update calmar loss
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@ -11,7 +11,7 @@ from typing import Any, Dict
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from pandas import DataFrame
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from freqtrade.constants import Config
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from freqtrade.data.metrics import calculate_max_drawdown
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from freqtrade.data.metrics import calculate_calmar
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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@ -23,42 +23,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
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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: Config,
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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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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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config: Config, *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 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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_, _, _, _, _, max_drawdown = calculate_max_drawdown(
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results, value_col="profit_abs"
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)
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except ValueError:
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max_drawdown = 0
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if max_drawdown != 0:
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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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starting_balance = config['dry_run_wallet']
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calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
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# print(expected_returns_mean, max_drawdown, calmar_ratio)
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return -calmar_ratio
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