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Add csi500 benchmark for MLP model. (#1215)
* Add csi500 benchmark for MLP model. * Update MLP metric for Alpha158 dataset. Co-authored-by: vincilee <vincilee1994@outlook.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Jinge Wang
102
examples/benchmarks/MLP/workflow_config_mlp_Alpha158_csi500.yaml
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102
examples/benchmarks/MLP/workflow_config_mlp_Alpha158_csi500.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 csi500
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benchmark: &benchmark SH000905
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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:
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- <MODEL>
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- <DATASET>
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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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lr_decay: 0.96
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lr_decay_steps: 100
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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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@@ -0,0 +1,89 @@
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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 csi500
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benchmark: &benchmark SH000905
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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: 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:
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- <MODEL>
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- <DATASET>
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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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lr_decay: 0.96
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lr_decay_steps: 100
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optimizer: adam
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max_steps: 8000
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batch_size: 4096
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GPU: 0
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pt_model_kwargs:
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input_dim: 360
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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
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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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@@ -110,6 +110,7 @@ python run_all_model.py run 3 lightgbm Alpha158 csi500 # for models with random
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| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
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|------------|----------|-------------|-------------|-------------|-------------|-------------------|-------------------|--------------|
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| Linear | Alpha158 | 0.0332±0.00 | 0.3044±0.00 | 0.0462±0.00 | 0.4326±0.00 | 0.0382±0.00 | 0.1723±0.00 | -0.4876±0.00 |
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| MLP | Alpha158 | 0.0229±0.01 | 0.2181±0.05 | 0.0360±0.00 | 0.3409±0.02 | 0.0043±0.02 | 0.0602±0.27 | -0.2184±0.04 |
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| LightGBM | Alpha158 | 0.0399±0.00 | 0.4065±0.00 | 0.0482±0.00 | 0.5101±0.00 | 0.1284±0.00 | 1.5650±0.00 | -0.0635±0.00 |
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| CatBoost | Alpha158 | 0.0345±0.00 | 0.2855±0.00 | 0.0417±0.00 | 0.3740±0.00 | 0.0496±0.00 | 0.5977±0.00 | -0.1496±0.00 |
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| DoubleEnsemble | Alpha158 | 0.0380±0.00 | 0.3659±0.00 | 0.0442±0.00 | 0.4324±0.00 | 0.0382±0.00 | 0.1723±0.00 | -0.4876±0.00 |
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@@ -117,6 +118,7 @@ python run_all_model.py run 3 lightgbm Alpha158 csi500 # for models with random
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### Alpha360 dataset
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| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
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|------------|----------|-------------|-------------|-------------|-------------|-------------------|-------------------|--------------|
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| MLP | Alpha360 | 0.0258±0.00 | 0.2021±0.02 | 0.0426±0.00 | 0.3840±0.02 | 0.0022±0.02 | 0.0301±0.26 | -0.2064±0.02 |
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| LightGBM | Alpha360 | 0.0400±0.00 | 0.3605±0.00 | 0.0536±0.00 | 0.5431±0.00 | 0.0505±0.00 | 0.7658±0.02 | -0.1880±0.00 |
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| CatBoost | Alpha360 | 0.0382±0.00 | 0.3229±0.00 | 0.0489±0.00 | 0.4649±0.00 | 0.0297±0.00 | 0.4227±0.02 | -0.1499±0.01 |
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| DoubleEnsemble | Alpha360 | 0.0361±0.00 | 0.3092±0.00 | 0.0499±0.00 | 0.4793±0.00 | 0.0382±0.00 | 0.1723±0.02 | -0.4876±0.00 |
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