mirror of
https://github.com/freqtrade/freqtrade.git
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374 lines
14 KiB
Python
374 lines
14 KiB
Python
import logging
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import random
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from abc import abstractmethod
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from enum import Enum
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from typing import Optional, Type, Union
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import gym
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import numpy as np
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import pandas as pd
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from gym import spaces
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from gym.utils import seeding
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from pandas import DataFrame
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logger = logging.getLogger(__name__)
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class BaseActions(Enum):
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"""
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Default action space, mostly used for type handling.
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"""
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Neutral = 0
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Long_enter = 1
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Long_exit = 2
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Short_enter = 3
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Short_exit = 4
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class Positions(Enum):
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Short = 0
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Long = 1
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Neutral = 0.5
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def opposite(self):
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return Positions.Short if self == Positions.Long else Positions.Long
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class BaseEnvironment(gym.Env):
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"""
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Base class for environments. This class is agnostic to action count.
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Inherited classes customize this to include varying action counts/types,
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See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
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"""
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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, can_short: bool = False, pair: str = "",
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df_raw: DataFrame = DataFrame()):
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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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:param prices: dataframe of prices to be used in the training environment
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:param window_size: size of window (temporal) to pass to the agent
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:param reward_kwargs: extra config settings assigned by user in `rl_config`
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:param starting_point: start at edge of window or not
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:param id: string id of the environment (used in backend for multiprocessed env)
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:param seed: Sets the seed of the environment higher in the gym.Env object
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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: dict = config
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self.rl_config: dict = config['freqai']['rl_config']
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self.add_state_info: bool = self.rl_config.get('add_state_info', False)
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self.id: str = id
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self.max_drawdown: float = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
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self.compound_trades: bool = config['stake_amount'] == 'unlimited'
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self.pair: str = pair
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self.raw_features: DataFrame = df_raw
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if self.config.get('fee', None) is not None:
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self.fee = self.config['fee']
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else:
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self.fee = fee
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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: bool = can_short
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self.live: bool = 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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logger.warning("add_state_info is not available in backtesting. Deactivating.")
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self.seed(seed)
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self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
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def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
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reward_kwargs: dict, starting_point=True):
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"""
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Resets the environment when the agent fails (in our case, if the drawdown
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exceeds the user set max_training_drawdown_pct)
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:param df: dataframe of features
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:param prices: dataframe of prices to be used in the training environment
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:param window_size: size of window (temporal) to pass to the agent
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:param reward_kwargs: extra config settings assigned by user in `rl_config`
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:param starting_point: start at edge of window or not
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"""
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self.signal_features: DataFrame = df
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self.prices: DataFrame = prices
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self.window_size: int = window_size
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self.starting_point: bool = starting_point
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self.rr: float = reward_kwargs["rr"]
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self.profit_aim: float = reward_kwargs["profit_aim"]
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# # spaces
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if self.add_state_info:
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self.total_features = self.signal_features.shape[1] + 3
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else:
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self.total_features = self.signal_features.shape[1]
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self.shape = (window_size, self.total_features)
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self.set_action_space()
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self.observation_space = spaces.Box(
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low=-1, high=1, shape=self.shape, dtype=np.float32)
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# episode
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self._start_tick: int = self.window_size
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self._end_tick: int = len(self.prices) - 1
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self._done: bool = False
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self._current_tick: int = self._start_tick
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self._last_trade_tick: Optional[int] = None
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self._position = Positions.Neutral
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self._position_history: list = [None]
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self.total_reward: float = 0
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self._total_profit: float = 1
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self._total_unrealized_profit: float = 1
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self.history: dict = {}
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self.trade_history: list = []
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@abstractmethod
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def set_action_space(self):
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"""
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Unique to the environment action count. Must be inherited.
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"""
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def seed(self, seed: int = 1):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def tensorboard_log(self, metric: str, value: Optional[Union[int, float]] = None,
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inc: Optional[bool] = None, category: str = "custom"):
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"""
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Function builds the tensorboard_metrics dictionary
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to be parsed by the TensorboardCallback. This
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function is designed for tracking incremented objects,
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events, actions inside the training environment.
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For example, a user can call this to track the
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frequency of occurence of an `is_valid` call in
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their `calculate_reward()`:
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def calculate_reward(self, action: int) -> float:
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if not self._is_valid(action):
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self.tensorboard_log("invalid")
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return -2
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:param metric: metric to be tracked and incremented
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:param value: `metric` value
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:param inc: (deprecated) sets whether the `value` is incremented or not
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:param category: `metric` category
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"""
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increment = True if value is None else False
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value = 1 if increment else value
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if category not in self.tensorboard_metrics:
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self.tensorboard_metrics[category] = {}
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if not increment or metric not in self.tensorboard_metrics[category]:
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self.tensorboard_metrics[category][metric] = value
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else:
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self.tensorboard_metrics[category][metric] += value
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def reset_tensorboard_log(self):
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self.tensorboard_metrics = {}
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def reset(self):
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"""
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Reset is called at the beginning of every episode
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"""
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self.reset_tensorboard_log()
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self._done = False
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if self.starting_point is True:
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if self.rl_config.get('randomize_starting_position', False):
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length_of_data = int(self._end_tick / 4)
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start_tick = random.randint(self.window_size + 1, length_of_data)
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self._start_tick = start_tick
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self._position_history = (self._start_tick * [None]) + [self._position]
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else:
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self._position_history = (self.window_size * [None]) + [self._position]
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self._current_tick = self._start_tick
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self._last_trade_tick = None
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self._position = Positions.Neutral
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self.total_reward = 0.
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self._total_profit = 1. # unit
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self.history = {}
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self.trade_history = []
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self.portfolio_log_returns = np.zeros(len(self.prices))
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self._profits = [(self._start_tick, 1)]
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self.close_trade_profit = []
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self._total_unrealized_profit = 1
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return self._get_observation()
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@abstractmethod
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def step(self, action: int):
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"""
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Step depeneds on action types, this must be inherited.
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"""
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return
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def _get_observation(self):
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"""
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This may or may not be independent of action types, user can inherit
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this in their custom "MyRLEnv"
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"""
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features_window = self.signal_features[(
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self._current_tick - self.window_size):self._current_tick]
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if self.add_state_info:
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features_and_state = DataFrame(np.zeros((len(features_window), 3)),
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columns=['current_profit_pct',
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'position',
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'trade_duration'],
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index=features_window.index)
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features_and_state['current_profit_pct'] = self.get_unrealized_profit()
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features_and_state['position'] = self._position.value
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features_and_state['trade_duration'] = self.get_trade_duration()
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features_and_state = pd.concat([features_window, features_and_state], axis=1)
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return features_and_state
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else:
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return features_window
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def get_trade_duration(self):
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"""
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Get the trade duration if the agent is in a trade
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"""
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if self._last_trade_tick is None:
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return 0
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else:
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return self._current_tick - self._last_trade_tick
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def get_unrealized_profit(self):
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"""
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Get the unrealized profit if the agent is in a trade
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"""
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if self._last_trade_tick is None:
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return 0.
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if self._position == Positions.Neutral:
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return 0.
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elif self._position == Positions.Short:
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current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
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last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
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return (last_trade_price - current_price) / last_trade_price
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elif self._position == Positions.Long:
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current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
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last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
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return (current_price - last_trade_price) / last_trade_price
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else:
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return 0.
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@abstractmethod
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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. This is
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unique to the actions in the environment, and therefore must be
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inherited.
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"""
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return True
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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.This is
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unique to the actions in the environment, and therefore must be
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inherited.
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"""
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return True
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def add_entry_fee(self, price):
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return price * (1 + self.fee)
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def add_exit_fee(self, price):
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return price / (1 + self.fee)
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def _update_history(self, info):
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if not self.history:
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self.history = {key: [] for key in info.keys()}
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for key, value in info.items():
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self.history[key].append(value)
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@abstractmethod
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def calculate_reward(self, action: int) -> float:
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"""
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An example reward function. This is the one function that users will likely
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wish to inject their own creativity into.
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:param action: int = The action made by the agent for the current candle.
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:return:
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float = the reward to give to the agent for current step (used for optimization
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of weights in NN)
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"""
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def _update_unrealized_total_profit(self):
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"""
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Update the unrealized total profit incase of episode end.
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"""
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if self._position in (Positions.Long, Positions.Short):
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pnl = self.get_unrealized_profit()
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if self.compound_trades:
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# assumes unit stake and compounding
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unrl_profit = self._total_profit * (1 + pnl)
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else:
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# assumes unit stake and no compounding
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unrl_profit = self._total_profit + pnl
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self._total_unrealized_profit = unrl_profit
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def _update_total_profit(self):
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pnl = self.get_unrealized_profit()
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if self.compound_trades:
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# assumes unit stake and compounding
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self._total_profit = self._total_profit * (1 + pnl)
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else:
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# assumes unit stake and no compounding
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self._total_profit += pnl
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def current_price(self) -> float:
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return self.prices.iloc[self._current_tick].open
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def get_actions(self) -> Type[Enum]:
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"""
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Used by SubprocVecEnv to get actions from
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initialized env for tensorboard callback
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"""
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return self.actions
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# Keeping around incase we want to start building more complex environment
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# templates in the future.
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# def most_recent_return(self):
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# """
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# Calculate the tick to tick return if in a trade.
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# Return is generated from rising prices in Long
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# and falling prices in Short positions.
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# The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
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# """
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# # Long positions
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# if self._position == Positions.Long:
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# current_price = self.prices.iloc[self._current_tick].open
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# previous_price = self.prices.iloc[self._current_tick - 1].open
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# if (self._position_history[self._current_tick - 1] == Positions.Short
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# or self._position_history[self._current_tick - 1] == Positions.Neutral):
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# previous_price = self.add_entry_fee(previous_price)
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# return np.log(current_price) - np.log(previous_price)
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# # Short positions
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# if self._position == Positions.Short:
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# current_price = self.prices.iloc[self._current_tick].open
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# previous_price = self.prices.iloc[self._current_tick - 1].open
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# if (self._position_history[self._current_tick - 1] == Positions.Long
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# or self._position_history[self._current_tick - 1] == Positions.Neutral):
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# previous_price = self.add_exit_fee(previous_price)
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# return np.log(previous_price) - np.log(current_price)
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# return 0
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# def update_portfolio_log_returns(self, action):
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# self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
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