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107 lines
4.0 KiB
Python
107 lines
4.0 KiB
Python
"""
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FreqAI generic functions
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"""
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import logging
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Any, Dict, Tuple
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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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logger = logging.getLogger(__name__)
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def get_full_model_path(config: Config) -> Path:
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"""
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Returns default FreqAI model path
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:param config: Configuration dictionary
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"""
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freqai_config: Dict[str, Any] = config["freqai"]
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return Path(
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config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))
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)
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def get_timerange_from_ready_models(models_path: Path) -> Tuple[TimeRange, str, Dict[str, Any]]:
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"""
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Returns timerange information based on a FreqAI model directory
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:param models_path: FreqAI model path
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:returns: a Tuple with (backtesting_timerange: Timerange calculated from directory,
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backtesting_string_timerange: str timerange calculated from
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directory (format example '20020822-20220830'), \
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pairs_end_dates: Dict with pair and model end training dates info)
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"""
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all_models_end_dates = []
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pairs_end_dates: Dict[str, Any] = {}
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if not models_path.is_dir():
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raise OperationalException(
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'Model folders not found. Saved models are required '
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'to run backtest with the freqai-backtest-live-models option'
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)
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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 = (
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f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}"
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"_model.joblib"
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)
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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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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 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 backtest with the freqai-backtest-live-models option'
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)
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if len(all_models_end_dates) == 1:
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logger.warning(
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"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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)
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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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end_date_string_timerange = stop
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if (
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finish_timestamp < int(datetime.now(tz=timezone.utc).timestamp()) and
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datetime.now(tz=timezone.utc).strftime('%Y%m%d') != stop.strftime('%Y%m%d')
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):
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# add 1 day to string timerange to ensure BT module will load all dataframe data
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end_date_string_timerange = stop + timedelta(days=1)
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backtesting_string_timerange = (
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f"{start.strftime('%Y%m%d')}-{end_date_string_timerange.strftime('%Y%m%d')}"
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)
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backtesting_timerange = TimeRange(
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'date', 'date', min(all_models_end_dates), max(all_models_end_dates)
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)
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return backtesting_timerange, backtesting_string_timerange, pairs_end_dates
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