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653c082e7a
* Waiting for bin data * Complete readme * CI * Add inst filter by time * Update qlib/data/dataset/processor.py * typo * Fix time filter bug * Add Filter and set Universe * Complete data pipeline * Fix Provider Logger Info Args * Add DQN; a minor bugfix in ppo reward. * update readme. modify assertion logic in strategy check. * Fix Doc issues and fix black * Fix pylint Error --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
63 lines
1.4 KiB
YAML
Executable File
63 lines
1.4 KiB
YAML
Executable File
simulator:
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data_granularity: 5
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time_per_step: 30
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vol_limit: null
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env:
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concurrency: 48
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parallel_mode: shmem
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action_interpreter:
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class: CategoricalActionInterpreter
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kwargs:
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values: 4
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max_step: 8
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module_path: qlib.rl.order_execution.interpreter
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state_interpreter:
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class: FullHistoryStateInterpreter
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kwargs:
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data_dim: 5
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data_ticks: 48 # 48 = 240 min / 5 min
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max_step: 8
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processed_data_provider:
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class: PickleProcessedDataProvider
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module_path: qlib.rl.data.pickle_styled
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kwargs:
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data_dir: ./data/pickle_dataframe/feature
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module_path: qlib.rl.order_execution.interpreter
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reward:
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class: PPOReward
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kwargs:
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max_step: 8
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start_time_index: 0
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end_time_index: 46 # 46 = (240 - 5) min / 5 min - 1
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module_path: qlib.rl.order_execution.reward
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data:
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source:
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order_dir: ./data/orders
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data_dir: ./data/pickle_dataframe/backtest
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total_time: 240
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default_start_time_index: 0
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default_end_time_index: 235
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proc_data_dim: 5
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num_workers: 0
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queue_size: 20
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network:
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class: Recurrent
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module_path: qlib.rl.order_execution.network
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policy:
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class: PPO # PPO, DQN
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kwargs:
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lr: 0.0001
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module_path: qlib.rl.order_execution.policy
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runtime:
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seed: 42
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use_cuda: false
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trainer:
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max_epoch: 500
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repeat_per_collect: 25
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earlystop_patience: 50
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episode_per_collect: 10000
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batch_size: 1024
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val_every_n_epoch: 4
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checkpoint_path: ./outputs/ppo
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checkpoint_every_n_iters: 1
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