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Init benchmarks
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4
examples/benchmarks/DNN/requirements.txt
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examples/benchmarks/DNN/requirements.txt
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pandas==1.1.2
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numpy==1.17.4
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scikit_learn==0.23.2
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torch==1.7.0
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62
examples/benchmarks/DNN/workflow_config_dnn.yaml
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examples/benchmarks/DNN/workflow_config_dnn.yaml
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provider_uri: "~/.qlib/qlib_data/cn_data"
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market: &market csi300
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benchmark: &benchmark SH000300
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data_handler_config: &data_handler_config
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start_time: 2008-01-01
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end_time: 2020-08-01
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fit_start_time: 2008-01-01
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fit_end_time: 2014-12-31
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instruments: *market
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port_analysis_config: &port_analysis_config
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy.strategy
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kwargs:
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topk: 50
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n_drop: 5
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backtest:
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verbose: False
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limit_threshold: 0.095
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account: 100000000
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benchmark: *benchmark
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deal_price: close
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open_cost: 0.0005
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close_cost: 0.0015
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min_cost: 5
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task:
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model:
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class: DNNModelPytorch
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module_path: qlib.contrib.model.pytorch_nn
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kwargs:
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input_dim: 360
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output_dim: 1
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layers: [256, 512, 1024, 512, 256, 128, 64]
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lr: 0.001
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max_steps: 300
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batch_size: 2000
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early_stop_rounds: 50
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eval_steps: 20
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lr_decay: 0.96
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lr_decay_steps: 100
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optimizer: gd
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loss: mse
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: ALPHA360_Denoise
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module_path: qlib.contrib.data.handler
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kwargs: *data_handler_config
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segments:
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train: [2008-01-01, 2014-12-31]
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valid: [2015-01-01, 2016-12-31]
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test: [2017-01-01, 2020-08-01]
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record:
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- class: SignalRecord
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module_path: qlib.workflow.record_temp
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kwargs: {}
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- class: PortAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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config: *port_analysis_config
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