mirror of
https://github.com/microsoft/qlib.git
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14d54aa2a1
* Add util function to help automatically get horizon * Reformat for CI * Leverage horizon change * Udpate config yaml * Update for formatting * Adapt to pickled handler * Fix CI error * remove blank * Fix lint * Update tests * Remove redundant check * modify the code as suggested * format code with pylint * fix pytest error --------- Co-authored-by: Linlang <Lv.Linlang@hotmail.com>
128 lines
2.9 KiB
YAML
128 lines
2.9 KiB
YAML
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: 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: 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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num_states: &num_states 3
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memory_mode: &memory_mode sample
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tra_config: &tra_config
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num_states: *num_states
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rnn_arch: LSTM
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hidden_size: 32
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num_layers: 1
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dropout: 0.0
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tau: 1.0
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src_info: LR_TPE
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model_config: &model_config
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input_size: 158
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hidden_size: 256
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num_layers: 2
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rnn_arch: LSTM
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use_attn: True
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dropout: 0.2
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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: TRAModel
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module_path: qlib.contrib.model.pytorch_tra
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kwargs:
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tra_config: *tra_config
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model_config: *model_config
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model_type: RNN
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lr: 1e-3
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n_epochs: 100
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max_steps_per_epoch:
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early_stop: 20
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logdir: output/Alpha158_full
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seed: 0
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lamb: 1.0
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rho: 0.99
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alpha: 0.5
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transport_method: router
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memory_mode: *memory_mode
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eval_train: False
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eval_test: True
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pretrain: True
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init_state:
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freeze_model: False
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freeze_predictors: False
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dataset:
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class: MTSDatasetH
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module_path: qlib.contrib.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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seq_len: 60
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input_size:
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num_states: *num_states
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batch_size: 1024
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n_samples:
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memory_mode: *memory_mode
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drop_last: True
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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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