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Add model template;
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Young
15
examples/benchmarks/GeneralPtNN/README.md
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15
examples/benchmarks/GeneralPtNN/README.md
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# Introduction
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What is GeneralPtNN
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- Fix previous design that fail to support both Time-series and tabular data
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- Now you can just replace the Pytorch model structure to run a NN model.
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We provide an example to demonstrate the effectiveness of the current design.
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- `workflow_config_gru.yaml` align with previous results [GRU(Kyunghyun Cho, et al.)](../README.md#Alpha158 dataset)
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- `workflow_config_mlp.yaml` align with previous results [MLP](../README.md#Alpha158 dataset)
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# TODO
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We will align existing models to current design.
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97
examples/benchmarks/GeneralPtNN/workflow_config_gru.yaml
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examples/benchmarks/GeneralPtNN/workflow_config_gru.yaml
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qlib_init:
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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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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infer_processors:
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- class: FilterCol
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kwargs:
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fields_group: feature
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col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10",
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"ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5",
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"RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
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]
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- class: RobustZScoreNorm
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kwargs:
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fields_group: feature
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clip_outlier: true
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- class: Fillna
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kwargs:
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fields_group: feature
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learn_processors:
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- class: DropnaLabel
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- class: CSRankNorm
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kwargs:
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fields_group: label
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label: ["Ref($close, -2) / Ref($close, -1) - 1"]
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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
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kwargs:
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signal: <PRED>
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topk: 50
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n_drop: 5
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backtest:
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start_time: 2017-01-01
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end_time: 2020-08-01
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account: 100000000
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benchmark: *benchmark
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exchange_kwargs:
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limit_threshold: 0.095
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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: GRU
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module_path: qlib.contrib.model.pytorch_gru_ts
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kwargs:
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d_feat: 20
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hidden_size: 64
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num_layers: 2
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dropout: 0.0
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n_epochs: 200
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lr: 2e-4
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early_stop: 10
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batch_size: 800
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metric: loss
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loss: mse
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n_jobs: 20
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GPU: 0
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dataset:
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class: TSDatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: Alpha158
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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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step_len: 20
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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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model: <MODEL>
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dataset: <DATASET>
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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ana_long_short: False
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ann_scaler: 252
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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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98
examples/benchmarks/GeneralPtNN/workflow_config_mlp.yaml
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98
examples/benchmarks/GeneralPtNN/workflow_config_mlp.yaml
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qlib_init:
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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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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infer_processors: [
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{
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"class" : "DropCol",
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"kwargs":{"col_list": ["VWAP0"]}
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},
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{
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"class" : "CSZFillna",
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"kwargs":{"fields_group": "feature"}
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}
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]
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learn_processors: [
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{
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"class" : "DropCol",
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"kwargs":{"col_list": ["VWAP0"]}
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},
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{
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"class" : "DropnaProcessor",
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"kwargs":{"fields_group": "feature"}
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},
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"DropnaLabel",
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{
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"class": "CSZScoreNorm",
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"kwargs": {"fields_group": "label"}
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}
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]
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process_type: "independent"
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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
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kwargs:
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signal: <PRED>
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topk: 50
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n_drop: 5
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backtest:
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start_time: 2017-01-01
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end_time: 2020-08-01
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account: 100000000
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benchmark: *benchmark
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exchange_kwargs:
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limit_threshold: 0.095
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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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loss: mse
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lr: 0.002
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optimizer: adam
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max_steps: 8000
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batch_size: 8192
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GPU: 0
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weight_decay: 0.0002
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pt_model_kwargs:
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input_dim: 157
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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: Alpha158
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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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model: <MODEL>
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dataset: <DATASET>
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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ana_long_short: False
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ann_scaler: 252
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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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