make base 3ac and base 5ac environments. TDQN defaults to 3AC.

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -31,7 +31,7 @@ def mean_over_std(x):
     return mean / std if std > 0 else 0
 
 
-class BaseRLEnv(gym.Env):
+class Base3ActionRLEnv(gym.Env):
 
     metadata = {'render.modes': ['human']}
 

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -0,0 +1,364 @@
+import logging
+from enum import Enum
+# from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+
+import gym
+import numpy as np
+from gym import spaces
+from gym.utils import seeding
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Long_buy = 1
+    Long_sell = 2
+    Short_buy = 3
+    Short_sell = 4
+
+
+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 Base5ActionRLEnv(gym.Env):
+    """
+    Base class for a 5 action environment
+    """
+    metadata = {'render.modes': ['human']}
+
+    def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
+        assert df.ndim == 2
+
+        self.seed()
+        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])
+        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 = self.window_size
+        self._end_tick = len(self.prices) - 1
+        self._done = None
+        self._current_tick = None
+        self._last_trade_tick = None
+        self._position = Positions.Neutral
+        self._position_history = None
+        self.total_reward = None
+        self._total_profit = None
+        self._first_rendering = None
+        self.history = None
+        self.trade_history = []
+
+        # self.A_t, self.B_t = 0.000639, 0.00001954
+        self.r_t_change = 0.
+
+        self.returns_report = []
+
+    def seed(self, seed=None):
+        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._first_rendering = True
+        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.r_t_change = 0.
+
+        self.returns_report = []
+
+        return self._get_observation()
+
+    def step(self, action):
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self.update_portfolio_log_returns(action)
+
+        self._update_profit(action)
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+
+        trade_type = None
+        if self.is_tradesignal(action):  # exclude 3 case not trade
+            # Update position
+            """
+            Action: Neutral, position: Long ->  Close Long
+            Action: Neutral, position: Short -> Close Short
+
+            Action: Long, position: Neutral -> Open Long
+            Action: Long, position: Short -> Close Short and Open Long
+
+            Action: Short, position: Neutral -> Open Short
+            Action: Short, position: Long -> Close Long and Open Short
+            """
+
+            if action == Actions.Neutral.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+            elif action == Actions.Long_buy.value:
+                self._position = Positions.Long
+                trade_type = "long"
+            elif action == Actions.Short_buy.value:
+                self._position = Positions.Short
+                trade_type = "short"
+            elif action == Actions.Long_sell.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+            elif action == Actions.Short_sell.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+            else:
+                print("case not defined")
+
+            # Update last trade tick
+            self._last_trade_tick = self._current_tick
+
+            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 < 0.2:
+            self._done = True
+
+        self._position_history.append(self._position)
+        observation = self._get_observation()
+        info = dict(
+            tick=self._current_tick,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value
+        )
+        self._update_history(info)
+
+        return observation, step_reward, self._done, info
+
+    # def processState(self, state):
+    #     return state.to_numpy()
+
+    # def convert_mlp_Policy(self, obs_):
+    #     pass
+
+    def _get_observation(self):
+        return self.signal_features[(self._current_tick - self.window_size):self._current_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_buy_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_sell_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_sell_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_buy_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):
+        # trade signal
+        """
+        not trade signal is :
+        Action: Neutral, position: Neutral -> Nothing
+        Action: Long, position: Long -> Hold Long
+        Action: Short, position: Short -> Hold Short
+        """
+        return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
+                    (action == Actions.Short_buy.value and self._position == Positions.Short) or
+                    (action == Actions.Short_sell.value and self._position == Positions.Short) or
+                    (action == Actions.Short_buy.value and self._position == Positions.Long) or
+                    (action == Actions.Short_sell.value and self._position == Positions.Long) or
+
+                    (action == Actions.Long_buy.value and self._position == Positions.Long) or
+                    (action == Actions.Long_sell.value and self._position == Positions.Long) or
+                    (action == Actions.Long_buy.value and self._position == Positions.Short) or
+                    (action == Actions.Long_sell.value and self._position == Positions.Short))
+
+    def _is_trade(self, action: Actions):
+        return ((action == Actions.Long_buy.value and self._position == Positions.Short) or
+                (action == Actions.Short_buy.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.Short_sell and self._position == Positions.Long) or
+                (action == Actions.Neutral.Long_sell and self._position == Positions.Short)
+                )
+
+    def is_hold(self, action):
+        return ((action == Actions.Short.value and self._position == Positions.Short)
+                or (action == Actions.Long.value and self._position == Positions.Long))
+
+    def add_buy_fee(self, price):
+        return price * (1 + self.fee)
+
+    def add_sell_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())
+
+    def _update_profit(self, action):
+        # if self._is_trade(action) or self._done:
+        if self._is_trade(action) or self._done:
+            pnl = self.get_unrealized_profit()
+
+            if self._position == Positions.Long:
+                self._total_profit = self._total_profit + self._total_profit * pnl
+                self._profits.append((self._current_tick, self._total_profit))
+                self.close_trade_profit.append(pnl)
+
+            if self._position == Positions.Short:
+                self._total_profit = self._total_profit + self._total_profit * pnl
+                self._profits.append((self._current_tick, self._total_profit))
+                self.close_trade_profit.append(pnl)
+
+    def most_recent_return(self, action):
+        """
+        We support Long, Neutral and Short positions.
+        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
+            # if action == Actions.Short.value or action == Actions.Neutral.value:
+            if action == Actions.Short_buy.value or action == Actions.Neutral.value:
+                current_price = self.add_sell_fee(current_price)
+
+            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_buy_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
+            # if action == Actions.Long.value or action == Actions.Neutral.value:
+            if action == Actions.Long_buy.value or action == Actions.Neutral.value:
+                current_price = self.add_buy_fee(current_price)
+
+            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_sell_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 get_trading_log_return(self):
+        return self.portfolio_log_returns[self._start_tick:]
+
+    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
+
+    def get_bnh_log_return(self):
+        return np.diff(np.log(self.prices['open'][self._start_tick:]))
+
+    def calculate_reward(self, action):
+
+        if self._last_trade_tick is None:
+            return 0.
+
+        # close long
+        if action == Actions.Long_sell.value and self._position == Positions.Long:
+            last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
+            current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
+            return float(np.log(current_price) - np.log(last_trade_price))
+
+        if action == Actions.Long_sell.value and self._position == Positions.Long:
+            if self.close_trade_profit[-1] > self.profit_aim * self.rr:
+                last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
+                current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
+                return float((np.log(current_price) - np.log(last_trade_price)) * 2)
+
+        # close short
+        if action == Actions.Short_buy.value and self._position == Positions.Short:
+            last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
+            current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
+            return float(np.log(last_trade_price) - np.log(current_price))
+
+        if action == Actions.Short_buy.value and self._position == Positions.Short:
+            if self.close_trade_profit[-1] > self.profit_aim * self.rr:
+                last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
+                current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
+                return float((np.log(last_trade_price) - np.log(current_price)) * 2)
+
+        return 0.

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -8,7 +8,7 @@ from pandas import DataFrame
 from abc import abstractmethod
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
-from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
+from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
 from freqtrade.persistence import Trade
 
 logger = logging.getLogger(__name__)
@@ -165,7 +165,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             hist_preds_df[return_str] = 0
 
 
-class MyRLEnv(BaseRLEnv):
+class MyRLEnv(Base3ActionRLEnv):
 
     def step(self, action):
         self._done = False

freqtrade/freqai/prediction_models/ReinforcementLearningPPO.py

@@ -10,7 +10,7 @@ from stable_baselines3 import PPO
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.monitor import Monitor
 # from stable_baselines3.common.vec_env import SubprocVecEnv
-from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
+from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 
 
@@ -67,7 +67,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
         return model
 
 
-class MyRLEnv(BaseRLEnv):
+class MyRLEnv(Base3ActionRLEnv):
     """
     User can override any function in BaseRLEnv and gym.Env
     """

freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py

@@ -1,17 +1,14 @@
 import logging
 from typing import Any, Dict  # Optional
-from enum import Enum
-import numpy as np
 import torch as th
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.monitor import Monitor
 # from stable_baselines3.common.vec_env import SubprocVecEnv
-from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv
+from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 from freqtrade.freqai.RL.TDQNagent import TDQN
 from stable_baselines3.common.buffers import ReplayBuffer
-from gym import spaces
+import numpy as np
-from gym.utils import seeding
 
 logger = logging.getLogger(__name__)
 
@@ -71,233 +68,66 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
         return model
 
 
-class Actions(Enum):
+class MyRLEnv(Base3ActionRLEnv):
-    Neutral = 0
-    Long_buy = 1
-    Long_sell = 2
-    Short_buy = 3
-    Short_sell = 4
-
-
-class Positions(Enum):
-    Short = 0
-    Long = 1
-    Neutral = 0.5
-
-    def opposite(self):
-        return Positions.Short if self == Positions.Long else Positions.Long
-
-
-class MyRLEnv(BaseRLEnv):
     """
-    User can override any function in BaseRLEnv and gym.Env. Here the user
+    User can override any function in BaseRLEnv and gym.Env
-    Adds 5 actions.
     """
 
-    metadata = {'render.modes': ['human']}
+    def calculate_reward(self, action):
-
-    def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
-        assert df.ndim == 2
-
-        self.seed()
-        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])
-        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 = self.window_size
-        self._end_tick = len(self.prices) - 1
-        self._done = None
-        self._current_tick = None
-        self._last_trade_tick = None
-        self._position = Positions.Neutral
-        self._position_history = None
-        self.total_reward = None
-        self._total_profit = None
-        self._first_rendering = None
-        self.history = None
-        self.trade_history = []
-
-        # self.A_t, self.B_t = 0.000639, 0.00001954
-        self.r_t_change = 0.
-
-        self.returns_report = []
-
-    def seed(self, seed=None):
-        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._first_rendering = True
-        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.r_t_change = 0.
-
-        self.returns_report = []
-
-        return self._get_observation()
-
-    def step(self, action):
-        self._done = False
-        self._current_tick += 1
-
-        if self._current_tick == self._end_tick:
-            self._done = True
-
-        self.update_portfolio_log_returns(action)
-
-        self._update_profit(action)
-        step_reward = self.calculate_reward(action)
-        self.total_reward += step_reward
-
-        trade_type = None
-        if self.is_tradesignal(action):  # exclude 3 case not trade
-            # Update position
-            """
-            Action: Neutral, position: Long ->  Close Long
-            Action: Neutral, position: Short -> Close Short
-
-            Action: Long, position: Neutral -> Open Long
-            Action: Long, position: Short -> Close Short and Open Long
-
-            Action: Short, position: Neutral -> Open Short
-            Action: Short, position: Long -> Close Long and Open Short
-            """
-
-            if action == Actions.Neutral.value:
-                self._position = Positions.Neutral
-                trade_type = "neutral"
-            elif action == Actions.Long_buy.value:
-                self._position = Positions.Long
-                trade_type = "long"
-            elif action == Actions.Short_buy.value:
-                self._position = Positions.Short
-                trade_type = "short"
-            elif action == Actions.Long_sell.value:
-                self._position = Positions.Neutral
-                trade_type = "neutral"
-            elif action == Actions.Short_sell.value:
-                self._position = Positions.Neutral
-                trade_type = "neutral"
-            else:
-                print("case not defined")
-
-            # Update last trade tick
-            self._last_trade_tick = self._current_tick
-
-            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 < 0.2:
-            self._done = True
-
-        self._position_history.append(self._position)
-        observation = self._get_observation()
-        info = dict(
-            tick=self._current_tick,
-            total_reward=self.total_reward,
-            total_profit=self._total_profit,
-            position=self._position.value
-        )
-        self._update_history(info)
-
-        return observation, step_reward, self._done, info
-
-    def _get_observation(self):
-        return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
-
-    def get_unrealized_profit(self):
 
         if self._last_trade_tick is None:
             return 0.
 
-        if self._position == Positions.Neutral:
+        # close long
-            return 0.
+        if (action == Actions.Short.value or
-        elif self._position == Positions.Short:
+                action == Actions.Neutral.value) and self._position == Positions.Long:
-            current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
-            last_trade_price = self.add_sell_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_sell_fee(self.prices.iloc[self._current_tick].open)
             last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
-            return (current_price - last_trade_price) / last_trade_price
+            current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
-        else:
+            return float(np.log(current_price) - np.log(last_trade_price))
-            return 0.
 
-    def is_tradesignal(self, action):
+        # close short
-        # trade signal
+        if (action == Actions.Long.value or
-        """
+                action == Actions.Neutral.value) and self._position == Positions.Short:
-        not trade signal is :
+            last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
-        Action: Neutral, position: Neutral -> Nothing
+            current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
-        Action: Long, position: Long -> Hold Long
+            return float(np.log(last_trade_price) - np.log(current_price))
-        Action: Short, position: Short -> Hold Short
-        """
-        return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
-                    (action == Actions.Short_buy.value and self._position == Positions.Short) or
-                    (action == Actions.Short_sell.value and self._position == Positions.Short) or
-                    (action == Actions.Short_buy.value and self._position == Positions.Long) or
-                    (action == Actions.Short_sell.value and self._position == Positions.Long) or
 
-                    (action == Actions.Long_buy.value and self._position == Positions.Long) or
+        return 0.
-                    (action == Actions.Long_sell.value and self._position == Positions.Long) or
-                    (action == Actions.Long_buy.value and self._position == Positions.Short) or
-                    (action == Actions.Long_sell.value and self._position == Positions.Short))
 
-    def _is_trade(self, action):
+# User can inherit and customize 5 action environment
-        return ((action == Actions.Long_buy.value and self._position == Positions.Short) or
+# class MyRLEnv(Base5ActionRLEnv):
-                (action == Actions.Short_buy.value and self._position == Positions.Long) or
+#     """
-                (action == Actions.Neutral.value and self._position == Positions.Long) or
+#     User can override any function in BaseRLEnv and gym.Env. Here the user
-                (action == Actions.Neutral.value and self._position == Positions.Short) or
+#     Adds 5 actions.
+#     """
 
-                (action == Actions.Neutral.Short_sell and self._position == Positions.Long) or
+#     def calculate_reward(self, action):
-                (action == Actions.Neutral.Long_sell and self._position == Positions.Short)
-                )
 
-    def is_hold(self, action):
+#         if self._last_trade_tick is None:
-        return ((action == Actions.Short.value and self._position == Positions.Short)
+#             return 0.
-                or (action == Actions.Long.value and self._position == Positions.Long))
 
-    def add_buy_fee(self, price):
+#         # close long
-        return price * (1 + self.fee)
+#         if action == Actions.Long_sell.value and self._position == Positions.Long:
+#             last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
+#             current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
+#             return float(np.log(current_price) - np.log(last_trade_price))
 
-    def add_sell_fee(self, price):
+#         if action == Actions.Long_sell.value and self._position == Positions.Long:
-        return price / (1 + self.fee)
+#             if self.close_trade_profit[-1] > self.profit_aim * self.rr:
+#                 last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
+#                 current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
+#                 return float((np.log(current_price) - np.log(last_trade_price)) * 2)
 
-    def _update_history(self, info):
+#         # close short
-        if not self.history:
+#         if action == Actions.Short_buy.value and self._position == Positions.Short:
-            self.history = {key: [] for key in info.keys()}
+#             last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
+#             current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
+#             return float(np.log(last_trade_price) - np.log(current_price))
 
-        for key, value in info.items():
+#         if action == Actions.Short_buy.value and self._position == Positions.Short:
-            self.history[key].append(value)
+#             if self.close_trade_profit[-1] > self.profit_aim * self.rr:
+#                 last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
+#                 current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
+#                 return float((np.log(last_trade_price) - np.log(current_price)) * 2)
+
+#         return 0.