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Please provide a description of the function:def QA_fetch_get_option_50etf_contract_time_to_market():
'''
#🛠todo 获取期权合约的上市日期 ？ 暂时没有。
:return: list Series
'''
result = QA_fetch_get_option_list('tdx')
# pprint.pprint(result)
# category market code name desc code
'''
fix here :
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
result['meaningful_name'] = None
C:\work_new\QUANTAXIS\QUANTAXIS\QAFetch\QATdx.py:1468: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
'''
# df = pd.DataFrame()
rows = []
result['meaningful_name'] = None
for idx in result.index:
# pprint.pprint((idx))
strCategory = result.loc[idx, "category"]
strMarket = result.loc[idx, "market"]
strCode = result.loc[idx, "code"] # 10001215
strName = result.loc[idx, 'name'] # 510050C9M03200
strDesc = result.loc[idx, 'desc'] # 10001215
if strName.startswith("510050"):
# print(strCategory,' ', strMarket, ' ', strCode, ' ', strName, ' ', strDesc, )
if strName.startswith("510050C"):
putcall = '50ETF,认购期权'
elif strName.startswith("510050P"):
putcall = '50ETF,认沽期权'
else:
putcall = "Unkown code name ： " + strName
expireMonth = strName[7:8]
if expireMonth == 'A':
expireMonth = "10月"
elif expireMonth == 'B':
expireMonth = "11月"
elif expireMonth == 'C':
expireMonth = "12月"
else:
expireMonth = expireMonth + '月'
# 第12位期初设为“M”，并根据合约调整次数按照“A”至“Z”依序变更，如变更为“A”表示期权合约发生首次调整，变更为“B”表示期权合约发生第二次调整，依此类推；
# fix here : M ??
if strName[8:9] == "M":
adjust = "未调整"
elif strName[8:9] == 'A':
adjust = " 第1次调整"
elif strName[8:9] == 'B':
adjust = " 第2调整"
elif strName[8:9] == 'C':
adjust = " 第3次调整"
elif strName[8:9] == 'D':
adjust = " 第4次调整"
elif strName[8:9] == 'E':
adjust = " 第5次调整"
elif strName[8:9] == 'F':
adjust = " 第6次调整"
elif strName[8:9] == 'G':
adjust = " 第7次调整"
elif strName[8:9] == 'H':
adjust = " 第8次调整"
elif strName[8:9] == 'I':
adjust = " 第9次调整"
elif strName[8:9] == 'J':
adjust = " 第10次调整"
else:
adjust = " 第10次以上的调整，调整代码 %s" + strName[8:9]
executePrice = strName[9:]
result.loc[idx, 'meaningful_name'] = '%s,到期月份:%s,%s,行权价:%s' % (
putcall, expireMonth, adjust, executePrice)
row = result.loc[idx]
rows.append(row)
return rows
|
[] |
Please provide a description of the function:def QA_fetch_get_commodity_option_CF_contract_time_to_market():
'''
铜期权 CU 开头 上期证
豆粕 M开头 大商所
白糖 SR开头 郑商所
测试中发现，行情不太稳定 ？ 是 通达信 IP 的问题 ？
'''
result = QA_fetch_get_option_list('tdx')
# pprint.pprint(result)
# category market code name desc code
# df = pd.DataFrame()
rows = []
result['meaningful_name'] = None
for idx in result.index:
# pprint.pprint((idx))
strCategory = result.loc[idx, "category"]
strMarket = result.loc[idx, "market"]
strCode = result.loc[idx, "code"] #
strName = result.loc[idx, 'name'] #
strDesc = result.loc[idx, 'desc'] #
# 如果同时获取， 不同的 期货交易所数据， pytdx会 connection close 连接中断？
# if strName.startswith("CU") or strName.startswith("M") or strName.startswith('SR'):
if strName.startswith("CF"):
# print(strCategory,' ', strMarket, ' ', strCode, ' ', strName, ' ', strDesc, )
row = result.loc[idx]
rows.append(row)
return rows
pass
|
[] |
Please provide a description of the function:def QA_fetch_get_exchangerate_list(ip=None, port=None):
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
return extension_market_list.query('market==10 or market==11').query('category==4')
|
[
"汇率列表\n\n Keyword Arguments:\n ip {[type]} -- [description] (default: {None})\n port {[type]} -- [description] (default: {None})\n\n ## 汇率 EXCHANGERATE\n 10 4 基本汇率 FE\n 11 4 交叉汇率 FX\n\n\n "
] |
Please provide a description of the function:def QA_fetch_get_future_day(code, start_date, end_date, frequence='day', ip=None, port=None):
'期货数据 日线'
ip, port = get_extensionmarket_ip(ip, port)
apix = TdxExHq_API()
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
with apix.connect(ip, port):
code_market = extension_market_list.query(
'code=="{}"'.format(code)).iloc[0]
data = pd.concat(
[apix.to_df(apix.get_instrument_bars(
_select_type(frequence),
int(code_market.market),
str(code),
(int(lens / 700) - i) * 700, 700)) for i in range(int(lens / 700) + 1)],
axis=0)
try:
# 获取商品期货会报None
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)
except Exception as exp:
print("code is ", code)
print(exp.__str__)
return None
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]))
|
[] |
Please provide a description of the function:def QA_fetch_get_future_min(code, start, end, frequence='1min', ip=None, port=None):
'期货数据 分钟线'
ip, port = get_extensionmarket_ip(ip, port)
apix = TdxExHq_API()
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens * 2.5
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens * 2.5
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens * 2.5
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens * 2.5
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens * 2.5
if lens > 20800:
lens = 20800
# print(lens)
with apix.connect(ip, port):
code_market = extension_market_list.query(
'code=="{}"'.format(code)).iloc[0]
data = pd.concat([apix.to_df(apix.get_instrument_bars(frequence, int(code_market.market), str(
code), (int(lens / 700) - i) * 700, 700)) for i in range(int(lens / 700) + 1)], axis=0)
# print(data)
# print(data.datetime)
data = data \
.assign(tradetime=data['datetime'].apply(str), code=str(code)) \
.assign(datetime=pd.to_datetime(data['datetime'].apply(QA_util_future_to_realdatetime, 1))) \
.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)
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))[start:end].sort_index()
|
[] |
Please provide a description of the function:def QA_fetch_get_future_transaction(code, start, end, retry=4, ip=None, port=None):
'期货历史成交分笔'
ip, port = get_extensionmarket_ip(ip, port)
apix = TdxExHq_API()
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
real_start, real_end = QA_util_get_real_datelist(start, end)
if real_start is None:
return None
real_id_range = []
with apix.connect(ip, port):
code_market = extension_market_list.query(
'code=="{}"'.format(code)).iloc[0]
data = pd.DataFrame()
for index_ in range(trade_date_sse.index(real_start), trade_date_sse.index(real_end) + 1):
try:
data_ = __QA_fetch_get_future_transaction(
code, trade_date_sse[index_], retry, int(code_market.market), apix)
if len(data_) < 1:
return None
except Exception as e:
print(e)
QA_util_log_info('Wrong in Getting {} history transaction data in day {}'.format(
code, trade_date_sse[index_]))
else:
QA_util_log_info('Successfully Getting {} history transaction data in day {}'.format(
code, trade_date_sse[index_]))
data = data.append(data_)
if len(data) > 0:
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)[0:19]))
else:
return None
|
[] |
Please provide a description of the function:def QA_fetch_get_future_transaction_realtime(code, ip=None, port=None):
'期货历史成交分笔'
ip, port = get_extensionmarket_ip(ip, port)
apix = TdxExHq_API()
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
code_market = extension_market_list.query(
'code=="{}"'.format(code)).iloc[0]
with apix.connect(ip, port):
data = pd.DataFrame()
data = pd.concat([apix.to_df(apix.get_transaction_data(
int(code_market.market), code, (30 - i) * 1800)) for i in range(31)], axis=0)
return data.assign(datetime=pd.to_datetime(data['date'])).assign(date=lambda x: str(x)[0:10]) \
.assign(code=str(code)).assign(order=range(len(data.index))).set_index('datetime', drop=False,
inplace=False)
|
[] |
Please provide a description of the function:def QA_fetch_get_future_realtime(code, ip=None, port=None):
'期货实时价格'
ip, port = get_extensionmarket_ip(ip, port)
apix = TdxExHq_API()
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
__data = pd.DataFrame()
code_market = extension_market_list.query(
'code=="{}"'.format(code)).iloc[0]
with apix.connect(ip, port):
__data = apix.to_df(apix.get_instrument_quote(
int(code_market.market), code))
__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'])
|
[] |
Please provide a description of the function:def concat(lists):
return lists[0].new(
pd.concat([lists.data for lists in lists]).drop_duplicates()
)
|
[
"类似于pd.concat 用于合并一个list里面的多个DataStruct,会自动去重\n\n\n\n Arguments:\n lists {[type]} -- [DataStruct1,DataStruct2,....,DataStructN]\n\n Returns:\n [type] -- new DataStruct\n "
] |
Please provide a description of the function:def datastruct_formater(
data,
frequence=FREQUENCE.DAY,
market_type=MARKET_TYPE.STOCK_CN,
default_header=[]
):
if isinstance(data, list):
try:
res = pd.DataFrame(data, columns=default_header)
if frequence is FREQUENCE.DAY:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_day(
res.assign(date=pd.to_datetime(res.date)
).set_index(['date',
'code'],
drop=False),
dtype='stock_day'
)
elif frequence in [FREQUENCE.ONE_MIN,
FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN]:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_min(
res.assign(datetime=pd.to_datetime(res.datetime)
).set_index(['datetime',
'code'],
drop=False),
dtype='stock_min'
)
except:
pass
elif isinstance(data, np.ndarray):
try:
res = pd.DataFrame(data, columns=default_header)
if frequence is FREQUENCE.DAY:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_day(
res.assign(date=pd.to_datetime(res.date)
).set_index(['date',
'code'],
drop=False),
dtype='stock_day'
)
elif frequence in [FREQUENCE.ONE_MIN,
FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN]:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_min(
res.assign(datetime=pd.to_datetime(res.datetime)
).set_index(['datetime',
'code'],
drop=False),
dtype='stock_min'
)
except:
pass
elif isinstance(data, pd.DataFrame):
index = data.index
if isinstance(index, pd.MultiIndex):
pass
elif isinstance(index, pd.DatetimeIndex):
pass
elif isinstance(index, pd.Index):
pass
|
[
"一个任意格式转化为DataStruct的方法\n \n Arguments:\n data {[type]} -- [description]\n \n Keyword Arguments:\n frequence {[type]} -- [description] (default: {FREQUENCE.DAY})\n market_type {[type]} -- [description] (default: {MARKET_TYPE.STOCK_CN})\n default_header {list} -- [description] (default: {[]})\n \n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def from_tushare(dataframe, dtype='day'):
if dtype in ['day']:
return QA_DataStruct_Stock_day(
dataframe.assign(date=pd.to_datetime(dataframe.date)
).set_index(['date',
'code'],
drop=False),
dtype='stock_day'
)
elif dtype in ['min']:
return QA_DataStruct_Stock_min(
dataframe.assign(datetime=pd.to_datetime(dataframe.datetime)
).set_index(['datetime',
'code'],
drop=False),
dtype='stock_min'
)
|
[
"dataframe from tushare\n\n Arguments:\n dataframe {[type]} -- [description]\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QDS_StockDayWarpper(func):
def warpper(*args, **kwargs):
data = func(*args, **kwargs)
if isinstance(data.index, pd.MultiIndex):
return QA_DataStruct_Stock_day(data)
else:
return QA_DataStruct_Stock_day(
data.assign(date=pd.to_datetime(data.date)
).set_index(['date',
'code'],
drop=False),
dtype='stock_day'
)
return warpper
|
[
"\n 日线QDS装饰器\n "
] |
Please provide a description of the function:def QDS_StockMinWarpper(func, *args, **kwargs):
def warpper(*args, **kwargs):
data = func(*args, **kwargs)
if isinstance(data.index, pd.MultiIndex):
return QA_DataStruct_Stock_min(data)
else:
return QA_DataStruct_Stock_min(
data.assign(datetime=pd.to_datetime(data.datetime)
).set_index(['datetime',
'code'],
drop=False),
dtype='stock_min'
)
return warpper
|
[
"\n 分钟线QDS装饰器\n "
] |
Please provide a description of the function:def QA_fetch_get_stock_adj(code, end=''):
pro = get_pro()
adj = pro.adj_factor(ts_code=code, trade_date=end)
return adj
|
[
"获取股票的复权因子\n \n Arguments:\n code {[type]} -- [description]\n \n Keyword Arguments:\n end {str} -- [description] (default: {''})\n \n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def cover_time(date):
datestr = str(date)[0:8]
date = time.mktime(time.strptime(datestr, '%Y%m%d'))
return date
|
[
"\n 字符串 '20180101' 转变成 float 类型时间 类似 time.time() 返回的类型\n :param date: 字符串str -- 格式必须是 20180101 ，长度8\n :return: 类型float\n "
] |
Please provide a description of the function:def new(self, data):
temp = copy(self)
temp.__init__(data)
return temp
|
[
"通过data新建一个stock_block\n\n Arguments:\n data {[type]} -- [description]\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def view_code(self):
return self.data.groupby(level=1).apply(
lambda x:
[item for item in x.index.remove_unused_levels().levels[0]]
)
|
[
"按股票排列的查看blockname的视图\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def get_code(self, code):
# code= [code] if isinstance(code,str) else
return self.new(self.data.loc[(slice(None), code), :])
|
[
"getcode 获取某一只股票的板块\n\n Arguments:\n code {str} -- 股票代码\n\n Returns:\n DataStruct -- [description]\n "
] |
Please provide a description of the function:def get_block(self, block_name):
# block_name = [block_name] if isinstance(
# block_name, str) else block_name
# return QA_DataStruct_Stock_block(self.data[self.data.blockname.apply(lambda x: x in block_name)])
return self.new(self.data.loc[(block_name, slice(None)), :])
|
[
"getblock 获取板块, block_name是list或者是单个str\n\n Arguments:\n block_name {[type]} -- [description]\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def get_both_code(self, code):
return self.new(self.data.loc[(slice(None), code), :])
|
[
"get_both_code 获取几个股票相同的版块\n \n Arguments:\n code {[type]} -- [description]\n \n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_get_tick(code, start, end, market):
res = None
if market == MARKET_TYPE.STOCK_CN:
res = QATdx.QA_fetch_get_stock_transaction(code, start, end)
elif market == MARKET_TYPE.FUTURE_CN:
res = QATdx.QA_fetch_get_future_transaction(code, start, end)
return res
|
[
"\n 统一的获取期货/股票tick的接口\n "
] |
Please provide a description of the function:def QA_get_realtime(code, market):
res = None
if market == MARKET_TYPE.STOCK_CN:
res = QATdx.QA_fetch_get_stock_realtime(code)
elif market == MARKET_TYPE.FUTURE_CN:
res = QATdx.QA_fetch_get_future_realtime(code)
return res
|
[
"\n 统一的获取期货/股票实时行情的接口\n "
] |
Please provide a description of the function:def QA_quotation(code, start, end, frequence, market, source=DATASOURCE.TDX, output=OUTPUT_FORMAT.DATAFRAME):
res = None
if market == MARKET_TYPE.STOCK_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
res = QA_DataStruct_Stock_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_min(
code, start, end, frequence=frequence)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.FUTURE_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_future_day(code, start, end)
res = QA_DataStruct_Future_day(res.set_index(['date', 'code']))
elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_future_min(
code, start, end, frequence=frequence)
res = QA_DataStruct_Future_min(
res.set_index(['datetime', 'code']))
# 指数代码和股票代码是冲突重复的， sh000001 上证指数 000001 是不同的
elif market == MARKET_TYPE.INDEX_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
elif market == MARKET_TYPE.OPTION_CN:
if source == DATASOURCE.MONGO:
#res = QAQueryAdv.QA_fetch_option_day_adv(code, start, end)
raise NotImplementedError('CURRENT NOT FINISH THIS METHOD')
# print(type(res))
if output is OUTPUT_FORMAT.DATAFRAME:
return res.data
elif output is OUTPUT_FORMAT.DATASTRUCT:
return res
elif output is OUTPUT_FORMAT.NDARRAY:
return res.to_numpy()
elif output is OUTPUT_FORMAT.JSON:
return res.to_json()
elif output is OUTPUT_FORMAT.LIST:
return res.to_list()
|
[
"一个统一的获取k线的方法\n 如果使用mongo,从本地数据库获取,失败则在线获取\n\n Arguments:\n code {str/list} -- 期货/股票的代码\n start {str} -- 开始日期\n end {str} -- 结束日期\n frequence {enum} -- 频率 QA.FREQUENCE\n market {enum} -- 市场 QA.MARKET_TYPE\n source {enum} -- 来源 QA.DATASOURCE\n output {enum} -- 输出类型 QA.OUTPUT_FORMAT\n "
] |
Please provide a description of the function:def QA_util_random_with_zh_stock_code(stockNumber=10):
'''
随机生成股票代码
:param stockNumber: 生成个数
:return: ['60XXXX', '00XXXX', '300XXX']
'''
codeList = []
pt = 0
for i in range(stockNumber):
if pt == 0:
#print("random 60XXXX")
iCode = random.randint(600000, 609999)
aCode = "%06d" % iCode
elif pt == 1:
#print("random 00XXXX")
iCode = random.randint(600000, 600999)
aCode = "%06d" % iCode
elif pt == 2:
#print("random 00XXXX")
iCode = random.randint(2000, 9999)
aCode = "%06d" % iCode
elif pt == 3:
#print("random 300XXX")
iCode = random.randint(300000, 300999)
aCode = "%06d" % iCode
elif pt == 4:
#print("random 00XXXX")
iCode = random.randint(2000, 2999)
aCode = "%06d" % iCode
pt = (pt + 1) % 5
codeList.append(aCode)
return codeList
|
[] |
Please provide a description of the function:def QA_util_random_with_topic(topic='Acc', lens=8):
_list = [chr(i) for i in range(65,
91)] + [chr(i) for i in range(97,
123)
] + [str(i) for i in range(10)]
num = random.sample(_list, lens)
return '{}_{}'.format(topic, ''.join(num))
|
[
"\n 生成account随机值\n\n Acc+4数字id+4位大小写随机\n\n "
] |
Please provide a description of the function:def update_pos(self, price, amount, towards):
temp_cost = amount*price * \
self.market_preset.get('unit_table', 1)
# if towards == ORDER_DIRECTION.SELL_CLOSE:
if towards == ORDER_DIRECTION.BUY:
# 股票模式/ 期货买入开仓
self.volume_long_today += amount
elif towards == ORDER_DIRECTION.SELL:
# 股票卖出模式:
# 今日买入仓位不能卖出
if self.volume_long_his > amount:
self.volume_long_his -= amount
elif towards == ORDER_DIRECTION.BUY_OPEN:
# 增加保证金
self.margin_long += temp_cost * \
self.market_preset['buy_frozen_coeff']
# 重算开仓均价
self.open_price_long = (
self.open_price_long * self.volume_long + amount*price) / (amount + self.volume_long)
# 重算持仓均价
self.position_price_long = (
self.position_price_long * self.volume_long + amount * price) / (amount + self.volume_long)
# 增加今仓数量 ==> 会自动增加volume_long
self.volume_long_today += amount
#
self.open_cost_long += temp_cost
elif towards == ORDER_DIRECTION.SELL_OPEN:
# 增加保证金
self.margin_short += temp_cost * \
self.market_preset['sell_frozen_coeff']
# 重新计算开仓/持仓成本
self.open_price_short = (
self.open_price_short * self.volume_short + amount*price) / (amount + self.volume_short)
self.position_price_short = (
self.position_price_short * self.volume_short + amount * price) / (amount + self.volume_short)
self.open_cost_short += temp_cost
self.volume_short_today += amount
elif towards == ORDER_DIRECTION.BUY_CLOSETODAY:
if self.volume_short_today > amount:
self.position_cost_short = self.position_cost_short * \
(self.volume_short-amount)/self.volume_short
self.open_cost_short = self.open_cost_short * \
(self.volume_short-amount)/self.volume_short
self.volume_short_today -= amount
# close_profit = (self.position_price_short - price) * volume * position->ins->volume_multiple;
#self.volume_short_frozen_today += amount
# 释放保证金
# TODO
# self.margin_short
#self.open_cost_short = price* amount
elif towards == ORDER_DIRECTION.SELL_CLOSETODAY:
if self.volume_long_today > amount:
self.position_cost_long = self.position_cost_long * \
(self.volume_long - amount)/self.volume_long
self.open_cost_long = self.open_cost_long * \
(self.volume_long-amount)/self.volume_long
self.volume_long_today -= amount
elif towards == ORDER_DIRECTION.BUY_CLOSE:
# 有昨仓先平昨仓
self.position_cost_short = self.position_cost_short * \
(self.volume_short-amount)/self.volume_short
self.open_cost_short = self.open_cost_short * \
(self.volume_short-amount)/self.volume_short
if self.volume_short_his >= amount:
self.volume_short_his -= amount
else:
self.volume_short_today -= (amount - self.volume_short_his)
self.volume_short_his = 0
elif towards == ORDER_DIRECTION.SELL_CLOSE:
# 有昨仓先平昨仓
self.position_cost_long = self.position_cost_long * \
(self.volume_long - amount)/self.volume_long
self.open_cost_long = self.open_cost_long * \
(self.volume_long-amount)/self.volume_long
if self.volume_long_his >= amount:
self.volume_long_his -= amount
else:
self.volume_long_today -= (amount - self.volume_long_his)
self.volume_long_his -= amount
|
[
"支持股票/期货的更新仓位\n\n Arguments:\n price {[type]} -- [description]\n amount {[type]} -- [description]\n towards {[type]} -- [description]\n\n margin: 30080\n margin_long: 0\n margin_short: 30080\n open_cost_long: 0\n open_cost_short: 419100\n open_price_long: 4193\n open_price_short: 4191\n position_cost_long: 0\n position_cost_short: 419100\n position_price_long: 4193\n position_price_short: 4191\n position_profit: -200\n position_profit_long: 0\n position_profit_short: -200\n "
] |
Please provide a description of the function:def settle(self):
self.volume_long_his += self.volume_long_today
self.volume_long_today = 0
self.volume_long_frozen_today = 0
self.volume_short_his += self.volume_short_today
self.volume_short_today = 0
self.volume_short_frozen_today = 0
|
[
"收盘后的结算事件\n "
] |
Please provide a description of the function:def close_available(self):
return {
'volume_long': self.volume_long - self.volume_long_frozen,
'volume_short': self.volume_short - self.volume_short_frozen
}
|
[
"可平仓数量\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def orderAction(self, order:QA_Order):
return self.pms[order.code][order.order_id].receive_order(order)
|
[
"\n 委托回报\n "
] |
Please provide a description of the function:def QA_SU_save_stock_min(client=DATABASE, ui_log=None, ui_progress=None):
# 导入聚宽模块且进行登录
try:
import jqdatasdk
# 请自行将 JQUSERNAME 和 JQUSERPASSWD 修改为自己的账号密码
jqdatasdk.auth("JQUSERNAME", "JQUSERPASSWD")
except:
raise ModuleNotFoundError
# 股票代码格式化
code_list = list(
map(
lambda x: x + ".XSHG" if x[0] == "6" else x + ".XSHE",
QA_fetch_get_stock_list().code.unique().tolist(),
))
coll = client.stock_min
coll.create_index([
("code", pymongo.ASCENDING),
("time_stamp", pymongo.ASCENDING),
("date_stamp", pymongo.ASCENDING),
])
err = []
def __transform_jq_to_qa(df, code, type_):
if df is None or len(df) == 0:
raise ValueError("没有聚宽数据")
df = df.reset_index().rename(columns={
"index": "datetime",
"volume": "vol",
"money": "amount"
})
df["code"] = code
df["date"] = df.datetime.map(str).str.slice(0, 10)
df = df.set_index("datetime", drop=False)
df["date_stamp"] = df["date"].apply(lambda x: QA_util_date_stamp(x))
df["time_stamp"] = (
df["datetime"].map(str).apply(lambda x: QA_util_time_stamp(x)))
df["type"] = type_
return df[[
"open",
"close",
"high",
"low",
"vol",
"amount",
"datetime",
"code",
"date",
"date_stamp",
"time_stamp",
"type",
]]
def __saving_work(code, coll):
QA_util_log_info(
"##JOB03 Now Saving STOCK_MIN ==== {}".format(code), ui_log=ui_log)
try:
for type_ in ["1min", "5min", "15min", "30min", "60min"]:
col_filter = {"code": str(code)[0:6], "type": type_}
ref_ = coll.find(col_filter)
end_time = str(now_time())[0:19]
if coll.count_documents(col_filter) > 0:
start_time = ref_[coll.count_documents(
col_filter) - 1]["datetime"]
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min",
"5min",
"15min",
"30min",
"60min"].index(type_),
str(code)[0:6],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = jqdatasdk.get_price(
security=code,
start_date=start_time,
end_date=end_time,
frequency=type_.split("min")[0]+"m",
)
__data = __transform_jq_to_qa(
df, code=code[:6], type_=type_)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
else:
start_time = "2015-01-01 09:30:00"
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min",
"5min",
"15min",
"30min",
"60min"].index(type_),
str(code)[0:6],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
__data == __transform_jq_to_qa(
jqdatasdk.get_price(
security=code,
start_date=start_time,
end_date=end_time,
frequency=type_.split("min")[0]+"m",
),
code=code[:6],
type_=type_
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
# 聚宽之多允许三个线程连接
executor = ThreadPoolExecutor(max_workers=2)
res = {
executor.submit(__saving_work, code_list[i_], coll)
for i_ in range(len(code_list))
}
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(code_list)),
ui_log=ui_log
)
strProgress = "DOWNLOAD PROGRESS {} ".format(
str(float(count / len(code_list) * 100))[0:4] + "%")
intProgress = int(count / len(code_list) * 10000.0)
QA_util_log_info(
strProgress,
ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info("SUCCESS", ui_log=ui_log)
else:
QA_util_log_info(" ERROR CODE \n ", ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"\n 聚宽实现方式\n save current day's stock_min data\n ",
"\n 处理 jqdata 分钟数据为 qa 格式，并存入数据库\n 1. jdatasdk 数据格式:\n open close high low volume money\n 2018-12-03 09:31:00 10.59 10.61 10.61 10.59 8339100.0 88377836.0\n 2. 与 QUANTAXIS.QAFetch.QATdx.QA_fetch_get_stock_min 获取数据进行匹配，具体处理详见相应源码\n\n open close high low vol amount ...\n datetime\n 2018-12-03 09:31:00 10.99 10.90 10.99 10.90 2.211700e+06 2.425626e+07 ...\n "
] |
Please provide a description of the function:def execute(command, shell=None, working_dir=".", echo=False, echo_indent=0):
if shell is None:
shell = True if isinstance(command, str) else False
p = Popen(command, stdin=PIPE, stdout=PIPE,
stderr=STDOUT, shell=shell, cwd=working_dir)
if echo:
stdout = ""
while p.poll() is None:
# This blocks until it receives a newline.
line = p.stdout.readline()
print(" " * echo_indent, line, end="")
stdout += line
# Read any last bits
line = p.stdout.read()
print(" " * echo_indent, line, end="")
print()
stdout += line
else:
stdout, _ = p.communicate()
return (p.returncode, stdout)
|
[
"Execute a command on the command-line.\n :param str,list command: The command to run\n :param bool shell: Whether or not to use the shell. This is optional; if\n ``command`` is a basestring, shell will be set to True, otherwise it will\n be false. You can override this behavior by setting this parameter\n directly.\n :param str working_dir: The directory in which to run the command.\n :param bool echo: Whether or not to print the output from the command to\n stdout.\n :param int echo_indent: Any number of spaces to indent the echo for clarity\n :returns: tuple: (return code, stdout)\n Example\n >>> from executor import execute\n >>> return_code, text = execute(\"dir\")\n "
] |
Please provide a description of the function:def QA_data_calc_marketvalue(data, xdxr):
'使用数据库数据计算复权'
mv = xdxr.query('category!=6').loc[:,
['shares_after',
'liquidity_after']].dropna()
res = pd.concat([data, mv], axis=1)
res = res.assign(
shares=res.shares_after.fillna(method='ffill'),
lshares=res.liquidity_after.fillna(method='ffill')
)
return res.assign(mv=res.close*res.shares*10000, liquidity_mv=res.close*res.lshares*10000).drop(['shares_after', 'liquidity_after'], axis=1)\
.loc[(slice(data.index.remove_unused_levels().levels[0][0],data.index.remove_unused_levels().levels[0][-1]),slice(None)),:]
|
[] |
Please provide a description of the function:def MACD_JCSC(dataframe, SHORT=12, LONG=26, M=9):
CLOSE = dataframe.close
DIFF = QA.EMA(CLOSE, SHORT) - QA.EMA(CLOSE, LONG)
DEA = QA.EMA(DIFF, M)
MACD = 2*(DIFF-DEA)
CROSS_JC = QA.CROSS(DIFF, DEA)
CROSS_SC = QA.CROSS(DEA, DIFF)
ZERO = 0
return pd.DataFrame({'DIFF': DIFF, 'DEA': DEA, 'MACD': MACD, 'CROSS_JC': CROSS_JC, 'CROSS_SC': CROSS_SC, 'ZERO': ZERO})
|
[
"\n 1.DIF向上突破DEA，买入信号参考。\n 2.DIF向下跌破DEA，卖出信号参考。\n "
] |
Please provide a description of the function:def _create(self, cache_file):
conn = sqlite3.connect(cache_file)
cur = conn.cursor()
cur.execute("PRAGMA foreign_keys = ON")
cur.execute('''
CREATE TABLE jobs(
hash TEXT NOT NULL UNIQUE PRIMARY KEY, description TEXT NOT NULL,
last_run REAL, next_run REAL, last_run_result INTEGER)''')
cur.execute('''
CREATE TABLE history(
hash TEXT, description TEXT, time REAL, result INTEGER,
FOREIGN KEY(hash) REFERENCES jobs(hash))''')
conn.commit()
conn.close()
|
[
"Create the tables needed to store the information."
] |
Please provide a description of the function:def get(self, id):
self.cur.execute("SELECT * FROM jobs WHERE hash=?", (id,))
item = self.cur.fetchone()
if item:
return dict(zip(
("id", "description", "last-run", "next-run", "last-run-result"),
item))
return None
|
[
"Retrieves the job with the selected ID.\n :param str id: The ID of the job\n :returns: The dictionary of the job if found, None otherwise\n "
] |
Please provide a description of the function:def update(self, job):
self.cur.execute('''UPDATE jobs
SET last_run=?,next_run=?,last_run_result=? WHERE hash=?''', (
job["last-run"], job["next-run"], job["last-run-result"], job["id"]))
|
[
"Update last_run, next_run, and last_run_result for an existing job.\n :param dict job: The job dictionary\n :returns: True\n "
] |
Please provide a description of the function:def add_job(self, job):
self.cur.execute("INSERT INTO jobs VALUES(?,?,?,?,?)", (
job["id"], job["description"], job["last-run"], job["next-run"], job["last-run-result"]))
return True
|
[
"Adds a new job into the cache.\n :param dict job: The job dictionary\n :returns: True\n "
] |
Please provide a description of the function:def add_result(self, job):
self.cur.execute(
"INSERT INTO history VALUES(?,?,?,?)",
(job["id"], job["description"], job["last-run"], job["last-run-result"]))
return True
|
[
"Adds a job run result to the history table.\n :param dict job: The job dictionary\n :returns: True\n "
] |
Please provide a description of the function:def QA_data_tick_resample_1min(tick, type_='1min', if_drop=True):
tick = tick.assign(amount=tick.price * tick.vol)
resx = pd.DataFrame()
_dates = set(tick.date)
for date in sorted(list(_dates)):
_data = tick.loc[tick.date == date]
# morning min bar
_data1 = _data[time(9,
25):time(11,
30)].resample(
type_,
closed='left',
base=30,
loffset=type_
).apply(
{
'price': 'ohlc',
'vol': 'sum',
'code': 'last',
'amount': 'sum'
}
)
_data1.columns = _data1.columns.droplevel(0)
# do fix on the first and last bar
# 某些股票某些日期没有集合竞价信息，譬如 002468 在 2017 年 6 月 5 日的数据
if len(_data.loc[time(9, 25):time(9, 25)]) > 0:
_data1.loc[time(9,
31):time(9,
31),
'open'] = _data1.loc[time(9,
26):time(9,
26),
'open'].values
_data1.loc[time(9,
31):time(9,
31),
'high'] = _data1.loc[time(9,
26):time(9,
31),
'high'].max()
_data1.loc[time(9,
31):time(9,
31),
'low'] = _data1.loc[time(9,
26):time(9,
31),
'low'].min()
_data1.loc[time(9,
31):time(9,
31),
'vol'] = _data1.loc[time(9,
26):time(9,
31),
'vol'].sum()
_data1.loc[time(9,
31):time(9,
31),
'amount'] = _data1.loc[time(9,
26):time(9,
31),
'amount'].sum()
# 通达信分笔数据有的有 11:30 数据，有的没有
if len(_data.loc[time(11, 30):time(11, 30)]) > 0:
_data1.loc[time(11,
30):time(11,
30),
'high'] = _data1.loc[time(11,
30):time(11,
31),
'high'].max()
_data1.loc[time(11,
30):time(11,
30),
'low'] = _data1.loc[time(11,
30):time(11,
31),
'low'].min()
_data1.loc[time(11,
30):time(11,
30),
'close'] = _data1.loc[time(11,
31):time(11,
31),
'close'].values
_data1.loc[time(11,
30):time(11,
30),
'vol'] = _data1.loc[time(11,
30):time(11,
31),
'vol'].sum()
_data1.loc[time(11,
30):time(11,
30),
'amount'] = _data1.loc[time(11,
30):time(11,
31),
'amount'].sum()
_data1 = _data1.loc[time(9, 31):time(11, 30)]
# afternoon min bar
_data2 = _data[time(13,
0):time(15,
0)].resample(
type_,
closed='left',
base=30,
loffset=type_
).apply(
{
'price': 'ohlc',
'vol': 'sum',
'code': 'last',
'amount': 'sum'
}
)
_data2.columns = _data2.columns.droplevel(0)
# 沪市股票在 2018-08-20 起，尾盘 3 分钟集合竞价
if (pd.Timestamp(date) <
pd.Timestamp('2018-08-20')) and (tick.code.iloc[0][0] == '6'):
# 避免出现 tick 数据没有 1:00 的值
if len(_data.loc[time(13, 0):time(13, 0)]) > 0:
_data2.loc[time(15,
0):time(15,
0),
'high'] = _data2.loc[time(15,
0):time(15,
1),
'high'].max()
_data2.loc[time(15,
0):time(15,
0),
'low'] = _data2.loc[time(15,
0):time(15,
1),
'low'].min()
_data2.loc[time(15,
0):time(15,
0),
'close'] = _data2.loc[time(15,
1):time(15,
1),
'close'].values
else:
# 避免出现 tick 数据没有 15:00 的值
if len(_data.loc[time(13, 0):time(13, 0)]) > 0:
_data2.loc[time(15,
0):time(15,
0)] = _data2.loc[time(15,
1):time(15,
1)].values
_data2 = _data2.loc[time(13, 1):time(15, 0)]
resx = resx.append(_data1).append(_data2)
resx['vol'] = resx['vol'] * 100.0
resx['volume'] = resx['vol']
resx['type'] = '1min'
if if_drop:
resx = resx.dropna()
return resx.reset_index().drop_duplicates().set_index(['datetime', 'code'])
|
[
"\n tick 采样为 分钟数据\n 1. 仅使用将 tick 采样为 1 分钟数据\n 2. 仅测试过，与通达信 1 分钟数据达成一致\n 3. 经测试，可以匹配 QA.QA_fetch_get_stock_transaction 得到的数据，其他类型数据未测试\n demo:\n df = QA.QA_fetch_get_stock_transaction(package='tdx', code='000001', \n start='2018-08-01 09:25:00',\n end='2018-08-03 15:00:00')\n df_min = QA_data_tick_resample_1min(df)\n "
] |
Please provide a description of the function:def QA_data_tick_resample(tick, type_='1min'):
tick = tick.assign(amount=tick.price * tick.vol)
resx = pd.DataFrame()
_temp = set(tick.index.date)
for item in _temp:
_data = tick.loc[str(item)]
_data1 = _data[time(9,
31):time(11,
30)].resample(
type_,
closed='right',
base=30,
loffset=type_
).apply(
{
'price': 'ohlc',
'vol': 'sum',
'code': 'last',
'amount': 'sum'
}
)
_data2 = _data[time(13,
1):time(15,
0)].resample(
type_,
closed='right',
loffset=type_
).apply(
{
'price': 'ohlc',
'vol': 'sum',
'code': 'last',
'amount': 'sum'
}
)
resx = resx.append(_data1).append(_data2)
resx.columns = resx.columns.droplevel(0)
return resx.reset_index().drop_duplicates().set_index(['datetime', 'code'])
|
[
"tick采样成任意级别分钟线\n\n Arguments:\n tick {[type]} -- transaction\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_data_ctptick_resample(tick, type_='1min'):
resx = pd.DataFrame()
_temp = set(tick.TradingDay)
for item in _temp:
_data = tick.query('TradingDay=="{}"'.format(item))
try:
_data.loc[time(20, 0):time(21, 0), 'volume'] = 0
except:
pass
_data.volume = _data.volume.diff()
_data = _data.assign(amount=_data.LastPrice * _data.volume)
_data0 = _data[time(0,
0):time(2,
30)].resample(
type_,
closed='right',
base=30,
loffset=type_
).apply(
{
'LastPrice': 'ohlc',
'volume': 'sum',
'code': 'last',
'amount': 'sum'
}
)
_data1 = _data[time(9,
0):time(11,
30)].resample(
type_,
closed='right',
base=30,
loffset=type_
).apply(
{
'LastPrice': 'ohlc',
'volume': 'sum',
'code': 'last',
'amount': 'sum'
}
)
_data2 = _data[time(13,
1):time(15,
0)].resample(
type_,
closed='right',
base=30,
loffset=type_
).apply(
{
'LastPrice': 'ohlc',
'volume': 'sum',
'code': 'last',
'amount': 'sum'
}
)
_data3 = _data[time(21,
0):time(23,
59)].resample(
type_,
closed='left',
loffset=type_
).apply(
{
'LastPrice': 'ohlc',
'volume': 'sum',
'code': 'last',
'amount': 'sum'
}
)
resx = resx.append(_data0).append(_data1).append(_data2).append(_data3)
resx.columns = resx.columns.droplevel(0)
return resx.reset_index().drop_duplicates().set_index(['datetime',
'code']).sort_index()
|
[
"tick采样成任意级别分钟线\n\n Arguments:\n tick {[type]} -- transaction\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_data_min_resample(min_data, type_='5min'):
try:
min_data = min_data.reset_index().set_index('datetime', drop=False)
except:
min_data = min_data.set_index('datetime', drop=False)
CONVERSION = {
'code': 'first',
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'vol': 'sum',
'amount': 'sum'
} if 'vol' in min_data.columns else {
'code': 'first',
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum',
'amount': 'sum'
}
resx = pd.DataFrame()
for item in set(min_data.index.date):
min_data_p = min_data.loc[str(item)]
n = min_data_p['{} 21:00:00'.format(item):].resample(
type_,
base=30,
closed='right',
loffset=type_
).apply(CONVERSION)
d = min_data_p[:'{} 11:30:00'.format(item)].resample(
type_,
base=30,
closed='right',
loffset=type_
).apply(CONVERSION)
f = min_data_p['{} 13:00:00'.format(item):].resample(
type_,
closed='right',
loffset=type_
).apply(CONVERSION)
resx = resx.append(d).append(f)
return resx.dropna().reset_index().set_index(['datetime', 'code'])
|
[
"分钟线采样成大周期\n\n\n 分钟线采样成子级别的分钟线\n\n\n time+ OHLC==> resample\n Arguments:\n min {[type]} -- [description]\n raw_type {[type]} -- [description]\n new_type {[type]} -- [description]\n "
] |
Please provide a description of the function:def QA_data_futuremin_resample(min_data, type_='5min'):
min_data.tradeime = pd.to_datetime(min_data.tradetime)
CONVERSION = {
'code': 'first',
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'trade': 'sum',
'tradetime': 'last',
'date': 'last'
}
resx = min_data.resample(
type_,
closed='right',
loffset=type_
).apply(CONVERSION)
return resx.dropna().reset_index().set_index(['datetime', 'code'])
|
[
"期货分钟线采样成大周期\n\n\n 分钟线采样成子级别的分钟线\n\n future:\n\n vol ==> trade\n amount X\n "
] |
Please provide a description of the function:def QA_data_day_resample(day_data, type_='w'):
# return day_data_p.assign(open=day_data.open.resample(type_).first(),high=day_data.high.resample(type_).max(),low=day_data.low.resample(type_).min(),\
# vol=day_data.vol.resample(type_).sum() if 'vol' in day_data.columns else day_data.volume.resample(type_).sum(),\
# amount=day_data.amount.resample(type_).sum()).dropna().set_index('date')
try:
day_data = day_data.reset_index().set_index('date', drop=False)
except:
day_data = day_data.set_index('date', drop=False)
CONVERSION = {
'code': 'first',
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'vol': 'sum',
'amount': 'sum'
} if 'vol' in day_data.columns else {
'code': 'first',
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum',
'amount': 'sum'
}
return day_data.resample(
type_,
closed='right'
).apply(CONVERSION).dropna().reset_index().set_index(['date',
'code'])
|
[
"日线降采样\n\n Arguments:\n day_data {[type]} -- [description]\n\n Keyword Arguments:\n type_ {str} -- [description] (default: {'w'})\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_SU_save_stock_info(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_stock_info(client=client)
|
[
"save stock info\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_list(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_stock_list(client=client)
|
[
"save stock_list\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_index_list(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_index_list(client=client)
|
[
"save index_list\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_etf_list(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_etf_list(client=client)
|
[
"save etf_list\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_future_day(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_future_day(client=client)
|
[
"save future_day\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_future_day_all(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_future_day_all(client=client)
|
[
"save future_day_all\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_future_min(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_future_min(client=client)
|
[
"save future_min\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_future_min_all(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_future_min_all(client=client)
|
[
"[summary]\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_day(engine, client=DATABASE, paralleled=False):
engine = select_save_engine(engine, paralleled=paralleled)
engine.QA_SU_save_stock_day(client=client)
|
[
"save stock_day\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_option_commodity_min(engine, client=DATABASE):
'''
:param engine:
:param client:
:return:
'''
engine = select_save_engine(engine)
engine.QA_SU_save_option_commodity_min(client=client)
|
[] |
Please provide a description of the function:def QA_SU_save_option_commodity_day(engine, client=DATABASE):
'''
:param engine:
:param client:
:return:
'''
engine = select_save_engine(engine)
engine.QA_SU_save_option_commodity_day(client=client)
|
[] |
Please provide a description of the function:def QA_SU_save_stock_min(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_stock_min(client=client)
|
[
"save stock_min\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_index_day(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_index_day(client=client)
|
[
"save index_day\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_index_min(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_index_min(client=client)
|
[
"save index_min\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_etf_day(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_etf_day(client=client)
|
[
"save etf_day\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_etf_min(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_etf_min(client=client)
|
[
"save etf_min\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_xdxr(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_stock_xdxr(client=client)
|
[
"save stock_xdxr\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_block(engine, client=DATABASE):
engine = select_save_engine(engine)
engine.QA_SU_save_stock_block(client=client)
|
[
"save stock_block\n\n Arguments:\n engine {[type]} -- [description]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def select_save_engine(engine, paralleled=False):
'''
select save_engine , tushare ts Tushare 使用 Tushare 免费数据接口， tdx 使用通达信数据接口
:param engine: 字符串Str
:param paralleled: 是否并行处理；默认为False
:return: sts means save_tushare_py or stdx means save_tdx_py
'''
if engine in ['tushare', 'ts', 'Tushare']:
return sts
elif engine in ['tdx']:
if paralleled:
return stdx_parallelism
else:
return stdx
elif engine in ['gm', 'goldenminer']:
return sgm
elif engine in ['jq', 'joinquant']:
return sjq
else:
print('QA Error QASU.main.py call select_save_engine with parameter %s is None of thshare, ts, Thshare, or tdx', engine)
|
[] |
Please provide a description of the function:def QA_fetch_stock_day(code, start, end, format='numpy', frequence='day', collections=DATABASE.stock_day):
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)}}, {"_id": 0}, batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.assign(volume=res.vol, date=pd.to_datetime(
res.date)).drop_duplicates((['date', 'code'])).query('volume>1').set_index('date', drop=False)
res = res.ix[:, ['code', 'open', 'high', 'low',
'close', 'volume', 'amount', 'date']]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print("QA Error QA_fetch_stock_day format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" " % format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_day data parameter start=%s end=%s is not right' % (start, end))
|
[
"'获取股票日线'\n\n Returns:\n [type] -- [description]\n\n 感谢@几何大佬的提示\n https://docs.mongodb.com/manual/tutorial/project-fields-from-query-results/#return-the-specified-fields-and-the-id-field-only\n\n "
] |
Please provide a description of the function:def QA_fetch_stock_min(code, start, end, format='numpy', frequence='1min', collections=DATABASE.stock_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
else:
print("QA Error QA_fetch_stock_min parameter frequence=%s is none of 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m" % frequence)
__data = []
# code checking
code = QA_util_code_tolist(code)
cursor = collections.find({
'code': {'$in': code}, "time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
}, 'type': frequence
}, {"_id": 0}, batch_size=10000)
res = pd.DataFrame([item for item in cursor])
try:
res = res.assign(volume=res.vol, datetime=pd.to_datetime(
res.datetime)).query('volume>1').drop_duplicates(['datetime', 'code']).set_index('datetime', drop=False)
# return res
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print("QA Error QA_fetch_stock_min format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" " % format)
return None
|
[] |
Please provide a description of the function:def QA_fetch_stock_list(collections=DATABASE.stock_list):
'获取股票列表'
return pd.DataFrame([item for item in collections.find()]).drop('_id', axis=1, inplace=False).set_index('code', drop=False)
|
[] |
Please provide a description of the function:def QA_fetch_etf_list(collections=DATABASE.etf_list):
'获取ETF列表'
return pd.DataFrame([item for item in collections.find()]).drop('_id', axis=1, inplace=False).set_index('code', drop=False)
|
[] |
Please provide a description of the function:def QA_fetch_index_list(collections=DATABASE.index_list):
'获取指数列表'
return pd.DataFrame([item for item in collections.find()]).drop('_id', axis=1, inplace=False).set_index('code', drop=False)
|
[] |
Please provide a description of the function:def QA_fetch_stock_terminated(collections=DATABASE.stock_terminated):
'获取股票基本信息 , 已经退市的股票列表'
# 🛠todo 转变成 dataframe 类型数据
return pd.DataFrame([item for item in collections.find()]).drop('_id', axis=1, inplace=False).set_index('code', drop=False)
|
[] |
Please provide a description of the function:def QA_fetch_stock_basic_info_tushare(collections=DATABASE.stock_info_tushare):
'''
purpose:
tushare 股票列表数据库
code,代码
name,名称
industry,所属行业
area,地区
pe,市盈率
outstanding,流通股本(亿)
totals,总股本(亿)
totalAssets,总资产(万)
liquidAssets,流动资产
fixedAssets,固定资产
reserved,公积金
reservedPerShare,每股公积金
esp,每股收益
bvps,每股净资
pb,市净率
timeToMarket,上市日期
undp,未分利润
perundp, 每股未分配
rev,收入同比(%)
profit,利润同比(%)
gpr,毛利率(%)
npr,净利润率(%)
holders,股东人数
add by tauruswang,
:param collections: stock_info_tushare 集合
:return:
'''
'获取股票基本信息'
items = [item for item in collections.find()]
# 🛠todo 转变成 dataframe 类型数据
return items
|
[] |
Please provide a description of the function:def QA_fetch_stock_full(date, format='numpy', collections=DATABASE.stock_day):
'获取全市场的某一日的数据'
Date = str(date)[0:10]
if QA_util_date_valid(Date) is True:
__data = []
for item in collections.find({
"date_stamp": QA_util_date_stamp(Date)}, batch_size=10000):
__data.append([str(item['code']), float(item['open']), float(item['high']), float(
item['low']), float(item['close']), float(item['vol']), item['date']])
# 多种数据格式
if format in ['n', 'N', 'numpy']:
__data = numpy.asarray(__data)
elif format in ['list', 'l', 'L']:
__data = __data
elif format in ['P', 'p', 'pandas', 'pd']:
__data = DataFrame(__data, columns=[
'code', 'open', 'high', 'low', 'close', 'volume', 'date'])
__data['date'] = pd.to_datetime(__data['date'])
__data = __data.set_index('date', drop=False)
else:
print("QA Error QA_fetch_stock_full format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" " % format)
return __data
else:
QA_util_log_info(
'QA Error QA_fetch_stock_full data parameter date=%s not right' % date)
|
[] |
Please provide a description of the function:def QA_fetch_index_min(
code,
start, end,
format='numpy',
frequence='1min',
collections=DATABASE.index_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
__data = []
code = QA_util_code_tolist(code)
cursor = collections.find({
'code': {'$in': code}, "time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
}, 'type': frequence
}, {"_id": 0}, batch_size=10000)
if format in ['dict', 'json']:
return [data for data in cursor]
# for item in cursor:
__data = pd.DataFrame([item for item in cursor])
__data = __data.assign(datetime=pd.to_datetime(__data['datetime']))
# __data.append([str(item['code']), float(item['open']), float(item['high']), float(
# item['low']), float(item['close']), int(item['up_count']), int(item['down_count']), float(item['vol']), float(item['amount']), item['datetime'], item['time_stamp'], item['date'], item['type']])
# __data = DataFrame(__data, columns=[
# 'code', 'open', 'high', 'low', 'close', 'up_count', 'down_count', 'volume', 'amount', 'datetime', 'time_stamp', 'date', 'type'])
# __data['datetime'] = pd.to_datetime(__data['datetime'])
__data = __data.set_index('datetime', drop=False)
if format in ['numpy', 'np', 'n']:
return numpy.asarray(__data)
elif format in ['list', 'l', 'L']:
return numpy.asarray(__data).tolist()
elif format in ['P', 'p', 'pandas', 'pd']:
return __data
|
[] |
Please provide a description of the function:def QA_fetch_future_min(
code,
start, end,
format='numpy',
frequence='1min',
collections=DATABASE.future_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
__data = []
code = QA_util_code_tolist(code, auto_fill=False)
cursor = collections.find({
'code': {'$in': code}, "time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
}, 'type': frequence
}, batch_size=10000)
if format in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
__data.append([str(item['code']), float(item['open']), float(item['high']), float(
item['low']), float(item['close']), float(item['position']), float(item['price']), float(item['trade']),
item['datetime'], item['tradetime'], item['time_stamp'], item['date'], item['type']])
__data = DataFrame(__data, columns=[
'code', 'open', 'high', 'low', 'close', 'position', 'price', 'trade', 'datetime', 'tradetime', 'time_stamp', 'date', 'type'])
__data['datetime'] = pd.to_datetime(__data['datetime'])
__data = __data.set_index('datetime', drop=False)
if format in ['numpy', 'np', 'n']:
return numpy.asarray(__data)
elif format in ['list', 'l', 'L']:
return numpy.asarray(__data).tolist()
elif format in ['P', 'p', 'pandas', 'pd']:
return __data
|
[] |
Please provide a description of the function:def QA_fetch_future_list(collections=DATABASE.future_list):
'获取期货列表'
return pd.DataFrame([item for item in collections.find()]).drop('_id', axis=1, inplace=False).set_index('code', drop=False)
|
[] |
Please provide a description of the function:def QA_fetch_ctp_tick(code, start, end, frequence, format='pd', collections=DATABASE.ctp_tick):
code = QA_util_code_tolist(code, auto_fill=False)
cursor = collections.find({
'InstrumentID': {'$in': code}, "time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
}, 'type': frequence
}, {"_id": 0}, batch_size=10000)
hq = pd.DataFrame([data for data in cursor]).replace(1.7976931348623157e+308,
numpy.nan).replace('', numpy.nan).dropna(axis=1)
p1 = hq.loc[:, ['ActionDay', 'AskPrice1', 'AskVolume1', 'AveragePrice', 'BidPrice1',
'BidVolume1', 'HighestPrice', 'InstrumentID', 'LastPrice',
'OpenInterest', 'TradingDay', 'UpdateMillisec',
'UpdateTime', 'Volume']]
p1 = p1.assign(datetime=p1.ActionDay.apply(QA_util_date_int2str)+' '+p1.UpdateTime + (p1.UpdateMillisec/1000000).apply(lambda x: str('%.6f' % x)[1:]),
code=p1.InstrumentID)
p1.datetime = pd.to_datetime(p1.datetime)
return p1.set_index(p1.datetime)
|
[
"仅供存储的ctp tick使用\n\n Arguments:\n code {[type]} -- [description]\n\n Keyword Arguments:\n format {str} -- [description] (default: {'pd'})\n collections {[type]} -- [description] (default: {DATABASE.ctp_tick})\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_fetch_stock_xdxr(code, format='pd', collections=DATABASE.stock_xdxr):
'获取股票除权信息/数据库'
code = QA_util_code_tolist(code)
data = pd.DataFrame([item for item in collections.find(
{'code': {'$in': code}}, batch_size=10000)]).drop(['_id'], axis=1)
data['date'] = pd.to_datetime(data['date'])
return data.set_index('date', drop=False)
|
[] |
Please provide a description of the function:def QA_fetch_quotations(date=datetime.date.today(), db=DATABASE):
'获取全部实时5档行情的存储结果'
try:
collections = db.get_collection(
'realtime_{}'.format(date))
data = pd.DataFrame([item for item in collections.find(
{}, {"_id": 0}, batch_size=10000)])
return data.assign(date=pd.to_datetime(data.datetime.apply(lambda x: str(x)[0:10]))).assign(datetime=pd.to_datetime(data.datetime)).set_index(['datetime', 'code'], drop=False).sort_index()
except Exception as e:
raise e
|
[] |
Please provide a description of the function:def QA_fetch_account(message={}, db=DATABASE):
collection = DATABASE.account
return [res for res in collection.find(message, {"_id": 0})]
|
[
"get the account\n\n Arguments:\n query_mes {[type]} -- [description]\n\n Keyword Arguments:\n collection {[type]} -- [description] (default: {DATABASE})\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_fetch_risk(message={}, params={"_id": 0, 'assets': 0, 'timeindex': 0, 'totaltimeindex': 0, 'benchmark_assets': 0, 'month_profit': 0}, db=DATABASE):
collection = DATABASE.risk
return [res for res in collection.find(message, params)]
|
[
"get the risk message\n\n Arguments:\n query_mes {[type]} -- [description]\n\n Keyword Arguments:\n collection {[type]} -- [description] (default: {DATABASE})\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_fetch_user(user_cookie, db=DATABASE):
collection = DATABASE.account
return [res for res in collection.find({'user_cookie': user_cookie}, {"_id": 0})]
|
[
"\n get the user\n\n Arguments:\n user_cookie : str the unique cookie_id for a user\n Keyword Arguments:\n db: database for query\n\n Returns:\n list --- [ACCOUNT]\n "
] |
Please provide a description of the function:def QA_fetch_strategy(message={}, db=DATABASE):
collection = DATABASE.strategy
return [res for res in collection.find(message, {"_id": 0})]
|
[
"get the account\n\n Arguments:\n query_mes {[type]} -- [description]\n\n Keyword Arguments:\n collection {[type]} -- [description] (default: {DATABASE})\n\n Returns:\n [type] -- [description]\n "
] |
Please provide a description of the function:def QA_fetch_lhb(date, db=DATABASE):
'获取某一天龙虎榜数据'
try:
collections = db.lhb
return pd.DataFrame([item for item in collections.find(
{'date': date}, {"_id": 0})]).set_index('code', drop=False).sort_index()
except Exception as e:
raise e
|
[] |
Please provide a description of the function:def QA_fetch_financial_report(code, report_date, ltype='EN', db=DATABASE):
if isinstance(code, str):
code = [code]
if isinstance(report_date, str):
report_date = [QA_util_date_str2int(report_date)]
elif isinstance(report_date, int):
report_date = [report_date]
elif isinstance(report_date, list):
report_date = [QA_util_date_str2int(item) for item in report_date]
collection = db.financial
num_columns = [item[:3] for item in list(financial_dict.keys())]
CH_columns = [item[3:] for item in list(financial_dict.keys())]
EN_columns = list(financial_dict.values())
#num_columns.extend(['283', '_id', 'code', 'report_date'])
# CH_columns.extend(['283', '_id', 'code', 'report_date'])
#CH_columns = pd.Index(CH_columns)
#EN_columns = list(financial_dict.values())
#EN_columns.extend(['283', '_id', 'code', 'report_date'])
#EN_columns = pd.Index(EN_columns)
try:
if code is not None and report_date is not None:
data = [item for item in collection.find(
{'code': {'$in': code}, 'report_date': {'$in': report_date}}, {"_id": 0}, batch_size=10000)]
elif code is None and report_date is not None:
data = [item for item in collection.find(
{'report_date': {'$in': report_date}}, {"_id": 0}, batch_size=10000)]
elif code is not None and report_date is None:
data = [item for item in collection.find(
{'code': {'$in': code}}, {"_id": 0}, batch_size=10000)]
else:
data = [item for item in collection.find({}, {"_id": 0})]
if len(data) > 0:
res_pd = pd.DataFrame(data)
if ltype in ['CH', 'CN']:
cndict = dict(zip(num_columns, CH_columns))
cndict['283'] = '283'
try:
cndict['284'] = '284'
cndict['285'] = '285'
cndict['286'] = '286'
except:
pass
cndict['code'] = 'code'
cndict['report_date'] = 'report_date'
res_pd.columns = res_pd.columns.map(lambda x: cndict[x])
elif ltype is 'EN':
endict = dict(zip(num_columns, EN_columns))
endict['283'] = '283'
try:
endict['284'] = '284'
endict['285'] = '285'
endict['286'] = '286'
except:
pass
endict['code'] = 'code'
endict['report_date'] = 'report_date'
res_pd.columns = res_pd.columns.map(lambda x: endict[x])
if res_pd.report_date.dtype == numpy.int64:
res_pd.report_date = pd.to_datetime(
res_pd.report_date.apply(QA_util_date_int2str))
else:
res_pd.report_date = pd.to_datetime(res_pd.report_date)
return res_pd.replace(-4.039810335e+34, numpy.nan).set_index(['report_date', 'code'], drop=False)
else:
return None
except Exception as e:
raise e
|
[
"获取专业财务报表\n Arguments:\n code {[type]} -- [description]\n report_date {[type]} -- [description]\n Keyword Arguments:\n ltype {str} -- [description] (default: {'EN'})\n db {[type]} -- [description] (default: {DATABASE})\n Raises:\n e -- [description]\n Returns:\n pd.DataFrame -- [description]\n "
] |
Please provide a description of the function:def QA_fetch_stock_divyield(code, start, end=None, format='pd', collections=DATABASE.stock_divyield):
'获取股票日线'
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
__data = []
cursor = collections.find({
'a_stockcode': {'$in': code}, "dir_dcl_date": {
"$lte": end,
"$gte": start}}, {"_id": 0}, batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop_duplicates(
(['dir_dcl_date', 'a_stockcode']))
res = res.ix[:, ['a_stockcode', 'a_stocksname', 'div_info', 'div_type_code', 'bonus_shr',
'cash_bt', 'cap_shr', 'epsp', 'ps_cr', 'ps_up', 'reg_date', 'dir_dcl_date',
'a_stockcode1', 'ex_divi_date', 'prg']]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print("QA Error QA_fetch_stock_divyield format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" " % format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_divyield data parameter start=%s end=%s is not right' % (start, end))
|
[] |
Please provide a description of the function:def QA_SU_save_stock_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
save stock_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
'''
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll_stock_day = client.stock_day
coll_stock_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_stock_day):
try:
QA_util_log_info(
'##JOB01 Now Saving STOCK_DAY==== {}'.format(str(code)),
ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
ref = coll_stock_day.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_STOCK_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_stock_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
QA_util_get_next_day(start_date),
end_date,
'00'
)
)
)
# 当前数据库中没有这个代码的股票数据， 从1990-01-01 开始下载所有的数据
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_STOCK_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_stock_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
start_date,
end_date,
'00'
)
)
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(stock_list)):
QA_util_log_info('The {} of Total {}'.format(item, len(stock_list)))
strProgressToLog = 'DOWNLOAD PROGRESS {} {}'.format(
str(float(item / len(stock_list) * 100))[0:4] + '%',
ui_log
)
intProgressToLog = int(float(item / len(stock_list) * 100))
QA_util_log_info(
strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog
)
__saving_work(stock_list[item], coll_stock_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save stock day ^_^', ui_log)
else:
QA_util_log_info('ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log)
|
[] |
Please provide a description of the function:def QA_SU_save_stock_week(client=DATABASE, ui_log=None, ui_progress=None):
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll_stock_week = client.stock_week
coll_stock_week.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_stock_week):
try:
QA_util_log_info(
'##JOB01 Now Saving STOCK_WEEK==== {}'.format(str(code)),
ui_log=ui_log
)
ref = coll_stock_week.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
if ref.count() > 0:
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_STOCK_WEEK \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
coll_stock_week.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
QA_util_get_next_day(start_date),
end_date,
'00',
frequence='week'
)
)
)
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_STOCK_WEEK \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
coll_stock_week.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
start_date,
end_date,
'00',
frequence='week'
)
)
)
except:
err.append(str(code))
for item in range(len(stock_list)):
QA_util_log_info(
'The {} of Total {}'.format(item,
len(stock_list)),
ui_log=ui_log
)
strProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(item / len(stock_list) * 100))[0:4] + '%'
)
intProgress = int(float(item / len(stock_list) * 100))
QA_util_log_info(
strProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
__saving_work(stock_list[item], coll_stock_week)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"save stock_week\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_xdxr(client=DATABASE, ui_log=None, ui_progress=None):
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
# client.drop_collection('stock_xdxr')
try:
coll = client.stock_xdxr
coll.create_index(
[('code',
pymongo.ASCENDING),
('date',
pymongo.ASCENDING)],
unique=True
)
except:
client.drop_collection('stock_xdxr')
coll = client.stock_xdxr
coll.create_index(
[('code',
pymongo.ASCENDING),
('date',
pymongo.ASCENDING)],
unique=True
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB02 Now Saving XDXR INFO ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_xdxr(str(code))),
ordered=False
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(stock_list)),
ui_log=ui_log
)
strLogInfo = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 100))
QA_util_log_info(
strLogInfo,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_], coll)
|
[
"[summary]\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_min(client=DATABASE, ui_log=None, ui_progress=None):
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB03 Now Saving STOCK_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB03.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_stock_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::]
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB03.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_stock_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
executor = ThreadPoolExecutor(max_workers=4)
# executor.map((__saving_work, stock_list[i_], coll),URLS)
res = {
executor.submit(__saving_work,
stock_list[i_],
coll)
for i_ in range(len(stock_list))
}
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(stock_list)),
ui_log=ui_log
)
strProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(stock_list) * 100))[0:4] + '%'
)
intProgress = int(count / len(stock_list) * 10000.0)
QA_util_log_info(
strProgress,
ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"save stock_min\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_index_day(client=DATABASE, ui_log=None, ui_progress=None):
__index_list = QA_fetch_get_stock_list('index')
coll = client.index_day
coll.create_index(
[('code',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll):
try:
ref_ = coll.find({'code': str(code)[0:6]})
end_time = str(now_time())[0:10]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['date']
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
if start_time != end_time:
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
QA_util_get_next_day(start_time),
end_time
)
)
)
else:
try:
start_time = '1990-01-01'
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
start_time,
end_time
)
)
)
except:
start_time = '2009-01-01'
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
start_time,
end_time
)
)
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(str(code))
QA_util_log_info(err, ui_log=ui_log)
for i_ in range(len(__index_list)):
# __saving_work('000001')
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(__index_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(__index_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(__index_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(__index_list.index[i_][0], coll)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"save index_day\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_index_min(client=DATABASE, ui_log=None, ui_progress=None):
__index_list = QA_fetch_get_stock_list('index')
coll = client.index_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB05 Now Saving Index_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB05.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_index_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB05.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_index_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(__saving_work,
__index_list.index[i_][0],
coll)
for i_ in range(len(__index_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(__index_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(__index_list) * 10000.0))
QA_util_log_info(
'The {} of Total {}'.format(count,
len(__index_list)),
ui_log=ui_log
)
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"save index_min\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_list(client=DATABASE, ui_log=None, ui_progress=None):
client.drop_collection('stock_list')
coll = client.stock_list
coll.create_index('code')
try:
# 🛠todo 这个应该是第一个任务 JOB01， 先更新股票列表！！
QA_util_log_info(
'##JOB08 Now Saving STOCK_LIST ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
stock_list_from_tdx = QA_fetch_get_stock_list()
pandas_data = QA_util_to_json_from_pandas(stock_list_from_tdx)
coll.insert_many(pandas_data)
QA_util_log_info(
"完成股票列表获取",
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_stock_list exception!")
pass
|
[
"save stock_list\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_etf_list(client=DATABASE, ui_log=None, ui_progress=None):
try:
QA_util_log_info(
'##JOB16 Now Saving ETF_LIST ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
etf_list_from_tdx = QA_fetch_get_stock_list(type_="etf")
pandas_data = QA_util_to_json_from_pandas(etf_list_from_tdx)
if len(pandas_data) > 0:
# 获取到数据后才进行drop collection 操作
client.drop_collection('etf_list')
coll = client.etf_list
coll.create_index('code')
coll.insert_many(pandas_data)
QA_util_log_info(
"完成ETF列表获取",
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_etf_list exception!")
pass
|
[
"save etf_list\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_block(client=DATABASE, ui_log=None, ui_progress=None):
client.drop_collection('stock_block')
coll = client.stock_block
coll.create_index('code')
try:
QA_util_log_info(
'##JOB09 Now Saving STOCK_BlOCK ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_block('tdx'))
)
QA_util_log_info(
'tdx Block ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
# 🛠todo fixhere here 获取同花顺板块， 还是调用tdx的
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_block('ths'))
)
QA_util_log_info(
'ths Block ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=8000
)
QA_util_log_info(
'完成股票板块获取=',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_stock_block exception!")
pass
|
[
"save stock_block\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_info(client=DATABASE, ui_log=None, ui_progress=None):
client.drop_collection('stock_info')
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_info
coll.create_index('code')
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB10 Now Saving STOCK INFO ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_info(str(code)))
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
# __saving_work('000001')
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 10000.0))
QA_util_log_info('The {} of Total {}'.format(i_, len(stock_list)))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_], coll)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"save stock_info\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_stock_transaction(
client=DATABASE,
ui_log=None,
ui_progress=None
):
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_transaction
coll.create_index('code')
err = []
def __saving_work(code):
QA_util_log_info(
'##JOB11 Now Saving STOCK_TRANSACTION ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(
# 🛠todo str(stock_list[code]) 参数不对？
QA_fetch_get_stock_transaction(
str(code),
'1990-01-01',
str(now_time())[0:10]
)
)
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
# __saving_work('000001')
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(stock_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_])
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[
"save stock_transaction\n\n Keyword Arguments:\n client {[type]} -- [description] (default: {DATABASE})\n "
] |
Please provide a description of the function:def QA_SU_save_option_commodity_day(
client=DATABASE,
ui_log=None,
ui_progress=None
):
'''
:param client:
:return:
'''
_save_option_commodity_cu_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_m_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_sr_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_ru_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_cf_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_c_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
|
[] |
Please provide a description of the function:def QA_SU_save_option_commodity_min(
client=DATABASE,
ui_log=None,
ui_progress=None
):
'''
:param client:
:return:
'''
# 测试中发现， 一起回去，容易出现错误，每次获取一个品种后 ，更换服务ip继续获取 ？
_save_option_commodity_cu_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_sr_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_m_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_ru_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_cf_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_c_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
|
[] |
Please provide a description of the function:def QA_SU_save_option_min(client=DATABASE, ui_log=None, ui_progress=None):
'''
:param client:
:return:
'''
option_contract_list = QA_fetch_get_option_contract_time_to_market()
coll_option_min = client.option_day_min
coll_option_min.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
# 索引 code
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB13 Now Saving Option 50ETF MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:8], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB13.{} Now Saving Option 50ETF {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
QA_util_log_info(
" 写入 新增历史合约记录数 {} ".format(len(__data))
)
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB13.{} Now Option 50ETF {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
QA_util_log_info(
" 写入 新增合约记录数 {} ".format(len(__data))
)
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(
__saving_work,
option_contract_list[i_]["code"],
coll_option_min
)
for i_ in range(len(option_contract_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(option_contract_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(option_contract_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(option_contract_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[] |
Please provide a description of the function:def QA_SU_save_option_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
:param client:
:return:
'''
option_contract_list = QA_fetch_get_option_50etf_contract_time_to_market()
coll_option_day = client.option_day
coll_option_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
# 索引 code
def __saving_work(code, coll_option_day):
try:
QA_util_log_info(
'##JOB12 Now Saving OPTION_DAY==== {}'.format(str(code)),
ui_log=ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
# 期权代码 从 10000001 开始编码 10001228
ref = coll_option_day.find({'code': str(code)[0:8]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
' 上次获取期权日线数据的最后日期是 {}'.format(start_date),
ui_log=ui_log
)
QA_util_log_info(
'UPDATE_OPTION_DAY \n 从上一次下载数据开始继续 Trying update {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
start_date0 = QA_util_get_next_day(start_date)
df0 = QA_fetch_get_option_day(
code=code,
start_date=start_date0,
end_date=end_date,
frequence='day',
ip=None,
port=None
)
retCount = df0.iloc[:, 0].size
QA_util_log_info(
"日期从开始{}-结束{} , 合约代码{} , 返回了{}条记录 , 准备写入数据库".format(
start_date0,
end_date,
code,
retCount
),
ui_log=ui_log
)
coll_option_day.insert_many(
QA_util_to_json_from_pandas(df0)
)
else:
QA_util_log_info(
"^已经获取过这天的数据了^ {}".format(start_date),
ui_log=ui_log
)
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_OPTION_DAY \n 从新开始下载数据 Trying update {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
df0 = QA_fetch_get_option_day(
code=code,
start_date=start_date,
end_date=end_date,
frequence='day',
ip=None,
port=None
)
retCount = df0.iloc[:, 0].size
QA_util_log_info(
"日期从开始{}-结束{} , 合约代码{} , 获取了{}条记录 , 准备写入数据库^_^ ".format(
start_date,
end_date,
code,
retCount
),
ui_log=ui_log
)
coll_option_day.insert_many(
QA_util_to_json_from_pandas(df0)
)
else:
QA_util_log_info(
"*已经获取过这天的数据了* {}".format(start_date),
ui_log=ui_log
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(option_contract_list)):
QA_util_log_info(
'The {} of Total {}'.format(item,
len(option_contract_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(item / len(option_contract_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(item / len(option_contract_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(option_contract_list[item].code, coll_option_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save option day ^_^ ', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log)
|
[] |
Please provide a description of the function:def QA_SU_save_future_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
save future_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
:return:
'''
future_list = [
item for item in QA_fetch_get_future_list().code.unique().tolist()
if str(item)[-2:] in ['L8',
'L9']
]
coll_future_day = client.future_day
coll_future_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_future_day):
try:
QA_util_log_info(
'##JOB12 Now Saving Future_DAY==== {}'.format(str(code)),
ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
ref = coll_future_day.find({'code': str(code)[0:4]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_Future_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_future_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_future_day(
str(code),
QA_util_get_next_day(start_date),
end_date
)
)
)
# 当前数据库中没有这个代码的股票数据， 从1990-01-01 开始下载所有的数据
else:
start_date = '2001-01-01'
QA_util_log_info(
'UPDATE_Future_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_future_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_future_day(
str(code),
start_date,
end_date
)
)
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(future_list)):
QA_util_log_info('The {} of Total {}'.format(item, len(future_list)))
strProgressToLog = 'DOWNLOAD PROGRESS {} {}'.format(
str(float(item / len(future_list) * 100))[0:4] + '%',
ui_log
)
intProgressToLog = int(float(item / len(future_list) * 100))
QA_util_log_info(
strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog
)
__saving_work(future_list[item], coll_future_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save future day ^_^', ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log)
|
[] |
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