126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
import logging
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from enum import Enum
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from gym import spaces
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from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
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logger = logging.getLogger(__name__)
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class Actions(Enum):
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Neutral = 0
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Buy = 1
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Sell = 2
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class Base3ActionRLEnv(BaseEnvironment):
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"""
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Base class for a 3 action environment
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.actions = Actions
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def set_action_space(self):
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self.action_space = spaces.Discrete(len(Actions))
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def step(self, action: int):
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"""
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Logic for a single step (incrementing one candle in time)
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by the agent
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:param: action: int = the action type that the agent plans
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to take for the current step.
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:returns:
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observation = current state of environment
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step_reward = the reward from `calculate_reward()`
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_done = if the agent "died" or if the candles finished
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info = dict passed back to openai gym lib
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"""
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self._done = False
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self._current_tick += 1
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if self._current_tick == self._end_tick:
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self._done = True
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self._update_unrealized_total_profit()
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step_reward = self.calculate_reward(action)
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self.total_reward += step_reward
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self.tensorboard_log(self.actions._member_names_[action], category="actions")
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trade_type = None
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if self.is_tradesignal(action):
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if action == Actions.Buy.value:
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if self._position == Positions.Short:
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self._update_total_profit()
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self._position = Positions.Long
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trade_type = "long"
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self._last_trade_tick = self._current_tick
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elif action == Actions.Sell.value and self.can_short:
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if self._position == Positions.Long:
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self._update_total_profit()
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self._position = Positions.Short
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trade_type = "short"
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self._last_trade_tick = self._current_tick
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elif action == Actions.Sell.value and not self.can_short:
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self._update_total_profit()
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self._position = Positions.Neutral
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trade_type = "neutral"
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self._last_trade_tick = None
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else:
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print("case not defined")
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if trade_type is not None:
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self.trade_history.append(
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{'price': self.current_price(), 'index': self._current_tick,
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'type': trade_type})
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if (self._total_profit < self.max_drawdown or
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self._total_unrealized_profit < self.max_drawdown):
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self._done = True
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self._position_history.append(self._position)
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info = dict(
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tick=self._current_tick,
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action=action,
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total_reward=self.total_reward,
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total_profit=self._total_profit,
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position=self._position.value,
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trade_duration=self.get_trade_duration(),
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current_profit_pct=self.get_unrealized_profit()
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)
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observation = self._get_observation()
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self._update_history(info)
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return observation, step_reward, self._done, info
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def is_tradesignal(self, action: int) -> bool:
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"""
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Determine if the signal is a trade signal
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e.g.: agent wants a Actions.Buy while it is in a Positions.short
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"""
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return (
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(action == Actions.Buy.value and self._position == Positions.Neutral)
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or (action == Actions.Sell.value and self._position == Positions.Long)
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or (action == Actions.Sell.value and self._position == Positions.Neutral
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and self.can_short)
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or (action == Actions.Buy.value and self._position == Positions.Short
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and self.can_short)
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)
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def _is_valid(self, action: int) -> bool:
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"""
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Determine if the signal is valid.
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e.g.: agent wants a Actions.Sell while it is in a Positions.Long
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"""
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if self.can_short:
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return action in [Actions.Buy.value, Actions.Sell.value, Actions.Neutral.value]
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else:
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if action == Actions.Sell.value and self._position != Positions.Long:
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return False
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return True
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