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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":"-207:data2","SourceOutputPortId":"-812:data"},{"DestinationInputPortId":"-187:input_data","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":"2016-01-08","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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15)\n","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":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","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":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-107","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":"-107"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-107"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-107","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-142","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":"# 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> 0:\n for instrument in stock_hold_now:\n context.order_target(symbol(instrument), 0)\n print(today,'大盘风控止损触发,全仓卖出')\n return\n \n if context.trading_day_index % 5 != 0:\n return\n \n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\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":"def bigquant_run(context):\n #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度\n # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间\n start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') \n df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])\n benckmark_data=df[df.instrument=='000001.HIX']\n #计算上证指数5日涨幅\n benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(1)-1\n #计算大盘风控条件,如果5日涨幅小于-4%则设置风险状态risk为1,否则为0\n benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)\n #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)\n benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))\n #设置日期为索引\n benckmark_data.set_index('date',inplace=True)\n #把风控序列输出给全局变量context.benckmark_risk\n context.benckmark_risk=benckmark_data['risk']\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# 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    In [13]:
    # 本代码由可视化策略环境自动生成 2020年9月11日 10:56
    # 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 = 2
        # 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 = 0.4
        context.options['hold_days'] = 1
    # 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):
        today = data.current_dt.strftime('%Y-%m-%d')
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
        #大盘风控模块,读取风控数据    
        benckmark_risk=context.benckmark_risk[today]
        context.symbol
        #当risk为1时,市场有风险,全部平仓,不再执行其它操作
        if benckmark_risk > 0:
            for instrument in stock_hold_now:
                context.order_target(symbol(instrument), 0)
            print(today,'大盘风控止损触发,全仓卖出')
            return
        
        if context.trading_day_index % 5 != 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)
    
    def m19_prepare_bigquant_run(context):
        #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
        # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间
        start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') 
        df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])
        benckmark_data=df[df.instrument=='000001.HIX']
        #计算上证指数5日涨幅
        benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(1)-1
        #计算大盘风控条件,如果5日涨幅小于-4%则设置风险状态risk为1,否则为0
        benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)
        #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
        benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        #设置日期为索引
        benckmark_data.set_index('date',inplace=True)
        #把风控序列输出给全局变量context.benckmark_risk
        context.benckmark_risk=benckmark_data['risk']
    
    
    m1 = M.instruments.v2(
        start_date='2016-01-08',
        end_date='2019-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        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)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = 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
    close_0
    rank_swing_volatility_60_0
    swing_volatility_30_0
    ta_rsi_14_0
    ta_cci_14_0
    avg_turn_5
    rank_return_20
    avg_amount_5
    rank_avg_turn_5
    mean(turn_0*return_0, 15)
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m6 = M.cached.v3(
        input_1=m15.data,
        run=m6_run_bigquant_run,
        post_run=m6_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m6.data_1,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m24 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m10 = M.join.v3(
        data1=m7.data,
        data2=m24.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m5 = M.dropnan.v2(
        input_data=m10.data
    )
    
    m12 = M.input_features.v1(
        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
    rank_swing_volatility_60_0
    swing_volatility_30_0
    ta_rsi_14_0
    #ta_cci_14_0
    #avg_turn_5
    #rank_return_20
    #avg_amount_5
    #rank_avg_turn_5
    #mean(turn_0*return_0, 15)
    """
    )
    
    m16 = M.instruments.v2(
        start_date='2019-01-04',
        end_date='2020-09-10',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m18 = M.general_feature_extractor.v7(
        instruments=m16.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m20 = M.cached.v3(
        input_1=m18.data,
        run=m20_run_bigquant_run,
        post_run=m20_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m23 = M.join.v3(
        data1=m20.data_1,
        data2=m26.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m22 = M.dropnan.v2(
        input_data=m23.data
    )
    
    m9 = M.advanced_auto_labeler.v2(
        instruments=m16.data,
        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)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m8 = M.join.v3(
        data1=m9.data,
        data2=m23.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m11 = M.dropnan.v2(
        input_data=m8.data
    )
    
    m4 = M.stock_ranker.v2(
        training_ds=m5.data,
        features=m12.data,
        test_ds=m11.data,
        predict_ds=m22.data,
        learning_algorithm='排序',
        number_of_leaves=5,
        minimum_docs_per_leaf=1000,
        number_of_trees=10,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        slim_data=True
    )
    
    m19 = M.trade.v4(
        instruments=m16.data,
        options_data=m4.predictions,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='open',
        capital_base=500000,
        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=''
    )
    
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4e213818940748009675fde31ba5e4aa"}/bigcharts-data-end
    • 收益率91.0%
    • 年化收益率48.7%
    • 基准收益率54.54%
    • 阿尔法0.28
    • 贝塔0.38
    • 夏普比率2.27
    • 胜率0.49
    • 盈亏比2.19
    • 收益波动率16.77%
    • 信息比率0.04
    • 最大回撤9.52%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-37fb37d8f20849eda6653a57d7d803df"}/bigcharts-data-end
    In [14]:
    #m13.result.best_params_
    
    In [15]:
    #dt = m4.predictions.read_df()[-30000:]
    #dt.to_csv('3.csv')