import logging
import random
from abc import abstractmethod
from enum import Enum
from typing import Optional, Type, Union

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 BaseActions(Enum):
    """
    Default action space, mostly used for type handling.
    """
    Neutral = 0
    Long_enter = 1
    Long_exit = 2
    Short_enter = 3
    Short_exit = 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 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 = {}, live: bool = False,
                 fee: float = 0.0015):
        """
        Initializes the training/eval environment.
        :param df: dataframe of features
        :param prices: dataframe of prices to be used in the training environment
        :param window_size: size of window (temporal) to pass to the agent
        :param reward_kwargs: extra config settings assigned by user in `rl_config`
        :param starting_point: start at edge of window or not
        :param id: string id of the environment (used in backend for multiprocessed env)
        :param seed: Sets the seed of the environment higher in the gym.Env object
        :param config: Typical user configuration file
        :param live: Whether or not this environment is active in dry/live/backtesting
        :param fee: The fee to use for environmental interactions.
        """
        self.config = config
        self.rl_config = config['freqai']['rl_config']
        self.add_state_info = self.rl_config.get('add_state_info', False)
        self.id = id
        self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
        self.compound_trades = config['stake_amount'] == 'unlimited'
        if self.config.get('fee', None) is not None:
            self.fee = self.config['fee']
        else:
            self.fee = fee

        # set here to default 5Ac, but all children envs can override this
        self.actions: Type[Enum] = BaseActions
        self.tensorboard_metrics: dict = {}
        self.live = live
        if not self.live and self.add_state_info:
            self.add_state_info = False
            logger.warning("add_state_info is not available in backtesting. Deactivating.")
        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):
        """
        Resets the environment when the agent fails (in our case, if the drawdown
        exceeds the user set max_training_drawdown_pct)
        :param df: dataframe of features
        :param prices: dataframe of prices to be used in the training environment
        :param window_size: size of window (temporal) to pass to the agent
        :param reward_kwargs: extra config settings assigned by user in `rl_config`
        :param starting_point: start at edge of window or not
        """
        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"]

        # # spaces
        if self.add_state_info:
            self.total_features = self.signal_features.shape[1] + 3
        else:
            self.total_features = self.signal_features.shape[1]
        self.shape = (window_size, self.total_features)
        self.set_action_space()
        self.observation_space = spaces.Box(
            low=-1, high=1, 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 tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
        """
        Function builds the tensorboard_metrics dictionary
        to be parsed by the TensorboardCallback. This
        function is designed for tracking incremented objects,
        events, actions inside the training environment.
        For example, a user can call this to track the
        frequency of occurence of an `is_valid` call in
        their `calculate_reward()`:

        def calculate_reward(self, action: int) -> float:
            if not self._is_valid(action):
                self.tensorboard_log("is_valid")
                return -2

        :param metric: metric to be tracked and incremented
        :param value: value to increment `metric` by
        :param inc: sets whether the `value` is incremented or not
        """
        if not inc or metric not in self.tensorboard_metrics:
            self.tensorboard_metrics[metric] = value
        else:
            self.tensorboard_metrics[metric] += value

    def reset_tensorboard_log(self):
        self.tensorboard_metrics = {}

    def reset(self):
        """
        Reset is called at the beginning of every episode
        """
        self.reset_tensorboard_log()

        self._done = False

        if self.starting_point is True:
            if self.rl_config.get('randomize_starting_position', False):
                length_of_data = int(self._end_tick / 4)
                start_tick = random.randint(self.window_size + 1, length_of_data)
                self._start_tick = start_tick
            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]
        if self.add_state_info:
            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
        else:
            return features_window

    def get_trade_duration(self):
        """
        Get the trade duration if the agent is in a trade
        """
        if self._last_trade_tick is None:
            return 0
        else:
            return self._current_tick - self._last_trade_tick

    def get_unrealized_profit(self):
        """
        Get the unrealized profit if the agent is in a trade
        """
        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) -> bool:
        """
        Determine if the signal is a trade signal. This is
        unique to the actions in the environment, and therefore must be
        inherited.
        """
        return True

    def _is_valid(self, action: int) -> bool:
        """
        Determine if the signal is valid.This is
        unique to the actions in the environment, and therefore must be
        inherited.
        """
        return True

    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: int) -> float:
        """
        An example reward function. This is the one function that users will likely
        wish to inject their own creativity into.
        :param action: int = The action made by the agent for the current candle.
        :return:
        float = the reward to give to the agent for current step (used for optimization
            of weights in NN)
        """

    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 unit stake and compounding
            self._total_profit = self._total_profit * (1 + pnl)
        else:
            # assumes unit stake and no compounding
            self._total_profit += pnl

    def current_price(self) -> float:
        return self.prices.iloc[self._current_tick].open

    def get_actions(self) -> Type[Enum]:
        """
        Used by SubprocVecEnv to get actions from
        initialized env for tensorboard callback
        """
        return self.actions

    # Keeping around incase we want to start building more complex environment
    # templates in the future.
    # def most_recent_return(self):
    #     """
    #     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 update_portfolio_log_returns(self, action):
    #     self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)