tensortrade.strategies.tensorforce_trading_strategy module¶
-
class
tensortrade.strategies.tensorforce_trading_strategy.
TensorforceTradingStrategy
(environment, agent_spec, network_spec, **kwargs)[source]¶ Bases:
tensortrade.strategies.trading_strategy.TradingStrategy
A trading strategy capable of self tuning, training, and evaluating with Tensorforce.
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__init__
(environment, agent_spec, network_spec, **kwargs)[source]¶ - Parameters
environment (
TradingEnvironment
) – A TradingEnvironment instance for the agent to trade within.agent_spec (
Dict
[~KT, ~VT]) – A specification dictionary for the Tensorforce agent.network_sepc – A specification dictionary for the Tensorforce agent’s model network.
kwargs (optional) – Optional keyword arguments to adjust the strategy.
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property
agent
¶ A Tensorforce Agent instance that will learn the strategy.
- Return type
Agent
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restore_agent
(path, model_path=None)[source]¶ Deserialize the strategy’s learning agent from a file. :type path:
str
:param path: The str path of the file the agent specification is stored in.The .json file extension will be automatically appended if not provided.
- Parameters
model_path (optional) – The str path of the file or directory the agent checkpoint is stored in. If not provided, the model_path will default to {path_without_dot_json}/agents.
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run
(steps=None, episodes=None, testing=True, 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 (
bool
) – 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, model_path=None, append_timestep=False)[source]¶ Serialize the learning agent to a file for restoring later. :type path:
str
:param path: The str path of the file to store the agent specification in.The .json file extension will be automatically appended if not provided.
- Parameters
model_path (optional) – The str path of the directory to store the agent checkpoints in. If not provided, the model_path will default to {path_without_dot_json}/agents.
append_timestep (
bool
) – Whether the timestep should be appended to filename to prevent overwriting previous models. Defaults to False.
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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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