110 lines
5.6 KiB
Python
110 lines
5.6 KiB
Python
from datetime import datetime, timedelta
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import talib.abstract as ta
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from freqtrade.persistence import Trade
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from freqtrade.strategy import CategoricalParameter
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from freqtrade.strategy import DecimalParameter, IntParameter
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# og @tirail
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# author @Jooopieeert#0239
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ma_types = {
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'SMA': ta.SMA,
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'EMA': ta.EMA,
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}
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class SMAOG(IStrategy):
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INTERFACE_VERSION = 2
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buy_params = {
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"base_nb_candles_buy": 26,
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"buy_trigger": "SMA",
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"low_offset": 0.968,
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"pair_is_bad_0_threshold": 0.555,
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"pair_is_bad_1_threshold": 0.172,
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"pair_is_bad_2_threshold": 0.198,
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}
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sell_params = {
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"base_nb_candles_sell": 28,
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"high_offset": 0.985,
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"sell_trigger": "EMA",
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}
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base_nb_candles_buy = IntParameter(16, 45, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False, load=True)
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base_nb_candles_sell = IntParameter(16, 45, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False, load=True)
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low_offset = DecimalParameter(0.8, 0.99, default=buy_params['low_offset'], space='buy', optimize=False, load=True)
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high_offset = DecimalParameter(0.8, 1.1, default=sell_params['high_offset'], space='sell', optimize=False, load=True)
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buy_trigger = CategoricalParameter(ma_types.keys(), default=buy_params['buy_trigger'], space='buy', optimize=False, load=True)
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sell_trigger = CategoricalParameter(ma_types.keys(), default=sell_params['sell_trigger'], space='sell', optimize=False, load=True)
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pair_is_bad_0_threshold = DecimalParameter(0.0, 0.600, default=0.220, space='buy', optimize=True, load=True)
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pair_is_bad_1_threshold = DecimalParameter(0.0, 0.350, default=0.090, space='buy', optimize=True, load=True)
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pair_is_bad_2_threshold = DecimalParameter(0.0, 0.200, default=0.060, space='buy', optimize=True, load=True)
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timeframe = '5m'
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stoploss = -0.23
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minimal_roi = {"0": 10,}
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trailing_stop = True
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trailing_only_offset_is_reached = True
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.02
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use_sell_signal = True
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sell_profit_only = False
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ignore_roi_if_buy_signal = False
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process_only_new_candles = True
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startup_candle_count = 400
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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if not self.config['runmode'].value == 'hyperopt':
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dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
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dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
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dataframe['pair_is_bad'] = (
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(((dataframe['open'].rolling(144).min() - dataframe['close']) / dataframe[
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'close']) >= self.pair_is_bad_0_threshold.value) |
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(((dataframe['open'].rolling(12).min() - dataframe['close']) / dataframe[
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'close']) >= self.pair_is_bad_1_threshold.value) |
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(((dataframe['open'].rolling(2).min() - dataframe['close']) / dataframe[
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'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')
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dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
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dataframe['rsi_exit'] = ta.RSI(dataframe, timeperiod=2)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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if self.config['runmode'].value == 'hyperopt':
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dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
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dataframe['pair_is_bad'] = (
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(((dataframe['open'].rolling(144).min() - dataframe['close']) / dataframe[
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'close']) >= self.pair_is_bad_0_threshold.value) |
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(((dataframe['open'].rolling(12).min() - dataframe['close']) / dataframe[
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'close']) >= self.pair_is_bad_1_threshold.value) |
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(((dataframe['open'].rolling(2).min() - dataframe['close']) / dataframe[
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'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')
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dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
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dataframe['rsi_exit'] = ta.RSI(dataframe, timeperiod=2)
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dataframe.loc[
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(
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(dataframe['ema_50'] > dataframe['ema_200']) &
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(dataframe['close'] > dataframe['ema_200']) &
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(dataframe['pair_is_bad'] < 1) &
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(dataframe['close'] < dataframe['ma_offset_buy']) &
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(dataframe['volume'] > 0)
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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if self.config['runmode'].value == 'hyperopt':
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dataframe['ma_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
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dataframe.loc[
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(
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(dataframe['close'] > dataframe['ma_offset_sell']) &
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(
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(dataframe['open'] < dataframe['open'].shift(1)) |
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(dataframe['rsi_exit'] < 50) |
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(dataframe['rsi_exit'] < dataframe['rsi_exit'].shift(1))
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) &
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(dataframe['volume'] > 0)
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),
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'sell'] = 1
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return dataframe
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