64 lines
1.8 KiB
Python
64 lines
1.8 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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import logging
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import os
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from datetime import datetime
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import numpy as np
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from pandas import DataFrame, Timedelta
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from freqtrade.data.btanalysis import calculate_outstanding_balance
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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logger = logging.getLogger(__name__)
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interval = os.getenv("FQT_TIMEFRAME") or "5m"
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slippage = 0.0005
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target = 0
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annualize = np.sqrt(365 * (Timedelta("1D") / Timedelta(interval)))
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logger.info(f"SortinoLossBalance target is set to: {target}")
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class SortinoLossBalance(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(
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results: DataFrame,
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trade_count: int,
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min_date: datetime,
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max_date: datetime,
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*args,
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**kwargs,
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) -> 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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hloc = kwargs["processed"]
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timeframe = SortinoLossBalance.timeframe
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balance_total = calculate_outstanding_balance(results, timeframe, hloc)
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returns = balance_total.mean()
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# returns = balance_total.values.mean()
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downside_returns = np.where(balance_total < 0, balance_total, 0)
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downside_risk = np.sqrt((downside_returns ** 2).sum() / len(hloc))
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if downside_risk != 0.0:
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sortino_ratio = (returns - target) / downside_risk * annualize
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else:
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sortino_ratio = -np.iinfo(np.int32).max
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# print(expected_returns_mean, down_stdev, sortino_ratio)
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return -sortino_ratio
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