if __name__ == '__main__':
factor_name = 'factor_ma10'
path = LocalDataPath.path + LocalDataFolderName.FACTOR.value + '/'
factor_ma5 = get_local_data(path, factor_name + '.h5')
# 指数数据不全，需要删一部分因子数据
factor_ma5 = factor_ma5[factor_ma5.index < datetime.datetime(2020, 1, 1)]
kline_object = GetKlineData()
market_data = kline_object.cache_all_stock_data(
dividend_type=RightsAdjustment.BACKWARD.value, field=['close'])
market_close_data = kline_object.get_market_data(market_data,
field=['close'])
# 指数行情，沪深300代替
all_index_data = kline_object.cache_all_index_data()
benchmark_df = kline_object.get_market_data(
all_index_data, stock_code=['000300.SH'],
field=['close']).to_frame(name='close')
# 沪深300 的日线，有脏数据，后续单独处理
if datetime.datetime(2016, 1, 1) in benchmark_df.index:
benchmark_df = benchmark_df.drop(datetime.datetime(2016, 1, 1))
regression_analysis_obj = RegressionAnalysis(factor_ma5, 'factor_ma5',
market_close_data,
benchmark_df)
regression_analysis_obj.cal_factor_return('float_value_inverse')
regression_analysis_obj.cal_t_value_statistics()
regression_analysis_obj.cal_net_analysis()
regression_analysis_obj.cal_acf()
regression_analysis_obj.save_regression_analysis_result(factor_name)
class StrategyBase(metaclass=ABCMeta):
def __init__(self, strategy_name='ma_strategy'):
self._strategy_name = strategy_name
self._run_mode = RunMode.BACKTESTING.value
self._account = []
self._capital = 1000000
self._start = datetime(2017, 1, 1)
self._end = datetime(2018, 1, 1)
self._benchmark = '000300.SH'
self._period = Period.DAILY.value # 后续支持1min 3min 5min 等多周期
self._universe = []
self._rights_adjustment = RightsAdjustment.FROWARD.value
self._time_tag = 0
# 数据缓存开关
self._daily_data_cache = False
self._one_min_data_cache = False
# 取数据
self._get_data = GetKlineData()
self.bar_index = 0
@property
def strategy_name(self):
return self._strategy_name
@strategy_name.setter
def strategy_name(self, value):
self._strategy_name = value
@property
def run_mode(self):
return self._run_mode
@run_mode.setter
def run_mode(self, value):
self._run_mode = value
@property
def account(self):
return self._account
@account.setter
def account(self, value):
self._account = value
@property
def capital(self):
return self._capital
@capital.setter
def capital(self, value):
self._capital = value
@property
def start(self):
return self._start
@start.setter
def start(self, value):
self._start = value
@property
def end(self):
return self._end
@end.setter
def end(self, value):
self._end = value
@property
def benchmark(self):
return self._benchmark
@benchmark.setter
def benchmark(self, value):
self._benchmark = value
@property
def period(self):
return self._period
@period.setter
def period(self, value):
self._period = value
@property
def universe(self):
return self._universe
@universe.setter
def universe(self, value):
self._universe.extend(value)
@property
def rights_adjustment(self):
return self._rights_adjustment
@rights_adjustment.setter
def rights_adjustment(self, value):
self._rights_adjustment = value
@property
def time_tag(self):
return self._time_tag
@time_tag.setter
def time_tag(self, value):
self._time_tag = value
@property
def daily_data_cache(self):
return self._daily_data_cache
@daily_data_cache.setter
def daily_data_cache(self, value):
self._daily_data_cache = value
@property
def one_min_data_cache(self):
return self._one_min_data_cache
@one_min_data_cache.setter
def one_min_data_cache(self, value):
self._one_min_data_cache = value
# 回测滑点设置
def set_slippage(self,
stock_type=StockType.STOCK.value,
slippage_type=SlippageType.SLIPPAGE_FIX.value,
value=0):
Environment.slippage_dict[stock_type] = {
'slippage_type': slippage_type,
'value': value
}
# 回测手续费和印花税
def set_commission(self,
stock_type=StockType.STOCK.value,
tax=0,
open_commission=0,
close_commission=0,
close_today_commission=0,
min_commission=0):
Environment.commission_dict[stock_type] = {
'tax': tax,
'open_commission': open_commission,
'close_commission': close_commission,
'close_today_commission': close_today_commission,
'min_commission': min_commission
}
def run(self, save_trade_record=False):
self.initialize()
# 初始化 account_data
if self.account:
for account in self.account:
Environment.current_account_data = AccountData()
Environment.current_account_data.account_id = generate_random_id.generate_random_id(
account)
Environment.current_account_data.total_balance = self.capital[
account]
Environment.current_account_data.available = self.capital[
account]
# Environment.logger(Environment.current_account_data.account_id, Environment.current_account_data.available)
Environment.bar_account_data_list.append(
Environment.current_account_data)
# if self.run_mode == RunMode.TRADE.value:
# self.end = self._get_data.get_end_time_tag(benchmark=self.benchmark, period=Period.DAILY.value)
# 缓存数据开关，和bar_index的计算
if self.period == Period.DAILY.value:
self.daily_data_cache = True
elif self.period == Period.ONE_MIN.value:
self.one_min_data_cache = True
#
security_list = copy.copy(self.universe)
security_list = list(set(security_list))
if self._daily_data_cache:
Environment.daily_data = self._get_data.cache_all_stock_data(
dividend_type=self.rights_adjustment)
Environment.index_daily_data = self._get_data.cache_all_index_data(
)
if self.one_min_data_cache:
Environment.one_min_data = self._get_data.cache_all_stock_data(
period=Period.ONE_MIN.value)
if self.period == Period.DAILY.value:
Environment.benchmark_index = [
i for i in Environment.index_daily_data['close'][
self.benchmark].index if self.start <= i <= self.end
]
elif self.period == Period.ONE_MIN.value:
Environment.benchmark_index = [
data_transfer.date_to_millisecond(str(int(i)), '%Y%m%d') for i
in Environment.one_min_data['open'].ix[self.benchmark].index
if i >= data_transfer.date_str_to_int(self.start)
]
self.bar_index = 0
while True:
try:
# Environment.logger(self.time_tag, Environment.benchmark_index)
self.time_tag = Environment.benchmark_index[self.bar_index]
except IndexError:
if self.run_mode == RunMode.BACKTESTING.value:
if save_trade_record:
run_backtesting_analysis_engine(self)
break
# elif self.run_mode == RunMode.TRADE.value:
# 读取最新tick, 更新最新的分钟或者日线
# if 读取最新tick, 更新最新的分钟或者日线 == done:
# daily_data.append(new_day_data)
# self.bar_index += 1
# benchmark_index.append(new_day_time_tag)
else:
run_bar_engine(self)
@abstractmethod
def initialize(self):
"""
设置运行模式，回测、实盘
设置on_bar运行周期，分钟线、日线
设置缓存历史数据范围、实时行情订阅
设置回测的各种设置。手续费、滑点、回测区间等
:return:
"""
pass
def on_bar(self, event):
"""
支持分钟线、日线的处理
:param event:
:return:
"""
Environment.logger('abstractmethod handle_bar')
pass
def on_quote(self, event):
"""
level1行情数据的推送会自动触发该方法的调用,每只股票数据推送都调用
:param event:
:return:
"""
pass
def on_transaction(self, event):
"""
逐笔成交行情数据的推送会自动触发该方法的调用,每只股票数据推送都调用
:param event:
:return:
"""
pass
def on_entrust(self, event):
"""
逐笔委托行情数据的推送会自动触发该方法的调用,每只股票数据推送都调用
:param event:
:return:
"""
pass
def on_order(self, event):
"""
策略订单成交信息的更新会自动触发该方法的调用。
:param event:
:return:
"""
pass
def on_cancel_order(self, event):
"""
策略订单撤单信息的更新会自动触发该方法的调用。
:param event:
:return:
"""
pass
def on_trade(self, event):
"""
策略订单委托信息的更新会自动触发该方法的调用。
:param event:
:return:
"""
pass
def on_account(self, event):
"""
策略账户信息的更新会自动触发该方法的调用。支持多账户，股票，期货
:param event:
:return:
"""
pass
