from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class Strategy001_custom_sell_4(IStrategy):
    INTERFACE_VERSION = 3
    '\n    Strategy 001_custom_exit\n    author@: Gerald Lonlas, froggleston\n    github@: https://github.com/freqtrade/freqtrade-strategies\n\n    How to use it?\n    > python3 ./freqtrade/main.py -s Strategy001_custom_exit\n    '


    minimal_roi = {'60': 0.01, '30': 0.03, '20': 0.04, '0': 0.05}


    stoploss = -0.1

    timeframe = '5m'

    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02

    process_only_new_candles = False

    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = False

    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    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.
        """
        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['rsi'] = ta.RSI(dataframe, 14)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """  # green bar
        dataframe.loc[qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) & (dataframe['ha_close'] > dataframe['ema20']) & (dataframe['ha_open'] < dataframe['ha_close']), 'enter_long'] = 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
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """  # red bar
        dataframe.loc[qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) & (dataframe['ha_close'] < dataframe['ema20']) & (dataframe['ha_open'] > dataframe['ha_close']), 'exit_long'] = 1
        return dataframe

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        """
        Sell only when matching some criteria other than those used to generate the exit signal
        :return: str exit_reason, if any, otherwise None
        """

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)

        current_candle = dataframe.iloc[-1].squeeze()

        if current_candle['rsi'] > 70 and current_profit > 0:
            return 'rsi_profit_exit'

        return None