Move validate_backtest_data and get_timeframe to histoyr

freqtrade/optimize/__init__.py

@@ -1,49 +1 @@
-# pragma pylint: disable=missing-docstring
-
-import logging
-from datetime import datetime
-from typing import Dict, Tuple
-import operator
-
-import arrow
-from pandas import DataFrame
-
 from freqtrade.optimize.default_hyperopt import DefaultHyperOpts  # noqa: F401
-
-logger = logging.getLogger(__name__)
-
-
-def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
-    """
-    Get the maximum timeframe for the given backtest data
-    :param data: dictionary with preprocessed backtesting data
-    :return: tuple containing min_date, max_date
-    """
-    timeframe = [
-        (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
-        for frame in data.values()
-    ]
-    return min(timeframe, key=operator.itemgetter(0))[0], \
-        max(timeframe, key=operator.itemgetter(1))[1]
-
-
-def validate_backtest_data(data: Dict[str, DataFrame], min_date: datetime,
-                           max_date: datetime, ticker_interval_mins: int) -> bool:
-    """
-    Validates preprocessed backtesting data for missing values and shows warnings about it that.
-
-    :param data: dictionary with preprocessed backtesting data
-    :param min_date: start-date of the data
-    :param max_date: end-date of the data
-    :param ticker_interval_mins: ticker interval in minutes
-    """
-    # total difference in minutes / interval-minutes
-    expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
-    found_missing = False
-    for pair, df in data.items():
-        dflen = len(df)
-        if dflen < expected_frames:
-            found_missing = True
-            logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
-                           pair, expected_frames, dflen, expected_frames - dflen)
-    return found_missing