backtest_live_models - params validation and get timerange from live models in BT
This commit is contained in:
Wagner Costa Santos
parent
f3f3917da3
commit
5880f7a638
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@ -25,7 +25,8 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
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ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
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"enable_protections", "dry_run_wallet", "timeframe_detail",
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"strategy_list", "export", "exportfilename",
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"backtest_breakdown", "backtest_cache"]
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"backtest_breakdown", "backtest_cache",
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"freqai_backtest_live_models"]
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ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
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"position_stacking", "use_max_market_positions",
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@ -668,4 +668,10 @@ AVAILABLE_CLI_OPTIONS = {
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help='Specify additional lookup path for freqaimodels.',
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metavar='PATH',
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),
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"freqai_backtest_live_models": Arg(
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'--freqai-backtest-live-models',
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help='Run backtest with ready models.',
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action='store_true',
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default=False,
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),
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}
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@ -86,6 +86,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
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_validate_unlimited_amount(conf)
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_validate_ask_orderbook(conf)
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_validate_freqai_hyperopt(conf)
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_validate_freqai_backtest(conf)
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_validate_consumers(conf)
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validate_migrated_strategy_settings(conf)
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@ -334,6 +335,21 @@ def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
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'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
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def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
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freqai_enabled = conf.get('freqai', {}).get('enabled', False)
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timerange = conf.get('timerange')
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freqai_backtest_live_models = conf.get('freqai_backtest_live_models', False)
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if freqai_backtest_live_models and freqai_enabled and timerange:
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raise OperationalException(
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'Using timerange parameter is not supported with '
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'--freqai-backtest-live-models parameter.')
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if freqai_backtest_live_models and not freqai_enabled:
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raise OperationalException(
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'Using --freqai-backtest-live-models parameter is only '
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'supported with a FreqAI strategy.')
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def _validate_consumers(conf: Dict[str, Any]) -> None:
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emc_conf = conf.get('external_message_consumer', {})
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if emc_conf.get('enabled', False):
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@ -277,6 +277,9 @@ class Configuration:
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self._args_to_config(config, argname='disableparamexport',
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logstring='Parameter --disableparamexport detected: {} ...')
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self._args_to_config(config, argname='freqai_backtest_live_models',
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logstring='Parameter --freqai-backtest-live-models detected ...')
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# Edge section:
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if 'stoploss_range' in self.args and self.args["stoploss_range"]:
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txt_range = eval(self.args["stoploss_range"])
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@ -21,6 +21,7 @@ from freqtrade.configuration import TimeRange
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from freqtrade.constants import Config
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import timeframe_to_seconds
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from freqtrade.freqai import freqai_util
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from freqtrade.strategy.interface import IStrategy
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@ -62,7 +63,6 @@ class FreqaiDataKitchen:
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live: bool = False,
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pair: str = "",
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):
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self.backtest_live_models = False # temp
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self.data: Dict[str, Any] = {}
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self.data_dictionary: Dict[str, DataFrame] = {}
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self.config = config
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@ -81,16 +81,21 @@ class FreqaiDataKitchen:
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self.svm_model: linear_model.SGDOneClassSVM = None
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self.keras: bool = self.freqai_config.get("keras", False)
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self.set_all_pairs()
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self.backtest_live_models = config.get("freqai_backtest_live_models", False)
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if not self.live:
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if not self.config["timerange"]:
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if (not self.config.get("timerange") and
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not self.backtest_live_models):
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raise OperationalException(
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'Please pass --timerange if you intend to use FreqAI for backtesting.')
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self.full_path = freqai_util.get_full_model_path(self.config)
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self.full_timerange = self.create_fulltimerange(
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self.config["timerange"], self.freqai_config.get("train_period_days", 0)
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)
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if self.backtest_live_models:
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self.get_timerange_from_ready_models()
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self.set_timerange_from_ready_models()
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(self.training_timeranges,
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self.backtesting_timeranges) = self.split_timerange_live_models()
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else:
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@ -118,10 +123,7 @@ class FreqaiDataKitchen:
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metadata: dict = strategy furnished pair metadata
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trained_timestamp: int = timestamp of most recent training
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"""
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self.full_path = Path(
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self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
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)
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self.full_path = freqai_util.get_full_model_path(self.config)
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self.data_path = Path(
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self.full_path
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/ f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
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@ -1035,11 +1037,6 @@ class FreqaiDataKitchen:
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start = datetime.fromtimestamp(backtest_timerange.startts, tz=timezone.utc)
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stop = datetime.fromtimestamp(backtest_timerange.stopts, tz=timezone.utc)
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full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
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self.full_path = Path(
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self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}"
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)
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config_path = Path(self.config["config_files"][0])
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if not self.full_path.is_dir():
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@ -1292,10 +1289,10 @@ class FreqaiDataKitchen:
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)
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return file_exists
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def get_timerange_from_ready_models(self):
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def set_timerange_from_ready_models(self):
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backtesting_timerange, \
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backtesting_string_timerange, \
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pairs_end_dates = self.gen_get_timerange_from_ready_models(self.full_path)
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pairs_end_dates = freqai_util.get_timerange_from_ready_models(self.full_path)
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self.backtest_live_models_data = {
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"backtesting_timerange": backtesting_timerange,
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"backtesting_string_timerange": backtesting_string_timerange,
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@ -1303,43 +1300,53 @@ class FreqaiDataKitchen:
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}
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return
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def gen_get_timerange_from_ready_models(self, models_path: Path):
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all_models_end_dates = []
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pairs_end_dates: Dict[str, Any] = {}
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for model_dir in models_path.iterdir():
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if str(model_dir.name).startswith("sub-train"):
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model_end_date = int(model_dir.name.split("_")[1])
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pair = model_dir.name.split("_")[0].replace("sub-train-", "")
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model_file_name = (f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}")
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model_file_name = f"{model_file_name}_model.joblib"
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# def get_timerange_from_ready_models(self, models_path: Path):
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# all_models_end_dates = []
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# pairs_end_dates: Dict[str, Any] = {}
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# for model_dir in models_path.iterdir():
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# if str(model_dir.name).startswith("sub-train"):
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# model_end_date = int(model_dir.name.split("_")[1])
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# pair = model_dir.name.split("_")[0].replace("sub-train-", "")
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# model_file_name = (f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}"
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# "_model.joblib")
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model_path_file = Path(model_dir / model_file_name)
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if model_path_file.is_file():
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if pair not in pairs_end_dates:
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pairs_end_dates[pair] = []
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# model_path_file = Path(model_dir / model_file_name)
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# if model_path_file.is_file():
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# if pair not in pairs_end_dates:
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# pairs_end_dates[pair] = []
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pairs_end_dates[pair].append({
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"model_end_date": model_end_date,
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"model_path_file": model_path_file,
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"model_dir": model_dir
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})
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# pairs_end_dates[pair].append({
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# "model_end_date": model_end_date,
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# "model_path_file": model_path_file,
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# "model_dir": model_dir
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# })
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if model_end_date not in all_models_end_dates:
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all_models_end_dates.append(model_end_date)
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# if model_end_date not in all_models_end_dates:
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# all_models_end_dates.append(model_end_date)
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finish_timestamp = int(datetime.now(tz=timezone.utc).timestamp())
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if len(all_models_end_dates) > 1:
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# After last model end date, use the same period from previous model
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# to finish the backtest
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all_models_end_dates.sort(reverse=True)
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finish_timestamp = all_models_end_dates[0] + \
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(all_models_end_dates[0] - all_models_end_dates[1])
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# if len(all_models_end_dates) == 0:
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# raise OperationalException(
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# 'At least 1 saved model is required to '
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# 'run backtesting with the backtest_live_models option'
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# )
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all_models_end_dates.append(finish_timestamp)
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all_models_end_dates.sort()
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start = datetime.fromtimestamp(min(all_models_end_dates), tz=timezone.utc)
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stop = datetime.fromtimestamp(max(all_models_end_dates), tz=timezone.utc)
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backtesting_string_timerange = f"{start.strftime('%Y%m%d')}-{stop.strftime('%Y%m%d')}"
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backtesting_timerange = TimeRange('date', 'date', min(all_models_end_dates),
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max(all_models_end_dates))
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return backtesting_timerange, backtesting_string_timerange, pairs_end_dates
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# if len(all_models_end_dates) == 1:
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# logger.warning(f"Only 1 model was found. Backtesting will run with the "
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# "timerange from the end of the training date to the current date")
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# finish_timestamp = int(datetime.now(tz=timezone.utc).timestamp())
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# if len(all_models_end_dates) > 1:
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# # After last model end date, use the same period from previous model
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# # to finish the backtest
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# all_models_end_dates.sort(reverse=True)
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# finish_timestamp = all_models_end_dates[0] + \
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# (all_models_end_dates[0] - all_models_end_dates[1])
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# all_models_end_dates.append(finish_timestamp)
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# all_models_end_dates.sort()
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# start = datetime.fromtimestamp(min(all_models_end_dates), tz=timezone.utc)
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# stop = datetime.fromtimestamp(max(all_models_end_dates), tz=timezone.utc)
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# backtesting_string_timerange = f"{start.strftime('%Y%m%d')}-{stop.strftime('%Y%m%d')}"
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# backtesting_timerange = TimeRange('date', 'date', min(all_models_end_dates),
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# max(all_models_end_dates))
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# return backtesting_timerange, backtesting_string_timerange, pairs_end_dates
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@ -1,783 +0,0 @@
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import logging
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import shutil
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import threading
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import time
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from abc import ABC, abstractmethod
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from collections import deque
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from datetime import datetime, timezone
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from pathlib import Path
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from threading import Lock
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from typing import Any, Dict, List, Tuple
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import numpy as np
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import pandas as pd
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from numpy.typing import NDArray
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
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from freqtrade.enums import RunMode
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import timeframe_to_seconds
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from freqtrade.freqai.data_drawer import FreqaiDataDrawer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.utils import plot_feature_importance
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from freqtrade.strategy.interface import IStrategy
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pd.options.mode.chained_assignment = None
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logger = logging.getLogger(__name__)
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class IFreqaiModel(ABC):
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"""
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Class containing all tools for training and prediction in the strategy.
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Base*PredictionModels inherit from this class.
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Record of contribution:
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FreqAI was developed by a group of individuals who all contributed specific skillsets to the
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project.
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Conception and software development:
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Robert Caulk @robcaulk
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Theoretical brainstorming:
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Elin Törnquist @th0rntwig
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Code review, software architecture brainstorming:
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@xmatthias
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Beta testing and bug reporting:
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@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
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Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
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"""
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def __init__(self, config: Config) -> None:
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self.config = config
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self.assert_config(self.config)
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self.freqai_info: Dict[str, Any] = config["freqai"]
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self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
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"data_split_parameters", {})
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self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
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"model_training_parameters", {})
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self.retrain = False
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self.first = True
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self.set_full_path()
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self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
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self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
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if self.save_backtest_models:
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logger.info('Backtesting module configured to save all models.')
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
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self.scanning = False
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self.ft_params = self.freqai_info["feature_parameters"]
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self.keras: bool = self.freqai_info.get("keras", False)
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if self.keras and self.ft_params.get("DI_threshold", 0):
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self.ft_params["DI_threshold"] = 0
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logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
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self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
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if self.ft_params.get("inlier_metric_window", 0):
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self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
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self.pair_it = 0
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self.pair_it_train = 0
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self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
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self.train_queue = self._set_train_queue()
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self.last_trade_database_summary: DataFrame = {}
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self.current_trade_database_summary: DataFrame = {}
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self.analysis_lock = Lock()
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self.inference_time: float = 0
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self.train_time: float = 0
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self.begin_time: float = 0
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self.begin_time_train: float = 0
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self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
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self.continual_learning = self.freqai_info.get('continual_learning', False)
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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def __getstate__(self):
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"""
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Return an empty state to be pickled in hyperopt
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"""
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return ({})
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def assert_config(self, config: Config) -> None:
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if not config.get("freqai", {}):
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raise OperationalException("No freqai parameters found in configuration file.")
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def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
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"""
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Entry point to the FreqaiModel from a specific pair, it will train a new model if
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necessary before making the prediction.
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:param dataframe: Full dataframe coming from strategy - it contains entire
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backtesting timerange + additional historical data necessary to train
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the model.
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:param metadata: pair metadata coming from strategy.
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:param strategy: Strategy to train on
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"""
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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self.dd.set_pair_dict_info(metadata)
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if self.live:
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self.inference_timer('start')
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self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
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dk = self.start_live(dataframe, metadata, strategy, self.dk)
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# For backtesting, each pair enters and then gets trained for each window along the
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# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
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# (backtest window, i.e. window immediately following the training window).
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# FreqAI slides the window and sequentially builds the backtesting results before returning
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# the concatenated results for the full backtesting period back to the strategy.
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elif not self.follow_mode:
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self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
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if(self.dk.backtest_live_models):
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logger.info(
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f"Backtesting {len(self.dk.backtesting_timeranges)} timeranges (Live Models)")
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else:
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logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
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dataframe = self.dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
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)
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dk = self.start_backtesting(dataframe, metadata, self.dk)
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# else:
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# dk = self.start_backtesting_live_models(dataframe, metadata, self.dk)
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dataframe = dk.remove_features_from_df(dk.return_dataframe)
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self.clean_up()
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if self.live:
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self.inference_timer('stop')
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return dataframe
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def clean_up(self):
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"""
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Objects that should be handled by GC already between coins, but
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are explicitly shown here to help demonstrate the non-persistence of these
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objects.
|
||||
"""
|
||||
self.model = None
|
||||
self.dk = None
|
||||
|
||||
def shutdown(self):
|
||||
"""
|
||||
Cleans up threads on Shutdown, set stop event. Join threads to wait
|
||||
for current training iteration.
|
||||
"""
|
||||
logger.info("Stopping FreqAI")
|
||||
self._stop_event.set()
|
||||
|
||||
logger.info("Waiting on Training iteration")
|
||||
for _thread in self._threads:
|
||||
_thread.join()
|
||||
|
||||
def start_scanning(self, *args, **kwargs) -> None:
|
||||
"""
|
||||
Start `self._start_scanning` in a separate thread
|
||||
"""
|
||||
_thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
|
||||
self._threads.append(_thread)
|
||||
_thread.start()
|
||||
|
||||
def _start_scanning(self, strategy: IStrategy) -> None:
|
||||
"""
|
||||
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
|
||||
to improve model youth. This function is agnostic to data preparation/collection/storage,
|
||||
it simply trains on what ever data is available in the self.dd.
|
||||
:param strategy: IStrategy = The user defined strategy class
|
||||
"""
|
||||
while not self._stop_event.is_set():
|
||||
time.sleep(1)
|
||||
pair = self.train_queue[0]
|
||||
|
||||
# ensure pair is avaialble in dp
|
||||
if pair not in strategy.dp.current_whitelist():
|
||||
self.train_queue.popleft()
|
||||
logger.warning(f'{pair} not in current whitelist, removing from train queue.')
|
||||
continue
|
||||
|
||||
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
|
||||
|
||||
dk = FreqaiDataKitchen(self.config, self.live, pair)
|
||||
dk.set_paths(pair, trained_timestamp)
|
||||
(
|
||||
retrain,
|
||||
new_trained_timerange,
|
||||
data_load_timerange,
|
||||
) = dk.check_if_new_training_required(trained_timestamp)
|
||||
dk.set_paths(pair, new_trained_timerange.stopts)
|
||||
|
||||
if retrain:
|
||||
self.train_timer('start')
|
||||
try:
|
||||
self.extract_data_and_train_model(
|
||||
new_trained_timerange, pair, strategy, dk, data_load_timerange
|
||||
)
|
||||
except Exception as msg:
|
||||
logger.warning(f'Training {pair} raised exception {msg}, skipping.')
|
||||
|
||||
self.train_timer('stop')
|
||||
|
||||
# only rotate the queue after the first has been trained.
|
||||
self.train_queue.rotate(-1)
|
||||
|
||||
self.dd.save_historic_predictions_to_disk()
|
||||
|
||||
def start_backtesting(
|
||||
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
|
||||
) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for backtesting. For backtesting, each pair enters and then gets
|
||||
trained for each window along the sliding window defined by "train_period_days"
|
||||
(training window) and "backtest_period_days" (backtest window, i.e. window immediately
|
||||
following the training window). FreqAI slides the window and sequentially builds
|
||||
the backtesting results before returning the concatenated results for the full
|
||||
backtesting period back to the strategy.
|
||||
:param dataframe: DataFrame = strategy passed dataframe
|
||||
:param metadata: Dict = pair metadata
|
||||
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:return:
|
||||
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
"""
|
||||
|
||||
self.pair_it += 1
|
||||
train_it = 0
|
||||
# Loop enforcing the sliding window training/backtesting paradigm
|
||||
# tr_train is the training time range e.g. 1 historical month
|
||||
# tr_backtest is the backtesting time range e.g. the week directly
|
||||
# following tr_train. Both of these windows slide through the
|
||||
# entire backtest
|
||||
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
|
||||
pair = metadata["pair"]
|
||||
(_, _, _) = self.dd.get_pair_dict_info(pair)
|
||||
train_it += 1
|
||||
total_trains = len(dk.backtesting_timeranges)
|
||||
self.training_timerange = tr_train
|
||||
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
|
||||
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
|
||||
|
||||
trained_timestamp = tr_train
|
||||
tr_train_startts_str = datetime.fromtimestamp(
|
||||
tr_train.startts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
tr_train_stopts_str = datetime.fromtimestamp(
|
||||
tr_train.stopts,
|
||||
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
|
||||
if not dk.backtest_live_models:
|
||||
logger.info(
|
||||
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
|
||||
f" from {tr_train_startts_str}"
|
||||
f" to {tr_train_stopts_str}, {train_it}/{total_trains} "
|
||||
"trains"
|
||||
)
|
||||
|
||||
timestamp_model_id = int(trained_timestamp.stopts)
|
||||
if dk.backtest_live_models:
|
||||
timestamp_model_id = int(tr_backtest.startts)
|
||||
|
||||
dk.data_path = Path(
|
||||
dk.full_path / f"sub-train-{pair.split('/')[0]}_{timestamp_model_id}"
|
||||
)
|
||||
|
||||
dk.set_new_model_names(pair, timestamp_model_id)
|
||||
|
||||
if dk.check_if_backtest_prediction_exists():
|
||||
self.dd.load_metadata(dk)
|
||||
if not dk.backtest_live_models:
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
|
||||
append_df = dk.get_backtesting_prediction()
|
||||
dk.append_predictions(append_df)
|
||||
else:
|
||||
if not self.model_exists(dk):
|
||||
if dk.backtest_live_models:
|
||||
raise OperationalException(
|
||||
"Training models is not allowed "
|
||||
"in backtest_live_models backtesting "
|
||||
"mode"
|
||||
)
|
||||
dk.find_features(dataframe_train)
|
||||
self.model = self.train(dataframe_train, pair, dk)
|
||||
self.dd.pair_dict[pair]["trained_timestamp"] = int(
|
||||
trained_timestamp.stopts)
|
||||
|
||||
if self.save_backtest_models:
|
||||
logger.info('Saving backtest model to disk.')
|
||||
self.dd.save_data(self.model, pair, dk)
|
||||
else:
|
||||
self.model = self.dd.load_data(pair, dk)
|
||||
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
|
||||
pred_df, do_preds = self.predict(dataframe_backtest, dk)
|
||||
append_df = dk.get_predictions_to_append(pred_df, do_preds)
|
||||
dk.append_predictions(append_df)
|
||||
dk.save_backtesting_prediction(append_df)
|
||||
|
||||
dk.fill_predictions(dataframe)
|
||||
return dk
|
||||
|
||||
def start_live(
|
||||
self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
|
||||
) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for dry/live. This function will check if a retraining should be
|
||||
performed, and if so, retrain and reset the model.
|
||||
:param dataframe: DataFrame = strategy passed dataframe
|
||||
:param metadata: Dict = pair metadata
|
||||
:param strategy: IStrategy = currently employed strategy
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:returns:
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
"""
|
||||
|
||||
# update follower
|
||||
if self.follow_mode:
|
||||
self.dd.update_follower_metadata()
|
||||
|
||||
# get the model metadata associated with the current pair
|
||||
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
|
||||
|
||||
# if the metadata doesn't exist, the follower returns null arrays to strategy
|
||||
if self.follow_mode and return_null_array:
|
||||
logger.info("Returning null array from follower to strategy")
|
||||
self.dd.return_null_values_to_strategy(dataframe, dk)
|
||||
return dk
|
||||
|
||||
# append the historic data once per round
|
||||
if self.dd.historic_data:
|
||||
self.dd.update_historic_data(strategy, dk)
|
||||
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
|
||||
|
||||
if not self.follow_mode:
|
||||
|
||||
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
|
||||
trained_timestamp
|
||||
)
|
||||
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
|
||||
|
||||
# load candle history into memory if it is not yet.
|
||||
if not self.dd.historic_data:
|
||||
self.dd.load_all_pair_histories(data_load_timerange, dk)
|
||||
|
||||
if not self.scanning:
|
||||
self.scanning = True
|
||||
self.start_scanning(strategy)
|
||||
|
||||
elif self.follow_mode:
|
||||
dk.set_paths(metadata["pair"], trained_timestamp)
|
||||
logger.info(
|
||||
"FreqAI instance set to follow_mode, finding existing pair "
|
||||
f"using { self.identifier }"
|
||||
)
|
||||
|
||||
# load the model and associated data into the data kitchen
|
||||
self.model = self.dd.load_data(metadata["pair"], dk)
|
||||
|
||||
with self.analysis_lock:
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
)
|
||||
|
||||
if not self.model:
|
||||
logger.warning(
|
||||
f"No model ready for {metadata['pair']}, returning null values to strategy."
|
||||
)
|
||||
self.dd.return_null_values_to_strategy(dataframe, dk)
|
||||
return dk
|
||||
|
||||
# ensure user is feeding the correct indicators to the model
|
||||
self.check_if_feature_list_matches_strategy(dataframe, dk)
|
||||
|
||||
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
|
||||
|
||||
return dk
|
||||
|
||||
def build_strategy_return_arrays(
|
||||
self, dataframe: DataFrame, dk: FreqaiDataKitchen, pair: str, trained_timestamp: int
|
||||
) -> None:
|
||||
|
||||
# hold the historical predictions in memory so we are sending back
|
||||
# correct array to strategy
|
||||
|
||||
if pair not in self.dd.model_return_values:
|
||||
# first predictions are made on entire historical candle set coming from strategy. This
|
||||
# allows FreqUI to show full return values.
|
||||
pred_df, do_preds = self.predict(dataframe, dk)
|
||||
if pair not in self.dd.historic_predictions:
|
||||
self.set_initial_historic_predictions(pred_df, dk, pair)
|
||||
self.dd.set_initial_return_values(pair, pred_df)
|
||||
|
||||
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
||||
return
|
||||
elif self.dk.check_if_model_expired(trained_timestamp):
|
||||
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
|
||||
do_preds = np.ones(2, dtype=np.int_) * 2
|
||||
dk.DI_values = np.zeros(2)
|
||||
logger.warning(
|
||||
f"Model expired for {pair}, returning null values to strategy. Strategy "
|
||||
"construction should take care to consider this event with "
|
||||
"prediction == 0 and do_predict == 2"
|
||||
)
|
||||
else:
|
||||
# remaining predictions are made only on the most recent candles for performance and
|
||||
# historical accuracy reasons.
|
||||
pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
|
||||
|
||||
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
|
||||
self.fit_live_predictions(dk, pair)
|
||||
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
|
||||
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
||||
|
||||
return
|
||||
|
||||
def check_if_feature_list_matches_strategy(
|
||||
self, dataframe: DataFrame, dk: FreqaiDataKitchen
|
||||
) -> None:
|
||||
"""
|
||||
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
|
||||
to a folder holding existing models.
|
||||
:param dataframe: DataFrame = strategy provided dataframe
|
||||
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
|
||||
current coin/bot loop
|
||||
"""
|
||||
dk.find_features(dataframe)
|
||||
if "training_features_list_raw" in dk.data:
|
||||
feature_list = dk.data["training_features_list_raw"]
|
||||
else:
|
||||
feature_list = dk.data['training_features_list']
|
||||
if dk.training_features_list != feature_list:
|
||||
raise OperationalException(
|
||||
"Trying to access pretrained model with `identifier` "
|
||||
"but found different features furnished by current strategy."
|
||||
"Change `identifier` to train from scratch, or ensure the"
|
||||
"strategy is furnishing the same features as the pretrained"
|
||||
"model. In case of --strategy-list, please be aware that FreqAI "
|
||||
"requires all strategies to maintain identical "
|
||||
"populate_any_indicator() functions"
|
||||
)
|
||||
|
||||
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Base data cleaning method for train.
|
||||
Functions here improve/modify the input data by identifying outliers,
|
||||
computing additional metrics, adding noise, reducing dimensionality etc.
|
||||
"""
|
||||
|
||||
ft_params = self.freqai_info["feature_parameters"]
|
||||
|
||||
if ft_params.get('inlier_metric_window', 0):
|
||||
dk.compute_inlier_metric(set_='train')
|
||||
if self.freqai_info["data_split_parameters"]["test_size"] > 0:
|
||||
dk.compute_inlier_metric(set_='test')
|
||||
|
||||
if ft_params.get(
|
||||
"principal_component_analysis", False
|
||||
):
|
||||
dk.principal_component_analysis()
|
||||
|
||||
if ft_params.get("use_SVM_to_remove_outliers", False):
|
||||
dk.use_SVM_to_remove_outliers(predict=False)
|
||||
|
||||
if ft_params.get("DI_threshold", 0):
|
||||
dk.data["avg_mean_dist"] = dk.compute_distances()
|
||||
|
||||
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
|
||||
if dk.pair in self.dd.old_DBSCAN_eps:
|
||||
eps = self.dd.old_DBSCAN_eps[dk.pair]
|
||||
else:
|
||||
eps = None
|
||||
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
|
||||
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
|
||||
|
||||
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
|
||||
dk.add_noise_to_training_features()
|
||||
|
||||
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
|
||||
"""
|
||||
Base data cleaning method for predict.
|
||||
Functions here are complementary to the functions of data_cleaning_train.
|
||||
"""
|
||||
ft_params = self.freqai_info["feature_parameters"]
|
||||
|
||||
if ft_params.get('inlier_metric_window', 0):
|
||||
dk.compute_inlier_metric(set_='predict')
|
||||
|
||||
if ft_params.get(
|
||||
"principal_component_analysis", False
|
||||
):
|
||||
dk.pca_transform(self.dk.data_dictionary['prediction_features'])
|
||||
|
||||
if ft_params.get("use_SVM_to_remove_outliers", False):
|
||||
dk.use_SVM_to_remove_outliers(predict=True)
|
||||
|
||||
if ft_params.get("DI_threshold", 0):
|
||||
dk.check_if_pred_in_training_spaces()
|
||||
|
||||
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
|
||||
dk.use_DBSCAN_to_remove_outliers(predict=True)
|
||||
|
||||
def model_exists(
|
||||
self,
|
||||
dk: FreqaiDataKitchen,
|
||||
scanning: bool = False,
|
||||
) -> bool:
|
||||
"""
|
||||
Given a pair and path, check if a model already exists
|
||||
:param pair: pair e.g. BTC/USD
|
||||
:param path: path to model
|
||||
:return:
|
||||
:boolean: whether the model file exists or not.
|
||||
"""
|
||||
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
|
||||
file_exists = path_to_modelfile.is_file()
|
||||
if file_exists and not scanning:
|
||||
logger.info("Found model at %s", dk.data_path / dk.model_filename)
|
||||
elif not scanning:
|
||||
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
|
||||
return file_exists
|
||||
|
||||
def set_full_path(self) -> None:
|
||||
self.full_path = Path(
|
||||
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
|
||||
)
|
||||
self.full_path.mkdir(parents=True, exist_ok=True)
|
||||
shutil.copy(
|
||||
self.config["config_files"][0],
|
||||
Path(self.full_path, Path(self.config["config_files"][0]).name),
|
||||
)
|
||||
|
||||
def extract_data_and_train_model(
|
||||
self,
|
||||
new_trained_timerange: TimeRange,
|
||||
pair: str,
|
||||
strategy: IStrategy,
|
||||
dk: FreqaiDataKitchen,
|
||||
data_load_timerange: TimeRange,
|
||||
):
|
||||
"""
|
||||
Retrieve data and train model.
|
||||
:param new_trained_timerange: TimeRange = the timerange to train the model on
|
||||
:param metadata: dict = strategy provided metadata
|
||||
:param strategy: IStrategy = user defined strategy object
|
||||
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
||||
:param data_load_timerange: TimeRange = the amount of data to be loaded
|
||||
for populate_any_indicators
|
||||
(larger than new_trained_timerange so that
|
||||
new_trained_timerange does not contain any NaNs)
|
||||
"""
|
||||
|
||||
corr_dataframes, base_dataframes = self.dd.get_base_and_corr_dataframes(
|
||||
data_load_timerange, pair, dk
|
||||
)
|
||||
|
||||
with self.analysis_lock:
|
||||
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
|
||||
strategy, corr_dataframes, base_dataframes, pair
|
||||
)
|
||||
|
||||
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
|
||||
|
||||
# find the features indicated by strategy and store in datakitchen
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
|
||||
model = self.train(unfiltered_dataframe, pair, dk)
|
||||
|
||||
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
|
||||
dk.set_new_model_names(pair, int(new_trained_timerange.stopts))
|
||||
self.dd.save_data(model, pair, dk)
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
|
||||
plot_feature_importance(model, pair, dk)
|
||||
|
||||
if self.freqai_info.get("purge_old_models", False):
|
||||
self.dd.purge_old_models()
|
||||
|
||||
def set_initial_historic_predictions(
|
||||
self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str
|
||||
) -> None:
|
||||
"""
|
||||
This function is called only if the datadrawer failed to load an
|
||||
existing set of historic predictions. In this case, it builds
|
||||
the structure and sets fake predictions off the first training
|
||||
data. After that, FreqAI will append new real predictions to the
|
||||
set of historic predictions.
|
||||
|
||||
These values are used to generate live statistics which can be used
|
||||
in the strategy for adaptive values. E.g. &*_mean/std are quantities
|
||||
that can computed based on live predictions from the set of historical
|
||||
predictions. Those values can be used in the user strategy to better
|
||||
assess prediction rarity, and thus wait for probabilistically favorable
|
||||
entries relative to the live historical predictions.
|
||||
|
||||
If the user reuses an identifier on a subsequent instance,
|
||||
this function will not be called. In that case, "real" predictions
|
||||
will be appended to the loaded set of historic predictions.
|
||||
:param: df: DataFrame = the dataframe containing the training feature data
|
||||
:param: model: Any = A model which was `fit` using a common library such as
|
||||
catboost or lightgbm
|
||||
:param: dk: FreqaiDataKitchen = object containing methods for data analysis
|
||||
:param: pair: str = current pair
|
||||
"""
|
||||
|
||||
self.dd.historic_predictions[pair] = pred_df
|
||||
hist_preds_df = self.dd.historic_predictions[pair]
|
||||
|
||||
for label in hist_preds_df.columns:
|
||||
if hist_preds_df[label].dtype == object:
|
||||
continue
|
||||
hist_preds_df[f'{label}_mean'] = 0
|
||||
hist_preds_df[f'{label}_std'] = 0
|
||||
|
||||
hist_preds_df['do_predict'] = 0
|
||||
|
||||
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
|
||||
hist_preds_df['DI_values'] = 0
|
||||
|
||||
for return_str in dk.data['extra_returns_per_train']:
|
||||
hist_preds_df[return_str] = 0
|
||||
|
||||
# # for keras type models, the conv_window needs to be prepended so
|
||||
# # viewing is correct in frequi
|
||||
if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
|
||||
n_lost_points = self.freqai_info.get('conv_width', 2)
|
||||
zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
|
||||
columns=hist_preds_df.columns)
|
||||
self.dd.historic_predictions[pair] = pd.concat(
|
||||
[zeros_df, hist_preds_df], axis=0, ignore_index=True)
|
||||
|
||||
def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
|
||||
"""
|
||||
Fit the labels with a gaussian distribution
|
||||
"""
|
||||
import scipy as spy
|
||||
|
||||
# add classes from classifier label types if used
|
||||
full_labels = dk.label_list + dk.unique_class_list
|
||||
|
||||
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
|
||||
dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
|
||||
for label in full_labels:
|
||||
if self.dd.historic_predictions[dk.pair][label].dtype == object:
|
||||
continue
|
||||
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
|
||||
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
|
||||
|
||||
return
|
||||
|
||||
def inference_timer(self, do='start'):
|
||||
"""
|
||||
Timer designed to track the cumulative time spent in FreqAI for one pass through
|
||||
the whitelist. This will check if the time spent is more than 1/4 the time
|
||||
of a single candle, and if so, it will warn the user of degraded performance
|
||||
"""
|
||||
if do == 'start':
|
||||
self.pair_it += 1
|
||||
self.begin_time = time.time()
|
||||
elif do == 'stop':
|
||||
end = time.time()
|
||||
self.inference_time += (end - self.begin_time)
|
||||
if self.pair_it == self.total_pairs:
|
||||
logger.info(
|
||||
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
|
||||
if self.inference_time > 0.25 * self.base_tf_seconds:
|
||||
logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
|
||||
" avoid blinding open trades and degrading performance.")
|
||||
self.pair_it = 0
|
||||
self.inference_time = 0
|
||||
return
|
||||
|
||||
def train_timer(self, do='start'):
|
||||
"""
|
||||
Timer designed to track the cumulative time spent training the full pairlist in
|
||||
FreqAI.
|
||||
"""
|
||||
if do == 'start':
|
||||
self.pair_it_train += 1
|
||||
self.begin_time_train = time.time()
|
||||
elif do == 'stop':
|
||||
end = time.time()
|
||||
self.train_time += (end - self.begin_time_train)
|
||||
if self.pair_it_train == self.total_pairs:
|
||||
logger.info(
|
||||
f'Total time spent training pairlist {self.train_time:.2f} seconds')
|
||||
self.pair_it_train = 0
|
||||
self.train_time = 0
|
||||
return
|
||||
|
||||
def get_init_model(self, pair: str) -> Any:
|
||||
if pair not in self.dd.model_dictionary or not self.continual_learning:
|
||||
init_model = None
|
||||
else:
|
||||
init_model = self.dd.model_dictionary[pair]
|
||||
|
||||
return init_model
|
||||
|
||||
def _set_train_queue(self):
|
||||
"""
|
||||
Sets train queue from existing train timestamps if they exist
|
||||
otherwise it sets the train queue based on the provided whitelist.
|
||||
"""
|
||||
current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
|
||||
if not self.dd.pair_dict:
|
||||
logger.info('Set fresh train queue from whitelist. '
|
||||
f'Queue: {current_pairlist}')
|
||||
return deque(current_pairlist)
|
||||
|
||||
best_queue = deque()
|
||||
|
||||
pair_dict_sorted = sorted(self.dd.pair_dict.items(),
|
||||
key=lambda k: k[1]['trained_timestamp'])
|
||||
for pair in pair_dict_sorted:
|
||||
if pair[0] in current_pairlist:
|
||||
best_queue.append(pair[0])
|
||||
for pair in current_pairlist:
|
||||
if pair not in best_queue:
|
||||
best_queue.appendleft(pair)
|
||||
|
||||
logger.info('Set existing queue from trained timestamps. '
|
||||
f'Best approximation queue: {best_queue}')
|
||||
return best_queue
|
||||
|
||||
# Following methods which are overridden by user made prediction models.
|
||||
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
|
||||
|
||||
@abstractmethod
|
||||
def train(self, unfiltered_df: DataFrame, pair: str,
|
||||
dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahandler
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:param unfiltered_df: Full dataframe for the current training period
|
||||
:param metadata: pair metadata from strategy.
|
||||
:return: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
Most regressors use the same function names and arguments e.g. user
|
||||
can drop in LGBMRegressor in place of CatBoostRegressor and all data
|
||||
management will be properly handled by Freqai.
|
||||
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
return
|
||||
|
||||
@abstractmethod
|
||||
def predict(
|
||||
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
|
||||
) -> Tuple[DataFrame, NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param unfiltered_df: Full dataframe for the current backtest period.
|
||||
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:param first: boolean = whether this is the first prediction or not.
|
||||
:return:
|
||||
:predictions: np.array of predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
|
||||
"""
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def get_full_model_path(config: Config) -> Path:
|
||||
freqai_config: Dict[str, Any] = config["freqai"]
|
||||
return Path(
|
||||
config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))
|
||||
)
|
||||
|
||||
|
||||
def get_timerange_from_ready_models(models_path: Path):
|
||||
all_models_end_dates = []
|
||||
pairs_end_dates: Dict[str, Any] = {}
|
||||
for model_dir in models_path.iterdir():
|
||||
if str(model_dir.name).startswith("sub-train"):
|
||||
model_end_date = int(model_dir.name.split("_")[1])
|
||||
pair = model_dir.name.split("_")[0].replace("sub-train-", "")
|
||||
model_file_name = (
|
||||
f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}"
|
||||
"_model.joblib"
|
||||
)
|
||||
|
||||
model_path_file = Path(model_dir / model_file_name)
|
||||
if model_path_file.is_file():
|
||||
if pair not in pairs_end_dates:
|
||||
pairs_end_dates[pair] = []
|
||||
|
||||
pairs_end_dates[pair].append({
|
||||
"model_end_date": model_end_date,
|
||||
"model_path_file": model_path_file,
|
||||
"model_dir": model_dir
|
||||
})
|
||||
|
||||
if model_end_date not in all_models_end_dates:
|
||||
all_models_end_dates.append(model_end_date)
|
||||
|
||||
if len(all_models_end_dates) == 0:
|
||||
raise OperationalException(
|
||||
'At least 1 saved model is required to '
|
||||
'run backtesting with the backtest_live_models option'
|
||||
)
|
||||
|
||||
if len(all_models_end_dates) == 1:
|
||||
logger.warning(
|
||||
"Only 1 model was found. Backtesting will run with the "
|
||||
"timerange from the end of the training date to the current date"
|
||||
)
|
||||
|
||||
finish_timestamp = int(datetime.now(tz=timezone.utc).timestamp())
|
||||
if len(all_models_end_dates) > 1:
|
||||
# After last model end date, use the same period from previous model
|
||||
# to finish the backtest
|
||||
all_models_end_dates.sort(reverse=True)
|
||||
finish_timestamp = all_models_end_dates[0] + \
|
||||
(all_models_end_dates[0] - all_models_end_dates[1])
|
||||
|
||||
all_models_end_dates.append(finish_timestamp)
|
||||
all_models_end_dates.sort()
|
||||
start = datetime.fromtimestamp(min(all_models_end_dates), tz=timezone.utc)
|
||||
stop = datetime.fromtimestamp(max(all_models_end_dates), tz=timezone.utc)
|
||||
backtesting_string_timerange = f"{start.strftime('%Y%m%d')}-{stop.strftime('%Y%m%d')}"
|
||||
backtesting_timerange = TimeRange(
|
||||
'date', 'date', min(all_models_end_dates), max(all_models_end_dates)
|
||||
)
|
||||
return backtesting_timerange, backtesting_string_timerange, pairs_end_dates
|
||||
|
|
@ -25,6 +25,7 @@ from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType
|
|||
from freqtrade.exceptions import DependencyException, OperationalException
|
||||
from freqtrade.exchange import (amount_to_contract_precision, price_to_precision,
|
||||
timeframe_to_minutes, timeframe_to_seconds)
|
||||
from freqtrade.freqai import freqai_util
|
||||
from freqtrade.mixins import LoggingMixin
|
||||
from freqtrade.optimize.backtest_caching import get_strategy_run_id
|
||||
from freqtrade.optimize.bt_progress import BTProgress
|
||||
|
|
@ -134,6 +135,12 @@ class Backtesting:
|
|||
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
|
||||
self.precision_mode = self.exchange.precisionMode
|
||||
|
||||
if self.config.get('freqai_backtest_live_models', False):
|
||||
freqai_model_path = freqai_util.get_full_model_path(self.config)
|
||||
_, live_models_timerange, _ = freqai_util.get_timerange_from_ready_models(
|
||||
freqai_model_path)
|
||||
self.config['timerange'] = live_models_timerange
|
||||
|
||||
self.timerange = TimeRange.parse_timerange(
|
||||
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue