无法对基金进行特征抽取

策略分享
新手专区
标签: #<Tag:0x00007f8c608219c0> #<Tag:0x00007f8c60821808>

(congcong009) #1
克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-50:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-57:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-102:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-199:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-57:input_data","SourceOutputPortId":"-50:data"},{"DestinationInputPortId":"-192:input_data","SourceOutputPortId":"-57:data"},{"DestinationInputPortId":"-102:options_data","SourceOutputPortId":"-192:data"},{"DestinationInputPortId":"-50:instruments","SourceOutputPortId":"-199:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"# #号开始的表示注释\n# 多个特征,每行一个,可以包含基础特征和衍生特征\nbuy_condition=where(mean(close_0,5)>mean(close_0,10),1,0)\nsell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_FUND","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"-50","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"60","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-50"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-50"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-50","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"Comment":"","CommentCollapsed":true},{"Id":"-57","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-57"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-57"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-57","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"Comment":"","CommentCollapsed":true},{"Id":"-102","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\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","ValueType":"Literal","LinkedGlobalParameter":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 # 获取今日的日期\n today = data.current_dt.strftime('%Y-%m-%d') \n # 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\n stock_hold_now = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 记录用于买入股票的可用现金,因为是早盘卖股票,需要记录卖出的股票市值并在买入下单前更新可用现金;\n # 如果是早盘买尾盘卖,则卖出时不需更新可用现金,因为尾盘卖出股票所得现金无法使用\n cash_for_buy = context.portfolio.cash \n \n try:\n buy_stock = context.daily_stock_buy[today] # 当日符合买入条件的股票\n except:\n buy_stock=[] # 如果没有符合条件的股票,就设置为空\n \n try:\n sell_stock = context.daily_stock_sell[today] # 当日符合卖出条件的股票\n except:\n sell_stock=[] # 如果没有符合条件的股票,就设置为空\n \n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ] \n # 需要调仓的股票:已有持仓且不符合卖出条件的股票\n stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]\n \n # 如果有卖出信号\n if len(stock_to_sell)>0:\n for instrument in stock_to_sell:\n sid = context.symbol(instrument) # 将标的转化为equity格式\n cur_position = context.portfolio.positions[sid].amount # 持仓\n if cur_position > 0 and data.can_trade(sid):\n context.order_target_percent(sid, 0) # 全部卖出 \n # 因为设置的是早盘卖出早盘买入,需要根据卖出的股票更新可用现金;如果设置尾盘卖出早盘买入,则不需更新可用现金(可以删除下面的语句)\n cash_for_buy += stock_hold_now[instrument]\n \n # 如果有买入信号/有持仓\n if len(stock_to_buy)+len(stock_to_adjust)>0:\n weight = 1/(len(stock_to_buy)+len(stock_to_adjust)) # 每只股票的比重为等资金比例持有\n for instrument in stock_to_buy+stock_to_adjust:\n sid = context.symbol(instrument) # 将标的转化为equity格式\n if data.can_trade(sid):\n context.order_target_value(sid, weight*cash_for_buy) # 买入","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n df = context.options['data'].read_df()\n\n # 函数:求满足开仓条件的股票列表\n def open_pos_con(df):\n return list(df[df['buy_condition']>0].instrument)\n\n # 函数:求满足平仓条件的股票列表\n def close_pos_con(df):\n return list(df[df['sell_condition']>0].instrument)\n\n # 每日买入股票的数据框\n context.daily_stock_buy= df.groupby('date').apply(open_pos_con)\n # 每日卖出股票的数据框\n context.daily_stock_sell= df.groupby('date').apply(close_pos_con)","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-102"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-102","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":3,"Comment":"","CommentCollapsed":true},{"Id":"-192","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-192"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-192"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-192","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"Comment":"","CommentCollapsed":true},{"Id":"-199","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, start_date,end_date):\n # 示例代码如下。在这里编写您的代码\n ins=input_1.read_pickle()['instruments']\n start_date=input_1.read_pickle()['start_date']\n end_date=input_1.read_pickle()['end_date']\n df=DataSource('bar1d_CN_FUND').read(start_date=start_date,end_date=end_date)\n #df = df[['date', 'close', 'instrument']].rename(columns={'close': 'close_0'})\n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-199"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-199"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-199"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-199","OutputType":null},{"Name":"data_2","NodeId":"-199","OutputType":null},{"Name":"data_3","NodeId":"-199","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='1371.5379028320312,28.033611297607422,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='721.9622192382812,-34.22268199920654,200,200'/><NodePosition Node='-50' Position='1066.953857421875,207.8907470703125,200,200'/><NodePosition Node='-57' Position='1232.655517578125,323.9873962402344,200,200'/><NodePosition Node='-102' Position='1047,531,200,200'/><NodePosition Node='-192' Position='1251.726806640625,430.0504150390625,200,200'/><NodePosition Node='-199' Position='963.61767578125,100.04830169677734,200,200'/></NodePositions><NodeGroups /></DataV1>"},"IsDraft":true,"ParentExperimentId":null,"WebService":{"IsWebServiceExperiment":false,"Inputs":[],"Outputs":[],"Parameters":[{"Name":"交易日期","Value":"","ParameterDefinition":{"Name":"交易日期","FriendlyName":"交易日期","DefaultValue":"","ParameterType":"String","HasDefaultValue":true,"IsOptional":true,"ParameterRules":[],"HasRules":false,"MarkupType":0,"CredentialDescriptor":null}}],"WebServiceGroupId":null,"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions></NodePositions><NodeGroups /></DataV1>"},"DisableNodesUpdate":false,"Category":"user","Tags":[],"IsPartialRun":true}
    In [6]:
    # 本代码由可视化策略环境自动生成 2020年11月10日 14:21
    # 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。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m5_run_bigquant_run(input_1, start_date,end_date):
        # 示例代码如下。在这里编写您的代码
        ins=input_1.read_pickle()['instruments']
        start_date=input_1.read_pickle()['start_date']
        end_date=input_1.read_pickle()['end_date']
        df=DataSource('bar1d_CN_FUND').read(start_date=start_date,end_date=end_date)
        #df = df[['date', 'close', 'instrument']].rename(columns={'close': 'close_0'})
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m5_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m3_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 m3_handle_data_bigquant_run(context, data):
        # 获取今日的日期
        today = data.current_dt.strftime('%Y-%m-%d')  
        # 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
        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,因为是早盘卖股票,需要记录卖出的股票市值并在买入下单前更新可用现金;
        # 如果是早盘买尾盘卖,则卖出时不需更新可用现金,因为尾盘卖出股票所得现金无法使用
        cash_for_buy = context.portfolio.cash    
        
        try:
            buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
        except:
            buy_stock=[]  # 如果没有符合条件的股票,就设置为空
        
        try:
            sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
        except:
            sell_stock=[] # 如果没有符合条件的股票,就设置为空
        
        # 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 ]  
        # 需要调仓的股票:已有持仓且不符合卖出条件的股票
        stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]
        
        # 如果有卖出信号
        if len(stock_to_sell)>0:
            for instrument in stock_to_sell:
                sid = context.symbol(instrument) # 将标的转化为equity格式
                cur_position = context.portfolio.positions[sid].amount # 持仓
                if cur_position > 0 and data.can_trade(sid):
                    context.order_target_percent(sid, 0) # 全部卖出 
                    # 因为设置的是早盘卖出早盘买入,需要根据卖出的股票更新可用现金;如果设置尾盘卖出早盘买入,则不需更新可用现金(可以删除下面的语句)
                    cash_for_buy += stock_hold_now[instrument]
        
        # 如果有买入信号/有持仓
        if len(stock_to_buy)+len(stock_to_adjust)>0:
            weight = 1/(len(stock_to_buy)+len(stock_to_adjust)) # 每只股票的比重为等资金比例持有
            for instrument in stock_to_buy+stock_to_adjust:
                sid = context.symbol(instrument) # 将标的转化为equity格式
                if  data.can_trade(sid):
                    context.order_target_value(sid, weight*cash_for_buy) # 买入
    # 回测引擎:准备数据,只执行一次
    def m3_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)
    
        # 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_stock_buy= df.groupby('date').apply(open_pos_con)
        # 每日卖出股票的数据框
        context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
    
    m1 = 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(mean(close_0,5)>mean(close_0,10),1,0)
    sell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)""",
        m_cached=False
    )
    
    m2 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2016-01-01'),
        end_date=T.live_run_param('trading_date', '2019-01-01'),
        market='CN_FUND',
        instrument_list='',
        max_count=0
    )
    
    m5 = M.cached.v3(
        input_1=m2.data,
        run=m5_run_bigquant_run,
        post_run=m5_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m7 = M.general_feature_extractor.v7(
        instruments=m5.data_1,
        features=m1.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m8 = M.derived_feature_extractor.v3(
        input_data=m7.data,
        features=m1.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m4 = M.dropnan.v2(
        input_data=m8.data
    )
    
    m3 = M.trade.v4(
        instruments=m2.data,
        options_data=m4.data,
        start_date='',
        end_date='',
        initialize=m3_initialize_bigquant_run,
        handle_data=m3_handle_data_bigquant_run,
        prepare=m3_prepare_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=''
    )
    
    ---------------------------------------------------------------------------
    UnpicklingError                           Traceback (most recent call last)
    <ipython-input-6-90dbb8961511> in <module>()
         49     start_date='',
         50     end_date='',
    ---> 51     before_start_days=60
         52 )
         53 
    
    UnpicklingError: invalid load key, 'H'.

    你好,请问我尝试对基金做特征提取,但报错如上。我不确定是否是自定义数据源这里出了问题,还请教帮忙指导下呢。

    谢谢。


    (adhaha111) #2

    您好,基础特征抽取模块的第一个入参需要的是代码列表(一个字典,含有 instruments, start_date, end_date)三个字段,您传入的是一个 DataFrame 对象


    (congcong009) #3

    你好,我大概知道你的意思,是数据格式的问题,但不是很清楚应该怎么调整,能烦请提供一个可参考的例子么?


    (adhaha111) #4

    您可以选择不用python自定义模块,直接把代码列表的传过去;也可以不要基础特征抽取模块:

    克隆策略

      {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-57:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-102:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-199:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-192:input_data","SourceOutputPortId":"-57:data"},{"DestinationInputPortId":"-102:options_data","SourceOutputPortId":"-192:data"},{"DestinationInputPortId":"-57:input_data","SourceOutputPortId":"-199:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"# #号开始的表示注释\n# 多个特征,每行一个,可以包含基础特征和衍生特征\nbuy_condition=where(mean(close,5)>mean(close,10),1,0)\nsell_condition=where(mean(close,5)<mean(close,10),1,0)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_FUND","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"-57","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-57"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-57"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-57","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"Comment":"","CommentCollapsed":true},{"Id":"-102","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\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","ValueType":"Literal","LinkedGlobalParameter":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 # 获取今日的日期\n today = data.current_dt.strftime('%Y-%m-%d') \n # 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\n stock_hold_now = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 记录用于买入股票的可用现金,因为是早盘卖股票,需要记录卖出的股票市值并在买入下单前更新可用现金;\n # 如果是早盘买尾盘卖,则卖出时不需更新可用现金,因为尾盘卖出股票所得现金无法使用\n cash_for_buy = context.portfolio.cash \n \n try:\n buy_stock = context.daily_stock_buy[today] # 当日符合买入条件的股票\n except:\n buy_stock=[] # 如果没有符合条件的股票,就设置为空\n \n try:\n sell_stock = context.daily_stock_sell[today] # 当日符合卖出条件的股票\n except:\n sell_stock=[] # 如果没有符合条件的股票,就设置为空\n \n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ] \n # 需要调仓的股票:已有持仓且不符合卖出条件的股票\n stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]\n \n # 如果有卖出信号\n if len(stock_to_sell)>0:\n for instrument in stock_to_sell:\n sid = context.symbol(instrument) # 将标的转化为equity格式\n cur_position = context.portfolio.positions[sid].amount # 持仓\n if cur_position > 0 and data.can_trade(sid):\n context.order_target_percent(sid, 0) # 全部卖出 \n # 因为设置的是早盘卖出早盘买入,需要根据卖出的股票更新可用现金;如果设置尾盘卖出早盘买入,则不需更新可用现金(可以删除下面的语句)\n cash_for_buy += stock_hold_now[instrument]\n \n # 如果有买入信号/有持仓\n if len(stock_to_buy)+len(stock_to_adjust)>0:\n weight = 1/(len(stock_to_buy)+len(stock_to_adjust)) # 每只股票的比重为等资金比例持有\n for instrument in stock_to_buy+stock_to_adjust:\n sid = context.symbol(instrument) # 将标的转化为equity格式\n if data.can_trade(sid):\n context.order_target_value(sid, weight*cash_for_buy) # 买入","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n df = context.options['data'].read_df()\n\n # 函数:求满足开仓条件的股票列表\n def open_pos_con(df):\n return list(df[df['buy_condition']>0].instrument)\n\n # 函数:求满足平仓条件的股票列表\n def close_pos_con(df):\n return list(df[df['sell_condition']>0].instrument)\n\n # 每日买入股票的数据框\n context.daily_stock_buy= df.groupby('date').apply(open_pos_con)\n # 每日卖出股票的数据框\n context.daily_stock_sell= df.groupby('date').apply(close_pos_con)","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-102"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-102"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-102","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":3,"Comment":"","CommentCollapsed":true},{"Id":"-192","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-192"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-192"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-192","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"Comment":"","CommentCollapsed":true},{"Id":"-199","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, start_date,end_date):\n # 示例代码如下。在这里编写您的代码\n ins=input_1.read_pickle()['instruments']\n start_date=input_1.read_pickle()['start_date']\n end_date=input_1.read_pickle()['end_date']\n df=DataSource('bar1d_CN_FUND').read(start_date=start_date,end_date=end_date)\n #df = df[['date', 'close', 'instrument']].rename(columns={'close': 'close_0'})\n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-199"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-199"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-199"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-199","OutputType":null},{"Name":"data_2","NodeId":"-199","OutputType":null},{"Name":"data_3","NodeId":"-199","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='1371,27.17345428466797,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='721,-34,200,200'/><NodePosition Node='-57' Position='1161.7437744140625,246.95794677734375,200,200'/><NodePosition Node='-102' Position='1047,531,200,200'/><NodePosition Node='-192' Position='1251,430,200,200'/><NodePosition Node='-199' Position='967.1328125,95.04074096679688,200,200'/></NodePositions><NodeGroups /></DataV1>"},"IsDraft":true,"ParentExperimentId":null,"WebService":{"IsWebServiceExperiment":false,"Inputs":[],"Outputs":[],"Parameters":[{"Name":"交易日期","Value":"","ParameterDefinition":{"Name":"交易日期","FriendlyName":"交易日期","DefaultValue":"","ParameterType":"String","HasDefaultValue":true,"IsOptional":true,"ParameterRules":[],"HasRules":false,"MarkupType":0,"CredentialDescriptor":null}}],"WebServiceGroupId":null,"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions></NodePositions><NodeGroups /></DataV1>"},"DisableNodesUpdate":false,"Category":"user","Tags":[],"IsPartialRun":true}
      In [5]:
      # 本代码由可视化策略环境自动生成 2020年11月10日 15:51
      # 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。
      
      
      # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
      def m5_run_bigquant_run(input_1, start_date,end_date):
          # 示例代码如下。在这里编写您的代码
          ins=input_1.read_pickle()['instruments']
          start_date=input_1.read_pickle()['start_date']
          end_date=input_1.read_pickle()['end_date']
          df=DataSource('bar1d_CN_FUND').read(start_date=start_date,end_date=end_date)
          #df = df[['date', 'close', 'instrument']].rename(columns={'close': 'close_0'})
          data_1 = DataSource.write_df(df)
          return Outputs(data_1=data_1, data_2=None, data_3=None)
      
      # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
      def m5_post_run_bigquant_run(outputs):
          return outputs
      
      # 回测引擎:初始化函数,只执行一次
      def m3_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 m3_handle_data_bigquant_run(context, data):
          # 获取今日的日期
          today = data.current_dt.strftime('%Y-%m-%d')  
          # 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
          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,因为是早盘卖股票,需要记录卖出的股票市值并在买入下单前更新可用现金;
          # 如果是早盘买尾盘卖,则卖出时不需更新可用现金,因为尾盘卖出股票所得现金无法使用
          cash_for_buy = context.portfolio.cash    
          
          try:
              buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
          except:
              buy_stock=[]  # 如果没有符合条件的股票,就设置为空
          
          try:
              sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
          except:
              sell_stock=[] # 如果没有符合条件的股票,就设置为空
          
          # 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 ]  
          # 需要调仓的股票:已有持仓且不符合卖出条件的股票
          stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]
          
          # 如果有卖出信号
          if len(stock_to_sell)>0:
              for instrument in stock_to_sell:
                  sid = context.symbol(instrument) # 将标的转化为equity格式
                  cur_position = context.portfolio.positions[sid].amount # 持仓
                  if cur_position > 0 and data.can_trade(sid):
                      context.order_target_percent(sid, 0) # 全部卖出 
                      # 因为设置的是早盘卖出早盘买入,需要根据卖出的股票更新可用现金;如果设置尾盘卖出早盘买入,则不需更新可用现金(可以删除下面的语句)
                      cash_for_buy += stock_hold_now[instrument]
          
          # 如果有买入信号/有持仓
          if len(stock_to_buy)+len(stock_to_adjust)>0:
              weight = 1/(len(stock_to_buy)+len(stock_to_adjust)) # 每只股票的比重为等资金比例持有
              for instrument in stock_to_buy+stock_to_adjust:
                  sid = context.symbol(instrument) # 将标的转化为equity格式
                  if  data.can_trade(sid):
                      context.order_target_value(sid, weight*cash_for_buy) # 买入
      # 回测引擎:准备数据,只执行一次
      def m3_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)
      
          # 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_stock_buy= df.groupby('date').apply(open_pos_con)
          # 每日卖出股票的数据框
          context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
      
      m1 = 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(mean(close,5)>mean(close,10),1,0)
      sell_condition=where(mean(close,5)<mean(close,10),1,0)""",
          m_cached=False
      )
      
      m2 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2016-01-01'),
          end_date=T.live_run_param('trading_date', '2019-01-01'),
          market='CN_FUND',
          instrument_list='',
          max_count=0
      )
      
      m5 = M.cached.v3(
          input_1=m2.data,
          run=m5_run_bigquant_run,
          post_run=m5_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m8 = M.derived_feature_extractor.v3(
          input_data=m5.data_1,
          features=m1.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m4 = M.dropnan.v2(
          input_data=m8.data
      )
      
      m3 = M.trade.v4(
          instruments=m2.data,
          options_data=m4.data,
          start_date='',
          end_date='',
          initialize=m3_initialize_bigquant_run,
          handle_data=m3_handle_data_bigquant_run,
          prepare=m3_prepare_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=''
      )
      

      (congcong009) #5

      哦明白了,感谢指导,谢谢啦!