克隆策略

    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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.test_data = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只\n context.stock_count = 10\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = 1/context.stock_count\n #持仓周期\n context.hold_days = 5\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n \n if context.trading_day_index % context.hold_days != 0:\n return \n \n today = data.current_dt.strftime('%Y-%m-%d')\n # 获取当前持仓\n positions = {e.symbol: p.amount for e, p in context.portfolio.positions.items()}\n \n # 按日期过滤得到今日数据\n today_data = context.test_data[context.test_data.date == today]\n #今日需要买入的股票\n stocks_buy = today_data.instrument.iloc[0:context.stock_count].to_list()\n #卖出\n for instrument in positions.keys():\n if instrument not in stocks_buy:\n context.order_target(context.symbol(instrument), 0)\n #买入\n cash_per_instrument = context.portfolio.portfolio_value * context.stock_weights\n for instrument in stocks_buy:\n if instrument not in positions.keys():\n context.order_value(context.symbol(instrument), cash_per_instrument)\n \n ","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 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。\ndef bigquant_run(context, data):\n 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    In [1]:
    # 本代码由可视化策略环境自动生成 2021年10月29日 11:34
    # 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 m7_initialize_bigquant_run(context):
        # 加载预测数据
        context.test_data = context.options['data'].read_df()
    
        # 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))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        context.stock_count = 10
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = 1/context.stock_count
        #持仓周期
        context.hold_days = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m7_handle_data_bigquant_run(context, data):
        
        if context.trading_day_index % context.hold_days != 0:
            return 
        
        today = data.current_dt.strftime('%Y-%m-%d')
        # 获取当前持仓
        positions = {e.symbol: p.amount for e, p in context.portfolio.positions.items()}
            
        # 按日期过滤得到今日数据
        today_data = context.test_data[context.test_data.date == today]
        #今日需要买入的股票
        stocks_buy = today_data.instrument.iloc[0:context.stock_count].to_list()
        #卖出
        for instrument in positions.keys():
            if instrument not in stocks_buy:
                context.order_target(context.symbol(instrument), 0)
        #买入
        cash_per_instrument = context.portfolio.portfolio_value * context.stock_weights
        for instrument in stocks_buy:
            if instrument not in positions.keys():
                context.order_value(context.symbol(instrument), cash_per_instrument)
           
        
    # 回测引擎:准备数据,只执行一次
    def m7_prepare_bigquant_run(context):
        pass
    
    # 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 m7_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2014-01-01',
        end_date='2021-10-25',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.input_features.v1(
        features='myrank=rank_fs_roe_ttm_0+rank_fs_net_profit_qoq_0-rank_pb_lf_0'
    )
    
    m3 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m2.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m4 = M.derived_feature_extractor.v3(
        input_data=m3.data,
        features=m2.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m6 = M.chinaa_stock_filter.v1(
        input_data=m4.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m5 = M.sort.v5(
        input_ds=m6.data,
        sort_by='myrank',
        group_by='date',
        keep_columns='--',
        ascending=False
    )
    
    m7 = M.trade.v4(
        instruments=m1.data,
        options_data=m5.sorted_data,
        start_date='',
        end_date='',
        initialize=m7_initialize_bigquant_run,
        handle_data=m7_handle_data_bigquant_run,
        prepare=m7_prepare_bigquant_run,
        before_trading_start=m7_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    • 收益率394.07%
    • 年化收益率23.59%
    • 基准收益率113.71%
    • 阿尔法0.14
    • 贝塔0.96
    • 夏普比率0.79
    • 胜率0.54
    • 盈亏比1.25
    • 收益波动率27.96%
    • 信息比率0.05
    • 最大回撤39.07%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2794bc604a80428e95a8858cde5daa9b"}/bigcharts-data-end