50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
"""
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SortinoHyperOptLoss
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This module defines the alternative HyperOptLoss class which can be used for
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Hyperoptimization.
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"""
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from datetime import datetime
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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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class SortinoHyperOptLoss(IHyperOptLoss):
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"""
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Defines the loss function for hyperopt.
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This implementation uses the Sortino Ratio calculation.
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"""
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@staticmethod
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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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*args, **kwargs) -> float:
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"""
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Objective function, returns smaller number for more optimal results.
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Uses Sortino Ratio calculation.
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"""
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total_profit = results["profit_ratio"]
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days_period = (max_date - min_date).days
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# adding slippage of 0.1% per trade
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total_profit = total_profit - 0.0005
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expected_returns_mean = total_profit.sum() / days_period
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results['downside_returns'] = 0
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results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
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down_stdev = np.std(results['downside_returns'])
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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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sortino_ratio = -20.
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# print(expected_returns_mean, down_stdev, sortino_ratio)
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return -sortino_ratio
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