Fix exception when few pairs with no data do not result in aborting backtest.
Exception is triggered by backtesting 20210301-20210501 range with BAKE/USDT pair (binance). Pair data starts on 2021-04-30 12:00:00 and after adjusting for startup candles pair dataframe is empty. Solution: Since there are other pairs with enough data - skip pairs with no data and issue a warning. Exception: ``` Traceback (most recent call last): File "/home/rk/src/freqtrade/freqtrade/main.py", line 37, in main return_code = args['func'](args) File "/home/rk/src/freqtrade/freqtrade/commands/optimize_commands.py", line 53, in start_backtesting backtesting.start() File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 502, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 474, in backtest_one_strategy results = self.backtest( File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 365, in backtest data: Dict = self._get_ohlcv_as_lists(processed) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 199, in _get_ohlcv_as_lists pair_data.loc[:, 'buy'] = 0 # cleanup from previous run File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 692, in __setitem__ iloc._setitem_with_indexer(indexer, value, self.name) File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 1587, in _setitem_with_indexer raise ValueError( ValueError: cannot set a frame with no defined index and a scalar ```
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@ -9,7 +9,7 @@ from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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from typing import Any, Dict, List, Optional, Tuple
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from pandas import DataFrame, NaT
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
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from freqtrade.constants import DATETIME_PRINT_FORMAT
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@ -457,13 +457,21 @@ class Backtesting:
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preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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preprocessed[pair] = trim_dataframe(df, timerange,
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startup_candles=self.required_startup)
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min_date, max_date = history.get_timerange(preprocessed)
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if min_date is NaT or max_date is NaT:
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for pair in list(preprocessed):
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df = preprocessed[pair]
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df = trim_dataframe(df, timerange, startup_candles=self.required_startup)
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if len(df) > 0:
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preprocessed[pair] = df
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else:
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logger.warning(f'{pair} has no data left after adjusting for startup candles, '
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f'skipping.')
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del preprocessed[pair]
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if not preprocessed:
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raise OperationalException(
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"No data left after adjusting for startup candles.")
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min_date, max_date = history.get_timerange(preprocessed)
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logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(max_date - min_date).days} days).')
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