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minor hyperopt output improvements

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hroff-1902 2019-05-10 10:54:44 +03:00
parent 43c2cf8e1c
commit 0f43e0bb7d
1 changed files with 17 additions and 8 deletions
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25
freqtrade/optimize/hyperopt.py
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@ -33,6 +33,7 @@ from freqtrade.resolvers import HyperOptResolver
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
INITIAL_POINTS = 30
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization 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') TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
TRIALSDATA_PICKLE = os.path.join('user_data', 'hyperopt_results.pickle') 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 Log results if it is better than any previous evaluation
""" """
if self.config.get('print_all', False) or results['loss'] < self.current_best_loss: print_all = self.config.get('print_all', False)
current = results['current_tries'] 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'] total = results['total_tries']
res = results['result'] res = results['result']
loss = results['loss'] loss = results['loss']
self.current_best_loss = results['loss'] self.current_best_loss = results['loss']
log_msg = f'\n{current:5d}/{total}: {res}. Loss {loss:.5f}' log_msg = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
print(log_msg) 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: else:
print('.', end='') print('.', end='')
sys.stdout.flush() sys.stdout.flush()
@ -228,13 +235,13 @@ class Hyperopt(Backtesting):
Return the format result in a string Return the format result in a string
""" """
trades = len(results.index) trades = len(results.index)
avg_profit = results.profit_percent.mean() * 100.0 avg_profit = results.profit_percent.mean()
total_profit = results.profit_abs.sum() total_profit = results.profit_abs.sum()
stake_cur = self.config['stake_currency'] stake_cur = self.config['stake_currency']
profit = results.profit_percent.sum() profit = results.profit_percent.sum()
duration = results.trade_duration.mean() 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'Total profit {total_profit: 11.8f} {stake_cur} '
f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.') f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
@ -243,7 +250,7 @@ class Hyperopt(Backtesting):
self.hyperopt_space(), self.hyperopt_space(),
base_estimator="ET", base_estimator="ET",
acq_optimizer="auto", acq_optimizer="auto",
n_initial_points=30, n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count}, acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.config.get('hyperopt_random_state', None) random_state=self.config.get('hyperopt_random_state', None)
) )
@ -301,9 +308,11 @@ class Hyperopt(Backtesting):
self.trials += f_val self.trials += f_val
for j in range(jobs): for j in range(jobs):
current = i * jobs + j
self.log_results({ self.log_results({
'loss': f_val[j]['loss'], 'loss': f_val[j]['loss'],
'current_tries': i * jobs + j, 'current_tries': current,
'initial_point': current < INITIAL_POINTS,
'total_tries': self.total_tries, 'total_tries': self.total_tries,
'result': f_val[j]['result'], 'result': f_val[j]['result'],
}) })