freqtrade_origin/freqtrade/optimize/hyperopt_loss_calmar.py

"""
CalmarHyperOptLoss

This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime

import numpy as np
from freqtrade.data.btanalysis import calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss
from pandas import DataFrame


class CalmarHyperOptLoss(IHyperOptLoss):
    """
    Defines the loss function for hyperopt.

    This implementation uses the Calmar Ratio calculation.
    """

    @staticmethod
    def hyperopt_loss_function(results: DataFrame, trade_count: int,
                               min_date: datetime, max_date: datetime,
                               *args, **kwargs) -> float:
        """
        Objective function, returns smaller number for more optimal results.

        Uses Calmar Ratio calculation.
        """
        total_profit = results["profit_ratio"]
        days_period = (max_date - min_date).days

        # adding slippage of 0.1% per trade
        total_profit = total_profit - 0.0005
        expected_returns_mean = total_profit.sum() / days_period

        # calculate max drawdown
        try:
            _,_,_,high_val,low_val = calculate_max_drawdown(results)
            max_drawdown = (high_val - low_val) / high_val
        except ValueError:
            max_drawdown = 0

        if max_drawdown != 0 and trade_count > 1000:
            calmar_ratio = expected_returns_mean / max_drawdown * np.sqrt(365)
        else:
            calmar_ratio = -20.

        return -calmar_ratio