Use backtesting output for hyperopt results
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Matthias
parent
545cba7fd8
commit
6aaaad29d7
@ -8,7 +8,7 @@ import locale
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import logging
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import random
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import warnings
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from datetime import datetime
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from datetime import datetime, timezone
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from math import ceil
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from operator import itemgetter
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from pathlib import Path
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@ -30,6 +30,8 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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from freqtrade.optimize.optimize_reports import generate_strategy_stats
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from freqtrade.persistence.pairlock_middleware import PairLocks
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from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
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from freqtrade.strategy import IStrategy
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@ -79,8 +81,7 @@ class Hyperopt:
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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strategy = str(self.config['strategy'])
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self.results_file = (self.config['user_data_dir'] /
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'hyperopt_results' /
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self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
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f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
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self.data_pickle_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_tickerdata.pkl')
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@ -246,6 +247,7 @@ class Hyperopt:
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Used Optimize function. Called once per epoch to optimize whatever is configured.
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Keep this function as optimized as possible!
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"""
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backtest_start_time = datetime.now(timezone.utc)
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params_dict = self._get_params_dict(raw_params)
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params_details = self._get_params_details(params_dict)
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@ -284,19 +286,31 @@ class Hyperopt:
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max_open_trades=self.max_open_trades,
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position_stacking=self.position_stacking,
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enable_protections=self.config.get('enable_protections', False),
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)
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return self._get_results_dict(backtesting_results, min_date, max_date,
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backtest_end_time = datetime.now(timezone.utc)
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bt_result = {
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'results': backtesting_results,
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'config': self.backtesting.strategy.config,
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'locks': PairLocks.get_all_locks(),
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'final_balance': self.backtesting.wallets.get_total(
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self.backtesting.strategy.config['stake_currency']),
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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}
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return self._get_results_dict(bt_result, min_date, max_date,
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params_dict, params_details,
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processed=processed)
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def _get_results_dict(self, backtesting_results, min_date, max_date,
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params_dict, params_details, processed: Dict[str, DataFrame]):
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results_metrics = self._calculate_results_metrics(backtesting_results)
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results_explanation = self._format_results_explanation_string(results_metrics)
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trade_count = results_metrics['trade_count']
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total_profit = results_metrics['total_profit']
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strat_stats = generate_strategy_stats(processed, '', backtesting_results,
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min_date, max_date, market_change=0)
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results_explanation = self._format_results_explanation_string(strat_stats)
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trade_count = strat_stats['total_trades']
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total_profit = strat_stats['profit_total']
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# If this evaluation contains too short amount of trades to be
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# interesting -- consider it as 'bad' (assigned max. loss value)
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@ -304,48 +318,32 @@ class Hyperopt:
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# path. We do not want to optimize 'hodl' strategies.
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loss: float = MAX_LOSS
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if trade_count >= self.config['hyperopt_min_trades']:
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loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
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loss = self.calculate_loss(results=backtesting_results['results'],
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trade_count=trade_count,
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min_date=min_date.datetime, max_date=max_date.datetime,
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config=self.config, processed=processed)
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return {
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'loss': loss,
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'params_dict': params_dict,
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'params_details': params_details,
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'results_metrics': results_metrics,
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'results_metrics': strat_stats,
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'results_explanation': results_explanation,
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'total_profit': total_profit,
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}
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def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
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wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
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draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
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losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
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return {
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'trade_count': len(backtesting_results.index),
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'wins': wins,
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'draws': draws,
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'losses': losses,
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'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
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'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
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'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
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'total_profit': backtesting_results['profit_abs'].sum(),
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'profit': backtesting_results['profit_ratio'].sum() * 100.0,
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'duration': backtesting_results['trade_duration'].mean(),
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}
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def _format_results_explanation_string(self, results_metrics: Dict) -> str:
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"""
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Return the formatted results explanation in a string
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"""
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stake_cur = self.config['stake_currency']
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return (f"{results_metrics['trade_count']:6d} trades. "
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return (f"{results_metrics['total_trades']:6d} trades. "
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f"{results_metrics['wins']}/{results_metrics['draws']}"
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f"/{results_metrics['losses']} Wins/Draws/Losses. "
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f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
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f"Median profit {results_metrics['median_profit']: 6.2f}%. "
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f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
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f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
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f"Avg duration {results_metrics['duration']:5.1f} min."
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f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
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f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
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f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_cur} "
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f"({results_metrics['profit_total']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
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f"Avg duration {results_metrics['holding_avg']} min."
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).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
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def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
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@ -12,7 +12,7 @@ from colorama import Fore, Style
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from pandas import isna, json_normalize
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import round_dict
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from freqtrade.misc import round_coin_value, round_dict
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logger = logging.getLogger(__name__)
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@ -169,11 +169,24 @@ class HyperoptTools():
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# Ensure compatibility with older versions of hyperopt results
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trials['results_metrics.winsdrawslosses'] = 'N/A'
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if 'results_metrics.total_trades' in trials:
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# New mode, using backtest result for metrics
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trials['results_metrics.winsdrawslosses'] = trials.apply(
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lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
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f"{x['results_metrics.losses']:>4}", axis=1)
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trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
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'results_metrics.winsdrawslosses',
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'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
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'results_metrics.profit_total', 'results_metrics.holding_avg',
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'loss', 'is_initial_point', 'is_best']]
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else:
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# Legacy mode
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.winsdrawslosses',
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'results_metrics.avg_profit', 'results_metrics.total_profit',
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'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'Objective',
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'is_initial_point', 'is_best']
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@ -188,21 +201,23 @@ class HyperoptTools():
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lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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lambda x: f'{x:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
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if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
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lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
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)
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stake_currency = config['stake_currency']
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trials['Profit'] = trials.apply(
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lambda x: '{:,.8f} {} {}'.format(
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x['Total profit'], config['stake_currency'],
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lambda x: '{} {}'.format(
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round_coin_value(x['Total profit'], stake_currency),
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'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
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).rjust(25+len(config['stake_currency']))
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if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
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).rjust(25+len(stake_currency))
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if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
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axis=1
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)
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trials = trials.drop(columns=['Total profit'])
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