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Merge branch 'develop' into feat/externalsignals

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This commit is contained in:
Timothy Pogue
2022-09-12 07:28:08 -06:00
parent 715a71465d a93e355175
commit 85b43a7c34
10 changed files with 78 additions and 62 deletions
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4
freqtrade/commands/arguments.py
View File

@@ -53,8 +53,8 @@ ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one
"print_csv", "base_currencies", "quote_currencies", "list_pairs_all",
"trading_mode"]
ARGS_TEST_PAIRLIST = ["verbosity", "config", "quote_currencies", "print_one_column",
"list_pairs_print_json", "exchange"]
ARGS_TEST_PAIRLIST = ["user_data_dir", "verbosity", "config", "quote_currencies",
"print_one_column", "list_pairs_print_json", "exchange"]
ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]

3
freqtrade/commands/cli_options.py
View File

@@ -393,7 +393,8 @@ AVAILABLE_CLI_OPTIONS = {
# Download data
"pairs_file": Arg(
'--pairs-file',
help='File containing a list of pairs to download.',
help='File containing a list of pairs. '
'Takes precedence over --pairs or pairs configured in the configuration.',
metavar='FILE',
),
"days": Arg(

2
freqtrade/data/dataprovider.py
View File

@@ -206,7 +206,7 @@ class DataProvider:
"""
_candle_type = CandleType.from_string(
candle_type) if candle_type != '' else self._config['candle_type_def']
saved_pair = (pair, str(timeframe), _candle_type)
saved_pair: PairWithTimeframe = (pair, str(timeframe), _candle_type)
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))

97
freqtrade/optimize/hyperopt.py
View File

@@ -290,7 +290,7 @@ class Hyperopt:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
def generate_optimizer(self, raw_params: List[Any]) -> Dict[str, Any]:
"""
Used Optimize function.
Called once per epoch to optimize whatever is configured.
@@ -410,9 +410,11 @@ class Hyperopt:
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
def run_optimizer_parallel(self, parallel, asked, i) -> List:
def run_optimizer_parallel(
self, parallel: Parallel, asked: List[List]) -> List[Dict[str, Any]]:
""" Start optimizer in a parallel way """
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@@ -491,6 +493,53 @@ class Hyperopt:
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def get_progressbar_widgets(self):
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
return widgets
def evaluate_result(self, val: Dict[str, Any], current: int, is_random: bool):
"""
Evaluate results returned from generate_optimizer
"""
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug("Optimizer epoch evaluated: %s", val)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
logger.info(f"Using optimizer random state: {self.random_state}")
@@ -526,26 +575,7 @@ class Hyperopt:
logger.info(f'Effective number of parallel workers used: {jobs}')
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
widgets = self.get_progressbar_widgets()
with progressbar.ProgressBar(
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
widgets=widgets
@@ -558,32 +588,15 @@ class Hyperopt:
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
f_val = self.run_optimizer_parallel(parallel, asked)
self.opt.tell(asked, [v['loss'] for v in f_val])
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
self.evaluate_result(val, current, is_random[j])
pbar.update(current)