E0V1E

from datetime import datetime
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
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce
import warnings

warnings.simplefilter(action='ignore', category=RuntimeWarning)
TMP_HOLD = []
TMP_HOLD1 = []


class E0V1E(IStrategy):
    minimal_roi = {'0': 1}
    timeframe = '5m'
    process_only_new_candles = True
    startup_candle_count = 240
    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,
    }

    stoploss = -0.25
    trailing_stop = False
    trailing_stop_positive = 0.002
    trailing_stop_positive_offset = 0.05
    trailing_only_offset_is_reached = True

    use_custom_stoploss = True

    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(20, 70, default=40, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=42, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(
        0.900, 1, default=0.973, decimals=3, space='buy', optimize=is_optimize_32
    )
    buy_cti_32 = DecimalParameter(
        -1, 1, default=0.69, decimals=2, space='buy', optimize=is_optimize_32
    )

    sell_fastx = IntParameter(50, 100, default=84, space='sell', optimize=True)

    cci_opt = False
    sell_loss_cci = IntParameter(low=0, high=600, default=120, space='sell', optimize=cci_opt)
    sell_loss_cci_profit = DecimalParameter(
        -0.15, 0, default=-0.05, decimals=2, space='sell', optimize=cci_opt
    )

    @property
    def protections(self):

        return [{'method': 'CooldownPeriod', 'stop_duration_candles': 18}]

    def custom_stoploss(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs
    ) -> float:

        if current_profit >= 0.05:
            return -0.002

        if str(trade.enter_tag) == 'buy_new' and current_profit >= 0.03:
            return -0.003

        return None

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # buy_1 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)

        # profit sell indicators
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastk'] = stoch_fast['fastk']

        dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)

        dataframe['ma120'] = ta.MA(dataframe, timeperiod=120)
        dataframe['ma240'] = ta.MA(dataframe, timeperiod=240)

        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_new = (
            (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1))
            & (dataframe['rsi_fast'] < 34)
            & (dataframe['rsi'] > 28)
            & (dataframe['close'] < dataframe['sma_15'] * 0.96)
            & (dataframe['cti'] < self.buy_cti_32.value)
        )

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

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

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

        min_profit = trade.calc_profit_ratio(trade.min_rate)

        if (
            current_candle['close'] > current_candle['ma120']
            and current_candle['close'] > current_candle['ma240']
        ):
            if trade.id not in TMP_HOLD:
                TMP_HOLD.append(trade.id)

        if (trade.open_rate - current_candle['ma120']) / trade.open_rate >= 0.1:
            if trade.id not in TMP_HOLD1:
                TMP_HOLD1.append(trade.id)

        if current_profit > 0:
            if current_candle['fastk'] > self.sell_fastx.value:
                return 'fastk_profit_sell'

        if min_profit <= -0.1:
            if current_profit > self.sell_loss_cci_profit.value:
                if current_candle['cci'] > self.sell_loss_cci.value:
                    return 'cci_loss_sell'

        if trade.id in TMP_HOLD1 and current_candle['close'] < current_candle['ma120']:
            TMP_HOLD1.remove(trade.id)
            return 'ma120_sell_fast'

        if (
            trade.id in TMP_HOLD
            and current_candle['close'] < current_candle['ma120']
            and current_candle['close'] < current_candle['ma240']
        ):
            if min_profit <= -0.1:
                TMP_HOLD.remove(trade.id)
                return 'ma120_sell'

        return None

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, ['exit_long', 'exit_tag']] = (0, 'long_out')
        return dataframe