stable/freqtrade/freqai/RL/Base3ActionRLEnv.py
2022-12-16 22:18:49 +03:00

126 lines
4.2 KiB
Python

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