adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'develop' into pr/yazeed/3008

Browse Source
This commit is contained in:
Matthias
2020-08-14 06:58:09 +02:00
parent c9711678fd 067d1fd72a
commit 05bd099f51
160 changed files with 7932 additions and 2670 deletions
Download Patch File Download Diff File Expand all files Collapse all files

153
freqtrade/optimize/hyperopt.py
View File

@@ -12,7 +12,7 @@ from math import ceil
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from pprint import pformat
from typing import Any, Dict, List, Optional
import rapidjson
@@ -49,9 +49,9 @@ logger = logging.getLogger(__name__)
INITIAL_POINTS = 30
# Keep no more than 2*SKOPT_MODELS_MAX_NUM models
# in the skopt models list
SKOPT_MODELS_MAX_NUM = 10
# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption
SKOPT_MODEL_QUEUE_SIZE = 10
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
@@ -75,8 +75,8 @@ class Hyperopt:
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
self.trials_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@@ -88,10 +88,10 @@ class Hyperopt:
else:
logger.info("Continuing on previous hyperopt results.")
self.num_trials_saved = 0
self.num_epochs_saved = 0
# Previous evaluations
self.trials: List = []
self.epochs: List = []
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -132,7 +132,7 @@ class Hyperopt:
"""
Remove hyperopt pickle files to restart hyperopt.
"""
for f in [self.data_pickle_file, self.trials_file]:
for f in [self.data_pickle_file, self.results_file]:
p = Path(f)
if p.is_file():
logger.info(f"Removing `{p}`.")
@@ -151,27 +151,26 @@ class Hyperopt:
# and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params)}
def save_trials(self, final: bool = False) -> None:
def _save_results(self) -> None:
"""
Save hyperopt trials to file
Save hyperopt results to file
"""
num_trials = len(self.trials)
if num_trials > self.num_trials_saved:
logger.debug(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}'.")
num_epochs = len(self.epochs)
if num_epochs > self.num_epochs_saved:
logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
dump(self.epochs, self.results_file)
self.num_epochs_saved = num_epochs
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
@staticmethod
def _read_trials(trials_file: Path) -> List:
def _read_results(results_file: Path) -> List:
"""
Read hyperopt trials file
Read hyperopt results from file
"""
logger.info("Reading Trials from '%s'", trials_file)
trials = load(trials_file)
return trials
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
def _get_params_details(self, params: Dict) -> Dict:
"""
@@ -231,6 +230,9 @@ class Hyperopt:
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
@@ -245,11 +247,24 @@ class Hyperopt:
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
print(header, space_params.get('stoploss'))
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
else:
print(header)
pprint(space_params, indent=4)
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
@@ -304,8 +319,9 @@ class Hyperopt:
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
@@ -375,27 +391,35 @@ class Hyperopt:
# Verification for overwrite
if path.isfile(csv_file):
logger.error("CSV-File already exists!")
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error("Filed to create CSV-File!")
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
@@ -418,7 +442,7 @@ class Hyperopt:
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
print("CSV-File created!")
logger.info(f"CSV file created: {csv_file}")
def has_space(self, space: str) -> bool:
"""
@@ -570,43 +594,28 @@ class Hyperopt:
n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.random_state,
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
def fix_optimizer_models_list(self) -> None:
"""
WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
This may cease working when skopt updates if implementation of this intrinsic
part changes.
"""
n = len(self.opt.models) - SKOPT_MODELS_MAX_NUM
# Keep no more than 2*SKOPT_MODELS_MAX_NUM models in the skopt models list,
# remove the old ones. These are actually of no use, the current model
# from the estimator is the only one used in the skopt optimizer.
# Freqtrade code also does not inspect details of the models.
if n >= SKOPT_MODELS_MAX_NUM:
logger.debug(f"Fixing skopt models list, removing {n} old items...")
del self.opt.models[0:n]
def run_optimizer_parallel(self, parallel, asked, i) -> List:
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@staticmethod
def load_previous_results(trials_file: Path) -> List:
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
trials: List = []
if trials_file.is_file() and trials_file.stat().st_size > 0:
trials = Hyperopt._read_trials(trials_file)
if trials[0].get('is_best') is None:
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = Hyperopt._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with Hyperopt results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
return trials
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@@ -632,8 +641,9 @@ class Hyperopt:
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.trials = self.load_previous_results(self.trials_file)
self.epochs = self.load_previous_results(self.results_file)
cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
@@ -669,7 +679,7 @@ class Hyperopt:
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
with progressbar.ProgressBar(
maxval=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
widgets=widgets
) as pbar:
EVALS = ceil(self.total_epochs / jobs)
@@ -682,7 +692,6 @@ class Hyperopt:
asked = self.opt.ask(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
# Calculate progressbar outputs
for j, val in enumerate(f_val):
@@ -703,23 +712,25 @@ class Hyperopt:
if is_best:
self.current_best_loss = val['loss']
self.trials.append(val)
self.epochs.append(val)
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self.save_trials()
self._save_results()
pbar.update(current)
except KeyboardInterrupt:
print('User interrupted..')
self.save_trials(final=True)
self._save_results()
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.trials:
sorted_trials = sorted(self.trials, key=itemgetter('loss'))
results = sorted_trials[0]
self.print_epoch_details(results, self.total_epochs, self.print_json)
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.