total loss depends on multi criterias
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@ -77,8 +77,8 @@ class CalmarHyperOptLoss(IHyperOptLoss):
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"""
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"""
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expected_trade_loss_penalty = 1 - norm.cdf(trade_count-NB_EXPECTED_TRADES,-NB_EXPECTED_TRADES,(NB_EXPECTED_TRADES*(2/3))*EXPECTED_TRADES_WEIGHT)
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expected_trade_loss_penalty = 1 - norm.cdf(trade_count-NB_EXPECTED_TRADES,-NB_EXPECTED_TRADES,(NB_EXPECTED_TRADES*(2/3))*EXPECTED_TRADES_WEIGHT)
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# feel free to add other criterias (e.g avg expected time duration)
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# want to see the loss -> shorturl.at/DHX34
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loss = (calmar_loss * CALMAR_LOSS_WEIGHT)
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loss = calmar_loss + expected_trade_loss_penalty
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# print('calmar_ratio {}'.format(calmar_ratio))
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# print('calmar_ratio {}'.format(calmar_ratio))
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