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Simplify timerange handling

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This commit is contained in:
Matthias
2022-11-10 18:26:14 +01:00
parent 57313dd961
commit 0f9c5f8d41
6 changed files with 55 additions and 33 deletions
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23
freqtrade/freqai/freqai_interface.py
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@@ -13,7 +13,7 @@ from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
from freqtrade.constants import Config
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
@@ -788,14 +788,8 @@ class IFreqaiModel(ABC):
:return: if the data exists or not
"""
if self.config.get("freqai_backtest_live_models", False) and len(dataframe_backtest) == 0:
tr_backtest_startts_str = datetime.fromtimestamp(
tr_backtest.startts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
tr_backtest_stopts_str = datetime.fromtimestamp(
tr_backtest.stopts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
logger.info(f"No data found for pair {pair} from {tr_backtest_startts_str} "
f" from {tr_backtest_startts_str} to {tr_backtest_stopts_str}. "
logger.info(f"No data found for pair {pair} from "
f"from { tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "
"Probably more than one training within the same candle period.")
return False
return True
@@ -810,18 +804,11 @@ class IFreqaiModel(ABC):
:param pair: the current pair
:param total_trains: total trains (total number of slides for the sliding window)
"""
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 self.config.get("freqai_backtest_live_models", False):
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} "
f" from {tr_train.start_fmt} "
f"to {tr_train.stop_fmt}, {train_it}/{total_trains} "
"trains"
)
# Following methods which are overridden by user made prediction models.