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

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -1,14 +1,11 @@
 import logging
 from enum import Enum
-from typing import Optional
 
-import gym
-import numpy as np
 from gym import spaces
-from gym.utils import seeding
-from pandas import DataFrame
-import pandas as pd
-from abc import abstractmethod
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -19,95 +16,13 @@ class Actions(Enum):
     Short_enter = 3
 
 
-
-class Positions(Enum):
-    Short = 0
-    Long = 1
-    Neutral = 0.5
-
-    def opposite(self):
-        return Positions.Short if self == Positions.Long else Positions.Long
-
-
-def mean_over_std(x):
-    std = np.std(x, ddof=1)
-    mean = np.mean(x)
-    return mean / std if std > 0 else 0
-
-
-class Base4ActionRLEnv(gym.Env):
+class Base4ActionRLEnv(BaseEnvironment):
     """
-    Base class for a 5 action environment
+    Base class for a 4 action environment
     """
-    metadata = {'render.modes': ['human']}
 
-    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)
-
-    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)
+    def set_action_space(self):
         self.action_space = spaces.Discrete(len(Actions))
-        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.history: dict = {}
-        self.trade_history: list = []
-
-    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 = []
-
-        return self._get_observation()
 
     def step(self, action: int):
         self._done = False
@@ -181,43 +96,6 @@ class Base4ActionRLEnv(gym.Env):
 
         return observation, step_reward, self._done, info
 
-    def _get_observation(self):
-        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.
-
     def is_tradesignal(self, action: int):
         # trade signal
         """
@@ -228,7 +106,7 @@ class Base4ActionRLEnv(gym.Env):
                     (action == Actions.Neutral.value and self._position == Positions.Short) or
                     (action == Actions.Neutral.value and self._position == Positions.Long) or
                     (action == Actions.Short_enter.value and self._position == Positions.Short) or
-                    (action == Actions.Short_enter.value and self._position == Positions.Long) or                 
+                    (action == Actions.Short_enter.value and self._position == Positions.Long) or
                     (action == Actions.Exit.value and self._position == Positions.Neutral) or
                     (action == Actions.Long_enter.value and self._position == Positions.Long) or
                     (action == Actions.Long_enter.value and self._position == Positions.Short))
@@ -240,7 +118,7 @@ class Base4ActionRLEnv(gym.Env):
         e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
         """
         # Agent should only try to exit if it is in position
-        if action in (Actions.Exit.value):
+        if action == Actions.Exit.value:
             if self._position not in (Positions.Short, Positions.Long):
                 return False
 
@@ -250,97 +128,3 @@ class Base4ActionRLEnv(gym.Env):
                 return False
 
         return True
-
-    def _is_trade(self, action: Actions):
-        return ((action == Actions.Long_enter.value and self._position == Positions.Neutral) or
-                (action == Actions.Short_enter.value and self._position == Positions.Neutral))
-
-    def is_hold(self, action):
-        return ((action == Actions.Short_enter.value and self._position == Positions.Short) or
-                (action == Actions.Long_enter.value and self._position == Positions.Long) or
-                (action == Actions.Neutral.value and self._position == Positions.Long) or
-                (action == Actions.Neutral.value and self._position == Positions.Short) or
-                (action == Actions.Neutral.value and self._position == Positions.Neutral))
-
-    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)
-
-    def get_sharpe_ratio(self):
-        return mean_over_std(self.get_portfolio_log_returns())
-
-    @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_profit(self, action):
-        if self._is_trade(action) or self._done:
-            pnl = self.get_unrealized_profit()
-
-            if self._position in (Positions.Long, Positions.Short):
-                self._total_profit *= (1 + pnl)
-                self._profits.append((self._current_tick, self._total_profit))
-                self.close_trade_profit.append(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