# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # isort: skip_file # --- Do not remove these libs --- from functools import reduce from typing import Any, Callable, Dict, List import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from skopt.space import Categorical, Dimension, Integer, Real # noqa from freqtrade.optimize.hyperopt_interface import IHyperOpt # -------------------------------- # Add your lib to import here import talib.abstract as ta # noqa import freqtrade.vendor.qtpylib.indicators as qtpylib class SampleHyperOpt(IHyperOpt): """ This is a sample Hyperopt to inspire you. More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/ You should: - Rename the class name to some unique name. - Add any methods you want to build your hyperopt. - Add any lib you need to build your hyperopt. An easier way to get a new hyperopt file is by using `freqtrade new-hyperopt --hyperopt MyCoolHyperopt`. You must keep: - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator. The methods roi_space, generate_roi_table and stoploss_space are not required and are provided by default. However, you may override them if you need 'roi' and 'stoploss' spaces that differ from the defaults offered by Freqtrade. Sample implementation of these methods will be copied to `user_data/hyperopts` when creating the user-data directory using `freqtrade create-userdir --userdir user_data`, or is available online under the following URL: https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py. """ @staticmethod def indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching buy 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'), Integer(75, 90, name='short-mfi-value'), Integer(55, 85, name='short-fastd-value'), Integer(50, 80, name='short-adx-value'), Integer(60, 80, name='short-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(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger') ] @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. """ long_conditions = [] short_conditions = [] # GUARDS AND TRENDS if 'mfi-enabled' in params and params['mfi-enabled']: long_conditions.append(dataframe['mfi'] < params['mfi-value']) short_conditions.append(dataframe['mfi'] > params['short-mfi-value']) if 'fastd-enabled' in params and params['fastd-enabled']: long_conditions.append(dataframe['fastd'] < params['fastd-value']) short_conditions.append(dataframe['fastd'] > params['short-fastd-value']) if 'adx-enabled' in params and params['adx-enabled']: long_conditions.append(dataframe['adx'] > params['adx-value']) short_conditions.append(dataframe['adx'] < params['short-adx-value']) if 'rsi-enabled' in params and params['rsi-enabled']: long_conditions.append(dataframe['rsi'] < params['rsi-value']) short_conditions.append(dataframe['rsi'] > params['short-rsi-value']) # TRIGGERS if 'trigger' in params: if params['trigger'] == 'boll': long_conditions.append(dataframe['close'] < dataframe['bb_lowerband']) short_conditions.append(dataframe['close'] > dataframe['bb_upperband']) if params['trigger'] == 'macd_cross_signal': long_conditions.append(qtpylib.crossed_above( dataframe['macd'], dataframe['macdsignal'] )) short_conditions.append(qtpylib.crossed_below( dataframe['macd'], dataframe['macdsignal'] )) if params['trigger'] == 'sar_reversal': long_conditions.append(qtpylib.crossed_above( dataframe['close'], dataframe['sar'] )) short_conditions.append(qtpylib.crossed_below( dataframe['close'], dataframe['sar'] )) # Check that volume is not 0 long_conditions.append(dataframe['volume'] > 0) short_conditions.append(dataframe['volume'] > 0) if long_conditions: dataframe.loc[ reduce(lambda x, y: x & y, long_conditions), 'buy'] = 1 if short_conditions: dataframe.loc[ reduce(lambda x, y: x & y, short_conditions), 'enter_short'] = 1 return dataframe return populate_buy_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'), Integer(1, 25, name='exit-short-mfi-value'), Integer(1, 50, name='exit-short-fastd-value'), Integer(1, 50, name='exit-short-adx-value'), Integer(1, 40, name='exit-short-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-boll', 'sell-macd_cross_signal', 'sell-sar_reversal'], name='sell-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. """ exit_long_conditions = [] exit_short_conditions = [] # GUARDS AND TRENDS if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']: exit_long_conditions.append(dataframe['mfi'] > params['sell-mfi-value']) exit_short_conditions.append(dataframe['mfi'] < params['exit-short-mfi-value']) if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']: exit_long_conditions.append(dataframe['fastd'] > params['sell-fastd-value']) exit_short_conditions.append(dataframe['fastd'] < params['exit-short-fastd-value']) if 'sell-adx-enabled' in params and params['sell-adx-enabled']: exit_long_conditions.append(dataframe['adx'] < params['sell-adx-value']) exit_short_conditions.append(dataframe['adx'] > params['exit-short-adx-value']) if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']: exit_long_conditions.append(dataframe['rsi'] > params['sell-rsi-value']) exit_short_conditions.append(dataframe['rsi'] < params['exit-short-rsi-value']) # TRIGGERS if 'sell-trigger' in params: if params['sell-trigger'] == 'sell-boll': exit_long_conditions.append(dataframe['close'] > dataframe['bb_upperband']) exit_short_conditions.append(dataframe['close'] < dataframe['bb_lowerband']) if params['sell-trigger'] == 'sell-macd_cross_signal': exit_long_conditions.append(qtpylib.crossed_above( dataframe['macdsignal'], dataframe['macd'] )) exit_short_conditions.append(qtpylib.crossed_below( dataframe['macdsignal'], dataframe['macd'] )) if params['sell-trigger'] == 'sell-sar_reversal': exit_long_conditions.append(qtpylib.crossed_above( dataframe['sar'], dataframe['close'] )) exit_short_conditions.append(qtpylib.crossed_below( dataframe['sar'], dataframe['close'] )) # Check that volume is not 0 exit_long_conditions.append(dataframe['volume'] > 0) exit_short_conditions.append(dataframe['volume'] > 0) if exit_long_conditions: dataframe.loc[ reduce(lambda x, y: x & y, exit_long_conditions), 'sell'] = 1 if exit_short_conditions: dataframe.loc[ reduce(lambda x, y: x & y, exit_short_conditions), 'exit_short'] = 1 return dataframe return populate_sell_trend