# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
import numpy as np
from functools import reduce
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import DecimalParameter
from freqtrade.persistence import Trade
from datetime import datetime
from freqtrade.strategy import stoploss_from_open

def MOST(dataframe, length=8, percent=2, MAtype=1):
    """Partial implementation of MOST indicator."""
    data = dataframe.copy()
    # Compute basic upper and lower bands
    if MAtype == 1:
        data['exma'] = ta.EMA(data, timeperiod=length)
    elif MAtype == 2:
        data['exma'] = ta.DEMA(data, timeperiod=length)
    elif MAtype == 3:
        data['exma'] = ta.T3(data, timeperiod=length)
    data['basic_ub'] = data['exma'] * (1 + percent / 100)
    data['basic_lb'] = data['exma'] * (1 - percent / 100)
    # Compute final upper and lower bands
    data['final_ub'] = 0.0
    data['final_lb'] = 0.0
    for i in range(length, len(data)):
        data['final_ub'].iat[i] = data['basic_ub'].iat[i] if data['basic_ub'].iat[i] < data['final_ub'].iat[i - 1] or data['exma'].iat[i - 1] > data['final_ub'].iat[i - 1] else data['final_ub'].iat[i - 1]
        data['final_lb'].iat[i] = data['basic_lb'].iat[i] if data['basic_lb'].iat[i] > data['final_lb'].iat[i - 1] or data['exma'].iat[i - 1] < data['final_lb'].iat[i - 1] else data['final_lb'].iat[i - 1]
    # Set the MOST value
    data['most'] = 0.0
    for i in range(length, len(data)):
        data['most'].iat[i] = data['final_ub'].iat[i] if data['most'].iat[i - 1] == data['final_ub'].iat[i - 1] and data['exma'].iat[i] <= data['final_ub'].iat[i] else data['final_lb'].iat[i] if data['most'].iat[i - 1] == data['final_ub'].iat[i - 1] and data['exma'].iat[i] > data['final_ub'].iat[i] else data['final_lb'].iat[i] if data['most'].iat[i - 1] == data['final_lb'].iat[i - 1] and data['exma'].iat[i] >= data['final_lb'].iat[i] else data['final_ub'].iat[i] if data['most'].iat[i - 1] == data['final_lb'].iat[i - 1] and data['exma'].iat[i] < data['final_lb'].iat[i] else 0.0
    # Mark the trend direction up/down
    data['trend'] = np.where(data['most'] > 0.0, np.where(data['exma'] < data['most'], 0, 1), np.NaN)
    # Remove basic and final bands from the columns
    data.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
    data.fillna(0, inplace=True)
    return data

class MostOfAll(IStrategy):
    INTERFACE_VERSION = 3
    '\n        My second humble strategy using a MOST alike indicator\n        Changelog:\n            0.9 Initial version, improvements needed\n\n        https://github.com/cyberjunky/freqtrade-strategies\n        https://www.tradingview.com/scripts/most/\n\n    '
    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.2
    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.015
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30
    # Custom stoploss
    use_custom_stoploss = True
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.08, '36': 0.031, '50': 0.021, '60': 0.01, '70': 0}

    @property
    def plot_config(self):
        """Buildin plot config."""
        # Main plot indicators (Moving averages, ...)
        # Subplots - each dict defines one additional plot
        return {'main_plot': {'most': {'color': 'darkpurple'}, 'exma': {'color': 'green'}}, 'subplots': {'trend': {'trend': {'color': 'blue'}}}}
    # hard stoploss profit
    pHSL = DecimalParameter(-0.5, -0.04, default=-0.99, decimals=3, space='exit', load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='exit', load=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.009, decimals=3, space='exit', load=True)
    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.04, decimals=3, space='exit', load=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.02, decimals=3, space='exit', load=True)
    # Custom stoploss

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        """Custom stoploss calculation with thresholds and based on linear curve."""
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        # Only for hyperopt invalid return
        if sl_profit >= current_profit:
            return -0.99
        return stoploss_from_open(sl_profit, current_profit)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """
        # MOST
        most_df = MOST(dataframe, length=14)
        dataframe['most'] = most_df['most']
        dataframe['exma'] = most_df['exma']
        dataframe['trend'] = most_df['trend']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        If the bullish fractal is active and below the teeth of the gator -> entry
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        conditions = []
        conditions.append(qtpylib.crossed_above(dataframe['most'], dataframe['exma']) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        If the bearish fractal is active and above the teeth of the gator -> exit
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        conditions = []
        conditions.append(qtpylib.crossed_above(dataframe['exma'], dataframe['most']) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit'] = 1
        return dataframe