270 lines
11 KiB
Python
270 lines
11 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# isort: skip_file
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta # noqa
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class AdvancedSampleHyperOpt(IHyperOpt):
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"""
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This is a sample hyperopt to inspire you.
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Feel free to customize it.
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More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
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You should:
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- Rename the class name to some unique name.
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- Add any methods you want to build your hyperopt.
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- Add any lib you need to build your hyperopt.
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You must keep:
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- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
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The methods roi_space, generate_roi_table and stoploss_space are not required
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and are provided by default.
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However, you may override them if you need the
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'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
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This sample illustrates how to override these methods.
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"""
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
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"""
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dataframe['adx'] = ta.ADX(dataframe)
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe['sar'] = ta.SAR(dataframe)
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return dataframe
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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return [
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Integer(10, 25, name='mfi-value'),
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Integer(15, 45, name='fastd-value'),
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Integer(20, 50, name='adx-value'),
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Integer(20, 40, name='rsi-value'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='fastd-enabled'),
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Categorical([True, False], name='adx-enabled'),
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Categorical([True, False], name='rsi-enabled'),
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Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
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]
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use
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"""
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conditions = []
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# GUARDS AND TRENDS
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if 'mfi-enabled' in params and params['mfi-enabled']:
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conditions.append(dataframe['mfi'] < params['mfi-value'])
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if 'fastd-enabled' in params and params['fastd-enabled']:
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conditions.append(dataframe['fastd'] < params['fastd-value'])
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if 'adx-enabled' in params and params['adx-enabled']:
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conditions.append(dataframe['adx'] > params['adx-value'])
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if 'rsi-enabled' in params and params['rsi-enabled']:
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conditions.append(dataframe['rsi'] < params['rsi-value'])
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'bb_lower':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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if params['trigger'] == 'macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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))
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if params['trigger'] == 'sar_reversal':
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conditions.append(qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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))
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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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return populate_buy_trend
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@staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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Integer(75, 100, name='sell-mfi-value'),
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Integer(50, 100, name='sell-fastd-value'),
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Integer(50, 100, name='sell-adx-value'),
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Integer(60, 100, name='sell-rsi-value'),
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Categorical([True, False], name='sell-mfi-enabled'),
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Categorical([True, False], name='sell-fastd-enabled'),
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Categorical([True, False], name='sell-adx-enabled'),
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Categorical([True, False], name='sell-rsi-enabled'),
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Categorical(['sell-bb_upper',
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'sell-macd_cross_signal',
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'sell-sar_reversal'], name='sell-trigger')
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]
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@staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by hyperopt
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use
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"""
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# print(params)
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conditions = []
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# GUARDS AND TRENDS
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if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
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conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
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if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
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conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
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if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
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conditions.append(dataframe['adx'] < params['sell-adx-value'])
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if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
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conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
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# TRIGGERS
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if 'sell-trigger' in params:
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if params['sell-trigger'] == 'sell-bb_upper':
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conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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if params['sell-trigger'] == 'sell-macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macdsignal'], dataframe['macd']
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))
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if params['sell-trigger'] == 'sell-sar_reversal':
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conditions.append(qtpylib.crossed_above(
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dataframe['sar'], dataframe['close']
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))
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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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return populate_sell_trend
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@staticmethod
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def generate_roi_table(params: Dict) -> Dict[int, float]:
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"""
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Generate the ROI table that will be used by Hyperopt
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This implementation generates the default legacy Freqtrade ROI tables.
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Change it if you need different number of steps in the generated
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ROI tables or other structure of the ROI tables.
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Please keep it aligned with parameters in the 'roi' optimization
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hyperspace defined by the roi_space method.
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"""
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roi_table = {}
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roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
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roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
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roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
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return roi_table
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@staticmethod
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def roi_space() -> List[Dimension]:
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"""
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Values to search for each ROI steps
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Override it if you need some different ranges for the parameters in the
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'roi' optimization hyperspace.
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Please keep it aligned with the implementation of the
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generate_roi_table method.
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"""
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return [
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Integer(10, 120, name='roi_t1'),
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Integer(10, 60, name='roi_t2'),
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Integer(10, 40, name='roi_t3'),
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SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
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SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
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SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
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]
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@staticmethod
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def stoploss_space() -> List[Dimension]:
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"""
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Stoploss Value to search
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Override it if you need some different range for the parameter in the
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'stoploss' optimization hyperspace.
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"""
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return [
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SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
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]
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@staticmethod
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def trailing_space() -> List[Dimension]:
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"""
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Create a trailing stoploss space.
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You may override it in your custom Hyperopt class.
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"""
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return [
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# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
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# is used. Otherwise hyperopt will vary other parameters that won't have effect if
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# trailing_stop is set False.
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# This parameter is included into the hyperspace dimensions rather than assigning
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# it explicitly in the code in order to have it printed in the results along with
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# other 'trailing' hyperspace parameters.
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Categorical([True], name='trailing_stop'),
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SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
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# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
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# so this intermediate parameter is used as the value of the difference between
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# them. The value of the 'trailing_stop_positive_offset' is constructed in the
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# generate_trailing_params() method.
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# This is similar to the hyperspace dimensions used for constructing the ROI tables.
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SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
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Categorical([True, False], name='trailing_only_offset_is_reached'),
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]
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