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Merge remote-tracking branch 'upstream/develop' into hyperopt-trailing-space

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This commit is contained in:
hroff-1902
2019-12-01 03:28:23 +03:00
parent f862b4d0f0 e0e0bad7c1
commit 69b0767165
71 changed files with 1505 additions and 487 deletions
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5
freqtrade/optimize/backtesting.py
View File

@@ -13,7 +13,8 @@ from pandas import DataFrame
from tabulate import tabulate
from freqtrade import OperationalException
from freqtrade.configuration import TimeRange, remove_credentials
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.data import history
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -75,10 +76,12 @@ class Backtesting:
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver(stratconf).strategy)
validate_config_consistency(stratconf)
else:
# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "

5
freqtrade/optimize/edge_cli.py
View File

@@ -9,7 +9,8 @@ from typing import Any, Dict
from tabulate import tabulate
from freqtrade import constants
from freqtrade.configuration import TimeRange, remove_credentials
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.edge import Edge
from freqtrade.exchange import Exchange
from freqtrade.resolvers import StrategyResolver
@@ -35,6 +36,8 @@ class EdgeCli:
self.exchange = Exchange(self.config)
self.strategy = StrategyResolver(self.config).strategy
validate_config_consistency(self.config)
self.edge = Edge(config, self.exchange, self.strategy)
# Set refresh_pairs to false for edge-cli (it must be true for edge)
self.edge._refresh_pairs = False

49
freqtrade/optimize/hyperopt.py
View File

@@ -23,7 +23,7 @@ from skopt import Optimizer
from skopt.space import Dimension
from freqtrade.data.history import get_timeframe, trim_dataframe
from freqtrade.misc import round_dict
from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F4
@@ -77,6 +77,8 @@ class Hyperopt:
# Previous evaluations
self.trials: List = []
self.num_trials_saved = 0
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
self.backtesting.strategy.advise_indicators = \
@@ -132,13 +134,18 @@ class Hyperopt:
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
def save_trials(self) -> None:
def save_trials(self, final: bool = False) -> None:
"""
Save hyperopt trials to file
"""
if self.trials:
logger.info("Saving %d evaluations to '%s'", len(self.trials), self.trials_file)
num_trials = len(self.trials)
if num_trials > self.num_trials_saved:
logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
dump(self.trials, self.trials_file)
self.num_trials_saved = num_trials
if final:
logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
f"saved to '{self.trials_file}'.")
def read_trials(self) -> List:
"""
@@ -153,6 +160,12 @@ class Hyperopt:
"""
Display Best hyperopt result
"""
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.
if not self.trials:
print("No epochs evaluated yet, no best result.")
return
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
params = best_result['params']
@@ -209,12 +222,20 @@ class Hyperopt:
print('Trailing stop:')
pprint(self.space_params(params, 'trailing', 5), indent=4)
def is_best(self, results) -> bool:
return results['loss'] < self.current_best_loss
def log_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
"""
print_all = self.config.get('print_all', False)
is_best_loss = results['loss'] < self.current_best_loss
is_best_loss = self.is_best(results)
if not print_all:
print('.', end='' if results['current_epoch'] % 100 != 0 else None) # type: ignore
sys.stdout.flush()
if print_all or is_best_loss:
if is_best_loss:
self.current_best_loss = results['loss']
@@ -229,13 +250,9 @@ class Hyperopt:
print(log_str)
else:
print(f'\n{log_str}')
else:
print('.', end='')
sys.stdout.flush()
def format_results_logstring(self, results) -> str:
# Output human-friendly index here (starting from 1)
current = results['current_epoch'] + 1
current = results['current_epoch']
total = self.total_epochs
res = results['results_explanation']
loss = results['loss']
@@ -460,15 +477,19 @@ class Hyperopt:
self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
for j in range(jobs):
current = i * jobs + j
# Use human-friendly index here (starting from 1)
current = i * jobs + j + 1
val = f_val[j]
val['current_epoch'] = current
val['is_initial_point'] = current < INITIAL_POINTS
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best(val)
self.log_results(val)
self.trials.append(val)
logger.debug(f"Optimizer epoch evaluated: {val}")
if is_best or current % 100 == 0:
self.save_trials()
except KeyboardInterrupt:
print('User interrupted..')
self.save_trials()
self.save_trials(final=True)
self.log_trials_result()