Quandomo2020
an event-driver quantative trading platform.
Install / Use
/learn @domodo2012/Quandomo2020README
quandomo 框架概述 一、数据存贮与使用 1、数据存贮 从最基础的远程数据库中取数据，以相同的结构保存到本地（sqlite）数据库中。
2、数据使用 回测时，将数据从本地数据库中读取后，逐个时间戳形成市场事件推送入队列。 然后从队列中取数据，针对不同的事件类型，已注册的不同监控函数对应响应。
二、整体框架 1、后台事务在策略的父类中进行，具体的策略本身继承之后，重写 handle_bar、handle_order、handle_trade 等方法，实现具体的策略。 2、回测时，将数据从本地数据库中读出，逐个时间戳新建市场事件并推送入队列，之后从队列中取事件，不同的事件类型有已注册的不同监控函数响应，从而驱动策略运行。
三、例程
ExampleStrategy.py
-- coding: utf-8 --
Import … from … import ...
class TrialStrategyStock(StrategyBaseBacktestStock): def init(self, universe_limit): super(TrialStrategyStock, self).init(universe_limit)
def init_strategy(self, log_full_path: list, color_log=True, start_str='20180101', end_str='20200222'): # 确定参数值
self.gateway = 'ctp'
self.run_mode = RunMode.BACKTESTING
self.start = date_str_to_int(start_str)
self.end = date_str_to_int(end_str)
self.account = [{'name': 'acc0', 'equity': 200000}]
self.benchmark = "000300.SH"
# self.rights_adjustment = RightsAdjustment.NONE # 只在画 K 线时有效，对策略运行无影响
# 动态股票池（沪深300指数成分股）
self.is_universe_dynamic = '000300.SH'
# 固定股票池
# self.is_universe_dynamic = False
# self.universe = ['000562.SZ', '600710.SH', '600757.SH', '600761.SH']
# 设置回测滑点
self.set_slippage(symbol_type=Product.STOCK.value, slippage_type=Slippage.FIX, value=0.01)
# 设置沪市交易成本
self.set_commission(symbol_type=Product.STOCK_SH, tax=0.001, open_commission=0.0003,
close_commission=0.0005,
close_today_commission=0, min_commission=5)
# 设置深市交易成本
self.set_commission(symbol_type=Product.STOCK_SZ, tax=0.001, open_commission=0.0003,
close_commission=0.0003,
close_today_commission=0, min_commission=5)
if not color_log:
self.context.logger = Logger(log_full_path[0])
else:
self.context.logger = ColorLogger(log_full_path[0] + log_full_path[1]) # color log
def calc_position_size(self, account_available, symbol_price):
psize = account_available / len(self.universe) * 0.9 / symbol_price
psize = int(psize / 100) * 100
return psize
def handle_bar(self, event_bar):
self.context.logger.info("handle_bar() @ {0}".format(event_bar.dt))
self.activate_trade_signal = False
available_position_dict = self.context.bar_position_data_dict
account_data = self.context.current_account_data
current_date_int = date_str_to_int(event_bar.dt)
cur_start_date = to_datetime(event_bar.dt) - Timedelta(days=60)
cur_start_date_int = date_str_to_int(str(cur_start_date)[:10])
# 循环处理旧持仓
for pos_symbol in available_position_dict.keys():
close_price = self.get_data.get_market_data(self.context.daily_data,
all_symbol_code=[pos_symbol],
field=["close"],
start=cur_start_date_int,
end=current_date_int)
if len(close_price) > 0:
# 指标计算
ma5 = talib.MA(array(close_price), timeperiod=5)
ma20 = talib.MA(array(close_price), timeperiod=20)
if current_date_int in close_price.keys() and (not isnull(ma5[-1])) and (not isnull(ma20[-1])):
if ma5[-1] < ma20[-1]:
if available_position_dict[pos_symbol].volume > 0 and available_position_dict[pos_symbol].direction == Direction.LONG:
self.activate_trade_signal = True
pos_abs = abs(available_position_dict[pos_symbol].volume)
comments = "限价卖出"
self.sell(event_bar.dt, self.account[0]['name'], pos_symbol,
close_price.iloc[-1], pos_abs, False, comments)
self.context.logger.info(
"-- 资金账号 {3} 发出委托卖出 {0} {1} 股，委托价为 {2}".format(pos_symbol, pos_abs,
close_price.iloc[-1],
self.account[0]['name']))
# 循环遍历股票池，看是否有新信号
for symbol in self.universe:
# 取当前股票的数据
close_price = self.get_data.get_market_data(self.context.daily_data,
all_symbol_code=[symbol],
field=["close"],
start=cur_start_date_int,
end=current_date_int)
# close_array = array(close_price)
if len(close_price) > 0:
# 指标计算
ma5 = talib.MA(array(close_price), timeperiod=5)
ma20 = talib.MA(array(close_price), timeperiod=20)
# 数据有效时才判断
if current_date_int in close_price.keys() and (not isnull(ma5[-1])) and (not isnull(ma20[-1])):
# 如果5日均线突破20日均线，并且没有持仓，则买入这只股票100股，委托价为当前bar的收盘价
if ma5[-1] > ma20[-1]:
# live模式下，此处需要更新一次持仓状态，这样判别持仓才准确
if symbol not in available_position_dict.keys():
self.activate_trade_signal = True
comments = "限价买入"
# psize = self.calc_position_size(account_data.available, close_price.iloc[-1])
psize = 300
if psize > 0:
self.buy(event_bar.dt, self.account[0]['name'], symbol, close_price.iloc[-1],
psize, False, comments)
self.context.logger.info(
"-- 资金账号 {0} 发出委托买入 {1} {2} 股，委托价为 {3}".format(self.account[0]['name'],
symbol, psize, close_price.iloc[-1]))
def handle_order(self, event_order):
"""针对委托单状态变动的响应"""
self.context.logger.info('-- {0} 的委托单 {1} 当前状态是 {2}'.format(event_order.data.symbol,
event_order.data.order_id,
event_order.data.status))
def handle_trade(self, event_trade):
"""成交后立即添加止损止盈"""
self.context.logger.info('-- {0} 的委托单 {1} 已成交，如有必要请立即添加止损止盈'.format(event_trade.data.symbol,
event_trade.data.order_id))
if name == "main": output_path = 'D:/python projects/quandomo/doc/' log_name = '{0}.log'.format(strftime('%Y-%m-%d_%H%M%S')) is_color_log = True start_date = '20160104' # hs300 成分股数据开始于 20050408 end_date = '20190902' universe_limit = 5
# 测试运行完整个策略所需时间
time_test = Timer(True)
with time_test:
trial_strategy = TrialStrategyStock(universe_limit)
trial_strategy.init_strategy([output_path, log_name], is_color_log, start_date, end_date)
trial_strategy.run_strategy()
trial_strategy.strategy_analysis(output_path) # 绩效分析
trial_strategy.show_results(output_path) # 运行结果展示
四、结果展示 1、log 文件（节选） [32m[2020-11-02 12:33:17,289] [INFO]- handle_bar() @ 20170519[0m [32m[2020-11-02 12:33:17,301] [INFO]- handle_bar() @ 20170522[0m [32m[2020-11-02 12:33:17,312] [INFO]- handle_bar() @ 20170523[0m [32m[2020-11-02 12:33:17,325] [INFO]- handle_bar() @ 20170524[0m [32m[2020-11-02 12:33:17,338] [INFO]- -- 600000.SH 今日 20170525 除权除息，所持仓位量价相应变动[0m [32m[2020-11-02 12:33:17,339] [INFO]- handle_bar() @ 20170525[0m [32m[2020-11-02 12:33:17,343] [INFO]- -- 资金账号 acc0 发出委托卖出 600000.SH 389.99999999999994 股，委托价为 12.93[0m [32m[2020-11-02 12:33:17,348] [INFO]- -- 资金账号 acc0 发出委托买入 000001.SZ 300 股，委托价为 9.1[0m [32m[2020-11-02 12:33:17,352] [INFO]- -- 资金账号 acc0 发出委托买入 000002.SZ 300 股，委托价为 20.5[0m [32m[2020-11-02 12:33:17,356] [INFO]- -- 600000.SH 的委托单 order_45210397 当前状态是 Status.NOT_TRADED[0m [32m[2020-11-02 12:33:17,356] [INFO]- -- 000001.SZ 的委托单 order_23461750 当前状态是 Status.NOT_TRADED[0m [32m[2020-11-02 12:33:17,357] [INFO]- -- 000002.SZ 的委托单 order_07982456 当前状态是 Status.NOT_TRADED[0m [32m[2020-11-02 12:33:17,359] [INFO]- -- 600000.SH 的委托单 order_45210397 无法成交，撤回重发[0m [32m[2020-11-02 12:33:17,360] [INFO]- -- 600000.SH 的委托单 order_45210397 当前状态是 Status.WITHDRAW[0m [32m[2020-11-02 12:33:17,360] [INFO]- -- 资金账号 acc0 发出委托卖出 600000.SH 389.99999999999994 股，委托价为 12.81[0m [32m[2020-11-02 12:33:17,361] [INFO]- -- 600000.SH 的委托单 order_78069125 当前状态是 Status.NOT_TRADED[0m [32m[2020-11-02 12:33:17,361] [INFO]- -- 600000.SH 的委托单 order_78069125 当前状态是 Status.ALL_TRADED[0m [32m[2020-11-02 12:33:17,362] [INFO]- -- 600000.SH 的委托单 order_78069125 于 20170526 成交，成交价为 12.92[0m [32m[2020-11-02 12:33:17,363] [INFO]- -- 600000.SH 的委托单 order_78069125 已成交，如有必要请立即添加止损止盈[0m [32m[2020-11-02 12:33:17,364] [INFO]- -- 000001.SZ 的委托单 order_23461750 当前状态是 Status.ALL_TRADED[0m [32m[2020-11-02 12:33:17,366] [INFO]- -- 000001.SZ 的委托单 order_23461750 于 20170526 成交，成交价为 9.09[0m [32m[2020-11-02 12:33:17,366] [INFO]- -- 000001.SZ 的委托单 order_23461750 已成交，如有必要请立即添加止损止盈[0m [32m[2020-11-02 12:33:17,367] [INFO]- -- 000002.SZ 的委托单 order_07982456 当前状态是 Status.ALL_TRADED[0m [32m[2020-11-02 12:33:17,368] [INFO]- -- 000002.SZ 的委托单 order_07982456 于 20170526 成交，成交价为 20.31[0m [32m[2020-11-02 12:33:17,369] [INFO]- -- 000002.SZ 的委托单 order_07982456 已成交，如有必要请立即添加止损止盈[0m [32m[2020-11-02 12:33:17,370] [INFO]- handle_bar() @ 20170526[0m [32m[2020-11-02 12:33:17,388] [INFO]- handle_bar() @ 20170531[0m [32m[2020-1
