65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
"""
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CalmarHyperOptLoss
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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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from math import sqrt as msqrt
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from typing import Any, Dict
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from pandas import DataFrame
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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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class CalmarHyperOptLoss(IHyperOptLoss):
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"""
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Defines the loss function for hyperopt.
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This implementation uses the Calmar 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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config: Dict,
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processed: Dict[str, DataFrame],
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backtest_stats: Dict[str, Any],
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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 Calmar Ratio calculation.
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"""
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total_profit = backtest_stats["profit_total"]
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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 * 100
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# calculate max drawdown
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try:
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_, _, _, high_val, low_val = calculate_max_drawdown(
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results, value_col="profit_abs"
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)
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max_drawdown = (high_val - low_val) / high_val
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except ValueError:
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max_drawdown = 0
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if max_drawdown != 0 and trade_count > 2000:
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calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
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else:
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# Define high (negative) calmar ratio to be clear that this is NOT optimal.
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calmar_ratio = -20.0
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# print(expected_returns_mean, max_drawdown, calmar_ratio)
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return -calmar_ratio
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