mirror of
https://github.com/freqtrade/freqtrade.git
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64d4a52a56
Improve the RL learning process by selecting random start point for the agent, it can help to block the agent to only learn on the selected period of time, while improving the quality of the model.
307 lines
12 KiB
Python
307 lines
12 KiB
Python
import logging
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from abc import abstractmethod
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from enum import Enum
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from typing import Optional
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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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import random
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from freqtrade.data.dataprovider import DataProvider
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logger = logging.getLogger(__name__)
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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 = {},
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dp: Optional[DataProvider] = None):
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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 dp: dataprovider from freqtrade
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"""
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self.config = config
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self.rl_config = config['freqai']['rl_config']
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self.add_state_info = self.rl_config.get('add_state_info', False)
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self.id = id
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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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self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
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self.compound_trades = config['stake_amount'] == 'unlimited'
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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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elif dp is not None:
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self.fee = dp._exchange.get_fee(symbol=dp.current_whitelist()[0]) # type: ignore
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else:
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self.fee = 0.0015
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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.df = df
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self.signal_features = self.df
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self.prices = prices
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self.window_size = window_size
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self.starting_point = starting_point
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self.rr = reward_kwargs["rr"]
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self.profit_aim = 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 reset(self):
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self._done = False
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if self.starting_point is True:
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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_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 (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_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 (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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# 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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