def QA_fetch_get_index_min(code, start, end, level='1min', ip=best_ip, port=7709):
'指数分钟线'
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 _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 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_index_day(code, start_date, end_date, frequence='day', ip=best_ip['stock']['ip'], port=best_ip['stock']['port']):
'指数日线'
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_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 _getKLineData(self):
api = TdxHq_API(auto_retry=True)
if api.connect('119.147.212.81', 7709):
self._KLineData = api.to_df(
api.get_index_bars(9, 1, self._stockCode, 0,
self._stockOberCount)) # 返回DataFrame
# print(self._KLineData)
api.disconnect()
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_index_day(code, start_date, end_date, ip=best_ip, port=7709):
'指数日线'
QA_util_log_info(code)
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_index_bars(
9, 1 if str(code)[0] in ['0', '8', '9'] else 0, str(code), (9 - i) * 800, 800)) for i in range(10)], 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_index_day(code, start_date, end_date, frequence='day', ip=None, port=None):
"""指数日线
1- sh
0 -sz
Arguments:
code {[type]} -- [description]
start_date {[type]} -- [description]
end_date {[type]} -- [description]
Keyword Arguments:
frequence {str} -- [description] (default: {'day'})
ip {[type]} -- [description] (default: {None})
port {[type]} -- [description] (default: {None})
Returns:
[type] -- [description]
"""
ip, port = get_mainmarket_ip(ip, port)
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_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_index_day(code,
start_date,
end_date,
ip='119.147.212.81',
port=7709):
api = TdxHq_API()
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_index_bars(9, 1, code, (9 - i) * 800, 800)
data = api.to_df(data)
data['date'] = data['datetime'].apply(lambda x: x[0:10])
data['date'] = pd.to_datetime(data['date'])
data = data.set_index('date', drop=False)
data = data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
return data[start_date:end_date]
def ping(ip, port=7709, multithread=False):
#print(ip, port, multithread)
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', 1, 800)
if x:
success = True
y = api.get_security_bars(7, 0, '000001', 800, 800)
if x and y:
success = True
except Exception as e:
success = False
#print(e)
endtime = time.time()
#print(success, endtime - starttime)
return (success, endtime - starttime, ip, port)
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]
from pytdx.hq import TdxHq_API
import pandas as pd
import datetime
from Event.Event import EventType
from Stock.AnalyseKLine import AnalyseKLine
#dd = DIFNewHighLogic()
from Stock.KLine import KLine, KLineType
api = TdxHq_API(auto_retry=True)
if api.connect('119.147.212.81', 7709):
data = api.to_df(api.get_index_bars(9, 1, '000905', 0, 500)) # 返回DataFrame
start = datetime.datetime.now()
ak = KLine(KLineType.LINE_1_D)
ak.updateKLineData(data)
end = datetime.datetime.now()
print(end - start)
print("*" * 50)
print(ak.getData())
print("*" * 50)
api.disconnect()
quit()
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 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)
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
print(data2)
print("获取股票行情")
stocks = api.get_security_quotes([(0, "000002"), (1, "600300")])
print(stocks)
print("获取k线")
data = api.get_security_bars(9, 0, '000001', 4, 3)
print(data)
print("获取 深市 股票数量")
print(api.get_security_count(0))
print("获取股票列表")
stocks = api.get_security_list(1, 255)
print(stocks)
print("获取指数k线")
data = api.get_index_bars(9, 1, '000001', 1, 2)
print(data)
print("查询分时行情")
data = api.get_minute_time_data(1, '600300')
print(data)
print("查询历史分时行情")
data = api.get_history_minute_time_data(1, '600300', 20161209)
print(data)
print("查询分时成交")
data = api.get_transaction_data(1, '000002', 0, 30)
print(data)
print("查询历史分时成交")
data = api.get_history_transaction_data(2, '600302', 0, 10, 20170209)
print(data)
print("查询公司信息目录")
data = api.get_company_info_category(1, '000003')
class TdxHelper:
ip_list = [{
'ip': '119.147.212.81',
'port': 7709
}, {
'ip': '60.12.136.250',
'port': 7709
}]
def __init__(self):
#连接tdx接口
self.api = TdxHq_API()
if not self.api.connect('60.12.136.250', 7709):
print("服务器连接失败！")
# pandas数据显示设置
pd.set_option('display.max_columns', None) # 显示所有列
#pd.set_option('display.max_rows', None) # 显示所有行
# mysql对象
self.mysql = mysqlHelper(config.mysql_host, config.mysql_username,
bluedothe.mysql_password, config.mysql_dbname)
# pandas的mysql对象
self.engine = create_engine(
f'mysql+pymysql://{config.mysql_username}:{bluedothe.mysql_password}@{config.mysql_host}/{config.mysql_dbname}?charset=utf8'
)
#断开tdx接口连接
def close_connect(self):
self.api.disconnect()
#获取k线，最后一个参数day,说明需要获取的数量，本接口只获取从最近交易日往前的数据
#输入参数：五个参数分别为：category（k线),市场代码(0:深圳，1:上海),股票代码,开始位置(从最近交易日向前取，0表示最近交易日),返回的记录条数
#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
#返回值：open,close,high,low,vol,amount,year,month,day,hour,minute,datetime
# csv格式：code,ts_code,trade_date(缩写）,trade_time,time_index,open,high,low,close,amount,volume
def get_security_bars(self, category, market, code, start=0, count=240):
dict = {0: 'SZ', 1: 'SH'}
ts_code = code + "." + dict[market]
order = [
'code', 'ts_code', 'trade_date', 'trade_time', 'time_index',
'open', 'high', 'low', 'close', 'amount', 'volume'
]
#df = self.api.get_security_bars(9, 0, '000001', 0, 10) # 返回普通list
df = self.api.to_df(
self.api.get_security_bars(category, market, code, start,
count)) # 返回DataFrame
if df.empty: return df
df.insert(0, 'ts_code', ts_code)
df.insert(0, 'code', code)
df['trade_time'] = df['datetime'].apply(lambda x: str(x)[11:19])
df['time_index'] = df['trade_time'].apply(
lambda x: datatime_util.stockTradeTime2Index(x))
df['trade_date'] = df['datetime'].apply(
lambda x: (str(x)[0:10]).replace('-', ''))
df.rename(columns={'vol': 'volume'}, inplace=True)
df.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'],
axis=1,
inplace=True)
df['volume'] = df['volume'].apply(lambda x: int(x)) #取整
df.loc[df['amount'] == 5.877471754111438e-39,
'amount'] = 0 #列值根据条件筛选后修改为0
df = df[order]
filename = config.tdx_csv_minline1_all + ts_code + ".csv"
if os.path.isfile(filename):
df.to_csv(filename,
index=False,
mode='a',
header=False,
sep=',',
encoding="utf_8_sig")
else:
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
print("新增加的一分钟all股票数据：", filename)
# 获取1分钟k线，最后一个参数说明需要获取的数量，本接口只获取从最近交易日往前的数据
# 输入参数：五个参数分别为：category（k线),市场代码(0:深圳，1:上海),股票代码,开始位置(从最近交易日向前取，0表示最近交易日),返回的记录条数
# 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
# 返回值：open,close,high,low,vol,amount,year,month,day,hour,minute,datetime
# csv格式：code,ts_code,trade_date(缩写）,trade_time,time_index,open,high,low,close,amount,volume
def get_security_bars_minute1(self, category, market, code, start, count):
dict = {0: 'SZ', 1: 'SH'}
ts_code = code + "." + dict[market]
order = [
'code', 'ts_code', 'trade_date', 'trade_time', 'time_index',
'open', 'high', 'low', 'close', 'amount', 'volume'
]
# df = self.api.get_security_bars(9, 0, '000001', 0, 10) # 返回普通list
df = self.api.to_df(
self.api.get_security_bars(category, market, code, start,
count)) # 返回DataFrame
if df.empty: return
df.insert(0, 'ts_code', ts_code)
df.insert(0, 'code', code)
df['trade_time'] = df['datetime'].apply(lambda x: str(x)[11:19])
df['time_index'] = df['trade_time'].apply(
lambda x: datatime_util.stockTradeTime2Index(x))
df['trade_date'] = df['datetime'].apply(
lambda x: (str(x)[0:10]).replace('-', ''))
df.rename(columns={'vol': 'volume'}, inplace=True)
df.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'],
axis=1,
inplace=True)
df['volume'] = df['volume'].apply(lambda x: int(x)) # 取整
df.loc[df['amount'] == 5.877471754111438e-39,
'amount'] = 0 # 列值根据条件筛选后修改为0
df = df[order]
#过滤掉停牌的数据，在tdx中，停牌股票也能取到数据，价格是前一交易日的收盘价，所以只能用成交量或成交金额为0来判断
#1按日期分组后取出成交量为0的日期；2循环过滤掉成交量为0的日期的数据。
dfg = df.groupby(by='trade_date').mean() #分组
dfg['trade_date'] = dfg.index
dfg = dfg[dfg.volume == 0] #条件过滤，保留满足条件的数据
for trade_date in dfg['trade_date'].values:
df = df[(df['trade_date'] != trade_date)] # 每个条件要用括号()括起来
return df
#可以获取多只股票的行情信息
#返回值：market,code,active1,price,last_close,open,high,low,reversed_bytes0,reversed_bytes1,vol,cur_vol,amount,s_vol,
#reversed_bytes2,reversed_bytes3,bid1,ask1,bid_vol1,ask_vol1,bid2,ask2,bid_vol2,ask_vol2,bid3,ask3,bid_vol3,ask_vol3,bid4,
#ask4,bid_vol4,ask_vol4,bid5,ask5,bid_vol5,ask_vol5,reversed_bytes4,reversed_bytes5,reversed_bytes6,reversed_bytes7,
#reversed_bytes8,reversed_bytes9,active2
def get_security_quotes(self):
df = self.api.to_df(
self.api.get_security_quotes([(0, '000001'), (1, '600300')]))
print(df)
# 获取市场股票数量
#返回值：value
def get_security_count(self):
df = self.api.to_df(self.api.get_security_count(0)) #0 - 深圳， 1 - 上海
print(df)
# 获取股票列表，返回值里面除了股票，还有国债等
#返回值：code,volunit,decimal_point,name,pre_close
def get_security_list(self):
df = self.api.to_df(self.api.get_security_list(
0, 10000)) # 市场代码, 起始位置 如： 0,0 或 1,100
print(df)
# 获取指数k线
#输入参数同股票k线接口
# 返回值：open,close,high,low,vol,amount,year,month,day,hour,minute,datetime,up_count down_count
def get_index_bars(self):
index_dict_cn = {
"上证指数": "999999",
"深证成指": "399001",
"中小板指": "399005",
"创业板指": "399006",
"深证综指": "399106",
"上证50": "000016",
"沪深300": "000300"
}
index_dict = {
"sh": "999999",
"sz": "399001",
"zxb": "399005",
"cyb": "399006",
"szz": "399106",
"sz50": "000016",
"hs300": "000300"
}
for key in index_dict.keys():
df = self.api.to_df(
self.api.get_index_bars(9, 1, index_dict[key], 0, 2))
print(df)
# 查询分时行情，最近交易日的数据，一分钟一条记录
#返回值：price,vol
def get_minute_time_data(self):
df = self.api.to_df(self.api.get_minute_time_data(
1, '600300')) #市场代码， 股票代码
print(df)
# 查询历史分时行情
# 返回值：price,vol
def get_history_minute_time_data(self):
df = self.api.to_df(
self.api.get_history_minute_time_data(TDXParams.MARKET_SH,
'603887',
20200420)) #市场代码， 股票代码，时间
print(df)
# 查询分笔成交，最近交易日数据
#返回值：time，price，vol，num，buyorsell
def get_transaction_data(self):
df = self.api.to_df(
self.api.get_transaction_data(TDXParams.MARKET_SZ, '000001', 0,
30)) #市场代码， 股票代码，起始位置， 数量
print(df)
# 查询历史分笔成交
#返回值:time,price,vol,buyorsell
def get_history_transaction_data(self):
df = self.api.to_df(
self.api.get_history_transaction_data(
TDXParams.MARKET_SZ, '000001', 0, 10,
20170209)) #市场代码， 股票代码，起始位置，日期 数量
print(df)
# 查询公司信息目录,返回的不是具体数据
#返回值:name,filename,start,length
def get_company_info_category(self):
df = self.api.to_df(
self.api.get_company_info_category(TDXParams.MARKET_SZ,
'000001')) #市场代码， 股票代码
print(df)
# 读取公司信息详情
#返回值:value
def get_company_info_content(self):
df = self.api.to_df(
self.api.get_company_info_content(
0, '000001', '000001.txt', 0,
1000)) #市场代码， 股票代码, 文件名, 起始位置， 数量
print(df)
# 读取除权除息信息
#返回值:year,month,day,category,name,fenhong,peigujia,songzhuangu,peigu
def get_xdxr_info(self):
df = self.api.to_df(self.api.get_xdxr_info(1, '600300')) #市场代码， 股票代码
print(df)
# 读取财务信息
#返回值:market,code,liutongguben,province,industry,updated_date,ipo_date,zongguben,guojiagu,faqirenfarengu,farengu,bgu,hgu,zhigonggu,
#zongzichan,liudongzichan,gudingzichan,wuxingzichan,gudongrenshu,liudongfuzhai,changqifuzhai,zibengongjijin,jingzichan,zhuyingshouru,
#zhuyinglirun,yingshouzhangkuan,yingyelirun,touzishouyu,jingyingxianjinliu,zongxianjinliu,cunhuo,lirunzonghe,shuihoulirun,jinglirun,weifenlirun,baoliu1,baoliu2
def get_finance_info(self):
df = self.api.to_df(self.api.get_finance_info(1,
'600300')) #市场代码， 股票代码
print(df)
# 读取k线信息
# 返回值:value
def get_k_data(self):
df = self.api.to_df(
self.api.get_k_data('600300', '2017-07-03',
'2017-07-10')) #股票代码， 开始时间， 结束时间
print(df)
# 读取板块信息
#返回值：blockname, block_type, code_index, code
""" BLOCK_SZ = "block_zs.dat";BLOCK_FG = "block_fg.dat";BLOCK_GN = "block_gn.dat";BLOCK_DEFAULT = "block.dat" """
def get_and_parse_block_info(self):
##指数板块 风格板块 概念板块 一般板块
block_filename = [
"block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat"
]
for block in block_filename:
df = self.api.to_df(
self.api.get_and_parse_block_info(block)) #板块文件名称
filename = config.tdx_csv_block + block[0:-4] + ".csv"
if os.path.isfile(filename):
os.remove(filename)
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
else:
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
# 读取板块信息，多个类型封装到一个df对象中返回
# 返回值：data_source, block_category, block_type, block_name, block_code, ts_code, create_time
def update_block_member(self):
##指数板块 风格板块 概念板块 一般板块
#block_filename = ["block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat"]
block_filename = ["block_zs.dat", "block_fg.dat",
"block_gn.dat"] #block.dat中的数据都包含在其他版块里了，这个可以去掉
data_source = "tdx"
dfall = None
for block in block_filename:
df = self.api.to_df(
self.api.get_and_parse_block_info(block)) # 板块文件名称
df['data_source'] = data_source
if block == "block.dat":
df['block_category'] = data_source + ".yb"
else:
df['block_category'] = data_source + "." + block[6:8]
df['block_type'] = df['block_type'].map(lambda x: str(x))
df['block_type'] = df['block_category'].str.cat(
df['block_type'], sep=".") #, sep = "."
df['block_code'] = "" #使用pd直接插入到数据库时，字段不能是None值
df['ts_code'] = df['code'].apply(lambda x: x + ".SH"
if x[0:1] == "6" else x + ".SZ")
if (dfall is not None) and (not dfall.empty):
dfall = dfall.append(df, ignore_index=True)
else:
dfall = df
if (dfall is None) or (dfall.empty): return None
dfall.rename(columns={'blockname': 'block_name'}, inplace=True)
dfall['create_time'] = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
dfall = dfall[[
'data_source', 'block_category', 'block_type', 'block_name',
'block_code', 'ts_code', 'create_time'
]] #列重排序
#分组统计
dfg = dfall.groupby(by=[
'data_source', 'block_category', 'block_type', 'block_name',
'block_code'
],
as_index=False).count() # 分组求每组数量
dfg.rename(columns={'ts_code': 'member_count'},
inplace=True) #ts_code列数值为汇总值，需要重命名
dfg['create_time'] = time.strftime(
'%Y-%m-%d %H:%M:%S',
time.localtime(time.time())) #create_time列数值为汇总值，需要重新赋值
delete_condition = f"data_source = '{data_source}'"
mysql_script.df2db_update(delete_condition=delete_condition,
block_basic_df=dfg,
block_member_df=dfall)
return (len(dfg), len(dfall))
#获取一段时间的1分钟数据，因为每次调用接口只能返回3天的分钟数据（240*3)，需要分多次调用
#返回值：0没有提取到数据；1提取到数据
def get_minute1_data(self, category, market, code, start_date, end_date):
init_start_date = start_date.replace('-', '')
init_end_date = end_date.replace('-', '')
day = datatime_util.diffrentPeriod(datatime_util.DAILY, start_date,
end_date)
df = self.get_security_bars_minute1(category, market, code, 0,
240 * 3) # 返回DataFrame
if df is None or df.empty:
print('{0}没有交易数据'.format(code))
return 0
print(market, '--', code, '--', start_date, '--', end_date)
#print("最大值：",df.groupby('datetime').max())
#print(df.describe()) #df数据统计
data_start_date = df.min()['trade_date']
data_end_date = df.max()['trade_date']
start_date = start_date.replace('-', '')
end_date = end_date.replace('-', '')
if data_end_date < start_date or end_date < data_start_date:
print("采集时间在数据范围之外，退出函数")
return 0
elif end_date > data_end_date:
end_date = data_end_date
if start_date < data_start_date:
#最近三天的数据中，去掉无用的数据后即是最终数据
#需要取的数据还有三天前的数据，需要继续向前取
n = (day - 3) // 3
m = (day - 3) % 3
for i in range(0, n):
dfn = self.get_security_bars_minute1(category, market, code,
240 * 3 * (i + 1),
240 * 3) # 返回DataFrame
if (dfn is not None) and (not dfn.empty):
df = dfn.append(df, ignore_index=True)
if m > 0:
dfn = self.get_security_bars_minute1(category, market, code,
240 * 3 * (n + 1),
240 * m)
if (dfn is not None) and (not dfn.empty):
df = dfn.append(df, ignore_index=True)
df = df.sort_values(by=['trade_date', 'time_index'],
axis=0,
ascending=True)
#过滤掉start_date, end_date之外的数据
df = df[(df['trade_date'] >= str(init_start_date)) &
(df['trade_date'] <= str(init_end_date))] #每个条件要用括号()括起来
dict = {0: 'SZ', 1: 'SH'}
ts_code = code + "." + dict[market]
filename = config.tdx_csv_minline1_all + ts_code + ".csv"
if os.path.isfile(filename):
df.to_csv(filename,
index=False,
mode='a',
header=False,
sep=',',
encoding="utf_8_sig")
print("更新一分钟all股票数据：", filename)
else:
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
print("新增加的一分钟all股票数据：", filename)
class SP(object):
def __init__(self,userid="account4",rate=1.2,products="1",limit=2,total=1000000,mock=True,server="http://192.168.118.1:65000"):
if products == "1":
self.products = {'880414': {'args': (3, 12, 90), 'stocklst': {}}, '880456': {'args': (6, 28, 40), 'stocklst': {}},
'880476': {'args': (5, 20, 90), 'stocklst': {}}, '880440': {'args': (4, 8, 85), 'stocklst': {}},
'880424': {'args': (4, 16, 110),'stocklst': {}}, '880448': {'args': (3, 14, 60), 'stocklst': {}},
'880454': {'args': (4, 8, 85), 'stocklst': {}}, '880493': {'args': (3, 24, 55), 'stocklst': {}},
'880344': {'args': (4, 4, 30), 'stocklst': {}}, '880301': {'args': (6, 20, 30), 'stocklst': {}},
'880464': {'args': (4, 14, 50), 'stocklst': {}}, '880459': {'args': (3, 16, 80), 'stocklst': {}},
'880380': {'args': (5, 22, 70), 'stocklst': {}}, '880472': {'args': (3, 32, 170),'stocklst': {}},
'880421': {'args': (3, 16, 45), 'stocklst': {}}, '880471': {'args': (4, 12, 40), 'stocklst': {}},
'880453': {'args': (6, 14, 30), 'stocklst': {}}, '880350': {'args': (4, 26, 30), 'stocklst': {}},
'880447': {'args': (5, 20, 170),'stocklst': {}}, '880351': {'args': (3, 6, 65), 'stocklst': {}},
'880390': {'args': (3, 14, 65), 'stocklst': {}}, '880406': {'args': (4, 16, 50), 'stocklst': {}},
'880305': {'args': (3, 14, 95), 'stocklst': {}}, '880492': {'args': (3, 30, 105),'stocklst': {}},
'880387': {'args': (5, 20, 70), 'stocklst': {}}, '880418': {'args': (4, 16, 100),'stocklst': {}},
'880367': {'args': (5, 14, 110),'stocklst': {}}, '880398': {'args': (5, 26, 100),'stocklst': {}},
'880437': {'args': (4, 8, 85), 'stocklst': {}}, '880474': {'args': (4, 24, 45), 'stocklst': {}},
'880324': {'args': (6, 22, 75), 'stocklst': {}}, '880335': {'args': (3, 12, 75), 'stocklst': {}},
'880372': {'args': (6, 20, 105),'stocklst': {}}, '880491': {'args': (4, 16, 20), 'stocklst': {}},
'880490': {'args': (3, 26, 110),'stocklst': {}}, '880431': {'args': (7, 12, 40), 'stocklst': {}},
'880432': {'args': (3, 10, 110),'stocklst': {}}, '880318': {'args': (4, 20, 90), 'stocklst': {}},
'880497': {'args': (3, 8, 40), 'stocklst': {}}, '880494': {'args': (3, 30, 110),'stocklst': {}},
'880400': {'args': (3, 10, 90), 'stocklst': {}}, '880360': {'args': (3, 16, 70), 'stocklst': {}},
'880310': {'args': (3, 12, 20), 'stocklst': {}}, '880423': {'args': (5, 16, 30), 'stocklst': {}},
'880430': {'args': (7, 12, 25), 'stocklst': {}}, '880422': {'args': (6, 8, 35), 'stocklst': {}},
'880465': {'args': (4, 12, 95), 'stocklst': {}}, '880482': {'args': (3, 20, 180),'stocklst': {}},
'880355': {'args': (3, 18, 45), 'stocklst': {}}, '880473': {'args': (3, 6, 40), 'stocklst': {}},
'880446': {'args': (3, 8, 45), 'stocklst': {}}, '880452': {'args': (3, 32, 90), 'stocklst': {}},
'880455': {'args': (5, 6, 20), 'stocklst': {}}, '880399': {'args': (3, 8, 85), 'stocklst': {}},
'880330': {'args': (5, 12, 80), 'stocklst': {}}, '880489': {'args': (3, 18, 190),'stocklst': {}}}
elif products == "2": #资金比较少是，选择配置部分行业
self.products = {'880414': {'args': (3, 12, 90), 'stocklst': {}}, '880456': {'args': (6, 28, 40), 'stocklst': {}},
'880476': {'args': (5, 20, 90), 'stocklst': {}}, '880440': {'args': (4, 8, 85), 'stocklst': {}},
'880424': {'args': (4, 16, 110),'stocklst': {}}, '880448': {'args': (3, 14, 60), 'stocklst': {}},
'880454': {'args': (4, 8, 85), 'stocklst': {}}, '880493': {'args': (3, 24, 55), 'stocklst': {}},
'880344': {'args': (4, 4, 30), 'stocklst': {}}, '880301': {'args': (6, 20, 30), 'stocklst': {}},
'880464': {'args': (4, 14, 50), 'stocklst': {}}, '880459': {'args': (3, 16, 80), 'stocklst': {}},
'880380': {'args': (5, 22, 70), 'stocklst': {}}, '880472': {'args': (3, 32, 170),'stocklst': {}},
'880421': {'args': (3, 16, 45), 'stocklst': {}}, '880471': {'args': (4, 12, 40), 'stocklst': {}},
'880453': {'args': (6, 14, 30), 'stocklst': {}}, '880350': {'args': (4, 26, 30), 'stocklst': {}},
'880447': {'args': (5, 20, 170),'stocklst': {}}, '880351': {'args': (3, 6, 65), 'stocklst': {}},
'880390': {'args': (3, 14, 65), 'stocklst': {}}, '880406': {'args': (4, 16, 50), 'stocklst': {}},
'880305': {'args': (3, 14, 95), 'stocklst': {}}, '880492': {'args': (3, 30, 105),'stocklst': {}},
'880387': {'args': (5, 20, 70), 'stocklst': {}}, '880418': {'args': (4, 16, 100),'stocklst': {}},
}
self.server = server
self.datatype = 1
self.userid = userid
self.limit = limit
self.api = TdxHq_API(heartbeat=True)
self.trader = None
self.trading = False
self.mock = mock
self.rate = rate #持仓杠杆率
self.total = total #默认持仓资金
self.TDX_IP_SETS = STOCK_IP_SETS
self.file_incon = FILE_INCON
self.file_tdxhy = FILE_TDXHY
self.file_tdxzs = FILE_TDXZS
def connect(self):
for ip in self.TDX_IP_SETS:
try:
if self.api.connect(ip, 7709):
return
except:
pass
def getdata(self,product,market=1,number=5000,pn=400):
data = []
for i in range(int(number/pn)+1):
temp = self.api.get_index_bars(self.datatype, market, product, (int(number/pn)-i)*pn,pn)
if not temp or len(temp)<pn:
self.connect()
for _ in range(2):
temp = self.api.get_index_bars(self.datatype, market, product, (int(number/pn)-i)*pn,pn)
if not temp or len(temp)<pn:
logger.info("record not reach the limit!")
else:
break
data += temp
df = self.api.to_df(data)[["open","close","high","low","datetime"]]
df.set_index("datetime",inplace=True,drop=False)
return df
def disconnect(self):
logger.info("[DISCONNECT]:statrt disconnect!!!!!")
if self.istradeday:
self.api.disconnect()
logger.info("[DISCONNECT]:disconnect finished!!!!!")
def updatetotal(self):
'''更新总资金
'''
accountinfo,holdlists = self.trader.position()
nhg = holdlists[holdlists[u"证券代码"].map(lambda x:x in ["SZRQ88","SHRQ88"])][u"最新市值"].sum()
self.total = accountinfo.ix["总资产"]["人民币"]-nhg
return self.total
def set_permoney(self):
'''单个品种资金上限
'''
self.permoney = self.total * self.rate /len(self.products)
return self.permoney
def _get_incon(self,):
'''获取行业分类代码
'''
f= open(self.file_incon, "rb")
data = f.read()
strings = data.decode("gbk", 'ignore').rstrip("\x00").replace("\r\n","\n")
data = strings.split("######")
rst = {}
for hystr in data:
key = re.findall(r'#.*',hystr)
if key == ['#TDXNHY']:
hylst = hystr.replace("#TDXNHY","").strip("\n").split("\n")
for item in hylst:
k,v = item.split("|")
rst[k] = [v]
return rst
def _get_tdxhy(self,islocal=True):
'''获取股票和行业对应列表
'''
if islocal:
stocklist = HY_WEIGHT.keys()
else:
stocklist = list(ts.get_stock_basics().index) #获取全市场股票代码
rst = self._get_incon()
f= open(self.file_tdxhy, "rb")
data = f.read().decode("gbk", 'ignore').rstrip("\x00").replace("\r\n","\n").strip("\n").split("\n")
for i in data:
_,code,tdxhy,_,_ = i.split("|")
if tdxhy != "T00" and code in stocklist:
rst[tdxhy].append(code)
return rst
def _get_tdxzs(self,islocal=True):
'''生成通达性版块代码对应股票列表
'''
dct = {}
rst = self._get_tdxhy(islocal=islocal)
f= open(self.file_tdxzs, "rb")
data = f.read().decode("gbk", 'ignore').rstrip("\x00").replace("\r\n","\n").strip("\n").split("\n")
for i in data:
name,code,_,_,_,hy = i.split("|")
code = int(code)
if 880301<=code and 880497>=code and hy in rst.keys() :
k = hy[:5]
if not dct.__contains__(k):
dct[k] = {"name":"","code":"","stocklist":[]}
if k==hy:
dct[k]["name"] = name
dct[k]["code"] = code
dct[k]["stocklist"].extend(rst[hy][1:])
return dct
def get_tdxhy_list(self,islocal=True):
'''获取通达信行业板块指数对应的股票列表
'''
return self._get_tdxzs(islocal)
def get_weight(self,htlist={},islocal=True):
'''获取行业板块个股权重，流动市值为权重系数
备注：回测是为方便处理，以最后一天的权重系数作为历史上的权重
'''
if islocal:
self.weight = HY_WEIGHT
else:
if not htlist:
htlist = self.get_tdxhy_list(islocal)
tasks = []
for v in htlist.values():
tasks.append(gevent.spawn(self.get_latest_ltsz,v["stocklist"]))
gevent.joinall(tasks)
return self.weight
def get_latest_ltsz(self,stocks=[]):
'''获取最新流通市值,千万为单位，取整
'''
unit = 10000000
for code in stocks:
mk = self._select_market_code(code)
print(mk,code)
try:
ltgb = self.api.get_finance_info(mk,code)["liutongguben"]
price = self.api.get_security_bars(4,mk,code,0,1)[0]["close"]
ltsz = int(ltgb*price/unit)
self.weight[code] = ltsz
except:
print("*****",code)
return
def set_instrument(self):
'''设置交易股票
'''
func = lambda x :1 if x.startswith("6") else 0
weight = self.get_weight()
for v in self.get_tdxhy_list().values():
code = str(v["code"])
if not self.products.__contains__(code):continue
stocks = [(i,weight[i]) for i in v["stocklist"]]
limit_stocks = sorted(stocks,key=lambda x:x[1],reverse=True)[:self.limit]
total = sum([i[1] for i in limit_stocks])
for i in limit_stocks:
market = func(i[0])
price = self.api.get_security_bars(4,market,i[0],0,1)[0]["close"]
number = int(self.permoney*i[1]/total/price/100)*100
self.products[code]["stocklst"][i[0]] = number
return self.products
def judgetradeday(self,):
today = datetime.datetime.today().date().strftime('%Y-%m-%d')
df = ts.trade_cal()
return df[(df["calendarDate"]==today)].isOpen.values[0]
@property
def istradeday(self):
if not hasattr(self, "_istradeday"):
self._istradeday = self.judgetradeday()
return self._istradeday
def initial(self):
'''每天初始化设置
'''
logger.info("[INITIAL]:start initial !!!!!!")
if not self.istradeday:
self.trading = False
return
self.trading = True
logger.info("[INITIAL]:try to create connect... ")
self.connect()
self.trader = trade(UserID=self.userid,api=self.api,mock=self.mock,server=self.server)
logger.info("[INITIAL]:connect successful!")
logger.info("[INITIAL]:initial account info...")
self.updatetotal() #更新账户总资金
self.set_permoney() #设置单个品种资金上限
logger.info("[INITIAL]:set per product money limit:{}".format(self.permoney))
self.set_instrument() #设置交易股票和手数
logger.info("[INITIAL]:set stock list succcessful !!!")
logger.info("[INITIAL]:initial finished!!!!!!")
def handledata(self,df,args=[]):
df.loc[:,"number"] = range(df.shape[0])
s,m,l = args
for i in args:#5 15 60 D
key = str(5*i)
df.loc[:,key+"high"] = df["high"].rolling(i).max()
df.loc[:,key+"low"] = df["low"].rolling(i).min()
df.loc[:,key+"atr"] = (df[key+"high"]-df[key+"low"]).rolling(10*i).mean()
df.loc[:,key+"med"] = (df[key+"high"]+df[key+"low"])/2
df.loc[:,key+"HH"] = df[key+"med"] + 1.5*df[key+"atr"]
df.loc[:,key+"LL"] = df[key+"med"] - 1.5*df[key+"atr"]
df.loc[:,key+'HHmax'] = df[key+'HH'].rolling(10*i).max()
df.loc[:,key+'LLmin'] = df[key+'LL'].rolling(10*i).min()
df.loc[df[key+'HH']>=df[key+'HHmax'],key+'hmark'] = df["number"]
df.loc[df[key+'LL']<=df[key+'LLmin'],key+'lmark'] = df["number"]
df[key+'hmark'].fillna(method="ffill",inplace=True)
df[key+'hmark'].fillna(0,inplace=True)
df[key+'lmark'].fillna(method="ffill",inplace=True)
df[key+'lmark'].fillna(0,inplace=True)
df.loc[:,key+'UP'] = df[key+'hmark'] >= df[key+'lmark']
# debuginfo.append({key+'hmark':df.iloc[-1][key+'hmark'],key+'lmark':df.iloc[-1][key+'lmark']})
df.fillna(method="ffill",inplace=True)
df.dropna(inplace=True)
# logger.info("trademessage:{}".format(debuginfo))
result = (df.iloc[-1][str(5*l)+"UP"] >0)&(df.iloc[-1][str(5*s)+"UP"]>0)
result |= (df.iloc[-1][str(5*l)+"UP"]>0)&(df.iloc[-1][str(5*m)+"UP"]>0)
result |= (df.iloc[-1][str(5*l)+"UP"]<=0)&(df.iloc[-1][str(5*m)+"UP"]>0)&(df.iloc[-1][str(5*s)+"UP"]>0)
return result
def sync(self,idx,director=True):
stocks = self.products[idx]["stocklst"]
for stock,number in stocks.items():
if not director: number = 0 #空信号,清仓
#判断现有持仓
try:
code = stock
h_number = self.hd_df.ix[code]["证券数量"]
except:
h_number = 0
logger.info("[RUN]:{},{},{}".format(stock,h_number,number))
#补仓差
cangcha = int((number-h_number)/100)*100
if h_number>0 and abs(cangcha)/h_number<0.2: #如果有持仓，同时仓差小于10% 不进行更改，为了处理频繁加减仓达到问题
continue
if cangcha>0:
logger.info("[RUN]:buy code:{}, number:{}".format(stock,number-h_number))
self.buy(stock,cangcha)
elif cangcha<0:
couldsell = self.hd_df.ix[code]["可卖数量"]
logger.info("[RUN]:sell code:{}, number:{},couldsell:{}".format(stock,h_number-number,couldsell))
if couldsell >0:
self.sell(stock,min(-cangcha,couldsell))
#
def buy(self,stock,number):
self.trader.buy(stock, number)
def sell(self,stock,number):
self.trader.sell(stock, number)
def check_position(self,status):
'''检查仓位情况
'''
self.handleposition()
handlelist = []
for ins,v in status.items():
if ins not in self.g_df.index :
handlelist.append(ins)
elif self.g_df.ix[ins]["Position"] != v["number"]:
handlelist.append(ins)
return handlelist
def handleposition(self):
'''计算多仓，空仓，以及昨仓和今仓
'''
self.trader.cancelorder() #先尝试撤单
_,holdlists = self.trader.position()
self.hd_df = holdlists
if holdlists.shape[0]>0:
self.hd_df.set_index("证券代码",inplace=True)
self.hd_df.index = self.hd_df.index.astype(np.str).map(lambda x:x if len(x)>=6 else "0"*(6-len(x))+x)
return self.hd_df
def run(self):
logger.info("[RUN]:start run !!!!!")
if not self.trading:
return
self.handleposition()
rst = {}
for idx in list(self.products.keys()):
director = self.handledata(self.getdata(idx,market=1),self.products[idx]["args"]) #用指数出信号
logger.info("[RUN]:trademessage: block:{}, director:{}".format(idx,director))
self.sync(idx,director)
rst[idx] = {"up":director,"number":self.products[idx]["stocklst"],"product":idx}
logger.info("[RUN]:lastest position status:{}".format(rst))
logger.info("[RUN]:run finished !!!!!")
class SP(object):
def __init__(self,
userid="account1",
number=2,
mock=True,
server="http://192.168.118.1:65000"):
''' 根据实际账户资金，以股指1手为基本对冲单位进行配置。
'''
self.products = {
'000905': {
'args': (3, 6, 65),
'stocklst': {
"510500": 0
}
}
}
self.datatype = 0
self.userid = userid
self.api = TdxHq_API(heartbeat=True)
self.trader = None
self.trading = False
self.mock = mock
self.permoney = 200 #指数一个点对应的金额
self.number = number #指数的倍数
self.server = server
self.TDX_IP_SETS = STOCK_IP_SETS
if mock: self.judgerate = 100
else: self.judgerate = 10
def connect(self):
for ip in self.TDX_IP_SETS:
try:
if self.api.connect(ip, 7709):
return
except:
pass
def getdata(self, product, market=1, number=10000, pn=400):
data = []
for i in range(int(number / pn) + 1):
temp = self.api.get_index_bars(self.datatype, market, product,
(int(number / pn) - i) * pn, pn)
if not temp or len(temp) < pn:
for _ in range(2):
temp = self.api.get_index_bars(self.datatype, market,
product,
(int(number / pn) - i) * pn,
pn)
if not temp or len(temp) < pn:
logger.info("record not reach the limit!")
else:
break
try:
data += temp
except:
self.connect()
df = self.api.to_df(data)[["open", "close", "high", "low", "datetime"]]
df.set_index("datetime", inplace=True, drop=False)
return df
def disconnect(self):
logger.info("[DISCONNECT]:statrt disconnect!!!!!")
if self.istradeday:
self.api.disconnect()
logger.info("[DISCONNECT]:disconnect finished!!!!!")
def set_number(self):
'''设置日内交易的单次手数
'''
price = self.api.get_security_quotes([(1, '510500')])[0]["last_close"]
index_price = self.api.get_security_quotes([(1, '000905')
])[0]["last_close"]
self.products["000905"]["stocklst"]["510500"] = int(
index_price * self.number * self.permoney / price /
self.judgerate) * 100
def judgetradeday(self, ):
today = datetime.datetime.today().date().strftime('%Y-%m-%d')
df = ts.trade_cal()
return df[(df["calendarDate"] == today)].isOpen.values[0]
@property
def istradeday(self):
if not hasattr(self, "_istradeday"):
self._istradeday = self.judgetradeday()
return self._istradeday
def initial(self):
'''每天初始化设置
'''
logger.info("[INITIAL]:start initial !!!!")
if not self.istradeday:
self.trading = False
return
self.trading = True
logger.info("[INITIAL]:try to create connect... ")
self.connect()
self.trader = trade(UserID=self.userid,
api=self.api,
mock=self.mock,
server=self.server)
logger.info("[INITIAL]:connect successful!")
self.set_number() #设置手数
logger.info("[INITIAL]:initial finished !!!!!")
def handledata(self, df, args=[]):
df.loc[:, "number"] = range(df.shape[0])
s, m, l = args
for i in args: #5 15 60 D
key = str(5 * i)
df.loc[:, key + "high"] = df["high"].rolling(i).max()
df.loc[:, key + "low"] = df["low"].rolling(i).min()
df.loc[:, key + "atr"] = (df[key + "high"] -
df[key + "low"]).rolling(10 * i).mean()
df.loc[:, key + "med"] = (df[key + "high"] + df[key + "low"]) / 2
df.loc[:, key + "HH"] = df[key + "med"] + 1.5 * df[key + "atr"]
df.loc[:, key + "LL"] = df[key + "med"] - 1.5 * df[key + "atr"]
df.loc[:, key + 'HHmax'] = df[key + 'HH'].rolling(10 * i).max()
df.loc[:, key + 'LLmin'] = df[key + 'LL'].rolling(10 * i).min()
df.loc[df[key + 'HH'] >= df[key + 'HHmax'],
key + 'hmark'] = df["number"]
df.loc[df[key + 'LL'] <= df[key + 'LLmin'],
key + 'lmark'] = df["number"]
df[key + 'hmark'].fillna(method="ffill", inplace=True)
df[key + 'hmark'].fillna(0, inplace=True)
df[key + 'lmark'].fillna(method="ffill", inplace=True)
df[key + 'lmark'].fillna(0, inplace=True)
df.loc[:, key + 'UP'] = df[key + 'hmark'] >= df[key + 'lmark']
# debuginfo.append({key+'hmark':df.iloc[-1][key+'hmark'],key+'lmark':df.iloc[-1][key+'lmark']})
df.fillna(method="ffill", inplace=True)
df.dropna(inplace=True)
# logger.info("trademessage:{}".format(debuginfo))
result = (df.iloc[-1][str(5 * l) + "UP"] >
0) & (df.iloc[-1][str(5 * s) + "UP"] > 0)
result |= (df.iloc[-1][str(5 * l) + "UP"] >
0) & (df.iloc[-1][str(5 * m) + "UP"] > 0)
result |= (df.iloc[-1][str(5 * l) + "UP"] <= 0) & (df.iloc[-1][
str(5 * m) + "UP"] > 0) & (df.iloc[-1][str(5 * s) + "UP"] > 0)
return result
def sync(self, idx, director=True):
stocks = self.products[idx]["stocklst"]
for stock, number in stocks.items():
if not director: number = 2 * number #空信号,清仓
else: number = 4 * number
#判断现有持仓
try:
h_number = self.hd_df.ix[stock]["证券数量"]
except:
h_number = 0
#补仓差
cangcha = int((number - h_number) / 100) * 100
if h_number > 0 and abs(
cangcha
) / number < 0.05: #如果有持仓，同时仓差小于5% 不进行更改，为了处理频繁加减仓达到问题
return
if cangcha > 0:
logger.info("[RUN]:buy code:{}, number:{}".format(
stock, number - h_number))
self.buy(stock, cangcha)
elif cangcha < 0:
logger.info("[RUN]:sell code:{}, number:{}".format(
stock, h_number - number))
lastholdnumber = self.hd_df.ix[stock]["可卖数量"]
self.sell(stock, min(-cangcha, lastholdnumber))
def buy(self, stock, number, jump=0.02):
self.trader.buy(stock, number, jump=jump)
def sell(self, stock, number, jump=0.02):
self.trader.sell(stock, number, jump=jump)
def check_position(self, status):
'''检查仓位情况
'''
self.handleposition()
handlelist = []
for ins, v in status.items():
if ins not in self.g_df.index:
handlelist.append(ins)
elif self.g_df.ix[ins]["Position"] != v["number"]:
handlelist.append(ins)
return handlelist
def handleposition(self):
'''计算多仓，空仓，以及昨仓和今仓
'''
self.trader.cancelorder() #先尝试撤单
_, holdlists = self.trader.position()
self.hd_df = holdlists
if holdlists.shape[0] > 0:
self.hd_df.set_index("证券代码", inplace=True)
self.hd_df.index = self.hd_df.index.astype(np.str)
return self.hd_df
def run(self):
logger.info("[RUN]:start run !!!!!")
if not self.trading:
return
self.handleposition()
rst = {}
for idx in list(self.products.keys()):
director = self.handledata(self.getdata(idx, market=1),
self.products[idx]["args"]) #用指数出信号
logger.info("[RUN]:trademessage: block:{}, director:{}".format(
idx, director))
self.sync(idx, director)
rst[idx] = {
"up": director,
"number": self.products[idx]["stocklst"],
"product": idx
}
logger.info("[RUN]:lastest position status:{}".format(rst))
logger.info("[RUN]:run finished !!!!!")
def nhg(self):
'''尾盘自动进行逆回购
'''
if not self.mock:
self.trader.autobuy()
class TdxData:
def __init__(self):
self.api = TdxHq_API(heartbeat=True)
self.dbUtil = DBUtil.getInstance()
def get_security_quotes(self, code, type):
return self.api.get_security_quotes([(type, code)])
# 支持板块及个股
def days(self, code, type, bk=False, all=False, day=5):
category = int(config.getByKey('TDX_CATEGORY'))
try:
with self.api.connect(TDX_IP, TDX_PORT):
data = []
if all:
if bk:
for i in range(10):
data += self.api.get_index_bars(
category, type, code, (9 - i) * 800, 800)
else:
for i in range(10):
data += self.api.get_security_bars(
category, type, code, (9 - i) * 800, 800)
if len(data) > 0:
df = self.api.to_df(data).drop([
'amount', 'year', 'month', 'day', 'hour', 'minute'
],
axis=1)
df['trade_date'] = df['datetime'].apply(
lambda x: x[0:10].replace('-', ''))
df = df.drop(['datetime'], axis=1)
df = df.sort_values(by=['trade_date'],
axis=0,
ascending=False)
return df
else:
return self.api.to_df(data)
else:
if bk:
data = self.api.get_index_bars(category, type, code, 0,
day) # 返回DataFrame
else:
data = self.api.get_security_bars(
category, type, code, 0, day)
if len(data) > 0:
df = self.api.to_df(data).drop([
'amount', 'year', 'month', 'day', 'hour', 'minute'
],
axis=1)
df['trade_date'] = df['datetime'].apply(
lambda x: x[0:10].replace('-', ''))
df = df.drop(['datetime'], axis=1)
df = df.sort_values(by=['trade_date'],
axis=0,
ascending=False)
return df
else:
return self.api.to_df(data)
except Exception as e:
logging.info("暂不支持类型,代码：%s:%s" % (code, e))
return self.api.to_df([])
# F10 查询公司信息目录
def get_company_info_category(self, code, type):
with self.api.connect(TDX_IP, TDX_PORT):
df = pd.DataFrame(self.api.get_company_info_category(type, code))
df['txt'] = None
return df
return []
def get_company_info_content(self, code, type, df):
with self.api.connect(TDX_IP, TDX_PORT):
return self.api.get_company_info_content(type, code,
df['filename'].values[0],
df['start'].values[0],
df['length'].values[0])
return ""
# 查询财务数据
def get_finance_info(self, code, type):
with self.api.connect(TDX_IP, TDX_PORT):
return self.api.get_finance_info(type, code)
return ''
# 每年更新一次，板块个股关系
def updateBk(self):
with self.api.connect(TDX_IP, TDX_PORT):
"""
# 获取股票所属板块信息
# 板块相关参数
BLOCK_SZ = "block_zs.dat"
BLOCK_FG = "block_fg.dat"
BLOCK_GN = "block_gn.dat"
BLOCK_DEFAULT = "block.dat"
"""
bk_zs = self.api.to_df(
self.api.get_and_parse_block_info("block_zs.dat")) #指数板块
bk_fg = self.api.to_df(
self.api.get_and_parse_block_info("block_fg.dat")) #风格板块
bk_gn = self.api.to_df(
self.api.get_and_parse_block_info("block_gn.dat")) #概念板块
bk_default = self.api.to_df(
self.api.get_and_parse_block_info("block.dat")) # 默认
self.dbUtil.to_sql(bk_gn,
'stock_bk_gn',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_fg,
'stock_bk_fg',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_zs,
'stock_bk_zs',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_default,
'stock_bk_default',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
# 获取股票列表
tmp1 = self.api.to_df(self.api.get_security_list(0, 0)) # 深圳
tmp1['type'] = 0
tmp2 = self.api.to_df(self.api.get_security_list(1, 0)) # 上海
tmp2['type'] = 1
tmp = tmp1.append(tmp2)
self.dbUtil.to_sql(tmp,
'stock_bk',
if_exists='replace',
index=False,
dtype=STOCK_BK)
def updateGD(self, code, type):
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/ShareholderResearch/ShareholderResearchAjax?code=%s%s' % (
type.lower(), code)
html = urllib.request.urlopen(url).read()
# 将字符串转换成字典
data = json.loads(html.decode('utf-8'))
# gdrs 股东人数,sdgd 十大股东 ，sdltgd 十大流通股东
df_gdrs = pd.DataFrame(data['gdrs'])
df_gdrs['code'] = code
try:
db_df_gdrs = self.dbUtil.read_sql(
"select * from stock_gdrs where code ='%s'" % code)
# 数据合并
df_gdrs = df_gdrs.append(db_df_gdrs).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_gdrs,
'stock_gdrs',
if_exists='append',
index=False,
dtype=STOCK_GDRS_DTYPE)
sdgd = []
for i in range(len(data['sdgd'])):
sdgd += data['sdgd'][i]['sdgd']
df_sdgd = pd.DataFrame(sdgd)
df_sdgd['code'] = code
try:
db_df_sdgd = self.dbUtil.read_sql(
"select * from stock_sdgd where code ='%s'" % code)
df_sdgd = df_sdgd.append(db_df_sdgd).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_sdgd,
'stock_sdgd',
if_exists='append',
index=False,
dtype=STOCK_SDGD_DTYPE)
sdltgd = []
for i in range(len(data['sdltgd'])):
sdltgd += data['sdltgd'][i]['sdltgd']
df_sdltgd = pd.DataFrame(sdltgd)
df_sdltgd['code'] = code
# 获取后与数据库中的数据进行merge,首次表不存在，会抛异常
try:
db_df_sdltgd = self.dbUtil.read_sql(
"select * from stock_sdltgd where code ='%s'" % code)
df_sdltgd = df_sdltgd.append(db_df_sdltgd).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_sdltgd,
'stock_sdltgd',
if_exists='append',
index=False,
dtype=STOCK_SDGD_DTYPE)
# 没季度更新一次
def updateGDs(self):
codes = self.dbUtil.read_sql("select ts_code from stock_basic")
tmp = codes['ts_code'].str.split('.', expand=True)
for index, row in tmp.iterrows():
try:
self.updateGD(row[0], row[1])
logging.info('%s更新结束,当前索引%s' % (row[0], index))
except Exception as e:
logging.info('%s更新失败,当前索引%s' % (row[0], index))
# 分红
# 分红地址http://data.eastmoney.com/yjfp/201812.html
def updateFh(self, rq):
url = 'http://data.eastmoney.com/DataCenter_V3/yjfp/getlist.ashx?filter=(ReportingPeriod=^%s^)' % rq
html = requests.get(url)
# 将字符串转换成字典
data = json.loads(html.text)['data']
if len(data) == 0:
return 0
df = pd.DataFrame(data)
df['ReportingPeriod'] = df['ReportingPeriod'].apply(lambda x: x[0:10])
# 首次需要将df_fh制空，因为表还不存在
if self.dbUtil.is_exist("stock_fh"):
db_fh = self.dbUtil.read_sql(
"select * from stock_fh where ReportingPeriod = '%s'" %
df['ReportingPeriod'][0])
if db_fh.empty: # 不存在当前日期的分红信息，进行拼接
self.dbUtil.to_sql(df,
'stock_fh',
if_exists='append',
index=False)
return 1
else:
pass
else:
self.dbUtil.to_sql(df, 'stock_fh', if_exists='append', index=False)
return 1
# 更新历年分红
def updateFhYears(self):
now = int(time.strftime("%Y", time.localtime())) + 1
lastYear = int(self._getFhMaxYear())
for i in range(lastYear, now): #初始化时开启
type = self.updateFh('%s-06-30' % i)
logging.info('%s-06-30%s' % (i, '成功' if type == 1 else '失败'))
self.updateFh('%s-12-31' % i)
logging.info('%s-12-31%s' % (i, '成功' if type == 1 else '失败'))
def _getFhMaxYear(self):
if self.dbUtil.is_exist('stock_fh'):
try:
df = self.dbUtil.read_sql(
'select substr(max(t.ReportingPeriod ),0,5) year from stock_fh t'
)
return df['year'][0].values
except Exception as e:
pass
return 1991
@classmethod
def getInstance(cls):
if not hasattr(TdxData, "_instance"):
TdxData._instance = TdxData()
return TdxData._instance
class StdQuotes(object):
"""股票市场实时行情"""
# __slots__ =
def __init__(self, **kwargs):
self.config = None
try:
self.config = json.loads(
os.path.join(os.environ['HOME'], '.mootdx/config.josn'))
except Exception as e:
self.config = None
self.client = TdxHq_API(**kwargs)
if not self.config:
self.bestip = os.environ.setdefault("MOOTDX_SERVER",
'202.108.253.131:7709')
self.bestip = self.bestip.split(':')
self.bestip[1] = int(self.bestip[1])
else:
self.bestip = self.config.get('SERVER')
# K线
def bars(self, symbol='000001', category='9', start='0', offset='100'):
'''
获取实时日K线数据
:param symbol: 股票代码
:param category: 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
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_security_bars(int(category), int(market),
str(symbol), int(start),
int(offset))
return self.client.to_df(data)
# 分时数据
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
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_minute_time_data(int(market), symbol)
return self.client.to_df(data)
# 分时历史数据
def minute_his(self, symbol='', datetime='20161209'):
'''
分时历史数据
:param market:
:param symbol:
:param datetime:
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_history_minute_time_data(
int(market), symbol, datetime)
return self.client.to_df(data)
def trans(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
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_transaction_data(int(market), symbol,
int(start), int(market))
return self.client.to_df(data)
def trans_his(self, symbol='', start=0, offset=10, date=''):
'''
查询历史分笔成交
:param market: 市场代码
: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
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_history_transaction_data(
int(market), symbol, int(start), int(offset), date)
return self.client.to_df(data)
def company(self, symbol='', detail='category', *args, **kwargs):
'''
企业信息获取
:param symbol:
:param detail:
:param args:
:param kwargs:
:return:
'''
pass
def company_category(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
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_company_info_category(int(market), symbol)
return self.client.to_df(data)
def company_content(self, symbol='', file='', start=0, offset=10):
'''
读取公司信息详情
:param market: 市场代码
:param symbol: 股票代码
:param file: 文件名
:param start: 起始位置
:param offset: 数量
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_company_info_content(
int(market), symbol, file, int(start), int(offset))
return self.client.to_df(data)
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
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_xdxr_info(int(market), symbol)
return self.client.to_df(data)
def k(self, symbol='', begin=None, end=None):
'''
读取k线信息
:param symbol:
:param begin:
:param end:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
data = self.client.get_k_data(symbol, begin, end)
return data
def index(self,
symbol='000001',
market='sh',
category='9',
start='0',
offset='100'):
'''
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 category: 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
'''
market = 1 if market == 'sh' else 0
with self.client.connect(*self.bestip):
data = self.client.get_index_bars(int(category), int(market),
str(symbol), int(start),
int(offset))
return self.client.to_df(data)
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):
data = self.client.get_and_parse_block_info(tofile)
return self.client.to_df(data)
def batch(self, method='', offset=100, *args, **kwargs):
'''
批量下载相关数据
:param method:
:param offset:
:return:
'''
pass
