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Ptnn4both datatypes and alignment tests (#1827)
* Init model for both dataset * Remove some deprecated code * Add model template; * We must align with previous results * We choose another mode as the initial version * Almost success to run GRU * Successfully run training * Passed general_nn test * gru test * Alignment test passed * comment * fix readme & minor errors * general nn updates & benchmarks * Update examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml --------- Co-authored-by: Young <[email protected]> Co-authored-by: you-n-g <[email protected]>
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| # Introduction | ||
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| What is GeneralPtNN | ||
| - Fix previous design that fail to support both Time-series and tabular data | ||
| - 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. | ||
| - `workflow_config_gru.yaml` align with previous results [GRU(Kyunghyun Cho, et al.)](../README.md#Alpha158-dataset) | ||
| - `workflow_config_gru2mlp.yaml` to demonstrate we can convert config from time-series to tabular data with minimal changes | ||
| - You only have to change the net & dataset class to make the conversion. | ||
| - `workflow_config_mlp.yaml` achieved similar functionality with [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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| - The result of `workflow_config_mlp.yaml` is different with the result of [MLP](../README.md#Alpha158-dataset) since GeneralPtNN has a different stopping method compared to previous implementations. Specificly, GeneralPtNN controls training according to epoches, whereas previous methods controlled by max_steps. |
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examples/benchmarks/GeneralPtNN/workflow_config_gru.yaml
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| 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: 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"] | ||
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| port_analysis_config: &port_analysis_config | ||
| strategy: | ||
| class: TopkDropoutStrategy | ||
| module_path: qlib.contrib.strategy | ||
| kwargs: | ||
| signal: <PRED> | ||
| 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: GeneralPTNN | ||
| module_path: qlib.contrib.model.pytorch_general_nn | ||
| kwargs: | ||
| n_epochs: 200 | ||
| lr: 2e-4 | ||
| early_stop: 10 | ||
| batch_size: 800 | ||
| metric: loss | ||
| loss: mse | ||
| n_jobs: 20 | ||
| GPU: 0 | ||
| pt_model_uri: "qlib.contrib.model.pytorch_gru_ts.GRUModel" | ||
| pt_model_kwargs: { | ||
| "d_feat": 20, | ||
| "hidden_size": 64, | ||
| "num_layers": 2, | ||
| "dropout": 0., | ||
| } | ||
| 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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examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml
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| 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: 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: <PRED> | ||
| 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: GeneralPTNN | ||
| module_path: qlib.contrib.model.pytorch_general_nn | ||
| kwargs: | ||
| lr: 1e-3 | ||
| n_epochs: 1 | ||
| batch_size: 800 | ||
| loss: mse | ||
| optimizer: adam | ||
| pt_model_uri: "qlib.contrib.model.pytorch_nn.Net" | ||
| pt_model_kwargs: | ||
| input_dim: 20 | ||
| layers: [20,] | ||
| dataset: | ||
| class: DatasetH | ||
| 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] | ||
| 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 +1,98 @@ | ||
| 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" : "DropCol", | ||
| "kwargs":{"col_list": ["VWAP0"]} | ||
| }, | ||
| { | ||
| "class" : "CSZFillna", | ||
| "kwargs":{"fields_group": "feature"} | ||
| } | ||
| ] | ||
| learn_processors: [ | ||
| { | ||
| "class" : "DropCol", | ||
| "kwargs":{"col_list": ["VWAP0"]} | ||
| }, | ||
| { | ||
| "class" : "DropnaProcessor", | ||
| "kwargs":{"fields_group": "feature"} | ||
| }, | ||
| "DropnaLabel", | ||
| { | ||
| "class": "CSZScoreNorm", | ||
| "kwargs": {"fields_group": "label"} | ||
| } | ||
| ] | ||
| process_type: "independent" | ||
|
|
||
| port_analysis_config: &port_analysis_config | ||
| strategy: | ||
| class: TopkDropoutStrategy | ||
| module_path: qlib.contrib.strategy | ||
| kwargs: | ||
| signal: <PRED> | ||
| 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: GeneralPTNN | ||
| module_path: qlib.contrib.model.pytorch_general_nn | ||
| kwargs: | ||
| # FIXME: wrong parameters. | ||
| lr: 2e-3 | ||
| batch_size: 8192 | ||
| loss: mse | ||
| weight_decay: 0.0002 | ||
| optimizer: adam | ||
| pt_model_uri: "qlib.contrib.model.pytorch_nn.Net" | ||
| pt_model_kwargs: | ||
| input_dim: 157 | ||
| dataset: | ||
| class: DatasetH | ||
| 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] | ||
| 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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