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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='m24', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2015-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2016-07-01'), # 数据结束日期\n train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=100, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数\n train_data_min_days=100, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=100, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds.read_df() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = start_date\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[result[predict_mid].data_1 for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': 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[2019-09-03 09:47:58.930908] INFO: bigquant: input_features.v1 开始运行..
[2019-09-03 09:47:59.028134] INFO: bigquant: input_features.v1 运行完成[0.097225s].
[2019-09-03 09:47:59.031551] INFO: bigquant: instruments.v2 开始运行..
[2019-09-03 09:47:59.167891] INFO: bigquant: instruments.v2 运行完成[0.136318s].
[2019-09-03 09:47:59.171529] INFO: bigquant: instruments.v2 开始运行..
[2019-09-03 09:47:59.289589] INFO: bigquant: instruments.v2 运行完成[0.118042s].
[2019-09-03 09:47:59.356272] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-09-03 09:48:05.317094] INFO: 基础特征抽取: 年份 2015, 特征行数=234600
[2019-09-03 09:48:05.444001] INFO: 基础特征抽取: 总行数: 234600
[2019-09-03 09:48:05.456075] INFO: bigquant: general_feature_extractor.v7 运行完成[6.099799s].
[2019-09-03 09:48:05.459915] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
[2019-09-03 09:48:06.657132] INFO: 自动标注(股票): 加载历史数据: 234600 行
[2019-09-03 09:48:06.660054] INFO: 自动标注(股票): 开始标注 ..
[2019-09-03 09:48:08.043039] INFO: bigquant: advanced_auto_labeler.v2 运行完成[2.583107s].
[2019-09-03 09:48:08.086253] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-09-03 09:48:09.133051] INFO: 基础特征抽取: 年份 2015, 特征行数=202913
[2019-09-03 09:48:09.219651] INFO: 基础特征抽取: 总行数: 202913
[2019-09-03 09:48:09.227343] INFO: bigquant: general_feature_extractor.v7 运行完成[1.141078s].
[2019-09-03 09:48:09.379144] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-09-03 09:48:10.972219] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.414s
[2019-09-03 09:48:12.454066] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 1.479s
[2019-09-03 09:48:13.918275] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 1.460s
[2019-09-03 09:48:13.922936] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.003s
[2019-09-03 09:48:15.393929] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.469s
[2019-09-03 09:48:16.687181] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.290s
[2019-09-03 09:48:18.206715] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.517s
[2019-09-03 09:48:18.362830] INFO: derived_feature_extractor: /y_2015, 234600
[2019-09-03 09:48:18.716598] INFO: bigquant: derived_feature_extractor.v3 运行完成[9.337434s].
[2019-09-03 09:48:18.719846] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:48:19.138261] INFO: bigquant: standardlize.v8 运行完成[0.418391s].
[2019-09-03 09:48:19.141980] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-09-03 09:48:20.619701] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.292s
[2019-09-03 09:48:22.707114] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 2.085s
[2019-09-03 09:48:24.320316] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 1.611s
[2019-09-03 09:48:24.326783] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.004s
[2019-09-03 09:48:25.803911] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.474s
[2019-09-03 09:48:27.233578] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.428s
[2019-09-03 09:48:28.930219] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.695s
[2019-09-03 09:48:29.086637] INFO: derived_feature_extractor: /y_2015, 202913
[2019-09-03 09:48:29.523498] INFO: bigquant: derived_feature_extractor.v3 运行完成[10.381498s].
[2019-09-03 09:48:29.570102] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:48:30.526011] INFO: bigquant: standardlize.v8 运行完成[0.955899s].
[2019-09-03 09:48:30.529828] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:48:31.323477] INFO: bigquant: standardlize.v8 运行完成[0.793651s].
[2019-09-03 09:48:31.369932] INFO: bigquant: join.v3 开始运行..
[2019-09-03 09:48:31.986806] INFO: join: /data, 行数=169862/183844, 耗时=0.380524s
[2019-09-03 09:48:32.145661] INFO: join: 最终行数: 169862
[2019-09-03 09:48:32.149426] INFO: bigquant: join.v3 运行完成[0.779488s].
[2019-09-03 09:48:32.203987] INFO: bigquant: dl_convert_to_bin.v2 开始运行..
[2019-09-03 09:48:32.730580] INFO: bigquant: dl_convert_to_bin.v2 运行完成[0.526591s].
[2019-09-03 09:48:32.843030] INFO: bigquant: dl_convert_to_bin.v2 开始运行..
[2019-09-03 09:48:33.394229] INFO: bigquant: dl_convert_to_bin.v2 运行完成[0.551196s].
[2019-09-03 09:48:33.485534] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:48:33.534875] INFO: bigquant: 命中缓存
[2019-09-03 09:48:33.538106] INFO: bigquant: cached.v3 运行完成[0.052554s].
[2019-09-03 09:48:33.542102] INFO: bigquant: dl_model_train.v1 开始运行..
[2019-09-03 09:48:33.934007] INFO: dl_model_train: 准备训练,训练样本个数:169862,迭代次数:5
[2019-09-03 09:48:55.661873] INFO: dl_model_train: 训练结束,耗时:21.73s
[2019-09-03 09:48:55.792548] INFO: bigquant: dl_model_train.v1 运行完成[22.25043s].
[2019-09-03 09:48:55.796243] INFO: bigquant: dl_model_predict.v1 开始运行..
[2019-09-03 09:48:57.401288] INFO: bigquant: dl_model_predict.v1 运行完成[1.605038s].
[2019-09-03 09:48:57.407503] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:48:58.133430] INFO: bigquant: cached.v3 运行完成[0.725891s].
[2019-09-03 09:48:58.144946] INFO: bigquant: input_features.v1 开始运行..
[2019-09-03 09:48:58.256207] INFO: bigquant: input_features.v1 运行完成[0.111252s].
[2019-09-03 09:48:58.259547] INFO: bigquant: instruments.v2 开始运行..
[2019-09-03 09:48:58.434447] INFO: bigquant: instruments.v2 运行完成[0.174888s].
[2019-09-03 09:48:58.438398] INFO: bigquant: instruments.v2 开始运行..
[2019-09-03 09:48:58.589190] INFO: bigquant: instruments.v2 运行完成[0.150795s].
[2019-09-03 09:48:58.655172] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-09-03 09:49:00.031891] INFO: 基础特征抽取: 年份 2015, 特征行数=226287
[2019-09-03 09:49:00.217879] INFO: 基础特征抽取: 总行数: 226287
[2019-09-03 09:49:00.236368] INFO: bigquant: general_feature_extractor.v7 运行完成[1.581157s].
[2019-09-03 09:49:00.242654] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
[2019-09-03 09:49:00.893865] INFO: 自动标注(股票): 加载历史数据: 226287 行
[2019-09-03 09:49:00.903691] INFO: 自动标注(股票): 开始标注 ..
[2019-09-03 09:49:02.305776] INFO: bigquant: advanced_auto_labeler.v2 运行完成[2.063112s].
[2019-09-03 09:49:02.386152] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-09-03 09:49:03.706779] INFO: 基础特征抽取: 年份 2015, 特征行数=132185
[2019-09-03 09:49:04.735769] INFO: 基础特征抽取: 年份 2016, 特征行数=91913
[2019-09-03 09:49:04.995628] INFO: 基础特征抽取: 总行数: 224098
[2019-09-03 09:49:05.006544] INFO: bigquant: general_feature_extractor.v7 运行完成[2.620388s].
[2019-09-03 09:49:05.222311] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-09-03 09:49:07.362213] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.754s
[2019-09-03 09:49:08.930775] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 1.566s
[2019-09-03 09:49:10.421632] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 1.482s
[2019-09-03 09:49:10.427486] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.003s
[2019-09-03 09:49:11.764034] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.334s
[2019-09-03 09:49:13.334199] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.568s
[2019-09-03 09:49:14.840106] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.502s
[2019-09-03 09:49:15.131020] INFO: derived_feature_extractor: /y_2015, 226287
[2019-09-03 09:49:15.623586] INFO: bigquant: derived_feature_extractor.v3 运行完成[10.401269s].
[2019-09-03 09:49:15.628096] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:49:16.356850] INFO: bigquant: standardlize.v8 运行完成[0.728695s].
[2019-09-03 09:49:16.362063] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-09-03 09:49:18.257053] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.576s
[2019-09-03 09:49:20.029688] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 1.770s
[2019-09-03 09:49:22.208379] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 2.176s
[2019-09-03 09:49:22.216689] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.006s
[2019-09-03 09:49:23.672046] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.453s
[2019-09-03 09:49:25.214009] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.538s
[2019-09-03 09:49:26.778408] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.562s
[2019-09-03 09:49:26.941520] INFO: derived_feature_extractor: /y_2015, 132185
[2019-09-03 09:49:27.101616] INFO: derived_feature_extractor: /y_2016, 91913
[2019-09-03 09:49:27.621252] INFO: bigquant: derived_feature_extractor.v3 运行完成[11.259176s].
[2019-09-03 09:49:27.675645] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:49:28.999026] INFO: bigquant: standardlize.v8 运行完成[1.323367s].
[2019-09-03 09:49:29.005858] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:49:30.374692] INFO: bigquant: standardlize.v8 运行完成[1.368807s].
[2019-09-03 09:49:30.445007] INFO: bigquant: join.v3 开始运行..
[2019-09-03 09:49:31.273311] INFO: join: /data, 行数=159227/175188, 耗时=0.419585s
[2019-09-03 09:49:31.536823] INFO: join: 最终行数: 159227
[2019-09-03 09:49:31.541427] INFO: bigquant: join.v3 运行完成[1.096412s].
[2019-09-03 09:49:31.610710] INFO: bigquant: dl_convert_to_bin.v2 开始运行..
[2019-09-03 09:49:32.412444] INFO: bigquant: dl_convert_to_bin.v2 运行完成[0.801631s].
[2019-09-03 09:49:32.615567] INFO: bigquant: dl_convert_to_bin.v2 开始运行..
[2019-09-03 09:49:33.169663] INFO: bigquant: dl_convert_to_bin.v2 运行完成[0.554096s].
[2019-09-03 09:49:33.254784] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:49:33.319865] INFO: bigquant: 命中缓存
[2019-09-03 09:49:33.322440] INFO: bigquant: cached.v3 运行完成[0.067783s].
[2019-09-03 09:49:33.325607] INFO: bigquant: dl_model_train.v1 开始运行..
[2019-09-03 09:49:33.851800] INFO: dl_model_train: 准备训练,训练样本个数:159227,迭代次数:5
[2019-09-03 09:49:59.122774] INFO: dl_model_train: 训练结束,耗时:25.26s
[2019-09-03 09:50:00.279870] INFO: bigquant: dl_model_train.v1 运行完成[26.954252s].
[2019-09-03 09:50:00.290090] INFO: bigquant: dl_model_predict.v1 开始运行..
[2019-09-03 09:50:02.472155] INFO: bigquant: dl_model_predict.v1 运行完成[2.182053s].
[2019-09-03 09:50:02.478284] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:50:03.246056] INFO: bigquant: cached.v3 运行完成[0.767729s].
[2019-09-03 09:50:03.262104] INFO: bigquant: input_features.v1 开始运行..
[2019-09-03 09:50:03.662337] INFO: bigquant: input_features.v1 运行完成[0.400227s].
[2019-09-03 09:50:03.669871] INFO: bigquant: instruments.v2 开始运行..
[2019-09-03 09:50:03.946636] INFO: bigquant: instruments.v2 运行完成[0.276742s].
[2019-09-03 09:50:03.950548] INFO: bigquant: instruments.v2 开始运行..
[2019-09-03 09:50:04.111730] INFO: bigquant: instruments.v2 运行完成[0.161168s].
[2019-09-03 09:50:04.169651] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-09-03 09:50:05.574860] INFO: 基础特征抽取: 年份 2015, 特征行数=155628
[2019-09-03 09:50:06.745857] INFO: 基础特征抽取: 年份 2016, 特征行数=91913
[2019-09-03 09:50:06.869394] INFO: 基础特征抽取: 总行数: 247541
[2019-09-03 09:50:06.875931] INFO: bigquant: general_feature_extractor.v7 运行完成[2.706286s].
[2019-09-03 09:50:06.879207] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
[2019-09-03 09:50:08.037265] INFO: 自动标注(股票): 加载历史数据: 247541 行
[2019-09-03 09:50:08.042890] INFO: 自动标注(股票): 开始标注 ..
[2019-09-03 09:50:10.175222] INFO: bigquant: advanced_auto_labeler.v2 运行完成[3.295985s].
[2019-09-03 09:50:10.271417] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-09-03 09:50:12.335211] INFO: 基础特征抽取: 年份 2016, 特征行数=216267
[2019-09-03 09:50:12.704520] INFO: 基础特征抽取: 总行数: 216267
[2019-09-03 09:50:12.741922] INFO: bigquant: general_feature_extractor.v7 运行完成[2.470496s].
[2019-09-03 09:50:13.008663] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-09-03 09:50:15.139772] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.631s
[2019-09-03 09:50:17.016663] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 1.874s
[2019-09-03 09:50:18.779938] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 1.757s
[2019-09-03 09:50:18.789102] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.006s
[2019-09-03 09:50:20.468712] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.677s
[2019-09-03 09:50:22.340953] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.870s
[2019-09-03 09:50:24.407039] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 2.063s
[2019-09-03 09:50:24.708133] INFO: derived_feature_extractor: /y_2015, 155628
[2019-09-03 09:50:25.102052] INFO: derived_feature_extractor: /y_2016, 91913
[2019-09-03 09:50:25.587205] INFO: bigquant: derived_feature_extractor.v3 运行完成[12.57853s].
[2019-09-03 09:50:25.595095] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:50:26.244656] INFO: bigquant: standardlize.v8 运行完成[0.649529s].
[2019-09-03 09:50:26.248125] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-09-03 09:50:28.190896] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.670s
[2019-09-03 09:50:30.694526] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 2.492s
[2019-09-03 09:50:33.095794] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 2.391s
[2019-09-03 09:50:33.100878] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.003s
[2019-09-03 09:50:35.264843] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 2.158s
[2019-09-03 09:50:37.357186] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 2.090s
[2019-09-03 09:50:39.081167] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.718s
[2019-09-03 09:50:39.237056] INFO: derived_feature_extractor: /y_2016, 216267
[2019-09-03 09:50:39.653159] INFO: bigquant: derived_feature_extractor.v3 运行完成[13.405034s].
[2019-09-03 09:50:39.696392] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:50:41.518344] INFO: bigquant: standardlize.v8 运行完成[1.821903s].
[2019-09-03 09:50:41.522020] INFO: bigquant: standardlize.v8 开始运行..
[2019-09-03 09:50:42.700298] INFO: bigquant: standardlize.v8 运行完成[1.178248s].
[2019-09-03 09:50:42.801244] INFO: bigquant: join.v3 开始运行..
[2019-09-03 09:50:43.626628] INFO: join: /data, 行数=180948/194925, 耗时=0.546676s
[2019-09-03 09:50:44.179912] INFO: join: 最终行数: 180948
[2019-09-03 09:50:44.184929] INFO: bigquant: join.v3 运行完成[1.383687s].
[2019-09-03 09:50:44.263002] INFO: bigquant: dl_convert_to_bin.v2 开始运行..
[2019-09-03 09:50:44.673381] INFO: bigquant: dl_convert_to_bin.v2 运行完成[0.410347s].
[2019-09-03 09:50:44.814221] INFO: bigquant: dl_convert_to_bin.v2 开始运行..
[2019-09-03 09:50:45.591727] INFO: bigquant: dl_convert_to_bin.v2 运行完成[0.777503s].
[2019-09-03 09:50:45.742454] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:50:45.859223] INFO: bigquant: 命中缓存
[2019-09-03 09:50:45.864432] INFO: bigquant: cached.v3 运行完成[0.121966s].
[2019-09-03 09:50:45.871533] INFO: bigquant: dl_model_train.v1 开始运行..
[2019-09-03 09:50:46.399603] INFO: dl_model_train: 准备训练,训练样本个数:180948,迭代次数:5
[2019-09-03 09:51:20.266201] INFO: dl_model_train: 训练结束,耗时:33.86s
[2019-09-03 09:51:20.398362] INFO: bigquant: dl_model_train.v1 运行完成[34.526799s].
[2019-09-03 09:51:20.401830] INFO: bigquant: dl_model_predict.v1 开始运行..
[2019-09-03 09:51:22.440885] INFO: bigquant: dl_model_predict.v1 运行完成[2.039041s].
[2019-09-03 09:51:22.453573] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:51:23.245942] INFO: bigquant: cached.v3 运行完成[0.792357s].
[2019-09-03 09:51:23.254716] INFO: bigquant: cached.v3 开始运行..
[2019-09-03 09:51:24.573757] INFO: bigquant: cached.v3 运行完成[1.31903s].
[2019-09-03 09:51:24.678316] INFO: bigquant: backtest.v8 开始运行..
[2019-09-03 09:51:24.686885] INFO: bigquant: biglearning backtest:V8.2.10
[2019-09-03 09:51:24.692140] INFO: bigquant: product_type:stock by specified
[2019-09-03 09:51:24.942752] INFO: bigquant: cached.v2 开始运行..
[2019-09-03 09:51:41.698210] INFO: bigquant: 读取股票行情完成:1326612
[2019-09-03 09:52:14.659580] INFO: bigquant: cached.v2 运行完成[49.716805s].
[2019-09-03 09:52:21.573077] INFO: algo: TradingAlgorithm V1.5.7
[2019-09-03 09:52:22.876146] INFO: algo: trading transform...
[2019-09-03 09:52:41.177505] INFO: Performance: Simulated 246 trading days out of 246.
[2019-09-03 09:52:41.179515] INFO: Performance: first open: 2015-07-01 09:30:00+00:00
[2019-09-03 09:52:41.181673] INFO: Performance: last close: 2016-07-01 15:00:00+00:00
[2019-09-03 09:52:53.138415] INFO: bigquant: backtest.v8 运行完成[88.460029s].
Epoch 1/5
- 4s - loss: 0.9815 - mean_squared_error: 0.9815
Epoch 2/5
- 4s - loss: 0.9759 - mean_squared_error: 0.9759
Epoch 3/5
- 4s - loss: 0.9751 - mean_squared_error: 0.9751
Epoch 4/5
- 4s - loss: 0.9743 - mean_squared_error: 0.9743
Epoch 5/5
- 4s - loss: 0.9738 - mean_squared_error: 0.9738
DataSource(65ee60d9a4e14897881e13d213351849T, v3)
Epoch 1/5
- 4s - loss: 0.9687 - mean_squared_error: 0.9687
Epoch 2/5
- 4s - loss: 0.9613 - mean_squared_error: 0.9613
Epoch 3/5
- 5s - loss: 0.9602 - mean_squared_error: 0.9602
Epoch 4/5
- 6s - loss: 0.9579 - mean_squared_error: 0.9579
Epoch 5/5
- 5s - loss: 0.9565 - mean_squared_error: 0.9565
DataSource(b01372df3ac4464fadd25e56581b3eccT, v3)
Epoch 1/5
- 8s - loss: 0.9732 - mean_squared_error: 0.9732
Epoch 2/5
- 7s - loss: 0.9677 - mean_squared_error: 0.9677
Epoch 3/5
- 6s - loss: 0.9665 - mean_squared_error: 0.9665
Epoch 4/5
- 6s - loss: 0.9653 - mean_squared_error: 0.9653
Epoch 5/5
- 6s - loss: 0.9642 - mean_squared_error: 0.9642
DataSource(eea8b6306cf648809d3a7b942bf6c696T, v3)
- 收益率29.49%
- 年化收益率30.31%
- 基准收益率-29.48%
- 阿尔法0.71
- 贝塔1.09
- 夏普比率0.72
- 胜率0.57
- 盈亏比0.94
- 收益波动率50.81%
- 信息比率0.14
- 最大回撤36.03%
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