2020-02-04 01:02:57 +00:00
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"""
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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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2020-09-28 17:39:41 +00:00
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from pandas import DataFrame
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2020-02-04 01:02:57 +00:00
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2023-01-07 00:19:06 +00:00
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from freqtrade.constants import Config
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from freqtrade.data.metrics import calculate_sortino
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2023-01-07 00:50:05 +00:00
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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2020-02-04 01:02:57 +00:00
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2023-01-07 00:46:46 +00:00
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2020-02-04 01:02:57 +00:00
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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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2020-02-07 00:18:15 +00:00
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This implementation uses the Sortino Ratio calculation.
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2020-02-04 01:02:57 +00:00
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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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2023-01-07 00:19:06 +00:00
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config: Config, *args, **kwargs) -> float:
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2020-02-04 01:02:57 +00:00
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"""
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Objective function, returns smaller number for more optimal results.
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2020-02-07 00:18:15 +00:00
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Uses Sortino Ratio calculation.
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2020-02-04 01:02:57 +00:00
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"""
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2023-01-07 00:19:06 +00:00
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starting_balance = config['dry_run_wallet']
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sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
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2020-02-04 01:02:57 +00:00
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# print(expected_returns_mean, down_stdev, sortino_ratio)
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return -sortino_ratio
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