tensortrade.strategies.stable_baselines_strategy module¶
-
class
tensortrade.strategies.stable_baselines_strategy.
StableBaselinesTradingStrategy
(environment, model=<class 'stable_baselines.deepq.dqn.DQN'>, policy='MlpPolicy', model_kwargs={}, **kwargs)[source]¶ Bases:
tensortrade.strategies.trading_strategy.TradingStrategy
A trading strategy capable of self tuning, training, and evaluating with stable-baselines.
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__init__
(environment, model=<class 'stable_baselines.deepq.dqn.DQN'>, policy='MlpPolicy', model_kwargs={}, **kwargs)[source]¶ - Parameters
environment (
TradingEnvironment
) – A TradingEnvironment instance for the agent to trade within.model (optional) – The RL model to create the agent with. Defaults to DQN.
policy (optional) – The RL policy to train the agent’s model with. Defaults to ‘MlpPolicy’.
model_kwargs (optional) – Any additional keyword arguments to adjust the model.
kwargs (optional) – Optional keyword arguments to adjust the strategy.
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property
environment
¶ A TradingEnvironment instance for the agent to trade within.
- Return type
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restore_agent
(path)[source]¶ Deserialize the strategy’s learning agent from a file.
- Parameters
path (
str
) – The str path of the file the agent specification is stored in.
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run
(steps=None, episodes=None, episode_callback=None)[source]¶ Evaluate the agent’s performance within the environment.
- Parameters
steps (
Optional
[int
]) – The number of steps to run the agent within the environment. Required if episodes is not passed.episodes (
Optional
[int
]) – The number of episodes to run the agent within the environment. Required if steps is not passed.testing – Whether or not the agent should be evaluated on the environment it is running in. Defaults to false.
episode_callback (optional) – A callback function for monitoring the agent’s progress within the environment.
- Return type
DataFrame
- Returns
A history of the agent’s trading performance during evaluation.
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save_agent
(path)[source]¶ Serialize the learning agent to a file for restoring later.
- Parameters
path (
str
) – The str path of the file to store the agent specification in.
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tune
(steps=None, episodes=None, callback=None)[source]¶ Tune the agent’s hyper-parameters and feature set for the environment.
- Parameters
steps_per_train – The number of steps per training of each hyper-parameter set.
steps_per_test – The number of steps per evaluation of each hyper-parameter set.
episode_callback (optional) – A callback function for monitoring progress of the tuning process.
- Return type
DataFrame
- Returns
A history of the agent’s trading performance during tuning.
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