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\n}\n","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":"0","type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_model","node_id":"-1098"},{"name":"training_data","node_id":"-1098"},{"name":"validation_data","node_id":"-1098"}],"output_ports":[{"name":"data","node_id":"-1098"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-1540","module_id":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","parameters":[{"name":"batch_size","value":"1024","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":0,"type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"trained_model","node_id":"-1540"},{"name":"input_data","node_id":"-1540"}],"output_ports":[{"name":"data","node_id":"-1540"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-2431","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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","type":"Literal","bound_global_parameter":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","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-2431"},{"name":"input_2","node_id":"-2431"},{"name":"input_3","node_id":"-2431"}],"output_ports":[{"name":"data_1","node_id":"-2431"},{"name":"data_2","node_id":"-2431"},{"name":"data_3","node_id":"-2431"}],"cacheable":true,"seq_num":24,"comment":"","comment_collapsed":true},{"node_id":"-243","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":1,"type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","type":"Literal","bound_global_parameter":null},{"name":"window_along_col","value":"instrument","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-243"},{"name":"features","node_id":"-243"}],"output_ports":[{"name":"data","node_id":"-243"}],"cacheable":true,"seq_num":26,"comment":"","comment_collapsed":true},{"node_id":"-251","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":1,"type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","type":"Literal","bound_global_parameter":null},{"name":"window_along_col","value":"instrument","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-251"},{"name":"features","node_id":"-251"}],"output_ports":[{"name":"data","node_id":"-251"}],"cacheable":true,"seq_num":27,"comment":"","comment_collapsed":true},{"node_id":"-436","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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[2022-06-02 16:30:33.536881] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..
[2022-06-02 16:30:33.548915] INFO: moduleinvoker: 命中缓存
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[2022-06-02 16:30:33.570723] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-02 16:30:33.589151] INFO: moduleinvoker: 命中缓存
[2022-06-02 16:30:33.590929] INFO: moduleinvoker: cached.v3 运行完成[0.020227s].
[2022-06-02 16:30:33.596216] INFO: moduleinvoker: instruments.v2 开始运行..
[2022-06-02 16:30:33.602194] INFO: moduleinvoker: 命中缓存
[2022-06-02 16:30:33.603525] INFO: moduleinvoker: instruments.v2 运行完成[0.007318s].
[2022-06-02 16:30:33.624769] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
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[2022-06-02 16:30:33.649724] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
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[2022-06-02 16:30:33.666701] INFO: moduleinvoker: standardlize.v8 开始运行..
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[2022-06-02 16:30:33.705356] INFO: moduleinvoker: fillnan.v1 开始运行..
[2022-06-02 16:30:33.715810] INFO: moduleinvoker: 命中缓存
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[2022-06-02 16:30:33.736378] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..
[2022-06-02 16:30:33.749763] INFO: moduleinvoker: 命中缓存
[2022-06-02 16:30:33.751561] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.015196s].
[2022-06-02 16:30:33.779199] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.007733s].
[2022-06-02 16:30:36.045324] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[2.252457s].
[2022-06-02 16:30:36.070390] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.006841s].
[2022-06-02 16:30:36.100203] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.02067s].
[2022-06-02 16:30:36.120330] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.012223s].
[2022-06-02 16:30:36.147032] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.0161s].
[2022-06-02 16:30:36.191095] INFO: moduleinvoker: cached.v3 开始运行..
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[2022-06-02 16:30:36.220289] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.062524s].
[2022-06-02 16:30:36.245898] INFO: moduleinvoker: dl_model_train.v1 开始运行..
[2022-06-02 16:30:36.257903] INFO: moduleinvoker: 命中缓存
[2022-06-02 16:30:36.264642] INFO: moduleinvoker: dl_model_train.v1 运行完成[0.018757s].
[2022-06-02 16:30:36.274817] INFO: moduleinvoker: dl_model_predict.v1 开始运行..
[2022-06-02 16:30:36.288355] INFO: moduleinvoker: 命中缓存
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[2022-06-02 16:30:38.655002] INFO: moduleinvoker: backtest.v8 开始运行..
[2022-06-02 16:30:38.661918] INFO: backtest: biglearning backtest:V8.6.2
[2022-06-02 16:30:38.663191] INFO: backtest: product_type:stock by specified
[2022-06-02 16:30:38.801037] INFO: moduleinvoker: cached.v2 开始运行..
[2022-06-02 16:30:38.816791] INFO: moduleinvoker: 命中缓存
[2022-06-02 16:30:38.820792] INFO: moduleinvoker: cached.v2 运行完成[0.01974s].
[2022-06-02 16:30:47.701504] INFO: algo: TradingAlgorithm V1.8.7
[2022-06-02 16:30:48.572874] INFO: algo: trading transform...
[2022-06-02 16:31:20.989423] INFO: Performance: Simulated 243 trading days out of 243.
[2022-06-02 16:31:20.991220] INFO: Performance: first open: 2021-01-04 09:30:00+00:00
[2022-06-02 16:31:20.992521] INFO: Performance: last close: 2021-12-31 15:00:00+00:00
[2022-06-02 16:31:31.806877] INFO: moduleinvoker: backtest.v8 运行完成[53.151883s].
[2022-06-02 16:31:31.808559] INFO: moduleinvoker: trade.v4 运行完成[55.456812s].
DataSource(ee47399e8b94420d913888ad88c8bf6eT)
- 收益率42.78%
- 年化收益率44.67%
- 基准收益率-5.2%
- 阿尔法0.48
- 贝塔0.43
- 夏普比率1.75
- 胜率0.5
- 盈亏比1.39
- 收益波动率20.67%
- 信息比率0.12
- 最大回撤12.3%
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