def QA_fetch_stock_day_adv(
code,
start='all', end=None,
if_drop_index=False,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.stock_day):
'''
:param code: 股票代码
:param start: 开始日期
:param end: 结束日期
:param if_drop_index:
:param collections: 默认数据库
:return: 如果股票代码不存 或者开始结束日期不存在 在返回 None ，合法返回 QA_DataStruct_Stock_day 数据
'''
'获取股票日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
res = QA_fetch_stock_day(code, start, end, format='pd')
if res is None:
# 🛠 todo 报告是代码不合法，还是日期不合法
print("💢 Error QA_fetch_stock_day_adv parameter code=%s , start=%s, end=%s call QA_fetch_stock_day return None"%(code,start,end))
return None
else:
res_reset_index = res.set_index(['date', 'code'], drop=if_drop_index)
# if res_reset_index is None:
# print("💢 Error QA_fetch_stock_day_adv set index 'datetime, code' return None")
# return None
return QA_DataStruct_Stock_day(res_reset_index)
def QA_fetch_stock_day_adv(code,
start='all',
end=None,
if_drop_index=False,
collections=DATABASE.stock_day):
'''
:param code: 股票代码
:param start: 开始日期
:param end: 结束日期
:param if_drop_index:
:param collections: 默认数据库
:return: 如果股票代码不存 或者开始结束日期不存在 在返回 None ，合法返回 QA_DataStruct_Stock_day 数据
'''
'获取股票日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
res = QA_fetch_stock_day(code, start, end, format='pd')
if res is None:
print("code=%s , start=%s, end=%s QA_fetch_stock_day return None" %
(code, start, end))
return None
else:
return QA_DataStruct_Stock_day(
res.set_index(['date', 'code'], drop=if_drop_index))
def _get_diffQA(self, code, end, start, typ='stock'):
# qm = self._qm
if typ == 'stock':
qm = F().createFetcher('stock')
else:
qm = F().createFetcher('index')
df = qm.get(code, start, end, frequence='day')
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
if typ == 'stock':
df2 = QA_fetch_stock_day(code, start, end, format='pd')
else:
df2 = QA_fetch_index_day(code, start, end, format='pd')
# self.assertTrue((df.columns.values == df2.columns.values).all(), "df.columns:{}".format(df.columns))
if len(df) != len(df2):
# show df df2
print("Fetcher index: {}\n {}".format(type(qm), qm.collectionsDay))
print("{}:{}\n{} {}\n df{} df2{}".format(len(df), len(df2), start,
end, df.tail(10),
df2.tail(10)))
self.assertTrue(np.array_equal(df, df2),
"df.columns:{}".format(df.columns))
self.assertTrue(
len(df) == len(df2),
"和QA返回的数据,长度不一致{}:{}\n{} {}\n df{} df2{}".format(
len(df), len(df2), start, end, df.tail(10), df2.tail(10)))
# 两种方式检测DataFrame数据一致性
obo = self.differOneByOne(df, df2)
self.assertTrue(df.equals(df2), "和QA返回的数据不一致{}".format(obo))
def QA_fetch_stock_day_adv(
code,
start='all', end=None,
if_drop_index=False,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.stock_day):
'''
:param code: 股票代码
:param start: 开始日期
:param end: 结束日期
:param if_drop_index:
:param collections: 默认数据库
:return: 如果股票代码不存 或者开始结束日期不存在 在返回 None ，合法返回 QA_DataStruct_Stock_day 数据
'''
'获取股票日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
res = QA_fetch_stock_day(code, start, end, format='pd')
if res is None:
# 🛠 todo 报告是代码不合法，还是日期不合法
print("💢 Error QA_fetch_stock_day_adv parameter code=%s , start=%s, end=%s call QA_fetch_stock_day return None"%(code,start,end))
return None
else:
res_reset_index = res.set_index(['date', 'code'], drop=if_drop_index)
# if res_reset_index is None:
# print("💢 Error QA_fetch_stock_day_adv set index 'datetime, code' return None")
# return None
return QA_DataStruct_Stock_day(res_reset_index);
def __init__(self):
super().__init__()
self.frequence = FREQUENCE.DAY
self.market_type = MARKET_TYPE.STOCK_CN
# self.stock_basics = QATs.get_stock_basics()
# self.time_to_Market_300439 = self.stock_basics.loc['300439', 'timeToMarket']
# self.time_to_Market_300439 = QA.QA_util_date_int2str(self.time_to_Market_300439)
# self.time_to_day = QA_util_datetime_to_strdate(QA.QA_util_date_today())
# print(self.time_to_Market_300439)
# print(self.time_to_day)
self.time_to_Market_300439 = '2015-04-22'
self.time_to_day = '2018-05-01'
self.df_from_Tdx = QA_fetch_stock_day('300439',
self.time_to_Market_300439,
self.time_to_day, 'pd')
# print(self.df_from_Tdx)
self.ma05 = QA_indicator_MA(self.df_from_Tdx, 5)
self.ma10 = QA_indicator_MA(self.df_from_Tdx, 10)
self.ma15 = QA_indicator_MA(self.df_from_Tdx, 15)
self.ma20 = QA_indicator_MA(self.df_from_Tdx, 20)
def __get_stock_day_data(self, __bid):
__data = QA_util_to_json_from_pandas(QA_fetch_stock_day(str(
__bid.code)[0:6], str(__bid.datetime)[0:10], str(__bid.datetime)[0:10], 'pd'))
if len(__data) == 0:
pass
else:
__data = __data[0]
return __data
def __get_data(__bid):
'隔离掉引擎查询数据库的行为'
__data = QA_util_to_json_from_pandas(QA_fetch_stock_day(str(
__bid.code)[0:6], str(__bid.date)[0:10], str(__bid.date)[0:10], 'pd'))
if len(__data) == 0:
pass
else:
__data = __data[0]
return __data
def _get_diffQA(self, code, days):
start = (datetime.datetime.now() - datetime.timedelta(days)).date()
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end)
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
df2 = QA_fetch_stock_day(code, start, end, format='pd')
# todo 全自动运行时会报错。单独测试不会报错？？
if len(df) !=len(df2):
print("_get_diffQA : {} {} {} {}".format(start, end,df.head(), df2.head()))
print('columns:\n {}\n{}'.format(df.columns, df2.columns))
print(df.equals(df2))
else:
print('数据长度{}'.format(len(df)))
self.assertTrue(len(df) == len(df2), "和QA返回的数据,长度不一致{}:{}".format(len(df), len(df2)))
# 两种方式检测DataFrame数据一致性
obo = self.differOneByOne(df2, df)
self.assertTrue(df.equals(df2), "和QA返回的数据不一致{}".format(obo))
return df
def get_data(self, code, start, end, if_fq):
if if_fq:
data = QA_util_to_json_from_pandas(
QA_fetch_stock_day_adv(code, start, end).to_qfq().data)
self.write({'result': data})
else:
data = QA_util_to_json_from_pandas(
QA_fetch_stock_day(code, start, end, format='pd'))
self.write({'result': data})
def get(self):
"""
采用了get_arguents来获取参数
默认参数: code-->000001 start-->2017-01-01 end-->today
"""
code = self.get_argument('code', default='000001')
start = self.get_argument('start', default='2017-01-01')
end = self.get_argument('end', default=str(datetime.date.today()))
data = QA_util_to_json_from_pandas(
QA_fetch_stock_day(code, start, end, format='pd'))
self.write({'result': data})
def get(self):
print(self.request.arguments)
code = self.request.arguments.get('code', '000001')
start = str(self.request.arguments.get('start', '2017-01-01'))
end = str(self.request.arguments.get('end', '2017-12-31'))
data = QA_fetch_stock_day(code[0].decode('utf-8'),
start,
end,
format='json')
print(start)
print(data)
self.write({'result': data, 'status': 200})
def test_getNumpy_diffQA(self):
"""和QA返回的数据对比一致性
"""
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
array1 = qm.getNumpy(code, start, end)
self.assertTrue(len(array1) > 0, "返回数据数量应该大于0。")
array2 = QA_fetch_stock_day(code, start, end)
self.assertTrue(len(array1) == len(array2), "和QA返回的数据,长度不一致")
# 两种方式检测numpy数据一致性
self.assertTrue(np.array_equal(array1, array2),
"和QA返回的数据不一致{}".format(""))
self.assertTrue((array1 == array2).all(), "和QA返回的数据不一致{}".format(
np.setdiff1d(array1, array2, assume_unique=True)))
def QA_fetch_stock_day_adv(code,
start='all',
end=None,
if_drop_index=False,
collections=DATABASE.stock_day):
'获取股票日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
return QA_DataStruct_Stock_day(
QA_fetch_stock_day(code, start, end,
format='pd').set_index(['date', 'code'],
drop=if_drop_index))
def __init__(self,
user_cookie,
portfolio_cookie,
account_cookie,
init_cash=100000,
init_hold={}):
super().__init__(user_cookie=user_cookie,
portfolio_cookie=portfolio_cookie,
account_cookie=account_cookie,
init_cash=init_cash,
init_hold=init_hold)
self.frequence = FREQUENCE.DAY
self.market_type = MARKET_TYPE.STOCK_CN
self.commission_coeff = 0.0001
self.tax_coeff = 0.001
self.reset_assets(100000)
# self.stock_basics = QATs.get_stock_basics()
# self.time_to_Market_300439 = self.stock_basics.loc['300439', 'timeToMarket']
# self.time_to_Market_300439 = QA.QA_util_date_int2str(self.time_to_Market_300439)
# self.time_to_day = QA_util_datetime_to_strdate(QA.QA_util_date_today())
# print(self.time_to_Market_300439)
# print(self.time_to_day)
self.time_to_Market_300439 = '2017-04-22'
self.time_to_day = '2017-07-01'
self.df_from_Tdx = QA_fetch_stock_day('300439',
self.time_to_Market_300439,
self.time_to_day, 'pd')
# print(self.df_from_Tdx)
self.ma05 = QA_indicator_MA(self.df_from_Tdx, 5)
self.ma10 = QA_indicator_MA(self.df_from_Tdx, 10)
self.ma15 = QA_indicator_MA(self.df_from_Tdx, 15)
self.ma20 = QA_indicator_MA(self.df_from_Tdx, 20)
def _saving_work(self, code):
def __QA_log_info(code, end_time, start_time):
def loginfo(prefix='', astr='', listCounts=5):
if len(self._loginfolist) < listCounts:
self._loginfolist.append(astr)
else:
str = ''
for i in range(len(self._loginfolist)):
str += self._loginfolist[i] + ' '
str += astr
QA_util_log_info(prefix.format(str), self.ui_log)
self._loginfolist.clear()
prefix = '##JOB02 Saving parallelism {}_DAY ==== Trying updating\n{}'.format(
"xdxr", '{}')
loginfo(prefix, ' {} from {} to {}'.format(code, start_time,
end_time))
try:
search_cond = {'code': str(code)[0:6]}
_col = get_coll(client=None,
cacheName="stock_xdxr",
tableName="stock_xdxr")
ref_ = _col.find(search_cond)
ref_count = _col.count_documents(search_cond)
end_time = str(now_time())[0:10]
_xdxr = QA_fetch_get_stock_xdxr(str(code))
if ref_count > 0:
start_time = ref_[ref_count - 1]['date']
__QA_log_info(code, end_time, start_time)
if start_time != end_time:
# 最后一次保存的数据之后的数据
xdxrdata = _xdxr[_xdxr['date'] > start_time]
if len(xdxrdata) > 0:
_col.insert_many(QA_util_to_json_from_pandas(xdxrdata),
ordered=False)
else:
# 第一次保存数据
try:
start_time = '1990-01-01'
__QA_log_info(code, end_time, start_time)
_col.insert_many(QA_util_to_json_from_pandas(_xdxr),
ordered=False)
except Exception as e:
pass
except Exception as e:
QA_util_log_info(e.args, ui_log=self.ui_log)
self.err.append(str(code))
QA_util_log_info(self.err, ui_log=self.ui_log)
try:
# 插入前复权数据
coll_adj = get_coll(client=None,
cacheName="stock_adj",
tableName="stock_adj")
data = QA_fetch_stock_day(str(code), '1990-01-01',
str(datetime.date.today()), 'pd')
qfq = _QA_data_stock_to_fq(data, _xdxr, 'qfq')
qfq = qfq.assign(date=qfq.date.apply(lambda x: str(x)[0:10]))
adjdata = QA_util_to_json_from_pandas(
qfq.loc[:, ['date', 'code', 'adj']])
# 如果没有新增幅圈数据，插入新增数据
ref_ = coll_adj.find(search_cond)
ref_count = coll_adj.count_documents(search_cond)
if ref_count > 0:
# 历史保存过数据
lastref = ref_[ref_count - 1]
del lastref['_id']
try:
# 存在相同的数据，则保存最近数据
adjdata.index(lastref) # 找不到对应的数据，会出发异常
start_time = lastref['date']
adjdata2 = QA_util_to_json_from_pandas(qfq.loc[slice(
pd.Timestamp(start_time), pd.Timestamp(
end_time))].loc[:, ['date', 'code', 'adj']][1:])
if (ref_count + len(adjdata2)) == len(adjdata):
#
if len(adjdata2) > 0:
coll_adj.insert_many(adjdata2)
else:
raise Exception("数据总量不匹配，重新存储")
except Exception as e:
# 分红数据有变化，删除以前保存的数据，重新保存新的数据
coll_adj.delete_many({'code': code})
coll_adj.insert_many(adjdata)
else:
# 第一次保存前复权数据
# print(adjdata)
coll_adj.insert_many(adjdata)
except Exception as e:
print(e)
def get(self):
code = self.get_argument('code', default='000001')
start_date = self.get_argument('start_date', default='2018-01-01')
end_date = self.get_argument('end_date', default='2018-12-31')
start_year = self.get_argument('start_year', default='2017')
stock_prices = QA_fetch_stock_day(code,
start_date,
end_date,
format='pandas')
financial_reports = QA_fetch_financial_report([code],
report_dates(
int(start_year)))
# PE= 股价/每股收益
# PB= 股价/每股净资产
# PEG=PE/（企业年盈利增长率*100）
price_pd = pd.DataFrame({
'code': stock_prices.code,
'date': stock_prices.date,
'close': stock_prices.close
})
reports_pd = pd.DataFrame({
'code':
financial_reports.code,
'report_date':
financial_reports.report_date,
'EPS':
financial_reports.EPS,
'netAssetsPerShare':
financial_reports.netAssetsPerShare,
'netProfitGrowthRate':
financial_reports.netProfitGrowthRate
})
reports_pd['report_date_str'] = reports_pd['report_date'].apply(
format_ts_date)
eps_sum = reports_pd.loc[reports_pd["report_date_str"] ==
"2017-12-31"].EPS.sum()
aps_sum = reports_pd.loc[reports_pd["report_date_str"] ==
"2017-12-31"].netAssetsPerShare.sum()
pgr_sum = reports_pd.loc[reports_pd["report_date_str"] ==
"2017-12-31"].netProfitGrowthRate.sum()
price_pd['date_str'] = price_pd['date'].apply(
lambda x: x.strftime("%Y-%m-%d"))
price_pd['eps_sum'] = price_pd['close'].apply(
lambda x: round(eps_sum, 2))
price_pd['aps_sum'] = price_pd['close'].apply(
lambda x: round(aps_sum, 2))
price_pd['pgr_sum'] = price_pd['close'].apply(
lambda x: round(pgr_sum, 2))
price_pd['pe'] = price_pd['close'].apply(
lambda x: round(x / eps_sum, 2))
price_pd['pb'] = price_pd['close'].apply(
lambda x: round(x / aps_sum, 2))
price_pd['peg'] = price_pd['pe'].apply(lambda x: round(x / pgr_sum, 2))
return self.write({
'stock_prices':
QA_util_to_json_from_pandas(price_pd),
'financial_reports':
QA_util_to_json_from_pandas(reports_pd)
})