Apollo13

from datetime import datetime
from datetime import timedelta
from functools import reduce
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy
from pandas import DataFrame

def to_minutes(**timdelta_kwargs):
    return int(timedelta(**timdelta_kwargs).total_seconds() / 60)



class Apollo13(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '15m'

    stoploss = -0.16
    startup_candle_count: int = 480
    trailing_stop = False
    use_custom_stoploss = True
    use_exit_signal = False

    buy_signal_1 = True
    buy_signal_2 = True
    buy_signal_3 = True
    minimal_roi = {'0': 10}
    s1_ema_xs = 3
    s1_ema_sm = 5
    s1_ema_md = 10
    s1_ema_xl = 50
    s1_ema_xxl = 240

    s2_ema_input = 50
    s2_ema_offset_input = -1
    s2_bb_sma_length = 49
    s2_bb_std_dev_length = 64
    s2_bb_lower_offset = 3
    s2_fib_sma_len = 50
    s2_fib_atr_len = 14
    s2_fib_lower_value = 4.236
    s3_ema_long = 50
    s3_ema_short = 20
    s3_ma_fast = 10
    s3_ma_slow = 20

    @property
    def protections(self):
        return [{'method': 'CooldownPeriod', 'stop_duration': to_minutes(minutes=0)}, {'method': 'MaxDrawdown', 'lookback_period': to_minutes(hours=12), 'trade_limit': 20, 'stop_duration': to_minutes(hours=1), 'max_allowed_drawdown': 0.2}, {'method': 'StoplossGuard', 'lookback_period': to_minutes(hours=6), 'trade_limit': 4, 'stop_duration': to_minutes(minutes=30), 'only_per_pair': False}, {'method': 'LowProfitPairs', 'lookback_period': to_minutes(hours=1, minutes=30), 'trade_limit': 2, 'stop_duration': to_minutes(hours=15), 'required_profit': 0.02}, {'method': 'LowProfitPairs', 'lookback_period': to_minutes(hours=6), 'trade_limit': 4, 'stop_duration': to_minutes(minutes=30), 'required_profit': 0.01}]

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

        dataframe['s1_ema_xs'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xs)
        dataframe['s1_ema_sm'] = ta.EMA(dataframe, timeperiod=self.s1_ema_sm)
        dataframe['s1_ema_md'] = ta.EMA(dataframe, timeperiod=self.s1_ema_md)
        dataframe['s1_ema_xl'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xl)
        dataframe['s1_ema_xxl'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xxl)
        s2_ema_value = ta.EMA(dataframe, timeperiod=self.s2_ema_input)
        s2_ema_xxl_value = ta.EMA(dataframe, timeperiod=200)
        dataframe['s2_ema'] = s2_ema_value - s2_ema_value * self.s2_ema_offset_input
        dataframe['s2_ema_xxl_off'] = s2_ema_xxl_value - s2_ema_xxl_value * self.s2_fib_lower_value
        dataframe['s2_ema_xxl'] = ta.EMA(dataframe, timeperiod=200)
        s2_bb_sma_value = ta.SMA(dataframe, timeperiod=self.s2_bb_sma_length)
        s2_bb_std_dev_value = ta.STDDEV(dataframe, self.s2_bb_std_dev_length)
        dataframe['s2_bb_std_dev_value'] = s2_bb_std_dev_value
        dataframe['s2_bb_lower_band'] = s2_bb_sma_value - s2_bb_std_dev_value * self.s2_bb_lower_offset
        s2_fib_atr_value = ta.ATR(dataframe, timeframe=self.s2_fib_atr_len)
        s2_fib_sma_value = ta.SMA(dataframe, timeperiod=self.s2_fib_sma_len)
        dataframe['s2_fib_lower_band'] = s2_fib_sma_value - s2_fib_atr_value * self.s2_fib_lower_value
        s3_bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['s3_bb_lowerband'] = s3_bollinger['lower']
        dataframe['s3_ema_long'] = ta.EMA(dataframe, timeperiod=self.s3_ema_long)
        dataframe['s3_ema_short'] = ta.EMA(dataframe, timeperiod=self.s3_ema_short)
        dataframe['s3_fast_ma'] = ta.EMA(dataframe['volume'] * dataframe['close'], self.s3_ma_fast) / ta.EMA(dataframe['volume'], self.s3_ma_fast)
        dataframe['s3_slow_ma'] = ta.EMA(dataframe['volume'] * dataframe['close'], self.s3_ma_slow) / ta.EMA(dataframe['volume'], self.s3_ma_slow)

        dataframe['fastMA'] = ta.EMA(dataframe['volume'] * dataframe['close'], 12) / ta.EMA(dataframe['volume'], 12)
        dataframe['slowMA'] = ta.EMA(dataframe['volume'] * dataframe['close'], 26) / ta.EMA(dataframe['volume'], 26)
        dataframe['vwmacd'] = dataframe['fastMA'] - dataframe['slowMA']
        dataframe['signal'] = ta.EMA(dataframe['vwmacd'], 9)
        dataframe['hist'] = dataframe['vwmacd'] - dataframe['signal']
        return dataframe

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

        if self.buy_signal_1:
            conditions = [dataframe['vwmacd'] < dataframe['signal'], dataframe['low'] < dataframe['s1_ema_xxl'], dataframe['close'] > dataframe['s1_ema_xxl'], qtpylib.crossed_above(dataframe['s1_ema_sm'], dataframe['s1_ema_md']), dataframe['s1_ema_xs'] < dataframe['s1_ema_xl'], dataframe['volume'] > 0]
            dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'buy_signal_1')
        if self.buy_signal_2:
            conditions = [qtpylib.crossed_above(dataframe['s2_fib_lower_band'], dataframe['s2_bb_lower_band']), dataframe['close'] < dataframe['s2_ema'], dataframe['volume'] > 0]
            dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'buy_signal_2')
        if self.buy_signal_3:
            conditions = [dataframe['low'] < dataframe['s3_bb_lowerband'], dataframe['high'] > dataframe['s3_slow_ma'], dataframe['high'] < dataframe['s3_ema_long'], dataframe['volume'] > 0]
            dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'buy_signal_3')
        if not all([self.buy_signal_1, self.buy_signal_2, self.buy_signal_3]):
            dataframe.loc[(), 'enter_long'] = 0
        return dataframe

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

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

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        if current_profit > 0.2:
            return 0.04
        if current_profit > 0.1:
            return 0.03
        if current_profit > 0.06:
            return 0.02
        if current_profit > 0.03:
            return 0.01

        if current_profit <= -0.1:
            if trade.open_date_utc + timedelta(hours=60) < current_time:

                return current_profit / 1.75
        if current_profit <= -0.08:
            if trade.open_date_utc + timedelta(hours=120) < current_time:

                return current_profit / 1.7
        return -1