# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement import talib.abstract as ta from pandas import DataFrame from typing import Dict, Any, Callable, List from functools import reduce import numpy from skopt.space import Categorical, Dimension, Integer, Real import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.optimize.hyperopt_interface import IHyperOpt class_name = 'SampleHyperOpts' # This class is a sample. Feel free to customize it. class SampleHyperOpts(IHyperOpt): """ This is a test hyperopt to inspire you. More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md You can: - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your hyperopt - Add any lib you need to build your hyperopt You must keep: - the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator, roi_space, generate_roi_table, stoploss_space """ @staticmethod def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['adx'] = ta.ADX(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['rsi'] = ta.RSI(dataframe) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['sar'] = ta.SAR(dataframe) return dataframe @staticmethod def buy_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the buy strategy parameters to be used by hyperopt """ def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Buy strategy Hyperopt will build and use """ conditions = [] # GUARDS AND TRENDS if 'mfi-enabled' in params and params['mfi-enabled']: conditions.append(dataframe['mfi'] < params['mfi-value']) if 'fastd-enabled' in params and params['fastd-enabled']: conditions.append(dataframe['fastd'] < params['fastd-value']) if 'adx-enabled' in params and params['adx-enabled']: conditions.append(dataframe['adx'] > params['adx-value']) if 'rsi-enabled' in params and params['rsi-enabled']: conditions.append(dataframe['rsi'] < params['rsi-value']) # TRIGGERS if 'trigger' in params: if params['trigger'] == 'bb_lower': conditions.append(dataframe['close'] < dataframe['bb_lowerband']) if params['trigger'] == 'macd_cross_signal': conditions.append(qtpylib.crossed_above( dataframe['macd'], dataframe['macdsignal'] )) if params['trigger'] == 'sar_reversal': conditions.append(qtpylib.crossed_above( dataframe['close'], dataframe['sar'] )) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe return populate_buy_trend @staticmethod def indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching strategy parameters """ return [ Integer(10, 25, name='mfi-value'), Integer(15, 45, name='fastd-value'), Integer(20, 50, name='adx-value'), Integer(20, 40, name='rsi-value'), Categorical([True, False], name='mfi-enabled'), Categorical([True, False], name='fastd-enabled'), Categorical([True, False], name='adx-enabled'), Categorical([True, False], name='rsi-enabled'), Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger') ] @staticmethod def sell_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the sell strategy parameters to be used by hyperopt """ def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Sell strategy Hyperopt will build and use """ # print(params) conditions = [] # GUARDS AND TRENDS if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']: conditions.append(dataframe['mfi'] > params['sell-mfi-value']) if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']: conditions.append(dataframe['fastd'] > params['sell-fastd-value']) if 'sell-adx-enabled' in params and params['sell-adx-enabled']: conditions.append(dataframe['adx'] > params['sell-adx-value']) if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']: conditions.append(dataframe['rsi'] > params['sell-rsi-value']) # TRIGGERS if 'sell-trigger' in params: if params['sell-trigger'] == 'sell-bb_lower': conditions.append(dataframe['close'] < dataframe['bb_lowerband']) if params['sell-trigger'] == 'sell-macd_cross_signal': conditions.append(qtpylib.crossed_above( dataframe['macd'], dataframe['macdsignal'] )) if params['sell-trigger'] == 'sell-sar_reversal': conditions.append(qtpylib.crossed_above( dataframe['close'], dataframe['sar'] )) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe return populate_sell_trend @staticmethod def sell_indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching sell strategy parameters """ return [ Integer(75, 100, name='sell-mfi-value'), Integer(50, 100, name='sell-fastd-value'), Integer(50, 100, name='sell-adx-value'), Integer(60, 100, name='sell-rsi-value'), Categorical([True, False], name='sell-mfi-enabled'), Categorical([True, False], name='sell-fastd-enabled'), Categorical([True, False], name='sell-adx-enabled'), Categorical([True, False], name='sell-rsi-enabled'), Categorical(['sell-bb_lower', 'sell-macd_cross_signal', 'sell-sar_reversal'], name='sell-trigger') ] @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: """ Generate the ROI table that will be used by Hyperopt """ roi_table = {} roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2'] roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1'] roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0 return roi_table @staticmethod def stoploss_space() -> List[Dimension]: """ Stoploss Value to search """ return [ Real(-0.5, -0.02, name='stoploss'), ] @staticmethod def roi_space() -> List[Dimension]: """ Values to search for each ROI steps """ return [ Integer(10, 120, name='roi_t1'), Integer(10, 60, name='roi_t2'), Integer(10, 40, name='roi_t3'), Real(0.01, 0.04, name='roi_p1'), Real(0.01, 0.07, name='roi_p2'), Real(0.01, 0.20, name='roi_p3'), ]