克隆策略
多条件选股策略¶
版本 v1.0
目录¶
### 多条件选股策略的交易规则
### 策略构建步骤
### 策略的实现
正文¶
一、多条件选股策略的交易规则¶
- 买入条件：满足 1）今日开盘价大于昨日收盘价；2）5日收盘价均线大于10日收盘价均线的股票按PE升序排名取前十名,次日以开盘价买入；
- 买入后，如果5日收盘价均线小于10日收盘价均线，则次日以开盘价卖出。
二、策略构建步骤¶
1、确定股票池和回测时间¶
- 通过证券代码列表输入要回测的单只/多只股票，以及回测的起止日期
2、确定买卖条件信号¶
- 在输入特征列表中通过表达式引擎定义 buy_condition=where((open_0>close_1)&(mean(close_0,5)>mean(close_0,10)),1,0)，实现买入信号。
- 在输入特征列表中通过表达式引擎定义 sell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)，实现卖出信号。
- 通过基础特征和衍生特征抽取模块实现买卖条件指标 buy_condition 和 sell_condition 数据的抽取。
- 通过缺失数据处理模块删去有缺失值的数据。
3、确定买卖原则¶
- 已有持仓中满足卖出条件的股票为卖出股票列表，需执行卖出操作并更新可用现金。
- 满足买入条件且没有持仓的股票为买入股票列表，需根据可用现金执行等资金买入操作,此例采用整百股数下单。
4、模拟回测¶
- 通过 trade 模块中的初始化函数定义交易手续费和滑点；
- 通过 trade 模块中的准备函数定义 context.daily_stock_buy 和 context.daily_stock_sell 变量来获取并存放每日符合买卖条件的股票列表；
- 通过 trade 模块中的主函数(handle函数)查看每日的买卖交易信号，按照买卖原则执行相应的买入/卖出操作。
三、策略的实现¶
可视化策略实现如下：
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In [12]:
# 本代码由可视化策略环境自动生成 2021年6月11日 19:50
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。
# 回测引擎：初始化函数，只执行一次
def m8_initialize_bigquant_run(context):
# 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
# 回测引擎：每日数据处理函数，每天执行一次
def m8_handle_data_bigquant_run(context, data):
# 回测引擎：每日数据处理函数，每天执行一次
today = data.current_dt.strftime('%Y-%m-%d') # 日期
# 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表和对应的最新市值
stock_hold_now = {e.symbol: p.amount * p.last_sale_price
for e, p in context.perf_tracker.position_tracker.positions.items()}
# 记录用于买入股票的可用现金
cash_for_buy = context.portfolio.cash
# 获取当日符合买入/卖出条件的股票列表
try:
buy_stock = context.daily_buy_stock[today] # 当日符合买入条件的股票
except:
buy_stock=[]
try:
sell_stock = context.daily_sell_stock[today] # 当日符合卖出条件的股票
except:
sell_stock = []
# 需要卖出的股票:已有持仓中符合卖出条件的股票
stock_to_sell = [i for i in stock_hold_now if i in sell_stock]
# 需要买入的股票:没有持仓且符合买入条件的股票
stock_to_buy = [i for i in buy_stock if i not in stock_hold_now]
# 卖出
for instrument in stock_to_sell:
# 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
# 如果返回真值，则可以正常下单，否则会出错
# 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式
if data.can_trade(context.symbol(instrument)):
# order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，即卖出全部股票，可参考回测文档
context.order_target_percent(context.symbol(instrument), 0)
# 开盘卖出后所得资金可用来买入股票，更新当日可用现金
cash_for_buy += stock_hold_now[instrument]
# 如果当天没有买入的股票，就返回
if len(stock_to_buy) == 0:
return
# 买入
for instrument in stock_to_buy:
# 利用当日可用现金使用等资金比例下单买入
cash = cash_for_buy / len(stock_to_buy)
if data.can_trade(context.symbol(instrument)):
current_price = data.current(context.symbol(instrument), 'price')
amount = math.floor(cash / current_price / 100) * 100
context.order(context.symbol(instrument), amount)
# 回测引擎：准备数据，只执行一次
def m8_prepare_bigquant_run(context):
# 加载预测数据
df = context.options['data'].read_df()
# 函数：求满足开仓条件的股票列表
def open_pos_con(df):
return list(df[df['buy_condition']>0].instrument)[:10]
# 函数：求满足平仓条件的股票列表
def close_pos_con(df):
return list(df[df['sell_condition']>0].instrument)
# 每日买入股票的数据框
context.daily_buy_stock= df.groupby('date').apply(open_pos_con)
# 每日卖出股票的数据框
context.daily_sell_stock= df.groupby('date').apply(close_pos_con)
# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m8_before_trading_start_bigquant_run(context, data):
pass
m1 = M.instruments.v2(
start_date='2017-06-01',
end_date=T.live_run_param('trading_date', '2017-07-01'),
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m2 = M.input_features.v1(
features="""
# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
buy_condition=where((open_0>close_1)&(mean(close_0,5)>mean(close_0,10)),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)
pe_ttm_0
in_csi300_0
"""
)
m5 = M.general_feature_extractor.v7(
instruments=m1.data,
features=m2.data,
start_date='',
end_date='',
before_start_days=60,
m_cached=False
)
m7 = M.derived_feature_extractor.v3(
input_data=m5.data,
features=m2.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False,
user_functions={}
)
m3 = M.filter.v3(
input_data=m7.data,
expr='in_csi300_0==1',
output_left_data=False
)
m9 = M.sort.v4(
input_ds=m3.data,
sort_by='pe_ttm_0',
group_by='date',
keep_columns='--',
ascending=True
)
m8 = M.trade.v4(
instruments=m1.data,
options_data=m9.sorted_data,
start_date='',
end_date='',
initialize=m8_initialize_bigquant_run,
handle_data=m8_handle_data_bigquant_run,
prepare=m8_prepare_bigquant_run,
before_trading_start=m8_before_trading_start_bigquant_run,
volume_limit=0.025,
order_price_field_buy='open',
order_price_field_sell='open',
capital_base=1000000,
auto_cancel_non_tradable_orders=True,
data_frequency='daily',
price_type='后复权',
product_type='股票',
plot_charts=True,
backtest_only=False,
benchmark=''
)
日志 34 条，错误日志
0 条
[2021-06-11 19:46:31.823267] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-06-11 19:46:31.835583] INFO: moduleinvoker: 命中缓存
[2021-06-11 19:46:31.840320] INFO: moduleinvoker: instruments.v2 运行完成[0.01702s].
[2021-06-11 19:46:31.844799] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-06-11 19:46:31.858400] INFO: moduleinvoker: 命中缓存
[2021-06-11 19:46:31.860913] INFO: moduleinvoker: input_features.v1 运行完成[0.016108s].
[2021-06-11 19:46:31.884032] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-06-11 19:46:32.899471] INFO: 基础特征抽取: 年份 2017, 特征行数=178502
[2021-06-11 19:46:32.985774] INFO: 基础特征抽取: 总行数: 178502
[2021-06-11 19:46:33.000834] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[1.116822s].
[2021-06-11 19:46:33.003990] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-06-11 19:46:34.583872] INFO: derived_feature_extractor: 提取完成 buy_condition=where((open_0>close_1)&(mean(close_0,5)>mean(close_0,10)),1,0), 0.812s
[2021-06-11 19:46:35.392789] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close_0,5)[2021-06-11 19:46:36.218391] INFO: derived_feature_extractor: /y_2017, 178502[2021-06-11 19:46:36.709494] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[3.705496s].[2021-06-11 19:46:36.713422] INFO: moduleinvoker: filter.v3 开始运行..[2021-06-11 19:46:36.728343] INFO: filter: 使用表达式 in_csi300_0==1 过滤[2021-06-11 19:46:36.898369] INFO: filter: 过滤 /y_2017, 16915/0/178502[2021-06-11 19:46:36.970136] INFO: moduleinvoker: filter.v3 运行完成[0.256693s].[2021-06-11 19:46:36.975432] INFO: moduleinvoker: sort.v4 开始运行..[2021-06-11 19:46:37.386601] INFO: moduleinvoker: sort.v4 运行完成[0.411149s].[2021-06-11 19:46:37.451224] INFO: moduleinvoker: backtest.v8 开始运行..[2021-06-11 19:46:37.465665] INFO: backtest: biglearning backtest:V8.5.0[2021-06-11 19:46:37.683246] INFO: backtest: product_type:stock by specified[2021-06-11 19:46:38.034313] INFO: moduleinvoker: cached.v2 开始运行..[2021-06-11 19:46:38.045427] INFO: moduleinvoker: 命中缓存[2021-06-11 19:46:38.048036] INFO: moduleinvoker: cached.v2 运行完成[0.013749s].[2021-06-11 19:46:39.755190] INFO: algo: TradingAlgorithm V1.8.3[2021-06-11 19:46:40.047103] INFO: algo: trading transform...[2021-06-11 19:46:40.876356] INFO: Performance: Simulated 22 trading days out of 22.[2021-06-11 19:46:40.878905] INFO: Performance: first open: 2017-06-01 09:30:00+00:00[2021-06-11 19:46:40.881773] INFO: Performance: last close: 2017-06-30 15:00:00+00:00[2021-06-11 19:46:50.654183] INFO: moduleinvoker: backtest.v8 运行完成[13.202955s].[2021-06-11 19:46:50.656848] INFO: moduleinvoker: trade.v4 运行完成[13.26277s].