refactor environment inheritence tree to accommodate flexible action types/counts. fix bug in train profit handling

freqtrade/freqai/RL/BaseEnvironment.py

@@ -0,0 +1,270 @@
+import logging
+from abc import abstractmethod
+from enum import Enum
+from typing import Optional
+
+import gym
+import numpy as np
+import pandas as pd
+from gym import spaces
+from gym.utils import seeding
+from pandas import DataFrame
+
+
+logger = logging.getLogger(__name__)
+
+
+class Positions(Enum):
+    Short = 0
+    Long = 1
+    Neutral = 0.5
+
+    def opposite(self):
+        return Positions.Short if self == Positions.Long else Positions.Long
+
+
+class BaseEnvironment(gym.Env):
+    """
+    Base class for environments. This class is agnostic to action count.
+    Inherited classes customize this to include varying action counts/types,
+    See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
+    """
+
+    def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
+                 reward_kwargs: dict = {}, window_size=10, starting_point=True,
+                 id: str = 'baseenv-1', seed: int = 1, config: dict = {}):
+
+        self.rl_config = config['freqai']['rl_config']
+        self.id = id
+        self.seed(seed)
+        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
+        self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
+        self.compound_trades = config['stake_amount'] == 'unlimited'
+
+    def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
+                  reward_kwargs: dict, starting_point=True):
+        self.df = df
+        self.signal_features = self.df
+        self.prices = prices
+        self.window_size = window_size
+        self.starting_point = starting_point
+        self.rr = reward_kwargs["rr"]
+        self.profit_aim = reward_kwargs["profit_aim"]
+
+        self.fee = 0.0015
+
+        # # spaces
+        self.shape = (window_size, self.signal_features.shape[1] + 3)
+        self.set_action_space()
+        self.observation_space = spaces.Box(
+            low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
+
+        # episode
+        self._start_tick: int = self.window_size
+        self._end_tick: int = len(self.prices) - 1
+        self._done: bool = False
+        self._current_tick: int = self._start_tick
+        self._last_trade_tick: Optional[int] = None
+        self._position = Positions.Neutral
+        self._position_history: list = [None]
+        self.total_reward: float = 0
+        self._total_profit: float = 1
+        self._total_unrealized_profit: float = 1
+        self.history: dict = {}
+        self.trade_history: list = []
+
+    @abstractmethod
+    def set_action_space(self):
+        """
+        Unique to the environment action count. Must be inherited.
+        """
+
+    def seed(self, seed: int = 1):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
+
+    def reset(self):
+
+        self._done = False
+
+        if self.starting_point is True:
+            self._position_history = (self._start_tick * [None]) + [self._position]
+        else:
+            self._position_history = (self.window_size * [None]) + [self._position]
+
+        self._current_tick = self._start_tick
+        self._last_trade_tick = None
+        self._position = Positions.Neutral
+
+        self.total_reward = 0.
+        self._total_profit = 1.  # unit
+        self.history = {}
+        self.trade_history = []
+        self.portfolio_log_returns = np.zeros(len(self.prices))
+
+        self._profits = [(self._start_tick, 1)]
+        self.close_trade_profit = []
+        self._total_unrealized_profit = 1
+
+        return self._get_observation()
+
+    @abstractmethod
+    def step(self, action: int):
+        """
+        Step depeneds on action types, this must be inherited.
+        """
+        return
+
+    def _get_observation(self):
+        """
+        This may or may not be independent of action types, user can inherit
+        this in their custom "MyRLEnv"
+        """
+        features_window = self.signal_features[(
+            self._current_tick - self.window_size):self._current_tick]
+        features_and_state = DataFrame(np.zeros((len(features_window), 3)),
+                                       columns=['current_profit_pct', 'position', 'trade_duration'],
+                                       index=features_window.index)
+
+        features_and_state['current_profit_pct'] = self.get_unrealized_profit()
+        features_and_state['position'] = self._position.value
+        features_and_state['trade_duration'] = self.get_trade_duration()
+        features_and_state = pd.concat([features_window, features_and_state], axis=1)
+        return features_and_state
+
+    def get_trade_duration(self):
+        if self._last_trade_tick is None:
+            return 0
+        else:
+            return self._current_tick - self._last_trade_tick
+
+    def get_unrealized_profit(self):
+
+        if self._last_trade_tick is None:
+            return 0.
+
+        if self._position == Positions.Neutral:
+            return 0.
+        elif self._position == Positions.Short:
+            current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
+            return (last_trade_price - current_price) / last_trade_price
+        elif self._position == Positions.Long:
+            current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
+            return (current_price - last_trade_price) / last_trade_price
+        else:
+            return 0.
+
+    @abstractmethod
+    def is_tradesignal(self, action: int):
+        # trade signal
+        """
+        Determine if the signal is a trade signal. This is
+        unique to the actions in the environment, and therefore must be
+        inherited.
+        """
+        return
+
+    def _is_valid(self, action: int):
+        # trade signal
+        """
+        Determine if the signal is valid.This is
+        unique to the actions in the environment, and therefore must be
+        inherited.
+        """
+        return
+
+    def add_entry_fee(self, price):
+        return price * (1 + self.fee)
+
+    def add_exit_fee(self, price):
+        return price / (1 + self.fee)
+
+    def _update_history(self, info):
+        if not self.history:
+            self.history = {key: [] for key in info.keys()}
+
+        for key, value in info.items():
+            self.history[key].append(value)
+
+    @abstractmethod
+    def calculate_reward(self, action):
+        """
+        Reward is created by BaseReinforcementLearningModel and can
+        be inherited/edited by the user made ReinforcementLearner file.
+        """
+
+        return 0.
+
+    def _update_unrealized_total_profit(self):
+        """
+        Update the unrealized total profit incase of episode end.
+        """
+        if self._position in (Positions.Long, Positions.Short):
+            pnl = self.get_unrealized_profit()
+            if self.compound_trades:
+                # assumes unit stake and compounding
+                unrl_profit = self._total_profit * (1 + pnl)
+            else:
+                # assumes unit stake and no compounding
+                unrl_profit = self._total_profit + pnl
+            self._total_unrealized_profit = unrl_profit
+
+    def _update_total_profit(self):
+        pnl = self.get_unrealized_profit()
+        if self.compound_trades:
+            # assumes unite stake and compounding
+            self._total_profit = self._total_profit * (1 + pnl)
+        else:
+            # assumes unit stake and no compounding
+            self._total_profit += pnl
+
+    def most_recent_return(self, action: int):
+        """
+        Calculate the tick to tick return if in a trade.
+        Return is generated from rising prices in Long
+        and falling prices in Short positions.
+        The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
+        """
+        # Long positions
+        if self._position == Positions.Long:
+            current_price = self.prices.iloc[self._current_tick].open
+            previous_price = self.prices.iloc[self._current_tick - 1].open
+
+            if (self._position_history[self._current_tick - 1] == Positions.Short
+                    or self._position_history[self._current_tick - 1] == Positions.Neutral):
+                previous_price = self.add_entry_fee(previous_price)
+
+            return np.log(current_price) - np.log(previous_price)
+
+        # Short positions
+        if self._position == Positions.Short:
+            current_price = self.prices.iloc[self._current_tick].open
+            previous_price = self.prices.iloc[self._current_tick - 1].open
+            if (self._position_history[self._current_tick - 1] == Positions.Long
+                    or self._position_history[self._current_tick - 1] == Positions.Neutral):
+                previous_price = self.add_exit_fee(previous_price)
+
+            return np.log(previous_price) - np.log(current_price)
+
+        return 0
+
+    def get_portfolio_log_returns(self):
+        return self.portfolio_log_returns[1:self._current_tick + 1]
+
+    def update_portfolio_log_returns(self, action):
+        self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
+
+    def current_price(self) -> float:
+        return self.prices.iloc[self._current_tick].open
+
+    def prev_price(self) -> float:
+        return self.prices.iloc[self._current_tick - 1].open
+
+    def sharpe_ratio(self):
+        if len(self.close_trade_profit) == 0:
+            return 0.
+        returns = np.array(self.close_trade_profit)
+        reward = (np.mean(returns) - 0. + 1e-9) / (np.std(returns) + 1e-9)
+        return reward