def __init__(self,
portfolio: Union[Portfolio, str],
action_scheme: Union[ActionScheme, str],
reward_scheme: Union[RewardScheme, str],
feed: DataFeed = None,
window_size: int = 1,
use_internal=True,
**kwargs):
"""
Arguments:
portfolio: The `Portfolio` of wallets used to submit and execute orders from.
action_scheme: The component for transforming an action into an `Order` at each timestep.
reward_scheme: The component for determining the reward at each timestep.
feed (optional): The pipeline of features to pass the observations through.
kwargs (optional): Additional arguments for tuning the environments, logging, etc.
"""
super().__init__()
self.portfolio = portfolio
self.action_scheme = action_scheme
self.reward_scheme = reward_scheme
self.feed = feed
self.window_size = window_size
self.use_internal = use_internal
if self.feed:
self._external_keys = self.feed.next().keys()
self.feed.reset()
self.history = ObservationHistory(window_size=window_size)
self._broker = Broker(exchanges=self.portfolio.exchanges)
self.clock = Clock()
self.action_space = None
self.observation_space = None
self.viewer = None
self._enable_logger = kwargs.get('enable_logger', False)
self._observation_dtype = kwargs.get('dtype', np.float32)
self._observation_lows = kwargs.get('observation_lows', 0)
self._observation_highs = kwargs.get('observation_highs', 1)
if self._enable_logger:
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)
self.compile()
def __init__(self,
portfolio: Union[Portfolio, str],
action_scheme: Union[ActionScheme, str],
reward_scheme: Union[RewardScheme, str],
feed: DataFeed = None,
window_size: int = 1,
use_internal: bool = True,
renderers: Union[str, List[str], List['BaseRenderer']] = 'screenlog',
**kwargs):
"""
Arguments:
portfolio: The `Portfolio` of wallets used to submit and execute orders from.
action_scheme: The component for transforming an action into an `Order` at each timestep.
reward_scheme: The component for determining the reward at each timestep.
feed (optional): The pipeline of features to pass the observations through.
renderers (optional): single or list of renderers for output by name or as objects.
String Values: 'screenlog', 'filelog', or 'plotly'. None for no rendering.
price_history (optional): OHLCV price history feed used for rendering
the chart. Required if render_mode is 'plotly'.
kwargs (optional): Additional arguments for tuning the environments, logging, etc.
"""
super().__init__()
self.portfolio = portfolio
self.action_scheme = action_scheme
self.reward_scheme = reward_scheme
self.feed = feed
self.window_size = window_size
self.use_internal = use_internal
self._price_history: pd.DataFrame = kwargs.get('price_history', None)
if self.feed:
self._external_keys = self.feed.next().keys()
self.feed.reset()
self.history = ObservationHistory(window_size=window_size)
self._broker = Broker(exchanges=self.portfolio.exchanges)
self.clock = Clock()
self.action_space = None
self.observation_space = None
if not renderers:
renderers = []
elif type(renderers) is not list:
renderers = [renderers]
self._renderers = []
for renderer in renderers:
if isinstance(renderer, str):
renderer = get(renderer)
self._renderers.append(renderer)
self._enable_logger = kwargs.get('enable_logger', False)
self._observation_dtype = kwargs.get('dtype', np.float32)
self._observation_lows = kwargs.get('observation_lows', -np.iinfo(np.int64).max)
self._observation_highs = kwargs.get('observation_highs', np.iinfo(np.int64).max)
self._max_allowed_loss = kwargs.get('max_allowed_loss', 0.1)
if self._enable_logger:
self.logger = logging.getLogger(kwargs.get('logger_name', __name__))
self.logger.setLevel(kwargs.get('log_level', logging.DEBUG))
self._max_episodes = None
self._max_steps = None
logging.getLogger('tensorflow').disabled = kwargs.get('disable_tensorflow_logger', True)
self.compile()
def test_basic_clock_increment():
clock = Clock()
clock.increment()
assert clock.step == 1
def test_basic_clock_init():
clock = Clock()
assert clock
assert clock.start == 0
assert clock.step == 0
def __init__(self,
portfolio: Union[Portfolio, str],
action_scheme: Union[ActionScheme, str],
reward_scheme: Union[RewardScheme, str],
feed: DataFeed = None,
window_size: int = 1,
use_internal: bool = True,
renderer: Union[str, List['AbstractRenderer']] = 'human',
**kwargs):
"""
Arguments:
portfolio: The `Portfolio` of wallets used to submit and execute orders from.
action_scheme: The component for transforming an action into an `Order` at each timestep.
reward_scheme: The component for determining the reward at each timestep.
feed (optional): The pipeline of features to pass the observations through.
render_mode (optional): rendering mode, 'human' or 'log'. None for no rendering.
chart_height (optioanl): int, the chart height for 'human' mode.
price_history (optional): OHLCV price history feed used for rendering
the chart. Required if render_mode is 'human'.
kwargs (optional): Additional arguments for tuning the environments, logging, etc.
"""
super().__init__()
self.portfolio = portfolio
self.action_scheme = action_scheme
self.reward_scheme = reward_scheme
self.feed = feed
self.window_size = window_size
self.use_internal = use_internal
self._price_history: pd.DataFrame = kwargs.get('price_history', None)
if self.feed:
self._external_keys = self.feed.next().keys()
self.feed.reset()
self.history = ObservationHistory(window_size=window_size)
self._broker = Broker(exchanges=self.portfolio.exchanges)
self.clock = Clock()
self.action_space = None
self.observation_space = None
if renderer == 'human':
self._renderer = [PlotlyTradingChart()]
else:
self._renderer = renderer if renderer else []
self._enable_logger = kwargs.get('enable_logger', False)
self._observation_dtype = kwargs.get('dtype', np.float32)
self._observation_lows = kwargs.get('observation_lows',
-np.iinfo(np.int32).max)
self._observation_highs = kwargs.get('observation_highs',
np.iinfo(np.int32).max)
self._max_allowed_loss = kwargs.get('max_allowed_loss', 0.1)
if self._enable_logger:
self.logger = logging.getLogger(kwargs.get('logger_name',
__name__))
self.logger.setLevel(kwargs.get('log_level', logging.DEBUG))
self._max_episodes = None
self._max_steps = None
logging.getLogger('tensorflow').disabled = kwargs.get(
'disable_tensorflow_logger', True)
self.compile()
