求助!麻烦各位帮忙看下这个cnn+lstm的策略,根据克隆改的,训练出来的结果loss和mae都是nan怎么解决呀

机器学习
策略分享
新手专区
标签: #<Tag:0x00007f49215baf88> #<Tag:0x00007f49215bae48> #<Tag:0x00007f49215bad08>

(Daniel129) #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":"-773:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-106:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-113:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-122:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-129:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-768:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-778:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-251:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-3895:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-3907:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-122:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-141:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-154:input_data","SourceOutputPortId":"-106:data"},{"DestinationInputPortId":"-768:input_1","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-9686:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-778:input_1","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-3880:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-2680:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_1"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-768:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"-773:data"},{"DestinationInputPortId":"-251:input_data","SourceOutputPortId":"-778:data"},{"DestinationInputPortId":"-3895:input_1","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-3907:input_1","SourceOutputPortId":"-251:data"},{"DestinationInputPortId":"-2712:inputs","SourceOutputPortId":"-2680:data"},{"DestinationInputPortId":"-3840:inputs","SourceOutputPortId":"-2712:data"},{"DestinationInputPortId":"-3872:inputs","SourceOutputPortId":"-3840:data"},{"DestinationInputPortId":"-356:inputs","SourceOutputPortId":"-3872:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-3880:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-3895:data_1"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-3907:data_1"},{"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":"-113:input_data","SourceOutputPortId":"-154:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-9686:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2014-07-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-07-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":" ","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\n# shift(close, -15) / shift(open, -1)-1\n# 计算收益:5日相对基准的收益率\nshift(close, -15) / shift(open, -1)-shift(benchmark_close, -15)/shift(benchmark_open, -1)\n# rank(shift(close, -15) / shift(open, -1))\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# 未来5日最大相对收益率\n# shift(cal_max_ret(close, open,benchmark_close,benchmark_open, 15), -1)\n# 未来5日最大相对收益率所在天数\n# shift(cal_max_ret_date(close, open,benchmark_close,benchmark_open, 15), -1) + 1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000906.HIX","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":"def cal_max_ret(df, close, open,benchmark_close,benchmark_open, N):\n ret = df.groupby('instrument', as_index=False, sort=False, group_keys=False).apply(lambda x: (pd.concat([x['close'].shift(-i) / x['open'] - x['benchmark_close'].shift(-i) / x['benchmark_open'] for i in range(N)], axis=1)).max(axis=1))\n \n return ret\n\ndef cal_max_ret_date(df, close, open,benchmark_close,benchmark_open, N):\n ret = df.groupby('instrument', as_index=False, sort=False, group_keys=False).apply(lambda x: pd.concat([x['close'].shift(-i) / x['open'] - x['benchmark_close'].shift(-i) / x['benchmark_open'] for i in range(N)], axis=1))\n return np.nanargmax(np.array(ret), axis=1)\nbigquant_run={'cal_max_ret':cal_max_ret, 'cal_max_ret_date': cal_max_ret_date}","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":"std(amount_0,15)\nstd(amount_0,20) \nstd(amount_0,6)\nrank((-1*((1-(open_0/close_0))**1)))\nta_ema(volume_0, 21) \n(close_0-mean(close_0,12))/mean(close_0,12)*100 \n(close_0-mean(close_0,24))/mean(close_0,24)*100 \n(close_0-delay(close_0,20))/delay(close_0,20)*100 \nta_ema(((high_0+low_0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,7) \nstd(volume_0,10)\n((close_0-open_0)/((high_0-low_0)+.001)) \nta_wma(close_0,5)/close_0 \n((close_0-low_0)-(high_0-close_0))/(high_0-close_0)\n((rank(max((amount_0/volume_0*adjust_factor_0-close_0),3))+rank(min((amount_0/volume_0*adjust_factor_0-close_0), 3)))*rank(delta(volume_0, 3)))\nta_wma(close_0,30)/close_0\n(rank((amount_0/volume_0*adjust_factor_0-close_0))/rank((amount_0/volume_0*adjust_factor_0 + close_0)))\n((-1*((low_0-close_0)*(open_0**5)))/((low_0-high_0)*(close_0**5)))\n-1*delta(((close_0-low_0)-(high_0-close_0))/(high_0-low_0),1)\nclose_0-delay(close_0,5)\nta_ema(close_0,5)/close_0\n(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)\n(close_0-delay(close_0,6))/delay(close_0,6)*100\nmean(mf_net_amount_m_0, 10)\nmean(mf_net_amount_l_0, 5)\nmean(mf_net_amount_l_0, 90)\n# fs_deducted_profit_ttm_0\n# fs_net_profit_ttm_0/market_cap_0\n# ta_adx_14_0\n# close_0>ta_atr(high_0, low_0, close_0, 14)\n# ta_obv_0\n# (close_0-close_12)/close_12>0.05\n# swing_volatility_30_0/swing_volatility_5_0\n# ta_sma_60_0\n# ta_wma_60_0\n# high_0>ta_bbands_middleband_28_0\n# close_0>ta_bbands_upperband_14_0\n# ta_rsi_28_0\nmarket_cap_float_0/market_cap_0\nrank(std(amount_0,15))\nrank_avg_amount_0/rank_avg_amount_8\nts_argmin(low_0,20)\nrank_return_30\n(low_1-close_0)/close_0\nta_bbands_lowerband_14_0\nmean(mf_net_pct_s_0,4)\namount_0/avg_amount_3\nreturn_0/return_5\nreturn_1/return_5\nrank_avg_amount_7/rank_avg_amount_10\nta_sma_10_0/close_0\nsqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0\navg_turn_15/(turn_0+1e-5)\nreturn_10\nmf_net_pct_s_0\n(close_0-open_0)/close_1","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-07-07","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-09-10","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":"60","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":"60","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-122"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-122"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-122","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-129","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-129"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-129"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-129","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-141","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只\n stock_count = 5\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.35\n context.hold_days = 5","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 positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n \n today = data.current_dt.strftime('%Y-%m-%d')\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 == today]\n \n #按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.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\n #---------------------START:大盘风控(含建仓期)--------------------------\n '''\n today_date = data.current_dt.strftime('%Y-%m-%d')\n positions_all = [equity.symbol for equity in context.portfolio.positions]\n dataprediction=context.dataprediction\n today_prediction=dataprediction[dataprediction.date==today_date].direction.values[0]\n # 满足空仓条件\n if today_prediction<0:\t\n if len(positions_all)>0:\n # 全部卖出后返回\n for i in positions_all:\n if data.can_trade(context.symbol(i)):\n context.order_target_percent(context.symbol(i), 0)\n print('风控执行',today_date)\n return\n #运行风控后当日结束,不再执行后续的买卖订单\n '''\n #------------------------END:大盘风控(含建仓期)---------------------------\n # 2. 根据需要加入移动止赢止损模块、固定天数卖出模块、ST或退市股卖出模块\n \n #------------------------START:止赢止损模块(含建仓期)---------------\n stock_sold = [] # 记录卖出的股票,防止多次卖出出现空单\n current_stopwin_stock=[]\n current_stoploss_stock = [] \n positions_cost={e.symbol:p.cost_basis for e,p in context.portfolio.positions.items()}\n if len(positions)>0:\n for instrument in positions.keys():\n stock_cost=positions_cost[instrument] \n stock_market_price=data.current(context.symbol(instrument),'price') \n #赚9%且为可交易状态就止盈\n if stock_market_price/stock_cost-1>=0.40 and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n current_stopwin_stock.append(instrument)\n # 亏5%并且为可交易状态就止损\n if stock_market_price/stock_cost-1 <= -0.030 and data.can_trade(context.symbol(instrument)): \n context.order_target_percent(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n current_stoploss_stock.append(instrument)\n if len(current_stopwin_stock)>0:\n print(today,'止盈股票列表',current_stopwin_stock)\n stock_sold += current_stopwin_stock\n if len(current_stoploss_stock)>0:\n print(today,'止损股票列表',current_stoploss_stock)\n stock_sold += current_stoploss_stock\n \n #--------------------------END: 止赢止损模块--------------------------\n #--------------------------START:持有固定天数卖出(不含建仓期)-----------\n# current_stopdays_stock = []\n# positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}\n# # 不是建仓期(在前hold_days属于建仓期)\n# if not is_staging:\n# for instrument in positions.keys():\n# #如果上面的止盈止损已经卖出过了,就不要重复卖出以防止产生空单\n# if instrument in stock_sold:\n# continue\n# # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数,比如datetime.timedelta(5)\n# if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(10) and data.can_trade(context.symbol(instrument)):\n# context.order_target_percent(context.symbol(instrument), 0)\n# current_stopdays_stock.append(instrument)\n# cash_for_sell -= positions[instrument]\n# if len(current_stopdays_stock)>0: \n# print(today,'固定天数卖出列表',current_stopdays_stock)\n# stock_sold += current_stopdays_stock\n #------------------------- END:持有固定天数卖出-----------------------\n \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StockRanker预测的排序末位淘汰\n \n if not is_staging and cash_for_sell > 0:\n# positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}\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 #if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(10) and data.can_trade(context.symbol(instrument)):\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 \n # 3. 生成买入订单:按StockRanker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":"\n# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n seq_len=5 #每个input的长度\n # 导入包\n from keras.layers.core import Dense, Activation, Dropout\n from keras.layers.recurrent import LSTM\n from keras.models import Sequential\n from keras import optimizers\n import tensorflow.keras as tf \n from sklearn.preprocessing import scale\n from tensorflow.keras.layers import Input, Dense, LSTM\n from tensorflow.keras.models import Model\n # 基础参数配置\n instrument = '000906.HIX' #股票代码\n #设置用于训练和回测的开始/结束日期\n train_length=seq_len*10\n start_date_temp= (pd.to_datetime(context.start_date) - datetime.timedelta(days=2*train_length)).strftime('%Y-%m-%d') # 多取几天的数据,这里取5倍\n len1=len(D.trading_days(start_date=start_date_temp, end_date=context.end_date)) \n len2=len(D.trading_days(start_date=context.start_date, end_date=context.end_date))\n distance=len1-len2\n trade_day=D.trading_days(start_date=start_date_temp, end_date=context.end_date)\n start_date = trade_day.iloc[distance-train_length][0].strftime('%Y-%m-%d')\n split_date = trade_day.iloc[distance-1][0].strftime('%Y-%m-%d')\n fields = ['close', 'open', 'high', 'low', 'amount', 'volume'] # features因子\n batch = 1024 #整数,指定进行梯度下降时每个batch包含的样本数,训练时一个batch的样本会被计算一次梯度下降,使目标函数优化一步\n \n # 数据导入以及初步处理\n data1 = D.history_data(instrument, start_date, context.end_date, fields)\n data1['return'] = data1['close'].shift(-5) / data1['open'].shift(-1) - 1 #计算未来5日收益率(未来第五日的收盘价/明日的开盘价)\n data1=data1[data1.amount>0]\n datatime = data1['date'][data1.date>split_date] #记录predictions的时间,回测要用\n data1['return'] = data1['return']\n data1['return'] = data1['return']*10 # 适当增大return范围,利于LSTM模型训练\n data1.reset_index(drop=True, inplace=True)\n scaledata = data1[fields]\n traindata = data1[data1.date<=split_date]\n \n # 数据处理:设定每个input(series×6features)以及数据标准化\n train_input = []\n train_output = []\n test_input = []\n for i in range(seq_len-1, len(traindata)):\n a = scale(scaledata[i+1-seq_len:i+1])\n train_input.append(a)\n c = data1['return'][i]\n train_output.append(c)\n for j in range(len(traindata), len(data1)):\n b = scale(scaledata[j+1-seq_len:j+1])\n test_input.append(b)\n\n\n # LSTM接受数组类型的输入\n train_x = np.array(train_input)\n train_y = np.array(train_output)\n test_x = np.array(test_input) \n\n # 自定义激活函数\n import tensorflow.keras as tf\n def tanh(x): \n return tf.tanh(x)\n # 构建神经网络层 1层LSTM层+3层Dense层\n # 用于1个输入情况\n lstm_input = Input(shape=(seq_len,len(fields)), name='lstm_input')\n lstm_output = LSTM(128,input_shape=(seq_len,len(fields)))(lstm_input)\n Dense_output_1 = Dense(32, activation='linear')(lstm_output)\n Dense_output_2 = Dense(1, activation='linear')(Dense_output_1)\n predictions = Dense(1)(Dense_output_2)\n model = Model(inputs=lstm_input, outputs=predictions)\n model.compile(optimizer='adam', loss='mse', metrics=['mse'])\n model.fit(train_x, train_y, batch_size=batch, nb_epoch=50, verbose=2)\n # 预测\n predictions = model.predict(test_x)\n # 如果预测值>0,取为1;如果预测值<=0,取为-1.为回测做准备\n for i in range(len(predictions)):\n if predictions[i]>0:\n predictions[i]=1\n elif predictions[i]<=0:\n predictions[i]=-1\n \n # 将预测值与时间整合作为回测数据\n cc = np.reshape(predictions,len(predictions), 1)\n dataprediction = pd.DataFrame()\n dataprediction['date'] = datatime\n dataprediction['direction']=np.round(cc)\n context.dataprediction=dataprediction\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"twap_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":"000906.HIX","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":"43,5","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":"mean_absolute_error","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"mae","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"1024","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"5","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":"-2431","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n pred_label = input_1.read_pickle()\n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-2431"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-2431","OutputType":null},{"Name":"data_2","NodeId":"-2431","OutputType":null},{"Name":"data_3","NodeId":"-2431","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-768","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-768"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-768"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-768","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-773","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"label","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-773"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-773"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-773","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-778","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-778"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-778"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-778","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"43","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-251","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"43","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-251"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-251"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-251","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2680","ModuleId":"BigQuantSpace.dl_layer_conv1d.dl_layer_conv1d-v1","ModuleParameters":[{"Name":"filters","Value":"60","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_size","Value":"3","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dilation_rate","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-2680"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2680","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2712","ModuleId":"BigQuantSpace.dl_layer_maxpooling1d.dl_layer_maxpooling1d-v1","ModuleParameters":[{"Name":"pool_size","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-2712"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2712","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3840","ModuleId":"BigQuantSpace.dl_layer_conv1d.dl_layer_conv1d-v1","ModuleParameters":[{"Name":"filters","Value":"60","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_size","Value":"3","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dilation_rate","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3840"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3840","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":32,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3872","ModuleId":"BigQuantSpace.dl_layer_maxpooling1d.dl_layer_maxpooling1d-v1","ModuleParameters":[{"Name":"pool_size","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3872"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3872","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":33,"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":"-3895","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 df = input_1.read_pickle()\n feature_len = len(input_2.read_pickle())\n \n \n df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))\n \n data_1 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3895"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3895","OutputType":null},{"Name":"data_2","NodeId":"-3895","OutputType":null},{"Name":"data_3","NodeId":"-3895","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3907","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 df = input_1.read_pickle()\n feature_len = len(input_2.read_pickle())\n \n \n df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))\n \n data_1 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-3907"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3907"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3907"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3907","OutputType":null},{"Name":"data_2","NodeId":"-3907","OutputType":null},{"Name":"data_3","NodeId":"-3907","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-356","ModuleId":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","ModuleParameters":[{"Name":"units","Value":"64","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_activation","Value":"hard_sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_initializer","Value":"Orthogonal","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"unit_forget_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_recurrent_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"recurrent_dropout","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"return_sequences","Value":"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":20,"IsPartOfPartialRun":null,"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":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-412","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"64","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"tanh","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-412"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-412","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":22,"IsPartOfPartialRun":null,"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":23,"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":"sigmoid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-440"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-440","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":28,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-154","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%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%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%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%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%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%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"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%3Afalse%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%3Atrue%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":"-154"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-154","OutputType":null},{"Name":"left_data","NodeId":"-154","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":29,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-9686","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%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%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%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%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%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%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"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%3Afalse%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%3Atrue%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":"-9686"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-9686","OutputType":null},{"Name":"left_data","NodeId":"-9686","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":30,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1540","ModuleId":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","ModuleParameters":[{"Name":"batch_size","Value":"1024","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trained_model","NodeId":"-1540"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1540"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1540","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true}],"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='394,20,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='220,173,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='713,-93,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='379,435,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1065,22,200,200'/><NodePosition Node='-106' Position='477,102,200,200'/><NodePosition Node='-113' Position='528,256,200,200'/><NodePosition Node='-122' Position='1058,135,200,200'/><NodePosition Node='-129' Position='1081,327,200,200'/><NodePosition Node='-141' Position='588,1008,200,200'/><NodePosition Node='-160' Position='-190,-143,200,200'/><NodePosition Node='-1098' Position='380,680,200,200'/><NodePosition Node='-2431' Position='773,891,200,200'/><NodePosition Node='-768' Position='531,329,200,200'/><NodePosition Node='-773' Position='230,329,200,200'/><NodePosition Node='-778' Position='1067,430,200,200'/><NodePosition Node='-243' Position='384,492,200,200'/><NodePosition Node='-251' Position='1061,508,200,200'/><NodePosition Node='-2680' Position='-64,-51,200,200'/><NodePosition Node='-2712' Position='-79,33,200,200'/><NodePosition Node='-3840' Position='-86,123,200,200'/><NodePosition Node='-3872' Position='-88,196,200,200'/><NodePosition Node='-3880' Position='-103,753,200,200'/><NodePosition Node='-3895' Position='444,579,200,200'/><NodePosition Node='-3907' Position='1056,590,200,200'/><NodePosition Node='-356' Position='-94,302,200,200'/><NodePosition Node='-567' Position='-94,377,200,200'/><NodePosition Node='-412' Position='-101,455,200,200'/><NodePosition Node='-574' Position='-107,530,200,200'/><NodePosition Node='-440' Position='-107,650,200,200'/><NodePosition Node='-154' Position='495,188,200,200'/><NodePosition Node='-9686' Position='1064,258,200,200'/><NodePosition Node='-1540' Position='623,802,200,200'/></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11日 15:38
    # 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。
    
    
    def cal_max_ret(df, close, open,benchmark_close,benchmark_open, N):
        ret = df.groupby('instrument', as_index=False, sort=False, group_keys=False).apply(lambda x: (pd.concat([x['close'].shift(-i) / x['open'] - x['benchmark_close'].shift(-i) / x['benchmark_open'] for i in range(N)], axis=1)).max(axis=1))
        
        return ret
    
    def cal_max_ret_date(df, close, open,benchmark_close,benchmark_open, N):
        ret = df.groupby('instrument', as_index=False, sort=False, group_keys=False).apply(lambda x: pd.concat([x['close'].shift(-i) / x['open'] - x['benchmark_close'].shift(-i) / x['benchmark_open'] for i in range(N)], axis=1))
        return np.nanargmax(np.array(ret), axis=1)
    m2_user_functions_bigquant_run={'cal_max_ret':cal_max_ret, 'cal_max_ret_date': cal_max_ret_date}
    # 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 m4_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m4_post_run_bigquant_run(outputs):
        return outputs
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m8_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m8_post_run_bigquant_run(outputs):
        return outputs
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m24_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 5
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.35
        context.hold_days = 5
    # 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):
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
        
        today = data.current_dt.strftime('%Y-%m-%d')
    #     # 按日期过滤得到今日的预测数据
    #     ranker_prediction = context.ranker_prediction[
    #         context.ranker_prediction.date == today]
        
        #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.hold_days # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.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()}
    
    
        #---------------------START:大盘风控(含建仓期)--------------------------
        '''
        today_date = data.current_dt.strftime('%Y-%m-%d')
        positions_all = [equity.symbol for equity in context.portfolio.positions]
        dataprediction=context.dataprediction
        today_prediction=dataprediction[dataprediction.date==today_date].direction.values[0]
        # 满足空仓条件
        if today_prediction<0:	
            if len(positions_all)>0:
                # 全部卖出后返回
                for i in positions_all:
                    if data.can_trade(context.symbol(i)):
                        context.order_target_percent(context.symbol(i), 0)
                        print('风控执行',today_date)
                        return
                    #运行风控后当日结束,不再执行后续的买卖订单
        '''
        #------------------------END:大盘风控(含建仓期)---------------------------
           # 2. 根据需要加入移动止赢止损模块、固定天数卖出模块、ST或退市股卖出模块
       
          #------------------------START:止赢止损模块(含建仓期)---------------
        stock_sold = [] # 记录卖出的股票,防止多次卖出出现空单
        current_stopwin_stock=[]
        current_stoploss_stock = []   
        positions_cost={e.symbol:p.cost_basis for e,p in context.portfolio.positions.items()}
        if len(positions)>0:
            for instrument in positions.keys():
                stock_cost=positions_cost[instrument]  
                stock_market_price=data.current(context.symbol(instrument),'price')  
                 #赚9%且为可交易状态就止盈
                if stock_market_price/stock_cost-1>=0.40 and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(context.symbol(instrument),0)
                    cash_for_sell -= positions[instrument]
                    current_stopwin_stock.append(instrument)
                # 亏5%并且为可交易状态就止损
                if stock_market_price/stock_cost-1 <= -0.030 and data.can_trade(context.symbol(instrument)):   
                    context.order_target_percent(context.symbol(instrument),0)
                    cash_for_sell -= positions[instrument]
                    current_stoploss_stock.append(instrument)
            if len(current_stopwin_stock)>0:
                print(today,'止盈股票列表',current_stopwin_stock)
                stock_sold += current_stopwin_stock
            if len(current_stoploss_stock)>0:
                print(today,'止损股票列表',current_stoploss_stock)
                stock_sold += current_stoploss_stock
                 
        #--------------------------END: 止赢止损模块--------------------------
            #--------------------------START:持有固定天数卖出(不含建仓期)-----------
    #     current_stopdays_stock = []
    #     positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}
    #     # 不是建仓期(在前hold_days属于建仓期)
    #     if not is_staging:
    #         for instrument in positions.keys():
    #             #如果上面的止盈止损已经卖出过了,就不要重复卖出以防止产生空单
    #             if instrument in stock_sold:
    #                 continue
    #             # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数,比如datetime.timedelta(5)
    #             if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(10) and data.can_trade(context.symbol(instrument)):
    #                 context.order_target_percent(context.symbol(instrument), 0)
    #                 current_stopdays_stock.append(instrument)
    #                 cash_for_sell -= positions[instrument]
    #         if len(current_stopdays_stock)>0:        
    #             print(today,'固定天数卖出列表',current_stopdays_stock)
    #             stock_sold += current_stopdays_stock
        #-------------------------  END:持有固定天数卖出-----------------------
        
        
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按StockRanker预测的排序末位淘汰
        
        if not is_staging and cash_for_sell > 0:
    #         positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}
            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)
            #if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(10) and data.can_trade(context.symbol(instrument)):
            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StockRanker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):
        seq_len=5    #每个input的长度
        # 导入包
        from keras.layers.core import Dense, Activation, Dropout
        from keras.layers.recurrent import LSTM
        from keras.models import Sequential
        from keras import optimizers
        import tensorflow.keras as tf   
        from sklearn.preprocessing import scale
        from tensorflow.keras.layers import Input, Dense, LSTM
        from tensorflow.keras.models import Model
        # 基础参数配置
        instrument = '000906.HIX'  #股票代码
        #设置用于训练和回测的开始/结束日期
        train_length=seq_len*10
        start_date_temp= (pd.to_datetime(context.start_date) - datetime.timedelta(days=2*train_length)).strftime('%Y-%m-%d') # 多取几天的数据,这里取5倍
        len1=len(D.trading_days(start_date=start_date_temp, end_date=context.end_date)) 
        len2=len(D.trading_days(start_date=context.start_date, end_date=context.end_date))
        distance=len1-len2
        trade_day=D.trading_days(start_date=start_date_temp, end_date=context.end_date)
        start_date = trade_day.iloc[distance-train_length][0].strftime('%Y-%m-%d')
        split_date = trade_day.iloc[distance-1][0].strftime('%Y-%m-%d')
        fields = ['close', 'open', 'high', 'low', 'amount', 'volume']  # features因子
        batch = 1024 #整数,指定进行梯度下降时每个batch包含的样本数,训练时一个batch的样本会被计算一次梯度下降,使目标函数优化一步
        
        # 数据导入以及初步处理
        data1 = D.history_data(instrument, start_date, context.end_date, fields)
        data1['return'] = data1['close'].shift(-5) / data1['open'].shift(-1) - 1 #计算未来5日收益率(未来第五日的收盘价/明日的开盘价)
        data1=data1[data1.amount>0]
        datatime = data1['date'][data1.date>split_date]  #记录predictions的时间,回测要用
        data1['return'] = data1['return']
        data1['return'] = data1['return']*10  # 适当增大return范围,利于LSTM模型训练
        data1.reset_index(drop=True, inplace=True)
        scaledata = data1[fields]
        traindata = data1[data1.date<=split_date]
        
        # 数据处理:设定每个input(series×6features)以及数据标准化
        train_input = []
        train_output = []
        test_input = []
        for i in range(seq_len-1, len(traindata)):
            a = scale(scaledata[i+1-seq_len:i+1])
            train_input.append(a)
            c = data1['return'][i]
            train_output.append(c)
        for j in range(len(traindata), len(data1)):
            b = scale(scaledata[j+1-seq_len:j+1])
            test_input.append(b)
    
    
        # LSTM接受数组类型的输入
        train_x = np.array(train_input)
        train_y = np.array(train_output)
        test_x = np.array(test_input) 
    
       # 自定义激活函数
        import tensorflow.keras as tf
        def tanh(x): 
            return tf.tanh(x)
        # 构建神经网络层 1层LSTM层+3层Dense层
        # 用于1个输入情况
        lstm_input = Input(shape=(seq_len,len(fields)), name='lstm_input')
        lstm_output = LSTM(128,input_shape=(seq_len,len(fields)))(lstm_input)
        Dense_output_1 = Dense(32, activation='linear')(lstm_output)
        Dense_output_2 = Dense(1, activation='linear')(Dense_output_1)
        predictions = Dense(1)(Dense_output_2)
        model = Model(inputs=lstm_input, outputs=predictions)
        model.compile(optimizer='adam', loss='mse', metrics=['mse'])
        model.fit(train_x, train_y, batch_size=batch, nb_epoch=50, verbose=2)
        # 预测
        predictions = model.predict(test_x)
        # 如果预测值>0,取为1;如果预测值<=0,取为-1.为回测做准备
        for i in range(len(predictions)):
            if predictions[i]>0:
                predictions[i]=1
            elif predictions[i]<=0:
                predictions[i]=-1
                
        # 将预测值与时间整合作为回测数据
        cc = np.reshape(predictions,len(predictions), 1)
        dataprediction = pd.DataFrame()
        dataprediction['date'] = datatime
        dataprediction['direction']=np.round(cc)
        context.dataprediction=dataprediction
    
    
    m1 = M.instruments.v2(
        start_date='2014-07-01',
        end_date='2019-07-01',
        market='CN_STOCK_A',
        instrument_list=' ',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    # shift(close, -15) / shift(open, -1)-1
    # 计算收益:5日相对基准的收益率
    shift(close, -15) / shift(open, -1)-shift(benchmark_close, -15)/shift(benchmark_open, -1)
    # rank(shift(close, -15) / shift(open, -1))
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 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)
    # 未来5日最大相对收益率
    # shift(cal_max_ret(close, open,benchmark_close,benchmark_open, 15), -1)
    # 未来5日最大相对收益率所在天数
    # shift(cal_max_ret_date(close, open,benchmark_close,benchmark_open, 15), -1) + 1""",
        start_date='',
        end_date='',
        benchmark='000906.HIX',
        drop_na_label=True,
        cast_label_int=False,
        user_functions=m2_user_functions_bigquant_run
    )
    
    m13 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label'
    )
    
    m3 = M.input_features.v1(
        features="""std(amount_0,15)
    std(amount_0,20) 
    std(amount_0,6)
    rank((-1*((1-(open_0/close_0))**1)))
    ta_ema(volume_0, 21) 
    (close_0-mean(close_0,12))/mean(close_0,12)*100  
    (close_0-mean(close_0,24))/mean(close_0,24)*100 
    (close_0-delay(close_0,20))/delay(close_0,20)*100 
    ta_ema(((high_0+low_0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,7) 
    std(volume_0,10)
    ((close_0-open_0)/((high_0-low_0)+.001)) 
    ta_wma(close_0,5)/close_0 
    ((close_0-low_0)-(high_0-close_0))/(high_0-close_0)
    ((rank(max((amount_0/volume_0*adjust_factor_0-close_0),3))+rank(min((amount_0/volume_0*adjust_factor_0-close_0), 3)))*rank(delta(volume_0, 3)))
    ta_wma(close_0,30)/close_0
    (rank((amount_0/volume_0*adjust_factor_0-close_0))/rank((amount_0/volume_0*adjust_factor_0 + close_0)))
    ((-1*((low_0-close_0)*(open_0**5)))/((low_0-high_0)*(close_0**5)))
    -1*delta(((close_0-low_0)-(high_0-close_0))/(high_0-low_0),1)
    close_0-delay(close_0,5)
    ta_ema(close_0,5)/close_0
    (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)
    (close_0-delay(close_0,6))/delay(close_0,6)*100
    mean(mf_net_amount_m_0, 10)
    mean(mf_net_amount_l_0, 5)
    mean(mf_net_amount_l_0, 90)
    # fs_deducted_profit_ttm_0
    # fs_net_profit_ttm_0/market_cap_0
    # ta_adx_14_0
    # close_0>ta_atr(high_0, low_0, close_0, 14)
    # ta_obv_0
    # (close_0-close_12)/close_12>0.05
    # swing_volatility_30_0/swing_volatility_5_0
    # ta_sma_60_0
    # ta_wma_60_0
    # high_0>ta_bbands_middleband_28_0
    # close_0>ta_bbands_upperband_14_0
    # ta_rsi_28_0
    market_cap_float_0/market_cap_0
    rank(std(amount_0,15))
    rank_avg_amount_0/rank_avg_amount_8
    ts_argmin(low_0,20)
    rank_return_30
    (low_1-close_0)/close_0
    ta_bbands_lowerband_14_0
    mean(mf_net_pct_s_0,4)
    amount_0/avg_amount_3
    return_0/return_5
    return_1/return_5
    rank_avg_amount_7/rank_avg_amount_10
    ta_sma_10_0/close_0
    sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
    avg_turn_15/(turn_0+1e-5)
    return_10
    mf_net_pct_s_0
    (close_0-open_0)/close_1"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m29 = M.chinaa_stock_filter.v1(
        input_data=m15.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m29.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m14 = M.standardlize.v8(
        input_1=m16.data,
        input_2=m3.data,
        columns_input=''
    )
    
    m7 = M.join.v3(
        data1=m13.data,
        data2=m14.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m7.data,
        features=m3.data,
        window_size=5,
        feature_clip=43,
        flatten=True,
        window_along_col='instrument'
    )
    
    m4 = M.cached.v3(
        input_1=m26.data,
        input_2=m3.data,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-07-07'),
        end_date=T.live_run_param('trading_date', '2020-09-10'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m30 = M.chinaa_stock_filter.v1(
        input_data=m17.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m30.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m25 = M.standardlize.v8(
        input_1=m18.data,
        input_2=m3.data,
        columns_input=''
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m25.data,
        features=m3.data,
        window_size=5,
        feature_clip=43,
        flatten=True,
        window_along_col='instrument'
    )
    
    m8 = M.cached.v3(
        input_1=m27.data,
        input_2=m3.data,
        run=m8_run_bigquant_run,
        post_run=m8_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_layer_input.v1(
        shape='43,5',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m10 = M.dl_layer_conv1d.v1(
        inputs=m6.data,
        filters=60,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=1,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m12 = M.dl_layer_maxpooling1d.v1(
        inputs=m10.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m32 = M.dl_layer_conv1d.v1(
        inputs=m12.data,
        filters=60,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=1,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m33 = M.dl_layer_maxpooling1d.v1(
        inputs=m32.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m20 = M.dl_layer_lstm.v1(
        inputs=m33.data,
        units=64,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        recurrent_initializer='Orthogonal',
        bias_initializer='Zeros',
        unit_forget_bias=True,
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        recurrent_regularizer='None',
        recurrent_regularizer_l1=0,
        recurrent_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        recurrent_constraint='None',
        bias_constraint='None',
        dropout=0,
        recurrent_dropout=0,
        return_sequences=False,
        implementation='0',
        name=''
    )
    
    m21 = M.dl_layer_dropout.v1(
        inputs=m20.data,
        rate=0.5,
        noise_shape='',
        name=''
    )
    
    m22 = M.dl_layer_dense.v1(
        inputs=m21.data,
        units=64,
        activation='tanh',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m23 = M.dl_layer_dropout.v1(
        inputs=m22.data,
        rate=0.5,
        noise_shape='',
        name=''
    )
    
    m28 = M.dl_layer_dense.v1(
        inputs=m23.data,
        units=1,
        activation='sigmoid',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m34 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m28.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m34.data,
        training_data=m4.data_1,
        optimizer='Adam',
        loss='mean_absolute_error',
        metrics='mae',
        batch_size=1024,
        epochs=5,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m8.data_1,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m18.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m24.data_1,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='twap_8',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000906.HIX'
    )
    
    [2020-09-11 15:18:51.008918] WARNING tensorflow: `implementation=0` has been deprecated, and now defaults to `implementation=1`.Please update your layer call.
    
    Train on 2521203 samples
    Epoch 1/5
    2521203/2521203 - 1027s - loss: nan - mae: nan
    Epoch 2/5
    
    ---------------------------------------------------------------------------
    KeyboardInterrupt                         Traceback (most recent call last)
    <ipython-input-1-76965159cd6f> in <module>()
        675     epochs=5,
        676     n_gpus=0,
    --> 677     verbose='2:每个epoch输出一行记录'
        678 )
        679 
    
    KeyboardInterrupt: 
    In [ ]:
    '''
    # factor=shift(close_0,15) / close_0
    # factor = rank(ts_argmax(signedpower(where(((close_0/shift(close_0,1)-1) < 0), std((close_0/shift(close_0,1)-1), 20), close_0), 2), 5)) -0.5
    shift(ta_rsi(close,14),1)/60
    shift(ta_rsi(close,14),1)/100
    (close_0-mean(close_0,12))/mean(close_0,12)*100
    rank(std(amount_0,15))
    rank_avg_amount_0/rank_avg_amount_8
    ts_argmin(low_0,20)
    rank_return_30
    (low_1-close_0)/close_0
    ta_bbands_lowerband_14_0
    mean(mf_net_pct_s_0,4)
    amount_0/avg_amount_3
    return_0/return_5
    return_1/return_5
    rank_avg_amount_7/rank_avg_amount_10
    ta_sma_10_0/close_0
    sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
    avg_turn_15/(turn_0+1e-5)
    return_10
    mf_net_pct_s_0
    (close_0-open_0)/close_1
    
    '''
    

    第一次训练,5次迭代全部都为nan,但是整体都能跑完,回测也可以运行,就是回测结果和对照曲线没有差别。
    自己调整了下,目前训练的结果loss和mae依然都是nan,我运行到第二轮训练就先取消了。

    目前训练的结果为:
    Train on 2085214 samples Epoch 1/5

    2085214/2085214 - 620s - loss: nan - mae: nan


    (Daniel129) #2

    感觉可能是和输入特征有关, 换了特征因子后有的时候就能成功运行出结果,但具体原因是什么还是搞不太明白,有没有老师能够解答下


    (adhaha111) #3

    这个问题的原因很可能是您的特征中存在nan的值,您可以添加个nan的处理模块再试试呢


    (Daniel129) #5

    我在衍生特征抽取模块已经勾选了删除na数据,能麻烦请教下还需要怎么添加nan的处理模块呢?麻烦大佬了。


    (adhaha111) #6

    您可以在数据传入训练模块之前,使用"缺失数据处理"模块再处理一次,因为流程中的各种处理会产生nan值的:
    image


    (Daniel129) #7

    感谢感谢,那就是把“缺失数据处理“模块放到“连接数据”模块后可以吗?


    (adhaha111) #8

    嗯嗯,您可以试下还会不会出现nan


    (Daniel129) #9

    添加了缺失数据处理模块试了下, 现在训练结果正常了, 感谢~!!