class QA_Backtest_stock_day():
account = QA_Account()
market = QA_Market()
bid = QA_QAMarket_bid()
setting = QA_Setting()
clients = setting.client
user = setting.QA_setting_user_name
market_data = []
def __init__(self):
self.account = QA_Account()
self.market = QA_Market()
self.bid = QA_QAMarket_bid()
self.setting = QA_Setting()
self.clients = self.setting.client
self.user = self.setting.QA_setting_user_name
@classmethod
def backtest_init(__backtest_cls, func, *arg, **kwargs):
def __init_backtest(__backtest_cls, *arg, **kwargs):
__backtest_cls.QA_backtest_init(__backtest_cls)
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_init_inside(__backtest_cls)
return __init_backtest(__backtest_cls)
@classmethod
def before_backtest(__backtest_cls, func, *arg, **kwargs):
# yield __backtest_cls.cash
func(*arg, **kwargs)
__backtest_cls.QA_backtest_start(__backtest_cls)
@classmethod
def end_backtest(__backtest_cls, func, *arg, **kwargs):
# yield __backtest_cls.cash
__backtest_cls.__end_of_backtest(__backtest_cls, func, *arg, **kwargs)
return func(*arg, **kwargs)
def __QA_backtest_set_bid_model(self):
if self.backtest_bid_model == 'market_price':
self.bid.bid['price'] = 'market_price'
self.bid.bid['bid_model'] = 'auto'
elif self.backtest_bid_model == 'close_price':
self.bid.bid['price'] = 'close_price'
self.bid.bid['bid_model'] = 'auto'
elif self.backtest_bid_model == 'strategy':
self.bid.bid['price'] = 0
self.bid.bid['bid_model'] = 'strategy'
else:
QA_util_log_info('support bid model')
sys.exit()
def __QA_backtest_set_save_model(self):
pass
def QA_backtest_init(self):
"""既然是被当做装饰器使用,就需要把变量设置放在装饰函数的前面,把函数放在装饰函数的后面"""
# 设置回测的开始结束时间
self.strategy_start_date = str('2017-01-05')
self.strategy_end_date = str('2017-07-01')
# 设置回测标的,是一个list对象,不过建议只用一个标的
# gap是回测时,每日获取数据的前推日期(交易日)
self.strategy_gap = int(60)
# 设置全局的数据库地址,回测用户名,密码,并初始化
self.setting.QA_util_sql_mongo_ip = str('127.0.0.1')
self.setting.QA_setting_user_name = str('admin')
self.setting.QA_setting_user_password = str('admin')
self.setting.QA_setting_init()
# 回测的名字
self.strategy_name = str('example')
# 股票的交易日历,真实回测的交易周期,和交易周期在交易日历中的id
self.trade_list = QA_fetch_trade_date(
self.setting.client.quantaxis.trade_date)
self.benchmark_code = 'hs300'
"""
这里会涉及一个区间的问题,开始时间是要向后推,而结束时间是要向前推,1代表向后推,-1代表向前推
"""
self.start_real_date = QA_util_get_real_date(self.strategy_start_date,
self.trade_list, 1)
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(self.strategy_end_date,
self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
self.strategy_stock_list = ['000001', '000002', '000004']
self.account.init_assest = 1000000
self.backtest_bid_model = 'market_price'
def __QA_backtest_init_inside(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
# 重新初始账户资产
self.setting.QA_setting_init()
self.account.init()
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
self.market_data = QA_fetch_stocklist_day(
self.strategy_stock_list, self.setting.client.quantaxis.stock_day,
[
self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id]
])
def QA_backtest_start(self, *args, **kwargs):
"""
这个是回测流程开始的入口
"""
assert len(self.strategy_stock_list) > 0
assert len(self.trade_list) > 0
assert isinstance(self.start_real_date, str)
assert isinstance(self.end_real_date, str)
#self.__QA_backtest_init_inside()
assert len(self.market_data) == len(self.strategy_stock_list)
QA_util_log_info('QUANTAXIS Backtest Engine Initial Successfully')
QA_util_log_info('Basical Info: \n' + tabulate(
[[str(__version__), str(self.strategy_name)]],
headers=('Version', 'Strategy_name')))
QA_util_log_info('Stock_List: \n' +
tabulate([self.strategy_stock_list]))
# 初始化报价模式
self.__QA_backtest_set_bid_model(self)
self.__messages = []
"""
try:
# 在末尾增加一个回调给策略
outside_handle.on_start(self)
except:
pass
# 加载外部策略
self.__QA_backest_handle_data(outside_handle)
"""
@classmethod
def load_strategy(__backtest_cls, func, *arg, **kwargs):
'策略加载函数'
# 首先判断是否能满足回测的要求`
_info = {}
_info['stock_list'] = __backtest_cls.strategy_stock_list
__messages = {}
__backtest_cls.__init_cash_per_stock = int(
float(__backtest_cls.account.init_assest) /
len(__backtest_cls.strategy_stock_list))
# 策略的交易日循环
for i in range(int(__backtest_cls.start_real_id),
int(__backtest_cls.end_real_id) - 1, 1):
__backtest_cls.running_date = __backtest_cls.trade_list[i]
QA_util_log_info(
'=================daily hold list====================')
QA_util_log_info('in the begining of ' +
__backtest_cls.running_date)
QA_util_log_info(
tabulate(
__backtest_cls.account.message['body']['account']['hold']))
func(*arg, **kwargs)
#最后一天
__backtest_cls.__end_of_trading(__backtest_cls)
def __start_of_every_day(self, func, *arg, **kwargs):
func(self, *arg, **kwargs)
def __end_of_every_day(self, func, *arg, **kwargs):
func(self, *arg, **kwargs)
def __backtest_every_day_trading(self, i, func, *arg, **kwargs):
# 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 = func(self, *arg, **kwargs)
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
def __end_of_trading(self, *arg, **kwargs):
# 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
self.__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)
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(
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)
# QA.QA_SU_save_backtest_message(analysis_message, self.setting.client)
def QA_backtest_excute_bid(self, __result, __date, __hold, __code,
__amount):
"""
这里是处理报价的逻辑部分
2017/7/19 修改
"""
"""
2017/8/4再次修改:
我们需要的是一个简化的报单api
买入/卖出
股票代码
买入/卖出数量
参考实盘的下单接口:
tradex:
nCategory - 委托业务的种类
0 买入
1 卖出
2 融资买入
3 融券卖出
4 买券还券
5 卖券还款
6 现券还券
nOrderType - 委托报价方式
0 限价委托； 上海限价委托/ 深圳限价委托
1 市价委托(深圳对方最优价格)
2 市价委托(深圳本方最优价格)
3 市价委托(深圳即时成交剩余撤销)
4 市价委托(上海五档即成剩撤/ 深圳五档即成剩撤)
5 市价委托(深圳全额成交或撤销)
6 市价委托(上海五档即成转限价)
sAccount - 股东代码
sStockCode - 证券代码
sPrice - 价格
sVolume - 委托证券的股数
返回值：
errinfo - 出错时函数抛出的异常信息；
result - 查询到的数据。
然后去检查
1.当前账户是否持仓该股票
2.当前账户现金是否足以支持购买该股票
返回
3.是否委托(委托id)
4.是否成交(成交id)
5.成交笔数
"""
self.__QA_backtest_set_bid_model()
if self.bid.bid['bid_model'] == 'strategy':
__bid_price = __result['price']
else:
__bid_price = self.bid.bid['price']
__bid = self.bid.bid
__bid['order_id'] = str(random.random())
__bid['user'] = self.setting.QA_setting_user_name
__bid['strategy'] = self.strategy_name
__bid['code'] = __code
__bid['date'] = __date
__bid['price'] = __bid_price
__bid['amount'], __bid['amount_model'] = self.__QA_bid_amount(
__result['amount'], __amount)
if __result['if_buy'] == 1:
# 这是买入的情况
__bid['towards'] = 1
__message = self.market.receive_bid(__bid, self.setting.client)
if float(self.account.message['body']['account']['cash'][-1]) > \
float(__message['body']['bid']['price']) * \
float(__message['body']['bid']['amount']):
# 这里是买入资金充足的情况
# 不去考虑
pass
else:
# 如果买入资金不充足,则按照可用资金去买入
__message['body']['bid']['amount'] = int(
float(self.account.message['body']['account']['cash'][-1])
/ float(
float(str(__message['body']['bid']['price'])[0:5]) *
100)) * 100
if __message['body']['bid']['amount'] > 0:
# 这个判断是为了 如果买入资金不充足,所以买入报了一个0量单的情况
#如果买入量>0, 才判断为成功交易
self.account.QA_account_receive_deal(__message)
elif __result['if_buy'] == 0:
# 如果买入状态为0,则不进行任何买入操作
pass
# 下面是卖出操作,这里在卖出前需要考虑一个是否有仓位的问题:
# 因为在股票中是不允许卖空操作的,所以这里是股票的交易引擎和期货的交易引擎的不同所在
if __result['if_sell'] == 1 and __hold == 1:
__bid['towards'] = -1
__message = self.market.receive_bid(__bid, self.setting.client)
self.account.QA_account_receive_deal(__message)
def __QA_bid_amount(self, __strategy_amount, __amount):
if __strategy_amount == 'mean':
return float(
float(self.account.message['body']['account']['cash'][-1]) /
len(self.strategy_stock_list)), 'price'
elif __strategy_amount == 'half':
return __amount * 0.5, 'amount'
elif __strategy_amount == 'all':
return __amount, 'amount'
def __QA_get_data_from_market(self, __id, stock_id):
# x=[x[6] for x in self.market_data]
if __id > self.strategy_gap + 1:
index_of_day = __id
index_of_start = index_of_day - self.strategy_gap + 1
return self.market_data[stock_id][index_of_start:index_of_day + 1]
# 从账户中更新数据
def __QA_data_handle(self, __id, __stock_id):
market_data = self.__QA_get_data_from_market(__id, __stock_id)
__message = self.account.message
return {'market': market_data, 'account': __message}
class QA_Backtest():
account = QA_Account()
market = QA_Market()
bid = QA_QAMarket_bid()
setting = QA_Setting()
clients = setting.client
user = setting.QA_setting_user_name
"""
backtest 类不应该只是一个简单的构造函数,他应该包含一个回测框架常用的方法和一些定制化的需求实现
@yutiansut
2017/6/19
需要做一个内部的setting表,以dict格式,value是函数名
@yutiansut
2017/6/27
"""
def QA_backtest_load_strategy(self, ):
__f_strategy_path = self.__backtest_setting['strategy']['file_path']
__f_strategy_path = __f_strategy_path.split(':')
if __f_strategy_path[0] == 'file' and __f_strategy_path[1] == 'py':
if __f_strategy_path[2] == 'local':
__current_dir = os.getcwd()
try:
if os.path.exists(
os.path.exists(
str(__current_dir) +
'\\backtest_strategy.py')):
__file_path = os.path.exists(
str(__current_dir) + '\\backtest_strategy.py')
except:
return "wrong with strategy file in current dir"
elif os.path.exists(__f_strategy_path[2]):
__file_path = __f_strategy_path[2]
else:
QA_util_log_info('error with loading strategy file')
sys.exit()
try:
import __file_path as QUANTAXIS_Strategy
QA_util_log_info(dir(QUANTAXIS_Strategy))
except:
QA_util_log_info('wrong with loading strategy')
def QA_backtest_import_setting(self, __setting_file_path='10x'):
if __setting_file_path == '10x':
__current_dir = os.getcwd()
try:
if os.path.exists(
os.path.exists(
str(__current_dir) + '\\backtest_setting.ini')):
__file_path = str(__current_dir) + '\\backtest_setting.ini'
except:
return "wrong with config file in current dir"
elif os.path.exists(__setting_file_path):
__file_path = os.path.exists(
str(__current_dir) + '\\backtest_setting.ini')
self.__backtest_setting = configparser.ConfigParser()
self.__backtest_setting.read(__file_path)
def __QA_backtest_set_stock_list(self):
self.strategy_stock_list = []
__t_strategy_stock_list = self.__backtest_setting['account'][
'stock_list']
__t_strategy_stock_list = __t_strategy_stock_list.split(':')
if __t_strategy_stock_list[0] == 'file':
if __t_strategy_stock_list[1] == 'csv':
if __t_strategy_stock_list[2] == 'local':
__current_dir = os.getcwd()
try:
if os.path.exists(
os.path.exists(
str(__current_dir) + '\\stock_list.csv')):
__stock_list_file_path = str(__current_dir) + \
'\\stock_list.csv'
else:
QA_util_log_info(
"wrong with csv file in current dir, \
the name should be \\stock_list.csv")
except:
QA_util_log_info("wrong with csv file in current dir, \
the name should be \\stock_list.csv")
else:
try:
if os.path.exists(__t_strategy_stock_list[2]):
__stock_list_file_path = __t_strategy_stock_list[2]
else:
QA_util_log_info(
"wrong with csv file in current dir, \
the name should be \\stock_list.csv")
except:
QA_util_log_info("wrong with csv file in current dir, \
the name should be \\stock_list.csv")
with open(__stock_list_file_path, 'r') as csv_file:
__data = csv.reader(csv_file)
for item in __data:
self.strategy_stock_list.append(item[0])
elif __t_strategy_stock_list[1] == 'json':
if __t_strategy_stock_list[2] == 'local':
__current_dir = os.getcwd()
try:
if os.path.exists(
os.path.exists(
str(__current_dir) + '\\stock_list.json')):
__stock_list_file_path = str(__current_dir) + \
'\\stock_list.json'
except:
return "wrong with csv file in current dir, \
the name should be \\stock_list.json"
else:
try:
if os.path.exists(__t_strategy_stock_list[2]):
__stock_list_file_path = __t_strategy_stock_list[2]
else:
return "wrong with csv file in current dir, \
the name should be \\stock_list.json"
except:
return "wrong with csv file in current dir, \
the name should be \\stock_list.json"
elif __t_strategy_stock_list[0] == 'mongo':
try:
import pymongo
coll = pymongo.MongoClient().__t_strategy_stock_list[1].split(
'-')[0].__t_strategy_stock_list[1].split['-'][1]
assert isinstance(__t_strategy_stock_list[2], str)
return coll.find(__t_strategy_stock_list[2])
except:
QA_util_log_info(
'something wrong with import stock_list from mongodb')
elif __t_strategy_stock_list[0] == 'data':
if __t_strategy_stock_list[1] == 'list':
self.strategy_stock_list = __t_strategy_stock_list[2]
def __QA_backtest_set_bid_model(self):
if self.__backtest_setting['bid']['bid_model'] == 'market_price':
self.bid.bid['price'] = 'market_price'
self.bid.bid['bid_model'] = 'auto'
elif self.__backtest_setting['bid']['bid_model'] == 'close_price':
self.bid.bid['price'] = 'close_price'
self.bid.bid['bid_model'] = 'auto'
elif self.__backtest_setting['bid']['bid_model'] == 'strategy':
self.bid.bid['price'] = 0
self.bid.bid['bid_model'] = 'strategy'
else:
QA_util_log_info('support bid model')
sys.exit()
def __QA_backtest_set_save_model(self):
pass
def QA_backtest_init(self):
# 设置回测的开始结束时间
try:
self.QA_backtest_import_setting()
except:
sys.exit()
self.strategy_start_date = str(
self.__backtest_setting['backtest']['strategy_start_date'])
self.strategy_end_date = str(
self.__backtest_setting['backtest']['strategy_end_date'])
# 设置回测标的,是一个list对象,不过建议只用一个标的
# gap是回测时,每日获取数据的前推日期(交易日)
self.strategy_gap = int(
self.__backtest_setting['backtest']['strategy_gap'])
# 设置全局的数据库地址,回测用户名,密码,并初始化
self.setting.QA_util_sql_mongo_ip = str(
self.__backtest_setting['backtest']['database_ip'])
self.setting.QA_setting_user_name = str(
self.__backtest_setting['backtest']['username'])
self.setting.QA_setting_user_password = str(
self.__backtest_setting['backtest']['password'])
self.setting.QA_setting_init()
# 回测的名字
self.strategy_name = str(
self.__backtest_setting['backtest']['strategy_name'])
# 股票的交易日历,真实回测的交易周期,和交易周期在交易日历中的id
self.trade_list = QA_fetch_trade_date(
self.setting.client.quantaxis.trade_date)
self.benchmark_code = self.__backtest_setting['backtest'][
'benchmark_code']
"""
这里会涉及一个区间的问题,开始时间是要向后推,而结束时间是要向前推,1代表向后推,-1代表向前推
"""
self.start_real_date = QA_util_get_real_date(self.strategy_start_date,
self.trade_list, 1)
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(self.strategy_end_date,
self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
self.__QA_backtest_set_stock_list()
self.account.init_assest = self.__backtest_setting['account'][
'account_assets']
def __QA_backtest_init_inside(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
# 重新初始账户资产
self.account.init()
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
self.market_data = QA_fetch_stocklist_day(
self.strategy_stock_list, self.setting.client.quantaxis.stock_day,
[
self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id]
])
def QA_backtest_start(self, outside_handle, *args, **kwargs):
"""
这个是回测流程开始的入口
"""
assert len(self.strategy_stock_list) > 0
assert len(self.trade_list) > 0
assert isinstance(self.start_real_date, str)
assert isinstance(self.end_real_date, str)
self.__QA_backtest_init_inside()
assert len(self.market_data) == len(self.strategy_stock_list)
QA_util_log_info('QUANTAXIS Backtest Engine Initial Successfully')
QA_util_log_info('Basical Info: \n' + tabulate(
[[str(__version__), str(self.strategy_name)]],
headers=('Version', 'Strategy_name')))
QA_util_log_info('Stock_List: \n' +
tabulate([self.strategy_stock_list]))
# 初始化报价模式
self.__QA_backtest_set_bid_model()
try:
# 在末尾增加一个回调给策略
outside_handle.on_start(self)
except:
pass
# 加载外部策略
self.__QA_backest_handle_data(outside_handle)
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)
# QA.QA_SU_save_backtest_message(analysis_message, self.setting.client)
def __QA_backtest_excute_bid(self, __result, __date, __hold, __code,
__amount):
"""
这里是处理报价的逻辑部分
2017/7/19 修改
__result传进来的变量重新区分: 现在需要有 if_buy, if_sell
因为需要对于: 持仓状态下继续购买进行进一步的支持*简单的情形就是 浮盈加仓
if_buy, if_sell都需要传入
现在的 买卖状态 和 持仓状态 是解耦的
"""
# 为了兼容性考虑,我们会在开始的时候检查是否有这些变量
if 'if_buy' not in list(__result.keys()):
__result['if_buy'] = 0
if 'if_sell' not in list(__result.keys()):
__result['if_sell'] = 0
self.__QA_backtest_set_bid_model()
if self.bid.bid['bid_model'] == 'strategy':
__bid_price = __result['price']
else:
__bid_price = self.bid.bid['price']
__bid = self.bid.bid
__bid['order_id'] = str(random.random())
__bid['user'] = self.setting.QA_setting_user_name
__bid['strategy'] = self.strategy_name
__bid['code'] = __code
__bid['date'] = __date
__bid['price'] = __bid_price
__bid['amount'], __bid['amount_model'] = self.__QA_bid_amount(
__result['amount'], __amount)
if __result['if_buy'] == 1:
# 这是买入的情况
__bid['towards'] = 1
__message = self.market.receive_bid(__bid, self.setting.client)
if float(self.account.message['body']['account']['cash'][-1]) > \
float(__message['body']['bid']['price']) * \
float(__message['body']['bid']['amount']):
# 这里是买入资金充足的情况
# 不去考虑
pass
else:
# 如果买入资金不充足,则按照可用资金去买入
__message['body']['bid']['amount'] = int(
float(self.account.message['body']['account']['cash'][-1])
/ float(
float(str(__message['body']['bid']['price'])[0:5]) *
100)) * 100
if __message['body']['bid']['amount'] > 0:
# 这个判断是为了 如果买入资金不充足,所以买入报了一个0量单的情况
#如果买入量>0, 才判断为成功交易
self.account.QA_account_receive_deal(__message)
elif __result['if_buy'] == 0:
# 如果买入状态为0,则不进行任何买入操作
pass
# 下面是卖出操作,这里在卖出前需要考虑一个是否有仓位的问题:
# 因为在股票中是不允许卖空操作的,所以这里是股票的交易引擎和期货的交易引擎的不同所在
if __result['if_sell'] == 1 and __hold == 1:
__bid['towards'] = -1
__message = self.market.receive_bid(__bid, self.setting.client)
self.account.QA_account_receive_deal(__message)
def __QA_bid_amount(self, __strategy_amount, __amount):
if __strategy_amount == 'mean':
return float(
float(self.account.message['body']['account']['cash'][-1]) /
len(self.strategy_stock_list)), 'price'
elif __strategy_amount == 'half':
return __amount * 0.5, 'amount'
elif __strategy_amount == 'all':
return __amount, 'amount'
def __QA_get_data_from_market(self, __id, stock_id):
# x=[x[6] for x in self.market_data]
if __id > self.strategy_gap + 1:
index_of_day = __id
index_of_start = index_of_day - self.strategy_gap + 1
return self.market_data[stock_id][index_of_start:index_of_day + 1]
# 从账户中更新数据
def __QA_data_handle(self, __id, __stock_id):
market_data = self.__QA_get_data_from_market(__id, __stock_id)
__message = self.account.message
return {'market': market_data, 'account': __message}
class QA_Backtest_stock_day():
'因为涉及很多不继承类的情况,所以先单列出来'
account = QA_Account()
market = QA_Market()
bid = QA_QAMarket_bid()
setting = QA_Setting()
clients = setting.client
user = setting.QA_setting_user_name
market_data = []
today = ''
def __init__(self):
self.account = QA_Account()
self.market = QA_Market()
self.bid = QA_QAMarket_bid()
self.setting = QA_Setting()
self.clients = self.setting.client
self.user = self.setting.QA_setting_user_name
self.market_data = []
self.today = ''
def __QA_backtest_init(self):
"""既然是被当做装饰器使用,就需要把变量设置放在装饰函数的前面,把函数放在装饰函数的后面"""
# 设置回测的开始结束时间
self.strategy_start_date = str('2017-01-05')
self.strategy_end_date = str('2017-07-01')
# 设置回测标的,是一个list对象,不过建议只用一个标的
# gap是回测时,每日获取数据的前推日期(交易日)
self.strategy_gap = int(60)
# 设置全局的数据库地址,回测用户名,密码,并初始化
self.setting.QA_util_sql_mongo_ip = str('127.0.0.1')
self.setting.QA_setting_user_name = str('admin')
self.setting.QA_setting_user_password = str('admin')
self.setting.QA_setting_init()
# 回测的名字
self.strategy_name = str('example')
# 股票的交易日历,真实回测的交易周期,和交易周期在交易日历中的id
self.trade_list = QA_fetch_trade_date(
self.setting.client.quantaxis.trade_date)
self.benchmark_code = 'hs300'
"""
这里会涉及一个区间的问题,开始时间是要向后推,而结束时间是要向前推,1代表向后推,-1代表向前推
"""
self.strategy_stock_list = ['000001', '000002', '000004']
self.account.init_assest = 1000000
self.backtest_bid_model = 'market_price'
def __QA_backtest_init_class(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
# 重新初始账户资产
self.setting.QA_setting_init()
self.account.init()
self.start_real_date = QA_util_get_real_date(self.strategy_start_date,
self.trade_list, 1)
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(self.strategy_end_date,
self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
self.market_data = QA_fetch_stocklist_day(
self.strategy_stock_list, self.setting.client.quantaxis.stock_day,
[
self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id]
])
def __QA_backtest_start(self, *args, **kwargs):
"""
这个是回测流程开始的入口
"""
assert len(self.strategy_stock_list) > 0
assert len(self.trade_list) > 0
assert isinstance(self.start_real_date, str)
assert isinstance(self.end_real_date, str)
assert len(self.market_data) == len(self.strategy_stock_list)
QA_util_log_info('QUANTAXIS Backtest Engine Initial Successfully')
QA_util_log_info('Basical Info: \n' + tabulate(
[[str(__version__), str(self.strategy_name)]],
headers=('Version', 'Strategy_name')))
QA_util_log_info('Stock_List: \n' +
tabulate([self.strategy_stock_list]))
# 初始化报价模式
self.__QA_backtest_set_bid_model(self)
self.__messages = []
def __QA_backtest_set_bid_model(self):
if self.backtest_bid_model == 'market_price':
self.bid.bid['price'] = 'market_price'
self.bid.bid['bid_model'] = 'auto'
elif self.backtest_bid_model == 'close_price':
self.bid.bid['price'] = 'close_price'
self.bid.bid['bid_model'] = 'auto'
elif self.backtest_bid_model == 'strategy':
self.bid.bid['price'] = 0
self.bid.bid['bid_model'] = 'strategy'
else:
QA_util_log_info('support bid model')
sys.exit()
def __check_state(self, bid_price, bid_amount):
pass
def __QA_bid_amount(self, __strategy_amount, __amount):
if __strategy_amount == 'mean':
return float(
float(self.account.message['body']['account']['cash'][-1]) /
len(self.strategy_stock_list)), 'price'
elif __strategy_amount == 'half':
return __amount * 0.5, 'amount'
elif __strategy_amount == 'all':
return __amount, 'amount'
def __QA_get_data_from_market(self, __id, stock_id):
if __id > self.strategy_gap + 1:
index_of_day = __id
index_of_start = index_of_day - self.strategy_gap + 1
return self.market_data[stock_id][index_of_start:index_of_day + 1]
else:
return self.market_data[stock_id][0:__id + 1]
def __QA_data_handle(self, __id, __stock_id):
"已经废弃"
market_data = self.__QA_get_data_from_market(__id, __stock_id)
__message = self.account.message
return {'market': market_data, 'account': __message}
def __backtest_every_day_trading(self, i, func, *arg, **kwargs):
# 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 = func(self, *arg, **kwargs)
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
def __end_of_trading(self, *arg, **kwargs):
# 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)
# 回测最后一天的交易处理
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
self.__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)
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(
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)
# QA.QA_SU_save_backtest_message(analysis_message, self.setting.client)
def QA_backtest_get_market_data(self, code, date):
'这个函数封装了关于获取的方式'
index_of_date = 0
index_of_code = self.strategy_stock_list.index(code)
if date in [l[6] for l in self.market_data[index_of_code]]:
index_of_date = [l[6] for l in self.market_data[index_of_code]
].index(date)
return self.__QA_get_data_from_market(self, index_of_date,
index_of_code)
def QA_backtest_hold_amount(self, __code):
__amount_hold = 0
for item in self.account.hold:
if __code in item:
__amount_hold += item[3]
return __amount_hold
def QA_backtest_get_OHLCV(self, __data):
'快速返回 OHLCV格式'
return (__data.T[1].astype(float).tolist(),
__data.T[2].astype(float).tolist(),
__data.T[3].astype(float).tolist(),
__data.T[4].astype(float).tolist(),
__data.T[5].astype(float).tolist())
def QA_backtest_send_order(self, __code: str, __amount: int,
__towards: int, __order: dict):
"""
2017/8/4
委托函数
在外部封装的一个报价接口,尽量满足和实盘一样的模式
输入
=============
买入/卖出
股票代码
买入/卖出数量
委托模式*
0 限价委托 LIMIT ORDER
1 市价委托 MARKET ORDER
2 严格模式(买入按最高价 卖出按最低价) STRICT ORDER
输出
=============
返回:
委托状态/委托id
成交状态/成交id/成交量/成交价
错误/错误id/
return bid_status,trade_status,error
"""
# 必须是100股的倍数
__amount = int(__amount / 100) * 100
# self.__QA_backtest_set_bid_model()
if __order['bid_model'] in [
'limit', 'Limit', 'Limited', 'limited', 'l', 'L', 0, '0'
]:
# 限价委托模式
__bid_price = __order['price']
elif __order['bid_model'] in [
'Market', 'market', 'MARKET', 'm', 'M', 1, '1'
]:
__bid_price = 'market_price'
elif __order['bid_model'] in ['strict', 'Strict', 's', 'S', '2', 2]:
__bid_price = 'strict_price'
elif __order['bid_model'] in [
'close', 'close_price', 'c', 'C', '3', 3
]:
__bid_price = 'close_price'
__bid = self.bid.bid
__bid['order_id'] = str(random.random())
__bid['user'] = self.setting.QA_setting_user_name
__bid['strategy'] = self.strategy_name
__bid['code'] = __code
__bid['date'] = self.running_date
__bid['price'] = __bid_price
__bid['amount'] = __amount
if __towards == 1:
# 这是买入的情况 买入的时候主要考虑的是能不能/有没有足够的钱来买入
__bid['towards'] = 1
__message = self.market.receive_bid(__bid, self.setting.client)
# 先扔进去买入,再通过返回的值来判定是否成功
if float(self.account.message['body']['account']['cash'][-1]) > \
float(__message['body']['bid']['price']) * \
float(__message['body']['bid']['amount']):
# 这里是买入资金充足的情况
# 不去考虑
pass
else:
# 如果买入资金不充足,则按照可用资金去买入
# 这里可以这样做的原因是在买入的时候 手续费为0
__message['body']['bid']['amount'] = int(
float(self.account.message['body']['account']['cash'][-1])
/ float(
float(str(__message['body']['bid']['price'])[0:5]) *
100)) * 100
if __message['body']['bid']['amount'] > 0:
# 这个判断是为了 如果买入资金不充足,所以买入报了一个0量单的情况
#如果买入量>0, 才判断为成功交易
self.account.QA_account_receive_deal(__message)
return __message
# 下面是卖出操作,这里在卖出前需要考虑一个是否有仓位的问题:
# 因为在股票中是不允许卖空操作的,所以这里是股票的交易引擎和期货的交易引擎的不同所在
elif __towards == -1:
# 如果是卖出操作 检查是否有持仓
# 股票中不允许有卖空操作
# 检查持仓面板
__amount_hold = self.QA_backtest_hold_amount(self, __code)
if __amount_hold > 0:
__bid['towards'] = -1
if __amount_hold >= __amount:
pass
else:
__bid['amount'] = __amount_hold
__message = self.market.receive_bid(__bid, self.setting.client)
if __message['header']['status'] == 200:
self.account.QA_account_receive_deal(__message)
return __message
else:
err_info = 'Error: Not Enough amount for code %s in hold list' % str(
__code)
QA_util_log_expection(err_info)
return err_info
else:
return "Error: No buy/sell towards"
def QA_backtest_sell_all(self):
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.today
__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
self.__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)
@classmethod
def load_strategy(__backtest_cls, func, *arg, **kwargs):
'策略加载函数'
# 首先判断是否能满足回测的要求`
_info = {}
_info['stock_list'] = __backtest_cls.strategy_stock_list
__messages = {}
__backtest_cls.__init_cash_per_stock = int(
float(__backtest_cls.account.init_assest) /
len(__backtest_cls.strategy_stock_list))
# 策略的交易日循环
for i in range(int(__backtest_cls.start_real_id),
int(__backtest_cls.end_real_id) - 1, 1):
__backtest_cls.running_date = __backtest_cls.trade_list[i]
QA_util_log_info(
'=================daily hold list====================')
QA_util_log_info('in the begining of ' +
__backtest_cls.running_date)
QA_util_log_info(
tabulate(
__backtest_cls.account.message['body']['account']['hold']))
__backtest_cls.today = __backtest_cls.running_date
func(*arg, **kwargs)
# 最后一天
__backtest_cls.__end_of_trading(__backtest_cls)
@classmethod
def backtest_init(__backtest_cls, func, *arg, **kwargs):
def __init_backtest(__backtest_cls, *arg, **kwargs):
__backtest_cls.__QA_backtest_init(__backtest_cls)
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_init_class(__backtest_cls)
return __init_backtest(__backtest_cls)
@classmethod
def before_backtest(__backtest_cls, func, *arg, **kwargs):
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_start(__backtest_cls)
@classmethod
def end_backtest(__backtest_cls, func, *arg, **kwargs):
# yield __backtest_cls.cash
__backtest_cls.__end_of_backtest(__backtest_cls, func, *arg, **kwargs)
return func(*arg, **kwargs)
class QA_Backtest_stock_day():
'最终目的还是实现一个通用的回测类'
backtest_type = 'day'
account = QA_Account()
market = QA_Market()
bid = QA_QAMarket_bid()
setting = QA_Setting()
clients = setting.client
user = setting.QA_setting_user_name
market_data = []
now = ''
today = ''
def __init__(self):
self.backtest_type = 'day'
self.account = QA_Account()
self.market = QA_Market()
self.bid = QA_QAMarket_bid()
self.setting = QA_Setting()
self.clients = self.setting.client
self.user = self.setting.QA_setting_user_name
self.market_data = []
self.now = ''
self.today = ''
def __QA_backtest_init(self):
"""既然是被当做装饰器使用,就需要把变量设置放在装饰函数的前面,把函数放在装饰函数的后面"""
# 设置回测的开始结束时间
self.strategy_start_date = str('2017-01-05')
self.strategy_end_date = str('2017-07-01')
# 设置回测标的,是一个list对象,不过建议只用一个标的
# gap是回测时,每日获取数据的前推日期(交易日)
self.strategy_gap = int(60)
# 设置全局的数据库地址,回测用户名,密码,并初始化
self.setting.QA_util_sql_mongo_ip = str('127.0.0.1')
self.setting.QA_setting_user_name = str('admin')
self.setting.QA_setting_user_password = str('admin')
self.setting.QA_setting_init()
# 回测的名字
self.strategy_name = str('example_min')
# 股票的交易日历,真实回测的交易周期,和交易周期在交易日历中的id
self.trade_list = QA_fetch_trade_date(
self.setting.client.quantaxis.trade_date)
self.benchmark_code = 'hs300'
"""
这里会涉及一个区间的问题,开始时间是要向后推,而结束时间是要向前推,1代表向后推,-1代表向前推
"""
self.strategy_stock_list = ['000001', '000002', '000004']
self.account.init_assest = 1000000
self.backtest_bid_model = 'market_price'
def __QA_backtest_prepare(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
# 重新初始账户资产
self.setting.QA_setting_init()
self.account.init()
self.start_real_date = QA_util_get_real_date(
self.strategy_start_date, self.trade_list, 1)
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(
self.strategy_end_date, self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
if self.backtest_type in ['day', 'd', '0x00']:
self.market_data = QA_fetch_stocklist_day(
self.strategy_stock_list,
[self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id]])
elif self.backtest_type in ['min', 'm', '0x01']:
self.market_data = QA_fetch_stocklist_min(
self.strategy_stock_list, [self.trade_list[
self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id]])
def __QA_backtest_start(self, *args, **kwargs):
"""
这个是回测流程开始的入口
"""
assert len(self.strategy_stock_list) > 0
assert len(self.trade_list) > 0
assert isinstance(self.start_real_date, str)
assert isinstance(self.end_real_date, str)
assert len(self.market_data) == len(self.strategy_stock_list)
QA_util_log_info('QUANTAXIS Backtest Engine Initial Successfully')
QA_util_log_info('Basical Info: \n' + tabulate(
[[str(__version__), str(self.strategy_name)]], headers=('Version', 'Strategy_name')))
QA_util_log_info('BACKTEST Cookie_ID is: ' +
str(self.account.account_cookie))
QA_util_log_info('Stock_List: \n' +
tabulate([self.strategy_stock_list]))
# 初始化报价模式
self.__QA_backtest_set_bid_model(self)
self.__messages = []
def __QA_backtest_set_bid_model(self):
if self.backtest_bid_model == 'market_price':
self.bid.price = 'market_price'
self.bid.bid_model = 'auto'
elif self.backtest_bid_model == 'close_price':
self.bid.price = 'close_price'
self.bid.bid_model = 'auto'
elif self.backtest_bid_model == 'strategy':
self.bid.price = 0
self.bid.bid_model = 'strategy'
else:
QA_util_log_info('support bid model')
sys.exit()
def __check_state(self, bid_price, bid_amount):
pass
def __QA_bid_amount(self, __strategy_amount, __amount):
if __strategy_amount == 'mean':
return float(float(self.account.message['body']['account']['cash'][-1]) /
len(self.strategy_stock_list)), 'price'
elif __strategy_amount == 'half':
return __amount * 0.5, 'amount'
elif __strategy_amount == 'all':
return __amount, 'amount'
def __end_of_trading(self, *arg, **kwargs):
# 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)
# 回测最后一天的交易处理
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
__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.type = '0x01'
__last_bid.amount_model = 'amount'
__message = self.market.receive_bid(
__last_bid)
_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)
# 直到市场不是为0状态位置,停止前推日期
self.__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)
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)
# QA.QA_SU_save_backtest_message(analysis_message, self.setting.client)
def QA_backtest_get_market_data(self, code, date, type_='numpy'):
'这个函数封装了关于获取的方式'
index_of_code = self.strategy_stock_list.index(code)
__res = self.market_data[index_of_code][:date].tail(self.strategy_gap)
if type_ in ['l', 'list', 'L']:
return np.asarray(__res).tolist()
elif type_ in ['pd', 'pandas', 'p']:
return __res
else:
return np.asarray(__res)
def QA_backtest_hold_amount(self, __code):
return sum(list(map(lambda item: item[3] if __code in item else 0, self.account.hold)))
def QA_backtest_get_OHLCV(self, __data):
'快速返回 OHLCV格式'
return (__data.T[1].astype(float).tolist(), __data.T[2].astype(float).tolist(),
__data.T[3].astype(float).tolist(
), __data.T[4].astype(float).tolist(),
__data.T[5].astype(float).tolist())
def QA_backtest_send_order(self, __code, __amount, __towards, __order):
"""
2017/8/4
委托函数
在外部封装的一个报价接口,尽量满足和实盘一样的模式
输入
=============
买入/卖出
股票代码
买入/卖出数量
委托模式*
0 限价委托 LIMIT ORDER
1 市价委托 MARKET ORDER
2 严格模式(买入按最高价 卖出按最低价) STRICT ORDER
输出
=============
返回:
委托状态/委托id
成交状态/成交id/成交量/成交价
错误/错误id/
return bid_status,trade_status,error
"""
# 必须是100股的倍数
__amount = int(__amount / 100) * 100
# self.__QA_backtest_set_bid_model()
if __order['bid_model'] in ['limit', 'Limit', 'Limited', 'limited', 'l', 'L', 0, '0']:
# 限价委托模式
__bid_price = __order['price']
elif __order['bid_model'] in ['Market', 'market', 'MARKET', 'm', 'M', 1, '1']:
__bid_price = 'market_price'
elif __order['bid_model'] in ['strict', 'Strict', 's', 'S', '2', 2]:
__bid_price = 'strict_price'
elif __order['bid_model'] in ['close', 'close_price', 'c', 'C', '3', 3]:
__bid_price = 'close_price'
__bid = self.bid
__bid.order_id = str(random.random())
__bid.user = self.setting.QA_setting_user_name
__bid.strategy = self.strategy_name
__bid.code = __code
__bid.date = self.running_date
__bid.datetime = self.running_date
__bid.sending_time = self.running_date
__bid.price = __bid_price
__bid.amount = __amount
if __towards == 1:
# 这是买入的情况 买入的时候主要考虑的是能不能/有没有足够的钱来买入
__bid.towards = 1
__message = self.market.receive_bid(
__bid)
# 先扔进去买入,再通过返回的值来判定是否成功
if float(self.account.message['body']['account']['cash'][-1]) > \
float(__message['body']['bid']['price']) * \
float(__message['body']['bid']['amount']):
# 这里是买入资金充足的情况
# 不去考虑
pass
else:
# 如果买入资金不充足,则按照可用资金去买入
# 这里可以这样做的原因是在买入的时候 手续费为0
__message['body']['bid']['amount'] = int(float(
self.account.message['body']['account']['cash'][-1]) / float(
float(str(__message['body']['bid']['price'])[0:5]) * 100)) * 100
if __message['body']['bid']['amount'] > 0:
# 这个判断是为了 如果买入资金不充足,所以买入报了一个0量单的情况
#如果买入量>0, 才判断为成功交易
self.messages=self.account.QA_account_receive_deal(__message)
return __message
# 下面是卖出操作,这里在卖出前需要考虑一个是否有仓位的问题:
# 因为在股票中是不允许卖空操作的,所以这里是股票的交易引擎和期货的交易引擎的不同所在
elif __towards == -1:
# 如果是卖出操作 检查是否有持仓
# 股票中不允许有卖空操作
# 检查持仓面板
__amount_hold = self.QA_backtest_hold_amount(self, __code)
if __amount_hold > 0:
__bid.towards = -1
__bid.amount = __amount_hold if __amount_hold < __amount else __bid.amount
__message = self.market.receive_bid(
__bid)
if __message['header']['status'] == 200:
self.messages=self.account.QA_account_receive_deal(__message)
return __message
else:
err_info = 'Error: Not Enough amount for code %s in hold list' % str(
__code)
QA_util_log_expection(err_info)
return err_info
else:
return "Error: No buy/sell towards"
def QA_backtest_check_order(self, order):
'用于检查委托单的状态'
"""
委托单被报入交易所会有一个回报,回报状态就是交易所返回的字段:
字段目前 2xx 是成功 4xx是失败 5xx是交易所无数据(停牌)
随着回测框架的不断升级,会有更多状态需要被管理:
200 委托成功,交易成功
203 委托成功,待成交
20
"""
pass
def QA_backtest_sell_all(self):
while len(self.account.hold) > 1:
__hold_list = self.account.hold[1::]
pre_del_id = []
def __sell(id_):
if __hold_list[id_][3] > 0:
__last_bid = self.bid
__last_bid.amount = int(__hold_list[id_][3])
__last_bid.order_id = str(random.random())
__last_bid.price = 'close_price'
__last_bid.code = str(__hold_list[id_][1])
__last_bid.date = self.now
__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.type = '0x01'
__last_bid.amount_model = 'amount'
__message = self.market.receive_bid(
__last_bid)
_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)
# 直到市场不是为0状态位置,停止前推日期
self.__messages = self.account.QA_account_receive_deal(
__message)
else:
pre_del_id.append(id_)
return pre_del_id
pre_del_id = reduce(lambda _, x: __sell(x),
range(len(__hold_list)))
pre_del_id.sort()
pre_del_id.reverse()
for item_x in pre_del_id:
__hold_list.pop(item_x)
@classmethod
def load_strategy(__backtest_cls, func, *arg, **kwargs):
'策略加载函数'
# 首先判断是否能满足回测的要求`
__messages = {}
__backtest_cls.__init_cash_per_stock = int(
float(__backtest_cls.account.init_assest) / len(__backtest_cls.strategy_stock_list))
# 策略的交易日循环
for i in range(int(__backtest_cls.start_real_id), int(__backtest_cls.end_real_id) - 1, 1):
__backtest_cls.running_date = __backtest_cls.trade_list[i]
QA_util_log_info(
'=================daily hold list====================')
QA_util_log_info('in the begining of ' +
__backtest_cls.running_date)
QA_util_log_info(
tabulate(__backtest_cls.account.message['body']['account']['hold']))
__backtest_cls.now = __backtest_cls.running_date
__backtest_cls.today = __backtest_cls.running_date
func(*arg, **kwargs)
# 最后一天
__backtest_cls.__end_of_trading(__backtest_cls)
@classmethod
def backtest_init(__backtest_cls, func, *arg, **kwargs):
def __init_backtest(__backtest_cls, *arg, **kwargs):
__backtest_cls.__QA_backtest_init(__backtest_cls)
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_prepare(__backtest_cls)
return __init_backtest(__backtest_cls)
@classmethod
def before_backtest(__backtest_cls, func, *arg, **kwargs):
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_start(__backtest_cls)
@classmethod
def end_backtest(__backtest_cls, func, *arg, **kwargs):
# yield __backtest_cls.cash
__backtest_cls.__end_of_backtest(__backtest_cls, func, *arg, **kwargs)
return func(*arg, **kwargs)
class QA_Backtest():
'最终目的还是实现一个通用的回测类'
backtest_type = 'day'
account = QA_Account()
market = QA_Market()
bid = QA_QAMarket_bid()
setting = QA_Setting()
clients = setting.client
user = setting.QA_setting_user_name
market_data = []
now = ''
today = ''
def __init__(self):
self.backtest_type = 'day'
self.account = QA_Account()
self.market = QA_Market()
self.bid = QA_QAMarket_bid()
self.setting = QA_Setting()
self.clients = self.setting.client
self.user = self.setting.QA_setting_user_name
self.market_data = []
self.now = ''
self.today = ''
def __QA_backtest_init(self):
"""既然是被当做装饰器使用,就需要把变量设置放在装饰函数的前面,把函数放在装饰函数的后面"""
# 设置回测的开始结束时间
self.strategy_start_date = str('2017-01-05')
self.strategy_end_date = str('2017-07-01')
# 设置回测标的,是一个list对象,不过建议只用一个标的
# gap是回测时,每日获取数据的前推日期(交易日)
self.strategy_gap = int(60)
# 设置全局的数据库地址,回测用户名,密码,并初始化
self.setting.QA_util_sql_mongo_ip = str('127.0.0.1')
self.setting.QA_setting_user_name = str('admin')
self.setting.QA_setting_user_password = str('admin')
self.setting.QA_setting_init()
# 回测的名字
self.strategy_name = str('example_min')
# 股票的交易日历,真实回测的交易周期,和交易周期在交易日历中的id
self.trade_list = QA_fetch_trade_date(
self.setting.client.quantaxis.trade_date)
self.benchmark_code = 'hs300'
"""
这里会涉及一个区间的问题,开始时间是要向后推,而结束时间是要向前推,1代表向后推,-1代表向前推
"""
self.strategy_stock_list = ['000001', '000002', '000004']
self.account.init_assest = 1000000
self.backtest_bid_model = 'market_price'
def __QA_backtest_prepare(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
# 重新初始账户资产
self.setting.QA_setting_init()
self.account.init()
self.start_real_date = QA_util_get_real_date(self.strategy_start_date,
self.trade_list, 1)
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(self.strategy_end_date,
self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
if self.backtest_type in ['day', 'd', '0x00']:
self.market_data = QA_fetch_stocklist_day(
self.strategy_stock_list, [
self.trade_list[self.start_real_id -
int(self.strategy_gap)],
self.trade_list[self.end_real_id]
])
elif self.backtest_type in ['min', 'm', '0x01']:
self.market_data = QA_fetch_stocklist_min(
self.strategy_stock_list, [
self.trade_list[self.start_real_id -
int(self.strategy_gap)],
self.trade_list[self.end_real_id]
])
def __QA_backtest_start(self, *args, **kwargs):
"""
这个是回测流程开始的入口
"""
assert len(self.strategy_stock_list) > 0
assert len(self.trade_list) > 0
assert isinstance(self.start_real_date, str)
assert isinstance(self.end_real_date, str)
assert len(self.market_data) == len(self.strategy_stock_list)
QA_util_log_info('QUANTAXIS Backtest Engine Initial Successfully')
QA_util_log_info('Basical Info: \n' + tabulate(
[[str(__version__), str(self.strategy_name)]],
headers=('Version', 'Strategy_name')))
QA_util_log_info('Stock_List: \n' +
tabulate([self.strategy_stock_list]))
# 初始化报价模式
self.__messages = []
def __check_state(self, bid_price, bid_amount):
pass
def __QA_bid_amount(self, __strategy_amount, __amount):
if __strategy_amount == 'mean':
return float(
float(self.account.message['body']['account']['cash'][-1]) /
len(self.strategy_stock_list)), 'price'
elif __strategy_amount == 'half':
return __amount * 0.5, 'amount'
elif __strategy_amount == 'all':
return __amount, 'amount'
def __end_of_trading(self, *arg, **kwargs):
# 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)
# 回测最后一天的交易处理
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
__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)
_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)
# 直到市场不是为0状态位置,停止前推日期
self.__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)
def __warp_bid(self, __bid, __order):
__market_data_for_backtest = self.QA_backtest_get_market_data(
self, __bid.code, __bid.date, 'np', 1)
__O, __H, __L, __C, __V = self.QA_backtest_get_OHLCV(
self, __market_data_for_backtest)
if __order['bid_model'] in [
'limit', 'Limit', 'Limited', 'limited', 'l', 'L', 0, '0'
]:
# 限价委托模式
__bid.price = __order['price']
elif __order['bid_model'] in [
'Market', 'market', 'MARKET', 'm', 'M', 1, '1'
]:
__bid.price = 0.5 * (float(__O[0]) + float(__C[0]))
elif __order['bid_model'] in ['strict', 'Strict', 's', 'S', '2', 2]:
__bid.price = float(__H[0]) if __bid.towards == 1 else float(
__L[0])
elif __order['bid_model'] in [
'close', 'close_price', 'c', 'C', '3', 3
]:
__bid.price = float(__C[0])
__bid.price = float('%.2f' % __bid.price)
return __bid
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_backtest_get_market_data(self,
code,
date,
type_='numpy',
gap_=None):
'这个函数封装了关于获取的方式'
gap_ = self.strategy_gap if gap_ is None else gap_
index_of_code = self.strategy_stock_list.index(code)
__res = self.market_data[index_of_code][:date].tail(gap_)
if type_ in ['l', 'list', 'L']:
return np.asarray(__res).tolist()
elif type_ in ['pd', 'pandas', 'p']:
return res
else:
return np.asarray(__res)
def QA_backtest_hold_amount(self, __code):
# return sum(list(map(lambda item: item[3] if __code in item else 0, self.account.hold)))
try:
return self.account.hold_available[__code]
except:
return 0
def QA_backtest_get_OHLCV(self, __data):
'快速返回 OHLCV格式'
return (__data.T[1].astype(float).tolist(),
__data.T[2].astype(float).tolist(),
__data.T[3].astype(float).tolist(),
__data.T[4].astype(float).tolist(),
__data.T[5].astype(float).tolist())
def QA_backtest_send_order(self, __code: str, __amount: int,
__towards: int, __order: dict):
"""
2017/8/4
委托函数
在外部封装的一个报价接口,尽量满足和实盘一样的模式
输入
=============
买入/卖出
股票代码
买入/卖出数量
委托模式*
0 限价委托 LIMIT ORDER
1 市价委托 MARKET ORDER
2 严格模式(买入按最高价 卖出按最低价) STRICT ORDER
"""
# 只需要维护未成交队列即可,无非是一个可用资金和可卖股票数量的调整
# 必须是100股的倍数
__amount = int(__amount / 100) * 100
# self.__QA_backtest_set_bid_model()
__bid = self.bid
(__bid.order_id, __bid.user, __bid.strategy, __bid.code, __bid.date,
__bid.datetime, __bid.sending_time, __bid.amount,
__bid.towards) = (str(random.random()),
self.setting.QA_setting_user_name,
self.strategy_name, __code, self.running_date,
self.running_date, self.running_date, __amount,
__towards)
__bid = self.__warp_bid(self, __bid, __order)
return self.__QA_backtest_send_bid(self, __bid)
# 先进行处理:
def __QA_backtest_send_bid(self, __bid):
if __bid.towards == 1:
# 扣费以下这个订单时的bar的open扣费
if self.account.cash_available > (__bid.price * __bid.amount):
# 这是买入的情况 买入的时候主要考虑的是能不能/有没有足够的钱来买入
__message = self.market.receive_bid(__bid)
self.account.cash_available -= (__bid.price * __bid.amount)
# 先扔进去买入,再通过返回的值来判定是否成功
if __message['header']['status'] == 200 and __message['body'][
'bid']['amount'] > 0:
# 这个判断是为了 如果买入资金不充足,所以买入报了一个0量单的情况
# 如果买入量>0, 才判断为成功交易
self.account.QA_account_receive_deal(__message)
return __message
else:
# self.account.cash_available += (__bid.price * __bid.amount) #报单还挂着 不能恢复
self.account.order_queue.append(__bid)
# 下面是卖出操作,这里在卖出前需要考虑一个是否有仓位的问题:
# 因为在股票中是不允许卖空操作的,所以这里是股票的交易引擎和期货的交易引擎的不同所在
elif __bid.towards == -1:
# 如果是卖出操作 检查是否有持仓
# 股票中不允许有卖空操作
# 检查持仓面板
__amount_hold = self.QA_backtest_hold_amount(self, __bid.code)
if __amount_hold > 0:
__bid.amount = __amount_hold if __amount_hold < __bid.amount else __bid.amount
self.account.hold_available[__bid.code] -= __bid.amount
__message = self.market.receive_bid(__bid)
print(__message)
if __message['header']['status'] == 200:
self.account.QA_account_receive_deal(__message)
return __message
else:
self.account.order_queue.append(__bid)
return __message
else:
err_info = 'Error: Not Enough amount for code %s in hold list' % str(
__bid.code)
QA_util_log_expection(err_info)
return err_info
else:
return "Error: No buy/sell towards"
def QA_backtest_check_order(self, order):
'用于检查委托单的状态'
"""
委托单被报入交易所会有一个回报,回报状态就是交易所返回的字段:
字段目前 2xx 是成功 4xx是失败 5xx是交易所无数据(停牌)
随着回测框架的不断升级,会有更多状态需要被管理:
200 委托成功,交易成功
203 委托成功,待成交
20
"""
pass
def QA_backtest_sell_all(self):
while len(self.account.hold) > 1:
__hold_list = self.account.hold[1::]
pre_del_id = []
def __sell(id_):
if __hold_list[item_][3] > 0:
__last_bid = self.bid
__last_bid.amount = int(__hold_list[id_][3])
__last_bid.order_id = str(random.random())
__last_bid.price = 'close_price'
__last_bid.code = str(__hold_list[id_][1])
__last_bid.date = self.now
__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)
_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)
# 直到市场不是为0状态位置,停止前推日期
self.__messages = self.account.QA_account_receive_deal(
__message)
else:
pre_del_id.append(id_)
return pre_del_id
pre_del_id = reduce(lambda _, x: __sell(x),
range(len(__hold_list)))
pre_del_id.sort()
pre_del_id.reverse()
for item_x in pre_del_id:
__hold_list.pop(item_x)
@classmethod
def load_strategy(__backtest_cls, func, *arg, **kwargs):
'策略加载函数'
# 首先判断是否能满足回测的要求`
__messages = {}
__backtest_cls.__init_cash_per_stock = int(
float(__backtest_cls.account.init_assest) /
len(__backtest_cls.strategy_stock_list))
# 策略的交易日循环
for i in range(int(__backtest_cls.start_real_id),
int(__backtest_cls.end_real_id) - 1, 1):
__backtest_cls.running_date = __backtest_cls.trade_list[i]
QA_util_log_info(
'=================daily hold list====================')
QA_util_log_info('in the begining of ' +
__backtest_cls.running_date)
QA_util_log_info(
tabulate(
__backtest_cls.account.message['body']['account']['hold']))
__backtest_cls.now = __backtest_cls.running_date
__backtest_cls.today = __backtest_cls.running_date
# 交易前同步持仓状态
__backtest_cls.account.cash_available = __backtest_cls.account.cash[
-1]
__temp_hold = pd.DataFrame(__backtest_cls.account.hold[1::],
columns=__backtest_cls.account.hold[0])
__temp_hold = __temp_hold.set_index('code', drop=False)
__backtest_cls.account.hold_available = __temp_hold[
'amount'].groupby('code').sum()
if __backtest_cls.backtest_type in ['day', 'd']:
for item in __backtest_cls.account.order_queue:
item.date, item.datetime = (__backtest_cls.today,
__backtest_cls.now)
__backtest_cls.__QA_backtest_send_bid(__backtest_cls, item)
# input()
func(*arg, **kwargs) # 发委托单
elif __backtest_cls.backtest_type in ['min', 'm']:
func(*arg, **kwargs) # 发委托单
# 队列循环批量发单
# 最后一天
__backtest_cls.__end_of_trading(__backtest_cls)
@classmethod
def backtest_init(__backtest_cls, func, *arg, **kwargs):
def __init_backtest(__backtest_cls, *arg, **kwargs):
__backtest_cls.__QA_backtest_init(__backtest_cls)
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_prepare(__backtest_cls)
return __init_backtest(__backtest_cls)
@classmethod
def before_backtest(__backtest_cls, func, *arg, **kwargs):
func(*arg, **kwargs)
__backtest_cls.__QA_backtest_start(__backtest_cls)
@classmethod
def end_backtest(__backtest_cls, func, *arg, **kwargs):
# yield __backtest_cls.cash
__backtest_cls.__end_of_backtest(__backtest_cls, func, *arg, **kwargs)
return func(*arg, **kwargs)