def __init__(self,
exchange: Union[Exchange, str],
action_scheme: Union[ActionScheme, str],
reward_scheme: Union[RewardScheme, str],
feature_pipeline: Union[FeaturePipeline, str] = None,
**kwargs):
"""
Arguments:
exchange: The `Exchange` that will be used to feed data from and execute trades within.
action_scheme: The component for transforming an action into a `Trade` at each timestep.
reward_scheme: The component for determining the reward at each timestep.
feature_pipeline (optional): The pipeline of features to pass the observations through.
kwargs (optional): Additional arguments for tuning the environment, logging, etc.
"""
super().__init__()
# str => exchange , 当前交易账号
self._exchange = exchanges.get(exchange) if isinstance(
exchange, str) else exchange
# str => 交易动作方案
self._action_scheme = actions.get(action_scheme) if isinstance(
action_scheme, str) else action_scheme
# str => 奖赏方案
self._reward_scheme = rewards.get(reward_scheme) if isinstance(
reward_scheme, str) else reward_scheme
# # str =》 特征转换管道
self._feature_pipeline = features.get(feature_pipeline) if isinstance(
feature_pipeline, str) else feature_pipeline
if feature_pipeline is not None:
self._exchange.feature_pipeline = feature_pipeline
# 链接交易动作方案与交易账号
self._action_scheme.exchange = self._exchange
# 链接奖赏方案与交易账号
self._reward_scheme.exchange = self._exchange
# 观测维度空间
self.observation_space = self._exchange.observation_space
# 动作维度空间
self.action_space = self._action_scheme.action_space
self.render_benchmarks: List[Dict] = kwargs.get(
'render_benchmarks', [])
self.viewer = None
self.logger = logging.getLogger(kwargs.get('logger_name', __name__))
self.logger.setLevel(kwargs.get('log_level', logging.DEBUG))
# 取消tensorflow的日志记录
logging.getLogger('tensorflow').disabled = kwargs.get(
'disable_tensorflow_logger', True)
self.reset()
def __init__(self,
exchange: Union[InstrumentExchange, str],
action_strategy: Union[ActionStrategy, str],
reward_strategy: Union[RewardStrategy, str],
feature_pipeline: Union[FeaturePipeline, str] = None,
**kwargs):
"""
Arguments:
exchange: The `InstrumentExchange` that will be used to feed data from and execute trades within.
action_strategy: The strategy for transforming an action into a `Trade` at each timestep.
reward_strategy: The strategy for determining the reward at each timestep.
feature_pipeline (optional): The pipeline of features to pass the observations through.
kwargs (optional): Additional arguments for tuning the environment, logging, etc.
"""
super().__init__()
self._exchange = exchanges.get(exchange) if isinstance(
exchange, str) else exchange
self._action_strategy = actions.get(action_strategy) if isinstance(
action_strategy, str) else action_strategy
self._reward_strategy = rewards.get(reward_strategy) if isinstance(
reward_strategy, str) else reward_strategy
self._feature_pipeline = features.get(feature_pipeline) if isinstance(
feature_pipeline, str) else feature_pipeline
if feature_pipeline is not None:
self._exchange.feature_pipeline = feature_pipeline
self._exchange.reset()
self._action_strategy.exchange = self._exchange
self._reward_strategy.exchange = self._exchange
self.observation_space = self._exchange.observation_space
self.action_space = self._action_strategy.action_space
self.logger = logging.getLogger(kwargs.get('logger_name', __name__))
self.logger.setLevel(kwargs.get('log_level', logging.DEBUG))
logging.getLogger('tensorflow').disabled = kwargs.get(
'disable_tensorflow_logger', True)
def feature_pipeline(self,
feature_pipeline: Union[FeaturePipeline, str] = None):
self._feature_pipeline = features.get(feature_pipeline) if isinstance(
feature_pipeline, str) else feature_pipeline