Add can_short param to base env
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@ -45,7 +45,7 @@ class BaseEnvironment(gym.Env):
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def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
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reward_kwargs: dict = {}, window_size=10, starting_point=True,
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id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
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fee: float = 0.0015):
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fee: float = 0.0015, can_short: bool = False):
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"""
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Initializes the training/eval environment.
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:param df: dataframe of features
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@ -58,6 +58,7 @@ class BaseEnvironment(gym.Env):
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:param config: Typical user configuration file
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:param live: Whether or not this environment is active in dry/live/backtesting
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:param fee: The fee to use for environmental interactions.
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:param can_short: Whether or not the environment can short
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"""
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self.config = config
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self.rl_config = config['freqai']['rl_config']
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@ -73,6 +74,7 @@ class BaseEnvironment(gym.Env):
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# set here to default 5Ac, but all children envs can override this
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self.actions: Type[Enum] = BaseActions
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self.tensorboard_metrics: dict = {}
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self.can_short = can_short
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self.live = live
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if not self.live and self.add_state_info:
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self.add_state_info = False
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@ -165,7 +165,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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env_info = {"window_size": self.CONV_WIDTH,
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"reward_kwargs": self.reward_params,
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"config": self.config,
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"live": self.live}
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"live": self.live,
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"can_short": self.can_short}
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if self.data_provider:
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env_info["fee"] = self.data_provider._exchange \
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.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
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@ -133,6 +133,7 @@ class IFreqaiModel(ABC):
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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self.dd.set_pair_dict_info(metadata)
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self.data_provider = strategy.dp
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self.can_short = strategy.can_short
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if self.live:
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self.inference_timer('start')
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