Minor improvements in data.history
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@@ -117,7 +117,7 @@ class Backtesting:
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fail_without_data=True,
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)
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min_date, max_date = history.get_timeframe(data)
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min_date, max_date = history.get_timerange(data)
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logger.info(
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'Loading data from %s up to %s (%s days)..',
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@@ -481,7 +481,7 @@ class Backtesting:
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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] = history.trim_dataframe(df, timerange)
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min_date, max_date = history.get_timeframe(preprocessed)
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min_date, max_date = history.get_timerange(preprocessed)
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logger.info(
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'Backtesting with data from %s up to %s (%s days)..',
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@@ -23,7 +23,7 @@ from joblib import (Parallel, cpu_count, delayed, dump, load,
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from pandas import DataFrame
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from freqtrade import OperationalException
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from freqtrade.data.history import get_timeframe, trim_dataframe
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from freqtrade.data.history import get_timerange, trim_dataframe
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from freqtrade.misc import plural, round_dict
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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@@ -369,7 +369,7 @@ class Hyperopt:
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processed = load(self.tickerdata_pickle)
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min_date, max_date = get_timeframe(processed)
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min_date, max_date = get_timerange(processed)
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backtesting_results = self.backtesting.backtest(
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{
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@@ -490,7 +490,7 @@ class Hyperopt:
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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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min_date, max_date = get_timeframe(data)
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min_date, max_date = get_timerange(data)
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logger.info(
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'Hyperopting with data from %s up to %s (%s days)..',
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