Source code for tensortrade.strategies.trading_strategy

# Copyright 2019 The TensorTrade Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import pandas as pd
import numpy as np

from abc import ABCMeta, abstractmethod
from typing import Union, List


[docs]class TradingStrategy(object, metaclass=ABCMeta): """An abstract trading strategy capable of self tuning, training, and evaluating."""
[docs] @abstractmethod def __init__(self, environment: 'TradingEnvironment'): """ Arguments: environment: A `TradingEnvironment` instance for the agent to trade within. """ self._environment = environment
@property def environment(self) -> 'TradingEnvironment': """A `TradingEnvironment` instance for the agent to trade within.""" return self._environment @environment.setter def environment(self, environment: 'TradingEnvironment'): self._environment = environment
[docs] @abstractmethod def restore_agent(self, path: str): """Deserialize the strategy's learning agent from a file. Arguments: path: The `str` path of where the strategy is stored. """ raise NotImplementedError
[docs] @abstractmethod def save_agent(self, path: str): """Serialize the strategy's learning agent to a file for restoring later. Arguments: path: The `str` path of where to store the strategy. """ raise NotImplementedError
[docs] @abstractmethod def tune(self, steps_per_train: int, steps_per_test: int, episode_callback=None) -> pd.DataFrame: """Tune the agent's hyper-parameters and feature set for the environment. Arguments: 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. Returns: A history of the agent's trading performance during tuning. """ raise NotImplementedError
[docs] @abstractmethod def run(self, steps: int = None, episodes: int = None, testing: bool = False, episode_callback=None) -> pd.DataFrame: """Evaluate the agent's performance within the environment. Arguments: steps: The number of steps to run the agent within the environment. Required if `episodes` is not passed. episodes: 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. Returns: A history of the agent's trading performance during evaluation. """ raise NotImplementedError