克隆策略
{"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":"-773:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15: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":"-768:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-778:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-251:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-122:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-141:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-113:input_data","SourceOutputPortId":"-106:data"},{"DestinationInputPortId":"-768:input_1","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-778:input_1","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-3880:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-356:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-6284:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_1"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-768:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"-773:data"},{"DestinationInputPortId":"-251:input_data","SourceOutputPortId":"-778:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-1098:validation_data","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-251:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-3880:data"},{"DestinationInputPortId":"-567:inputs","SourceOutputPortId":"-356:data"},{"DestinationInputPortId":"-6233:inputs","SourceOutputPortId":"-567:data"},{"DestinationInputPortId":"-3880:outputs","SourceOutputPortId":"-412:data"},{"DestinationInputPortId":"-6284:layer","SourceOutputPortId":"-6233:data"},{"DestinationInputPortId":"-412:inputs","SourceOutputPortId":"-6276:data"},{"DestinationInputPortId":"-6276:inputs","SourceOutputPortId":"-6284:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-01-01","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(close, -5) / shift(open, -1)-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# 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":"False","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":"(close_0-mean(close_0,12))/mean(close_0,12)*100\nrank(std(amount_0,15))\nrank_avg_amount_0/rank_avg_amount_8\nts_argmin(low_0,20)\nrank_return_30\n(low_1-close_0)/close_0\nta_bbands_lowerband_14_0\nmean(mf_net_pct_s_0,4)\namount_0/avg_amount_3\nreturn_0/return_5\nreturn_1/return_5\nrank_avg_amount_7/rank_avg_amount_10\nta_sma_10_0/close_0\nsqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0\navg_turn_15/(turn_0+1e-5)\nreturn_10\nmf_net_pct_s_0\n(close_0-open_0)/close_1","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":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-09-18","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":"-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":"30","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":"True","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":"30","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":"True","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":"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 = 0.1\n context.options['hold_days'] = 1","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":"before_trading_start","Value":"","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":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"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":"-160","ModuleId":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","ModuleParameters":[{"Name":"shape","Value":"2,18","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dtype","Value":"float32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sparse","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-160"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-160","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"RMSprop","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_optimizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"loss","Value":"mean_squared_error","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"mae","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"2048","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"200","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_model","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_data","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"validation_data","NodeId":"-1098"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1098","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1540","ModuleId":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","ModuleParameters":[{"Name":"batch_size","Value":"1024","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trained_model","NodeId":"-1540"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1540"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1540","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2431","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 pred_label = input_1.read_pickle()\n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-2431"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-2431","OutputType":null},{"Name":"data_2","NodeId":"-2431","OutputType":null},{"Name":"data_3","NodeId":"-2431","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-768","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-768"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-768"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-768","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-773","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"label","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-773"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-773"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-773","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-778","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-778"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-778"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-778","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"18","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-251","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"18","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-251"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-251"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-251","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3880","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3880"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-3880"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3880","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":34,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-356","ModuleId":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","ModuleParameters":[{"Name":"units","Value":"128","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_activation","Value":"hard_sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_initializer","Value":"Orthogonal","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"unit_forget_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"return_sequences","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"implementation","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-356"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-356","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-567","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"noise_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"seed","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-567"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-567","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-412","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-412"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-412","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1121","ModuleId":"BigQuantSpace.hyper_rolling_train.hyper_rolling_train-v1","ModuleParameters":[{"Name":"run","Value":"def bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历, TODO\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m24', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2019-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2020-09-18'), # 数据结束日期\n train_update_days=125, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=125, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数\n train_data_min_days=125, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=250, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None and train_data_max_days > 0:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = 0\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].data_1, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': trade}\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"run_now","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bq_graph","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"bq_graph_port","NodeId":"-1121"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-1121"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-1121"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-1121"}],"OutputPortsInternal":[{"Name":"result","NodeId":"-1121","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-6233","ModuleId":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","ModuleParameters":[{"Name":"units","Value":"128","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_activation","Value":"hard_sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_initializer","Value":"Orthogonal","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"unit_forget_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"return_sequences","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"implementation","Value":"2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-6233"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-6233","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-6276","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"noise_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"seed","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-6276"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-6276","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-6284","ModuleId":"BigQuantSpace.dl_layer_bidirectional.dl_layer_bidirectional-v1","ModuleParameters":[{"Name":"merge_mode","Value":"concat","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"weights","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"layer","NodeId":"-6284"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-6284"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-6284","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":22,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='366,-3,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='206,91,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='744,-7,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='448,376,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='966,73,200,200'/><NodePosition Node='-106' Position='527,95,200,200'/><NodePosition Node='-113' Position='528,197,200,200'/><NodePosition Node='-122' Position='970,170,200,200'/><NodePosition Node='-129' Position='973,261,200,200'/><NodePosition Node='-141' Position='328,763,200,200'/><NodePosition Node='-160' Position='-86,-2,200,200'/><NodePosition Node='-1098' Position='286,547,200,200'/><NodePosition Node='-1540' Position='333,620,200,200'/><NodePosition Node='-2431' Position='402,694,200,200'/><NodePosition Node='-768' Position='663,277,200,200'/><NodePosition Node='-773' Position='228,200,200,200'/><NodePosition Node='-778' Position='959,366,200,200'/><NodePosition Node='-243' Position='450,454,200,200'/><NodePosition Node='-251' Position='953,442,200,200'/><NodePosition Node='-3880' Position='-137,515,200,200'/><NodePosition Node='-356' Position='-87,71,200,200'/><NodePosition Node='-567' Position='-89,144,200,200'/><NodePosition Node='-412' Position='-90,439,200,200'/><NodePosition Node='-1121' Position='814,596,200,200'/><NodePosition Node='-6233' Position='-88,214,200,200'/><NodePosition Node='-6276' Position='-89,369,200,200'/><NodePosition Node='-6284' Position='-44,289,200,200'/></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 [4]:
# 本代码由可视化策略环境自动生成 2020年9月23日 16:34
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
# 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 m24_run_bigquant_run(input_1, input_2, input_3):
# 示例代码如下。在这里编写您的代码
pred_label = input_1.read_pickle()
df = input_2.read_df()
df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
return Outputs(data_1=DataSource.write_df(df), 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 m24_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 = 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 = 0.1
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):
# 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': '2018-01-01',
'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(close, -5) / shift(open, -1)-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))
# 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': False,
'm13': 'M.standardlize.v8',
'm13.input_1': T.Graph.OutputPort('m2.data'),
'm13.columns_input': 'label',
'm3': 'M.input_features.v1',
'm3.features': """(close_0-mean(close_0,12))/mean(close_0,12)*100
rank(std(amount_0,15))
rank_avg_amount_0/rank_avg_amount_8
ts_argmin(low_0,20)
rank_return_30
(low_1-close_0)/close_0
ta_bbands_lowerband_14_0
mean(mf_net_pct_s_0,4)
amount_0/avg_amount_3
return_0/return_5
return_1/return_5
rank_avg_amount_7/rank_avg_amount_10
ta_sma_10_0/close_0
sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
avg_turn_15/(turn_0+1e-5)
return_10
mf_net_pct_s_0
(close_0-open_0)/close_1""",
'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': 30,
'm16': 'M.derived_feature_extractor.v3',
'm16.input_data': T.Graph.OutputPort('m15.data'),
'm16.features': T.Graph.OutputPort('m3.data'),
'm16.date_col': 'date',
'm16.instrument_col': 'instrument',
'm16.drop_na': True,
'm16.remove_extra_columns': False,
'm14': 'M.standardlize.v8',
'm14.input_1': T.Graph.OutputPort('m16.data'),
'm14.input_2': T.Graph.OutputPort('m3.data'),
'm14.columns_input': '',
'm7': 'M.join.v3',
'm7.data1': T.Graph.OutputPort('m13.data'),
'm7.data2': T.Graph.OutputPort('m14.data'),
'm7.on': 'date,instrument',
'm7.how': 'inner',
'm7.sort': False,
'm26': 'M.dl_convert_to_bin.v2',
'm26.input_data': T.Graph.OutputPort('m7.data'),
'm26.features': T.Graph.OutputPort('m3.data'),
'm26.window_size': 2,
'm26.feature_clip': 18,
'm26.flatten': False,
'm26.window_along_col': 'instrument',
'm9': 'M.instruments.v2',
'm9.start_date': T.live_run_param('trading_date', '2019-01-01'),
'm9.end_date': T.live_run_param('trading_date', '2020-09-18'),
'm9.market': 'CN_STOCK_A',
'm9.instrument_list': '',
'm9.max_count': 0,
'm17': 'M.general_feature_extractor.v7',
'm17.instruments': T.Graph.OutputPort('m9.data'),
'm17.features': T.Graph.OutputPort('m3.data'),
'm17.start_date': '',
'm17.end_date': '',
'm17.before_start_days': 30,
'm18': 'M.derived_feature_extractor.v3',
'm18.input_data': T.Graph.OutputPort('m17.data'),
'm18.features': T.Graph.OutputPort('m3.data'),
'm18.date_col': 'date',
'm18.instrument_col': 'instrument',
'm18.drop_na': True,
'm18.remove_extra_columns': False,
'm25': 'M.standardlize.v8',
'm25.input_1': T.Graph.OutputPort('m18.data'),
'm25.input_2': T.Graph.OutputPort('m3.data'),
'm25.columns_input': '',
'm27': 'M.dl_convert_to_bin.v2',
'm27.input_data': T.Graph.OutputPort('m25.data'),
'm27.features': T.Graph.OutputPort('m3.data'),
'm27.window_size': 2,
'm27.feature_clip': 18,
'm27.flatten': False,
'm27.window_along_col': 'instrument',
'm6': 'M.dl_layer_input.v1',
'm6.shape': '2,18',
'm6.batch_shape': '',
'm6.dtype': 'float32',
'm6.sparse': False,
'm6.name': '',
'm10': 'M.dl_layer_lstm.v1',
'm10.inputs': T.Graph.OutputPort('m6.data'),
'm10.units': 128,
'm10.activation': 'tanh',
'm10.recurrent_activation': 'hard_sigmoid',
'm10.use_bias': True,
'm10.kernel_initializer': 'glorot_uniform',
'm10.recurrent_initializer': 'Orthogonal',
'm10.bias_initializer': 'Zeros',
'm10.unit_forget_bias': True,
'm10.kernel_regularizer': 'None',
'm10.kernel_regularizer_l1': 0,
'm10.kernel_regularizer_l2': 0,
'm10.recurrent_regularizer': 'None',
'm10.recurrent_regularizer_l1': 0,
'm10.recurrent_regularizer_l2': 0,
'm10.bias_regularizer': 'None',
'm10.bias_regularizer_l1': 0,
'm10.bias_regularizer_l2': 0,
'm10.activity_regularizer': 'None',
'm10.activity_regularizer_l1': 0,
'm10.activity_regularizer_l2': 0,
'm10.kernel_constraint': 'None',
'm10.recurrent_constraint': 'None',
'm10.bias_constraint': 'None',
'm10.dropout': 0,
'm10.recurrent_dropout': 0,
'm10.return_sequences': True,
'm10.implementation': '1',
'm10.name': '',
'm12': 'M.dl_layer_dropout.v1',
'm12.inputs': T.Graph.OutputPort('m10.data'),
'm12.rate': 0.2,
'm12.noise_shape': '',
'm12.name': '',
'm8': 'M.dl_layer_lstm.v1',
'm8.inputs': T.Graph.OutputPort('m12.data'),
'm8.units': 128,
'm8.activation': 'tanh',
'm8.recurrent_activation': 'hard_sigmoid',
'm8.use_bias': True,
'm8.kernel_initializer': 'glorot_uniform',
'm8.recurrent_initializer': 'Orthogonal',
'm8.bias_initializer': 'Zeros',
'm8.unit_forget_bias': True,
'm8.kernel_regularizer': 'None',
'm8.kernel_regularizer_l1': 0,
'm8.kernel_regularizer_l2': 0,
'm8.recurrent_regularizer': 'None',
'm8.recurrent_regularizer_l1': 0,
'm8.recurrent_regularizer_l2': 0,
'm8.bias_regularizer': 'None',
'm8.bias_regularizer_l1': 0,
'm8.bias_regularizer_l2': 0,
'm8.activity_regularizer': 'None',
'm8.activity_regularizer_l1': 0,
'm8.activity_regularizer_l2': 0,
'm8.kernel_constraint': 'None',
'm8.recurrent_constraint': 'None',
'm8.bias_constraint': 'None',
'm8.dropout': 0,
'm8.recurrent_dropout': 0,
'm8.return_sequences': True,
'm8.implementation': '2',
'm8.name': '',
'm22': 'M.dl_layer_bidirectional.v1',
'm22.layer': T.Graph.OutputPort('m8.data'),
'm22.inputs': T.Graph.OutputPort('m6.data'),
'm22.merge_mode': 'concat',
'm22.weights': '',
'm22.name': '',
'm21': 'M.dl_layer_dropout.v1',
'm21.inputs': T.Graph.OutputPort('m22.data'),
'm21.rate': 0.2,
'm21.noise_shape': '',
'm21.name': '',
'm20': 'M.dl_layer_dense.v1',
'm20.inputs': T.Graph.OutputPort('m21.data'),
'm20.units': 1,
'm20.activation': 'tanh',
'm20.use_bias': True,
'm20.kernel_initializer': 'glorot_uniform',
'm20.bias_initializer': 'Zeros',
'm20.kernel_regularizer': 'None',
'm20.kernel_regularizer_l1': 0,
'm20.kernel_regularizer_l2': 0,
'm20.bias_regularizer': 'None',
'm20.bias_regularizer_l1': 0,
'm20.bias_regularizer_l2': 0,
'm20.activity_regularizer': 'None',
'm20.activity_regularizer_l1': 0,
'm20.activity_regularizer_l2': 0,
'm20.kernel_constraint': 'None',
'm20.bias_constraint': 'None',
'm20.name': '',
'm34': 'M.dl_model_init.v1',
'm34.inputs': T.Graph.OutputPort('m6.data'),
'm34.outputs': T.Graph.OutputPort('m20.data'),
'm5': 'M.dl_model_train.v1',
'm5.input_model': T.Graph.OutputPort('m34.data'),
'm5.training_data': T.Graph.OutputPort('m26.data'),
'm5.validation_data': T.Graph.OutputPort('m26.data'),
'm5.optimizer': 'RMSprop',
'm5.loss': 'mean_squared_error',
'm5.metrics': 'mae',
'm5.batch_size': 2048,
'm5.epochs': 200,
'm5.n_gpus': 0,
'm5.verbose': '2:每个epoch输出一行记录',
'm11': 'M.dl_model_predict.v1',
'm11.trained_model': T.Graph.OutputPort('m5.data'),
'm11.input_data': T.Graph.OutputPort('m27.data'),
'm11.batch_size': 1024,
'm11.n_gpus': 0,
'm11.verbose': '2:每个epoch输出一行记录',
'm24': 'M.cached.v3',
'm24.input_1': T.Graph.OutputPort('m11.data'),
'm24.input_2': T.Graph.OutputPort('m18.data'),
'm24.run': m24_run_bigquant_run,
'm24.post_run': m24_post_run_bigquant_run,
'm24.input_ports': '',
'm24.params': '{}',
'm24.output_ports': '',
'm19': 'M.trade.v4',
'm19.instruments': T.Graph.OutputPort('m9.data'),
'm19.options_data': T.Graph.OutputPort('m24.data_1'),
'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,
'm19.order_price_field_buy': 'open',
'm19.order_price_field_sell': 'close',
'm19.capital_base': 1000000,
'm19.auto_cancel_non_tradable_orders': True,
'm19.data_frequency': 'daily',
'm19.price_type': '后复权',
'm19.product_type': '股票',
'm19.plot_charts': True,
'm19.backtest_only': False,
'm19.benchmark': '000300.SHA',
})
# g.run({})
def m4_run_bigquant_run(
bq_graph,
inputs,
trading_days_market='CN', # 使用那个市场的交易日历, TODO
train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
predict_mid='m24', # 预测 模块id
trade_mid='m19', # 回测 模块id
start_date='2019-01-01', # 数据开始日期
end_date=T.live_run_param('trading_date', '2020-09-18'), # 数据结束日期
train_update_days=125, # 更新周期,按交易日计算,每多少天更新一次
train_update_days_for_live=125, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数
train_data_min_days=125, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
train_data_max_days=250, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制
):
def merge_datasources(input_1):
df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
df = pd.concat(df_list)
instrument_data = {
'start_date': df['date'].min().strftime('%Y-%m-%d'),
'end_date': df['date'].max().strftime('%Y-%m-%d'),
'instruments': list(set(df['instrument'])),
}
return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))
def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
# 是否实盘模式
tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
is_live_run = T.live_run_param('trading_date', None) is not None
if is_live_run and train_update_days_for_live:
train_update_days = train_update_days_for_live
rollings = []
train_end_date = train_data_min_days
while train_end_date < len(tdays):
if train_data_max_days is not None and train_data_max_days > 0:
train_start_date = max(train_end_date - train_data_max_days, 0)
else:
train_start_date = 0
rollings.append({
'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
})
train_end_date += train_update_days
if not rollings:
raise Exception('没有滚动需要执行,请检查配置')
if is_live_run and rolling_count_for_live:
rollings = rollings[-rolling_count_for_live:]
return rollings
g = bq_graph
rolling_dates = gen_rolling_dates(
trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
# 训练和预测
results = []
for rolling in rolling_dates:
parameters = {}
# 先禁用回测
parameters[trade_mid + '.__enabled__'] = False
parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
# print('------ rolling_train:', parameters)
results.append(g.run(parameters))
# 合并预测结果并回测
mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].data_1, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
parameters = {}
parameters['*.__enabled__'] = False
parameters[trade_mid + '.__enabled__'] = True
parameters[trade_mid + '.instruments'] = mx.instrument_data
parameters[trade_mid + '.options_data'] = mx.data
trade = g.run(parameters)
return {'rollings': results, 'trade': trade}
m4 = M.hyper_rolling_train.v1(
run=m4_run_bigquant_run,
run_now=True,
bq_graph=g
)
日志 45 条,错误日志
2 条
[2020-09-23 16:32:06.830741] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-09-23 16:32:06.837343] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:06.838849] INFO: moduleinvoker: instruments.v2 运行完成[0.008111s].
[2020-09-23 16:32:06.841000] INFO: moduleinvoker: input_features.v1 开始运行..
[2020-09-23 16:32:06.846437] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:06.849565] INFO: moduleinvoker: input_features.v1 运行完成[0.008544s].
[2020-09-23 16:32:06.851939] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-09-23 16:32:06.857314] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:06.859014] INFO: moduleinvoker: instruments.v2 运行完成[0.007064s].
[2020-09-23 16:32:06.865170] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.003352s].
[2020-09-23 16:32:06.867655] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2020-09-23 16:32:06.872599] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:06.874164] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.006501s].
[2020-09-23 16:32:06.879767] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-09-23 16:32:06.884909] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:06.886112] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.006343s].
[2020-09-23 16:32:06.892410] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-09-23 16:32:06.897261] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:06.898634] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.006223s].
[2020-09-23 16:32:07.395368] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.494655s].
[2020-09-23 16:32:07.397812] INFO: moduleinvoker: standardlize.v8 开始运行..
[2020-09-23 16:32:07.405324] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.406824] INFO: moduleinvoker: standardlize.v8 运行完成[0.009015s].
[2020-09-23 16:32:07.408814] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-09-23 16:32:07.426239] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.427644] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.018823s].
[2020-09-23 16:32:07.429288] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-09-23 16:32:07.448952] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.450723] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.021405s].
[2020-09-23 16:32:07.475984] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.023009s].
[2020-09-23 16:32:07.477653] INFO: moduleinvoker: standardlize.v8 开始运行..
[2020-09-23 16:32:07.482916] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.484359] INFO: moduleinvoker: standardlize.v8 运行完成[0.006703s].
[2020-09-23 16:32:07.486525] INFO: moduleinvoker: standardlize.v8 开始运行..
[2020-09-23 16:32:07.491697] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.492692] INFO: moduleinvoker: standardlize.v8 运行完成[0.006165s].
[2020-09-23 16:32:07.666621] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.171397s].
[2020-09-23 16:32:07.669142] INFO: moduleinvoker: join.v3 开始运行..
[2020-09-23 16:32:07.674901] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.676596] INFO: moduleinvoker: join.v3 运行完成[0.00744s].
[2020-09-23 16:32:07.682950] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..
[2020-09-23 16:32:07.688854] INFO: moduleinvoker: 命中缓存
[2020-09-23 16:32:07.690494] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.007561s].
[2020-09-23 16:32:07.692871] ERROR: moduleinvoker: module name: dl_layer_bidirectional, module version: v1, trackeback: Traceback (most recent call last): ValueError: Please initialize `Bidirectional` layer with a `Layer` instance. You passed: Tensor("lstm_7/Identity:0", shape=(None, 2, 128), dtype=float32)
[2020-09-23 16:32:07.696777] ERROR: moduleinvoker: module name: hyper_rolling_train, module version: v1, trackeback: Traceback (most recent call last): ValueError: Please initialize `Bidirectional` layer with a `Layer` instance. You passed: Tensor("lstm_7/Identity:0", shape=(None, 2, 128), dtype=float32)