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GCN/GAT_INDUS MODEL #1476
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GCN/GAT_INDUS MODEL #1476
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102 changes: 102 additions & 0 deletions
102
examples/benchmarks/GATs/workflow_config_gats_indus_Alpha158.yaml
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| qlib_init: | ||
| provider_uri: "~/.qlib/qlib_data/cn_data" | ||
| region: cn | ||
| market: &market csi300 | ||
| benchmark: &benchmark SH000300 | ||
| experiment_name: gats_add_ind_ts | ||
| data_handler_config: &data_handler_config | ||
| start_time: 2008-01-01 | ||
| end_time: 2020-08-01 | ||
| fit_start_time: 2008-01-01 | ||
| fit_end_time: 2014-12-31 | ||
| instruments: *market | ||
| infer_processors: | ||
| - class: FilterCol | ||
| kwargs: | ||
| fields_group: feature | ||
| col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", | ||
| "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", | ||
| "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW" | ||
| ] | ||
| - class: RobustZScoreNorm | ||
| kwargs: | ||
| fields_group: feature | ||
| clip_outlier: true | ||
| - class: Fillna | ||
| kwargs: | ||
| fields_group: feature | ||
| learn_processors: | ||
| - class: DropnaLabel | ||
| - class: CSRankNorm | ||
| kwargs: | ||
| fields_group: label | ||
| label: ["Ref($close, -2) / Ref($close, -1) - 1"] | ||
| port_analysis_config: &port_analysis_config | ||
| strategy: | ||
| class: TopkDropoutStrategy | ||
| module_path: qlib.contrib.strategy | ||
| kwargs: | ||
| signal: | ||
| - <MODEL> | ||
| - <DATASET> | ||
| topk: 50 | ||
| n_drop: 5 | ||
| backtest: | ||
| start_time: 2017-01-01 | ||
| end_time: 2020-08-01 | ||
| account: 100000000 | ||
| benchmark: *benchmark | ||
| exchange_kwargs: | ||
| limit_threshold: 0.095 | ||
| deal_price: close | ||
| open_cost: 0.0005 | ||
| close_cost: 0.0015 | ||
| min_cost: 5 | ||
| task: | ||
| model: | ||
| class: GATs_ADD_IND | ||
| module_path: qlib.contrib.model.pytorch_gats_add_ind_ts | ||
| kwargs: | ||
| d_feat: 20 | ||
| hidden_size: 64 | ||
| num_layers: 2 | ||
| dropout: 0.7 | ||
| n_epochs: 200 | ||
| lr: 0.00001 | ||
| early_stop: 10 | ||
| metric: loss | ||
| loss: mse | ||
| base_model: LSTM | ||
| model_path: "benchmarks/LSTM/csi300_lstm_ts.pkl" | ||
| GPU: 0 | ||
| industrial_data_path: '~/stable_ind.csv' | ||
| industry_col: 'industry_citic' | ||
| smooth_perplexity: 1 | ||
| dataset: | ||
| class: TSDatasetH | ||
| module_path: qlib.data.dataset | ||
| kwargs: | ||
| handler: | ||
| class: Alpha158 | ||
| module_path: qlib.contrib.data.handler | ||
| kwargs: *data_handler_config | ||
| segments: | ||
| train: [2008-01-01, 2014-12-31] | ||
| valid: [2015-01-01, 2016-12-31] | ||
| test: [2017-01-01, 2020-08-01] | ||
| step_len: 20 | ||
| record: | ||
| - class: SignalRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| model: <MODEL> | ||
| dataset: <DATASET> | ||
| - class: SigAnaRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| ana_long_short: False | ||
| ann_scaler: 252 | ||
| - class: PortAnaRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| config: *port_analysis_config |
92 changes: 92 additions & 0 deletions
92
examples/benchmarks/GATs/workflow_config_gats_indus_Alpha360.yaml
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|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| qlib_init: | ||
| provider_uri: "~/.qlib/qlib_data/cn_data" | ||
| region: cn | ||
| market: &market csi300 | ||
| benchmark: &benchmark SH000300 | ||
| data_handler_config: &data_handler_config | ||
| start_time: 2008-01-01 | ||
| end_time: 2020-08-01 | ||
| fit_start_time: 2008-01-01 | ||
| fit_end_time: 2014-12-31 | ||
| instruments: *market | ||
| infer_processors: | ||
| - class: RobustZScoreNorm | ||
| kwargs: | ||
| fields_group: feature | ||
| clip_outlier: true | ||
| - class: Fillna | ||
| kwargs: | ||
| fields_group: feature | ||
| learn_processors: | ||
| - class: DropnaLabel | ||
| - class: CSRankNorm | ||
| kwargs: | ||
| fields_group: label | ||
| label: ["Ref($close, -2) / Ref($close, -1) - 1"] | ||
| port_analysis_config: &port_analysis_config | ||
| strategy: | ||
| class: TopkDropoutStrategy | ||
| module_path: qlib.contrib.strategy | ||
| kwargs: | ||
| signal: | ||
| - <MODEL> | ||
| - <DATASET> | ||
| topk: 50 | ||
| n_drop: 5 | ||
| backtest: | ||
| start_time: 2017-01-01 | ||
| end_time: 2020-08-01 | ||
| account: 100000000 | ||
| benchmark: *benchmark | ||
| exchange_kwargs: | ||
| limit_threshold: 0.095 | ||
| deal_price: close | ||
| open_cost: 0.0005 | ||
| close_cost: 0.0015 | ||
| min_cost: 5 | ||
| task: | ||
| model: | ||
| class: GATs_ADD_IND | ||
| module_path: qlib.contrib.model.pytorch_gats_add_ind | ||
| kwargs: | ||
| d_feat: 6 | ||
| hidden_size: 64 | ||
| num_layers: 2 | ||
| dropout: 0.7 | ||
| n_epochs: 200 | ||
| lr: 0.00001 | ||
| early_stop: 20 | ||
| metric: loss | ||
| loss: mse | ||
| base_model: LSTM | ||
| model_path: "benchmarks/LSTM/model_lstm_csi300.pkl" | ||
| GPU: 0 | ||
| industrial_data_path: '~/stable_ind.csv' | ||
| industry_col: 'industry_citic' | ||
| #smooth_perplexity: 1 | ||
| dataset: | ||
| class: DatasetH | ||
| module_path: qlib.data.dataset | ||
| kwargs: | ||
| class: Alpha360 | ||
| module_path: qlib.contrib.data.handler | ||
| kwargs: *data_handler_config | ||
| segments: | ||
| train: [2008-01-01, 2014-12-31] | ||
| valid: [2015-01-01, 2016-12-31] | ||
| test: [2017-01-01, 2020-08-01] | ||
| record: | ||
| - class: SignalRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| model: <MODEL> | ||
| dataset: <DATASET> | ||
| - class: SigAnaRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| ana_long_short: False | ||
| ann_scaler: 252 | ||
| - class: PortAnaRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| config: *port_analysis_config | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,4 @@ | ||
| # GCN | ||
| * The costing matrix is based on `stable_ind.csv`, which contains industry information that each instrument belongs to, collected in 2008. | ||
| * This code used in Qlib is implemented with PyTorch by ourselves. | ||
| * Paper: Graph Convolutional Networks https://arxiv.org/pdf/1609.02907.pdf |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,4 @@ | ||
| pandas==1.1.2 | ||
| numpy==1.21.0 | ||
| scikit_learn==0.23.2 | ||
| torch==1.7.0 |
102 changes: 102 additions & 0 deletions
102
examples/benchmarks/GCN/workflow_config_gcn_Alpha158.yaml
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| qlib_init: | ||
| provider_uri: "~/.qlib/qlib_data/cn_data" | ||
| region: cn | ||
| market: &market csi300 | ||
| benchmark: &benchmark SH000300 | ||
| experiment_name: workflow | ||
| data_handler_config: &data_handler_config | ||
| start_time: 2008-01-01 | ||
| end_time: 2020-08-01 | ||
| fit_start_time: 2008-01-01 | ||
| fit_end_time: 2014-12-31 | ||
| instruments: *market | ||
| infer_processors: | ||
| - class: FilterCol | ||
| kwargs: | ||
| fields_group: feature | ||
| col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", | ||
| "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", | ||
| "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW" | ||
| ] | ||
| - class: RobustZScoreNorm | ||
| kwargs: | ||
| fields_group: feature | ||
| clip_outlier: true | ||
| - class: Fillna | ||
| kwargs: | ||
| fields_group: feature | ||
| learn_processors: | ||
| - class: DropnaLabel | ||
| - class: CSRankNorm | ||
| kwargs: | ||
| fields_group: label | ||
| label: ["Ref($close, -2) / Ref($close, -1) - 1"] | ||
| port_analysis_config: &port_analysis_config | ||
| strategy: | ||
| class: TopkDropoutStrategy | ||
| module_path: qlib.contrib.strategy | ||
| kwargs: | ||
| signal: | ||
| - <MODEL> | ||
| - <DATASET> | ||
| topk: 50 | ||
| n_drop: 5 | ||
| backtest: | ||
| start_time: 2017-01-01 | ||
| end_time: 2020-08-01 | ||
| account: 100000000 | ||
| benchmark: *benchmark | ||
| exchange_kwargs: | ||
| limit_threshold: 0.095 | ||
| deal_price: close | ||
| open_cost: 0.0005 | ||
| close_cost: 0.0015 | ||
| min_cost: 5 | ||
| task: | ||
| model: | ||
| class: GCN | ||
| module_path: qlib.contrib.model.pytorch_gcn_ts | ||
| kwargs: | ||
| d_feat: 20 | ||
| hidden_size: 64 | ||
| num_layers: 2 | ||
| dropout: 0.5 | ||
| n_epochs: 200 | ||
| lr: 5e-5 | ||
| early_stop: 20 | ||
| metric: loss | ||
| loss: mse | ||
| base_model: LSTM | ||
| model_path: "benchmarks/LSTM/csi300_lstm_ts.pkl" | ||
| GPU: 0 | ||
| industrial_data_path: '~/stable_ind.csv' | ||
| industry_col: 'industry_citic' | ||
| adjacent_coef: 0.01 | ||
| dataset: | ||
| class: TSDatasetH | ||
| module_path: qlib.data.dataset | ||
| kwargs: | ||
| handler: | ||
| class: Alpha158 | ||
| module_path: qlib.contrib.data.handler | ||
| kwargs: *data_handler_config | ||
| segments: | ||
| train: [2008-01-01, 2014-12-31] | ||
| valid: [2015-01-01, 2016-12-31] | ||
| test: [2017-01-01, 2020-08-01] | ||
| step_len: 20 | ||
| record: | ||
| - class: SignalRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| model: <MODEL> | ||
| dataset: <DATASET> | ||
| - class: SigAnaRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| ana_long_short: False | ||
| ann_scaler: 252 | ||
| - class: PortAnaRecord | ||
| module_path: qlib.workflow.record_temp | ||
| kwargs: | ||
| config: *port_analysis_config |
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The indentation in these lines looks off, and the run_all_model.py file is not able to load the configuration file correctly.