472 lines
20 KiB
Python
472 lines
20 KiB
Python
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
|
|
|
|
"""
|
|
This module contains the hyperopt logic
|
|
"""
|
|
|
|
import logging
|
|
import random
|
|
import warnings
|
|
from datetime import datetime, timezone
|
|
from math import ceil
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
import progressbar
|
|
import rapidjson
|
|
from colorama import Fore, Style
|
|
from colorama import init as colorama_init
|
|
from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
|
|
from freqtrade.data.converter import trim_dataframes
|
|
from freqtrade.data.history import get_timerange
|
|
from freqtrade.misc import file_dump_json, plural
|
|
from freqtrade.optimize.backtesting import Backtesting
|
|
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
|
|
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
|
|
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
|
|
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
|
|
from freqtrade.optimize.hyperopt_tools import HyperoptTools
|
|
from freqtrade.optimize.optimize_reports import generate_strategy_stats
|
|
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
|
|
|
|
|
|
# Suppress scikit-learn FutureWarnings from skopt
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("ignore", category=FutureWarning)
|
|
from skopt import Optimizer
|
|
from skopt.space import Dimension
|
|
|
|
progressbar.streams.wrap_stderr()
|
|
progressbar.streams.wrap_stdout()
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
INITIAL_POINTS = 30
|
|
|
|
# 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
|
|
|
|
|
|
class Hyperopt:
|
|
"""
|
|
Hyperopt class, this class contains all the logic to run a hyperopt simulation
|
|
|
|
To run a backtest:
|
|
hyperopt = Hyperopt(config)
|
|
hyperopt.start()
|
|
"""
|
|
custom_hyperopt: IHyperOpt
|
|
|
|
def __init__(self, config: Dict[str, Any]) -> None:
|
|
self.buy_space: List[Dimension] = []
|
|
self.sell_space: List[Dimension] = []
|
|
self.roi_space: List[Dimension] = []
|
|
self.stoploss_space: List[Dimension] = []
|
|
self.trailing_space: List[Dimension] = []
|
|
self.dimensions: List[Dimension] = []
|
|
|
|
self.config = config
|
|
|
|
self.backtesting = Backtesting(self.config)
|
|
|
|
if not self.config.get('hyperopt'):
|
|
self.custom_hyperopt = HyperOptAuto(self.config)
|
|
else:
|
|
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
|
|
self.backtesting._set_strategy(self.backtesting.strategylist[0])
|
|
self.custom_hyperopt.strategy = self.backtesting.strategy
|
|
|
|
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(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)
|
|
|
|
self.current_best_loss = 100
|
|
|
|
self.clean_hyperopt()
|
|
|
|
self.num_epochs_saved = 0
|
|
self.current_best_epoch: Optional[Dict[str, Any]] = None
|
|
|
|
# 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 = ( # type: ignore
|
|
self.custom_hyperopt.populate_indicators) # type: ignore
|
|
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
|
|
self.backtesting.strategy.advise_buy = ( # type: ignore
|
|
self.custom_hyperopt.populate_buy_trend) # type: ignore
|
|
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
|
|
self.backtesting.strategy.advise_sell = ( # type: ignore
|
|
self.custom_hyperopt.populate_sell_trend) # type: ignore
|
|
|
|
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
|
|
if self.config.get('use_max_market_positions', True):
|
|
self.max_open_trades = self.config['max_open_trades']
|
|
else:
|
|
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
|
|
self.max_open_trades = 0
|
|
self.position_stacking = self.config.get('position_stacking', False)
|
|
|
|
if HyperoptTools.has_space(self.config, 'sell'):
|
|
# Make sure use_sell_signal is enabled
|
|
if 'ask_strategy' not in self.config:
|
|
self.config['ask_strategy'] = {}
|
|
self.config['ask_strategy']['use_sell_signal'] = True
|
|
|
|
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)
|
|
|
|
@staticmethod
|
|
def get_lock_filename(config: Dict[str, Any]) -> str:
|
|
|
|
return str(config['user_data_dir'] / 'hyperopt.lock')
|
|
|
|
def clean_hyperopt(self) -> None:
|
|
"""
|
|
Remove hyperopt pickle files to restart hyperopt.
|
|
"""
|
|
for f in [self.data_pickle_file, self.results_file]:
|
|
p = Path(f)
|
|
if p.is_file():
|
|
logger.info(f"Removing `{p}`.")
|
|
p.unlink()
|
|
|
|
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.')
|
|
|
|
# Return a dict where the keys are the names of the dimensions
|
|
# and the values are taken from the list of parameters.
|
|
return {d.name: v for d, v in zip(dimensions, raw_params)}
|
|
|
|
def _save_result(self, epoch: Dict) -> None:
|
|
"""
|
|
Save hyperopt results to file
|
|
Store one line per epoch.
|
|
While not a valid json object - this allows appending easily.
|
|
:param epoch: result dictionary for this epoch.
|
|
"""
|
|
with self.results_file.open('a') as f:
|
|
rapidjson.dump(epoch, f, default=str,
|
|
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}'.")
|
|
# 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)
|
|
|
|
def _get_params_details(self, params: Dict) -> Dict:
|
|
"""
|
|
Return the params for each space
|
|
"""
|
|
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, '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)
|
|
|
|
return result
|
|
|
|
def print_results(self, results) -> None:
|
|
"""
|
|
Log results if it is better than any previous evaluation
|
|
TODO: this should be moved to HyperoptTools too
|
|
"""
|
|
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.hyperopt_table_header = 2
|
|
|
|
def init_spaces(self):
|
|
"""
|
|
Assign the dimensions in the hyperoptimization space.
|
|
"""
|
|
|
|
if HyperoptTools.has_space(self.config, 'buy'):
|
|
logger.debug("Hyperopt has 'buy' space")
|
|
self.buy_space = self.custom_hyperopt.indicator_space()
|
|
|
|
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'):
|
|
logger.debug("Hyperopt has 'roi' space")
|
|
self.roi_space = self.custom_hyperopt.roi_space()
|
|
|
|
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'):
|
|
logger.debug("Hyperopt has 'trailing' space")
|
|
self.trailing_space = self.custom_hyperopt.trailing_space()
|
|
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
|
|
self.stoploss_space + self.trailing_space)
|
|
|
|
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
|
|
"""
|
|
Used Optimize function. Called once per epoch to optimize whatever is configured.
|
|
Keep this function as optimized as possible!
|
|
"""
|
|
backtest_start_time = datetime.now(timezone.utc)
|
|
params_dict = self._get_params_dict(self.dimensions, raw_params)
|
|
|
|
# Apply parameters
|
|
if HyperoptTools.has_space(self.config, 'roi'):
|
|
self.backtesting.strategy.minimal_roi = ( # type: ignore
|
|
self.custom_hyperopt.generate_roi_table(params_dict))
|
|
|
|
if HyperoptTools.has_space(self.config, 'buy'):
|
|
self.backtesting.strategy.advise_buy = ( # type: ignore
|
|
self.custom_hyperopt.buy_strategy_generator(params_dict))
|
|
|
|
if HyperoptTools.has_space(self.config, 'sell'):
|
|
self.backtesting.strategy.advise_sell = ( # type: ignore
|
|
self.custom_hyperopt.sell_strategy_generator(params_dict))
|
|
|
|
if HyperoptTools.has_space(self.config, 'stoploss'):
|
|
self.backtesting.strategy.stoploss = params_dict['stoploss']
|
|
|
|
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']
|
|
|
|
with self.data_pickle_file.open('rb') as f:
|
|
processed = load(f, mmap_mode='r')
|
|
bt_results = self.backtesting.backtest(
|
|
processed=processed,
|
|
start_date=self.min_date,
|
|
end_date=self.max_date,
|
|
max_open_trades=self.max_open_trades,
|
|
position_stacking=self.position_stacking,
|
|
enable_protections=self.config.get('enable_protections', False),
|
|
)
|
|
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()),
|
|
})
|
|
|
|
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]:
|
|
params_details = self._get_params_details(params_dict)
|
|
|
|
strat_stats = generate_strategy_stats(
|
|
processed, self.backtesting.strategy.get_strategy_name(),
|
|
backtesting_results, min_date, max_date, market_change=0
|
|
)
|
|
results_explanation = HyperoptTools.format_results_explanation_string(
|
|
strat_stats, self.config['stake_currency'])
|
|
|
|
not_optimized = self.backtesting.strategy.get_params_dict()
|
|
|
|
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)
|
|
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,
|
|
}
|
|
|
|
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
|
|
return Optimizer(
|
|
dimensions,
|
|
base_estimator="ET",
|
|
acq_optimizer="auto",
|
|
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 run_optimizer_parallel(self, parallel, asked, i) -> List:
|
|
return parallel(delayed(
|
|
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
|
|
|
|
def _set_random_state(self, random_state: Optional[int]) -> int:
|
|
return random_state or random.randint(1, 2**16 - 1)
|
|
|
|
def prepare_hyperopt_data(self) -> None:
|
|
data, timerange = self.backtesting.load_bt_data()
|
|
logger.info("Dataload complete. Calculating indicators")
|
|
|
|
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
|
|
|
|
# Trim startup period from analyzed dataframe
|
|
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
|
|
|
|
self.min_date, self.max_date = get_timerange(processed)
|
|
|
|
logger.info(f'Hyperopting with data from {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)..')
|
|
|
|
dump(processed, self.data_pickle_file)
|
|
|
|
def start(self) -> None:
|
|
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
|
|
logger.info(f"Using optimizer random state: {self.random_state}")
|
|
self.hyperopt_table_header = -1
|
|
# Initialize spaces ...
|
|
self.init_spaces()
|
|
|
|
self.prepare_hyperopt_data()
|
|
|
|
# We don't need exchange instance anymore while running hyperopt
|
|
self.backtesting.exchange.close()
|
|
self.backtesting.exchange._api = None # type: ignore
|
|
self.backtesting.exchange._api_async = None # type: ignore
|
|
# self.backtesting.exchange = None # type: ignore
|
|
self.backtesting.pairlists = None # type: ignore
|
|
|
|
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}')
|
|
|
|
self.opt = self.get_optimizer(self.dimensions, config_jobs)
|
|
|
|
if self.print_colorized:
|
|
colorama_init(autoreset=True)
|
|
|
|
try:
|
|
with Parallel(n_jobs=config_jobs) as parallel:
|
|
jobs = parallel._effective_n_jobs()
|
|
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(), ']',
|
|
]
|
|
with progressbar.ProgressBar(
|
|
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
|
|
widgets=widgets
|
|
) as pbar:
|
|
EVALS = ceil(self.total_epochs / jobs)
|
|
for i in range(EVALS):
|
|
# Correct the number of epochs to be processed for the last
|
|
# iteration (should not exceed self.total_epochs in total)
|
|
n_rest = (i + 1) * jobs - self.total_epochs
|
|
current_jobs = jobs - n_rest if n_rest > 0 else jobs
|
|
|
|
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])
|
|
|
|
# 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
|
|
self.print_results(val)
|
|
|
|
if is_best:
|
|
self.current_best_loss = val['loss']
|
|
self.current_best_epoch = val
|
|
|
|
self._save_result(val)
|
|
|
|
pbar.update(current)
|
|
|
|
except KeyboardInterrupt:
|
|
print('User interrupted..')
|
|
|
|
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.print_epoch_details(self.current_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.
|
|
print("No epochs evaluated yet, no best result.")
|