From dad4a49e81c3a01eb98166b6db20fb08bb1a3a9a Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 11 Sep 2021 09:06:57 +0200 Subject: [PATCH] Remove legacy hyperopt interface from hyperopt.py --- freqtrade/commands/cli_options.py | 2 +- freqtrade/optimize/hyperopt.py | 64 ++--- freqtrade/optimize/hyperopt_auto.py | 21 +- freqtrade/optimize/hyperopt_interface.py | 14 +- freqtrade/resolvers/hyperopt_resolver.py | 38 --- freqtrade/templates/sample_hyperopt.py | 174 ----------- .../templates/sample_hyperopt_advanced.py | 269 ------------------ 7 files changed, 25 insertions(+), 557 deletions(-) delete mode 100644 freqtrade/templates/sample_hyperopt.py delete mode 100644 freqtrade/templates/sample_hyperopt_advanced.py diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index cf7cb804c..a1790cb9a 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -209,7 +209,7 @@ AVAILABLE_CLI_OPTIONS = { ), "hyperopt_path": Arg( '--hyperopt-path', - help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.', + help='Specify additional lookup path for Hyperopt Loss functions.', metavar='PATH', ), "epochs": Arg( diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index e0b35df32..d37c68769 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -22,6 +22,7 @@ from pandas import DataFrame from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN from freqtrade.data.converter import trim_dataframes from freqtrade.data.history import get_timerange +from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, file_dump_json, plural from freqtrade.optimize.backtesting import Backtesting # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules @@ -30,7 +31,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401 from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer from freqtrade.optimize.optimize_reports import generate_strategy_stats -from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver +from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver # Suppress scikit-learn FutureWarnings from skopt @@ -80,8 +81,9 @@ class Hyperopt: self.custom_hyperopt = HyperOptAuto(self.config) self.auto_hyperopt = True else: - self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config) - self.auto_hyperopt = False + raise OperationalException( + "Using seperate Hyperopt files has been removed in 2021.9. Please convert " + "your existing Hyperopt file to the new Hyperoptable strategy interface") self.backtesting._set_strategy(self.backtesting.strategylist[0]) self.custom_hyperopt.strategy = self.backtesting.strategy @@ -103,31 +105,6 @@ class Hyperopt: self.num_epochs_saved = 0 self.current_best_epoch: Optional[Dict[str, Any]] = None - if not self.auto_hyperopt: - # Populate "fallback" functions here - # (hasattr is slow so should not be run during "regular" operations) - if hasattr(self.custom_hyperopt, 'populate_indicators'): - logger.warning( - "DEPRECATED: Using `populate_indicators()` in the hyperopt file is deprecated. " - "Please move these methods to your strategy." - ) - self.backtesting.strategy.populate_indicators = ( # type: ignore - self.custom_hyperopt.populate_indicators) # type: ignore - if hasattr(self.custom_hyperopt, 'populate_buy_trend'): - logger.warning( - "DEPRECATED: Using `populate_buy_trend()` in the hyperopt file is deprecated. " - "Please move these methods to your strategy." - ) - self.backtesting.strategy.populate_buy_trend = ( # type: ignore - self.custom_hyperopt.populate_buy_trend) # type: ignore - if hasattr(self.custom_hyperopt, 'populate_sell_trend'): - logger.warning( - "DEPRECATED: Using `populate_sell_trend()` in the hyperopt file is deprecated. " - "Please move these methods to your strategy." - ) - self.backtesting.strategy.populate_sell_trend = ( # type: ignore - self.custom_hyperopt.populate_sell_trend) # type: ignore - # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set if self.config.get('use_max_market_positions', True): self.max_open_trades = self.config['max_open_trades'] @@ -256,7 +233,7 @@ class Hyperopt: """ Assign the dimensions in the hyperoptimization space. """ - if self.auto_hyperopt and HyperoptTools.has_space(self.config, 'protection'): + if HyperoptTools.has_space(self.config, 'protection'): # Protections can only be optimized when using the Parameter interface logger.debug("Hyperopt has 'protection' space") # Enable Protections if protection space is selected. @@ -285,6 +262,15 @@ class Hyperopt: self.dimensions = (self.buy_space + self.sell_space + self.protection_space + self.roi_space + self.stoploss_space + self.trailing_space) + def assign_params(self, params_dict: Dict, category: str) -> None: + """ + Assign hyperoptable parameters + """ + for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category): + if attr.optimize: + # noinspection PyProtectedMember + attr.value = params_dict[attr_name] + def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict: """ Used Optimize function. @@ -296,18 +282,13 @@ class Hyperopt: # Apply parameters if HyperoptTools.has_space(self.config, 'buy'): - self.backtesting.strategy.advise_buy = ( # type: ignore - self.custom_hyperopt.buy_strategy_generator(params_dict)) + self.assign_params(params_dict, 'buy') if HyperoptTools.has_space(self.config, 'sell'): - self.backtesting.strategy.advise_sell = ( # type: ignore - self.custom_hyperopt.sell_strategy_generator(params_dict)) + self.assign_params(params_dict, 'sell') if HyperoptTools.has_space(self.config, 'protection'): - for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'): - if attr.optimize: - # noinspection PyProtectedMember - attr.value = params_dict[attr_name] + self.assign_params(params_dict, 'protection') if HyperoptTools.has_space(self.config, 'roi'): self.backtesting.strategy.minimal_roi = ( # type: ignore @@ -517,11 +498,10 @@ class Hyperopt: f"saved to '{self.results_file}'.") if self.current_best_epoch: - if self.auto_hyperopt: - HyperoptTools.try_export_params( - self.config, - self.backtesting.strategy.get_strategy_name(), - self.current_best_epoch) + HyperoptTools.try_export_params( + self.config, + self.backtesting.strategy.get_strategy_name(), + self.current_best_epoch) HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs, self.print_json) diff --git a/freqtrade/optimize/hyperopt_auto.py b/freqtrade/optimize/hyperopt_auto.py index 43e92d9c6..022f04a84 100644 --- a/freqtrade/optimize/hyperopt_auto.py +++ b/freqtrade/optimize/hyperopt_auto.py @@ -22,26 +22,6 @@ class HyperOptAuto(IHyperOpt): sell_indicator_space methods, but other hyperopt methods can be overridden as well. """ - def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable: - def populate_buy_trend(dataframe: DataFrame, metadata: dict): - for attr_name, attr in self.strategy.enumerate_parameters('buy'): - if attr.optimize: - # noinspection PyProtectedMember - attr.value = params[attr_name] - return self.strategy.populate_buy_trend(dataframe, metadata) - - return populate_buy_trend - - def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable: - def populate_sell_trend(dataframe: DataFrame, metadata: dict): - for attr_name, attr in self.strategy.enumerate_parameters('sell'): - if attr.optimize: - # noinspection PyProtectedMember - attr.value = params[attr_name] - return self.strategy.populate_sell_trend(dataframe, metadata) - - return populate_sell_trend - def _get_func(self, name) -> Callable: """ Return a function defined in Strategy.HyperOpt class, or one defined in super() class. @@ -61,6 +41,7 @@ class HyperOptAuto(IHyperOpt): yield attr.get_space(attr_name) def _get_indicator_space(self, category, fallback_method_name): + # TODO: is this necessary, or can we call "generate_space" directly? indicator_space = list(self._generate_indicator_space(category)) if len(indicator_space) > 0: return indicator_space diff --git a/freqtrade/optimize/hyperopt_interface.py b/freqtrade/optimize/hyperopt_interface.py index 500798627..814260f5e 100644 --- a/freqtrade/optimize/hyperopt_interface.py +++ b/freqtrade/optimize/hyperopt_interface.py @@ -5,7 +5,7 @@ This module defines the interface to apply for hyperopt import logging import math from abc import ABC -from typing import Any, Callable, Dict, List +from typing import Dict, List from skopt.space import Categorical, Dimension, Integer @@ -45,18 +45,6 @@ class IHyperOpt(ABC): IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED IHyperOpt.timeframe = str(config['timeframe']) - def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable: - """ - Create a buy strategy generator. - """ - raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy')) - - def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable: - """ - Create a sell strategy generator. - """ - raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell')) - def protection_space(self) -> List[Dimension]: """ Create a protection space. diff --git a/freqtrade/resolvers/hyperopt_resolver.py b/freqtrade/resolvers/hyperopt_resolver.py index 8327a4d13..6f0263e93 100644 --- a/freqtrade/resolvers/hyperopt_resolver.py +++ b/freqtrade/resolvers/hyperopt_resolver.py @@ -9,7 +9,6 @@ from typing import Dict from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS from freqtrade.exceptions import OperationalException -from freqtrade.optimize.hyperopt_interface import IHyperOpt from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss from freqtrade.resolvers import IResolver @@ -17,43 +16,6 @@ from freqtrade.resolvers import IResolver logger = logging.getLogger(__name__) -class HyperOptResolver(IResolver): - """ - This class contains all the logic to load custom hyperopt class - """ - object_type = IHyperOpt - object_type_str = "Hyperopt" - user_subdir = USERPATH_HYPEROPTS - initial_search_path = None - - @staticmethod - def load_hyperopt(config: Dict) -> IHyperOpt: - """ - Load the custom hyperopt class from config parameter - :param config: configuration dictionary - """ - if not config.get('hyperopt'): - raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify " - "the Hyperopt class to use.") - - hyperopt_name = config['hyperopt'] - - hyperopt = HyperOptResolver.load_object(hyperopt_name, config, - kwargs={'config': config}, - extra_dir=config.get('hyperopt_path')) - - if not hasattr(hyperopt, 'populate_indicators'): - logger.info("Hyperopt class does not provide populate_indicators() method. " - "Using populate_indicators from the strategy.") - if not hasattr(hyperopt, 'populate_buy_trend'): - logger.info("Hyperopt class does not provide populate_buy_trend() method. " - "Using populate_buy_trend from the strategy.") - if not hasattr(hyperopt, 'populate_sell_trend'): - logger.info("Hyperopt class does not provide populate_sell_trend() method. " - "Using populate_sell_trend from the strategy.") - return hyperopt - - class HyperOptLossResolver(IResolver): """ This class contains all the logic to load custom hyperopt loss class diff --git a/freqtrade/templates/sample_hyperopt.py b/freqtrade/templates/sample_hyperopt.py deleted file mode 100644 index ed1af7718..000000000 --- a/freqtrade/templates/sample_hyperopt.py +++ /dev/null @@ -1,174 +0,0 @@ -# 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'), - 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 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'] - )) - - # Check that volume is not 0 - conditions.append(dataframe['volume'] > 0) - - if conditions: - dataframe.loc[ - reduce(lambda x, y: x & y, conditions), - 'buy'] = 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'), - 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 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. - """ - 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'] - )) - - # Check that volume is not 0 - conditions.append(dataframe['volume'] > 0) - - if conditions: - dataframe.loc[ - reduce(lambda x, y: x & y, conditions), - 'sell'] = 1 - - return dataframe - - return populate_sell_trend diff --git a/freqtrade/templates/sample_hyperopt_advanced.py b/freqtrade/templates/sample_hyperopt_advanced.py deleted file mode 100644 index cc13b6ba3..000000000 --- a/freqtrade/templates/sample_hyperopt_advanced.py +++ /dev/null @@ -1,269 +0,0 @@ -# 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 freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, 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 AdvancedSampleHyperOpt(IHyperOpt): - """ - This is a sample hyperopt to inspire you. - Feel free to customize it. - - 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. - - 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 the - 'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade. - - This sample illustrates how to override these methods. - """ - @staticmethod - def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: - """ - This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class. - """ - 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 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'), - 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 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'] - )) - - # Check that volume is not 0 - conditions.append(dataframe['volume'] > 0) - - if conditions: - dataframe.loc[ - reduce(lambda x, y: x & y, conditions), - 'buy'] = 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'), - 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 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'] - )) - - # Check that volume is not 0 - conditions.append(dataframe['volume'] > 0) - - if conditions: - dataframe.loc[ - reduce(lambda x, y: x & y, conditions), - 'sell'] = 1 - - return dataframe - - return populate_sell_trend - - @staticmethod - def generate_roi_table(params: Dict) -> Dict[int, float]: - """ - Generate the ROI table that will be used by Hyperopt - - This implementation generates the default legacy Freqtrade ROI tables. - - Change it if you need different number of steps in the generated - ROI tables or other structure of the ROI tables. - - Please keep it aligned with parameters in the 'roi' optimization - hyperspace defined by the roi_space method. - """ - 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 roi_space() -> List[Dimension]: - """ - Values to search for each ROI steps - - Override it if you need some different ranges for the parameters in the - 'roi' optimization hyperspace. - - Please keep it aligned with the implementation of the - generate_roi_table method. - """ - return [ - Integer(10, 120, name='roi_t1'), - Integer(10, 60, name='roi_t2'), - Integer(10, 40, name='roi_t3'), - SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'), - SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'), - SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'), - ] - - @staticmethod - def stoploss_space() -> List[Dimension]: - """ - Stoploss Value to search - - Override it if you need some different range for the parameter in the - 'stoploss' optimization hyperspace. - """ - return [ - SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'), - ] - - @staticmethod - def trailing_space() -> List[Dimension]: - """ - Create a trailing stoploss space. - - You may override it in your custom Hyperopt class. - """ - return [ - # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace - # is used. Otherwise hyperopt will vary other parameters that won't have effect if - # trailing_stop is set False. - # This parameter is included into the hyperspace dimensions rather than assigning - # it explicitly in the code in order to have it printed in the results along with - # other 'trailing' hyperspace parameters. - Categorical([True], name='trailing_stop'), - - SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'), - - # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive', - # so this intermediate parameter is used as the value of the difference between - # them. The value of the 'trailing_stop_positive_offset' is constructed in the - # generate_trailing_params() method. - # This is similar to the hyperspace dimensions used for constructing the ROI tables. - SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'), - - Categorical([True, False], name='trailing_only_offset_is_reached'), - ]