def QA_fetch_index_day_adv(
code,
start, end=None,
if_drop_index=True,
# 🛠 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.index_day):
'''
:param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return:
'''
'获取指数日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# 🛠 todo 如果相等
res = QA_fetch_index_day(code, start, end, format='pd')
if res is None:
print("QA Error QA_fetch_index_day_adv parameter code=%s start=%s end=%s call QA_fetch_index_day return None" % (
code, start, end))
return None
else:
res_set_index = res.set_index(['date', 'code'], drop=if_drop_index)
# if res_set_index is None:
# print("QA Error QA_fetch_index_day_adv set index 'date, code' return None")
# return None
return QA_DataStruct_Index_day(res_set_index)
def QA_fetch_index_day_adv(
code,
start,
end=None,
if_drop_index=True,
# 🛠 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.index_day):
'''
:param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return:
'''
'获取指数日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# 🛠 todo 如果相等
res = QA_fetch_index_day(code, start, end, format='pd')
if res is None:
print(
"QA Error QA_fetch_index_day_adv parameter code=%s start=%s end=%s call QA_fetch_index_day return None"
% (code, start, end))
else:
res_set_index = res.set_index(['date', 'code'])
# if res_set_index is None:
# print("QA Error QA_fetch_index_day_adv set index 'date, code' return None")
# return None
return QA_DataStruct_Index_day(res_set_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 test_get_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)).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_index_day(code, start, end, format='pd')
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(df, df2)
# # df df2之间的区别
# df2 = df2.loc[:,
# [
# 'code',
# 'open',
# 'high',
# 'low',
# 'close',
# 'volume',
# 'amount',
# 'date'
# ]]
self.assertTrue(df.equals(df2), "和QA返回的数据不一致{}".format(obo))
def __get_index_day_data(self, __bid):
__data = QA_util_to_json_from_pandas(QA_fetch_index_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 QA_fetch_index_day_adv(code,
start,
end=None,
if_drop_index=False,
collections=DATABASE.index_day):
'获取指数日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
return QA_DataStruct_Index_day(
QA_fetch_index_day(code, start, end,
format='pd').set_index(['date', 'code'],
drop=if_drop_index))
def __end_of_backtest(self, *arg, **kwargs):
# 开始分析
QA_util_log_info('start analysis====\n' +
str(self.strategy_stock_list))
QA_util_log_info('=' * 10 + 'Trade History' + '=' * 10)
QA_util_log_info('\n' + tabulate(self.account.history,
headers=('date', 'code', 'price', 'towards',
'amounts', 'order_id', 'trade_id', 'commission')))
QA_util_log_info('\n' + tabulate(self.account.detail,
headers=('date', 'code', 'price', 'amounts', 'order_id',
'trade_id', 'sell_price', 'sell_order_id',
'sell_trade_id', 'sell_date', 'left_amount',
'commission')))
__exist_time = int(self.end_real_id) - int(self.start_real_id) + 1
self.__benchmark_data = QA_fetch_index_day(
self.benchmark_code, self.start_real_date,
self.end_real_date)
if len(self.__messages) > 1:
performace = QA_backtest_analysis_start(
self.setting.client, self.strategy_stock_list, self.__messages,
self.trade_list[self.start_real_id:self.end_real_id + 1],
self.market_data, self.__benchmark_data)
_backtest_mes = {
'user': self.setting.QA_setting_user_name,
'strategy': self.strategy_name,
'stock_list': performace['code'],
'start_time': self.strategy_start_date,
'end_time': self.strategy_end_date,
'account_cookie': self.account.account_cookie,
'annualized_returns': performace['annualized_returns'],
'benchmark_annualized_returns': performace['benchmark_annualized_returns'],
'assets': performace['assets'],
'benchmark_assets': performace['benchmark_assets'],
'trade_date': performace['trade_date'],
'total_date': performace['total_date'],
'win_rate': performace['win_rate'],
'alpha': performace['alpha'],
'beta': performace['beta'],
'sharpe': performace['sharpe'],
'vol': performace['vol'],
'benchmark_vol': performace['benchmark_vol'],
'max_drop': performace['max_drop'],
'exist': __exist_time,
'time': datetime.datetime.now()
}
QA_SU_save_backtest_message(_backtest_mes, self.setting.client)
QA_SU_save_account_message(self.__messages, self.setting.client)
QA_SU_save_account_to_csv(self.__messages)
def __QA_backest_handle_data(self, outside_handle):
'这个outside_handle就是一个外部的注入函数'
# 首先判断是否能满足回测的要求`
_info = {}
_info['stock_list'] = self.strategy_stock_list
__messages = {}
self.__init_cash_per_stock = int(
float(self.account.init_assest) / len(self.strategy_stock_list))
# 策略的交易日循环
for i in range(int(self.start_real_id), int(self.end_real_id) - 1, 1):
# 正在进行的交易日期
__running_date = self.trade_list[i]
QA_util_log_info(
'=================daily hold list====================')
QA_util_log_info('in the begining of ' + __running_date)
QA_util_log_info(
tabulate(self.account.message['body']['account']['hold']))
for __j in range(0, len(self.strategy_stock_list)):
if __running_date in [l[6] for l in self.market_data[__j]] and \
[l[6] for l in self.market_data[__j]].index(__running_date) \
> self.strategy_gap + 1:
__data = self.__QA_data_handle([
__l[6] for __l in self.market_data[__j]
].index(__running_date), __j)
__amount = 0
for item in __data['account']['body']['account']['hold']:
if self.strategy_stock_list[__j] in item:
__amount = __amount + item[3]
if __amount > 0:
__hold = 1
else:
__hold = 0
__result = outside_handle.predict(__data['market'],
__data['account'],
__hold, _info)
if float(self.account.message['body']['account']['cash']
[-1]) > 0:
self.__QA_backtest_excute_bid(
__result, __running_date, __hold,
str(self.strategy_stock_list[__j])[0:6], __amount)
else:
QA_util_log_info('not enough free money')
else:
pass
# 在回测的最后一天,平掉所有仓位(回测的最后一天是不买入的)
while len(self.account.hold) > 1:
__hold_list = self.account.hold[1::]
pre_del_id = []
for item_ in range(0, len(__hold_list)):
if __hold_list[item_][3] > 0:
__last_bid = self.bid.bid
__last_bid['amount'] = int(__hold_list[item_][3])
__last_bid['order_id'] = str(random.random())
__last_bid['price'] = 'close_price'
__last_bid['code'] = str(__hold_list[item_][1])
__last_bid['date'] = self.trade_list[self.end_real_id]
__last_bid['towards'] = -1
__last_bid['user'] = self.setting.QA_setting_user_name
__last_bid['strategy'] = self.strategy_name
__last_bid['bid_model'] = 'auto'
__last_bid['status'] = '0x01'
__last_bid['amount_model'] = 'amount'
__message = self.market.receive_bid(
__last_bid, self.setting.client)
_remains_day = 0
while __message['header']['status'] == 500:
# 停牌状态,这个时候按停牌的最后一天计算价值(假设平仓)
__last_bid['date'] = self.trade_list[self.end_real_id -
_remains_day]
_remains_day += 1
__message = self.market.receive_bid(
__last_bid, self.setting.client)
# 直到市场不是为0状态位置,停止前推日期
__messages = self.account.QA_account_receive_deal(
__message)
else:
pre_del_id.append(item_)
pre_del_id.sort()
pre_del_id.reverse()
for item_x in pre_del_id:
__hold_list.pop(item_x)
try:
# 在末尾增加一个回调给策略
outside_handle.on_end(__data['market'], __data['account'], __hold,
_info)
except:
pass
# 开始分析
QA_util_log_info('start analysis====\n' +
str(self.strategy_stock_list))
QA_util_log_info('=' * 10 + 'Trade History' + '=' * 10)
QA_util_log_info('\n' +
tabulate(self.account.history,
headers=('date', 'code', 'price', 'towards',
'amounts', 'order_id', 'trade_id',
'commission')))
QA_util_log_info(
tabulate(self.account.detail,
headers=('date', 'code', 'price', 'amounts', 'order_id',
'trade_id', 'sell_price', 'sell_order_id',
'sell_trade_id', 'sell_date', 'left_amount',
'commission')))
__exist_time = int(self.end_real_id) - int(self.start_real_id) + 1
self.__benchmark_data = QA_fetch_index_day(self.benchmark_code,
self.start_real_date,
self.end_real_date)
performace = QA_backtest_analysis_start(
self.setting.client, self.strategy_stock_list, __messages,
self.trade_list[self.start_real_id:self.end_real_id + 1],
self.market_data, self.__benchmark_data)
_backtest_mes = {
'user':
self.setting.QA_setting_user_name,
'strategy':
self.strategy_name,
'stock_list':
performace['code'],
'start_time':
self.strategy_start_date,
'end_time':
self.strategy_end_date,
'account_cookie':
self.account.account_cookie,
'annualized_returns':
performace['annualized_returns'],
'benchmark_annualized_returns':
performace['benchmark_annualized_returns'],
'assets':
performace['assets'],
'benchmark_assets':
performace['benchmark_assets'],
'trade_date':
performace['trade_date'],
'total_date':
performace['total_date'],
'win_rate':
performace['win_rate'],
'alpha':
performace['alpha'],
'beta':
performace['beta'],
'sharpe':
performace['sharpe'],
'vol':
performace['vol'],
'benchmark_vol':
performace['benchmark_vol'],
'max_drop':
performace['max_drop'],
'exist':
__exist_time,
'time':
datetime.datetime.now()
}
QA_SU_save_backtest_message(_backtest_mes, self.setting.client)
QA_SU_save_account_message(__messages, self.setting.client)
QA_SU_save_account_to_csv(__messages)
