except Exception as e:
print(e)
else:
return api
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def QA_fetch_get_stock_transaction_realtime(code, ip=None, port=None):
'实时分笔成交 包含集合竞价 buyorsell 1--sell 0--buy 2--盘前'
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()
try:
with api.connect(ip, port):
data = pd.DataFrame()
data = pd.concat([api.to_df(api.get_transaction_data(
_select_market_code(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()
return 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))).set_index('datetime', drop=False, inplace=False)
except:
return None
def QA_fetch_get_index_min(code, start, end, level='1min', ip=best_ip, port=7709):
api = TdxHq_API()
type_ = ''
if str(level) in ['5', '5m', '5min', 'five']:
level, type_ = 0, '5min'
elif str(level) in ['1', '1m', '1min', 'one']:
level, type_ = 8, '1min'
elif str(level) in ['15', '15m', '15min', 'fifteen']:
level, type_ = 1, '15min'
elif str(level) in ['30', '30m', '30min', 'half']:
level, type_ = 2, '30min'
elif str(level) in ['60', '60m', '60min', '1h']:
level, type_ = 3, '60min'
with api.connect(ip, port):
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([api.to_df(api.get_security_bars(
level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (25 - i) * 800, 800)) for i in range(26)], axis=0)
else:
data = pd.concat([api.to_df(api.get_index_bars(
level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (25 - i) * 800, 800)) for i in range(26)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
# data
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_index_day(code, start_date, end_date, level='day', ip=best_ip, port=7709):
'指数日线'
api = TdxHq_API()
if level in ['day', 'd', 'D', 'DAY', 'Day']:
level = 9
elif level in ['w', 'W', 'Week', 'week']:
level = 5
elif level in ['month', 'M', 'm', 'Month']:
level = 6
elif level in ['Q', 'Quarter', 'q']:
level = 10
elif level in ['y', 'Y', 'year', 'Year']:
level = 11
with api.connect(ip, port):
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([api.to_df(api.get_security_bars(
level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (25 - i) * 800, 800)) for i in range(26)], axis=0)
else:
data = pd.concat([api.to_df(api.get_index_bars(
level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (25 - i) * 800, 800)) for i in range(26)], axis=0)
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)[start_date:end_date]
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_stock_xdxr(code, ip=None, port=None):
'除权除息'
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()
market_code = _select_market_code(code)
with api.connect(ip, port):
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 = api.to_df(api.get_xdxr_info(market_code, code))
if len(data) >= 1:
data = data\
.assign(date=pd.to_datetime(data[['year', 'month', 'day']]))\
.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)
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
else:
return None
def QA_fetch_get_stock_block(ip=None, port=None):
'板块数据'
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()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")).assign(type='gn'),
api.to_df(api.get_and_parse_block_info(
"block.dat")).assign(type='yb'),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")).assign(type='zs'),
api.to_df(api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')])
if len(data) > 10:
return data.assign(source='tdx').drop(['block_type', 'code_index'], axis=1).set_index('code', drop=False, inplace=False).drop_duplicates()
else:
QA_util_log_info('Wrong with fetch block ')
def _get_bars(self, market, code, ktype):
data = []
tdx_market = self._trans_market(market)
if tdx_market is None:
print("tdx_market == None")
return data
tdx_ktype = self._trans_ktype(ktype)
if tdx_ktype is None:
print("tdx_ktype == None")
return data
try:
ip = self.getParam('ip')
port = self.getParam('port')
except:
ip = '119.147.212.81'
port = 7709
api = TdxHq_API(raise_exception=True)
with api.connect(ip, port):
if (market == 'SH' and code[:3] == '000') \
or (market == 'SZ' and code[:2] == '39'):
for i in range(self._max[ktype]):
data += api.get_index_bars(tdx_ktype, tdx_market, code,
(self._max[ktype]-1-i)*800,800)
else:
for i in range(self._max[ktype]):
data += api.get_security_bars(tdx_ktype, tdx_market, code,
(self._max[ktype]-1-i)*800,800)
return data
def __call__(self):
count = 0
connect = sqlite3.connect(self.sqlitefile)
try:
progress = ProgressBar(self)
api = TdxHq_API()
api.connect(self.ip, self.port)
count = import_time(connect, self.market, self.quotations, api,
self.dest_dir, max_days=self.max_days, progress=progress)
except Exception as e:
print(e)
finally:
connect.commit()
connect.close()
self.queue.put([self.task_name, self.market, 'TIME', None, count])
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=best_ip, port=7709):
api = TdxHq_API()
__data = pd.DataFrame()
code = [code] if type(code) is str else code
async def _get_security_quotes(__code):
"""
"""
assert isinstance(__code, str)
data=await api.get_security_quotes([(__select_market_code(__code), __code)])
data=api.to_df(data)
data['datetime'] = datetime.datetime.now()
return data[['datetime', 'code', 'open', 'high', 'low', 'price']]
with api.connect(ip, port):
coroutines = [asyncio.ensure_future(
_get_security_quotes(code_)) for code_ in code]
print(coroutines)
try:
loop = asyncio.get_event_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
res = loop.run_until_complete(asyncio.gather(*coroutines))
print(res)
return res
def ping(ip):
__time1 = datetime.datetime.now()
api = TdxHq_API()
try:
with api.connect(ip, 7709):
if len(api.get_security_list(0, 1)) > 800:
return datetime.datetime.now() - __time1
except:
return datetime.timedelta(9, 9, 0)
def ping(ip):
api = TdxHq_API()
__time1 = datetime.datetime.now()
try:
with api.connect(ip, 7709):
if len(api.get_security_list(0, 1)) > 800:
return datetime.datetime.now() - __time1
except:
print('Bad REPSONSE %s' % ip)
return datetime.timedelta(9, 9, 0)
def QA_fetch_get_index_min(code, start, end, frequence='1min', ip=None, port=None):
'指数分钟线'
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()
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens
if lens > 20800:
lens = 20800
with api.connect(ip, port):
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([api.to_df(api.get_security_bars(
frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
else:
data = pd.concat([api.to_df(api.get_index_bars(
frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(code=code)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
# data
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
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_latest(code, ip=best_ip, port=7709):
code = [code] if isinstance(code, str) else code
api = TdxHq_API(multithread=True)
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(
9, __select_market_code(item), item, 0, 1)).assign(code=item) for item in code], axis=0)
return data\
.assign(date=pd.to_datetime(data['datetime']
.apply(lambda x: x[0:10])), date_stamp=data['datetime']
.apply(lambda x: QA_util_date_stamp(str(x[0:10]))))\
.set_index('date', drop=False)\
.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
def get_tdx_kdata(security_item, start, end):
api = TdxHq_API()
with api.connect():
# open close high low vol amount date code
# KDATA_COLUMN = ['timestamp', 'code', 'low', 'open', 'close', 'high', 'volume', 'turnover', 'securityId']
df = api.get_k_data(security_item['code'], start, end)
df = df[['date', 'code', 'low', 'open', 'close', 'high', 'vol', 'amount']]
df['securityId'] = df['code'].apply(lambda x: 'stock_{}_{}'.format(get_exchange(x), x))
df['vol'] = df['vol'].apply(lambda x: x * 100)
df.columns = KDATA_COLUMN
return df
def _test_speed(self, ip, port=7709):
api = TdxHq_API(raise_exception=True, auto_retry=False)
_time = datetime.datetime.now()
try:
with api.connect(ip, port, time_out=0.05):
if len(api.get_security_list(0, 1)) > 800:
return (datetime.datetime.now() - _time).total_seconds()
else:
return datetime.timedelta(9, 9, 0).total_seconds()
except Exception as e:
return datetime.timedelta(9, 9, 0).total_seconds()
def QA_fetch_get_stock_info(code, ip=None, port=None):
'股票基本信息'
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()
market_code = _select_market_code(code)
with api.connect(ip, port):
return api.to_df(api.get_finance_info(market_code, code))
def ping(ip, port=7709, multithread=False):
api = TdxHq_API(multithread=multithread)
success = False
starttime = time.time()
try:
with api.connect(ip, port, time_out=1):
#x = api.get_security_count(0)
#x = api.get_index_bars(7, 1, '000001', 800, 100)
x = api.get_security_bars(7, 0, '000001', 800, 100)
if x:
success = True
except Exception as e:
success = False
endtime = time.time()
return (success, endtime - starttime, ip, port)
def QA_fetch_get_index_day(code, start_date, end_date, frequence='day', ip=None, port=None):
'指数日线'
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()
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
with api.connect(ip, port):
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([api.to_df(api.get_security_bars(
frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
else:
data = pd.concat([api.to_df(api.get_index_bars(
frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.assign(code=code)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)[start_date:end_date]
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_stock_list(type_='stock', ip=best_ip, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
if type_ in ['stock', 'gp']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 <= 30][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 != 2],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 == 6]]).assign(code=data['code'].apply(lambda x: str(x)))
elif type_ in ['index', 'zs']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 >= 399],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 == 0]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))
elif type_ in ['etf', 'ETF']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 15],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 51]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))
else:
return data.assign(code=data['code'].apply(lambda x: str(x)))
def QA_fetch_get_security_bars(code, _type, lens, ip=None, port=None):
"""按bar长度推算数据
Arguments:
code {[type]} -- [description]
_type {[type]} -- [description]
lens {[type]} -- [description]
Keyword Arguments:
ip {[type]} -- [description] (default: {best_ip})
port {[type]} -- [description] (default: {7709})
Returns:
[type] -- [description]
"""
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()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(_select_type(_type), _select_market_code(
code), code, (i - 1) * 800, 800)) for i in range(1, int(lens / 800) + 2)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=_type).set_index('datetime', drop=False, inplace=False).tail(lens)
if data is not None:
return data
else:
return None
def ping(ip, port=7709, type_='stock'):
api = TdxHq_API()
apix = TdxExHq_API()
__time1 = datetime.datetime.now()
try:
if type_ in ['stock']:
with api.connect(ip, port, time_out=0.7):
if len(api.get_security_list(0, 1)) > 800:
return datetime.datetime.now() - __time1
else:
print('BAD RESPONSE {}'.format(ip))
return datetime.timedelta(9, 9, 0)
elif type_ in ['future']:
with apix.connect(ip, port, time_out=0.7):
if apix.get_instrument_count() > 10000:
return datetime.datetime.now() - __time1
else:
print('️Bad FUTUREIP REPSONSE {}'.format(ip))
return datetime.timedelta(9, 9, 0)
except:
print('BAD RESPONSE {}'.format(ip))
return datetime.timedelta(9, 9, 0)
def QA_fetch_get_stock_latest(code, 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
code = [code] if isinstance(code, str) else code
api = TdxHq_API(multithread=True)
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(
9, _select_market_code(item), item, 0, 1)).assign(code=item) for item in code], axis=0)
return data\
.assign(date=pd.to_datetime(data['datetime']
.apply(lambda x: x[0:10])), date_stamp=data['datetime']
.apply(lambda x: QA_util_date_stamp(str(x[0:10]))))\
.set_index('date', drop=False)\
.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
def QA_fetch_get_stock_transaction(code, start, end, retry=2, ip=None, port=None):
'历史分笔成交 buyorsell 1--sell 0--buy 2--盘前'
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()
real_start, real_end = QA_util_get_real_datelist(start, end)
if real_start is None:
return None
real_id_range = []
with api.connect(ip, port):
data = pd.DataFrame()
for index_ in range(trade_date_sse.index(real_start), trade_date_sse.index(real_end) + 1):
try:
data_ = __QA_fetch_get_stock_transaction(
code, trade_date_sse[index_], retry, api)
if len(data_) < 1:
return None
except:
QA_util_log_info('Wrong in Getting {} history transaction data in day {}'.format(
code, trade_date_sse[index_]))
else:
QA_util_log_info('Successfully Getting {} history transaction data in day {}'.format(
code, trade_date_sse[index_]))
data = data.append(data_)
if len(data) > 0:
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)[0:19]))
else:
return None
def QA_fetch_get_stock_transaction(code, start, end, retry=2, ip=best_ip, port=7709):
api = TdxHq_API()
real_start, real_end = QA_util_get_real_datelist(start, end)
real_id_range = []
with api.connect(ip, port):
data = pd.DataFrame()
for index_ in range(trade_date_sse.index(real_start), trade_date_sse.index(real_end) + 1):
try:
data_ = __QA_fetch_get_stock_transaction(
code, trade_date_sse[index_], retry, api)
if len(data_) < 1:
return None
except:
QA_util_log_info('Wrong in Getting %s history transaction data in day %s' % (
code, trade_date_sse[index_]))
else:
QA_util_log_info('Successfully Getting %s history transaction data in day %s' % (
code, trade_date_sse[index_]))
data = data.append(data_)
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)[0:19]))
def QA_fetch_get_stock_xdxr(code,
ip=best_ip['stock']['ip'],
port=best_ip['stock']['port']):
'除权除息'
api = TdxHq_API()
market_code = _select_market_code(code)
with api.connect(ip, port):
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 = api.to_df(api.get_xdxr_info(market_code, code))
if len(data) >= 1:
data = data\
.assign(date=pd.to_datetime(data[['year', 'month', 'day']]))\
.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)
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
else:
return None
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 QA_fetch_get_stock_min(code, start, end, frequence='1min', ip=None, port=None):
ip, port = get_mainmarket_ip(ip, port)
api = TdxHq_API()
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens
if lens > 20800:
lens = 20800
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code(
str(code)), str(code), (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_list(type_='stock', ip=best_ip, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([
pd.concat([
api.to_df(api.get_security_list(j, i * 1000)).assign(
sse='sz' if j == 0 else 'sh').set_index(['code', 'sse'],
drop=False)
for i in range(int(api.get_security_count(j) / 1000) + 1)
],
axis=0) for j in range(2)
],
axis=0)
if type_ in ['stock', 'gp']:
return pd.concat([
data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(
lambda x: int(x)))['code'] // 10000 <= 30][data.assign(
code=data['code'].apply(lambda x: int(x)))['code'] //
100000 != 2],
data[data['sse'] == 'sh'][data.assign(
code=data['code'].apply(lambda x: int(x)))['code'] //
100000 == 6]
]).assign(code=data['code'].apply(lambda x: str(x)))
elif type_ in ['index', 'zs']:
return pd.concat([
data[data['sse'] == 'sz'][data.assign(
code=data['code'].apply(lambda x: int(x)))['code'] //
1000 >= 399],
data[data['sse'] == 'sh'][data.assign(
code=data['code'].apply(lambda x: int(x)))['code'] //
1000 == 0]
]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))
else:
return data.assign(code=data['code'].apply(lambda x: str(x)))
def download_index_bars(index_code, freq='day', market=None):
"""获取指数
market -> 市场代码 0:深圳，1:上海
"""
if not market:
if stock_code[0] == '3':
market = 0
else:
market = 1
category = FREQUENT[freq]
api = TdxHq_API()
with api.connect('119.147.212.81', 7709):
index = 0
stock_data = []
while True:
older_data = api.get_index_bars(category, market, index_code, index * 800, 800)
if older_data:
stock_data = older_data + stock_data
index += 1
else:
break
stock_df = api.to_df(stock_data) # 返回DataFrame
return stock_df[ct.STOCK_COLS]
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 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_list(type_='stock', 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()
with api.connect(ip, port):
data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
#data.code = data.code.apply(int)
sz = data.query('sse=="sz"')
sh = data.query('sse=="sh"')
sz = sz.assign(sec=sz.code.apply(for_sz))
sh = sh.assign(sec=sh.code.apply(for_sh))
if type_ in ['stock', 'gp']:
return pd.concat([sz, sh]).query('sec=="stock_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
elif type_ in ['index', 'zs']:
return pd.concat([sz, sh]).query('sec=="index_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
#.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
#.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
elif type_ in ['etf', 'ETF']:
return pd.concat([sz, sh]).query('sec=="etf_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
else:
return data.assign(code=data['code'].apply(lambda x: str(x))).assign(name=data['name'].apply(lambda x: str(x)[0:6]))
def __hq_bars(self, code, offset, frequency=9, index=False):
assert self.__hq.qsize() > 0
api = TdxHq_API()
if index is True:
market_func = get_index_market
bars_func = api.get_index_bars
else:
market_func = get_code_market
bars_func = api.get_security_bars
ip, port = self.__hq.get()
with api.connect(ip, port):
df = list()
for _code in code:
market = market_func(_code)
for _start, _count in offset:
df.append(
api.to_df(
bars_func(frequency, market, _code, _start,
_count)).assign(code=_code))
api.disconnect()
self.__hq.put((ip, port))
if len(df) < 1:
return None
return pandas.concat(df, sort=False)
def QA_fetch_get_stock_block(ip=best_ip['stock']['ip'],
port=best_ip['stock']['port']):
'板块数据'
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([
api.to_df(api.get_and_parse_block_info("block_gn.dat")).assign(
type='gn'),
api.to_df(
api.get_and_parse_block_info("block.dat")).assign(type='yb'),
api.to_df(api.get_and_parse_block_info("block_zs.dat")).assign(
type='zs'),
api.to_df(
api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')
])
if len(data) > 10:
return data.assign(source='tdx').drop(
['block_type', 'code_index'],
axis=1).set_index('code', drop=False,
inplace=False).drop_duplicates()
else:
QA_util_log_info('Wrong with fetch block ')
def QA_fetch_get_stock_transaction(code, start, end, retry=2, ip=best_ip, port=7709):
'逐笔成交'
api = TdxHq_API()
real_start, real_end = QA_util_get_real_datelist(start, end)
real_id_range = []
with api.connect(ip, port):
data = pd.DataFrame()
for index_ in range(trade_date_sse.index(real_start), trade_date_sse.index(real_end) + 1):
try:
data_ = __QA_fetch_get_stock_transaction(
code, trade_date_sse[index_], retry, api)
if len(data_) < 1:
return None
except:
QA_util_log_info('Wrong in Getting %s history transaction data in day %s' % (
code, trade_date_sse[index_]))
else:
QA_util_log_info('Successfully Getting %s history transaction data in day %s' % (
code, trade_date_sse[index_]))
data = data.append(data_)
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)[0:19]))
def QA_fetch_get_stock_day(code,
start_date,
end_date,
ip='119.147.212.81',
port=7709):
api = TdxHq_API()
market_code = __select_market_code(code)
start_date = QA_util_get_real_date(start_date, trade_date_sse, 1)
end_date = QA_util_get_real_date(end_date, trade_date_sse, -1)
with api.connect(ip, port):
data = []
for i in range(10):
data += api.get_security_bars(9, market_code, code, (9 - i) * 800,
800)
data = api.to_df(data)
data['date'] = data['datetime'].apply(lambda x: x[0:10])
data['date_stamp'] = data['date'].apply(
lambda x: QA_util_date_stamp(x))
data['date'] = pd.to_datetime(data['date'])
data = data.set_index('date', drop=False)
data['date'] = data['date'].apply(lambda x: str(x)[0:10])
data = data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
return data[start_date:end_date]
def QA_fetch_get_stock_min(code,
start,
end,
level='1min',
ip=best_ip,
port=7709):
api = TdxHq_API()
type_ = ''
if str(level) in ['5', '5m', '5min', 'five']:
level, type_ = 0, '5min'
elif str(level) in ['1', '1m', '1min', 'one']:
level, type_ = 8, '1min'
elif str(level) in ['15', '15m', '15min', 'fifteen']:
level, type_ = 1, '15min'
elif str(level) in ['30', '30m', '30min', 'half']:
level, type_ = 2, '30min'
elif str(level) in ['60', '60m', '60min', '1h']:
level, type_ = 3, '60min'
with api.connect(ip, port):
data = pd.concat([
api.to_df(
api.get_security_bars(level, __select_market_code(str(code)),
str(code), (25 - i) * 800, 800))
for i in range(26)
],
axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
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 _run(self):
src_dir = self.config['tdx']['dir']
dest_dir = self.config['hdf5']['dir']
hdf5_import_progress = {
'SH': {
'DAY': 0,
'1MIN': 0,
'5MIN': 0
},
'SZ': {
'DAY': 0,
'1MIN': 0,
'5MIN': 0
}
}
trans_progress = {'SH': 0, 'SZ': 0}
time_progress = {'SH': 0, 'SZ': 0}
#正在导入代码表
self.send_message(['INFO', '导入股票代码表'])
connect = sqlite3.connect(dest_dir + "/stock.db")
create_database(connect)
pytdx_api = TdxHq_API()
pytdx_api.connect(self.hosts[0][2], self.hosts[0][3])
count = import_stock_name(connect, pytdx_api, 'SH', self.quotations)
if count > 0:
self.send_message(['INFO', '上证新增股票数：%s' % count])
count = import_stock_name(connect, pytdx_api, 'SZ', self.quotations)
if count > 0:
self.send_message(['INFO', '深证新增股票数：%s' % count])
self.process_list.clear()
for task in self.tasks:
p = Process(target=task)
self.process_list.append(p)
p.start()
finished_count = len(self.tasks)
while finished_count > 0:
message = self.queue.get()
taskname, market, ktype, progress, total = message
if progress is None:
finished_count -= 1
if taskname in ('IMPORT_KDATA', 'IMPORT_TRANS', 'IMPORT_TIME'):
self.send_message(
[taskname, 'FINISHED', market, ktype, total])
else:
self.send_message([taskname, 'FINISHED'])
continue
if taskname == 'IMPORT_WEIGHT':
if market == 'INFO':
self.send_message(['INFO', ktype])
self.send_message([taskname, market, total])
elif taskname == 'IMPORT_FINANCE':
self.send_message([taskname, progress])
elif taskname == 'IMPORT_KDATA':
hdf5_import_progress[market][ktype] = progress
current_progress = (hdf5_import_progress['SH'][ktype] +
hdf5_import_progress['SZ'][ktype]) // 2
self.send_message([taskname, ktype, current_progress])
elif taskname == 'IMPORT_TRANS':
trans_progress[market] = progress
current_progress = (trans_progress['SH'] +
trans_progress['SZ']) // 2
self.send_message([taskname, ktype, current_progress])
elif taskname == 'IMPORT_TIME':
time_progress[market] = progress
current_progress = (time_progress['SH'] +
time_progress['SZ']) // 2
self.send_message([taskname, ktype, current_progress])
else:
print("Unknow task: ", taskname)
def test_raise_excepiton():
api = TdxHq_API(raise_exception=True)
with pytest.raises(TdxConnectionError):
with api.connect('8.8.8.8'):
pass
class TDX(object):
'''
This class is tong da xin data source.
We can use it to get down the stock datas.
Tushare can't get minter line and or year line.
TDX can search index of stock and funds.
'''
def __init__(self):
self.tdx_api = TdxHq_API()
self.__ip = '119.147.212.81' #输入IP
self.__port = 7709 #端口
self.__code = '600200'
self.__market = 1 #市场代码 0:深圳，1:上海
self._startdate = "2017-01-01"
self.today = datetime.date.today()
self._enddate = datetime.datetime.strftime(self.today, '%Y-%m-%d')
self.__mkt_segment = {
'sh': '60',
"sz": '00',
"cyb": "30",
} #segment 当前板块开始字符串
def __str__(self):
return 'TDX object (code : %s)' % self.code
@property
def IP(self): # self.IP
return self.__ip
@property
def PORT(self):
return self.__port
@property
def code(self): #定义stock code 属性
return self.__code
@code.setter #设定code
def code(self, code_input):
"""
The setter of the code property
"""
if not isinstance(code_input, str): #确定是否是字符串
raise ValueError('the code must string!')
if not len(code_input) == 6: #确定长度
raise ValueError('the code value error,the len must SIX !')
if code_input.startswith('60'): #确定表头
self.__market = 1
elif code_input.startswith('00'):
self.__market = 0
elif code_input.startswith('30'):
self.__market = 0
else:
raise ValueError('this code is not stock code')
self.__code = code_input
@property
def startdate(self): #开始日期
return self._startdate
@startdate.setter #设置日期
def startdate(self, date_input):
"""
The setter of the start date property
"""
if not isinstance(date_input, str):
raise ValueError('the date must string!')
if not len(date_input) == 8:
raise ValueError(
'the date value error,the date formet must xxxx-xx-xx !')
self._startdate = date_input
@property #结束日期
def enddate(self):
return self._enddate
@enddate.setter
def enddate(self, date_input):
"""
The setter of the start date property
"""
if not isinstance(date_input, str):
raise ValueError('the date must string!')
if not len(date_input) == 8:
raise ValueError(
'the date value error,the date formet must xxxx-xx-xx !')
self._enddate = date_input
def get_day_data_tdx(self): #获取K line
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_k_data(self.code, self.startdate,
self.enddate)
data = pandas.DataFrame(data)
data.date = data.date.apply(
lambda x: datetime.datetime.strptime(x, "%Y-%m-%d"))
return data
#TODO: 现在是用800点进行计数，以后会细化功能
def get_k_data_tdx(self, k_mode=9):
"""
获取k 线图，总计800 点
Parameters
----------
k_mode= 0-11
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
Returns
-------
"""
with self.tdx_api.connect(self.self.IP, self.self.PORT):
data = self.tdx_api.get_security_bars(k_mode, self.__market,
self.code, 0, 800)
data = pandas.DataFrame(data)
#data.date = data.date.apply(
# lambda x: datetime.datetime.strptime(x, "%Y-%m-%d"))
return data
def len_market(self): #市场有多少只股票
with self.tdx_api.connect(self.IP, self.PORT):
_len = self.tdx_api.get_security_count(self.__market)
return _len
def get_page_tdx(self, block=None):
if block is None:
market = 1
page = [0]
elif block in ['sh', 'SH']:
market = 1
page = [13, 14]
elif block in ['sz', 'SZ']:
print('block for shenzhen')
market = 0
page = [0, 1]
elif block in ['cyb', 'CYB']:
print('block for chuang ye ban')
market = 0
page = [7, 8]
else:
pass
code_list_df = pandas.DataFrame()
with self.tdx_api.connect(self.IP, self.PORT):
for pn in page:
data = self.tdx_api.get_security_list(market, pn * 1000)
data = pandas.DataFrame(data)
print(data)
code_list_df = code_list_df.append(data, ignore_index=True)
return code_list_df
def get_base_finace_tdx(self):
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_finance_info(0, '000001')
data = pandas.Series(data)
print(data)
def get_min_data(self):
from pytdx.params import TDXParams
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_history_minute_time_data(
TDXParams.MARKET_SH, self.code, 20161209)
data = pandas.DataFrame(data)
print(data)
#TODO: 需要确定 0: buy 1 : sell
def get_tick_data(self):
"""
历史分笔交易：time 顺序； price ; vol ;buyorsell [1:] [0:];
sh 60 13000-14000
Parameters
----------
Returns
-------
"""
data = pandas.DataFrame()
with self.tdx_api.connect(self.IP, self.PORT):
for i in [2000, 0000]:
df = self.tdx_api.get_history_transaction_data(
TDXParams.MARKET_SH, "600547", i, 2000, 20160308)
df = pandas.DataFrame(df)
data = data.append(df, ignore_index=True)
return data
def get_tick_today(self):
"""
Get every time the each deal for today.每组数最大len 2 k 所以要确定的数据长度
Parameters
----------
self:
Returns
-------
"""
with self.tdx_api.connect(self.IP, self.PORT):
data = pandas.DataFrame()
for i in [0, 2000]:
df = self.tdx_api.get_transaction_data(self.__market,
self.code, i, 2000)
df = pandas.DataFrame(df)
data = data.append(df, ignore_index=True)
return data
def get_block(self):
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_and_parse_block_info("block.dat")
data = pandas.DataFrame(data)
print(data)
def get_market_segment_list(self, mkt):
data = self.get_page_tdx(mkt)
self.code_list = pandas.DataFrame()
pbar = tqdm(total=len(data.code))
mkt_hard = self.mkt_segment[mkt]
for idx, __code in enumerate(data.code):
pbar.update(1)
if __code.startswith(mkt_hard, 0, 2):
self.code_list = self.code_list.append(data.loc[idx],
ignore_index=True)
return self.code_list
def get_sh_list(self):
return self.get_market_segment_list('sh')
def get_sz_list(self):
return self.get_market_segment_list('sz')
def get_cyb_list(self):
return self.get_market_segment_list('cyb')
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', level='day', ip=best_ip, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
if level in ['day', 'd', 'D', 'DAY', 'Day']:
level = 9
elif level in ['w', 'W', 'Week', 'week']:
level = 5
elif level in ['month', 'M', 'm', 'Month']:
level = 6
elif level in ['Q', 'Quarter', 'q']:
level = 10
elif level in ['y', 'Y', 'year', 'Year']:
level = 11
data = pd.concat([api.to_df(api.get_security_bars(level, __select_market_code(
code), code, (9 - i) * 800, 800)) for i in range(10)], axis=0)
if if_fq in ['00', 'bfq']:
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)[start_date:end_date]
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
elif if_fq in ['01', 'qfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.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']][bfq_data.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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['03', 'ddqfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], 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']][bfq_data.index[0]:end_date]], 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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['02', 'hfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.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']][bfq_data.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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['04', 'ddhfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], 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']][bfq_data.index[0]:end_date]], 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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
class TDXData(DataSource):
def __init__(self, db_interface: DBInterface = None, host: str = None, port: int = None):
super().__init__(db_interface)
if host is None:
conf = get_global_config()
host = conf['tdx_server']['host']
port = conf['tdx_server']['port']
self.api = TdxHq_API()
self.host = host
self.port = port
self._factor_param = utils.load_param('tdx_param.json')
self.stock_ticker = StockTickers(db_interface)
def connect(self):
self.api.connect(self.host, self.port)
def __enter__(self):
self.connect()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.api.disconnect()
def update_stock_minute(self):
"""更新股票分钟行情"""
table_name = '股票分钟行情'
db_timestamp = self.db_interface.get_latest_timestamp(table_name, dt.datetime(2015, 1, 1))
start_date = self.calendar.offset(db_timestamp.date(), 1)
end_date = dt.datetime.today()
dates = self.calendar.select_dates(start_date, end_date)
for date in dates:
self.get_stock_minute(date)
def get_stock_minute(self, date: dt.datetime) -> None:
"""获取 ``date`` 的股票分钟行情"""
minute_data = self._get_stock_minute(date)
auction_time = date + dt.timedelta(hours=9, minutes=25)
auction_db_data = self.db_interface.read_table('股票集合竞价数据', columns=['成交价', '成交量', '成交额'], dates=auction_time)
df = self.left_shift_minute_data(minute_data=minute_data, auction_db_data=auction_db_data)
self.db_interface.insert_df(df, '股票分钟行情')
def _get_stock_minute(self, date: dt.datetime) -> pd.DataFrame:
num_days = self.calendar.days_count(date, dt.date.today())
start_index = num_days * 60 * 4
tickers = self.stock_ticker.ticker(date)
storage = []
with tqdm(tickers) as pbar:
for ticker in tickers:
pbar.set_description(f'下载 {ticker} 在 {date} 的分钟数据')
code, market = self._split_ticker(ticker)
data = self.api.get_security_bars(category=8, market=market, code=code, start=start_index, count=240)
data = self._formatting_data(data, ticker)
storage.append(data)
pbar.update()
df = pd.concat(storage)
return df
def _formatting_data(self, info: OrderedDict, ticker: str) -> pd.DataFrame:
df = pd.DataFrame(info)
df['datetime'] = df['datetime'].apply(self.str2datetime)
df = df.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1).rename(self._factor_param['行情数据'], axis=1)
df['ID'] = ticker
df = df.set_index(['DateTime', 'ID'], drop=True)
return df
@staticmethod
def _split_ticker(ticker: str) -> [str, int]:
code, market_str = ticker.split('.')
market = 0 if market_str == 'SZ' else 1
return code, market
@staticmethod
def str2datetime(date: str) -> dt.datetime:
return dt.datetime.strptime(date, '%Y-%m-%d %H:%M')
def QA_fetch_get_stock_xdxr(code, ip='221.231.141.60', port=7709):
api = TdxHq_API()
market_code = __select_market_code(code)
with api.connect():
return api.to_df(api.get_xdxr_info(market_code, code))
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
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
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()
if __name__ == '__main__':
import os
import time
starttime = time.time()
dest_dir = "c:\\stock"
tdx_server = '119.147.212.81'
tdx_port = 7709
quotations = ['stock', 'fund']
connect = sqlite3.connect(dest_dir + "\\stock.db")
create_database(connect)
from pytdx.hq import TdxHq_API, TDXParams
api = TdxHq_API()
api.connect(tdx_server, tdx_port)
add_count = 0
"""
print("导入股票代码表")
add_count = import_stock_name(connect, api, 'SH', quotations)
add_count += import_stock_name(connect, api, 'SZ', quotations)
print("新增股票数：", add_count)
print("\n导入上证日线数据")
add_count = import_data(connect, 'SH', 'DAY', quotations, api, dest_dir, progress=ProgressBar)
print("\n导入数量：", add_count)
print("\n导入深证日线数据")
add_count = import_data(connect, 'SZ', 'DAY', quotations, api, dest_dir, progress=ProgressBar)
print("\n导入数量：", add_count)
("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')])))
def QA_fetch_get_stock_info(code, ip=best_ip, port=7709):
'股票财务数据'
api = TdxHq_API()
market_code = __select_market_code(code)
with api.connect(ip, port):
return api.to_df(api.get_finance_info(market_code, code))
def __call__(self):
total_count = 0
try:
if self.config.getboolean('hdf5', 'enable', fallback=True):
sqlite_file = "{}/stock.db".format(self.config['hdf5']['dir'])
connect = sqlite3.connect(sqlite_file, timeout=1800)
pytdx_import_weight = pytdx_import_weight_to_sqlite
self.logger.debug('use sqlite import weight')
else:
db_config = {
'user': self.config['mysql']['usr'],
'password': self.config['mysql']['pwd'],
'host': self.config['mysql']['host'],
'port': self.config['mysql']['port']
}
connect = mysql.connector.connect(**db_config)
pytdx_import_weight = pytdx_import_weight_to_mysql
self.logger.debug('use mysql import weight')
except Exception as e:
#self.queue.put([self.msg_name, str(e), -1, 0, total_count])
self.queue.put([self.msg_name, 'INFO', str(e), 0, 0])
self.queue.put([self.msg_name, '', 0, None, total_count])
return
try:
"""
download_dir = self.dest_dir + "/downloads"
if not os.path.lexists(download_dir):
os.makedirs(download_dir)
self.queue.put([self.msg_name, '正在下载钱龙权息信息...', 0, 0, 0])
net_file = urllib.request.urlopen(
'http://www.qianlong.com.cn/download/history/weight.rar', timeout=60
)
buffer = net_file.read()
self.queue.put([self.msg_name, '钱龙权息信息下载完成，正在校验是否存在更新...', 0, 0, 0])
new_md5 = hashlib.md5(buffer).hexdigest()
dest_filename = download_dir + '/weight.rar'
old_md5 = None
if os.path.exists(dest_filename):
with open(dest_filename, 'rb') as oldfile:
old_md5 = hashlib.md5(oldfile.read()).hexdigest()
#如果没变化不需要解压导入
if new_md5 != old_md5:
with open(dest_filename, 'wb') as file:
file.write(buffer)
self.queue.put([self.msg_name, '正在解压钱龙权息信息...', 0, 0, 0])
x = os.system('unrar x -o+ -inul {} {}'.format(dest_filename, download_dir))
if x != 0:
raise Exception("无法找到unrar命令！")
self.queue.put([self.msg_name, '正在导入钱龙权息数据...', 0, 0, 0])
total_count = qianlong_import_weight(connect, download_dir + '/weight', 'SH')
total_count += qianlong_import_weight(connect, download_dir + '/weight', 'SZ')
self.queue.put([self.msg_name, '导入钱龙权息数据完毕!', 0, 0, total_count])
else:
self.queue.put([self.msg_name, '钱龙权息数据无变化', 0, 0, 0])
"""
hosts = search_best_tdx()
api = TdxHq_API()
api.connect(hosts[0][2], hosts[0][3])
self.queue.put([self.msg_name, '正在导入权息数据...', 0, 0, 0])
total_count = pytdx_import_weight(api, connect, "SH")
total_count += pytdx_import_weight(api, connect, "SZ")
self.queue.put([self.msg_name, '导入权息数据完毕!', 0, 0, total_count])
#self.queue.put([self.msg_name, '下载通达信财务信息(上证)...', 0, 0, 0])
#x = pytdx_import_finance(connect, api, "SH")
#self.queue.put([self.msg_name, '下载通达信财务信息(深证)...', 0, 0, 0])
#x += pytdx_import_finance(connect, api, "SZ")
#self.queue.put([self.msg_name, '导入通达信财务信息完毕!', 0, 0, x])
api.disconnect()
except Exception as e:
self.logger.error(e)
#self.queue.put([self.msg_name, str(e), -1, 0, total_count])
self.queue.put([self.msg_name, 'INFO', str(e), 0, 0])
finally:
connect.commit()
connect.close()
self.queue.put([self.msg_name, '', 0, None, total_count])
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
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', frequence='day', ip=best_ip['stock'], port=7709):
"""获取日线及以上级别的数据
Arguments:
code {str:6} -- code 是一个单独的code 6位长度的str
start_date {str:10} -- 10位长度的日期 比如'2017-01-01'
end_date {str:10} -- 10位长度的日期 比如'2018-01-01'
Keyword Arguments:
if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权
frequency {str} -- day/week/month/quarter/year 也可以是简写 D/W/M/Q/Y
ip {str} -- [description] (default: best_ip['stock']) ip可以通过select_best_ip()函数重新获取
port {int} -- [description] (default: {7709})
Returns:
pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获取了一天,而当天停牌,返回None
Exception:
如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理
"""
api = TdxHq_API()
with api.connect(ip, port, time_out=0.7):
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['quarter', 'Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code(
code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
# 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了
if len(data) < 1:
return None
data = data[data['open'] != 0]
if if_fq in ['00', 'bfq']:
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
return data.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date].assign(date=data['date'].apply(lambda x: str(x)[0:10]))
elif if_fq in ['01', 'qfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
bfq_data = data.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
bfq_data = bfq_data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
#
if xdxr_data is not None:
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.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']][bfq_data.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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
else:
bfq_data['preclose'] = bfq_data['close'].shift(1)
bfq_data['adj'] = 1
return bfq_data[start_date:end_date]
elif if_fq in ['03', 'ddqfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], 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']][bfq_data.index[0]:end_date]], 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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['02', 'hfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.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']][bfq_data.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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['04', 'ddhfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], 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']][bfq_data.index[0]:end_date]], 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']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
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()
def _run(self):
src_dir = self.config['tdx']['dir']
dest_dir = self.config['hdf5']['dir']
hdf5_import_progress = {'SH': {'DAY': 0, '1MIN': 0, '5MIN': 0},
'SZ': {'DAY': 0, '1MIN': 0, '5MIN': 0}}
trans_progress = {'SH': 0, 'SZ': 0}
time_progress = {'SH': 0, 'SZ': 0}
#正在导入代码表
self.send_message(['INFO', '导入股票代码表'])
connect = sqlite3.connect(dest_dir + "/stock.db")
create_database(connect)
pytdx_api = TdxHq_API()
pytdx_api.connect(self.hosts[0][2], self.hosts[0][3])
count = import_stock_name(connect, pytdx_api, 'SH', self.quotations)
if count > 0:
self.send_message(['INFO', '上证新增股票数：%s' % count])
count = import_stock_name(connect, pytdx_api, 'SZ', self.quotations)
if count > 0:
self.send_message(['INFO', '深证新增股票数：%s' % count])
self.process_list.clear()
for task in self.tasks:
p = Process(target=task)
self.process_list.append(p)
p.start()
finished_count = len(self.tasks)
while finished_count > 0:
message = self.queue.get()
taskname, market, ktype, progress, total = message
if progress is None:
finished_count -= 1
if taskname in ('IMPORT_KDATA', 'IMPORT_TRANS', 'IMPORT_TIME'):
self.send_message([taskname, 'FINISHED', market, ktype, total])
else:
self.send_message([taskname, 'FINISHED'])
continue
if taskname == 'IMPORT_WEIGHT':
if market == 'INFO':
self.send_message(['INFO', ktype])
self.send_message([taskname, market, total])
elif taskname == 'IMPORT_FINANCE':
self.send_message([taskname, progress])
elif taskname == 'IMPORT_KDATA':
hdf5_import_progress[market][ktype] = progress
current_progress = (hdf5_import_progress['SH'][ktype] + hdf5_import_progress['SZ'][ktype]) // 2
self.send_message([taskname, ktype, current_progress])
elif taskname == 'IMPORT_TRANS':
trans_progress[market] = progress
current_progress = (trans_progress['SH'] + trans_progress['SZ']) // 2
self.send_message([taskname, ktype, current_progress])
elif taskname == 'IMPORT_TIME':
time_progress[market] = progress
current_progress = (time_progress['SH'] + time_progress['SZ']) // 2
self.send_message([taskname, ktype, current_progress])
else:
print("Unknow task: ", taskname)
