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    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-107:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-107:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-161:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-837:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-779:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-577:input_1","SourceOutputPortId":"-107:data"},{"DestinationInputPortId":"-819:input_data","SourceOutputPortId":"-107:data"},{"DestinationInputPortId":"-1188:training_ds","SourceOutputPortId":"-648:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-577:data_1"},{"DestinationInputPortId":"-1188:features","SourceOutputPortId":"-1475:data"},{"DestinationInputPortId":"-161:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-213:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-142:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-171:input_1","SourceOutputPortId":"-161:data"},{"DestinationInputPortId":"-837:input_data","SourceOutputPortId":"-161:data"},{"DestinationInputPortId":"-812:data1","SourceOutputPortId":"-171:data_1"},{"DestinationInputPortId":"-1188:predict_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-648:input_data","SourceOutputPortId":"-779:data"},{"DestinationInputPortId":"-187:input_data","SourceOutputPortId":"-812:data"},{"DestinationInputPortId":"-207:data2","SourceOutputPortId":"-812:data"},{"DestinationInputPortId":"-779:data2","SourceOutputPortId":"-819:data"},{"DestinationInputPortId":"-812:data2","SourceOutputPortId":"-837:data"},{"DestinationInputPortId":"-224:input_data","SourceOutputPortId":"-207:data"},{"DestinationInputPortId":"-207:data1","SourceOutputPortId":"-213:data"},{"DestinationInputPortId":"-1188:test_ds","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-142:options_data","SourceOutputPortId":"-1188:predictions"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","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-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\nshift(open, -5)/shift(open, -1)\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.05), all_quantile(label, 0.95))\n\n# 将分数映射到分类,这里使用20个分类\nall_wbins(label, 20)\n\n# 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na_label","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"cast_label_int","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"# 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1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n #print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","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 pass\n","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":"500000","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":"真实价格","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":"-142"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-142"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-142"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-142"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-142"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-142","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-648","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-648"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-648"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-648","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-577","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, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n input_df = input_1.read_df().reset_index(drop='True')\n import talib\n def cal_macd(df):\n # 取出close_0列的数据转化为float\n close = [float(x) for x in df['close_0']]\n # 调用talib计算MACD指标\n df['MACD'],df['MACDsignal'],df['MACDhist'] = talib.MACD(np.array(close),\n fastperiod=6, slowperiod=12, signalperiod=9)\n return df[['date','instrument','MACD','MACDsignal','MACDhist']]\n \n result = input_df.groupby('instrument').apply(cal_macd)\n \n data_1 = DataSource.write_df(result)\n return Outputs(data_1=data_1, data_2=None, data_3=None)","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":"-577"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-577"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-577"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-577","OutputType":null},{"Name":"data_2","NodeId":"-577","OutputType":null},{"Name":"data_3","NodeId":"-577","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1475","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# #号开始的表示注释,注释需单独一行\n# 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征\nMACD\nMACDsignal\nMACDhist\nrank_swing_volatility_60_0\nswing_volatility_30_0\nta_rsi_14_0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-1475"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1475","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-152","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2020-08-27","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","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":"-152"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-152","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-161","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":"90","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-161"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-161"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-161","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-171","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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nt","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"-207"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"-207"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-207","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-213","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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    In [13]:
    # 本代码由可视化策略环境自动生成 2020年8月28日 17:32
    # 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 m6_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        input_df = input_1.read_df().reset_index(drop='True')
        import talib
        def cal_macd(df):
            # 取出close_0列的数据转化为float
            close = [float(x) for x in df['close_0']]
             # 调用talib计算MACD指标
            df['MACD'],df['MACDsignal'],df['MACDhist'] = talib.MACD(np.array(close),
                                        fastperiod=6, slowperiod=12, signalperiod=9)
            return df[['date','instrument','MACD','MACDsignal','MACDhist']]
        
        result = input_df.groupby('instrument').apply(cal_macd)
        
        data_1 = DataSource.write_df(result)
        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 m6_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m20_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        input_df = input_1.read_df().reset_index(drop='True')
        import talib
        def cal_macd(df):
            # 取出close_0列的数据转化为float
            close = [float(x) for x in df['close_0']]
             # 调用talib计算MACD指标
            df['MACD'],df['MACDsignal'],df['MACDhist'] = talib.MACD(np.array(close),
                                        fastperiod=6, slowperiod=12, signalperiod=9)
            return df[['date','instrument','MACD','MACDsignal','MACDhist']]
        
        result = input_df.groupby('instrument').apply(cal_macd)
        
        data_1 = DataSource.write_df(result)
        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 m20_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = 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
        stock_count = 3
        # 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 = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.3
        context.options['hold_days'] = 5
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        
        if context.trading_day_index % 7 != 0:
            return
        
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            #print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2016-01-01',
        'm1.end_date': '2019-01-01',
        'm1.market': 'CN_STOCK_A',
        'm1.instrument_list': '',
        'm1.max_count': 0,
    
        'm2': 'M.advanced_auto_labeler.v2',
        'm2.instruments': T.Graph.OutputPort('m1.data'),
        'm2.label_expr': """# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(open, -5)/shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.05), all_quantile(label, 0.95))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': True,
    
        'm3': 'M.input_features.v1',
        'm3.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
    close_0
    rank_swing_volatility_60_0
    swing_volatility_30_0
    ta_rsi_14_0""",
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 90,
    
        'm6': 'M.cached.v3',
        'm6.input_1': T.Graph.OutputPort('m15.data'),
        'm6.run': m6_run_bigquant_run,
        'm6.post_run': m6_post_run_bigquant_run,
        'm6.input_ports': '',
        'm6.params': '{}',
        'm6.output_ports': '',
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m2.data'),
        'm7.data2': T.Graph.OutputPort('m6.data_1'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm24': 'M.derived_feature_extractor.v3',
        'm24.input_data': T.Graph.OutputPort('m15.data'),
        'm24.features': T.Graph.OutputPort('m3.data'),
        'm24.date_col': 'date',
        'm24.instrument_col': 'instrument',
        'm24.drop_na': False,
        'm24.remove_extra_columns': False,
        'm24.user_functions': {},
    
        'm10': 'M.join.v3',
        'm10.data1': T.Graph.OutputPort('m7.data'),
        'm10.data2': T.Graph.OutputPort('m24.data'),
        'm10.on': 'date,instrument',
        'm10.how': 'inner',
        'm10.sort': False,
    
        'm5': 'M.dropnan.v2',
        'm5.input_data': T.Graph.OutputPort('m10.data'),
    
        'm12': 'M.input_features.v1',
        'm12.features': """
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    MACD
    MACDsignal
    MACDhist
    rank_swing_volatility_60_0
    swing_volatility_30_0
    ta_rsi_14_0""",
    
        'm16': 'M.instruments.v2',
        'm16.start_date': '2019-01-01',
        'm16.end_date': '2020-08-27',
        'm16.market': 'CN_STOCK_A',
        'm16.instrument_list': '',
        'm16.max_count': 0,
    
        'm18': 'M.general_feature_extractor.v7',
        'm18.instruments': T.Graph.OutputPort('m16.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.start_date': '',
        'm18.end_date': '',
        'm18.before_start_days': 90,
    
        'm20': 'M.cached.v3',
        'm20.input_1': T.Graph.OutputPort('m18.data'),
        'm20.run': m20_run_bigquant_run,
        'm20.post_run': m20_post_run_bigquant_run,
        'm20.input_ports': '',
        'm20.params': '{}',
        'm20.output_ports': '',
    
        'm26': 'M.derived_feature_extractor.v3',
        'm26.input_data': T.Graph.OutputPort('m18.data'),
        'm26.features': T.Graph.OutputPort('m3.data'),
        'm26.date_col': 'date',
        'm26.instrument_col': 'instrument',
        'm26.drop_na': False,
        'm26.remove_extra_columns': False,
        'm26.user_functions': {},
    
        'm23': 'M.join.v3',
        'm23.data1': T.Graph.OutputPort('m20.data_1'),
        'm23.data2': T.Graph.OutputPort('m26.data'),
        'm23.on': 'date,instrument',
        'm23.how': 'inner',
        'm23.sort': False,
    
        'm22': 'M.dropnan.v2',
        'm22.input_data': T.Graph.OutputPort('m23.data'),
    
        'm9': 'M.advanced_auto_labeler.v2',
        'm9.instruments': T.Graph.OutputPort('m16.data'),
        'm9.label_expr': """# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(open, -5)/shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.05), all_quantile(label, 0.95))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        'm9.start_date': '',
        'm9.end_date': '',
        'm9.benchmark': '000300.SHA',
        'm9.drop_na_label': True,
        'm9.cast_label_int': True,
    
        'm8': 'M.join.v3',
        'm8.data1': T.Graph.OutputPort('m9.data'),
        'm8.data2': T.Graph.OutputPort('m23.data'),
        'm8.on': 'date,instrument',
        'm8.how': 'inner',
        'm8.sort': False,
    
        'm11': 'M.dropnan.v2',
        'm11.input_data': T.Graph.OutputPort('m8.data'),
    
        'm4': 'M.stock_ranker.v2',
        'm4.training_ds': T.Graph.OutputPort('m5.data'),
        'm4.features': T.Graph.OutputPort('m12.data'),
        'm4.test_ds': T.Graph.OutputPort('m11.data'),
        'm4.predict_ds': T.Graph.OutputPort('m22.data'),
        'm4.learning_algorithm': '排序',
        'm4.number_of_leaves': 10,
        'm4.minimum_docs_per_leaf': 4000,
        'm4.number_of_trees': 10,
        'm4.learning_rate': 0.01,
        'm4.max_bins': 1023,
        'm4.feature_fraction': 1,
        'm4.data_row_fraction': 1,
        'm4.ndcg_discount_base': 1,
        'm4.slim_data': True,
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m16.data'),
        'm19.options_data': T.Graph.OutputPort('m4.predictions'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.volume_limit': 0.025,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'open',
        'm19.capital_base': 500000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '真实价格',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '',
    })
    
    # g.run({})
    
    
    def m13_param_grid_builder_bigquant_run():
        param_grid = {}
    
        # 在这里设置需要调优的参数备选
        param_grid['m4.minimum_docs_per_leaf'] = [1000,2000,3000,4000,5000]
        param_grid['m4.learning_rate'] = [0.01, 0.05, 0.1, 0.2, 0.3]
        param_grid['m4.number_of_leaves'] = [10, 20, 30, 40]
        #param_grid['m4.max_bins'] = [512, 1024, 2048, 4096]
    
        return param_grid
    
    def m13_scoring_bigquant_run(result):
        score = result.get('m19').read_raw_perf()['algorithm_period_return'][-1]
        result['m19'].display()
        return score
    
    
    m13 = M.hyper_parameter_search.v1(
        param_grid_builder=m13_param_grid_builder_bigquant_run,
        scoring=m13_scoring_bigquant_run,
        search_algorithm='随机搜索',
        search_iterations=1,
        workers=3,
        worker_distributed_run=True,
        worker_silent=True,
        run_now=True,
        bq_graph=g
    )
    
    Fitting 1 folds for each of 1 candidates, totalling 1 fits
    [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
    [CV] m4.number_of_leaves=20, m4.minimum_docs_per_leaf=4000, m4.learning_rate=0.3 
    
    • 收益率62.21%
    • 年化收益率35.32%
    • 基准收益率57.15%
    • 阿尔法0.12
    • 贝塔0.59
    • 夏普比率1.79
    • 胜率0.55
    • 盈亏比1.35
    • 收益波动率15.92%
    • 信息比率0.0
    • 最大回撤11.26%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3d9ddb2b9d1149a19e24a63168b5a9ed"}/bigcharts-data-end
    • 收益率62.21%
    • 年化收益率35.32%
    • 基准收益率57.15%
    • 阿尔法0.12
    • 贝塔0.59
    • 夏普比率1.79
    • 胜率0.55
    • 盈亏比1.35
    • 收益波动率15.92%
    • 信息比率0.0
    • 最大回撤11.26%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-248e8c3466e244729176bd617c162e8b"}/bigcharts-data-end
    [CV]  m4.number_of_leaves=20, m4.minimum_docs_per_leaf=4000, m4.learning_rate=0.3, score=0.6221392761521711, total= 4.4min
    [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.5min remaining:    0.0s
    [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.5min finished
    
    In [14]:
    m13.result.best_params_
    
    Out[14]:
    {'m4.learning_rate': 0.3,
     'm4.minimum_docs_per_leaf': 4000,
     'm4.number_of_leaves': 20}
    In [15]:
    #dt = m4.predictions.read_df()[-30000:]
    #dt.to_csv('3.csv')