418 lines
15 KiB
Python
418 lines
15 KiB
Python
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
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"""
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This module contains the hyperopt logic
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"""
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import logging
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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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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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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, get_timeframe
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from freqtrade.optimize.backtesting import Backtesting
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logger = logging.getLogger(__name__)
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MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
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TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
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class Hyperopt(Backtesting):
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"""
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Hyperopt class, this class contains all the logic to run a hyperopt simulation
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To run a backtest:
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hyperopt = Hyperopt(config)
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hyperopt.start()
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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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# 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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self.total_tries = config.get('epochs', 0)
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self.current_best_loss = 100
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# max average trade duration in minutes
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# if eval ends with higher value, we consider it a failed eval
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self.max_accepted_trade_duration = 300
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# this is expexted avg profit * expected trade count
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# for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
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# check that the reported Σ% values do not exceed this!
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self.expected_max_profit = 3.0
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# Previous evaluations
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self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle')
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self.trials: List = []
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def get_args(self, params):
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dimensions = self.hyperopt_space()
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# Ensure the number of dimensions match
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# the number of parameters in the list x.
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if len(params) != len(dimensions):
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raise ValueError('Mismatch in number of search-space dimensions. '
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f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
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# Create a dict where the keys are the names of the dimensions
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# and the values are taken from the list of parameters x.
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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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"""
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if self.trials:
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logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file)
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dump(self.trials, self.trials_file)
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def read_trials(self) -> List:
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"""
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Read hyperopt trials file
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"""
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logger.info('Reading Trials from \'%s\'', self.trials_file)
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trials = load(self.trials_file)
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os.remove(self.trials_file)
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return trials
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def log_trials_result(self) -> None:
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"""
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Display Best hyperopt result
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"""
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results = sorted(self.trials, key=itemgetter('loss'))
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best_result = results[0]
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logger.info(
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'Best result:\n%s\nwith values:\n%s',
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best_result['result'],
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best_result['params']
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)
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if 'roi_t1' in best_result['params']:
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logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
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def log_results(self, results) -> None:
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"""
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Log results if it is better than any previous evaluation
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"""
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if results['loss'] < self.current_best_loss:
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current = results['current_tries']
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total = results['total_tries']
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res = results['result']
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loss = results['loss']
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self.current_best_loss = results['loss']
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log_msg = f'\n{current:5d}/{total}: {res}. Loss {loss:.5f}'
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print(log_msg)
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else:
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print('.', end='')
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sys.stdout.flush()
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def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:
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"""
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Objective function, returns smaller number for more optimal results
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"""
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trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
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profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
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duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
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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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"""
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if space in self.config['spaces'] or 'all' in self.config['spaces']:
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return True
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return False
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def hyperopt_space(self) -> List[Dimension]:
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"""
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Return the space to use during Hyperopt
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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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if self.has_space('roi'):
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spaces += Hyperopt.roi_space()
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if self.has_space('stoploss'):
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spaces += 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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if self.has_space('roi'):
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self.strategy.minimal_roi = self.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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if self.has_space('stoploss'):
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self.strategy.stoploss = params['stoploss']
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processed = load(TICKERDATA_PICKLE)
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min_date, max_date = get_timeframe(processed)
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results = self.backtest(
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{
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'stake_amount': self.config['stake_amount'],
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'processed': processed,
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'position_stacking': self.config.get('position_stacking', True),
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'start_date': min_date,
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'end_date': max_date,
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}
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)
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result_explanation = self.format_results(results)
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total_profit = results.profit_percent.sum()
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trade_count = len(results.index)
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trade_duration = results.trade_duration.mean()
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if trade_count == 0:
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return {
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'loss': MAX_LOSS,
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'params': params,
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'result': result_explanation,
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}
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loss = self.calculate_loss(total_profit, trade_count, trade_duration)
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return {
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'loss': loss,
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'params': params,
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'result': result_explanation,
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}
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def format_results(self, results: DataFrame) -> str:
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"""
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Return the format result in a string
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"""
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trades = len(results.index)
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avg_profit = results.profit_percent.mean() * 100.0
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total_profit = results.profit_abs.sum()
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stake_cur = self.config['stake_currency']
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profit = results.profit_percent.sum()
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duration = results.trade_duration.mean()
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return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
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f'Total profit {total_profit: 11.8f} {stake_cur} '
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f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
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def get_optimizer(self, cpu_count) -> Optimizer:
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return Optimizer(
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self.hyperopt_space(),
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base_estimator="ET",
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acq_optimizer="auto",
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n_initial_points=30,
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acq_optimizer_kwargs={'n_jobs': cpu_count}
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)
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def run_optimizer_parallel(self, parallel, asked) -> List:
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return parallel(delayed(self.generate_optimizer)(v) for v in asked)
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def load_previous_results(self):
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""" read trials file if we have one """
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if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
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self.trials = self.read_trials()
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logger.info(
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'Loaded %d previous evaluations from disk.',
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len(self.trials)
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)
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def start(self) -> None:
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timerange = Arguments.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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data = load_data(
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datadir=str(self.config.get('datadir')),
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pairs=self.config['exchange']['pair_whitelist'],
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ticker_interval=self.ticker_interval,
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timerange=timerange
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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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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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cpus = multiprocessing.cpu_count()
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logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
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opt = self.get_optimizer(cpus)
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EVALS = max(self.total_tries // cpus, 1)
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try:
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with Parallel(n_jobs=cpus) as parallel:
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for i in range(EVALS):
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asked = opt.ask(n_points=cpus)
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f_val = self.run_optimizer_parallel(parallel, asked)
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opt.tell(asked, [i['loss'] for i in f_val])
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self.trials += f_val
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for j in range(cpus):
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self.log_results({
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'loss': f_val[j]['loss'],
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'current_tries': i * cpus + j,
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'total_tries': self.total_tries,
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'result': f_val[j]['result'],
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})
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except KeyboardInterrupt:
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print('User interrupted..')
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self.save_trials()
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self.log_trials_result()
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def start(args: Namespace) -> None:
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"""
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Start Backtesting script
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:param args: Cli args from Arguments()
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:return: None
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"""
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# Remove noisy log messages
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logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
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# Initialize configuration
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# Monkey patch the configuration with hyperopt_conf.py
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configuration = Configuration(args)
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logger.info('Starting freqtrade in Hyperopt mode')
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config = configuration.load_config()
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config['exchange']['key'] = ''
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config['exchange']['secret'] = ''
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if config.get('strategy') and config.get('strategy') != 'DefaultStrategy':
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logger.error("Please don't use --strategy for hyperopt.")
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logger.error(
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"Read the documentation at "
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"https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md "
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"to understand how to configure hyperopt.")
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raise ValueError("--strategy configured but not supported for hyperopt")
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# Initialize backtesting object
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hyperopt = Hyperopt(config)
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hyperopt.start()
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