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bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m8', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2016-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2018-10-22'), # 数据结束日期\n train_update_days=250, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=None, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数\n train_data_min_days=250, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=250, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = start_date\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions,result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': trade}\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"run_now","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bq_graph","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"bq_graph_port","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-613"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-613"}],"OutputPortsInternal":[{"Name":"result","NodeId":"-613","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' 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[2023-04-17 13:23:46.108291] INFO: moduleinvoker: instruments.v2 开始运行..
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[2023-04-17 13:23:46.186821] INFO: moduleinvoker: 命中缓存
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[2023-04-17 13:23:46.792079] INFO: 自动标注(股票): 加载历史数据: 658578 行
[2023-04-17 13:23:46.793395] INFO: 自动标注(股票): 开始标注 ..
[2023-04-17 13:23:47.530469] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[1.273469s].
[2023-04-17 13:23:47.548525] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2023-04-17 13:23:47.967185] INFO: 基础特征抽取: 年份 2015, 特征行数=148558
[2023-04-17 13:23:49.236879] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
[2023-04-17 13:23:49.512171] INFO: 基础特征抽取: 年份 2017, 特征行数=17032
[2023-04-17 13:23:49.582047] INFO: 基础特征抽取: 总行数: 807136
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[2023-04-17 13:23:50.048471] INFO: 基础特征抽取: 年份 2016, 特征行数=157658
[2023-04-17 13:23:51.518270] INFO: 基础特征抽取: 年份 2017, 特征行数=743233
[2023-04-17 13:23:51.842111] INFO: 基础特征抽取: 年份 2018, 特征行数=39164
[2023-04-17 13:23:51.922634] INFO: 基础特征抽取: 总行数: 940055
[2023-04-17 13:23:51.926919] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[2.328944s].
[2023-04-17 13:23:51.937959] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
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[2023-04-17 13:23:53.064005] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.002s
[2023-04-17 13:23:53.066366] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.001s
[2023-04-17 13:23:53.438576] INFO: derived_feature_extractor: /y_2015, 148558
[2023-04-17 13:23:54.223125] INFO: derived_feature_extractor: /y_2016, 641546
[2023-04-17 13:23:54.577633] INFO: derived_feature_extractor: /y_2017, 17032
[2023-04-17 13:23:54.675647] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[2.737672s].
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[2023-04-17 13:23:55.907970] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.001s
[2023-04-17 13:23:55.910207] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.001s
[2023-04-17 13:23:56.262568] INFO: derived_feature_extractor: /y_2016, 157658
[2023-04-17 13:23:57.131125] INFO: derived_feature_extractor: /y_2017, 743233
[2023-04-17 13:23:57.559842] INFO: derived_feature_extractor: /y_2018, 39164
[2023-04-17 13:23:57.669605] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[2.985741s].
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[2023-04-17 13:24:02.297777] INFO: dropnan: /y_2018, 38914/39164
[2023-04-17 13:24:02.379557] INFO: dropnan: 行数: 928570/940055
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[2023-04-17 13:24:04.816709] INFO: StockRanker: 特征预处理 ..
[2023-04-17 13:24:05.500677] INFO: StockRanker: prepare data: training ..
[2023-04-17 13:24:06.160484] INFO: StockRanker: sort ..
[2023-04-17 13:24:11.747424] INFO: StockRanker训练: 11338aa2 准备训练: 586987 行数
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[2023-04-17 13:27:06.738776] INFO: 自动标注(股票): 加载历史数据: 765365 行
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[2023-04-17 13:27:09.130812] INFO: 基础特征抽取: 总行数: 809282
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[2023-04-17 13:27:10.518920] INFO: derived_feature_extractor: /y_2017, 164744
[2023-04-17 13:27:11.294769] INFO: derived_feature_extractor: /y_2018, 644538
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[2023-04-17 13:27:15.331580] INFO: dropnan: /y_2017, 163094/164744
[2023-04-17 13:27:16.436598] INFO: dropnan: /y_2018, 642449/644538
[2023-04-17 13:27:16.508074] INFO: dropnan: 行数: 805543/809282
[2023-04-17 13:27:16.516095] INFO: moduleinvoker: dropnan.v1 运行完成[1.626288s].
[2023-04-17 13:27:16.524565] INFO: moduleinvoker: dropnan.v1 开始运行..
[2023-04-17 13:27:16.697558] INFO: dropnan: /y_2016, 0/0
[2023-04-17 13:27:18.014343] INFO: dropnan: /y_2017, 679284/684644
[2023-04-17 13:27:18.033302] INFO: dropnan: /y_2018, 0/0
[2023-04-17 13:27:18.082495] INFO: dropnan: 行数: 679284/684644
[2023-04-17 13:27:18.087164] INFO: moduleinvoker: dropnan.v1 运行完成[1.562596s].
[2023-04-17 13:27:18.094093] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..
[2023-04-17 13:27:19.238955] INFO: StockRanker: 特征预处理 ..
[2023-04-17 13:27:20.008183] INFO: StockRanker: prepare data: training ..
[2023-04-17 13:27:20.764377] INFO: StockRanker: sort ..
[2023-04-17 13:27:26.866531] INFO: StockRanker训练: 85085afc 准备训练: 679284 行数
[2023-04-17 13:27:27.110495] INFO: StockRanker训练: 正在训练 ..
[2023-04-17 13:31:08.051007] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[229.956898s].
[2023-04-17 13:31:08.060004] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..
[2023-04-17 13:31:08.442668] INFO: StockRanker预测: /y_2017 ..
[2023-04-17 13:31:09.488760] INFO: StockRanker预测: /y_2018 ..
[2023-04-17 13:31:10.787747] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[2.727727s].
[2023-04-17 13:31:10.824492] INFO: moduleinvoker: cached.v3 开始运行..
[2023-04-17 13:31:11.826630] ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: AttributeError: 'DataFrame' object has no attribute 'ix'
[2023-04-17 13:31:11.832664] ERROR: moduleinvoker: module name: hyper_rolling_train, module version: v1, trackeback: AttributeError: 'DataFrame' object has no attribute 'ix'
bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a8fdb8d43e744293b8b4fc2bd9c8e3b5"}/bigcharts-data-end
bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a0476cc8f07d4d0488821a3a4a8377ce"}/bigcharts-data-end
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-3d8c6e6cbc33> in <module>
294
295
--> 296 m20 = M.hyper_rolling_train.v1(
297 run=m20_run_bigquant_run,
298 run_now=True,
<ipython-input-1-3d8c6e6cbc33> in m20_run_bigquant_run(bq_graph, inputs, trading_days_market, train_instruments_mid, test_instruments_mid, predict_mid, trade_mid, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
282
283 # 合并预测结果并回测
--> 284 mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions,result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
285 parameters = {}
286 parameters['*.__enabled__'] = False
<ipython-input-1-3d8c6e6cbc33> in merge_datasources(input_1)
223 ):
224 def merge_datasources(input_1):
--> 225 df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
226 df = pd.concat(df_list)
227 instrument_data = {
<ipython-input-1-3d8c6e6cbc33> in <listcomp>(.0)
223 ):
224 def merge_datasources(input_1):
--> 225 df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
226 df = pd.concat(df_list)
227 instrument_data = {
AttributeError: 'DataFrame' object has no attribute 'ix'