这个DNN模型如何设置滚动训练?老是报错

新手专区
标签: #<Tag:0x00007f313e28c2f8>

(tkyz) #1
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":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","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":"-1535:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-1547:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-1535: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":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-1547:input_data","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-168:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-682:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-224:inputs","SourceOutputPortId":"-168:data"},{"DestinationInputPortId":"-231:inputs","SourceOutputPortId":"-196:data"},{"DestinationInputPortId":"-196:inputs","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-238:inputs","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-682:outputs","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-682:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-1535:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-1547:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2014-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2017-12-31","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)\nwhere(label>0.5, NaN, label)\nwhere(label<-0.5, 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":"# #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_5-1\nreturn_10-1\nreturn_20-1\navg_amount_0/avg_amount_5-1\navg_amount_5/avg_amount_20-1\nrank_avg_amount_0-rank_avg_amount_5\nrank_avg_amount_5-rank_avg_amount_10\nrank_return_0-rank_return_5\nrank_return_5-rank_return_10\nbeta_csi300_30_0/10\nbeta_csi300_60_0/10\nswing_volatility_5_0/swing_volatility_30_0-1\nswing_volatility_30_0/swing_volatility_60_0-1\nta_atr_14_0/ta_atr_28_0-1\nta_sma_5_0/ta_sma_20_0-1\nta_sma_10_0/ta_sma_20_0-1\nta_sma_20_0/ta_sma_30_0-1\nta_sma_30_0/ta_sma_60_0-1\nta_rsi_14_0/100\nta_rsi_28_0/100\nta_cci_14_0/500\nta_cci_28_0/500\nbeta_industry_30_0/10\nbeta_industry_60_0/10\nta_sma(amount_0, 10)/ta_sma(amount_0, 20)-1\nta_sma(amount_0, 20)/ta_sma(amount_0, 30)-1\nta_sma(amount_0, 30)/ta_sma(amount_0, 60)-1\nta_sma(amount_0, 50)/ta_sma(amount_0, 100)-1\nta_sma(turn_0, 10)/ta_sma(turn_0, 20)-1\nta_sma(turn_0, 20)/ta_sma(turn_0, 30)-1\nta_sma(turn_0, 30)/ta_sma(turn_0, 60)-1\nta_sma(turn_0, 50)/ta_sma(turn_0, 100)-1\nhigh_0/low_0-1\nclose_0/open_0-1\nshift(close_0,1)/close_0-1\nshift(close_0,2)/close_0-1\nshift(close_0,3)/close_0-1\nshift(close_0,4)/close_0-1\nshift(close_0,5)/close_0-1\nshift(close_0,10)/close_0-1\nshift(close_0,20)/close_0-1\nta_sma(high_0-low_0, 5)/ta_sma(high_0-low_0, 20)-1\nta_sma(high_0-low_0, 10)/ta_sma(high_0-low_0, 20)-1\nta_sma(high_0-low_0, 20)/ta_sma(high_0-low_0, 30)-1\nta_sma(high_0-low_0, 30)/ta_sma(high_0-low_0, 60)-1\nta_sma(high_0-low_0, 50)/ta_sma(high_0-low_0, 100)-1\nrank_avg_amount_5\nrank_avg_turn_5\nrank_volatility_5_0\nrank_swing_volatility_5_0\nrank_avg_mf_net_amount_5\nrank_beta_industry_5_0\nrank_return_5\nrank_return_2\nstd(close_0,5)/std(close_0,20)-1\nstd(close_0,10)/std(close_0,20)-1\nstd(close_0,20)/std(close_0,30)-1\nstd(close_0,30)/std(close_0,60)-1\nstd(close_0,50)/std(close_0,100)-1\n","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2018-12-31","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":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":"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":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":"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":"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":0.025,"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":"59","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":"-168","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"256","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","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":"-168"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-168","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-196","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"128","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","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":"-196"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-196","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-224","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.9","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":"-224"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-224","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-231","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.9","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":"-231"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-231","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":22,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-238","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"linear","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":"-238"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-238","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":23,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-682","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-682"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-682"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-682","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"Adam","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":"mse","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"10240","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"2","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":"-1535","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v1","ModuleParameters":[{"Name":"window_size","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1535"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1535"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1535","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":10,"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":"-1547","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v1","ModuleParameters":[{"Name":"window_size","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1547"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1547"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1547","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":12,"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":"-613","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\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='2011-09-23', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2019-01-11'), # 数据结束日期\n train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=90, #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=548, # 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=548, # 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.read_df() 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:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = start_date\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 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":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"bq_graph_port","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-613"}],"OutputPortsInternal":[{"Name":"result","NodeId":"-613","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-11547","ModuleId":"BigQuantSpace.rolling_conf.rolling_conf-v1","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-12-31","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"rolling_update_days","Value":365,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"rolling_update_days_for_live","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"rolling_min_days","Value":730,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"rolling_max_days","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"rolling_count_for_live","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[],"OutputPortsInternal":[{"Name":"data","NodeId":"-11547","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":14,"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='207,62,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='764,4,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='250,375,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><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='823,1085,200,200'/><NodePosition Node='-160' Position='-283,55,200,200'/><NodePosition Node='-168' Position='-282,147,200,200'/><NodePosition Node='-196' Position='-278,321,200,200'/><NodePosition Node='-224' Position='-278,229,200,200'/><NodePosition Node='-231' Position='-276,405,200,200'/><NodePosition Node='-238' Position='-275,494,200,200'/><NodePosition Node='-682' Position='-225,659,200,200'/><NodePosition Node='-1098' Position='-50,765,200,200'/><NodePosition Node='-1535' Position='388,510,200,200'/><NodePosition Node='-1540' Position='249,844,200,200'/><NodePosition Node='-1547' Position='805,515,200,200'/><NodePosition Node='-2431' Position='533,940,200,200'/><NodePosition Node='-613' Position='-177,926,200,200'/><NodePosition Node='-11547' Position='1031,762,200,200'/></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 [1]:
    # 本代码由可视化策略环境自动生成 2019年1月20日 10:56
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def 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: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
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2014-01-01',
        'm1.end_date': '2017-12-31',
        '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)
    where(label>0.5, NaN, label)
    where(label<-0.5, NaN, label)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': False,
    
        'm3': 'M.input_features.v1',
        'm3.features': """# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    return_5-1
    return_10-1
    return_20-1
    avg_amount_0/avg_amount_5-1
    avg_amount_5/avg_amount_20-1
    rank_avg_amount_0-rank_avg_amount_5
    rank_avg_amount_5-rank_avg_amount_10
    rank_return_0-rank_return_5
    rank_return_5-rank_return_10
    beta_csi300_30_0/10
    beta_csi300_60_0/10
    swing_volatility_5_0/swing_volatility_30_0-1
    swing_volatility_30_0/swing_volatility_60_0-1
    ta_atr_14_0/ta_atr_28_0-1
    ta_sma_5_0/ta_sma_20_0-1
    ta_sma_10_0/ta_sma_20_0-1
    ta_sma_20_0/ta_sma_30_0-1
    ta_sma_30_0/ta_sma_60_0-1
    ta_rsi_14_0/100
    ta_rsi_28_0/100
    ta_cci_14_0/500
    ta_cci_28_0/500
    beta_industry_30_0/10
    beta_industry_60_0/10
    ta_sma(amount_0, 10)/ta_sma(amount_0, 20)-1
    ta_sma(amount_0, 20)/ta_sma(amount_0, 30)-1
    ta_sma(amount_0, 30)/ta_sma(amount_0, 60)-1
    ta_sma(amount_0, 50)/ta_sma(amount_0, 100)-1
    ta_sma(turn_0, 10)/ta_sma(turn_0, 20)-1
    ta_sma(turn_0, 20)/ta_sma(turn_0, 30)-1
    ta_sma(turn_0, 30)/ta_sma(turn_0, 60)-1
    ta_sma(turn_0, 50)/ta_sma(turn_0, 100)-1
    high_0/low_0-1
    close_0/open_0-1
    shift(close_0,1)/close_0-1
    shift(close_0,2)/close_0-1
    shift(close_0,3)/close_0-1
    shift(close_0,4)/close_0-1
    shift(close_0,5)/close_0-1
    shift(close_0,10)/close_0-1
    shift(close_0,20)/close_0-1
    ta_sma(high_0-low_0, 5)/ta_sma(high_0-low_0, 20)-1
    ta_sma(high_0-low_0, 10)/ta_sma(high_0-low_0, 20)-1
    ta_sma(high_0-low_0, 20)/ta_sma(high_0-low_0, 30)-1
    ta_sma(high_0-low_0, 30)/ta_sma(high_0-low_0, 60)-1
    ta_sma(high_0-low_0, 50)/ta_sma(high_0-low_0, 100)-1
    rank_avg_amount_5
    rank_avg_turn_5
    rank_volatility_5_0
    rank_swing_volatility_5_0
    rank_avg_mf_net_amount_5
    rank_beta_industry_5_0
    rank_return_5
    rank_return_2
    std(close_0,5)/std(close_0,20)-1
    std(close_0,10)/std(close_0,20)-1
    std(close_0,20)/std(close_0,30)-1
    std(close_0,30)/std(close_0,60)-1
    std(close_0,50)/std(close_0,100)-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': 0,
    
        '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,
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m2.data'),
        'm7.data2': T.Graph.OutputPort('m16.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm10': 'M.dl_convert_to_bin.v1',
        'm10.input_data': T.Graph.OutputPort('m7.data'),
        'm10.features': T.Graph.OutputPort('m3.data'),
        'm10.window_size': 1,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2018-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2018-12-31'),
        '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': 0,
    
        '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,
    
        'm12': 'M.dl_convert_to_bin.v1',
        'm12.input_data': T.Graph.OutputPort('m18.data'),
        'm12.features': T.Graph.OutputPort('m3.data'),
        'm12.window_size': 1,
    
        'm6': 'M.dl_layer_input.v1',
        'm6.shape': '59',
        'm6.batch_shape': '',
        'm6.dtype': 'float32',
        'm6.sparse': False,
        'm6.name': '',
    
        'm8': 'M.dl_layer_dense.v1',
        'm8.inputs': T.Graph.OutputPort('m6.data'),
        'm8.units': 256,
        'm8.activation': 'relu',
        'm8.use_bias': True,
        'm8.kernel_initializer': 'glorot_uniform',
        'm8.bias_initializer': 'Zeros',
        'm8.kernel_regularizer': 'None',
        'm8.kernel_regularizer_l1': 0,
        'm8.kernel_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.bias_constraint': 'None',
        'm8.name': '',
    
        'm21': 'M.dl_layer_dropout.v1',
        'm21.inputs': T.Graph.OutputPort('m8.data'),
        'm21.rate': 0.9,
        'm21.noise_shape': '',
        'm21.name': '',
    
        'm20': 'M.dl_layer_dense.v1',
        'm20.inputs': T.Graph.OutputPort('m21.data'),
        'm20.units': 128,
        'm20.activation': 'relu',
        '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': '',
    
        'm22': 'M.dl_layer_dropout.v1',
        'm22.inputs': T.Graph.OutputPort('m20.data'),
        'm22.rate': 0.9,
        'm22.noise_shape': '',
        'm22.name': '',
    
        'm23': 'M.dl_layer_dense.v1',
        'm23.inputs': T.Graph.OutputPort('m22.data'),
        'm23.units': 1,
        'm23.activation': 'linear',
        'm23.use_bias': True,
        'm23.kernel_initializer': 'glorot_uniform',
        'm23.bias_initializer': 'Zeros',
        'm23.kernel_regularizer': 'None',
        'm23.kernel_regularizer_l1': 0,
        'm23.kernel_regularizer_l2': 0,
        'm23.bias_regularizer': 'None',
        'm23.bias_regularizer_l1': 0,
        'm23.bias_regularizer_l2': 0,
        'm23.activity_regularizer': 'None',
        'm23.activity_regularizer_l1': 0,
        'm23.activity_regularizer_l2': 0,
        'm23.kernel_constraint': 'None',
        'm23.bias_constraint': 'None',
        'm23.name': '',
    
        'm4': 'M.dl_model_init.v1',
        'm4.inputs': T.Graph.OutputPort('m6.data'),
        'm4.outputs': T.Graph.OutputPort('m23.data'),
    
        'm5': 'M.dl_model_train.v1',
        'm5.input_model': T.Graph.OutputPort('m4.data'),
        'm5.training_data': T.Graph.OutputPort('m10.data'),
        'm5.optimizer': 'Adam',
        'm5.loss': 'mean_squared_error',
        'm5.metrics': 'mse',
        'm5.batch_size': 10240,
        'm5.epochs': 2,
        '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('m12.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.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.volume_limit': 0.025,
        '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',
    
        'm14': 'M.rolling_conf.v1',
        'm14.start_date': '2010-01-01',
        'm14.end_date': '2015-12-31',
        'm14.rolling_update_days': 365,
        'm14.rolling_min_days': 730,
        'm14.rolling_max_days': 0,
        'm14.rolling_count_for_live': 1,
    })
    
    # g.run({})
    
    
    def m13_run_bigquant_run(
        bq_graph,
        inputs,
        trading_days_market='CN', # 使用那个市场的交易日历
        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='2011-09-23', # 数据开始日期
        end_date=T.live_run_param('trading_date', '2019-01-11'), # 数据结束日期
        train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次
        train_update_days_for_live=90, #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=548, # 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=548, # 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.read_df() 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:
                    train_start_date = max(train_end_date - train_data_max_days, 0)
                else:
                    train_start_date = start_date
                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 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}
    
    
    m13 = M.hyper_rolling_train.v1(
        run=m13_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    Using TensorFlow backend.
    
    [2019-01-20 10:44:30.880685] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-20 10:44:30.894811] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:30.895826] INFO: bigquant: instruments.v2 运行完成[0.015191s].
    [2019-01-20 10:44:30.898147] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-20 10:44:30.914904] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:30.915921] INFO: bigquant: instruments.v2 运行完成[0.017774s].
    [2019-01-20 10:44:30.923401] INFO: 滚动运行配置: 生成了 5 次滚动,第一次 {'start_date': '2010-01-01', 'end_date': '2011-12-31'},最后一次 {'start_date': '2010-01-01', 'end_date': '2015-12-30'}
    [2019-01-20 10:44:30.936298] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-20 10:44:30.942397] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:30.943438] INFO: bigquant: input_features.v1 运行完成[0.007144s].
    [2019-01-20 10:44:30.996645] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-20 10:44:31.003145] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.004319] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.007688s].
    [2019-01-20 10:44:31.049684] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-20 10:44:31.098721] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.100039] INFO: bigquant: general_feature_extractor.v7 运行完成[0.050372s].
    [2019-01-20 10:44:31.105899] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-20 10:44:31.111104] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.111945] INFO: bigquant: general_feature_extractor.v7 运行完成[0.00606s].
    [2019-01-20 10:44:31.152381] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-20 10:44:31.158716] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.159641] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.007264s].
    [2019-01-20 10:44:31.161984] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-20 10:44:31.217691] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.219015] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.057017s].
    [2019-01-20 10:44:31.249680] INFO: bigquant: join.v3 开始运行..
    [2019-01-20 10:44:31.270451] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.271697] INFO: bigquant: join.v3 运行完成[0.022024s].
    [2019-01-20 10:44:31.278391] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
    [2019-01-20 10:44:31.283234] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.284208] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.0058s].
    [2019-01-20 10:44:31.319378] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
    [2019-01-20 10:44:31.325115] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.325968] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.006621s].
    [2019-01-20 10:44:31.371284] INFO: bigquant: cached.v3 开始运行..
    [2019-01-20 10:44:31.378197] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.379383] INFO: bigquant: cached.v3 运行完成[0.008102s].
    [2019-01-20 10:44:31.389119] INFO: bigquant: dl_model_train.v1 开始运行..
    [2019-01-20 10:44:31.397482] INFO: bigquant: 命中缓存
    [2019-01-20 10:44:31.399061] INFO: bigquant: dl_model_train.v1 运行完成[0.009945s].
    [2019-01-20 10:44:31.403554] INFO: bigquant: dl_model_predict.v1 开始运行..
    [2019-01-20 10:44:31.409732] INFO: bigquant: 命中缓存
    DataSource(7fce4e301b9611e9acd80a580a810233, v3)
    [2019-01-20 10:44:31.411186] INFO: bigquant: dl_model_predict.v1 运行完成[0.007615s].
    [2019-01-20 10:44:31.415674] INFO: bigquant: cached.v3 开始运行..
    
    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    <ipython-input-1-e89421a44510> in <module>()
        462     run=m13_run_bigquant_run,
        463     run_now=True,
    --> 464     bq_graph=g
        465 )
    
    <ipython-input-1-e89421a44510> in m13_run_bigquant_run(bq_graph, inputs, trading_days_market, train_instruments_mid, test_instruments_mid, predict_mid, trade_mid, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
        444         parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
        445         # print('------ rolling_train:', parameters)
    --> 446         results.append(g.run(parameters))
        447 
        448     # 合并预测结果并回测
    
    <ipython-input-1-e89421a44510> in m24_run_bigquant_run(input_1, input_2, input_3)
          8     pred_label = input_1.read_pickle()
          9     df = input_2.read_df()
    ---> 10     df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
         11     df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
         12     return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    TypeError: list indices must be integers or slices, not tuple

    (iQuant) #2

    可以试一下,因子太多了这个例子减少了一些,能跑通。可以自己调因子数量。问题出在预测值在第一期为空,在预测之后读取数据m24那里加了个容错。另外就是你的策略里有两个滚动模块,删掉了早期版的滚动模块,只保留了m13

    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":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","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":"-1535:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-1547:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-1535: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":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-1547:input_data","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-168:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-682:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-224:inputs","SourceOutputPortId":"-168:data"},{"DestinationInputPortId":"-231:inputs","SourceOutputPortId":"-196:data"},{"DestinationInputPortId":"-196:inputs","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-238:inputs","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-682:outputs","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-682:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-1535:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-1547:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2014-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2017-12-31","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)\nwhere(label>0.5, NaN, label)\nwhere(label<-0.5, 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":"# #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_5-1\nreturn_10-1\nreturn_20-1","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":"2018-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2018-12-31","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":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":"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":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":"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":"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":0.025,"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":"3","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":"-168","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"256","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","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":"-168"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-168","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-196","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"128","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","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":"-196"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-196","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-224","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.9","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":"-224"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-224","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-231","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.9","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":"-231"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-231","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":22,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-238","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"linear","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":"-238"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-238","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":23,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-682","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-682"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-682"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-682","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"Adam","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":"mse","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"10240","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"2","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":"-1535","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v1","ModuleParameters":[{"Name":"window_size","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1535"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1535"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1535","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":10,"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":"-1547","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v1","ModuleParameters":[{"Name":"window_size","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1547"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1547"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1547","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":12,"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 if pred_label==[]:\n df = pd.DataFrame({'pred_label':[], 'instrument':df.instrument, 'date':df.date})\n else:\n df = pd.DataFrame({'pred_label':list(pred_label[:,0]), 'instrument':list(df.instrument), 'date':list(df.date)},index=range(len(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":"-613","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\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='2018-01-23', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2019-01-11'), # 数据结束日期\n train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=100, #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=100, # 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=100, # 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.read_df() 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:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = start_date\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 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":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"bq_graph_port","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-613"}],"OutputPortsInternal":[{"Name":"result","NodeId":"-613","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":13,"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='207,62,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='764,1.5240936279296875,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='250,375,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><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='823,1085,200,200'/><NodePosition Node='-160' Position='-283,55,200,200'/><NodePosition Node='-168' Position='-282,147,200,200'/><NodePosition Node='-196' Position='-278,321,200,200'/><NodePosition Node='-224' Position='-278,229,200,200'/><NodePosition Node='-231' Position='-276,405,200,200'/><NodePosition Node='-238' Position='-275,494,200,200'/><NodePosition Node='-682' Position='-225,659,200,200'/><NodePosition Node='-1098' Position='-50,765,200,200'/><NodePosition Node='-1535' Position='388,510,200,200'/><NodePosition Node='-1540' Position='249,844,200,200'/><NodePosition Node='-1547' Position='805,515,200,200'/><NodePosition Node='-2431' Position='533,940,200,200'/><NodePosition Node='-613' Position='-177,926,200,200'/></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 [1]:
      # 本代码由可视化策略环境自动生成 2019年1月22日 19:07
      # 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()
          if pred_label==[]:
              df = pd.DataFrame({'pred_label':[], 'instrument':df.instrument, 'date':df.date})
          else:
              df = pd.DataFrame({'pred_label':list(pred_label[:,0]), 'instrument':list(df.instrument), 'date':list(df.date)},index=range(len(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: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
      
      
      g = T.Graph({
      
          'm1': 'M.instruments.v2',
          'm1.start_date': '2014-01-01',
          'm1.end_date': '2017-12-31',
          '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)
      where(label>0.5, NaN, label)
      where(label<-0.5, NaN, label)
      """,
          'm2.start_date': '',
          'm2.end_date': '',
          'm2.benchmark': '000300.SHA',
          'm2.drop_na_label': True,
          'm2.cast_label_int': False,
      
          'm3': 'M.input_features.v1',
          'm3.features': """# #号开始的表示注释
      # 多个特征,每行一个,可以包含基础特征和衍生特征
      return_5-1
      return_10-1
      return_20-1""",
      
          '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': 0,
      
          '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,
      
          'm7': 'M.join.v3',
          'm7.data1': T.Graph.OutputPort('m2.data'),
          'm7.data2': T.Graph.OutputPort('m16.data'),
          'm7.on': 'date,instrument',
          'm7.how': 'inner',
          'm7.sort': False,
      
          'm10': 'M.dl_convert_to_bin.v1',
          'm10.input_data': T.Graph.OutputPort('m7.data'),
          'm10.features': T.Graph.OutputPort('m3.data'),
          'm10.window_size': 1,
      
          'm9': 'M.instruments.v2',
          'm9.start_date': T.live_run_param('trading_date', '2018-01-01'),
          'm9.end_date': T.live_run_param('trading_date', '2018-12-31'),
          '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': 0,
      
          '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,
      
          'm12': 'M.dl_convert_to_bin.v1',
          'm12.input_data': T.Graph.OutputPort('m18.data'),
          'm12.features': T.Graph.OutputPort('m3.data'),
          'm12.window_size': 1,
      
          'm6': 'M.dl_layer_input.v1',
          'm6.shape': '3',
          'm6.batch_shape': '',
          'm6.dtype': 'float32',
          'm6.sparse': False,
          'm6.name': '',
      
          'm8': 'M.dl_layer_dense.v1',
          'm8.inputs': T.Graph.OutputPort('m6.data'),
          'm8.units': 256,
          'm8.activation': 'relu',
          'm8.use_bias': True,
          'm8.kernel_initializer': 'glorot_uniform',
          'm8.bias_initializer': 'Zeros',
          'm8.kernel_regularizer': 'None',
          'm8.kernel_regularizer_l1': 0,
          'm8.kernel_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.bias_constraint': 'None',
          'm8.name': '',
      
          'm21': 'M.dl_layer_dropout.v1',
          'm21.inputs': T.Graph.OutputPort('m8.data'),
          'm21.rate': 0.9,
          'm21.noise_shape': '',
          'm21.name': '',
      
          'm20': 'M.dl_layer_dense.v1',
          'm20.inputs': T.Graph.OutputPort('m21.data'),
          'm20.units': 128,
          'm20.activation': 'relu',
          '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': '',
      
          'm22': 'M.dl_layer_dropout.v1',
          'm22.inputs': T.Graph.OutputPort('m20.data'),
          'm22.rate': 0.9,
          'm22.noise_shape': '',
          'm22.name': '',
      
          'm23': 'M.dl_layer_dense.v1',
          'm23.inputs': T.Graph.OutputPort('m22.data'),
          'm23.units': 1,
          'm23.activation': 'linear',
          'm23.use_bias': True,
          'm23.kernel_initializer': 'glorot_uniform',
          'm23.bias_initializer': 'Zeros',
          'm23.kernel_regularizer': 'None',
          'm23.kernel_regularizer_l1': 0,
          'm23.kernel_regularizer_l2': 0,
          'm23.bias_regularizer': 'None',
          'm23.bias_regularizer_l1': 0,
          'm23.bias_regularizer_l2': 0,
          'm23.activity_regularizer': 'None',
          'm23.activity_regularizer_l1': 0,
          'm23.activity_regularizer_l2': 0,
          'm23.kernel_constraint': 'None',
          'm23.bias_constraint': 'None',
          'm23.name': '',
      
          'm4': 'M.dl_model_init.v1',
          'm4.inputs': T.Graph.OutputPort('m6.data'),
          'm4.outputs': T.Graph.OutputPort('m23.data'),
      
          'm5': 'M.dl_model_train.v1',
          'm5.input_model': T.Graph.OutputPort('m4.data'),
          'm5.training_data': T.Graph.OutputPort('m10.data'),
          'm5.optimizer': 'Adam',
          'm5.loss': 'mean_squared_error',
          'm5.metrics': 'mse',
          'm5.batch_size': 10240,
          'm5.epochs': 2,
          '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('m12.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.handle_data': m19_handle_data_bigquant_run,
          'm19.prepare': m19_prepare_bigquant_run,
          'm19.initialize': m19_initialize_bigquant_run,
          'm19.volume_limit': 0.025,
          '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 m13_run_bigquant_run(
          bq_graph,
          inputs,
          trading_days_market='CN', # 使用那个市场的交易日历
          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='2018-01-23', # 数据开始日期
          end_date=T.live_run_param('trading_date', '2019-01-11'), # 数据结束日期
          train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次
          train_update_days_for_live=100, #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=100, # 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=100, # 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.read_df() 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:
                      train_start_date = max(train_end_date - train_data_max_days, 0)
                  else:
                      train_start_date = start_date
                  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 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}
      
      
      m13 = M.hyper_rolling_train.v1(
          run=m13_run_bigquant_run,
          run_now=True,
          bq_graph=g
      )
      
      [2019-01-22 19:06:57.057794] INFO: bigquant: input_features.v1 开始运行..
      [2019-01-22 19:06:57.071771] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:57.072936] INFO: bigquant: input_features.v1 运行完成[0.015172s].
      [2019-01-22 19:06:57.076423] INFO: bigquant: instruments.v2 开始运行..
      [2019-01-22 19:06:57.080495] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:57.081698] INFO: bigquant: instruments.v2 运行完成[0.005278s].
      [2019-01-22 19:06:57.083608] INFO: bigquant: instruments.v2 开始运行..
      [2019-01-22 19:06:57.087743] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:57.088657] INFO: bigquant: instruments.v2 运行完成[0.005039s].
      
      Using TensorFlow backend.
      
      [2019-01-22 19:06:59.460628] INFO: bigquant: general_feature_extractor.v7 开始运行..
      [2019-01-22 19:06:59.466020] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.467433] INFO: bigquant: general_feature_extractor.v7 运行完成[0.006838s].
      [2019-01-22 19:06:59.474123] INFO: bigquant: general_feature_extractor.v7 开始运行..
      [2019-01-22 19:06:59.479875] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.480823] INFO: bigquant: general_feature_extractor.v7 运行完成[0.0067s].
      [2019-01-22 19:06:59.486929] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
      [2019-01-22 19:06:59.492049] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.492982] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.00607s].
      [2019-01-22 19:06:59.585673] INFO: bigquant: derived_feature_extractor.v3 开始运行..
      [2019-01-22 19:06:59.591796] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.592942] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.007302s].
      [2019-01-22 19:06:59.595751] INFO: bigquant: derived_feature_extractor.v3 开始运行..
      [2019-01-22 19:06:59.600069] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.600923] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.005165s].
      [2019-01-22 19:06:59.729844] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
      [2019-01-22 19:06:59.735073] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.736209] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.006323s].
      [2019-01-22 19:06:59.741800] INFO: bigquant: join.v3 开始运行..
      [2019-01-22 19:06:59.747084] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.747980] INFO: bigquant: join.v3 运行完成[0.006184s].
      [2019-01-22 19:06:59.785514] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
      [2019-01-22 19:06:59.790257] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.791208] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.005699s].
      [2019-01-22 19:06:59.894029] INFO: bigquant: cached.v3 开始运行..
      [2019-01-22 19:06:59.898887] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.899915] INFO: bigquant: cached.v3 运行完成[0.005905s].
      [2019-01-22 19:06:59.946349] INFO: bigquant: dl_model_train.v1 开始运行..
      [2019-01-22 19:06:59.951839] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.953416] INFO: bigquant: dl_model_train.v1 运行完成[0.007072s].
      [2019-01-22 19:06:59.957092] INFO: bigquant: dl_model_predict.v1 开始运行..
      [2019-01-22 19:06:59.961799] INFO: bigquant: 命中缓存
      DataSource(8778ebbc1e3511e9a4680a580a8102b4, v3)
      [2019-01-22 19:06:59.962767] INFO: bigquant: dl_model_predict.v1 运行完成[0.005675s].
      [2019-01-22 19:06:59.966939] INFO: bigquant: cached.v3 开始运行..
      [2019-01-22 19:06:59.971289] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.972009] INFO: bigquant: cached.v3 运行完成[0.005073s].
      [2019-01-22 19:06:59.976019] INFO: bigquant: input_features.v1 开始运行..
      [2019-01-22 19:06:59.980402] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.981139] INFO: bigquant: input_features.v1 运行完成[0.00512s].
      [2019-01-22 19:06:59.983707] INFO: bigquant: instruments.v2 开始运行..
      [2019-01-22 19:06:59.987577] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.988314] INFO: bigquant: instruments.v2 运行完成[0.004596s].
      [2019-01-22 19:06:59.990395] INFO: bigquant: instruments.v2 开始运行..
      [2019-01-22 19:06:59.994073] INFO: bigquant: 命中缓存
      [2019-01-22 19:06:59.994794] INFO: bigquant: instruments.v2 运行完成[0.004387s].
      [2019-01-22 19:07:00.003985] INFO: bigquant: general_feature_extractor.v7 开始运行..
      [2019-01-22 19:07:00.007961] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.008763] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004788s].
      [2019-01-22 19:07:00.013423] INFO: bigquant: general_feature_extractor.v7 开始运行..
      [2019-01-22 19:07:00.017328] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.018070] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004657s].
      [2019-01-22 19:07:00.020546] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
      [2019-01-22 19:07:00.024399] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.025393] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004858s].
      [2019-01-22 19:07:00.063187] INFO: bigquant: derived_feature_extractor.v3 开始运行..
      [2019-01-22 19:07:00.068509] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.069657] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.006508s].
      [2019-01-22 19:07:00.072506] INFO: bigquant: derived_feature_extractor.v3 开始运行..
      [2019-01-22 19:07:00.076928] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.077769] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.005243s].
      [2019-01-22 19:07:00.114717] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
      [2019-01-22 19:07:00.119609] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.120758] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.006047s].
      [2019-01-22 19:07:00.123638] INFO: bigquant: join.v3 开始运行..
      [2019-01-22 19:07:00.128736] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.129841] INFO: bigquant: join.v3 运行完成[0.006201s].
      [2019-01-22 19:07:00.179343] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
      [2019-01-22 19:07:00.185890] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.187142] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.007827s].
      [2019-01-22 19:07:00.275242] INFO: bigquant: cached.v3 开始运行..
      [2019-01-22 19:07:00.280102] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.281052] INFO: bigquant: cached.v3 运行完成[0.005918s].
      [2019-01-22 19:07:00.283049] INFO: bigquant: dl_model_train.v1 开始运行..
      [2019-01-22 19:07:00.286904] INFO: bigquant: 命中缓存
      [2019-01-22 19:07:00.287875] INFO: bigquant: dl_model_train.v1 运行完成[0.004823s].
      [2019-01-22 19:07:00.289705] INFO: bigquant: dl_model_predict.v1 开始运行..
      [2019-01-22 19:07:00.293526] INFO: bigquant: 命中缓存
      DataSource(b13ea5fe1e3511e992e00a580a8102b4, v3)
      [2019-01-22 19:07:00.294282] INFO: bigquant: dl_model_predict.v1 运行完成[0.004572s].
      [2019-01-22 19:07:00.297791] INFO: bigquant: cached.v3 开始运行..
      [2019-01-22 19:07:01.788050] INFO: bigquant: cached.v3 运行完成[1.490191s].
      [2019-01-22 19:07:01.793099] INFO: bigquant: cached.v3 开始运行..
      [2019-01-22 19:07:02.355600] INFO: bigquant: cached.v3 运行完成[0.562488s].
      [2019-01-22 19:07:02.396921] INFO: bigquant: backtest.v8 开始运行..
      [2019-01-22 19:07:02.399508] INFO: bigquant: biglearning backtest:V8.1.6
      [2019-01-22 19:07:02.400714] INFO: bigquant: product_type:stock by specified
      [2019-01-22 19:07:09.488083] INFO: bigquant: 读取股票行情完成:1317482
      [2019-01-22 19:07:26.205250] INFO: algo: TradingAlgorithm V1.4.2
      [2019-01-22 19:07:37.808873] INFO: algo: trading transform...
      [2019-01-22 19:07:42.594152] INFO: Performance: Simulated 136 trading days out of 136.
      [2019-01-22 19:07:42.595908] INFO: Performance: first open: 2018-06-26 09:30:00+00:00
      [2019-01-22 19:07:42.597187] INFO: Performance: last close: 2019-01-11 15:00:00+00:00
      
      • 收益率-8.17%
      • 年化收益率-14.61%
      • 基准收益率-13.08%
      • 阿尔法-0.07
      • 贝塔0.34
      • 夏普比率-0.64
      • 胜率0.49
      • 盈亏比1.01
      • 收益波动率24.68%
      • 信息比率0.02
      • 最大回撤23.28%
      [2019-01-22 19:07:44.242144] INFO: bigquant: backtest.v8 运行完成[41.845239s].
      

      (tkyz) #3

      我想回测14年到现在的,为什么14年的时段没有预测值呢?


      (tkyz) #4

      改了一下时间运行一会就停止啦


      (iQuant) #5

      收到您的问题,已提交给策略工程师,会尽快为您解答。