I sorted imports with isort
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@ -7,10 +7,9 @@ Hyperoptimization.
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from datetime import datetime
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from datetime import datetime
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import numpy as np
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import numpy as np
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from pandas import DataFrame
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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from freqtrade.data.btanalysis import calculate_max_drawdown
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from freqtrade.data.btanalysis import calculate_max_drawdown
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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from pandas import DataFrame
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class CalmarHyperOptLoss(IHyperOptLoss):
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class CalmarHyperOptLoss(IHyperOptLoss):
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@ -38,15 +37,14 @@ class CalmarHyperOptLoss(IHyperOptLoss):
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# calculate max drawdown
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# calculate max drawdown
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try:
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try:
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_, _, _, high_val, low_val = calculate_max_drawdown(results)
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_,_,_,high_val,low_val = calculate_max_drawdown(results)
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max_drawdown = (high_val - low_val) / high_val
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max_drawdown = (high_val - low_val) / high_val
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except ValueError:
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except ValueError:
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max_drawdown = 0
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max_drawdown = 0
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if max_drawdown > 0:
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if max_drawdown != 0 and trade_count > 1000:
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calmar_ratio = expected_returns_mean / max_drawdown * np.sqrt(365)
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calmar_ratio = expected_returns_mean / max_drawdown * np.sqrt(365)
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else:
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else:
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calmar_ratio = -20.
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calmar_ratio = -20.
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# print(calmar_ratio)
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return -calmar_ratio
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return -calmar_ratio
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