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_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_min(code, start, end, level='1min', ip=best_ip, port=7709):
api = TdxHq_API()
type_ = ''
if str(level) in ['5', '5m', '5min', 'five']:
level, type_ = 0, '5min'
elif str(level) in ['1', '1m', '1min', 'one']:
level, type_ = 8, '1min'
elif str(level) in ['15', '15m', '15min', 'fifteen']:
level, type_ = 1, '15min'
elif str(level) in ['30', '30m', '30min', 'half']:
level, type_ = 2, '30min'
elif str(level) in ['60', '60m', '60min', '1h']:
level, type_ = 3, '60min'
with api.connect(ip, port):
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([api.to_df(api.get_security_bars(
level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (25 - i) * 800, 800)) for i in range(26)], axis=0)
else:
data = pd.concat([api.to_df(api.get_index_bars(
level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (25 - i) * 800, 800)) for i in range(26)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
# data
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_block(ip=None, port=None):
'板块数据'
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")).assign(type='gn'),
api.to_df(api.get_and_parse_block_info(
"block.dat")).assign(type='yb'),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")).assign(type='zs'),
api.to_df(api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')])
if len(data) > 10:
return data.assign(source='tdx').drop(['block_type', 'code_index'], axis=1).set_index('code', drop=False, inplace=False).drop_duplicates()
else:
QA_util_log_info('Wrong with fetch block ')
def 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_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 get_a_quote_by_tdx(code_list):
"""
获取A股市场行情，该接口只获取A股市场行情
其他的行情使用拓展行情接口
code_list：数字或者字符串格式的list，调用def standard_tdx_code(code_list)来格式化市场信息及code
"""
if len(code_list) == 0:
return pd.DataFrame(columns=stock_tdx_columns)
# 一次最多获取50只股票的实时行情，如果code_list 多于50只，则50只股票取一次行情，然后进行拼接
tdx_api = TdxHq_API()
if len(code_list) >= 50:
tmp = div_list(code_list, 50)
df_tmp = pd.DataFrame(columns=stock_tdx_columns)
if tdx_api.connect('119.147.212.81', 7709):
for tmp_list in tmp:
tmp_list = standard_tdx_code(tmp_list)
tdx_data = tdx_api.to_df(tdx_api.get_security_quotes(tmp_list))
df_tmp = pd.concat([df_tmp, tdx_data])
df_tmp = df_tmp[stock_tdx_columns]
else:
tmp = standard_tdx_code(code_list)
if tdx_api.connect('119.147.212.81', 7709):
tdx_data = tdx_api.to_df(tdx_api.get_security_quotes(tmp))
df_tmp = tdx_data[stock_tdx_columns]
tdx_api.disconnect()
return process_tdx_price(df_tmp)
def 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, 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_stock_block(ip=best_ip, port=7709):
'板块数据'
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")),
api.to_df(api.get_and_parse_block_info("block.dat")),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")),
api.to_df(api.get_and_parse_block_info("block_fg.dat"))])
if len(data) > 10:
return data.assign(source='tdx').set_index('code', drop=False, inplace=False)
else:
QA_util_log_info('Wrong with fetch block ')
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', frequence='day', ip=None, port=None):
"""获取日线及以上级别的数据
Arguments:
code {str:6} -- code 是一个单独的code 6位长度的str
start_date {str:10} -- 10位长度的日期 比如'2017-01-01'
end_date {str:10} -- 10位长度的日期 比如'2018-01-01'
Keyword Arguments:
if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权
frequency {str} -- day/week/month/quarter/year 也可以是简写 D/W/M/Q/Y
ip {str} -- [description] (default: None) ip可以通过select_best_ip()函数重新获取
port {int} -- [description] (default: {None})
Returns:
pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获取了一天,而当天停牌,返回None
Exception:
如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理
"""
ip, port = get_mainmarket_ip(ip, port)
api = TdxHq_API()
with api.connect(ip, port, time_out=0.7):
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['quarter', 'Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code(
code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
# 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了
if len(data) < 1:
return None
data = data[data['open'] != 0]
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10])),
code=str(code),\
date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)
data = data.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date]
if if_fq in ['00','bfq']:
return data
else:
print('CURRENTLY NOT SUPPORT REALTIME FUQUAN')
return None
def QA_fetch_get_stock_xdxr(code, ip=None, port=None):
'除权除息'
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
market_code = _select_market_code(code)
with api.connect(ip, port):
category = {
'1': '除权除息', '2': '送配股上市', '3': '非流通股上市', '4': '未知股本变动', '5': '股本变化',
'6': '增发新股', '7': '股份回购', '8': '增发新股上市', '9': '转配股上市', '10': '可转债上市',
'11': '扩缩股', '12': '非流通股缩股', '13': '送认购权证', '14': '送认沽权证'}
data = api.to_df(api.get_xdxr_info(market_code, code))
if len(data) >= 1:
data = data\
.assign(date=pd.to_datetime(data[['year', 'month', 'day']]))\
.drop(['year', 'month', 'day'], axis=1)\
.assign(category_meaning=data['category'].apply(lambda x: category[str(x)]))\
.assign(code=str(code))\
.rename(index=str, columns={'panhouliutong': 'liquidity_after',
'panqianliutong': 'liquidity_before', 'houzongguben': 'shares_after',
'qianzongguben': 'shares_before'})\
.set_index('date', drop=False, inplace=False)
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
else:
return None
def QA_fetch_get_stock_min(code,
start,
end,
level,
ip='221.231.141.60',
port=7709):
api = TdxHq_API()
market_code = __select_market_code(code)
if str(level) in ['5', '5m', '5min', 'five']:
level = 0
elif str(level) in ['1', '1m', '1min', 'one']:
level = 8
elif str(level) in ['15', '15m', '15min', 'fifteen']:
level = 1
elif str(level) in ['30', '30m', '30min', 'half']:
level = 2
elif str(level) in ['60', '60m', '60min', '1h']:
level = 3
with api.connect(ip, port):
data = []
for i in range(26):
data += api.get_security_bars(level, market_code, code,
(25 - i) * 800, 800)
data = api.to_df(data)
data['datetime'] = pd.to_datetime(data['datetime'])
data['code'] = code
data = data.set_index('datetime', drop=False)
data = data.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1)
data['datetime'] = data['datetime'].apply(lambda x: str(x)[0:19])
data['date'] = data['datetime'].apply(lambda x: str(x)[0:10])
data['date_stamp'] = data['date'].apply(
lambda x: QA_util_date_stamp(x))
data['time_stamp'] = data['datetime'].apply(
lambda x: QA_util_time_stamp(x))
return data[start:end]
def fetch_get_stock_min(code, start, end, level='1min', ip=ac.TDX_BEST_IP, port=7709):
api = TdxHq_API()
type_ = ''
if str(level) in ['5', '5m', '5min', 'five']:
level, type_ = 0, '5min'
elif str(level) in ['1', '1m', '1min', 'one']:
level, type_ = 8, '1min'
elif str(level) in ['15', '15m', '15min', 'fifteen']:
level, type_ = 1, '15min'
elif str(level) in ['30', '30m', '30min', 'half']:
level, type_ = 2, '30min'
elif str(level) in ['60', '60m', '60min', '1h']:
level, type_ = 3, '60min'
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(level, __select_market_code(
str(code)), str(code), (25 - i) * 800, 800)) for i in range(26)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_transaction_realtime(code, ip=None, port=None):
'实时分笔成交 包含集合竞价 buyorsell 1--sell 0--buy 2--盘前'
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
try:
with api.connect(ip, port):
data = pd.DataFrame()
data = pd.concat([api.to_df(api.get_transaction_data(
_select_market_code(str(code)), code, (2 - i) * 2000, 2000)) for i in range(3)], axis=0)
if 'value' in data.columns:
data = data.drop(['value'], axis=1)
data = data.dropna()
day = datetime.date.today()
return data.assign(date=str(day)).assign(datetime=pd.to_datetime(data['time'].apply(lambda x: str(day) + ' ' + str(x))))\
.assign(code=str(code)).assign(order=range(len(data.index))).set_index('datetime', drop=False, inplace=False)
except:
return None
def __init__(self, code):
self.code = str(code)
self.market = 2
if code.startswith('002') or code.startswith('300') or code.startswith(
'000'):
self.market = TDXParams.MARKET_SZ
elif code.startswith('60'):
self.market = TDXParams.MARKET_SH
if self.market == 2:
raise Exception('code should be stock code')
api = TdxHq_API()
stock_path = Path.home().joinpath('stocks').joinpath(self.code)
if not Path.exists(stock_path):
Path.mkdir(stock_path)
xdxr_path = stock_path.joinpath('xdxr_' +
str(datetime.datetime.now().date()) +
'.csv')
if not Path.exists(xdxr_path):
with api.connect('119.147.212.81', 7709):
xdxr = api.to_df(api.get_xdxr_info(self.market, self.code))
xdxr.to_csv(xdxr_path)
self.xdxr = pd.read_csv(xdxr_path)
pg = self.xdxr.loc[self.xdxr['peigu'] > 0]
def QA_fetch_get_stock_min(code, start, end, frequence='1min', ip=best_ip['stock'], port=7709):
api = TdxHq_API()
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens
if lens > 20800:
lens = 20800
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code(
str(code)), str(code), (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_xdxr(code, ip=best_ip, port=7709):
'除权除息'
api = TdxHq_API()
market_code = __select_market_code(code)
with api.connect(ip, port):
category = {
'1': '除权除息',
'2': '送配股上市',
'3': '非流通股上市',
'4': '未知股本变动',
'5': '股本变化',
'6': '增发新股',
'7': '股份回购',
'8': '增发新股上市',
'9': '转配股上市',
'10': '可转债上市',
'11': '扩缩股',
'12': '非流通股缩股',
'13': '送认购权证',
'14': '送认沽权证'
}
data = api.to_df(api.get_xdxr_info(market_code, code))
data = data\
.assign(date=pd.to_datetime(data[['year', 'month', 'day']]))\
.drop(['year', 'month', 'day'], axis=1)\
.assign(category_meaning=data['category'].apply(lambda x: category[str(x)]))\
.assign(code=str(code))\
.rename(index=str, columns={'panhouliutong': 'liquidity_after',
'panqianliutong': 'liquidity_before', 'houzongguben': 'shares_after',
'qianzongguben': 'shares_before'})\
.set_index('date', drop=False, inplace=False)
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_stock_block(ip=best_ip['stock'], port=7709):
'板块数据'
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")).assign(type='gn'),
api.to_df(api.get_and_parse_block_info(
"block.dat")).assign(type='yb'),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")).assign(type='zs'),
api.to_df(api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')])
if len(data) > 10:
return data.assign(source='tdx').drop(['block_type', 'code_index'], axis=1).set_index('code', drop=False, inplace=False).drop_duplicates()
else:
QA_util_log_info('Wrong with fetch block ')
def QA_fetch_get_stock_list(type_='stock', ip=best_ip['stock'], port=7709):
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
#data.code = data.code.apply(int)
sz = data.query('sse=="sz"')
sh = data.query('sse=="sh"')
sz = sz.assign(sec=sz.code.apply(for_sz))
sh = sh.assign(sec=sh.code.apply(for_sh))
if type_ in ['stock', 'gp']:
return pd.concat([sz, sh]).query('sec=="stock_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
elif type_ in ['index', 'zs']:
return pd.concat([sz, sh]).query('sec=="index_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
#.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
#.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
elif type_ in ['etf', 'ETF']:
return pd.concat([sz, sh]).query('sec=="etf_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
else:
return data.assign(code=data['code'].apply(lambda x: str(x))).assign(name=data['name'].apply(lambda x: str(x)[0:6]))
def QA_fetch_get_stock_list(type_='stock', ip=best_ip, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
if type_ in ['stock', 'gp']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 <= 30][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 != 2],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 == 6]]).assign(code=data['code'].apply(lambda x: str(x)))
#.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
#.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
elif type_ in ['index', 'zs']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 >= 399],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 == 0]]) \
.sort_index()\
.assign(code=data['code'].apply(lambda x: str(x)))
#.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
#.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
elif type_ in ['etf', 'ETF']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 15],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 51]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))\
#.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
#.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
else:
return data.assign(code=data['code'].apply(lambda x: str(x)))
def QA_fetch_get_security_bars(code, _type, lens, ip=best_ip['stock'], port=7709):
"""按bar长度推算数据
Arguments:
code {[type]} -- [description]
_type {[type]} -- [description]
lens {[type]} -- [description]
Keyword Arguments:
ip {[type]} -- [description] (default: {best_ip})
port {[type]} -- [description] (default: {7709})
Returns:
[type] -- [description]
"""
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(_select_type(_type), _select_market_code(
code), code, (i - 1) * 800, 800)) for i in range(1, int(lens / 800) + 2)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=_type).set_index('datetime', drop=False, inplace=False).tail(lens)
if data is not None:
return data
else:
return None
def QA_fetch_get_stock_min(code,
start,
end,
level,
ip='221.231.141.60',
port=7709):
api = TdxHq_API()
market_code = __select_market_code(code)
if str(level) in ['5', '5m', '5min', 'five']:
level = 0
elif str(level) in ['1', '1m', '1min', 'one']:
level = 8
elif str(level) in ['15', '15m', '15min', 'fifteen']:
level = 1
elif str(level) in ['30', '30m', '30min', 'half']:
level = 2
elif str(level) in ['60', '60m', '60min', '1h']:
level = 3
with api.connect(ip, port):
data = []
for i in range(26):
data += api.get_security_bars(level, market_code, code,
(25 - i) * 800, 800)
data = api.to_df(data)
data['datetime'] = pd.to_datetime(data['datetime'])
data['code'] = code
data = data.set_index('datetime')
return data[start:end]
def QA_fetch_get_stock_info(code, ip=None, port=None):
'股票基本信息'
ip, port = get_mainmarket_ip(ip, port)
api = TdxHq_API()
market_code = _select_market_code(code)
with api.connect(ip, port):
return api.to_df(api.get_finance_info(market_code, code))
def get_xdxr_info(stock_code, market):
api = TdxHq_API()
xdxr_df = None
with api.connect('119.147.212.81', 7709):
xdxr_df = api.get_xdxr_info(market, stock_code)
xdxr_df = api.to_df(xdxr_df)
return xdxr_df
def QA_fetch_get_stock_info(code,
ip=best_ip['stock']['ip'],
port=best_ip['stock']['port']):
'股票基本信息'
api = TdxHq_API()
market_code = _select_market_code(code)
with api.connect(ip, port):
return api.to_df(api.get_finance_info(market_code, code))
def get_stock_count():
api = TdxHq_API()
if api.connect('119.147.212.81', 7709):
print(api.get_security_count(0)) #参数为市场代码
print(api.get_security_list(0, 0)) #第一个参数为市场代码，第二个参数为起始位置
print(api.get_security_count(1))
print(api.to_df(api.get_security_list(1, 0)))
api.disconnect()
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 fetch_bars(self, typ: SeType, code, start, end, freq):
try:
tdxApi = TdxHq_API()
ex_nbr = map_exchange_to_tdx_number[Exchange.from_type_and_code(typ, code)]
if typ == SeType.Stock or typ == SeType.ETF:
tdx_fetch_func = tdxApi.get_security_bars
elif typ == SeType.Index:
tdx_fetch_func = tdxApi.get_index_bars
else:
raise Exception('Unsupported se type')
#timeout is in seconds.
with tdxApi.connect(self.server['ip'], self.server['port'], time_out=3):
frequence = map_freq_to_tdx_number[freq][0]
days_between = date_time_utils.Date.from_str(end).delta_to(start).days + 1
if days_between > MAX_DAYS:
days_between = MAX_DAYS
max_count = int(map_freq_to_tdx_number[freq][1] * days_between)
tmp_list = []
n_start = 0
while n_start < max_count:
fetch_size = min(max_count - n_start, BATCH_SIZE)
tmp_res = tdx_fetch_func(
frequence,
ex_nbr,
code, n_start, fetch_size
)
if tmp_res is None or len(tmp_res) < 1:
break
n_start += len(tmp_res)
tmp_list.extend(tmp_res)
if n_start == 0:
print('No rows fetched for {}'.format(code))
# print('total fetched rows for {}: {}'.format(code, n_start))
data = tdxApi.to_df(tmp_list)
if len(data) < 1:
return None
data = data[data['open'] != 0]
cols_to_drop = ['year', 'month', 'day']
if freq.is_minutes():
data = data.assign(datetime=data['datetime'].apply(lambda x: str(x[0:19])),
date=data['datetime'].apply(lambda x: str(x[0:10])),
code=str(code)) \
.set_index('datetime', drop=True, inplace=False) \
.drop(cols_to_drop, axis=1)
else:
cols_to_drop.extend(['datetime', 'hour', 'minute'])
data = data.assign(datetime=data['datetime'],
date=data['datetime'].apply(lambda x: str(x[0:10])),
code=str(code)) \
.set_index('date', drop=True, inplace=False) \
.drop(cols_to_drop, axis=1)
return data
except Exception as e:
print('exception caught: {}'.format(e))
def fetch_security_bars(code, _type, lens, ip=ac.TDX_BEST_IP, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(__select_type(_type), __select_market_code(
code), code, (i - 1) * 800, 800)) for i in range(1, int(lens / 800) + 2)], axis=0)
if data is not None:
return data
else:
return None
def __hq_tick(self, code):
api = TdxHq_API()
ip, port = self.__hq.get()
with api.connect(ip, port):
df = api.get_security_quotes(code)
if df is not None:
df = api.to_df(df)
api.disconnect()
self.__hq.put((ip, port))
return df
def QA_fetch_get_index_day(code, start_date, end_date, frequence='day', ip=None, port=None):
'指数日线'
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
with api.connect(ip, port):
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([api.to_df(api.get_security_bars(
frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
else:
data = pd.concat([api.to_df(api.get_index_bars(
frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.assign(code=code)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)[start_date:end_date]
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=best_ip, port=7709):
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(__select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'code', 'open', 'high', 'low', 'price']]
return data.set_index('code', drop=False, inplace=False)
def get_block_info():
api = TdxHq_API()
if api.connect('119.147.212.81', 7709):
data = api.get_and_parse_block_info("block.dat") #一般板块
#print(api.get_and_parse_block_info("block_zs.dat")) #指数板块
#print(api.get_and_parse_block_info("block_fg.dat")) #风格板块
#print(api.get_and_parse_block_info("block_gn.dat")) #概念板块
datadf = api.to_df(data)
print(datadf)
api.disconnect()
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=best_ip, port=7709):
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(__select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'code', 'open', 'high', 'low', 'price']]
return data.set_index('code', drop=False, inplace=False)
def QA_fetch_get_stock_latest(code, ip=best_ip, port=7709):
code = [code] if isinstance(code, str) else code
api = TdxHq_API(multithread=True)
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(
9, __select_market_code(item), item, 0, 1)).assign(code=item) for item in code], axis=0)
return data\
.assign(date=pd.to_datetime(data['datetime']
.apply(lambda x: x[0:10])), date_stamp=data['datetime']
.apply(lambda x: QA_util_date_stamp(str(x[0:10]))))\
.set_index('date', drop=False)\
.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
def QA_fetch_get_stock_info(code, ip=None, port=None):
'股票基本信息'
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
market_code = _select_market_code(code)
with api.connect(ip, port):
return api.to_df(api.get_finance_info(market_code, code))
def QA_fetch_get_stock_list(type_='stock', ip=best_ip, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
if type_ in ['stock', 'gp']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 <= 30][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 != 2],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 == 6]]).assign(code=data['code'].apply(lambda x: str(x)))
elif type_ in ['index', 'zs']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 >= 399],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 == 0]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))
elif type_ in ['etf', 'ETF']:
return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 15],
data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 51]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))
else:
return data.assign(code=data['code'].apply(lambda x: str(x)))
def QA_fetch_get_security_bars(code, _type, lens, ip=None, port=None):
"""按bar长度推算数据
Arguments:
code {[type]} -- [description]
_type {[type]} -- [description]
lens {[type]} -- [description]
Keyword Arguments:
ip {[type]} -- [description] (default: {best_ip})
port {[type]} -- [description] (default: {7709})
Returns:
[type] -- [description]
"""
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(_select_type(_type), _select_market_code(
code), code, (i - 1) * 800, 800)) for i in range(1, int(lens / 800) + 2)], axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=_type).set_index('datetime', drop=False, inplace=False).tail(lens)
if data is not None:
return data
else:
return None
def QA_fetch_get_stock_latest(code, ip=None, port=None):
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
code = [code] if isinstance(code, str) else code
api = TdxHq_API(multithread=True)
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_security_bars(
9, _select_market_code(item), item, 0, 1)).assign(code=item) for item in code], axis=0)
return data\
.assign(date=pd.to_datetime(data['datetime']
.apply(lambda x: x[0:10])), date_stamp=data['datetime']
.apply(lambda x: QA_util_date_stamp(str(x[0:10]))))\
.set_index('date', drop=False)\
.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
def QA_fetch_depth_market_data(code=['000001', '000002'], ip=None, port=None):
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
__data = pd.DataFrame()
with api.connect(ip, port):
code = [code] if type(code) is str else code
for id_ in range(int(len(code) / 80) + 1):
__data = __data.append(api.to_df(api.get_security_quotes(
[(_select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
data = __data[['datetime', 'active1', 'active2', 'last_close', 'code', 'open', 'high', 'low', 'price', 'cur_vol',
's_vol', 'b_vol', 'vol', 'ask1', 'ask_vol1', 'bid1', 'bid_vol1', 'ask2', 'ask_vol2',
'bid2', 'bid_vol2', 'ask3', 'ask_vol3', 'bid3', 'bid_vol3', 'ask4',
'ask_vol4', 'bid4', 'bid_vol4', 'ask5', 'ask_vol5', 'bid5', 'bid_vol5']]
return data.set_index(['datetime', 'code'], drop=False, inplace=False)
def QA_fetch_get_stock_list(type_='stock', ip=None, port=None):
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
#data.code = data.code.apply(int)
sz = data.query('sse=="sz"')
sh = data.query('sse=="sh"')
sz = sz.assign(sec=sz.code.apply(for_sz))
sh = sh.assign(sec=sh.code.apply(for_sh))
if type_ in ['stock', 'gp']:
return pd.concat([sz, sh]).query('sec=="stock_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
elif type_ in ['index', 'zs']:
return pd.concat([sz, sh]).query('sec=="index_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
#.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
#.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
elif type_ in ['etf', 'ETF']:
return pd.concat([sz, sh]).query('sec=="etf_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
else:
return data.assign(code=data['code'].apply(lambda x: str(x))).assign(name=data['name'].apply(lambda x: str(x)[0:6]))
one_chunk = 0x7530
chuncks = size // one_chunk
if size % one_chunk != 0:
chuncks += 1
file_content = bytearray()
for seg in range(chuncks):
start = seg * one_chunk
piece_data = client.get_block_info(blockfile, start, size)
file_content.extend(piece_data)
return BlockReader().get_data(file_content, BlockReader_TYPE_FLAT)
if __name__ == '__main__':
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect():
# ret = api.get_block_info("block_zs.dat", 0, 100)
# print(len(ret))
# ret = api.get_and_parse_block_info("block_fg.dat")
# ret = api.get_and_parse_block_info("block_zs.dat")
# ret = api.get_and_parse_block_info("block_gn.dat")
# ret = api.get_and_parse_block_info("block.dat")
ret = api.get_and_parse_block_info("block.dat")
print(api.to_df(ret))
# \x80\x03 = 14:56
hour, minute, pos = get_time(body_buf, pos)
price_raw, pos = get_price(body_buf, pos)
vol, pos = get_price(body_buf, pos)
buyorsell, pos = get_price(body_buf, pos)
_, pos = get_price(body_buf, pos)
last_price = last_price + price_raw
tick = OrderedDict(
[
("time", "%02d:%02d" % (hour, minute)),
("price", float(last_price)/100),
("vol", vol),
("buyorsell", buyorsell),
]
)
ticks.append(tick)
return ticks
if __name__ == '__main__':
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect():
print(api.to_df(api.get_history_transaction_data(0, '000001', 0, 10, 20170811)))
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', level='day', ip=best_ip, port=7709):
api = TdxHq_API()
with api.connect(ip, port):
if level in ['day', 'd', 'D', 'DAY', 'Day']:
level = 9
elif level in ['w', 'W', 'Week', 'week']:
level = 5
elif level in ['month', 'M', 'm', 'Month']:
level = 6
elif level in ['Q', 'Quarter', 'q']:
level = 10
elif level in ['y', 'Y', 'year', 'Year']:
level = 11
data = pd.concat([api.to_df(api.get_security_bars(level, __select_market_code(
code), code, (9 - i) * 800, 800)) for i in range(10)], axis=0)
if if_fq in ['00', 'bfq']:
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)[start_date:end_date]
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
elif if_fq in ['01', 'qfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade']=True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['03', 'ddqfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade']=True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:end_date]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['02', 'hfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade']=True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['04', 'ddhfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade']=True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:end_date]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', frequence='day', ip=None, port=None):
"""获取日线及以上级别的数据
Arguments:
code {str:6} -- code 是一个单独的code 6位长度的str
start_date {str:10} -- 10位长度的日期 比如'2017-01-01'
end_date {str:10} -- 10位长度的日期 比如'2018-01-01'
Keyword Arguments:
if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权
frequency {str} -- day/week/month/quarter/year 也可以是简写 D/W/M/Q/Y
ip {str} -- [description] (default: None) ip可以通过select_best_ip()函数重新获取
port {int} -- [description] (default: {None})
Returns:
pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获取了一天,而当天停牌,返回None
Exception:
如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理
"""
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
with api.connect(ip, port, time_out=0.7):
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['quarter', 'Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code(
code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0)
# 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了
if len(data) < 1:
return None
data = data[data['open'] != 0]
if if_fq in ['00', 'bfq']:
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
return data.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date].assign(date=data['date'].apply(lambda x: str(x)[0:10]))
elif if_fq in ['01', 'qfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
bfq_data = data.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
bfq_data = bfq_data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
#
if xdxr_data is not None:
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
else:
bfq_data['preclose'] = bfq_data['close'].shift(1)
bfq_data['adj'] = 1
return bfq_data[start_date:end_date]
elif if_fq in ['03', 'ddqfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:end_date]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['02', 'hfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['04', 'ddhfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat([bfq_data, info[['category']]
[bfq_data.index[0]:end_date]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia',
'songzhuangu']][bfq_data.index[0]:end_date]], axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu']
* data['peigujia']) / (10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
class QA_Tdx_Executor():
def __init__(self, thread_num=2, timeout=1, *args, **kwargs):
self.thread_num = thread_num
self._queue = queue.Queue(maxsize=200)
self.api_no_connection = TdxHq_API()
self._api_worker = Thread(
target=self.api_worker, args=(), name='API Worker')
self._api_worker.start()
self.timeout = timeout
self.executor = ThreadPoolExecutor(self.thread_num)
def __getattr__(self, item):
try:
api = self.get_available()
func = api.__getattribute__(item)
def wrapper(*args, **kwargs):
res = self.executor.submit(func, *args, **kwargs)
self._queue.put(api)
return res
return wrapper
except:
return self.__getattr__(item)
def _queue_clean(self):
self._queue = queue.Queue(maxsize=200)
def _test_speed(self, ip, port=7709):
api = TdxHq_API(raise_exception=True, auto_retry=False)
_time = datetime.datetime.now()
# print(self.timeout)
try:
with api.connect(ip, port, time_out=1):
res = api.get_security_list(0, 1)
# print(res)
# print(len(res))
if len(api.get_security_list(0, 1)) > 800:
return (datetime.datetime.now() - _time).total_seconds()
else:
return datetime.timedelta(9, 9, 0).total_seconds()
except Exception as e:
return datetime.timedelta(9, 9, 0).total_seconds()
def get_market(self, code):
code = str(code)
if code[0] in ['5', '6', '9'] or code[:3] in ["009", "126", "110", "201", "202", "203", "204"]:
return 1
return 0
def get_frequence(self, frequence):
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
elif str(frequence) in ['5', '5m', '5min', 'five']:
frequence = 0
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence = 8
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence = 1
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence = 2
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence = 3
return frequence
@property
def ipsize(self):
return len(self._queue.qsize())
@property
def api(self):
return self.get_available()
def get_available(self):
if self._queue.empty() is False:
return self._queue.get_nowait()
else:
Timer(0, self.api_worker).start()
return self._queue.get()
def api_worker(self):
data = []
if self._queue.qsize() < 80:
for item in stock_ip_list:
if self._queue.full():
break
_sec = self._test_speed(ip=item['ip'], port=item['port'])
if _sec < self.timeout*3:
try:
self._queue.put(TdxHq_API(heartbeat=False).connect(
ip=item['ip'], port=item['port'], time_out=self.timeout*2))
except:
pass
else:
self._queue_clean()
Timer(0, self.api_worker).start()
Timer(300, self.api_worker).start()
def _singal_job(self, context, id_, time_out=0.7):
try:
_api = self.get_available()
__data = context.append(self.api_no_connection.to_df(_api.get_security_quotes(
[(self._select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
__data['datetime'] = datetime.datetime.now()
self._queue.put(_api) # 加入注销
return __data
except:
return self.singal_job(context, id_)
def get_realtime(self, code):
context = pd.DataFrame()
code = [code] if isinstance(code, str) is str else code
try:
for id_ in range(int(len(code) / 80) + 1):
context = self._singal_job(context, id_)
data = context[['datetime', 'last_close', 'code', 'open', 'high', 'low', 'price', 'cur_vol',
's_vol', 'b_vol', 'vol', 'ask1', 'ask_vol1', 'bid1', 'bid_vol1', 'ask2', 'ask_vol2',
'bid2', 'bid_vol2', 'ask3', 'ask_vol3', 'bid3', 'bid_vol3', 'ask4',
'ask_vol4', 'bid4', 'bid_vol4', 'ask5', 'ask_vol5', 'bid5', 'bid_vol5']]
data['datetime'] = data['datetime'].apply(lambda x: str(x))
return data.set_index('code', drop=False, inplace=False)
except:
return None
def get_realtime_concurrent(self, code):
code = [code] if isinstance(code, str) is str else code
try:
data = {self.get_security_quotes([(self.get_market(
x), x) for x in code[80 * pos:80 * (pos + 1)]]) for pos in range(int(len(code) / 80) + 1)}
return (pd.concat([self.api_no_connection.to_df(i.result()) for i in data]), datetime.datetime.now())
except:
pass
def get_security_bar_concurrent(self, code, _type, lens):
try:
data = {self.get_security_bars(self.get_frequence(_type), self.get_market(
str(code)), str(code), 0, lens) for code in code}
return [i.result() for i in data]
except:
raise Exception
def _get_security_bars(self, context, code, _type, lens):
try:
_api = self.get_available()
for i in range(1, int(lens / 800) + 2):
context.extend(_api.get_security_bars(self.get_frequence(
_type), self.get_market(str(code)), str(code), (i - 1) * 800, 800))
print(context)
self._queue.put(_api)
return context
except Exception as e:
return self._get_security_bars(context, code, _type, lens)
def get_security_bar(self, code, _type, lens):
code = [code] if isinstance(code, str) is str else code
context = []
try:
for item in code:
context = self._get_security_bars(context, item, _type, lens)
return context
except Exception as e:
raise e
def save_mongo(self, data, client=DATABASE):
database = DATABASE.get_collection(
'realtime_{}'.format(datetime.date.today()))
database.insert_many(QA_util_to_json_from_pandas(data))
)
rows.append(row)
return rows
def get_category_name(self, category_id):
if category_id in XDXR_CATEGORY_MAPPING:
return XDXR_CATEGORY_MAPPING[category_id]
else:
return str(category_id)
if __name__ == '__main__':
from pytdx.util.best_ip import select_best_ip
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect():
# 11 扩缩股
print(api.to_df(api.get_xdxr_info(1, '600381')))
# 12 非流通股缩股
#print(api.to_df(api.get_xdxr_info(1, '600339')))
# 13 送认购权证
#print(api.to_df(api.get_xdxr_info(1, '600008')))
# 14 送认沽权证
#print(api.to_df(api.get_xdxr_info(0, '000932')))
