对冲策略研究demo示例

策略分享
标签: #<Tag:0x00007fb3dcd2be40>

(华尔街的猫) #1

在策略研究中,如何进行对冲,可以先参考以下,后面我们会提供更完善的文档,也欢迎大家踊跃给我们提意见。

克隆策略

如果我们想看看通过股指期货对冲后的策略收益率,我们有如下两种方式:

  • 通过基准指数设置成股指期货指数,通过相对收益率来进行衡量

  • 直接在拥有多仓的时候,直接建股指期货的空头头寸

    {"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":"-106: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":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-106:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-113:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-122:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-129:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-122:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-141:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-688:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"DestinationInputPortId":"-463:input_data","SourceOutputPortId":"-86:data"},{"DestinationInputPortId":"-113:input_data","SourceOutputPortId":"-106:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-454:input_1","SourceOutputPortId":"-141:raw_perf"},{"DestinationInputPortId":"-1442:instruments","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-454:input_2","SourceOutputPortId":"-1442:raw_perf"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-463:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","SourceOutputPortId":"-688:data"},{"DestinationInputPortId":"-141:benchmark_ds","SourceOutputPortId":"-486:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-01-01","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(close, -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, 0.01), all_quantile(label, 0.99))\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":"# #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\nreturn_5\nreturn_10\nreturn_20\navg_amount_0/avg_amount_5\navg_amount_5/avg_amount_20\nrank_avg_amount_0/rank_avg_amount_5\nrank_avg_amount_5/rank_avg_amount_10\nrank_return_0\nrank_return_5\nrank_return_10\nrank_return_0/rank_return_5\nrank_return_5/rank_return_10\npe_ttm_0\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-bdf8-104b137c8601-43","ModuleId":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v5","ModuleParameters":[{"Name":"learning_algorithm","Value":"排序","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"number_of_leaves","Value":30,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"minimum_docs_per_leaf","Value":1000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"number_of_trees","Value":20,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"learning_rate","Value":0.1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_bins","Value":1023,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_fraction","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"test_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"base_model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"OutputPortsInternal":[{"Name":"model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","OutputType":null},{"Name":"feature_gains","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","OutputType":null},{"Name":"m_lazy_run","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"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":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","ModuleId":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","ModuleParameters":[{"Name":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"OutputPortsInternal":[{"Name":"predictions","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null},{"Name":"m_lazy_run","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"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":"2017-01-02","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"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-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"IsPartOfPartialRun":null,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-86","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-86"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-86","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-106","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":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-106"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-106"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-106","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-113","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":"-113"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-113"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-113","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-122","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":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-122"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-122"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-122","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-129","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":"-129"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-129"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-129","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-141","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","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 # 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":"# 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":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只\n stock_count = 5\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":"0.0251","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":"2000000","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":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-141"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-141","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-25","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2017-01-02","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_FUTURE","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"IC8888.CFE","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-25"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-25","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1442","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","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 # 获取当日指标数据\n print('index', context.trading_day_index)\n today = data.current_dt.strftime('%Y-%m-%d') # 当前交易日期\n context.extension['index'] += 1 \n \n instrument = context.future_symbol(context.instruments[0]) # 交易标的\n \n if context.extension['index'] == 1 :\n context.order(instrument, -1)\n print(today, '卖空全部')\n \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","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 # 设置是否是结算模式\n context.set_need_settle(False)\n # 设置最大杠杆\n context.set_max_leverage(1, 'fill_amap')\n if 'index' not in context.extension:\n context.extension['index'] = 0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"# 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。\ndef bigquant_run(context, data):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":"0","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":"2100001","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":"期货","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":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-1442"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-1442","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-454","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 \n perf1 = input_1.read_df()['algorithm_period_return']+1 # 净值\n perf2 = input_2.read_df()['algorithm_period_return']+1 # 净值\n net_pv = perf1-perf2+1 # 对冲后净值\n net_pv.name = 'net value after hedge'\n net_pv_ds = DataSource.write_df(net_pv)\n return Outputs(data_1=net_pv_ds)\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 net_pv = outputs.data_1.read_df()\n T.plot(net_pv, title='对冲后策略净值')\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":"-454"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-454"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-454"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-454","OutputType":null},{"Name":"data_2","NodeId":"-454","OutputType":null},{"Name":"data_3","NodeId":"-454","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-463","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-463"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-463","OutputType":null},{"Name":"left_data","NodeId":"-463","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":22,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-688","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-688"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-688","OutputType":null},{"Name":"left_data","NodeId":"-688","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-486","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 bm = DataSource('bar1d_CN_FUTURE').read(instruments=['IC8888.CFE'],start_date='2016-01-01',end_date='2017-01-02')\n bm.index = range(len(bm)) \n data_1 = DataSource.write_df(bm)\n return Outputs(data_1=data_1)\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":"-486"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-486"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-486"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-486","OutputType":null},{"Name":"data_2","NodeId":"-486","OutputType":null},{"Name":"data_3","NodeId":"-486","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"读取基准数据","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-bdf8-104b137c8601-8' Position='211,64,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,181,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='765,21,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='494,641,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='665,737,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1078,103,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='233,457,200,200'/><NodePosition Node='-86' Position='1079,408,200,200'/><NodePosition Node='-106' Position='381,188,200,200'/><NodePosition Node='-113' Position='385,280,200,200'/><NodePosition Node='-122' Position='1078,236,200,200'/><NodePosition Node='-129' Position='1081,327,200,200'/><NodePosition Node='-141' Position='919,892,200,200'/><NodePosition Node='-25' Position='1423,510,200,200'/><NodePosition Node='-1442' Position='1420,645,200,200'/><NodePosition Node='-454' Position='1233,1024,200,200'/><NodePosition Node='-463' Position='1076,498,200,200'/><NodePosition Node='-688' Position='470,532,200,200'/><NodePosition Node='-486' Position='1114,768,200,200'/></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 [2]:
    # 本代码由可视化策略环境自动生成 2019年3月5日 17:31
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m20_handle_data_bigquant_run(context, data):
        # 获取当日指标数据
        print('index', context.trading_day_index)
        today = data.current_dt.strftime('%Y-%m-%d') # 当前交易日期
        context.extension['index'] += 1 
        
        instrument = context.future_symbol(context.instruments[0]) # 交易标的
        
        if context.extension['index']  == 1 :
            context.order(instrument,  -1)
            print(today, '卖空全部')
            
              
    # 回测引擎:准备数据,只执行一次
    def m20_prepare_bigquant_run(context):
        pass
    # 回测引擎:初始化函数,只执行一次
    def m20_initialize_bigquant_run(context):
       # 设置是否是结算模式
        context.set_need_settle(False)
        # 设置最大杠杆
        context.set_max_leverage(1, 'fill_amap')
        if 'index' not in context.extension:
            context.extension['index'] = 0
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m20_before_trading_start_bigquant_run(context, data):
        pass
    
    # 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 m10_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        bm = DataSource('bar1d_CN_FUTURE').read(instruments=['IC8888.CFE'],start_date='2016-01-01',end_date='2017-01-02')
        bm.index = range(len(bm)) 
        data_1 = DataSource.write_df(bm)
        return Outputs(data_1=data_1)
    
    # 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 m10_post_run_bigquant_run(Outputs):
        return Outputs
    
    # 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):
        # 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
    
    # 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 = 5
        # 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 = 0.2
        context.options['hold_days'] = 5
    
    # 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 m21_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
         
        perf1 = input_1.read_df()['algorithm_period_return']+1 # 净值
        perf2 = input_2.read_df()['algorithm_period_return']+1 # 净值
        net_pv = perf1-perf2+1 # 对冲后净值
        net_pv.name = 'net value after hedge'
        net_pv_ds = DataSource.write_df(net_pv)
        return Outputs(data_1=net_pv_ds)
    
    # 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 m21_post_run_bigquant_run(outputs):
        net_pv = outputs.data_1.read_df()
        T.plot(net_pv, title='对冲后策略净值')
        return outputs
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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(close, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 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征
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = 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
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m5 = M.chinaa_stock_filter.v1(
        input_data=m13.data,
        index_constituent_cond=['中证500'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['全部'],
        output_left_data=False
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m5.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2016-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-02'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m22 = M.chinaa_stock_filter.v1(
        input_data=m14.data,
        index_constituent_cond=['中证500'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['全部'],
        output_left_data=False
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m22.data,
        m_lazy_run=False
    )
    
    m4 = M.instruments.v2(
        start_date='2016-01-01',
        end_date='2017-01-02',
        market='CN_FUTURE',
        instrument_list='IC8888.CFE',
        max_count=0
    )
    
    m20 = M.trade.v4(
        instruments=m4.data,
        start_date='',
        end_date='',
        handle_data=m20_handle_data_bigquant_run,
        prepare=m20_prepare_bigquant_run,
        initialize=m20_initialize_bigquant_run,
        before_trading_start=m20_before_trading_start_bigquant_run,
        volume_limit=0,
        order_price_field_buy='open',
        order_price_field_sell='open',
        capital_base=2100001,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='期货',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    m10 = M.cached.v3(
        run=m10_run_bigquant_run,
        post_run=m10_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports='',
        m_cached=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        benchmark_ds=m10.data_1,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_bigquant_run,
        volume_limit=0.0251,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=2000000,
        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='000300.SHA'
    )
    
    m21 = M.cached.v3(
        input_1=m19.raw_perf,
        input_2=m20.raw_perf,
        run=m21_run_bigquant_run,
        post_run=m21_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports='',
        m_cached=False
    )
    
    • 收益率-11.27%
    • 年化收益率-11.62%
    • 基准收益率-11.28%
    • 阿尔法-0.22
    • 贝塔-0.64
    • 夏普比率-0.82
    • 胜率0.42
    • 盈亏比0.81
    • 收益波动率16.89%
    • 信息比率-0.0
    • 最大回撤22.49%
    • 收益率-13.48%
    • 年化收益率-13.89%
    • 基准收益率-9.75%
    • 阿尔法-0.02
    • 贝塔1.03
    • 夏普比率-0.27
    • 胜率0.53
    • 盈亏比0.85
    • 收益波动率38.58%
    • 信息比率-0.01
    • 最大回撤29.1%

    【宽客学院】设置回测基准
    (scalaview) #2

    认真学习