Merge branch 'develop' into feat/short
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@@ -284,8 +284,8 @@ class Backtesting:
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{'pair': pair}
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).copy()
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# Trim startup period from analyzed dataframe
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df_analyzed = trim_dataframe(df_analyzed, self.timerange,
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startup_candles=self.required_startup)
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df_analyzed = processed[pair] = pair_data = trim_dataframe(
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df_analyzed, self.timerange, startup_candles=self.required_startup)
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# To avoid using data from future, we use buy/sell signals shifted
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# from the previous candle
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for col in headers[5:]:
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@@ -303,9 +303,6 @@ class Backtesting:
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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data[pair] = df_analyzed[headers].values.tolist()
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# Do not hold on to old data to reduce memory usage
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processed[pair] = pair_data = None
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return data
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def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
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@@ -648,7 +648,12 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
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headers.append('Drawdown')
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# Align drawdown string on the center two space separator.
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drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
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if 'max_drawdown_account' in strategy_results[0]:
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drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
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else:
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# Support for prior backtest results
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drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
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dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
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dd_pad_per = max([len(dd) for dd in drawdown])
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drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
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