# source: https://raw.githubusercontent.com/ssssi/freqtrade_strs/dd3396a3433738d386f9364e98886bac19447884/binance/E0V1E.py
from datetime import datetime
from typing import Optional, Union
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce


# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
    highest_high = dataframe["high"].rolling(center=False, window=period).max()
    lowest_low = dataframe["low"].rolling(center=False, window=period).min()

    wr = Series(
        (highest_high - dataframe["close"]) / (highest_high - lowest_low),
        name=f"{period} Williams %R",
    )

    return wr * -100


class Github_ssssi_freqtrade_strs__E0V1E__20221026_111522(IStrategy):
    minimal_roi = {
        "0": 100
    }

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    startup_candle_count = 20

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_market_ratio': 0.99
    }

    # Disabled
    stoploss = -0.1

    # Custom stoploss
    use_custom_stoploss = False

    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32)

    is_optimize_r_deadfish = True
    buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60, space='buy', optimize=is_optimize_r_deadfish)

    is_optimize_deadfish = True
    sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish)

    sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # NFI 32 indicators
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)

        dataframe['cti'] = pta.cti(dataframe["close"], length=20)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        # dead fish indicators
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']

        dataframe['bb_width'] = (
                (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])

        dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
        dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)

        dataframe['r_14'] = williams_r(dataframe, period=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy_1 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
                (dataframe['rsi'] > self.buy_rsi_32.value) &
                (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
                (dataframe['cti'] < self.buy_cti_32.value)
        )

        buy_2 = (
                (dataframe['ema_100'] < dataframe['ema_200'] * self.buy_r_deadfish_ema.value) &
                (dataframe['bb_width'] > self.buy_r_deadfish_bb_width.value) &
                (dataframe['close'] < dataframe['bb_middleband2'] * self.buy_r_deadfish_bb_factor.value) &
                (dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.buy_r_deadfish_volume_factor.value) &
                (dataframe['cti'] < self.buy_r_deadfish_cti.value) &
                (dataframe['r_14'] < self.buy_r_deadfish_r14.value)
        )

        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

        conditions.append(buy_2)
        dataframe.loc[buy_2, 'enter_tag'] += 'buy_2'

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'enter_long'] = 1

        return dataframe

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                    current_profit: float, **kwargs) -> Optional[Union[str, bool]]:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        if current_profit > 0:
            if current_candle["fastk"] > self.sell_fastx.value:
                return "sell_fastk"

        # stoploss - deadfish
        if ((current_profit < self.sell_deadfish_profit.value)
                and (current_candle['bb_width'] < self.sell_deadfish_bb_width.value)
                and (current_candle['close'] > current_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value)
                and (current_candle['volume_mean_12'] < current_candle[
                    'volume_mean_24'] * self.sell_deadfish_volume_factor.value)
        ):
            return "sell_stoploss_deadfish"

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')

        return dataframe