freqtrade_origin/freqtrade/optimize/default_hyperopt_loss.py

"""
ShortTradeDurHyperOptLoss
This module defines the default HyperoptLoss class which is being used for
Hyperoptimization.
"""
from math import exp

from pandas import DataFrame

from freqtrade.optimize.hyperopt import IHyperOptLoss


# Set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
TARGET_TRADES = 600

# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# expected max profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
EXPECTED_MAX_PROFIT = 3.0

# Max average trade duration in minutes.
# If eval ends with higher value, we consider it a failed eval.
MAX_ACCEPTED_TRADE_DURATION = 300


class ShortTradeDurHyperOptLoss(IHyperOptLoss):
    """
    Defines the default loss function for hyperopt
    """

    @staticmethod
    def hyperopt_loss_function(results: DataFrame, trade_count: int,
                               *args, **kwargs) -> float:
        """
        Objective function, returns smaller number for better results
        This is the Default algorithm
        Weights are distributed as follows:
        * 0.4 to trade duration
        * 0.25: Avoiding trade loss
        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
        """
        total_profit = results['profit_percent'].sum()
        trade_duration = results['trade_duration'].mean()

        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
        result = trade_loss + profit_loss + duration_loss
        return result


# Create an alias for This to allow the legacy Method to work as well.
DefaultHyperOptLoss = ShortTradeDurHyperOptLoss