def __init__(self):
super().__init__()
# 取指数成分股实例
self.index_member_obj = GetIndexMember()
# 取K线数据实例
self.data_class = GetKlineData()
# 取指标实例
self.indicator = SaveGetIndicator()
def __init__(self):
super().__init__()
# 取指数成分股实例
self.index_member_obj = GetIndexMember()
# 取K线数据实例
self.data_class = GetKlineData()
# 取指标实例
self.indicator = SaveGetIndicator()
# 取指标数据
self.ma5 = self.indicator.get_indicator('ma5')
self.ma10 = self.indicator.get_indicator('ma10')
self.now = time.time()
def __init__(self, strategy_name='ma_strategy'):
super().__init__()
self.strategy_name = strategy_name
# 取指数成分股实例
self.index_member_obj = GetIndexMember()
# 取K线数据实例
self.data_class = GetKlineData()
# 取指标实例
self.indicator = SaveGetIndicator()
# 取指标数据
self.ma5 = self.indicator.get_indicator('ma5')
self.ma10 = self.indicator.get_indicator('ma10')
self.now = time.time()
Environment.logger = Logger(strategy_name)
class MaStrategy(StrategyBase):
def __init__(self, strategy_name='ma_strategy'):
"""
用户定义类变量
取本地数据
:param strategy_name:
"""
super().__init__(strategy_name=strategy_name)
# 取指数成分股实例
self.index_member_obj = GetIndexMember()
# 取K线数据实例
self.data_class = GetKlineData()
# 取指标实例
self.indicator = SaveGetIndicator()
# 取指标数据
self.ma5 = self.indicator.get_indicator('ma5')
self.ma10 = self.indicator.get_indicator('ma10')
self.now = time.time()
Environment.logger = Logger(strategy_name)
def initialize(self):
# 设置运行模式，回测或者交易
self.run_mode = RunMode.BACKTESTING.value
# 设置回测资金账号
self.account = ['test0']
# 设置回测资金账号资金量
self.capital = {'test0': 2000000}
# 设置回测基准
self.benchmark = '000300.SH'
# 设置复权方式
self.rights_adjustment = RightsAdjustment.FROWARD.value
# 设置回测起止时间
self.start = datetime(2018, 1, 1)
self.end = datetime(2019, 1, 1)
# 设置运行周期
self.period = 'daily'
self.index_member_obj.get_all_index_members()
_, index_members_all = self.index_member_obj.get_index_members(
'000300.SH')
self.universe = index_members_all
# 设置在运行前是否缓存日线，分钟线等各个周期数据
self.daily_data_cache = True
Environment.logger.info(self.universe)
# 回测滑点设置，按固定值0.01,20-0.01 = 19.99;百分比0.01,20*(1-0.01) = 19.98;平仓时用'+'
self.set_slippage(stock_type=StockType.STOCK.value,
slippage_type=SlippageType.SLIPPAGE_FIX.value,
value=0.01)
# 回测股票手续费和印花税，卖出印花税，千分之一；开仓手续费，万分之三；平仓手续费，万分之三，最低手续费，５元
# 沪市，卖出有万分之二的过户费，加入到卖出手续费
self.set_commission(stock_type=StockType.STOCK_SH.value,
tax=0.001,
open_commission=0.0003,
close_commission=0.00032,
close_today_commission=0,
min_commission=5)
# 深市不加过户费
self.set_commission(stock_type=StockType.STOCK_SZ.value,
tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
close_today_commission=0,
min_commission=5)
def handle_bar(self, event):
Environment.logger.info('self.time_tag', self.time_tag, datetime.now(),
(time.time() - self.now) * 1000)
Environment.logger.debug(len(Environment.bar_position_data_list))
# 取当前bar的持仓情况
with Timer(True):
available_position_dict = {}
for position in Environment.bar_position_data_list:
available_position_dict[
position.instrument + '.' +
position.exchange] = position.position - position.frozen
index_member_list = self.index_member_obj.get_index_member_in_date(
self.time_tag)
close_price_all = self.data_class.get_market_data(
Environment.daily_data,
stock_code=index_member_list,
field=['close'],
start=self.time_tag,
end=self.time_tag)
# 循环遍历股票池
for stock in index_member_list:
# 取当前股票的收盘价
close_price = close_price_all['close'][stock]
if close_price:
ma5 = self.ma5[stock][self.time_tag]
ma20 = self.ma10[stock][self.time_tag]
if ma5 and ma20:
# 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，以收盘价为指定价交易
if ma5 > ma20 and stock not in available_position_dict.keys(
) and stock in index_member_list:
Trade(self).order_shares(
stock_code=stock,
shares=100,
price_type='fix',
order_price=close_price,
account_id=self.account[0])
Environment.logger.info('buy', stock, -1, 'fix',
close_price, self.account)
# 如果20日均线突破5日均线，并且有持仓，则卖出这只股票100股，以收盘价为指定价交易
elif ma5 < ma20 and stock in available_position_dict.keys(
):
Trade(self).order_shares(
stock_code=stock,
shares=-100,
price_type='fix',
order_price=close_price,
account_id=self.account[0])
Environment.logger.info('sell', stock, -1, 'fix',
close_price, self.account)
for stock in available_position_dict.keys():
if stock not in index_member_list:
Trade(self).order_shares(stock_code=stock,
shares=-100,
price_type='fix',
order_price=close_price,
account_id=self.account[0])
Environment.logger.info('sell not in index_member_list',
stock, -1, 'fix', close_price,
self.account)
self.now = time.time()
def save_ma(self, num):
save_get_indicator = SaveGetIndicator()
ma5 = self.kline['close'].rolling(num).mean()
save_get_indicator.save_indicator('ma' + str(num), ma5)
pass
return fit_result.resid
self.raw_data = self.raw_data.apply(cal_resid,
args=(
index_class_obj,
share_data,
method,
),
axis=1)
return self.raw_data
if __name__ == '__main__':
indicator_name = 'ma5'
factor_name = 'factor_' + indicator_name
indicator_data = SaveGetIndicator().get_indicator(indicator_name)
factor_pre_obj = FactorPreProcessing(indicator_data)
# 可根据时间和股票list过滤数据
data_filter = factor_pre_obj.data_filter()
# 去极值方法，四种
extreme_data = factor_pre_obj.extreme_processing(
dict(std={'sigma_multiple': 3}))
# extreme_data = factor_pre_obj.extreme_processing(dict(mad={'median_multiple': 1.483}))
# extreme_data = factor_pre_obj.extreme_processing(dict(quantile={'quantile_min': 0.025, 'quantile_max': 0.975}))
# extreme_data = factor_pre_obj.extreme_processing(dict(box_plot={'median_multiple': 3}))
# 中性化方法，可选择行业和流通市值中性
neutralize_data = factor_pre_obj.neutralize_processing(
dict(neutralize_method=[
NeutralizeMethod.INDUSTRY.value,
fit_result = model.fit()
# 残差作为中性化后的数据
return fit_result.resid
self.raw_data = self.raw_data.apply(cal_resid,
args=(
index_class_obj,
share_data,
method,
),
axis=1)
return self.raw_data
if __name__ == '__main__':
indicator_data = SaveGetIndicator().get_indicator('ma10')
factor_pre_obj = FactorPreProcessing(indicator_data)
# 可根据时间和股票list过滤数据
data_filter = factor_pre_obj.data_filter()
# 去极值方法，四种
extreme_data = factor_pre_obj.extreme_processing(
dict(std={'sigma_multiple': 3}))
# extreme_data = factor_pre_obj.extreme_processing(dict(mad={'median_multiple': 1.483}))
# extreme_data = factor_pre_obj.extreme_processing(dict(quantile={'quantile_min': 0.025, 'quantile_max': 0.975}))
# extreme_data = factor_pre_obj.extreme_processing(dict(box_plot={'median_multiple': 3}))
# 中性化方法，可选择行业和流通市值中性
neutralize_data = factor_pre_obj.neutralize_processing(
dict(neutralize_method=[
NeutralizeMethod.INDUSTRY.value,
class MaStrategy(StrategyBase):
def __init__(self):
super().__init__()
# 取指数成分股实例
self.index_member_obj = GetIndexMember()
# 取K线数据实例
self.data_class = GetKlineData()
# 取指标实例
self.indicator = SaveGetIndicator()
def initialize(self):
# 设置运行模式，回测或者交易
self.run_mode = RunMode.BACKTESTING.value
# 设置回测资金账号
self.account = ['test0', 'test1']
# 设置回测资金账号资金量
self.capital = {'test0': 2000000, 'test1': 1000}
# 设置回测基准
self.benchmark = '000300.SH'
# 设置复权方式
self.rights_adjustment = RightsAdjustment.NONE.value
# 设置回测起止时间
self.start = datetime(2018, 1, 1)
self.end = datetime(2019, 1, 1)
# 设置运行周期
self.period = 'daily'
_, index_members_all = self.index_member_obj.get_index_members(
'000300.SH')
self.universe = index_members_all
# 设置在运行前是否缓存日线，分钟线等各个周期数据
self.daily_data_cache = True
print(self.universe)
# 回测滑点设置，按固定值0.01,20-0.01 = 19.99;百分比0.01,20*(1-0.01) = 19.98;平仓时用'+'
self.set_slippage(stock_type=StockType.STOCK.value,
slippage_type=SlippageType.SLIPPAGE_FIX.value,
value=0.01)
# 回测股票手续费和印花税，卖出印花税，千分之一；开仓手续费，万分之三；平仓手续费，万分之三，最低手续费，５元
# 沪市，卖出有万分之二的过户费，加入到卖出手续费
self.set_commission(stock_type=StockType.STOCK_SH.value,
tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
close_today_commission=0,
min_commission=5)
# 深市不加过户费
self.set_commission(stock_type=StockType.STOCK_SZ.value,
tax=0.001,
open_commission=0.0003,
close_commission=0.0005,
close_today_commission=0,
min_commission=5)
# 取指标数据
self.ma5 = self.indicator.get_indicator('ma5')
self.ma10 = self.indicator.get_indicator('ma10')
self.now = time.time()
def handle_bar(self, event):
print('self.time_tag', self.time_tag, datetime.now(),
(time.time() - self.now) * 1000)
print(len(Environment.bar_position_data_list))
# 取当前bar的持仓情况
with Timer(True):
available_position_dict = {}
for position in Environment.bar_position_data_list:
available_position_dict[
position.instrument + '.' +
position.exchange] = position.position - position.frozen
index_member_list = self.index_member_obj.get_index_member_in_date(
self.time_tag)
close_price_all = self.data_class.get_market_data(
Environment.daily_data,
stock_code=index_member_list,
field=['close'],
start=self.time_tag,
end=self.time_tag)
# 循环遍历股票池
for stock in index_member_list:
# 取当前股票的收盘价
close_price = close_price_all['close'][stock]
# print(close_price, type(close_price))
# close_array = np.array(close_price)
if close_price:
# if len(close_array) > 0:
# # 利用talib计算MA
# try:
# ma5 = talib.MA(close_array[-20:], timeperiod=5)
# ma20 = talib.MA(close_array[-20:], timeperiod=20)
# except Exception as e:
# continue
# print('ma5', ma5[-1], ma20[-1], ma5[-1] > ma20[-1], len(available_position_dict.keys()))
# 过滤因为停牌没有数据
# if self.time_tag in close_price.index:
# print("qqqqqq", type(self.ma5), self.ma5)
ma5 = self.ma5[stock][self.time_tag]
ma20 = self.ma10[stock][self.time_tag]
if ma5 and ma20:
# 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，以收盘价为指定价交易
if ma5 > ma20 and stock not in available_position_dict.keys(
) and stock in index_member_list:
Trade(self).order_shares(stock_code=stock,
shares=100,
price_type='fix',
order_price=close_price,
account=self.account[0])
print('buy', stock, -1, 'fix', close_price,
self.account)
# 如果20日均线突破5日均线，并且有持仓，则卖出这只股票100股，以收盘价为指定价交易
elif ma5 < ma20 and stock in available_position_dict.keys(
):
Trade(self).order_shares(stock_code=stock,
shares=-100,
price_type='fix',
order_price=close_price,
account=self.account[0])
print('sell', stock, -1, 'fix', close_price,
self.account)
for stock in available_position_dict.keys():
if stock not in index_member_list:
Trade(self).order_shares(stock_code=stock,
shares=-100,
price_type='fix',
order_price=close_price,
account=self.account[0])
print('sell not in index_member_list', stock, -1, 'fix',
close_price, self.account)
self.now = time.time()