SMAOG

from datetime import datetime, timedelta
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy import CategoricalParameter
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# og        @tirail
# author    @Jooopieeert#0239
ma_types = {'SMA': ta.SMA, 'EMA': ta.EMA}

class SMAOG(IStrategy):
    INTERFACE_VERSION = 3
    entry_params = {'base_nb_candles_entry': 26, 'entry_trigger': 'SMA', 'low_offset': 0.968, 'pair_is_bad_0_threshold': 0.555, 'pair_is_bad_1_threshold': 0.172, 'pair_is_bad_2_threshold': 0.198}
    exit_params = {'base_nb_candles_exit': 28, 'high_offset': 0.985, 'exit_trigger': 'EMA'}
    base_nb_candles_entry = IntParameter(16, 45, default=entry_params['base_nb_candles_entry'], space='entry', optimize=False, load=True)
    base_nb_candles_exit = IntParameter(16, 45, default=exit_params['base_nb_candles_exit'], space='exit', optimize=False, load=True)
    low_offset = DecimalParameter(0.8, 0.99, default=entry_params['low_offset'], space='entry', optimize=False, load=True)
    high_offset = DecimalParameter(0.8, 1.1, default=exit_params['high_offset'], space='exit', optimize=False, load=True)
    entry_trigger = CategoricalParameter(ma_types.keys(), default=entry_params['entry_trigger'], space='entry', optimize=False, load=True)
    exit_trigger = CategoricalParameter(ma_types.keys(), default=exit_params['exit_trigger'], space='exit', optimize=False, load=True)
    pair_is_bad_0_threshold = DecimalParameter(0.0, 0.6, default=0.22, space='entry', optimize=True, load=True)
    pair_is_bad_1_threshold = DecimalParameter(0.0, 0.35, default=0.09, space='entry', optimize=True, load=True)
    pair_is_bad_2_threshold = DecimalParameter(0.0, 0.2, default=0.06, space='entry', optimize=True, load=True)
    timeframe = '5m'
    stoploss = -0.23
    minimal_roi = {'0': 10}
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    process_only_new_candles = True
    startup_candle_count = 400

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_entry'] = ma_types[self.entry_trigger.value](dataframe, int(self.base_nb_candles_entry.value)) * self.low_offset.value
            dataframe['ma_offset_exit'] = ma_types[self.exit_trigger.value](dataframe, int(self.base_nb_candles_exit.value)) * self.high_offset.value
            dataframe['pair_is_bad'] = (((dataframe['open'].rolling(144).min() - dataframe['close']) / dataframe['close'] >= self.pair_is_bad_0_threshold.value) | ((dataframe['open'].rolling(12).min() - dataframe['close']) / dataframe['close'] >= self.pair_is_bad_1_threshold.value) | ((dataframe['open'].rolling(2).min() - dataframe['close']) / dataframe['close'] >= self.pair_is_bad_2_threshold.value)).astype('int')
            dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
            dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
            dataframe['rsi_exit'] = ta.RSI(dataframe, timeperiod=2)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_entry'] = ma_types[self.entry_trigger.value](dataframe, int(self.base_nb_candles_entry.value)) * self.low_offset.value
            dataframe['pair_is_bad'] = (((dataframe['open'].rolling(144).min() - dataframe['close']) / dataframe['close'] >= self.pair_is_bad_0_threshold.value) | ((dataframe['open'].rolling(12).min() - dataframe['close']) / dataframe['close'] >= self.pair_is_bad_1_threshold.value) | ((dataframe['open'].rolling(2).min() - dataframe['close']) / dataframe['close'] >= self.pair_is_bad_2_threshold.value)).astype('int')
            dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
            dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe.loc[(dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200']) & (dataframe['pair_is_bad'] < 1) & (dataframe['close'] < dataframe['ma_offset_entry']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_exit'] = ta.EMA(dataframe, int(self.base_nb_candles_exit.value)) * self.high_offset.value
        dataframe.loc[(dataframe['close'] > dataframe['ma_offset_exit']) & ((dataframe['open'] < dataframe['open'].shift(1)) | (dataframe['rsi_exit'] < 50) | (dataframe['rsi_exit'] < dataframe['rsi_exit'].shift(1))) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe