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