from datetime import datetime

import talib.abstract as ta
from pandas import DataFrame

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exceptions import DependencyException
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy


class strategy_test_v2_6(IStrategy):
    """
    Strategy used by tests freqtrade bot.
    Please do not modify this strategy, it's  intended for internal use only.
    Please look at the SampleStrategy in the user_data/strategy directory
    or strategy repository https://github.com/freqtrade/freqtrade-strategies
    for samples and inspiration.
    """
    INTERFACE_VERSION = 2

    minimal_roi = {
        "40": 0.0,
        "30": 0.01,
        "20": 0.02,
        "0": 0.04
    }

    stoploss = -0.10

    timeframe = '5m'

    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False
    }

    startup_candle_count: int = 20

    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc',
    }

    position_adjustment_enable = 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.
        :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
        """



        dataframe['adx'] = ta.ADX(dataframe)

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        dataframe['minus_di'] = ta.MINUS_DI(dataframe)

        dataframe['plus_di'] = ta.PLUS_DI(dataframe)

        dataframe['rsi'] = ta.RSI(dataframe)

        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']

        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['rsi'] < 35) &
                (dataframe['fastd'] < 35) &
                (dataframe['adx'] > 30) &
                (dataframe['plus_di'] > 0.5)
            ) |
            (
                (dataframe['adx'] > 65) &
                (dataframe['plus_di'] > 0.5)
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (
                    (qtpylib.crossed_above(dataframe['rsi'], 70)) |
                    (qtpylib.crossed_above(dataframe['fastd'], 70))
                ) &
                (dataframe['adx'] > 10) &
                (dataframe['minus_di'] > 0)
            ) |
            (
                (dataframe['adx'] > 70) &
                (dataframe['minus_di'] > 0.5)
            ),
            'sell'] = 1
        return dataframe

    def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float,
                              current_profit: float, min_stake: float, max_stake: float, **kwargs):

        if current_profit < -0.0075:
            try:
                return self.wallets.get_trade_stake_amount(trade.pair, None)
            except DependencyException:
                pass

        return None