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Decoupled custom hyperopts from hyperopt.py
This commit is contained in:
Stephen Dade
@@ -9,22 +9,20 @@ import multiprocessing
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import os
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import sys
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from argparse import Namespace
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from functools import reduce
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from math import exp
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from operator import itemgetter
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from typing import Any, Callable, Dict, List
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from typing import Any, Dict, List
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import talib.abstract as ta
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from pandas import DataFrame
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from sklearn.externals.joblib import Parallel, delayed, dump, load
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from skopt import Optimizer
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from skopt.space import Categorical, Dimension, Integer, Real
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from skopt.space import Dimension
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.optimize import load_data
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from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.optimize.custom_hyperopt import CustomHyperOpt
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logger = logging.getLogger(__name__)
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@@ -42,6 +40,9 @@ class Hyperopt(Backtesting):
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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super().__init__(config)
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self.custom_hyperopt = CustomHyperOpt(self.config)
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# set TARGET_TRADES to suit your number concurrent trades so its realistic
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# to the number of days
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self.target_trades = 600
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@@ -74,24 +75,6 @@ class Hyperopt(Backtesting):
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arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
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return arg_dict
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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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['sar'] = ta.SAR(dataframe)
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return dataframe
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def save_trials(self) -> None:
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"""
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Save hyperopt trials to file
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@@ -149,59 +132,6 @@ class Hyperopt(Backtesting):
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result = trade_loss + profit_loss + duration_loss
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return result
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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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"""
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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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"""
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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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Real(0.01, 0.04, name='roi_p1'),
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Real(0.01, 0.07, name='roi_p2'),
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Real(0.01, 0.20, 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 search space
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"""
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return [
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Real(-0.5, -0.02, name='stoploss'),
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]
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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 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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def has_space(self, space: str) -> bool:
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"""
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Tell if a space value is contained in the configuration
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@@ -216,61 +146,19 @@ class Hyperopt(Backtesting):
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"""
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spaces: List[Dimension] = []
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if self.has_space('buy'):
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spaces += Hyperopt.indicator_space()
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spaces = {**spaces, **self.custom_hyperopt.indicator_space()}
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if self.has_space('roi'):
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spaces += Hyperopt.roi_space()
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spaces = {**spaces, **self.custom_hyperopt.roi_space()}
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if self.has_space('stoploss'):
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spaces += Hyperopt.stoploss_space()
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spaces = {**spaces, **self.custom_hyperopt.stoploss_space()}
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return spaces
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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 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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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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def generate_optimizer(self, _params) -> Dict:
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params = self.get_args(_params)
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def generate_optimizer(self, params: Dict) -> Dict:
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if self.has_space('roi'):
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self.strategy.minimal_roi = self.generate_roi_table(params)
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self.analyze.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
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if self.has_space('buy'):
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self.advise_buy = self.buy_strategy_generator(params)
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self.populate_buy_trend = self.custom_hyperopt.buy_strategy_generator(params)
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if self.has_space('stoploss'):
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self.strategy.stoploss = params['stoploss']
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@@ -351,7 +239,7 @@ class Hyperopt(Backtesting):
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)
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if self.has_space('buy'):
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self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
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self.strategy.advise_indicators = self.custom_hyperopt.populate_indicators # type: ignore
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dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
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self.exchange = None # type: ignore
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self.load_previous_results()
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