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Add --analyze-per-epoch - moving populate_analysis to the epoch process

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Matthias
2022-08-19 15:19:43 +02:00
parent 09f8904545
commit bc359675a2
6 changed files with 48 additions and 12 deletions
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34
freqtrade/optimize/hyperopt.py
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@@ -24,13 +24,15 @@ from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, 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_loss_interface import IHyperOptLoss
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.hyperopt_tools import (HyperoptStateContainer, HyperoptTools,
hyperopt_serializer)
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
@@ -74,10 +76,14 @@ class Hyperopt:
self.dimensions: List[Dimension] = []
self.config = config
self.min_date: datetime
self.max_date: datetime
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)
HyperoptStateContainer.set_state(HyperoptState.STARTUP)
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
@@ -290,6 +296,7 @@ class Hyperopt:
Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(self.dimensions, raw_params)
@@ -321,6 +328,10 @@ class Hyperopt:
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,
@@ -415,19 +426,24 @@ class Hyperopt:
return processed
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
data, self.timerange = self.backtesting.load_bt_data()
self.backtesting.load_bt_data_detail()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
if not self.analyze_per_epoch:
HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
preprocessed = self.advise_and_trim(data)
preprocessed = self.advise_and_trim(data)
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)..')
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
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:
dump(data, self.data_pickle_file)
def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
"""