# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement from functools import reduce from math import exp from typing import Any, Callable, Dict, List from datetime import datetime import numpy as np# noqa F401 import talib.abstract as ta from pandas import DataFrame from skopt.space import Categorical, Dimension, Integer, Real import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.optimize.hyperopt_interface import IHyperOpt # 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['bb_upperband'] = bollinger['upper'] 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'] )) if conditions: 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_upper': conditions.append(dataframe['close'] > dataframe['bb_upperband']) if params['sell-trigger'] == 'sell-macd_cross_signal': conditions.append(qtpylib.crossed_above( dataframe['macdsignal'], dataframe['macd'] )) if params['sell-trigger'] == 'sell-sar_reversal': conditions.append(qtpylib.crossed_above( dataframe['sar'], dataframe['close'] )) if conditions: 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_upper', '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'), ] def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators. Should be a copy of from strategy must align to populate_indicators in this file Only used when --spaces does not include buy """ dataframe.loc[ ( (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['mfi'] < 16) & (dataframe['adx'] > 25) & (dataframe['rsi'] < 21) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators. Should be a copy of from strategy must align to populate_indicators in this file Only used when --spaces does not include sell """ dataframe.loc[ ( (qtpylib.crossed_above( dataframe['macdsignal'], dataframe['macd'] )) & (dataframe['fastd'] > 54) ), 'sell'] = 1 return dataframe