From 0f43e0bb7d786f13f00f6b246851ac44c674231c Mon Sep 17 00:00:00 2001 From: hroff-1902 Date: Fri, 10 May 2019 10:54:44 +0300 Subject: [PATCH] minor hyperopt output improvements --- freqtrade/optimize/hyperopt.py | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 235a20156..06c7ae495 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -33,6 +33,7 @@ from freqtrade.resolvers import HyperOptResolver logger = logging.getLogger(__name__) +INITIAL_POINTS = 30 MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl') TRIALSDATA_PICKLE = os.path.join('user_data', 'hyperopt_results.pickle') @@ -120,14 +121,20 @@ class Hyperopt(Backtesting): """ Log results if it is better than any previous evaluation """ - if self.config.get('print_all', False) or results['loss'] < self.current_best_loss: - current = results['current_tries'] + print_all = self.config.get('print_all', False) + if print_all or results['loss'] < self.current_best_loss: + # Output human-friendly index here (starting from 1) + current = results['current_tries'] + 1 total = results['total_tries'] res = results['result'] loss = results['loss'] self.current_best_loss = results['loss'] - log_msg = f'\n{current:5d}/{total}: {res}. Loss {loss:.5f}' - print(log_msg) + log_msg = f'{current:5d}/{total}: {res} Objective: {loss:.5f}' + log_msg = f'*{log_msg}' if results['initial_point'] else f' {log_msg}' + if print_all: + print(log_msg) + else: + print('\n' + log_msg) else: print('.', end='') sys.stdout.flush() @@ -228,13 +235,13 @@ class Hyperopt(Backtesting): Return the format result in a string """ trades = len(results.index) - avg_profit = results.profit_percent.mean() * 100.0 + avg_profit = results.profit_percent.mean() total_profit = results.profit_abs.sum() stake_cur = self.config['stake_currency'] profit = results.profit_percent.sum() duration = results.trade_duration.mean() - return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. ' + return (f'{trades:6d} trades. Avg profit {avg_profit: 9.6f}%. ' f'Total profit {total_profit: 11.8f} {stake_cur} ' f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.') @@ -243,7 +250,7 @@ class Hyperopt(Backtesting): self.hyperopt_space(), base_estimator="ET", acq_optimizer="auto", - n_initial_points=30, + n_initial_points=INITIAL_POINTS, acq_optimizer_kwargs={'n_jobs': cpu_count}, random_state=self.config.get('hyperopt_random_state', None) ) @@ -301,9 +308,11 @@ class Hyperopt(Backtesting): self.trials += f_val for j in range(jobs): + current = i * jobs + j self.log_results({ 'loss': f_val[j]['loss'], - 'current_tries': i * jobs + j, + 'current_tries': current, + 'initial_point': current < INITIAL_POINTS, 'total_tries': self.total_tries, 'result': f_val[j]['result'], })