Adjust handling of zero stdev in loss functions
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@ -36,7 +36,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
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expected_returns_mean = total_profit.sum() / days_period
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up_stdev = np.std(total_profit)
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if (np.std(total_profit) != 0.):
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if up_stdev != 0:
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sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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@ -51,7 +51,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
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expected_returns_mean = total_profit.mean()
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up_stdev = total_profit.std()
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if (up_stdev != 0.):
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if up_stdev != 0:
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sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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@ -39,7 +39,7 @@ class SortinoHyperOptLoss(IHyperOptLoss):
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results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
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down_stdev = np.std(results['downside_returns'])
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if np.std(total_profit) != 0.0:
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if down_stdev != 0:
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sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
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else:
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# Define high (negative) sortino ratio to be clear that this is NOT optimal.
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@ -59,7 +59,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
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# where P = sum_daily["profit_percent_after_slippage"]
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down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
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if (down_stdev != 0.):
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if down_stdev != 0:
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sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
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else:
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# Define high (negative) sortino ratio to be clear that this is NOT optimal.
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