freqtrade_origin/freqtrade/optimize/hyperopt_loss_sharpe.py

"""
SharpeHyperOptLoss

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

import numpy as np
from pandas import DataFrame

from freqtrade.optimize.hyperopt import IHyperOptLoss


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

    This implementation uses the Sharpe 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 Sharpe 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
        up_stdev = np.std(total_profit)

        if up_stdev != 0:
            sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
        else:
            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
            sharp_ratio = -20.

        # print(expected_returns_mean, up_stdev, sharp_ratio)
        return -sharp_ratio