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82 lines
2.3 KiB
Python
82 lines
2.3 KiB
Python
"""
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CalmarHyperOptLossDaily
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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, date_range
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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class CalmarHyperOptLossDaily(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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resample_freq = "1D"
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slippage_per_trade_ratio = 0.0005
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days_in_year = 365
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# create the index within the min_date and end max_date
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t_index = date_range(
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start=min_date, end=max_date, freq=resample_freq, normalize=True
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)
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# apply slippage per trade to profit_total
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results.loc[:, "profit_ratio_after_slippage"] = (
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results["profit_ratio"] - slippage_per_trade_ratio
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)
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sum_daily = (
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results.resample(resample_freq, on="close_date")
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.agg({"profit_ratio_after_slippage": sum})
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.reindex(t_index)
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.fillna(0)
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)
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total_profit = sum_daily["profit_ratio_after_slippage"]
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expected_returns_mean = total_profit.mean() * 100
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# calculate max drawdown
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try:
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high_val = total_profit.max()
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low_val = total_profit.min()
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max_drawdown = (high_val - low_val) / high_val
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except (ValueError, ZeroDivisionError):
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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(days_in_year)
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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(t_index, sum_daily, total_profit)
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# print(expected_returns_mean, max_drawdown, calmar_ratio)
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return -calmar_ratio
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