Merge pull request #262 from TreborNamor/main
updated buy and sell parameters
This commit is contained in:
Matthias
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import talib.abstract as ta
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import numpy as np # noqa
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import pandas as pd
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from functools import reduce
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from pandas import DataFrame
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy import IStrategy, CategoricalParameter, DecimalParameter, IntParameter, RealParameter
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__author__ = "Robert Roman"
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__copyright__ = "Free For Use"
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__license__ = "MIT"
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__version__ = "1.0"
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__maintainer__ = "Robert Roman"
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__email__ = "robertroman7@gmail.com"
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__BTC_donation__ = "3FgFaG15yntZYSUzfEpxr5mDt1RArvcQrK"
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# Optimized With Sharpe Ratio and 1 year data
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# 199/40000: 30918 trades. 18982/3408/8528 Wins/Draws/Losses. Avg profit 0.39%. Median profit 0.65%. Total profit 119934.26007495 USDT ( 119.93%). Avg duration 8:12:00 min. Objective: -127.60220
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class Bandtastic(IStrategy):
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INTERFACE_VERSION = 2
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timeframe = '15m'
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# ROI table:
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minimal_roi = {
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"0": 0.162,
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"69": 0.097,
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"229": 0.061,
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"566": 0
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}
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# Stoploss:
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stoploss = -0.345
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.058
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trailing_only_offset_is_reached = False
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# Hyperopt Buy Parameters
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buy_fastema = IntParameter(low=1, high=236, default=211, space='buy', optimize=True, load=True)
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buy_slowema = IntParameter(low=1, high=126, default=364, space='buy', optimize=True, load=True)
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buy_rsi = IntParameter(low=15, high=70, default=52, space='buy', optimize=True, load=True)
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buy_mfi = IntParameter(low=15, high=70, default=30, space='buy', optimize=True, load=True)
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buy_rsi_enabled = CategoricalParameter([True, False], space='buy', optimize=True, default=False)
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buy_mfi_enabled = CategoricalParameter([True, False], space='buy', optimize=True, default=False)
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buy_ema_enabled = CategoricalParameter([True, False], space='buy', optimize=True, default=False)
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buy_trigger = CategoricalParameter(["bb_lower1", "bb_lower2", "bb_lower3", "bb_lower4"], default="bb_lower1", space="buy")
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# Hyperopt Sell Parameters
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sell_fastema = IntParameter(low=1, high=365, default=7, space='sell', optimize=True, load=True)
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sell_slowema = IntParameter(low=1, high=365, default=6, space='sell', optimize=True, load=True)
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sell_rsi = IntParameter(low=30, high=100, default=57, space='sell', optimize=True, load=True)
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sell_mfi = IntParameter(low=30, high=100, default=46, space='sell', optimize=True, load=True)
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sell_rsi_enabled = CategoricalParameter([True, False], space='sell', optimize=True, default=False)
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sell_mfi_enabled = CategoricalParameter([True, False], space='sell', optimize=True, default=True)
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sell_ema_enabled = CategoricalParameter([True, False], space='sell', optimize=True, default=False)
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sell_trigger = CategoricalParameter(["sell-bb_upper1", "sell-bb_upper2", "sell-bb_upper3", "sell-bb_upper4"], default="sell-bb_upper2", space="sell")
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe['mfi'] = ta.MFI(dataframe)
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# Bollinger Bands 1,2,3 and 4
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bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
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dataframe['bb_lowerband1'] = bollinger1['lower']
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dataframe['bb_middleband1'] = bollinger1['mid']
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dataframe['bb_upperband1'] = bollinger1['upper']
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bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband2'] = bollinger2['lower']
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dataframe['bb_middleband2'] = bollinger2['mid']
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dataframe['bb_upperband2'] = bollinger2['upper']
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bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
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dataframe['bb_lowerband3'] = bollinger3['lower']
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dataframe['bb_middleband3'] = bollinger3['mid']
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dataframe['bb_upperband3'] = bollinger3['upper']
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bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
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dataframe['bb_lowerband4'] = bollinger4['lower']
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dataframe['bb_middleband4'] = bollinger4['mid']
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dataframe['bb_upperband4'] = bollinger4['upper']
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# Build EMA rows - combine all ranges to a single set to avoid duplicate calculations.
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for period in set(
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list(self.buy_fastema.range)
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+ list(self.buy_slowema.range)
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+ list(self.sell_fastema.range)
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+ list(self.sell_slowema.range)
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):
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dataframe[f'EMA_{period}'] = ta.EMA(dataframe, timeperiod=period)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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# GUARDS
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if self.buy_rsi_enabled.value:
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conditions.append(dataframe['rsi'] < self.buy_rsi.value)
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if self.buy_mfi_enabled.value:
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conditions.append(dataframe['mfi'] < self.buy_mfi.value)
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if self.buy_ema_enabled.value:
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conditions.append(dataframe[f'EMA_{self.buy_fastema.value}'] > dataframe[f'EMA_{self.buy_slowema.value}'])
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# TRIGGERS
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if self.buy_trigger.value == 'bb_lower1':
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conditions.append(dataframe["close"] < dataframe['bb_lowerband1'])
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if self.buy_trigger.value == 'bb_lower2':
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conditions.append(dataframe["close"] < dataframe['bb_lowerband2'])
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if self.buy_trigger.value == 'bb_lower3':
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conditions.append(dataframe["close"] < dataframe['bb_lowerband3'])
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if self.buy_trigger.value == 'bb_lower4':
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conditions.append(dataframe["close"] < dataframe['bb_lowerband4'])
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# Check that volume is not 0
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conditions.append(dataframe['volume'] > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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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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conditions = []
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# GUARDS
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if self.sell_rsi_enabled.value:
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conditions.append(dataframe['rsi'] > self.sell_rsi.value)
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if self.sell_mfi_enabled.value:
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conditions.append(dataframe['mfi'] > self.sell_mfi.value)
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if self.sell_ema_enabled.value:
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conditions.append(dataframe[f'EMA_{self.sell_fastema.value}'] < dataframe[f'EMA_{self.sell_slowema.value}'])
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# TRIGGERS
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if self.sell_trigger.value == 'sell-bb_upper1':
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conditions.append(dataframe["close"] > dataframe['bb_upperband1'])
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if self.sell_trigger.value == 'sell-bb_upper2':
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conditions.append(dataframe["close"] > dataframe['bb_upperband2'])
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if self.sell_trigger.value == 'sell-bb_upper3':
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conditions.append(dataframe["close"] > dataframe['bb_upperband3'])
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if self.sell_trigger.value == 'sell-bb_upper4':
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conditions.append(dataframe["close"] > dataframe['bb_upperband4'])
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# Check that volume is not 0
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conditions.append(dataframe['volume'] > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell'] = 1
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return dataframe
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