LSTM滚动训练错误

策略分享
标签: #<Tag:0x00007f044d3d86c0>

(a1750271086) #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":"-8423:input_data","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":"-243:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-8427:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-12340:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-167:input_1","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":"-8427:input_data","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-4136:input_1","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-3880:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-356:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-3880:data"},{"DestinationInputPortId":"-567:inputs","SourceOutputPortId":"-356:data"},{"DestinationInputPortId":"-412:inputs","SourceOutputPortId":"-567:data"},{"DestinationInputPortId":"-574:inputs","SourceOutputPortId":"-412:data"},{"DestinationInputPortId":"-440:inputs","SourceOutputPortId":"-574:data"},{"DestinationInputPortId":"-3880:outputs","SourceOutputPortId":"-440:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-8419:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"-8423:data"},{"DestinationInputPortId":"-8419:input_data","SourceOutputPortId":"-8427:data"},{"DestinationInputPortId":"-304:input_data","SourceOutputPortId":"-21274:data"},{"DestinationInputPortId":"-21274:input_data","SourceOutputPortId":"-182:data"},{"DestinationInputPortId":"-182:input_data","SourceOutputPortId":"-167:data_1"},{"DestinationInputPortId":"-9214:input_data","SourceOutputPortId":"-21278:data"},{"DestinationInputPortId":"-21278:input_data","SourceOutputPortId":"-556:data_1"},{"DestinationInputPortId":"-556:input_1","SourceOutputPortId":"-4127:data"},{"DestinationInputPortId":"-4127:input_data","SourceOutputPortId":"-4136:data_1"},{"DestinationInputPortId":"-12340:input_data","SourceOutputPortId":"-9214:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-9214:data"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-12340:data"},{"DestinationInputPortId":"-243:input_data","SourceOutputPortId":"-304:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_2"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":" ","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\nshift(close, -5) / shift(open, -1)-1\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na_label","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"cast_label_int","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"close_0/mean(close_0,5)\nclose_0/mean(close_0,10)\nclose_0/mean(close_0,20)\nclose_0/open_0\nopen_0/mean(close_0,5)\nopen_0/mean(close_0,10)\nopen_0/mean(close_0,20)","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,"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,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-09-05","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,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"-106","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-106"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-106"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-106","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"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,"Comment":"","CommentCollapsed":true},{"Id":"-122","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-122"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-122"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-122","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"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,"Comment":"","CommentCollapsed":true},{"Id":"-141","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\nfrom zipline.finance.slippage import SlippageModel\nclass FixedPriceSlippage(SlippageModel):\n # 指定初始化函数\n def __init__(self, spreads, price_field_buy, price_field_sell):\n self.spreads = spreads\n self._price_field_buy = price_field_buy\n self._price_field_sell = price_field_sell \n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price_base = data.current(order.asset, price_field)\n # 买单的下单价格向上偏移 spreads百分比 , 卖单的下单价格向下偏移 spreads百分比\n price = price_base * (1.0 + self.spreads) if order.amount > 0 else price_base * (1.0 - self.spreads)\n else:\n price = order.limit\n # 返回希望成交的价格和数量\n return (price, order.amount) \n # 设置滑点模型\nfix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close',spreads=0.015)\ncontext.set_slippage(us_equities=fix_slippage)\n\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 = 0.05\n context.options['hold_days'] = 1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-141"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-141","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"Comment":"","CommentCollapsed":true},{"Id":"-160","ModuleId":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","ModuleParameters":[{"Name":"shape","Value":"30,7","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,"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":"binary_crossentropy","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"acc","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"2048","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"20","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,"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,"Comment":"","CommentCollapsed":true},{"Id":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"Comment":"","CommentCollapsed":true},{"Id":"-3880","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3880"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-3880"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3880","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":34,"Comment":"","CommentCollapsed":true},{"Id":"-356","ModuleId":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","ModuleParameters":[{"Name":"units","Value":"32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_activation","Value":"hard_sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_initializer","Value":"Orthogonal","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"unit_forget_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"return_sequences","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"implementation","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-356"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-356","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":10,"Comment":"","CommentCollapsed":true},{"Id":"-567","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"noise_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"seed","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-567"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-567","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":12,"Comment":"","CommentCollapsed":true},{"Id":"-412","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-412"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-412","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"Comment":"","CommentCollapsed":true},{"Id":"-574","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.5","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":"-574"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-574","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":21,"Comment":"","CommentCollapsed":true},{"Id":"-440","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","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":"-440"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-440","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":22,"Comment":"","CommentCollapsed":true},{"Id":"-8419","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-8419"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8419"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-8419","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"Comment":"","CommentCollapsed":true},{"Id":"-8423","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-8423"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8423"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-8423","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":23,"Comment":"","CommentCollapsed":true},{"Id":"-8427","ModuleId":"BigQuantSpace.winsorize.winsorize-v6","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"median_deviate","Value":3,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-8427"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8427"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-8427","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"Comment":"","CommentCollapsed":true},{"Id":"-3148","ModuleId":"BigQuantSpace.hyper_rolling_train.hyper_rolling_train-v1","ModuleParameters":[{"Name":"run","Value":"def bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历, TODO\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m24', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2019-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2020-09-05'), # 数据结束日期\n train_update_days=100, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=5, #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=250, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=250, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None and train_data_max_days > 0:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = 0\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': trade}\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"run_now","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bq_graph","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"bq_graph_port","NodeId":"-3148"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-3148"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3148"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3148"}],"OutputPortsInternal":[{"Name":"result","NodeId":"-3148","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":8,"Comment":"","CommentCollapsed":true},{"Id":"-21274","ModuleId":"BigQuantSpace.aa.aa-v5","ModuleParameters":[{"Name":"day_number","Value":"50","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-21274"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-21274","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":14,"Comment":"","CommentCollapsed":true},{"Id":"-182","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Atrue%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"delist_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-182"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-182","OutputType":null},{"Name":"left_data","NodeId":"-182","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":28,"Comment":"","CommentCollapsed":true},{"Id":"-167","ModuleId":"BigQuantSpace.filter_delist_stock.filter_delist_stock-v6","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-167"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-167","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":29,"Comment":"","CommentCollapsed":true},{"Id":"-21278","ModuleId":"BigQuantSpace.aa.aa-v5","ModuleParameters":[{"Name":"day_number","Value":"50","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-21278"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-21278","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":30,"Comment":"","CommentCollapsed":true},{"Id":"-556","ModuleId":"BigQuantSpace.filtet_st_stock_tomo.filtet_st_stock_tomo-v3","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-556"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-556","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":31,"Comment":"","CommentCollapsed":true},{"Id":"-4127","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Atrue%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"delist_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-4127"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-4127","OutputType":null},{"Name":"left_data","NodeId":"-4127","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":32,"Comment":"","CommentCollapsed":true},{"Id":"-4136","ModuleId":"BigQuantSpace.filter_delist_stock.filter_delist_stock-v6","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-4136"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-4136","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":33,"Comment":"","CommentCollapsed":true},{"Id":"-9214","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-9214"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-9214"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-9214","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":42,"Comment":"","CommentCollapsed":true},{"Id":"-12340","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-12340"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-12340"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-12340","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"Comment":"","CommentCollapsed":true},{"Id":"-304","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-304"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-304"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-304","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"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({'predictions':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','predictions'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-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":false,"moduleIdForCode":24,"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='394,21,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='270,191,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='723,17,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='477,429,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1043,20.805461883544922,200,200'/><NodePosition Node='-106' Position='553,121,200,200'/><NodePosition Node='-113' Position='599,199,200,200'/><NodePosition Node='-122' Position='1047,119,200,200'/><NodePosition Node='-129' Position='1048,195,200,200'/><NodePosition Node='-141' Position='827,879,200,200'/><NodePosition Node='-160' Position='-20,34,200,200'/><NodePosition Node='-1098' Position='549.225830078125,786.4032592773438,200,200'/><NodePosition Node='-1540' Position='532.0857543945312,876.4384765625,200,200'/><NodePosition Node='-243' Position='624.01611328125,710.4031982421875,200,200'/><NodePosition Node='-3880' Position='-14,493,200,200'/><NodePosition Node='-356' Position='-19,115,200,200'/><NodePosition Node='-567' Position='-19,192,200,200'/><NodePosition Node='-412' Position='-18,265,200,200'/><NodePosition Node='-574' Position='-17,339,200,200'/><NodePosition Node='-440' Position='-15,414,200,200'/><NodePosition Node='-8419' Position='651,352,200,200'/><NodePosition Node='-8423' Position='315,291,200,200'/><NodePosition Node='-8427' Position='646,269,200,200'/><NodePosition Node='-3148' Position='-6,596,200,200'/><NodePosition Node='-21274' Position='305,654,200,200'/><NodePosition Node='-182' Position='477,568,200,200'/><NodePosition Node='-167' Position='476,500,200,200'/><NodePosition Node='-21278' Position='1005,480,200,200'/><NodePosition Node='-556' Position='1005,410,200,200'/><NodePosition Node='-4127' Position='1050,337,200,200'/><NodePosition Node='-4136' Position='1049,268,200,200'/><NodePosition Node='-9214' Position='1052,549,200,200'/><NodePosition Node='-12340' Position='1100,617.6935424804688,200,200'/><NodePosition Node='-304' Position='623.1613159179688,635.9354858398438,200,200'/><NodePosition Node='-2431' Position='929.0966796875,787.9677124023438,200,200'/></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 [ ]:
    # 本代码由可视化策略环境自动生成 2020年9月5日 19:49
    # 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({'predictions':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','predictions'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
            # 指定初始化函数
        def __init__(self, spreads, price_field_buy, price_field_sell):
                self.spreads = spreads
                self._price_field_buy = price_field_buy
                self._price_field_sell = price_field_sell    
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
                if order.limit is None:
                    price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                    price_base = data.current(order.asset, price_field)
                   # 买单的下单价格向上偏移 spreads百分比 , 卖单的下单价格向下偏移 spreads百分比
                    price = price_base * (1.0 + self.spreads) if order.amount > 0 else price_base * (1.0 - self.spreads)
                else:
                    price = order.limit
                    # 返回希望成交的价格和数量
                return (price, order.amount)    
        # 设置滑点模型
    fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close',spreads=0.015)
    context.set_slippage(us_equities=fix_slippage)
    
    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 = 0.05
        context.options['hold_days'] = 1
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2018-01-01',
        'm1.end_date': '2019-01-01',
        'm1.market': 'CN_STOCK_A',
        'm1.instrument_list': ' ',
        'm1.max_count': 0,
    
        'm2': 'M.advanced_auto_labeler.v2',
        'm2.instruments': T.Graph.OutputPort('m1.data'),
        'm2.label_expr': """# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)-1
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': False,
    
        'm23': 'M.dropnan.v2',
        'm23.input_data': T.Graph.OutputPort('m2.data'),
    
        'm3': 'M.input_features.v1',
        'm3.features': """close_0/mean(close_0,5)
    close_0/mean(close_0,10)
    close_0/mean(close_0,20)
    close_0/open_0
    open_0/mean(close_0,5)
    open_0/mean(close_0,10)
    open_0/mean(close_0,20)""",
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 30,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m3.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': True,
        'm16.remove_extra_columns': False,
    
        'm13': 'M.winsorize.v6',
        'm13.input_data': T.Graph.OutputPort('m16.data'),
        'm13.features': T.Graph.OutputPort('m3.data'),
        'm13.columns_input': '',
        'm13.median_deviate': 3,
    
        'm4': 'M.dropnan.v2',
        'm4.input_data': T.Graph.OutputPort('m13.data'),
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m23.data'),
        'm7.data2': T.Graph.OutputPort('m4.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm29': 'M.filter_delist_stock.v6',
        'm29.input_1': T.Graph.OutputPort('m7.data'),
        'm29.m_cached': False,
    
        'm28': 'M.chinaa_stock_filter.v1',
        'm28.input_data': T.Graph.OutputPort('m29.data_1'),
        'm28.index_constituent_cond': ['上证50', '沪深300'],
        'm28.board_cond': ['上证主板', '深证主板'],
        'm28.industry_cond': ['全部'],
        'm28.st_cond': ['*ST', 'ST', '正常', '暂停上市'],
        'm28.delist_cond': ['全部'],
        'm28.output_left_data': False,
        'm28.m_cached': False,
    
        'm14': 'M.aa.v5',
        'm14.input_data': T.Graph.OutputPort('m28.data'),
        'm14.day_number': 50,
        'm14.m_cached': False,
    
        'm25': 'M.dropnan.v2',
        'm25.input_data': T.Graph.OutputPort('m14.data'),
    
        'm26': 'M.dl_convert_to_bin.v2',
        'm26.input_data': T.Graph.OutputPort('m25.data'),
        'm26.features': T.Graph.OutputPort('m3.data'),
        'm26.window_size': 30,
        'm26.feature_clip': 7,
        'm26.flatten': False,
        'm26.window_along_col': 'instrument',
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2019-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2020-09-05'),
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm17': 'M.general_feature_extractor.v7',
        'm17.instruments': T.Graph.OutputPort('m9.data'),
        'm17.features': T.Graph.OutputPort('m3.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 30,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': True,
        'm18.remove_extra_columns': False,
    
        'm33': 'M.filter_delist_stock.v6',
        'm33.input_1': T.Graph.OutputPort('m18.data'),
        'm33.m_cached': False,
    
        'm32': 'M.chinaa_stock_filter.v1',
        'm32.input_data': T.Graph.OutputPort('m33.data_1'),
        'm32.index_constituent_cond': ['上证50', '沪深300'],
        'm32.board_cond': ['上证主板', '深证主板'],
        'm32.industry_cond': ['全部'],
        'm32.st_cond': ['*ST', 'ST', '正常', '暂停上市'],
        'm32.delist_cond': ['全部'],
        'm32.output_left_data': False,
        'm32.m_cached': False,
    
        'm31': 'M.filtet_st_stock_tomo.v3',
        'm31.input_1': T.Graph.OutputPort('m32.data'),
        'm31.m_cached': False,
    
        'm30': 'M.aa.v5',
        'm30.input_data': T.Graph.OutputPort('m31.data_1'),
        'm30.day_number': 50,
        'm30.m_cached': False,
    
        'm42': 'M.dropnan.v2',
        'm42.input_data': T.Graph.OutputPort('m30.data'),
        'm42.m_cached': False,
    
        'm27': 'M.dl_convert_to_bin.v2',
        'm27.input_data': T.Graph.OutputPort('m42.data'),
        'm27.features': T.Graph.OutputPort('m3.data'),
        'm27.window_size': 30,
        'm27.feature_clip': 7,
        'm27.flatten': False,
        'm27.window_along_col': 'instrument',
    
        'm6': 'M.dl_layer_input.v1',
        'm6.shape': '30,7',
        'm6.batch_shape': '',
        'm6.dtype': 'float32',
        'm6.sparse': False,
        'm6.name': '',
    
        'm10': 'M.dl_layer_lstm.v1',
        'm10.inputs': T.Graph.OutputPort('m6.data'),
        'm10.units': 32,
        'm10.activation': 'tanh',
        'm10.recurrent_activation': 'hard_sigmoid',
        'm10.use_bias': True,
        'm10.kernel_initializer': 'glorot_uniform',
        'm10.recurrent_initializer': 'Orthogonal',
        'm10.bias_initializer': 'Zeros',
        'm10.unit_forget_bias': True,
        'm10.kernel_regularizer': 'L1L2',
        'm10.kernel_regularizer_l1': 0,
        'm10.kernel_regularizer_l2': .01,
        'm10.recurrent_regularizer': 'None',
        'm10.recurrent_regularizer_l1': 0,
        'm10.recurrent_regularizer_l2': 0,
        'm10.bias_regularizer': 'None',
        'm10.bias_regularizer_l1': 0,
        'm10.bias_regularizer_l2': 0,
        'm10.activity_regularizer': 'None',
        'm10.activity_regularizer_l1': 0,
        'm10.activity_regularizer_l2': 0,
        'm10.kernel_constraint': 'None',
        'm10.recurrent_constraint': 'None',
        'm10.bias_constraint': 'None',
        'm10.dropout': 0,
        'm10.recurrent_dropout': 0,
        'm10.return_sequences': False,
        'm10.implementation': '0',
        'm10.name': '',
    
        'm12': 'M.dl_layer_dropout.v1',
        'm12.inputs': T.Graph.OutputPort('m10.data'),
        'm12.rate': 0.5,
        'm12.noise_shape': '',
        'm12.name': '',
    
        'm20': 'M.dl_layer_dense.v1',
        'm20.inputs': T.Graph.OutputPort('m12.data'),
        'm20.units': 30,
        'm20.activation': 'tanh',
        'm20.use_bias': True,
        'm20.kernel_initializer': 'glorot_uniform',
        'm20.bias_initializer': 'Zeros',
        'm20.kernel_regularizer': 'L1L2',
        'm20.kernel_regularizer_l1': 0,
        'm20.kernel_regularizer_l2': .01,
        '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': '',
    
        'm21': 'M.dl_layer_dropout.v1',
        'm21.inputs': T.Graph.OutputPort('m20.data'),
        'm21.rate': 0.5,
        'm21.noise_shape': '',
        'm21.name': '',
    
        'm22': 'M.dl_layer_dense.v1',
        'm22.inputs': T.Graph.OutputPort('m21.data'),
        'm22.units': 1,
        'm22.activation': 'tanh',
        'm22.use_bias': True,
        'm22.kernel_initializer': 'glorot_uniform',
        'm22.bias_initializer': 'Zeros',
        'm22.kernel_regularizer': 'L1L2',
        'm22.kernel_regularizer_l1': 0,
        'm22.kernel_regularizer_l2': .01,
        'm22.bias_regularizer': 'None',
        'm22.bias_regularizer_l1': 0,
        'm22.bias_regularizer_l2': 0,
        'm22.activity_regularizer': 'None',
        'm22.activity_regularizer_l1': 0,
        'm22.activity_regularizer_l2': 0,
        'm22.kernel_constraint': 'None',
        'm22.bias_constraint': 'None',
        'm22.name': '',
    
        'm34': 'M.dl_model_init.v1',
        'm34.inputs': T.Graph.OutputPort('m6.data'),
        'm34.outputs': T.Graph.OutputPort('m22.data'),
    
        'm5': 'M.dl_model_train.v1',
        'm5.input_model': T.Graph.OutputPort('m34.data'),
        'm5.training_data': T.Graph.OutputPort('m26.data'),
        'm5.optimizer': 'Adam',
        'm5.loss': 'binary_crossentropy',
        'm5.metrics': 'acc',
        'm5.batch_size': 2048,
        'm5.epochs': 20,
        'm5.n_gpus': 0,
        'm5.verbose': '2:每个epoch输出一行记录',
    
        'm11': 'M.dl_model_predict.v1',
        'm11.trained_model': T.Graph.OutputPort('m5.data'),
        'm11.input_data': T.Graph.OutputPort('m27.data'),
        'm11.batch_size': 1024,
        'm11.n_gpus': 0,
        'm11.verbose': '2:每个epoch输出一行记录',
    
        'm24': 'M.cached.v3',
        'm24.input_1': T.Graph.OutputPort('m11.data'),
        'm24.input_2': T.Graph.OutputPort('m42.data'),
        'm24.run': m24_run_bigquant_run,
        'm24.post_run': m24_post_run_bigquant_run,
        'm24.input_ports': '',
        'm24.params': '{}',
        'm24.output_ports': '',
        'm24.m_cached': False,
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m24.data_2'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.volume_limit': 0,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 1000000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '后复权',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000300.SHA',
    })
    
    # g.run({})
    
    
    def m8_run_bigquant_run(
        bq_graph,
        inputs,
        trading_days_market='CN', # 使用那个市场的交易日历, TODO
        train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
        test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
        predict_mid='m24', # 预测 模块id
        trade_mid='m19', # 回测 模块id
        start_date='2019-01-01', # 数据开始日期
        end_date=T.live_run_param('trading_date', '2020-09-05'), # 数据结束日期
        train_update_days=100, # 更新周期,按交易日计算,每多少天更新一次
        train_update_days_for_live=5, #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=250, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
        train_data_max_days=250, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
        rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制
    ):
        def merge_datasources(input_1):
            df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
            df = pd.concat(df_list)
            instrument_data = {
                'start_date': df['date'].min().strftime('%Y-%m-%d'),
                'end_date': df['date'].max().strftime('%Y-%m-%d'),
                'instruments': list(set(df['instrument'])),
            }
            return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))
    
        def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
            # 是否实盘模式
            tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
            is_live_run = T.live_run_param('trading_date', None) is not None
    
            if is_live_run and train_update_days_for_live:
                train_update_days = train_update_days_for_live
    
            rollings = []
            train_end_date = train_data_min_days
            while train_end_date < len(tdays):
                if train_data_max_days is not None and train_data_max_days > 0:
                    train_start_date = max(train_end_date - train_data_max_days, 0)
                else:
                    train_start_date = 0
                rollings.append({
                    'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                    'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                    'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                    'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
                })
                train_end_date += train_update_days
    
            if not rollings:
                raise Exception('没有滚动需要执行,请检查配置')
    
            if is_live_run and rolling_count_for_live:
                rollings = rollings[-rolling_count_for_live:]
    
            return rollings
    
        g = bq_graph
    
        rolling_dates = gen_rolling_dates(
            trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
    
        # 训练和预测
        results = []
        for rolling in rolling_dates:
            parameters = {}
            # 先禁用回测
            parameters[trade_mid + '.__enabled__'] = False
            parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
            parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
            parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
            parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
            # print('------ rolling_train:', parameters)
            results.append(g.run(parameters))
    
        # 合并预测结果并回测
        mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
        parameters = {}
        parameters['*.__enabled__'] = False
        parameters[trade_mid + '.__enabled__'] = True
        parameters[trade_mid + '.instruments'] = mx.instrument_data
        parameters[trade_mid + '.options_data'] = mx.data
    
        trade = g.run(parameters)
    
        return {'rollings': results, 'trade': trade}
    
    
    m8 = M.hyper_rolling_train.v1(
        run=m8_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    [2020-09-05 19:45:13.859990] WARNING tensorflow: `implementation=0` has been deprecated, and now defaults to `implementation=1`.Please update your layer call.
    
    Train on 68694 samples
    Epoch 1/20
    68694/68694 - 6s - loss: 0.5434 - acc: 0.0139
    Epoch 2/20
    68694/68694 - 3s - loss: 0.4402 - acc: 0.0140
    Epoch 3/20
    68694/68694 - 3s - loss: 0.3613 - acc: 0.0140
    Epoch 4/20
    68694/68694 - 3s - loss: 0.2997 - acc: 0.0140
    Epoch 5/20
    68694/68694 - 3s - loss: 0.2518 - acc: 0.0140
    Epoch 6/20
    68694/68694 - 3s - loss: 0.2153 - acc: 0.0140
    Epoch 7/20
    68694/68694 - 3s - loss: 0.1876 - acc: 0.0140
    Epoch 8/20
    68694/68694 - 3s - loss: 0.1840 - acc: 0.0140
    Epoch 9/20
    68694/68694 - 3s - loss: 0.1730 - acc: 0.0140
    Epoch 10/20
    68694/68694 - 3s - loss: 0.1711 - acc: 0.0140
    Epoch 11/20
    68694/68694 - 3s - loss: 0.1661 - acc: 0.0140
    Epoch 12/20
    68694/68694 - 3s - loss: 0.1609 - acc: 0.0140
    Epoch 13/20
    68694/68694 - 3s - loss: 0.1581 - acc: 0.0140
    Epoch 14/20
    68694/68694 - 3s - loss: 0.1554 - acc: 0.0140
    Epoch 15/20
    68694/68694 - 3s - loss: 0.1523 - acc: 0.0140
    Epoch 16/20
    68694/68694 - 3s - loss: 0.1504 - acc: 0.0140
    Epoch 17/20
    68694/68694 - 3s - loss: 0.1498 - acc: 0.0140
    Epoch 18/20
    68694/68694 - 3s - loss: 0.1477 - acc: 0.0140
    Epoch 19/20
    68694/68694 - 3s - loss: 0.1464 - acc: 0.0140
    Epoch 20/20
    68694/68694 - 3s - loss: 0.1441 - acc: 0.0140
    
    28784/28784 - 0s
    DataSource(a009c094e4b943a4a19296840fe556aaT, v3)
    
    [2020-09-05 19:47:47.281267] WARNING tensorflow: `implementation=0` has been deprecated, and now defaults to `implementation=1`.Please update your layer call.
    

    (a1750271086) #2

    老师您好,在平台的模板策略中,我根据之前的帖子把m24模块的预测数据标签修正为’predictions’,滚动运行的预测数据接口试过填m11和m24,都不行。我这里也单独运行了m24模块,能够正确地输出‘predictions’数据,但是滚动训练就是运行不了。我也把数据缩短到很小的区间了,还是不行。能不能帮我看看呢?非常感谢


    (a1750271086) #3

    同时,所有的缓存加速都是关了的


    (a1750271086) #6

    我最后在DNN算法的模板上把这个问题解决了。我发现应该是激活函数何输出维度调错了,那LSTM这里应该怎么调呢?谢谢老师


    (adhaha111) #7

    您好,您可以分享下您最新的策略吗,这边看您上面的策略,发现滚动训练和 python自定义模块中都存在错误


    (a1750271086) #10

    就是这个策略

    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":"-8423:input_data","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":"-243:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-8427:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-12340:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-167:input_1","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":"-8427:input_data","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-4136:input_1","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-3880:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-356:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-3880:data"},{"DestinationInputPortId":"-567:inputs","SourceOutputPortId":"-356:data"},{"DestinationInputPortId":"-412:inputs","SourceOutputPortId":"-567:data"},{"DestinationInputPortId":"-574:inputs","SourceOutputPortId":"-412:data"},{"DestinationInputPortId":"-440:inputs","SourceOutputPortId":"-574:data"},{"DestinationInputPortId":"-3880:outputs","SourceOutputPortId":"-440:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-8419:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"-8423:data"},{"DestinationInputPortId":"-8419:input_data","SourceOutputPortId":"-8427:data"},{"DestinationInputPortId":"-304:input_data","SourceOutputPortId":"-21274:data"},{"DestinationInputPortId":"-21274:input_data","SourceOutputPortId":"-182:data"},{"DestinationInputPortId":"-182:input_data","SourceOutputPortId":"-167:data_1"},{"DestinationInputPortId":"-9214:input_data","SourceOutputPortId":"-21278:data"},{"DestinationInputPortId":"-21278:input_data","SourceOutputPortId":"-556:data_1"},{"DestinationInputPortId":"-556:input_1","SourceOutputPortId":"-4127:data"},{"DestinationInputPortId":"-4127:input_data","SourceOutputPortId":"-4136:data_1"},{"DestinationInputPortId":"-12340:input_data","SourceOutputPortId":"-9214:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-9214:data"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-12340:data"},{"DestinationInputPortId":"-243:input_data","SourceOutputPortId":"-304: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":"2015-01-01","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)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na_label","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"cast_label_int","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"close_0\nvolume_0\nturn_0\namount_0\nopen_0\nlow_0\nhigh_0\n","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","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":"2020-09-05","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"IsPartOfPartialRun":null,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"-106","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-106"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-106"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-106","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-113","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-113"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-113"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-113","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-122","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-122"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-122"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-122","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-129","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-129"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-129"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-129","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-141","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"def 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 = 0.05\n context.options['hold_days'] = 2\n# 回测引擎:初始化函数,只执行一次\n'''from zipline.finance.slippage import SlippageModel\nclass FixedPriceSlippage(SlippageModel):\n # 指定初始化函数\n def __init__(self, spreads, price_field_buy, price_field_sell):\n self.spreads = spreads\n self._price_field_buy = price_field_buy\n self._price_field_sell = price_field_sell \n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price_base = data.current(order.asset, price_field)\n # 买单的下单价格向上偏移 spreads百分比 , 卖单的下单价格向下偏移 spreads百分比\n price = price_base * (1.0 + self.spreads) if order.amount > 0 else price_base * (1.0 - self.spreads)\n else:\n price = order.limit\n # 返回希望成交的价格和数量\n return (price, order.amount) \n # 设置滑点模型\nfix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close',spreads=0.0003)\ncontext.set_slippage(us_equities=fix_slippage)'''","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"vwap_1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"vwap_8","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":"30,7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dtype","Value":"float32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sparse","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-160"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-160","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"Adam","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_optimizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"loss","Value":"binary_crossentropy","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"acc","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"2048","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"20","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_model","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_data","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"validation_data","NodeId":"-1098"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1098","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1540","ModuleId":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","ModuleParameters":[{"Name":"batch_size","Value":"2048","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":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3880","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3880"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-3880"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3880","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":34,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-356","ModuleId":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","ModuleParameters":[{"Name":"units","Value":"32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_activation","Value":"hard_sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_initializer","Value":"Orthogonal","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"unit_forget_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"return_sequences","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"implementation","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-356"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-356","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-567","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"noise_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"seed","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-567"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-567","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-412","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-412"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-412","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-574","ModuleId":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","ModuleParameters":[{"Name":"rate","Value":"0.1","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":"-574"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-574","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-440","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","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":"-440"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-440","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":22,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-8419","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-8419"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8419"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-8419","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-8423","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-8423"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8423"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-8423","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":23,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-8427","ModuleId":"BigQuantSpace.winsorize.winsorize-v6","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"median_deviate","Value":3,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-8427"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8427"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-8427","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-21274","ModuleId":"BigQuantSpace.aa.aa-v5","ModuleParameters":[{"Name":"day_number","Value":"50","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-21274"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-21274","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-182","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Atrue%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"delist_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-182"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-182","OutputType":null},{"Name":"left_data","NodeId":"-182","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":28,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-167","ModuleId":"BigQuantSpace.filter_delist_stock.filter_delist_stock-v6","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-167"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-167","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":29,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-21278","ModuleId":"BigQuantSpace.aa.aa-v5","ModuleParameters":[{"Name":"day_number","Value":"50","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-21278"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-21278","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":30,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-556","ModuleId":"BigQuantSpace.filtet_st_stock_tomo.filtet_st_stock_tomo-v3","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-556"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-556","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":31,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-4127","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Atrue%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"delist_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-4127"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-4127","OutputType":null},{"Name":"left_data","NodeId":"-4127","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":32,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-4136","ModuleId":"BigQuantSpace.filter_delist_stock.filter_delist_stock-v6","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-4136"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-4136","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":33,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-9214","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-9214"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-9214"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-9214","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":42,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-12340","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-12340"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-12340"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-12340","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-304","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-304"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-304"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-304","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"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({'predictions':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['predictions','date'],inplace=True, ascending=[False,True])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-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":false,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-942","ModuleId":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","ModuleParameters":[{"Name":"units","Value":"32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_activation","Value":"hard_sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_initializer","Value":"Orthogonal","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"unit_forget_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"L1L2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":".01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"return_sequences","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"implementation","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-942"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-942","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":8,"Comment":"","CommentCollapsed":true},{"Id":"-988","ModuleId":"BigQuantSpace.hyper_rolling_train.hyper_rolling_train-v1","ModuleParameters":[{"Name":"run","Value":"def bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历, TODO\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m24', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2018-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2020-09-05'), # 数据结束日期\n train_update_days=100, # 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[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None and train_data_max_days > 0:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = 0\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': trade}\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"run_now","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bq_graph","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"bq_graph_port","NodeId":"-988"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-988"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-988"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-988"}],"OutputPortsInternal":[{"Name":"result","NodeId":"-988","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":35,"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='394,21,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='270,191,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='723,17,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='477,429,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1043,20,200,200'/><NodePosition Node='-106' Position='553,121,200,200'/><NodePosition Node='-113' Position='599,199,200,200'/><NodePosition Node='-122' Position='1047,119,200,200'/><NodePosition Node='-129' Position='1048,195,200,200'/><NodePosition Node='-141' Position='827,879,200,200'/><NodePosition Node='-160' Position='-20,34,200,200'/><NodePosition Node='-1098' Position='549,784,200,200'/><NodePosition Node='-1540' Position='532,876,200,200'/><NodePosition Node='-243' Position='624,710,200,200'/><NodePosition Node='-3880' Position='-12,596.8709716796875,200,200'/><NodePosition Node='-356' Position='-19,115,200,200'/><NodePosition Node='-567' Position='-19,295.8709716796875,200,200'/><NodePosition Node='-412' Position='-18,368.8709716796875,200,200'/><NodePosition Node='-574' Position='-17,440.8709716796875,200,200'/><NodePosition Node='-440' Position='-15,517.8709716796875,200,200'/><NodePosition Node='-8419' Position='651,352,200,200'/><NodePosition Node='-8423' Position='315,291,200,200'/><NodePosition Node='-8427' Position='646,269,200,200'/><NodePosition Node='-21274' Position='306.29840087890625,689.0564575195312,200,200'/><NodePosition Node='-182' Position='477,568,200,200'/><NodePosition Node='-167' Position='476,500,200,200'/><NodePosition Node='-21278' Position='1005,480,200,200'/><NodePosition Node='-556' Position='1005,410,200,200'/><NodePosition Node='-4127' Position='1050,337,200,200'/><NodePosition Node='-4136' Position='1049,268,200,200'/><NodePosition Node='-9214' Position='1052,549,200,200'/><NodePosition Node='-12340' Position='1100,617,200,200'/><NodePosition Node='-304' Position='623,635,200,200'/><NodePosition Node='-2431' Position='929,785,200,200'/><NodePosition Node='-942' Position='-18.830634593963623,188.04032135009766,200,200'/><NodePosition Node='-988' Position='-79.4458236694336,763.2257690429688,200,200'/></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 2020年9月8日 09:31
      # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
      
      
      # 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({'predictions':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
          df.sort_values(['predictions','date'],inplace=True, ascending=[False,True])
          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
      
      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 = 0.05
          context.options['hold_days'] = 2
      # 回测引擎:初始化函数,只执行一次
      '''from zipline.finance.slippage import SlippageModel
      class FixedPriceSlippage(SlippageModel):
              # 指定初始化函数
          def __init__(self, spreads, price_field_buy, price_field_sell):
                  self.spreads = spreads
                  self._price_field_buy = price_field_buy
                  self._price_field_sell = price_field_sell    
          def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
                  if order.limit is None:
                      price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                      price_base = data.current(order.asset, price_field)
                     # 买单的下单价格向上偏移 spreads百分比 , 卖单的下单价格向下偏移 spreads百分比
                      price = price_base * (1.0 + self.spreads) if order.amount > 0 else price_base * (1.0 - self.spreads)
                  else:
                      price = order.limit
                      # 返回希望成交的价格和数量
                  return (price, order.amount)    
          # 设置滑点模型
      fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close',spreads=0.0003)
      context.set_slippage(us_equities=fix_slippage)'''
      # 回测引擎:每日数据处理函数,每天执行一次
      def m19_handle_data_bigquant_run(context, data):
          # 按日期过滤得到今日的预测数据
          ranker_prediction = context.ranker_prediction[
              context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
      
          # 1. 资金分配
          # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
          # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
          is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
          cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
          cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
          cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
          positions = {e.symbol: p.amount * p.last_sale_price
                       for e, p in context.perf_tracker.position_tracker.positions.items()}
      
          # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
          if not is_staging and cash_for_sell > 0:
              equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
              instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
              # print('rank order for sell %s' % instruments)
              for instrument in instruments:
                  context.order_target(context.symbol(instrument), 0)
                  cash_for_sell -= positions[instrument]
                  if cash_for_sell <= 0:
                      break
      
          # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
          buy_cash_weights = context.stock_weights
          buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
          max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
          for i, instrument in enumerate(buy_instruments):
              cash = cash_for_buy * buy_cash_weights[i]
              if cash > max_cash_per_instrument - positions.get(instrument, 0):
                  # 确保股票持仓量不会超过每次股票最大的占用资金量
                  cash = max_cash_per_instrument - positions.get(instrument, 0)
              if cash > 0:
                  context.order_value(context.symbol(instrument), cash)
      
      # 回测引擎:准备数据,只执行一次
      def m19_prepare_bigquant_run(context):
          pass
      
      
      g = T.Graph({
      
          'm1': 'M.instruments.v2',
          'm1.start_date': '2015-01-01',
          '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)
      """,
          'm2.start_date': '',
          'm2.end_date': '',
          'm2.benchmark': '000300.SHA',
          'm2.drop_na_label': True,
          'm2.cast_label_int': False,
      
          'm23': 'M.dropnan.v2',
          'm23.input_data': T.Graph.OutputPort('m2.data'),
      
          'm3': 'M.input_features.v1',
          'm3.features': """close_0
      volume_0
      turn_0
      amount_0
      open_0
      low_0
      high_0
      """,
      
          'm15': 'M.general_feature_extractor.v7',
          'm15.instruments': T.Graph.OutputPort('m1.data'),
          'm15.features': T.Graph.OutputPort('m3.data'),
          'm15.start_date': '',
          'm15.end_date': '',
          'm15.before_start_days': 30,
      
          'm16': 'M.derived_feature_extractor.v3',
          'm16.input_data': T.Graph.OutputPort('m15.data'),
          'm16.features': T.Graph.OutputPort('m3.data'),
          'm16.date_col': 'date',
          'm16.instrument_col': 'instrument',
          'm16.drop_na': True,
          'm16.remove_extra_columns': False,
      
          'm13': 'M.winsorize.v6',
          'm13.input_data': T.Graph.OutputPort('m16.data'),
          'm13.features': T.Graph.OutputPort('m3.data'),
          'm13.columns_input': '',
          'm13.median_deviate': 3,
      
          'm4': 'M.dropnan.v2',
          'm4.input_data': T.Graph.OutputPort('m13.data'),
      
          'm7': 'M.join.v3',
          'm7.data1': T.Graph.OutputPort('m23.data'),
          'm7.data2': T.Graph.OutputPort('m4.data'),
          'm7.on': 'date,instrument',
          'm7.how': 'inner',
          'm7.sort': False,
      
          'm29': 'M.filter_delist_stock.v6',
          'm29.input_1': T.Graph.OutputPort('m7.data'),
          'm29.m_cached': False,
      
          'm28': 'M.chinaa_stock_filter.v1',
          'm28.input_data': T.Graph.OutputPort('m29.data_1'),
          'm28.index_constituent_cond': ['上证50', '沪深300'],
          'm28.board_cond': ['上证主板', '深证主板'],
          'm28.industry_cond': ['全部'],
          'm28.st_cond': ['*ST', 'ST', '正常', '暂停上市'],
          'm28.delist_cond': ['全部'],
          'm28.output_left_data': False,
          'm28.m_cached': False,
      
          'm14': 'M.aa.v5',
          'm14.input_data': T.Graph.OutputPort('m28.data'),
          'm14.day_number': 50,
          'm14.m_cached': False,
      
          'm25': 'M.dropnan.v2',
          'm25.input_data': T.Graph.OutputPort('m14.data'),
      
          'm26': 'M.dl_convert_to_bin.v2',
          'm26.input_data': T.Graph.OutputPort('m25.data'),
          'm26.features': T.Graph.OutputPort('m3.data'),
          'm26.window_size': 30,
          'm26.feature_clip': 7,
          'm26.flatten': False,
          'm26.window_along_col': 'instrument',
      
          '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', '2020-09-05'),
          'm9.market': 'CN_STOCK_A',
          'm9.instrument_list': '',
          'm9.max_count': 0,
      
          'm17': 'M.general_feature_extractor.v7',
          'm17.instruments': T.Graph.OutputPort('m9.data'),
          'm17.features': T.Graph.OutputPort('m3.data'),
          'm17.start_date': '',
          'm17.end_date': '',
          'm17.before_start_days': 30,
      
          'm18': 'M.derived_feature_extractor.v3',
          'm18.input_data': T.Graph.OutputPort('m17.data'),
          'm18.features': T.Graph.OutputPort('m3.data'),
          'm18.date_col': 'date',
          'm18.instrument_col': 'instrument',
          'm18.drop_na': True,
          'm18.remove_extra_columns': False,
      
          'm33': 'M.filter_delist_stock.v6',
          'm33.input_1': T.Graph.OutputPort('m18.data'),
          'm33.m_cached': False,
      
          'm32': 'M.chinaa_stock_filter.v1',
          'm32.input_data': T.Graph.OutputPort('m33.data_1'),
          'm32.index_constituent_cond': ['上证50', '沪深300'],
          'm32.board_cond': ['上证主板', '深证主板'],
          'm32.industry_cond': ['全部'],
          'm32.st_cond': ['*ST', 'ST', '正常', '暂停上市'],
          'm32.delist_cond': ['全部'],
          'm32.output_left_data': False,
          'm32.m_cached': False,
      
          'm31': 'M.filtet_st_stock_tomo.v3',
          'm31.input_1': T.Graph.OutputPort('m32.data'),
          'm31.m_cached': False,
      
          'm30': 'M.aa.v5',
          'm30.input_data': T.Graph.OutputPort('m31.data_1'),
          'm30.day_number': 50,
          'm30.m_cached': False,
      
          'm42': 'M.dropnan.v2',
          'm42.input_data': T.Graph.OutputPort('m30.data'),
          'm42.m_cached': False,
      
          'm27': 'M.dl_convert_to_bin.v2',
          'm27.input_data': T.Graph.OutputPort('m42.data'),
          'm27.features': T.Graph.OutputPort('m3.data'),
          'm27.window_size': 30,
          'm27.feature_clip': 7,
          'm27.flatten': False,
          'm27.window_along_col': 'instrument',
      
          'm6': 'M.dl_layer_input.v1',
          'm6.shape': '30,7',
          'm6.batch_shape': '',
          'm6.dtype': 'float32',
          'm6.sparse': False,
          'm6.name': '',
      
          'm10': 'M.dl_layer_lstm.v1',
          'm10.inputs': T.Graph.OutputPort('m6.data'),
          'm10.units': 32,
          'm10.activation': 'tanh',
          'm10.recurrent_activation': 'hard_sigmoid',
          'm10.use_bias': True,
          'm10.kernel_initializer': 'glorot_uniform',
          'm10.recurrent_initializer': 'Orthogonal',
          'm10.bias_initializer': 'Zeros',
          'm10.unit_forget_bias': True,
          'm10.kernel_regularizer': 'L1L2',
          'm10.kernel_regularizer_l1': 0,
          'm10.kernel_regularizer_l2': .01,
          'm10.recurrent_regularizer': 'None',
          'm10.recurrent_regularizer_l1': 0,
          'm10.recurrent_regularizer_l2': 0,
          'm10.bias_regularizer': 'None',
          'm10.bias_regularizer_l1': 0,
          'm10.bias_regularizer_l2': 0,
          'm10.activity_regularizer': 'None',
          'm10.activity_regularizer_l1': 0,
          'm10.activity_regularizer_l2': 0,
          'm10.kernel_constraint': 'None',
          'm10.recurrent_constraint': 'None',
          'm10.bias_constraint': 'None',
          'm10.dropout': 0,
          'm10.recurrent_dropout': 0,
          'm10.return_sequences': False,
          'm10.implementation': '1',
          'm10.name': '',
      
          'm12': 'M.dl_layer_dropout.v1',
          'm12.inputs': T.Graph.OutputPort('m10.data'),
          'm12.rate': 0.1,
          'm12.noise_shape': '',
          'm12.name': '',
      
          'm20': 'M.dl_layer_dense.v1',
          'm20.inputs': T.Graph.OutputPort('m12.data'),
          'm20.units': 30,
          'm20.activation': 'tanh',
          'm20.use_bias': True,
          'm20.kernel_initializer': 'glorot_uniform',
          'm20.bias_initializer': 'Zeros',
          'm20.kernel_regularizer': 'L1L2',
          'm20.kernel_regularizer_l1': 0,
          'm20.kernel_regularizer_l2': .01,
          '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': '',
      
          'm21': 'M.dl_layer_dropout.v1',
          'm21.inputs': T.Graph.OutputPort('m20.data'),
          'm21.rate': 0.1,
          'm21.noise_shape': '',
          'm21.name': '',
      
          'm22': 'M.dl_layer_dense.v1',
          'm22.inputs': T.Graph.OutputPort('m21.data'),
          'm22.units': 1,
          'm22.activation': 'tanh',
          'm22.use_bias': True,
          'm22.kernel_initializer': 'glorot_uniform',
          'm22.bias_initializer': 'Zeros',
          'm22.kernel_regularizer': 'L1L2',
          'm22.kernel_regularizer_l1': 0,
          'm22.kernel_regularizer_l2': .01,
          'm22.bias_regularizer': 'None',
          'm22.bias_regularizer_l1': 0,
          'm22.bias_regularizer_l2': 0,
          'm22.activity_regularizer': 'None',
          'm22.activity_regularizer_l1': 0,
          'm22.activity_regularizer_l2': 0,
          'm22.kernel_constraint': 'None',
          'm22.bias_constraint': 'None',
          'm22.name': '',
      
          'm34': 'M.dl_model_init.v1',
          'm34.inputs': T.Graph.OutputPort('m6.data'),
          'm34.outputs': T.Graph.OutputPort('m22.data'),
      
          'm5': 'M.dl_model_train.v1',
          'm5.input_model': T.Graph.OutputPort('m34.data'),
          'm5.training_data': T.Graph.OutputPort('m26.data'),
          'm5.optimizer': 'Adam',
          'm5.loss': 'binary_crossentropy',
          'm5.metrics': 'acc',
          'm5.batch_size': 2048,
          'm5.epochs': 20,
          'm5.n_gpus': 0,
          'm5.verbose': '2:每个epoch输出一行记录',
      
          'm11': 'M.dl_model_predict.v1',
          'm11.trained_model': T.Graph.OutputPort('m5.data'),
          'm11.input_data': T.Graph.OutputPort('m27.data'),
          'm11.batch_size': 2048,
          '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('m42.data'),
          'm24.run': m24_run_bigquant_run,
          'm24.post_run': m24_post_run_bigquant_run,
          'm24.input_ports': '',
          'm24.params': '{}',
          'm24.output_ports': '',
          'm24.m_cached': False,
      
          'm19': 'M.trade.v4',
          'm19.instruments': T.Graph.OutputPort('m9.data'),
          'm19.options_data': T.Graph.OutputPort('m24.data_1'),
          'm19.start_date': '',
          'm19.end_date': '',
          'm19.initialize': m19_initialize_bigquant_run,
          'm19.handle_data': m19_handle_data_bigquant_run,
          'm19.prepare': m19_prepare_bigquant_run,
          'm19.volume_limit': 0,
          'm19.order_price_field_buy': 'vwap_1',
          'm19.order_price_field_sell': 'vwap_8',
          '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',
      
          'm8': 'M.dl_layer_lstm.v1',
          'm8.units': 32,
          'm8.activation': 'tanh',
          'm8.recurrent_activation': 'hard_sigmoid',
          'm8.use_bias': True,
          'm8.kernel_initializer': 'glorot_uniform',
          'm8.recurrent_initializer': 'Orthogonal',
          'm8.bias_initializer': 'Zeros',
          'm8.unit_forget_bias': True,
          'm8.kernel_regularizer': 'L1L2',
          'm8.kernel_regularizer_l1': 0,
          'm8.kernel_regularizer_l2': .01,
          'm8.recurrent_regularizer': 'None',
          'm8.recurrent_regularizer_l1': 0,
          'm8.recurrent_regularizer_l2': 0,
          'm8.bias_regularizer': 'None',
          'm8.bias_regularizer_l1': 0,
          'm8.bias_regularizer_l2': 0,
          'm8.activity_regularizer': 'None',
          'm8.activity_regularizer_l1': 0,
          'm8.activity_regularizer_l2': 0,
          'm8.kernel_constraint': 'None',
          'm8.recurrent_constraint': 'None',
          'm8.bias_constraint': 'None',
          'm8.dropout': 0,
          'm8.recurrent_dropout': 0,
          'm8.return_sequences': False,
          'm8.implementation': '1',
          'm8.name': '',
      })
      
      # g.run({})
      
      
      def m35_run_bigquant_run(
          bq_graph,
          inputs,
          trading_days_market='CN', # 使用那个市场的交易日历, TODO
          train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
          test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
          predict_mid='m24', # 预测 模块id
          trade_mid='m19', # 回测 模块id
          start_date='2018-01-01', # 数据开始日期
          end_date=T.live_run_param('trading_date', '2020-09-05'), # 数据结束日期
          train_update_days=100, # 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[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
              df = pd.concat(df_list)
              instrument_data = {
                  'start_date': df['date'].min().strftime('%Y-%m-%d'),
                  'end_date': df['date'].max().strftime('%Y-%m-%d'),
                  'instruments': list(set(df['instrument'])),
              }
              return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))
      
          def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
              # 是否实盘模式
              tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
              is_live_run = T.live_run_param('trading_date', None) is not None
      
              if is_live_run and train_update_days_for_live:
                  train_update_days = train_update_days_for_live
      
              rollings = []
              train_end_date = train_data_min_days
              while train_end_date < len(tdays):
                  if train_data_max_days is not None and train_data_max_days > 0:
                      train_start_date = max(train_end_date - train_data_max_days, 0)
                  else:
                      train_start_date = 0
                  rollings.append({
                      'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                      'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                      'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                      'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
                  })
                  train_end_date += train_update_days
      
              if not rollings:
                  raise Exception('没有滚动需要执行,请检查配置')
      
              if is_live_run and rolling_count_for_live:
                  rollings = rollings[-rolling_count_for_live:]
      
              return rollings
      
          g = bq_graph
      
          rolling_dates = gen_rolling_dates(
              trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
      
          # 训练和预测
          results = []
          for rolling in rolling_dates:
              parameters = {}
              # 先禁用回测
              parameters[trade_mid + '.__enabled__'] = False
              parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
              parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
              parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
              parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
              # print('------ rolling_train:', parameters)
              results.append(g.run(parameters))
      
          # 合并预测结果并回测
          mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
          parameters = {}
          parameters['*.__enabled__'] = False
          parameters[trade_mid + '.__enabled__'] = True
          parameters[trade_mid + '.instruments'] = mx.instrument_data
          parameters[trade_mid + '.options_data'] = mx.data
      
          trade = g.run(parameters)
      
          return {'rollings': results, 'trade': trade}
      
      
      m35 = M.hyper_rolling_train.v1(
          run=m35_run_bigquant_run,
          run_now=True,
          bq_graph=g
      )
      
      [2020-09-07 17:39:00.867018] WARNING tensorflow: `implementation=0` has been deprecated, and now defaults to `implementation=1`.Please update your layer call.
      
      Train on 194680 samples
      Epoch 1/20
      194680/194680 - 11s - loss: 0.3816 - acc: 0.0116
      Epoch 2/20
      194680/194680 - 7s - loss: 0.1902 - acc: 0.0116
      Epoch 3/20
      194680/194680 - 7s - loss: 0.1088 - acc: 0.0116
      Epoch 4/20
      194680/194680 - 7s - loss: 0.0755 - acc: 0.0116
      Epoch 5/20
      194680/194680 - 7s - loss: 0.0616 - acc: 0.0116
      Epoch 6/20
      194680/194680 - 7s - loss: 0.0556 - acc: 0.0116
      Epoch 7/20
      194680/194680 - 7s - loss: 0.0530 - acc: 0.0116
      Epoch 8/20
      194680/194680 - 7s - loss: 0.0518 - acc: 0.0116
      Epoch 9/20
      194680/194680 - 7s - loss: 0.0511 - acc: 0.0116
      Epoch 10/20
      194680/194680 - 7s - loss: 0.0508 - acc: 0.0116
      Epoch 11/20
      194680/194680 - 7s - loss: 0.0506 - acc: 0.0116
      Epoch 12/20
      194680/194680 - 7s - loss: 0.0505 - acc: 0.0116
      Epoch 13/20
      194680/194680 - 7s - loss: 0.0504 - acc: 0.0116
      Epoch 14/20
      194680/194680 - 7s - loss: 0.0503 - acc: 0.0116
      Epoch 15/20
      194680/194680 - 7s - loss: 0.0503 - acc: 0.0116
      Epoch 16/20
      194680/194680 - 7s - loss: 0.0502 - acc: 0.0116
      Epoch 17/20
      194680/194680 - 7s - loss: 0.0502 - acc: 0.0116
      Epoch 18/20
      194680/194680 - 7s - loss: 0.0501 - acc: 0.0116
      Epoch 19/20
      194680/194680 - 7s - loss: 0.0501 - acc: 0.0116
      Epoch 20/20
      194680/194680 - 7s - loss: 0.0501 - acc: 0.0116
      
      186961/186961 - 2s
      DataSource(9df30df2f7494ab482d51608d230e1c5T, v3)
      
      • 收益率25.53%
      • 年化收益率9.19%
      • 基准收益率18.34%
      • 阿尔法0.05
      • 贝塔0.28
      • 夏普比率0.64
      • 胜率0.5
      • 盈亏比1.4
      • 收益波动率9.83%
      • 信息比率0.0
      • 最大回撤12.98%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-da58caf1e03f48b8a216d0eb602145bd"}/bigcharts-data-end
      ---------------------------------------------------------------------------
      Exception                                 Traceback (most recent call last)
      <ipython-input-1-49d9c98fe279> in <module>()
          439     rolling_min_days=750,
          440     rolling_max_days=1000,
      --> 441     rolling_count_for_live=1
          442 )
      
      Exception: invalid module name: summertest003, version: v1, friendly_name: None, either bigquant_run or BigQuantModule is required

      (a1750271086) #11

      这个策略里面我一开始用的是用户模块里面的滚动运行配置,但是这个用了模块之后模型根本不会滚动运行。我想用平台的滚动训练模块,目前平台的模块怎么都接不到’prediction’数据,请老师帮我修改一下里面的错误,谢谢。


      (adhaha111) #12

      您好,将滚动训练模块中的 predictions 改为 data_1 再试试呢:


      (a1750271086) #15

      非常感谢老师,这个问题终于解决了