class Backtest(QA_Backtest):
'''
多线程模式回测示例
'''
def __init__(self, market_type, frequence, start, end, code_list,
commission_fee):
super().__init__(market_type, frequence, start, end, code_list,
commission_fee)
self.user = QA_User()
mastrategy = MAStrategy()
# maminstrategy = MAMINStrategy()
# maminstrategy.reset_assets(1000)
# self.portfolio, self.account = self.user.register_account(mastrategy)
self.user = QA_User(user_cookie='user_admin')
self.portfolio = self.user.new_portfolio('folio_admin')
self.portfolio, self.account = self.user.register_account(mastrategy)
def after_success(self):
QA_util_log_info(self.account.history_table)
risk = QA_Risk(self.account,
benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN)
print(risk().T)
self.account.save()
risk.save()
class Backtest(QA_Backtest):
def __init__(self, market_type, frequence, start, end, code_list, commission_fee):
super().__init__(market_type, frequence, start, end, code_list, commission_fee)
cpsstrategy = CaoPanShouStrategy()
self.user = QA_User(user_cookie='user_admin')
self.portfolio = self.user.new_portfolio('folio_admin_caopanshou')
# self.account = self.portfolio.new_account()
# self.portfolio, self.account = self.user.register_account(cpsstrategy, portfolio_cookie='folio_admin')
self.portfolio, self.account = self.user.register_account(cpsstrategy)
# account = QA_Account(user_cookie='user_admin',
# portfolio_cookie='portfolio_admin',
# account_cookie='account_admin')
# self.user = QA_User.register_account(account)
# self.portfolio, self.account = self.user.register_account(cpsstrategy)
def after_success(self):
QA_util_log_info(self.account.history_table)
risk = QA_Risk(self.account, benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN)
print(risk().T)
self.account.save()
risk.save()
class Backtest(QA_Backtest):
'''
多线程模式回测示例
'''
def __init__(self, market_type, frequence, start, end, code_list, commission_fee):
super().__init__(market_type, frequence, start, end, code_list, commission_fee)
self.user = QA_User()
mastrategy = MAStrategy()
maminstrategy = MAMINStrategy()
# maminstrategy.reset_assets(1000)
# self.portfolio, self.account = self.user.register_account(mastrategy)
self.user = QA_User(user_cookie='user_admin')
self.portfolio = self.user.new_portfolio('folio_admin')
self.portfolio, self.account = self.user.register_account(mastrategy)
def after_success(self):
QA_util_log_info(self.account.history_table)
risk = QA_Risk(self.account, benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN)
print(risk().T)
risk.plot_assets_curve()
risk.plot_dailyhold()
risk.plot_signal()
self.account.save()
risk.save()
class QA_Backtest():
"""BACKTEST
BACKTEST的主要目的:
- 引入时间轴环境,获取全部的数据,然后按生成器将数据迭代插入回测的BROKER
(这一个过程是模拟在真实情况中市场的时间变化和价格变化)
- BROKER有了新数据以后 会通知MARKET交易前置,MARKET告知已经注册的所有的ACCOUNT 有新的市场数据
- ACCOUNT 获取了新的市场函数,并将其插入他已有的数据中(update)
- ACCOUNT 底下注册的策略STRATEGY根据新的市场函数,产生新的买卖判断,综合生成信号
- 买卖判断通过交易前置发送给对应的BROKER,进行交易
- BROKER发送SETTLE指令 结束这一个bar的所有交易,进行清算
- 账户也进行清算,更新持仓,可卖,可用现金等
- 迭代循环直至结束回测
- 回测去计算这段时间的各个账户收益,并给出综合的最终结果
"""
def __init__(self,
market_type,
frequence,
start,
end,
code_list,
commission_fee,
username='******',
password='******',
portfolio_cookie='qatestportfolio'):
"""
:param market_type: 回测的市场 MARKET_TYPE.STOCK_CN ，
:param frequence: 'day' '1min' '5min' '15min' '30min' '60min'
:param start: 开始日期
:param end: 结束日期
:param code_list: 股票代码池
:param commission_fee: 交易佣金
"""
self.user = QA_User(username=username, password=password)
self.if_settled = False
self.account = None
self.portfolio = self.user.new_portfolio(portfolio_cookie)
# 🛠todo market_type 应该放在 QA_Market对象里的一个属性
self.market = QA_Market(if_start_orderthreading=True)
self.market_type = market_type
self.frequence = frequence
self.broker = QA_BacktestBroker(commission_fee)
self.broker_name = 'backtest_broker'
self.start = start
self.end = end
self.code_list = code_list
# 🛠todo 检查start日期和结束end日期是否正确
# 🛠todo 检查code list 是否合法
# 根据 市场类型，回测周期频率， 和股票代码列表 获取回测数据
if self.market_type is MARKET_TYPE.STOCK_CN and self.frequence is FREQUENCE.DAY:
# 获取日线级别的回测数据
self.ingest_data = QA_fetch_stock_day_adv(
self.code_list, self.start, self.end).to_qfq().panel_gen
elif self.market_type is MARKET_TYPE.STOCK_CN and self.frequence[
-3:] == 'min':
# 获取分钟级别的回测数据
self.ingest_data = QA_fetch_stock_min_adv(
self.code_list, self.start, self.end,
self.frequence).to_qfq().panel_gen
else:
QA_util_log_info("{} 的市场类型没有实现！".format(market_type))
def _generate_account(self):
"""
generate a simple account
"""
self.account = self.portfolio.new_account()
def start_market(self):
"""
start the market thread and register backtest broker thread
QAMarket 继承QATrader， QATrader 中有 trade_engine属性 ， trade_engine类型是QA_Engine从 QA_Thread继承
"""
# 启动 trade_engine 线程
self.market.start()
print('market start')
# 注册 backtest_broker ，并且启动和它关联线程QAThread 存放在 kernels 词典中， { 'broker_name': QAThread }
self.market.register(self.broker_name, self.broker)
# 通过 broke名字 新建立一个 QAAccount 放在的中 session字典中 session 是 { 'cookie' , QAAccount }
self.market.login(self.broker_name, self.account.account_cookie,
self.account)
self.market._sync_orders()
def run(self):
"""generator driven data flow
"""
# 如果出现了日期的改变 才会进行结算的事件
print('start: running')
_date = None
for data in self.ingest_data: # 对于在ingest_data中的数据
# <class 'QUANTAXIS.QAData.QADataStruct.QA_DataStruct_Stock_day'>
date = data.date[0]
print('current date : {}'.format(date))
print('current time : {}'.format(data.datetime[0]))
if self.market_type is MARKET_TYPE.STOCK_CN: # 如果是股票市场
if _date != date: # 如果新的date
# 前一天的交易日已经过去
# 往 broker 和 account 发送 settle 事件
try:
print('try to settle')
self.market._settle(self.broker_name)
self.market.next_tradeday()
except Exception as e:
raise e
# 基金 指数 期货
elif self.market_type in [
MARKET_TYPE.FUND_CN, MARKET_TYPE.INDEX_CN,
MARKET_TYPE.FUTURE_CN
]:
self.market._settle(self.broker_name)
self.broker.run(
QA_Event(event_type=ENGINE_EVENT.UPCOMING_DATA,
market_data=data))
# 生成 UPCOMING_DATA 事件放到 队列中去执行
self.market.upcoming_data(self.broker_name, data)
_date = date
# 最后收盘的平仓
self.market._settle(self.broker_name)
self.after_success()
def after_success(self):
"""called when all trading fininshed, for performance analysis
"""
print('after success')
for po in self.user.portfolio_list.values():
for ac in po.accounts.values():
print(ac.hold)
print(ac.history_table)
ac.save()
self.stop()
def stop(self):
"""stop all the market trade enging threads and all subthreads
"""
self.market.trade_engine.stop_all()
self.market.trade_engine.stop()
class QA_Backtest():
"""BACKTEST
BACKTEST的主要目的:
- 引入时间轴环境,获取全部的数据,然后按生成器将数据迭代插入回测的BROKER
(这一个过程是模拟在真实情况中市场的时间变化和价格变化)
- BROKER有了新数据以后 会通知MARKET交易前置,MARKET告知已经注册的所有的ACCOUNT 有新的市场数据
- ACCOUNT 获取了新的市场函数,并将其插入他已有的数据中(update)
- ACCOUNT 底下注册的策略STRATEGY根据新的市场函数,产生新的买卖判断,综合生成信号
- 买卖判断通过交易前置发送给对应的BROKER,进行交易
- BROKER发送SETTLE指令 结束这一个bar的所有交易,进行清算
- 账户也进行清算,更新持仓,可卖,可用现金等
- 迭代循环直至结束回测
- 回测去计算这段时间的各个账户收益,并给出综合的最终结果
"""
def __init__(
self,
market_type,
frequence,
start,
end,
code_list,
commission_fee,
username='******',
password='******',
portfolio_cookie='qatestportfolio'
):
"""
:param market_type: 回测的市场 MARKET_TYPE.STOCK_CN ，
:param frequence: 'day' '1min' '5min' '15min' '30min' '60min'
:param start: 开始日期
:param end: 结束日期
:param code_list: 股票代码池
:param commission_fee: 交易佣金
"""
self.user = QA_User(username=username, password=password)
self.if_settled = False
self.account = None
self.portfolio = self.user.new_portfolio(portfolio_cookie)
# 🛠todo market_type 应该放在 QA_Market对象里的一个属性
self.market = QA_Market(if_start_orderthreading=True)
self.market_type = market_type
self.frequence = frequence
self.broker = QA_BacktestBroker(commission_fee)
self.broker_name = 'backtest_broker'
self.start = start
self.end = end
self.code_list = code_list
# 🛠todo 检查start日期和结束end日期是否正确
# 🛠todo 检查code list 是否合法
# 根据 市场类型，回测周期频率， 和股票代码列表 获取回测数据
if self.market_type is MARKET_TYPE.STOCK_CN and self.frequence is FREQUENCE.DAY:
# 获取日线级别的回测数据
self.ingest_data = QA_fetch_stock_day_adv(
self.code_list,
self.start,
self.end
).to_qfq().panel_gen
elif self.market_type is MARKET_TYPE.STOCK_CN and self.frequence[
-3:] == 'min':
# 获取分钟级别的回测数据
self.ingest_data = QA_fetch_stock_min_adv(
self.code_list,
self.start,
self.end,
self.frequence
).to_qfq().panel_gen
else:
QA_util_log_info("{} 的市场类型没有实现！".format(market_type))
def _generate_account(self):
"""
generate a simple account
"""
self.account = self.portfolio.new_account()
def start_market(self):
"""
start the market thread and register backtest broker thread
QAMarket 继承QATrader， QATrader 中有 trade_engine属性 ， trade_engine类型是QA_Engine从 QA_Thread继承
"""
# 启动 trade_engine 线程
self.market.start()
print('market start')
# 注册 backtest_broker ，并且启动和它关联线程QAThread 存放在 kernels 词典中， { 'broker_name': QAThread }
self.market.register(self.broker_name, self.broker)
# 通过 broke名字 新建立一个 QAAccount 放在的中 session字典中 session 是 { 'cookie' , QAAccount }
self.market.login(
self.broker_name,
self.account.account_cookie,
self.account
)
self.market._sync_orders()
def run(self):
"""generator driven data flow
"""
# 如果出现了日期的改变 才会进行结算的事件
print('start: running')
_date = None
for data in self.ingest_data: # 对于在ingest_data中的数据
# <class 'QUANTAXIS.QAData.QADataStruct.QA_DataStruct_Stock_day'>
date = data.date[0]
print('current date : {}'.format(date))
print('current time : {}'.format(data.datetime[0]))
if self.market_type is MARKET_TYPE.STOCK_CN: # 如果是股票市场
if _date != date: # 如果新的date
# 前一天的交易日已经过去
# 往 broker 和 account 发送 settle 事件
try:
print('try to settle')
self.market._settle(self.broker_name)
self.market.next_tradeday()
except Exception as e:
raise e
# 基金 指数 期货
elif self.market_type in [MARKET_TYPE.FUND_CN,
MARKET_TYPE.INDEX_CN,
MARKET_TYPE.FUTURE_CN]:
self.market._settle(self.broker_name)
self.broker.run(
QA_Event(
event_type=ENGINE_EVENT.UPCOMING_DATA,
market_data=data
)
)
# 生成 UPCOMING_DATA 事件放到 队列中去执行
self.market.upcoming_data(self.broker_name, data)
_date = date
# 最后收盘的平仓
self.market._settle(self.broker_name)
self.after_success()
def after_success(self):
"""called when all trading fininshed, for performance analysis
"""
print('after success')
for po in self.user.portfolio_list.values():
for ac in po.accounts.values():
print(ac.hold)
print(ac.history_table)
ac.save()
self.stop()
def stop(self):
"""stop all the market trade enging threads and all subthreads
"""
self.market.trade_engine.stop_all()
self.market.trade_engine.stop()