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Merge pull request #1837 from hroff-1902/hyperopt-minor-1

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minor: hyperopt output improvements
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Matthias 2019-05-13 19:49:23 +02:00 committed by GitHub
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2 changed files with 29 additions and 17 deletions
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37
freqtrade/optimize/hyperopt.py
View File

@ -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')
@ -62,9 +63,11 @@ class Hyperopt(Backtesting):
# if eval ends with higher value, we consider it a failed eval
self.max_accepted_trade_duration = 300
# this is expexted avg profit * expected trade count
# for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
# check that the reported Σ% values do not exceed this!
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# self.expected_max_profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
self.expected_max_profit = 3.0
# Previous evaluations
@ -120,14 +123,20 @@ 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}'
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()
@ -204,8 +213,8 @@ class Hyperopt(Backtesting):
trade_count = len(results.index)
trade_duration = results.trade_duration.mean()
# If this evaluation contains too short small amount of trades
# to be interesting -- consider it as 'bad' (assign max. loss value)
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
# in order to cast this hyperspace point away from optimization
# path. We do not want to optimize 'hodl' strategies.
if trade_count < self.config['hyperopt_min_trades']:
@ -231,19 +240,19 @@ class Hyperopt(Backtesting):
avg_profit = results.profit_percent.mean() * 100.0
total_profit = results.profit_abs.sum()
stake_cur = self.config['stake_currency']
profit = results.profit_percent.sum()
profit = results.profit_percent.sum() * 100.0
duration = results.trade_duration.mean()
return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
f'Total profit {total_profit: 11.8f} {stake_cur} '
f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
def get_optimizer(self, cpu_count) -> Optimizer:
return Optimizer(
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)
)
@ -301,15 +310,17 @@ 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'],
})
logger.debug(f"Optimizer params: {f_val[j]['params']}")
for j in range(jobs):
logger.debug(f"Opimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
logger.debug(f"Optimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
except KeyboardInterrupt:
print('User interrupted..')

7
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -249,11 +249,12 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
'loss': 1,
'current_tries': 1,
'total_tries': 2,
'result': 'foo'
'result': 'foo.',
'initial_point': False
}
)
out, err = capsys.readouterr()
assert ' 1/2: foo. Loss 1.00000' in out
assert ' 2/2: foo. Objective: 1.00000' in out
def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
@ -459,7 +460,7 @@ def test_generate_optimizer(mocker, default_conf) -> None:
response_expected = {
'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'(0.0231Σ%). Avg duration 100.0 mins.',
'( 2.31Σ%). Avg duration 100.0 mins.',
'params': optimizer_param
}