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ruff format: hyperopt

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Matthias
2024-05-12 17:16:02 +02:00
parent f1ef537dfa
commit da7addcd98
6 changed files with 630 additions and 496 deletions
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391
freqtrade/optimize/hyperopt.py
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@@ -93,30 +93,36 @@ class Hyperopt:
self.backtesting = Backtesting(self.config)
self.pairlist = self.backtesting.pairlists.whitelist
self.custom_hyperopt: HyperOptAuto
self.analyze_per_epoch = self.config.get('analyze_per_epoch', False)
self.analyze_per_epoch = self.config.get("analyze_per_epoch", False)
HyperoptStateContainer.set_state(HyperoptState.STARTUP)
if not self.config.get('hyperopt'):
if not self.config.get("hyperopt"):
self.custom_hyperopt = HyperOptAuto(self.config)
else:
raise OperationalException(
"Using separate Hyperopt files has been removed in 2021.9. Please convert "
"your existing Hyperopt file to the new Hyperoptable strategy interface")
"your existing Hyperopt file to the new Hyperoptable strategy interface"
)
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.hyperopt_pickle_magic(self.backtesting.strategy.__class__.__bases__)
self.custom_hyperoptloss: IHyperOptLoss = HyperOptLossResolver.load_hyperoptloss(
self.config)
self.config
)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
f'strategy_{strategy}_{time_now}.fthypt')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
strategy = str(self.config["strategy"])
self.results_file: Path = (
self.config["user_data_dir"]
/ "hyperopt_results"
/ f"strategy_{strategy}_{time_now}.fthypt"
)
self.data_pickle_file = (
self.config["user_data_dir"] / "hyperopt_results" / "hyperopt_tickerdata.pkl"
)
self.total_epochs = config.get("epochs", 0)
self.current_best_loss = 100
@@ -127,24 +133,23 @@ class Hyperopt:
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if not self.config.get('use_max_market_positions', True):
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.backtesting.strategy.max_open_trades = float('inf')
config.update({'max_open_trades': self.backtesting.strategy.max_open_trades})
if not self.config.get("use_max_market_positions", True):
logger.debug("Ignoring max_open_trades (--disable-max-market-positions was used) ...")
self.backtesting.strategy.max_open_trades = float("inf")
config.update({"max_open_trades": self.backtesting.strategy.max_open_trades})
if HyperoptTools.has_space(self.config, 'sell'):
if HyperoptTools.has_space(self.config, "sell"):
# Make sure use_exit_signal is enabled
self.config['use_exit_signal'] = True
self.config["use_exit_signal"] = True
self.print_all = self.config.get('print_all', False)
self.print_all = self.config.get("print_all", False)
self.hyperopt_table_header = 0
self.print_colorized = self.config.get('print_colorized', False)
self.print_json = self.config.get('print_json', False)
self.print_colorized = self.config.get("print_colorized", False)
self.print_json = self.config.get("print_json", False)
@staticmethod
def get_lock_filename(config: Config) -> str:
return str(config['user_data_dir'] / 'hyperopt.lock')
return str(config["user_data_dir"] / "hyperopt.lock")
def clean_hyperopt(self) -> None:
"""
@@ -163,16 +168,15 @@ class Hyperopt:
to pickle as value.
"""
for modules in bases:
if modules.__name__ != 'IStrategy':
if modules.__name__ != "IStrategy":
cloudpickle.register_pickle_by_value(sys.modules[modules.__module__])
self.hyperopt_pickle_magic(modules.__bases__)
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
# the number of parameters in the list.
if len(raw_params) != len(dimensions):
raise ValueError('Mismatch in number of search-space dimensions.')
raise ValueError("Mismatch in number of search-space dimensions.")
# Return a dict where the keys are the names of the dimensions
# and the values are taken from the list of parameters.
@@ -186,18 +190,23 @@ class Hyperopt:
:param epoch: result dictionary for this epoch.
"""
epoch[FTHYPT_FILEVERSION] = 2
with self.results_file.open('a') as f:
rapidjson.dump(epoch, f, default=hyperopt_serializer,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
with self.results_file.open("a") as f:
rapidjson.dump(
epoch,
f,
default=hyperopt_serializer,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN,
)
f.write("\n")
self.num_epochs_saved += 1
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
logger.debug(
f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'."
)
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
file_dump_json(latest_filename, {"latest_hyperopt": str(self.results_file.name)}, log=False)
def _get_params_details(self, params: Dict) -> Dict:
"""
@@ -205,23 +214,26 @@ class Hyperopt:
"""
result: Dict = {}
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'protection'):
result['protection'] = {p.name: params.get(p.name) for p in self.protection_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in
self.custom_hyperopt.generate_roi_table(params).items()}
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
if HyperoptTools.has_space(self.config, 'trades'):
result['max_open_trades'] = {
'max_open_trades': self.backtesting.strategy.max_open_trades
if self.backtesting.strategy.max_open_trades != float('inf') else -1}
if HyperoptTools.has_space(self.config, "buy"):
result["buy"] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, "sell"):
result["sell"] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, "protection"):
result["protection"] = {p.name: params.get(p.name) for p in self.protection_space}
if HyperoptTools.has_space(self.config, "roi"):
result["roi"] = {
str(k): v for k, v in self.custom_hyperopt.generate_roi_table(params).items()
}
if HyperoptTools.has_space(self.config, "stoploss"):
result["stoploss"] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, "trailing"):
result["trailing"] = self.custom_hyperopt.generate_trailing_params(params)
if HyperoptTools.has_space(self.config, "trades"):
result["max_open_trades"] = {
"max_open_trades": self.backtesting.strategy.max_open_trades
if self.backtesting.strategy.max_open_trades != float("inf")
else -1
}
return result
@@ -231,19 +243,19 @@ class Hyperopt:
"""
result: Dict[str, Any] = {}
strategy = self.backtesting.strategy
if not HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in strategy.minimal_roi.items()}
if not HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {'stoploss': strategy.stoploss}
if not HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = {
'trailing_stop': strategy.trailing_stop,
'trailing_stop_positive': strategy.trailing_stop_positive,
'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
if not HyperoptTools.has_space(self.config, "roi"):
result["roi"] = {str(k): v for k, v in strategy.minimal_roi.items()}
if not HyperoptTools.has_space(self.config, "stoploss"):
result["stoploss"] = {"stoploss": strategy.stoploss}
if not HyperoptTools.has_space(self.config, "trailing"):
result["trailing"] = {
"trailing_stop": strategy.trailing_stop,
"trailing_stop_positive": strategy.trailing_stop_positive,
"trailing_stop_positive_offset": strategy.trailing_stop_positive_offset,
"trailing_only_offset_is_reached": strategy.trailing_only_offset_is_reached,
}
if not HyperoptTools.has_space(self.config, 'trades'):
result['max_open_trades'] = {'max_open_trades': strategy.max_open_trades}
if not HyperoptTools.has_space(self.config, "trades"):
result["max_open_trades"] = {"max_open_trades": strategy.max_open_trades}
return result
def print_results(self, results) -> None:
@@ -251,14 +263,17 @@ class Hyperopt:
Log results if it is better than any previous evaluation
TODO: this should be moved to HyperoptTools too
"""
is_best = results['is_best']
is_best = results["is_best"]
if self.print_all or is_best:
print(
HyperoptTools.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
self.config,
results,
self.total_epochs,
self.print_all,
self.print_colorized,
self.hyperopt_table_header,
)
)
self.hyperopt_table_header = 2
@@ -267,41 +282,47 @@ class Hyperopt:
"""
Assign the dimensions in the hyperoptimization space.
"""
if HyperoptTools.has_space(self.config, 'protection'):
if HyperoptTools.has_space(self.config, "protection"):
# Protections can only be optimized when using the Parameter interface
logger.debug("Hyperopt has 'protection' space")
# Enable Protections if protection space is selected.
self.config['enable_protections'] = True
self.config["enable_protections"] = True
self.backtesting.enable_protections = True
self.protection_space = self.custom_hyperopt.protection_space()
if HyperoptTools.has_space(self.config, 'buy'):
if HyperoptTools.has_space(self.config, "buy"):
logger.debug("Hyperopt has 'buy' space")
self.buy_space = self.custom_hyperopt.buy_indicator_space()
if HyperoptTools.has_space(self.config, 'sell'):
if HyperoptTools.has_space(self.config, "sell"):
logger.debug("Hyperopt has 'sell' space")
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if HyperoptTools.has_space(self.config, 'roi'):
if HyperoptTools.has_space(self.config, "roi"):
logger.debug("Hyperopt has 'roi' space")
self.roi_space = self.custom_hyperopt.roi_space()
if HyperoptTools.has_space(self.config, 'stoploss'):
if HyperoptTools.has_space(self.config, "stoploss"):
logger.debug("Hyperopt has 'stoploss' space")
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if HyperoptTools.has_space(self.config, 'trailing'):
if HyperoptTools.has_space(self.config, "trailing"):
logger.debug("Hyperopt has 'trailing' space")
self.trailing_space = self.custom_hyperopt.trailing_space()
if HyperoptTools.has_space(self.config, 'trades'):
if HyperoptTools.has_space(self.config, "trades"):
logger.debug("Hyperopt has 'trades' space")
self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space
+ self.max_open_trades_space)
self.dimensions = (
self.buy_space
+ self.sell_space
+ self.protection_space
+ self.roi_space
+ self.stoploss_space
+ self.trailing_space
+ self.max_open_trades_space
)
def assign_params(self, params_dict: Dict, category: str) -> None:
"""
@@ -323,104 +344,119 @@ class Hyperopt:
params_dict = self._get_params_dict(self.dimensions, raw_params)
# Apply parameters
if HyperoptTools.has_space(self.config, 'buy'):
self.assign_params(params_dict, 'buy')
if HyperoptTools.has_space(self.config, "buy"):
self.assign_params(params_dict, "buy")
if HyperoptTools.has_space(self.config, 'sell'):
self.assign_params(params_dict, 'sell')
if HyperoptTools.has_space(self.config, "sell"):
self.assign_params(params_dict, "sell")
if HyperoptTools.has_space(self.config, 'protection'):
self.assign_params(params_dict, 'protection')
if HyperoptTools.has_space(self.config, "protection"):
self.assign_params(params_dict, "protection")
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = (
self.custom_hyperopt.generate_roi_table(params_dict))
if HyperoptTools.has_space(self.config, "roi"):
self.backtesting.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(
params_dict
)
if HyperoptTools.has_space(self.config, 'stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss']
if HyperoptTools.has_space(self.config, "stoploss"):
self.backtesting.strategy.stoploss = params_dict["stoploss"]
if HyperoptTools.has_space(self.config, 'trailing'):
if HyperoptTools.has_space(self.config, "trailing"):
d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
self.backtesting.strategy.trailing_stop_positive_offset = \
d['trailing_stop_positive_offset']
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
self.backtesting.strategy.trailing_stop = d["trailing_stop"]
self.backtesting.strategy.trailing_stop_positive = d["trailing_stop_positive"]
self.backtesting.strategy.trailing_stop_positive_offset = d[
"trailing_stop_positive_offset"
]
self.backtesting.strategy.trailing_only_offset_is_reached = d[
"trailing_only_offset_is_reached"
]
if HyperoptTools.has_space(self.config, 'trades'):
if self.config["stake_amount"] == "unlimited" and \
(params_dict['max_open_trades'] == -1 or params_dict['max_open_trades'] == 0):
if HyperoptTools.has_space(self.config, "trades"):
if self.config["stake_amount"] == "unlimited" and (
params_dict["max_open_trades"] == -1 or params_dict["max_open_trades"] == 0
):
# Ignore unlimited max open trades if stake amount is unlimited
params_dict.update({'max_open_trades': self.config['max_open_trades']})
params_dict.update({"max_open_trades": self.config["max_open_trades"]})
updated_max_open_trades = int(params_dict['max_open_trades']) \
if (params_dict['max_open_trades'] != -1
and params_dict['max_open_trades'] != 0) else float('inf')
updated_max_open_trades = (
int(params_dict["max_open_trades"])
if (params_dict["max_open_trades"] != -1 and params_dict["max_open_trades"] != 0)
else float("inf")
)
self.config.update({'max_open_trades': updated_max_open_trades})
self.config.update({"max_open_trades": updated_max_open_trades})
self.backtesting.strategy.max_open_trades = updated_max_open_trades
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
with self.data_pickle_file.open("rb") as f:
processed = load(f, mmap_mode="r")
if self.analyze_per_epoch:
# Data is not yet analyzed, rerun populate_indicators.
processed = self.advise_and_trim(processed)
bt_results = self.backtesting.backtest(
processed=processed,
start_date=self.min_date,
end_date=self.max_date
processed=processed, start_date=self.min_date, end_date=self.max_date
)
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
bt_results.update(
{
"backtest_start_time": int(backtest_start_time.timestamp()),
"backtest_end_time": int(backtest_end_time.timestamp()),
}
)
return self._get_results_dict(bt_results, self.min_date, self.max_date,
params_dict,
processed=processed)
return self._get_results_dict(
bt_results, self.min_date, self.max_date, params_dict, processed=processed
)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
def _get_results_dict(
self, backtesting_results, min_date, max_date, params_dict, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
params_details = self._get_params_details(params_dict)
strat_stats = generate_strategy_stats(
self.pairlist, self.backtesting.strategy.get_strategy_name(),
backtesting_results, min_date, max_date, market_change=self.market_change,
self.pairlist,
self.backtesting.strategy.get_strategy_name(),
backtesting_results,
min_date,
max_date,
market_change=self.market_change,
is_hyperopt=True,
)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
strat_stats, self.config["stake_currency"]
)
not_optimized = self.backtesting.strategy.get_no_optimize_params()
not_optimized = deep_merge_dicts(not_optimized, self._get_no_optimize_details())
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
trade_count = strat_stats["total_trades"]
total_profit = strat_stats["profit_total"]
# 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.
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results['results'],
trade_count=trade_count,
min_date=min_date, max_date=max_date,
config=self.config, processed=processed,
backtest_stats=strat_stats)
if trade_count >= self.config["hyperopt_min_trades"]:
loss = self.calculate_loss(
results=backtesting_results["results"],
trade_count=trade_count,
min_date=min_date,
max_date=max_date,
config=self.config,
processed=processed,
backtest_stats=strat_stats,
)
return {
'loss': loss,
'params_dict': params_dict,
'params_details': params_details,
'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation,
'total_profit': total_profit,
"loss": loss,
"params_dict": params_dict,
"params_details": params_details,
"params_not_optimized": not_optimized,
"results_metrics": strat_stats,
"results_explanation": results_explanation,
"total_profit": total_profit,
}
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
@@ -439,16 +475,16 @@ class Hyperopt:
base_estimator=estimator,
acq_optimizer=acq_optimizer,
n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count},
acq_optimizer_kwargs={"n_jobs": cpu_count},
random_state=self.random_state,
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
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) for v in asked)
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) for v in asked
)
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@@ -462,7 +498,7 @@ class Hyperopt:
trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(trimmed)
if not self.market_change:
self.market_change = calculate_market_change(trimmed, 'close')
self.market_change = calculate_market_change(trimmed, "close")
# Real trimming will happen as part of backtesting.
return preprocessed
@@ -478,10 +514,12 @@ class Hyperopt:
preprocessed = self.advise_and_trim(data)
logger.info(f'Hyperopting with data from '
f'{self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
logger.info(
f"Hyperopting with data from "
f"{self.min_date.strftime(DATETIME_PRINT_FORMAT)} "
f"up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} "
f"({(self.max_date - self.min_date).days} days).."
)
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
else:
@@ -499,12 +537,14 @@ class Hyperopt:
5. Repeat until at least `n_points` points in the `asked_non_tried` list
6. Return a list with length truncated at `n_points`
"""
def unique_list(a_list):
new_list = []
for item in a_list:
if item not in new_list:
new_list.append(item)
return new_list
i = 0
asked_non_tried: List[List[Any]] = []
is_random_non_tried: List[bool] = []
@@ -516,18 +556,20 @@ class Hyperopt:
else:
asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
is_random = [True for _ in range(len(asked))]
is_random_non_tried += [rand for x, rand in zip(asked, is_random)
if x not in self.opt.Xi
and x not in asked_non_tried]
asked_non_tried += [x for x in asked
if x not in self.opt.Xi
and x not in asked_non_tried]
is_random_non_tried += [
rand
for x, rand in zip(asked, is_random)
if x not in self.opt.Xi and x not in asked_non_tried
]
asked_non_tried += [
x for x in asked if x not in self.opt.Xi and x not in asked_non_tried
]
i += 1
if asked_non_tried:
return (
asked_non_tried[:min(len(asked_non_tried), n_points)],
is_random_non_tried[:min(len(asked_non_tried), n_points)]
asked_non_tried[: min(len(asked_non_tried), n_points)],
is_random_non_tried[: min(len(asked_non_tried), n_points)],
)
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
@@ -536,8 +578,8 @@ class Hyperopt:
"""
Evaluate results returned from generate_optimizer
"""
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
val["current_epoch"] = current
val["is_initial_point"] = current <= INITIAL_POINTS
logger.debug("Optimizer epoch evaluated: %s", val)
@@ -546,18 +588,18 @@ class Hyperopt:
# 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
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_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'))
self.random_state = self._set_random_state(self.config.get("hyperopt_random_state"))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
# Initialize spaces ...
@@ -577,8 +619,8 @@ class Hyperopt:
cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
config_jobs = self.config.get("hyperopt_jobs", -1)
logger.info(f"Number of parallel jobs set as: {config_jobs}")
self.opt = self.get_optimizer(self.dimensions, config_jobs)
@@ -588,7 +630,7 @@ class Hyperopt:
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()
logger.info(f'Effective number of parallel workers used: {jobs}')
logger.info(f"Effective number of parallel workers used: {jobs}")
# Define progressbar
with Progress(
@@ -611,7 +653,7 @@ class Hyperopt:
# This allows dataprovider to load it's informative cache.
asked, is_random = self.get_asked_points(n_points=1)
f_val0 = self.generate_optimizer(asked[0])
self.opt.tell(asked, [f_val0['loss']])
self.opt.tell(asked, [f_val0["loss"]])
self.evaluate_result(f_val0, 1, is_random[0])
pbar.update(task, advance=1)
start += 1
@@ -625,7 +667,7 @@ class Hyperopt:
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked)
self.opt.tell(asked, [v['loss'] for v in f_val])
self.opt.tell(asked, [v["loss"] for v in f_val])
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
@@ -635,23 +677,26 @@ class Hyperopt:
pbar.update(task, advance=1)
except KeyboardInterrupt:
print('User interrupted..')
print("User interrupted..")
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
logger.info(
f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'."
)
if self.current_best_epoch:
HyperoptTools.try_export_params(
self.config,
self.backtesting.strategy.get_strategy_name(),
self.current_best_epoch)
self.config, self.backtesting.strategy.get_strategy_name(), self.current_best_epoch
)
HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)
HyperoptTools.show_epoch_details(
self.current_best_epoch, self.total_epochs, self.print_json
)
elif self.num_epochs_saved > 0:
print(
f"No good result found for given optimization function in {self.num_epochs_saved} "
f"{plural(self.num_epochs_saved, 'epoch')}.")
f"{plural(self.num_epochs_saved, 'epoch')}."
)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

53
freqtrade/optimize/hyperopt_auto.py
View File

@@ -3,6 +3,7 @@ HyperOptAuto class.
This module implements a convenience auto-hyperopt class, which can be used together with strategies
that implement IHyperStrategy interface.
"""
import logging
from contextlib import suppress
from typing import Callable, Dict, List
@@ -20,15 +21,17 @@ logger = logging.getLogger(__name__)
def _format_exception_message(space: str, ignore_missing_space: bool) -> None:
msg = (f"The '{space}' space is included into the hyperoptimization "
f"but no parameter for this space was found in your Strategy. "
)
msg = (
f"The '{space}' space is included into the hyperoptimization "
f"but no parameter for this space was found in your Strategy. "
)
if ignore_missing_space:
logger.warning(msg + "This space will be ignored.")
else:
raise OperationalException(
msg + f"Please make sure to have parameters for this space enabled for optimization "
f"or remove the '{space}' space from hyperoptimization.")
f"or remove the '{space}' space from hyperoptimization."
)
class HyperOptAuto(IHyperOpt):
@@ -44,7 +47,7 @@ class HyperOptAuto(IHyperOpt):
:param name: function name.
:return: a requested function.
"""
hyperopt_cls = getattr(self.strategy, 'HyperOpt', None)
hyperopt_cls = getattr(self.strategy, "HyperOpt", None)
default_func = getattr(super(), name)
if hyperopt_cls:
return getattr(hyperopt_cls, name, default_func)
@@ -63,36 +66,36 @@ class HyperOptAuto(IHyperOpt):
return indicator_space
else:
_format_exception_message(
category,
self.config.get("hyperopt_ignore_missing_space", False))
category, self.config.get("hyperopt_ignore_missing_space", False)
)
return []
def buy_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy')
def buy_indicator_space(self) -> List["Dimension"]:
return self._get_indicator_space("buy")
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell')
def sell_indicator_space(self) -> List["Dimension"]:
return self._get_indicator_space("sell")
def protection_space(self) -> List['Dimension']:
return self._get_indicator_space('protection')
def protection_space(self) -> List["Dimension"]:
return self._get_indicator_space("protection")
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)
return self._get_func("generate_roi_table")(params)
def roi_space(self) -> List['Dimension']:
return self._get_func('roi_space')()
def roi_space(self) -> List["Dimension"]:
return self._get_func("roi_space")()
def stoploss_space(self) -> List['Dimension']:
return self._get_func('stoploss_space')()
def stoploss_space(self) -> List["Dimension"]:
return self._get_func("stoploss_space")()
def generate_trailing_params(self, params: Dict) -> Dict:
return self._get_func('generate_trailing_params')(params)
return self._get_func("generate_trailing_params")(params)
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()
def trailing_space(self) -> List["Dimension"]:
return self._get_func("trailing_space")()
def max_open_trades_space(self) -> List['Dimension']:
return self._get_func('max_open_trades_space')()
def max_open_trades_space(self) -> List["Dimension"]:
return self._get_func("max_open_trades_space")()
def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)
def generate_estimator(self, dimensions: List["Dimension"], **kwargs) -> EstimatorType:
return self._get_func("generate_estimator")(dimensions=dimensions, **kwargs)

103
freqtrade/optimize/hyperopt_epoch_filters.py
View File

@@ -11,11 +11,10 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict, log: bool = True)
"""
Filter our items from the list of hyperopt results
"""
if filteroptions['only_best']:
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
epochs = [x for x in epochs
if x['results_metrics'].get('profit_total', 0) > 0]
if filteroptions["only_best"]:
epochs = [x for x in epochs if x["is_best"]]
if filteroptions["only_profitable"]:
epochs = [x for x in epochs if x["results_metrics"].get("profit_total", 0) > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
@@ -25,10 +24,12 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict, log: bool = True)
epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
if log:
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
logger.info(
f"{len(epochs)} "
+ ("best " if filteroptions["only_best"] else "")
+ ("profitable " if filteroptions["only_profitable"] else "")
+ "epochs found."
)
return epochs
@@ -36,93 +37,87 @@ def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
"""
Filter epochs with trade-counts > trades
"""
return [
x for x in epochs if x['results_metrics'].get('total_trades', 0) > trade_count
]
return [x for x in epochs if x["results_metrics"].get("total_trades", 0) > trade_count]
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions["filter_min_trades"] > 0:
epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions["filter_min_trades"])
if filteroptions['filter_min_trades'] > 0:
epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
if filteroptions['filter_max_trades'] > 0:
if filteroptions["filter_max_trades"] > 0:
epochs = [
x for x in epochs
if x['results_metrics'].get('total_trades') < filteroptions['filter_max_trades']
x
for x in epochs
if x["results_metrics"].get("total_trades") < filteroptions["filter_max_trades"]
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def get_duration_value(x):
# Duration in minutes ...
if 'holding_avg_s' in x['results_metrics']:
avg = x['results_metrics']['holding_avg_s']
if "holding_avg_s" in x["results_metrics"]:
avg = x["results_metrics"]["holding_avg_s"]
return avg // 60
raise OperationalException(
"Holding-average not available. Please omit the filter on average time, "
"or rerun hyperopt with this version")
"or rerun hyperopt with this version"
)
if filteroptions['filter_min_avg_time'] is not None:
if filteroptions["filter_min_avg_time"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if get_duration_value(x) > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
epochs = [x for x in epochs if get_duration_value(x) > filteroptions["filter_min_avg_time"]]
if filteroptions["filter_max_avg_time"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if get_duration_value(x) < filteroptions['filter_max_avg_time']
]
epochs = [x for x in epochs if get_duration_value(x) < filteroptions["filter_max_avg_time"]]
return epochs
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None:
if filteroptions["filter_min_avg_profit"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_mean', 0) * 100
> filteroptions['filter_min_avg_profit']
x
for x in epochs
if x["results_metrics"].get("profit_mean", 0) * 100
> filteroptions["filter_min_avg_profit"]
]
if filteroptions['filter_max_avg_profit'] is not None:
if filteroptions["filter_max_avg_profit"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_mean', 0) * 100
< filteroptions['filter_max_avg_profit']
x
for x in epochs
if x["results_metrics"].get("profit_mean", 0) * 100
< filteroptions["filter_max_avg_profit"]
]
if filteroptions['filter_min_total_profit'] is not None:
if filteroptions["filter_min_total_profit"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_total_abs', 0)
> filteroptions['filter_min_total_profit']
x
for x in epochs
if x["results_metrics"].get("profit_total_abs", 0)
> filteroptions["filter_min_total_profit"]
]
if filteroptions['filter_max_total_profit'] is not None:
if filteroptions["filter_max_total_profit"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_total_abs', 0)
< filteroptions['filter_max_total_profit']
x
for x in epochs
if x["results_metrics"].get("profit_total_abs", 0)
< filteroptions["filter_max_total_profit"]
]
return epochs
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None:
if filteroptions["filter_min_objective"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None:
epochs = [x for x in epochs if x["loss"] < filteroptions["filter_min_objective"]]
if filteroptions["filter_max_objective"] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
epochs = [x for x in epochs if x["loss"] > filteroptions["filter_max_objective"]]
return epochs

113
freqtrade/optimize/hyperopt_interface.py
View File

@@ -2,6 +2,7 @@
IHyperOpt interface
This module defines the interface to apply for hyperopt
"""
import logging
import math
from abc import ABC
@@ -30,6 +31,7 @@ class IHyperOpt(ABC):
Class attributes you can use:
timeframe -> int: value of the timeframe to use for the strategy
"""
timeframe: str
strategy: IStrategy
@@ -37,7 +39,7 @@ class IHyperOpt(ABC):
self.config = config
# Assign timeframe to be used in hyperopt
IHyperOpt.timeframe = str(config['timeframe'])
IHyperOpt.timeframe = str(config["timeframe"])
def generate_estimator(self, dimensions: List[Dimension], **kwargs) -> EstimatorType:
"""
@@ -45,7 +47,7 @@ class IHyperOpt(ABC):
Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
inheriting from RegressorMixin (from sklearn).
"""
return 'ET'
return "ET"
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
"""
@@ -55,10 +57,10 @@ class IHyperOpt(ABC):
You may override it in your custom Hyperopt class.
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
roi_table[0] = params["roi_p1"] + params["roi_p2"] + params["roi_p3"]
roi_table[params["roi_t3"]] = params["roi_p1"] + params["roi_p2"]
roi_table[params["roi_t3"] + params["roi_t2"]] = params["roi_p1"]
roi_table[params["roi_t3"] + params["roi_t2"] + params["roi_t1"]] = 0
return roi_table
@@ -96,49 +98,52 @@ class IHyperOpt(ABC):
roi_t_scale = timeframe_min / 5
roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
roi_limits = {
'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
'roi_t2_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t2_max': int(60 * roi_t_scale * roi_t_alpha),
'roi_t3_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t3_max': int(40 * roi_t_scale * roi_t_alpha),
'roi_p1_min': 0.01 * roi_p_scale * roi_p_alpha,
'roi_p1_max': 0.04 * roi_p_scale * roi_p_alpha,
'roi_p2_min': 0.01 * roi_p_scale * roi_p_alpha,
'roi_p2_max': 0.07 * roi_p_scale * roi_p_alpha,
'roi_p3_min': 0.01 * roi_p_scale * roi_p_alpha,
'roi_p3_max': 0.20 * roi_p_scale * roi_p_alpha,
"roi_t1_min": int(10 * roi_t_scale * roi_t_alpha),
"roi_t1_max": int(120 * roi_t_scale * roi_t_alpha),
"roi_t2_min": int(10 * roi_t_scale * roi_t_alpha),
"roi_t2_max": int(60 * roi_t_scale * roi_t_alpha),
"roi_t3_min": int(10 * roi_t_scale * roi_t_alpha),
"roi_t3_max": int(40 * roi_t_scale * roi_t_alpha),
"roi_p1_min": 0.01 * roi_p_scale * roi_p_alpha,
"roi_p1_max": 0.04 * roi_p_scale * roi_p_alpha,
"roi_p2_min": 0.01 * roi_p_scale * roi_p_alpha,
"roi_p2_max": 0.07 * roi_p_scale * roi_p_alpha,
"roi_p3_min": 0.01 * roi_p_scale * roi_p_alpha,
"roi_p3_max": 0.20 * roi_p_scale * roi_p_alpha,
}
logger.debug(f"Using roi space limits: {roi_limits}")
p = {
'roi_t1': roi_limits['roi_t1_min'],
'roi_t2': roi_limits['roi_t2_min'],
'roi_t3': roi_limits['roi_t3_min'],
'roi_p1': roi_limits['roi_p1_min'],
'roi_p2': roi_limits['roi_p2_min'],
'roi_p3': roi_limits['roi_p3_min'],
"roi_t1": roi_limits["roi_t1_min"],
"roi_t2": roi_limits["roi_t2_min"],
"roi_t3": roi_limits["roi_t3_min"],
"roi_p1": roi_limits["roi_p1_min"],
"roi_p2": roi_limits["roi_p2_min"],
"roi_p3": roi_limits["roi_p3_min"],
}
logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 3)}")
p = {
'roi_t1': roi_limits['roi_t1_max'],
'roi_t2': roi_limits['roi_t2_max'],
'roi_t3': roi_limits['roi_t3_max'],
'roi_p1': roi_limits['roi_p1_max'],
'roi_p2': roi_limits['roi_p2_max'],
'roi_p3': roi_limits['roi_p3_max'],
"roi_t1": roi_limits["roi_t1_max"],
"roi_t2": roi_limits["roi_t2_max"],
"roi_t3": roi_limits["roi_t3_max"],
"roi_p1": roi_limits["roi_p1_max"],
"roi_p2": roi_limits["roi_p2_max"],
"roi_p3": roi_limits["roi_p3_max"],
}
logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 3)}")
return [
Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
SKDecimal(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], decimals=3,
name='roi_p1'),
SKDecimal(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], decimals=3,
name='roi_p2'),
SKDecimal(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], decimals=3,
name='roi_p3'),
Integer(roi_limits["roi_t1_min"], roi_limits["roi_t1_max"], name="roi_t1"),
Integer(roi_limits["roi_t2_min"], roi_limits["roi_t2_max"], name="roi_t2"),
Integer(roi_limits["roi_t3_min"], roi_limits["roi_t3_max"], name="roi_t3"),
SKDecimal(
roi_limits["roi_p1_min"], roi_limits["roi_p1_max"], decimals=3, name="roi_p1"
),
SKDecimal(
roi_limits["roi_p2_min"], roi_limits["roi_p2_max"], decimals=3, name="roi_p2"
),
SKDecimal(
roi_limits["roi_p3_min"], roi_limits["roi_p3_max"], decimals=3, name="roi_p3"
),
]
def stoploss_space(self) -> List[Dimension]:
@@ -149,7 +154,7 @@ class IHyperOpt(ABC):
You may override it in your custom Hyperopt class.
"""
return [
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
SKDecimal(-0.35, -0.02, decimals=3, name="stoploss"),
]
def generate_trailing_params(self, params: Dict) -> Dict:
@@ -157,11 +162,12 @@ class IHyperOpt(ABC):
Create dict with trailing stop parameters.
"""
return {
'trailing_stop': params['trailing_stop'],
'trailing_stop_positive': params['trailing_stop_positive'],
'trailing_stop_positive_offset': (params['trailing_stop_positive'] +
params['trailing_stop_positive_offset_p1']),
'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
"trailing_stop": params["trailing_stop"],
"trailing_stop_positive": params["trailing_stop_positive"],
"trailing_stop_positive_offset": (
params["trailing_stop_positive"] + params["trailing_stop_positive_offset_p1"]
),
"trailing_only_offset_is_reached": params["trailing_only_offset_is_reached"],
}
def trailing_space(self) -> List[Dimension]:
@@ -177,18 +183,15 @@ class IHyperOpt(ABC):
# This parameter is included into the hyperspace dimensions rather than assigning
# it explicitly in the code in order to have it printed in the results along with
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
Categorical([True], name="trailing_stop"),
SKDecimal(0.01, 0.35, decimals=3, name="trailing_stop_positive"),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
SKDecimal(0.001, 0.1, decimals=3, name="trailing_stop_positive_offset_p1"),
Categorical([True, False], name="trailing_only_offset_is_reached"),
]
def max_open_trades_space(self) -> List[Dimension]:
@@ -198,7 +201,7 @@ class IHyperOpt(ABC):
You may override it in your custom Hyperopt class.
"""
return [
Integer(-1, 10, name='max_open_trades'),
Integer(-1, 10, name="max_open_trades"),
]
# This is needed for proper unpickling the class attribute timeframe
@@ -206,9 +209,9 @@ class IHyperOpt(ABC):
# Why do I still need such shamanic mantras in modern python?
def __getstate__(self):
state = self.__dict__.copy()
state['timeframe'] = self.timeframe
state["timeframe"] = self.timeframe
return state
def __setstate__(self, state):
self.__dict__.update(state)
IHyperOpt.timeframe = state['timeframe']
IHyperOpt.timeframe = state["timeframe"]

17
freqtrade/optimize/hyperopt_loss_interface.py
View File

@@ -17,15 +17,22 @@ class IHyperOptLoss(ABC):
Interface for freqtrade hyperopt Loss functions.
Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
"""
timeframe: str
@staticmethod
@abstractmethod
def hyperopt_loss_function(*, results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Config, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
**kwargs) -> float:
def hyperopt_loss_function(
*,
results: DataFrame,
trade_count: int,
min_date: datetime,
max_date: datetime,
config: Config,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
**kwargs,
) -> float:
"""
Objective function, returns smaller number for better results
"""

449
freqtrade/optimize/hyperopt_tools.py
View File

@@ -37,7 +37,8 @@ def hyperopt_serializer(x):
class HyperoptStateContainer:
""" Singleton class to track state of hyperopt"""
"""Singleton class to track state of hyperopt"""
state: HyperoptState = HyperoptState.OPTIMIZE
@classmethod
@@ -46,20 +47,21 @@ class HyperoptStateContainer:
class HyperoptTools:
@staticmethod
def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]:
"""
Get Strategy-location (filename) from strategy_name
"""
from freqtrade.resolvers.strategy_resolver import StrategyResolver
strategy_objs = StrategyResolver.search_all_objects(
config, False, config.get('recursive_strategy_search', False))
strategies = [s for s in strategy_objs if s['name'] == strategy_name]
config, False, config.get("recursive_strategy_search", False)
)
strategies = [s for s in strategy_objs if s["name"] == strategy_name]
if strategies:
strategy = strategies[0]
return Path(strategy['location'])
return Path(strategy["location"])
return None
@staticmethod
@@ -67,37 +69,40 @@ class HyperoptTools:
"""
Generate files
"""
final_params = deepcopy(params['params_not_optimized'])
final_params = deep_merge_dicts(params['params_details'], final_params)
final_params = deepcopy(params["params_not_optimized"])
final_params = deep_merge_dicts(params["params_details"], final_params)
final_params = {
'strategy_name': strategy_name,
'params': final_params,
'ft_stratparam_v': 1,
'export_time': datetime.now(timezone.utc),
"strategy_name": strategy_name,
"params": final_params,
"ft_stratparam_v": 1,
"export_time": datetime.now(timezone.utc),
}
logger.info(f"Dumping parameters to {filename}")
with filename.open('w') as f:
rapidjson.dump(final_params, f, indent=2,
default=hyperopt_serializer,
number_mode=HYPER_PARAMS_FILE_FORMAT
)
with filename.open("w") as f:
rapidjson.dump(
final_params,
f,
indent=2,
default=hyperopt_serializer,
number_mode=HYPER_PARAMS_FILE_FORMAT,
)
@staticmethod
def load_params(filename: Path) -> Dict:
"""
Load parameters from file
"""
with filename.open('r') as f:
with filename.open("r") as f:
params = rapidjson.load(f, number_mode=HYPER_PARAMS_FILE_FORMAT)
return params
@staticmethod
def try_export_params(config: Config, strategy_name: str, params: Dict):
if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False):
if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get("disableparamexport", False):
# Export parameters ...
fn = HyperoptTools.get_strategy_filename(config, strategy_name)
if fn:
HyperoptTools.export_params(params, strategy_name, fn.with_suffix('.json'))
HyperoptTools.export_params(params, strategy_name, fn.with_suffix(".json"))
else:
logger.warning("Strategy not found, not exporting parameter file.")
@@ -107,10 +112,10 @@ class HyperoptTools:
Tell if the space value is contained in the configuration
"""
# 'trailing' and 'protection spaces are not included in the 'default' set of spaces
if space in ('trailing', 'protection', 'trades'):
return any(s in config['spaces'] for s in [space, 'all'])
if space in ("trailing", "protection", "trades"):
return any(s in config["spaces"] for s in [space, "all"])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
return any(s in config["spaces"] for s in [space, "all", "default"])
@staticmethod
def _read_results(results_file: Path, batch_size: int = 10) -> Iterator[List[Any]]:
@@ -118,8 +123,9 @@ class HyperoptTools:
Stream hyperopt results from file
"""
import rapidjson
logger.info(f"Reading epochs from '{results_file}'")
with results_file.open('r') as f:
with results_file.open("r") as f:
data = []
for line in f:
data += [rapidjson.loads(line)]
@@ -131,7 +137,7 @@ class HyperoptTools:
@staticmethod
def _test_hyperopt_results_exist(results_file) -> bool:
if results_file.is_file() and results_file.stat().st_size > 0:
if results_file.suffix == '.pickle':
if results_file.suffix == ".pickle":
raise OperationalException(
"Legacy hyperopt results are no longer supported."
"Please rerun hyperopt or use an older version to load this file."
@@ -144,18 +150,18 @@ class HyperoptTools:
@staticmethod
def load_filtered_results(results_file: Path, config: Config) -> Tuple[List, int]:
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time'),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time'),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit'),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit'),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit'),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit'),
'filter_min_objective': config.get('hyperopt_list_min_objective'),
'filter_max_objective': config.get('hyperopt_list_max_objective'),
"only_best": config.get("hyperopt_list_best", False),
"only_profitable": config.get("hyperopt_list_profitable", False),
"filter_min_trades": config.get("hyperopt_list_min_trades", 0),
"filter_max_trades": config.get("hyperopt_list_max_trades", 0),
"filter_min_avg_time": config.get("hyperopt_list_min_avg_time"),
"filter_max_avg_time": config.get("hyperopt_list_max_avg_time"),
"filter_min_avg_profit": config.get("hyperopt_list_min_avg_profit"),
"filter_max_avg_profit": config.get("hyperopt_list_max_avg_profit"),
"filter_min_total_profit": config.get("hyperopt_list_min_total_profit"),
"filter_max_total_profit": config.get("hyperopt_list_max_total_profit"),
"filter_min_objective": config.get("hyperopt_list_min_objective"),
"filter_max_objective": config.get("hyperopt_list_max_objective"),
}
if not HyperoptTools._test_hyperopt_results_exist(results_file):
# No file found.
@@ -165,10 +171,11 @@ class HyperoptTools:
epochs = []
total_epochs = 0
for epochs_tmp in HyperoptTools._read_results(results_file):
if total_epochs == 0 and epochs_tmp[0].get('is_best') is None:
if total_epochs == 0 and epochs_tmp[0].get("is_best") is None:
raise OperationalException(
"The file with HyperoptTools results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
"of Freqtrade and cannot be loaded."
)
total_epochs += len(epochs_tmp)
epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False)
@@ -180,13 +187,18 @@ class HyperoptTools:
return epochs, total_epochs
@staticmethod
def show_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: Optional[str] = None) -> None:
def show_epoch_details(
results,
total_epochs: int,
print_json: bool,
no_header: bool = False,
header_str: Optional[str] = None,
) -> None:
"""
Display details of the hyperopt result
"""
params = results.get('params_details', {})
non_optimized = results.get('params_not_optimized', {})
params = results.get("params_details", {})
non_optimized = results.get("params_not_optimized", {})
# Default header string
if header_str is None:
@@ -198,23 +210,34 @@ class HyperoptTools:
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'protection',
'roi', 'stoploss', 'trailing', 'max_open_trades']:
for s in [
"buy",
"sell",
"protection",
"roi",
"stoploss",
"trailing",
"max_open_trades",
]:
HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=HYPER_PARAMS_FILE_FORMAT))
else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'protection',
"Protection hyperspace params:", non_optimized)
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
HyperoptTools._params_pretty_print(
params, 'max_open_trades', "Max Open Trades:", non_optimized)
params, "buy", "Buy hyperspace params:", non_optimized
)
HyperoptTools._params_pretty_print(
params, "sell", "Sell hyperspace params:", non_optimized
)
HyperoptTools._params_pretty_print(
params, "protection", "Protection hyperspace params:", non_optimized
)
HyperoptTools._params_pretty_print(params, "roi", "ROI table:", non_optimized)
HyperoptTools._params_pretty_print(params, "stoploss", "Stoploss:", non_optimized)
HyperoptTools._params_pretty_print(params, "trailing", "Trailing stop:", non_optimized)
HyperoptTools._params_pretty_print(
params, "max_open_trades", "Max Open Trades:", non_optimized
)
@staticmethod
def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
@@ -227,23 +250,23 @@ class HyperoptTools:
if len(space_non_optimized) > 0:
all_space_params = {**space_params, **space_non_optimized}
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(all_space_params)
elif space == 'roi':
if space in ["buy", "sell"]:
result_dict.setdefault("params", {}).update(all_space_params)
elif space == "roi":
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
result_dict['minimal_roi'] = {str(k): v for k, v in all_space_params.items()}
result_dict["minimal_roi"] = {str(k): v for k, v in all_space_params.items()}
else: # 'stoploss', 'trailing'
result_dict.update(all_space_params)
@staticmethod
def _params_pretty_print(
params, space: str, header: str, non_optimized: Optional[Dict] = None) -> None:
params, space: str, header: str, non_optimized: Optional[Dict] = None
) -> None:
if space in params or (non_optimized and space in non_optimized):
space_params = HyperoptTools._space_params(params, space, 5)
no_params = HyperoptTools._space_params(non_optimized, space, 5)
appendix = ''
appendix = ""
if not space_params and not no_params:
# No parameters - don't print
return
@@ -254,15 +277,18 @@ class HyperoptTools:
result = f"\n# {header}\n"
if space == "stoploss":
stoploss = safe_value_fallback2(space_params, no_params, space, space)
result += (f"stoploss = {stoploss}{appendix}")
result += f"stoploss = {stoploss}{appendix}"
elif space == "max_open_trades":
max_open_trades = safe_value_fallback2(space_params, no_params, space, space)
result += (f"max_open_trades = {max_open_trades}{appendix}")
result += f"max_open_trades = {max_open_trades}{appendix}"
elif space == "roi":
result = result[:-1] + f'{appendix}\n'
minimal_roi_result = rapidjson.dumps({
str(k): v for k, v in (space_params or no_params).items()
}, default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
result = result[:-1] + f"{appendix}\n"
minimal_roi_result = rapidjson.dumps(
{str(k): v for k, v in (space_params or no_params).items()},
default=str,
indent=4,
number_mode=rapidjson.NM_NATIVE,
)
result += f"minimal_roi = {minimal_roi_result}"
elif space == "trailing":
for k, v in (space_params or no_params).items():
@@ -291,177 +317,213 @@ class HyperoptTools:
"""
p = params.copy()
p.update(non_optimized)
result = '{\n'
result = "{\n"
for k, param in p.items():
result += " " * indent + f'"{k}": '
result += f'"{param}",' if isinstance(param, str) else f'{param},'
result += f'"{param}",' if isinstance(param, str) else f"{param},"
if k in non_optimized:
result += NON_OPT_PARAM_APPENDIX
result += "\n"
result += '}'
result += "}"
return result
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return bool(results['loss'] < current_best_loss)
return bool(results["loss"] < current_best_loss)
@staticmethod
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
"""
Return the formatted results explanation in a string
"""
return (f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean']:7.2%}. "
f"Median profit {results_metrics['profit_median']:7.2%}. "
f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
f"({results_metrics['profit_total']:8.2%}). "
f"Avg duration {results_metrics['holding_avg']} min."
)
return (
f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean']:7.2%}. "
f"Median profit {results_metrics['profit_median']:7.2%}. "
f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
f"({results_metrics['profit_total']:8.2%}). "
f"Avg duration {results_metrics['holding_avg']} min."
)
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
return (
("*" if results["is_initial_point"] else " ")
+ f"{results['current_epoch']:5d}/{total_epochs}: "
+ f"{results['results_explanation']} "
+ f"Objective: {results['loss']:.5f}"
)
@staticmethod
def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
trials['Best'] = ''
trials["Best"] = ""
if 'results_metrics.winsdrawslosses' not in trials.columns:
if "results_metrics.winsdrawslosses" not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials["results_metrics.winsdrawslosses"] = "N/A"
if not has_drawdown:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.max_drawdown_account'] = None
if 'is_random' not in trials.columns:
trials['is_random'] = False
trials["results_metrics.max_drawdown_account"] = None
if "is_random" not in trials.columns:
trials["is_random"] = False
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
trials["results_metrics.winsdrawslosses"] = trials.apply(
lambda x: generate_wins_draws_losses(
x['results_metrics.wins'], x['results_metrics.draws'],
x['results_metrics.losses']
), axis=1)
x["results_metrics.wins"], x["results_metrics.draws"], x["results_metrics.losses"]
),
axis=1,
)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'results_metrics.max_drawdown',
'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
'loss', 'is_initial_point', 'is_random', 'is_best']]
trials = trials[
[
"Best",
"current_epoch",
"results_metrics.total_trades",
"results_metrics.winsdrawslosses",
"results_metrics.profit_mean",
"results_metrics.profit_total_abs",
"results_metrics.profit_total",
"results_metrics.holding_avg",
"results_metrics.max_drawdown",
"results_metrics.max_drawdown_account",
"results_metrics.max_drawdown_abs",
"loss",
"is_initial_point",
"is_random",
"is_best",
]
]
trials.columns = [
'Best', 'Epoch', 'Trades', ' Win Draw Loss Win%', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
]
"Best",
"Epoch",
"Trades",
" Win Draw Loss Win%",
"Avg profit",
"Total profit",
"Profit",
"Avg duration",
"max_drawdown",
"max_drawdown_account",
"max_drawdown_abs",
"Objective",
"is_initial_point",
"is_random",
"is_best",
]
return trials
@staticmethod
def get_result_table(config: Config, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
def get_result_table(
config: Config,
results: list,
total_epochs: int,
highlight_best: bool,
print_colorized: bool,
remove_header: int,
) -> str:
"""
Log result table
"""
if not results:
return ''
return ""
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
has_account_drawdown = "results_metrics.max_drawdown_account" in trials.columns
trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
trials['is_profit'] = False
trials.loc[trials['is_initial_point'] | trials['is_random'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials["is_profit"] = False
trials.loc[trials["is_initial_point"] | trials["is_random"], "Best"] = "* "
trials.loc[trials["is_best"], "Best"] = "Best"
trials.loc[
(trials['is_initial_point'] | trials['is_random']) & trials['is_best'],
'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
(trials["is_initial_point"] | trials["is_random"]) & trials["is_best"], "Best"
] = "* Best"
trials.loc[trials["Total profit"] > 0, "is_profit"] = True
trials["Trades"] = trials["Trades"].astype(str)
# perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
trials["Epoch"] = trials["Epoch"].apply(
lambda x: "{}/{}".format(str(x).rjust(len(str(total_epochs)), " "), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
trials["Avg profit"] = trials["Avg profit"].apply(
lambda x: f"{x:,.2%}".rjust(7, " ") if not isna(x) else "--".rjust(7, " ")
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
if not isna(x) else "--".rjust(7, ' ')
trials["Avg duration"] = trials["Avg duration"].apply(
lambda x: f"{x:,.1f} m".rjust(7, " ")
if isinstance(x, float)
else f"{x}"
if not isna(x)
else "--".rjust(7, " ")
)
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
trials["Objective"] = trials["Objective"].apply(
lambda x: f"{x:,.5f}".rjust(8, " ") if x != 100000 else "N/A".rjust(8, " ")
)
stake_currency = config['stake_currency']
stake_currency = config["stake_currency"]
trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
lambda x: "{} {}".format(
fmt_coin(x['max_drawdown_abs'], stake_currency, keep_trailing_zeros=True),
(f"({x['max_drawdown_account']:,.2%})"
fmt_coin(x["max_drawdown_abs"], stake_currency, keep_trailing_zeros=True),
(
f"({x['max_drawdown_account']:,.2%})"
if has_account_drawdown
else f"({x['max_drawdown']:,.2%})"
).rjust(10, ' ')
).rjust(10, " "),
).rjust(25 + len(stake_currency))
if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
else '--'.rjust(25 + len(stake_currency)),
axis=1
if x["max_drawdown"] != 0.0 or x["max_drawdown_account"] != 0.0
else "--".rjust(25 + len(stake_currency)),
axis=1,
)
trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
trials = trials.drop(columns=["max_drawdown_abs", "max_drawdown", "max_drawdown_account"])
trials['Profit'] = trials.apply(
lambda x: '{} {}'.format(
fmt_coin(x['Total profit'], stake_currency, keep_trailing_zeros=True),
f"({x['Profit']:,.2%})".rjust(10, ' ')
trials["Profit"] = trials.apply(
lambda x: "{} {}".format(
fmt_coin(x["Total profit"], stake_currency, keep_trailing_zeros=True),
f"({x['Profit']:,.2%})".rjust(10, " "),
).rjust(25 + len(stake_currency))
if x['Total profit'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
axis=1
if x["Total profit"] != 0.0
else "--".rjust(25 + len(stake_currency)),
axis=1,
)
trials = trials.drop(columns=['Total profit'])
trials = trials.drop(columns=["Total profit"])
if print_colorized:
trials2 = trials.astype(str)
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
if trials.loc[i]["is_profit"]:
for j in range(len(trials.loc[i]) - 3):
trials2.iat[i, j] = f"{Fore.GREEN}{str(trials.iloc[i, j])}{Fore.RESET}"
if trials.loc[i]['is_best'] and highlight_best:
if trials.loc[i]["is_best"] and highlight_best:
for j in range(len(trials.loc[i]) - 3):
trials2.iat[i, j] = (
f"{Style.BRIGHT}{str(trials.iloc[i, j])}{Style.RESET_ALL}"
)
trials = trials2
del trials2
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
trials = trials.drop(columns=["is_initial_point", "is_best", "is_profit", "is_random"])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
trials.to_dict(orient="list"), tablefmt="orgtbl", headers="keys", stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
trials.to_dict(orient="list"), tablefmt="psql", headers="keys", stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
trials.to_dict(orient="list"), tablefmt="psql", headers="keys", stralign="right"
)
return table
@@ -479,56 +541,75 @@ class HyperoptTools:
return
try:
Path(csv_file).open('w+').close()
Path(csv_file).open("w+").close()
except OSError:
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["Best"] = ""
trials["Stake currency"] = config["stake_currency"]
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total', 'Stake currency',
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'results_metrics.trade_count_long', 'results_metrics.trade_count_short',
'loss', 'is_initial_point', 'is_best']
base_metrics = [
"Best",
"current_epoch",
"results_metrics.total_trades",
"results_metrics.profit_mean",
"results_metrics.profit_median",
"results_metrics.profit_total",
"Stake currency",
"results_metrics.profit_total_abs",
"results_metrics.holding_avg",
"results_metrics.trade_count_long",
"results_metrics.trade_count_short",
"loss",
"is_initial_point",
"is_best",
]
perc_multi = 100
param_metrics = [("params_dict." + param) for param in results[0]['params_dict'].keys()]
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', 'Median profit', 'Total profit',
'Stake currency', 'Profit', 'Avg duration',
'Trade count long', 'Trade count short',
'Objective',
'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
base_columns = [
"Best",
"Epoch",
"Trades",
"Avg profit",
"Median profit",
"Total profit",
"Stake currency",
"Profit",
"Avg duration",
"Trade count long",
"Trade count short",
"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)
trials['Median profit'] = trials['Median profit'] * perc_multi
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)
trials["Median profit"] = trials["Median profit"] * perc_multi
trials['Total profit'] = trials['Total profit'].apply(
lambda x: f'{x:,.8f}' if x != 0.0 else ""
trials["Total profit"] = trials["Total profit"].apply(
lambda x: f"{x:,.8f}" if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: f'{x:,.2f}' if not isna(x) else ""
trials["Profit"] = trials["Profit"].apply(lambda x: f"{x:,.2f}" if not isna(x) else "")
trials["Avg profit"] = trials["Avg profit"].apply(
lambda x: f"{x * perc_multi:,.2f}%" if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}' if x != 100000 else ""
trials["Objective"] = trials["Objective"].apply(
lambda x: f"{x:,.5f}" if x != 100000 else ""
)
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')
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")
logger.info(f"CSV file created: {csv_file}")