"""
IHyperOptLoss interface
This module defines the interface for the loss-function for hyperopts
"""

from datetime import datetime

from pandas import DataFrame
import numpy as np

from freqtrade.optimize.hyperopt import IHyperOptLoss


class SharpeHyperOptLoss(IHyperOptLoss):
    """
    Defines the a 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
        Using sharpe ratio calculation
        """
        total_profit = results.profit_percent
        days_period = (max_date - min_date).days

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

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

        # print(expected_yearly_return, np.std(total_profit), sharp_ratio)
        return -sharp_ratio