def __end_of_backtest(self, *arg, **kwargs):
# 开始分析
# 对于account.detail做一定的整理
self.account.detail = detail = pd.DataFrame(self.account.detail, columns=['date', 'code', 'price', 'amounts', 'order_id',
'trade_id', 'sell_price', 'sell_order_id',
'sell_trade_id', 'sell_date', 'left_amount',
'commission'])
self.account.detail['sell_average'] = self.account.detail['sell_price'].apply(
lambda x: mean(x))
self.account.detail['pnl_persentage'] = self.account.detail['sell_average'] - \
self.account.detail['price']
self.account.detail['pnl'] = self.account.detail['pnl_persentage'] * (
self.account.detail['amounts'] - self.account.detail['left_amount']) - self.account.detail['commission']
self.account.detail = self.account.detail.drop(
['order_id', 'trade_id', 'sell_order_id', 'sell_trade_id'], axis=1)
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=(self.account.detail.columns)))
__exist_time = int(self.end_real_id) - int(self.start_real_id) + 1
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.benchmark_data.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)
self.account.detail.to_csv(
'backtest-pnl--' + str(self.account.account_cookie) + '.csv')
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)
