add continual retraining feature, handly mypy typing reqs, improve docstrings

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -1,6 +1,6 @@
 import logging
 from enum import Enum
-# from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+from typing import Optional
 
 import gym
 import numpy as np
@@ -44,14 +44,14 @@ class Base5ActionRLEnv(gym.Env):
     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 = {}):
-        assert df.ndim == 2
 
         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, prices, window_size, reward_kwargs, starting_point=True):
+    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
@@ -69,18 +69,18 @@ class Base5ActionRLEnv(gym.Env):
             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._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 = None
-        self.total_reward = None
-        self._total_profit = None
-        self._first_rendering = None
-        self.history = None
-        self.trade_history = []
+        self._position_history: list = [None]
+        self.total_reward: float = 0
+        self._total_profit: float = 0
+        self._first_rendering: bool = False
+        self.history: dict = {}
+        self.trade_history: list = []
 
     def seed(self, seed: int = 1):
         self.np_random, seed = seeding.np_random(seed)
@@ -125,8 +125,7 @@ class Base5ActionRLEnv(gym.Env):
         self.total_reward += step_reward
 
         trade_type = None
-        if self.is_tradesignal(action):  # exclude 3 case not trade
-            # Update position
+        if self.is_tradesignal(action):
             """
             Action: Neutral, position: Long ->  Close Long
             Action: Neutral, position: Short -> Close Short
@@ -223,9 +222,8 @@ class Base5ActionRLEnv(gym.Env):
         # trade signal
         """
         not trade signal is :
-        Action: Neutral, position: Neutral -> Nothing
-        Action: Long, position: Long -> Hold Long
-        Action: Short, position: Short -> Hold Short
+        Determine if the signal is non sensical
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
         """
         return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
                     (action == Actions.Neutral.value and self._position == Positions.Short) or
@@ -292,7 +290,7 @@ class Base5ActionRLEnv(gym.Env):
 
     def most_recent_return(self, action: int):
         """
-        We support Long, Neutral and Short positions.
+        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.