freqtrade_origin/freqtrade/templates/sample_hyperopt_loss.py

from datetime import datetime
from math import exp

from pandas import DataFrame

from freqtrade.constants import Config
from freqtrade.optimize.hyperopt import IHyperOptLoss


# Define some constants:

# 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,
# self.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 SampleHyperOptLoss(IHyperOptLoss):
    """
    Defines the default loss function for hyperopt
    This is intended to give you some inspiration for your own loss function.

    The Function needs to return a number (float) - which becomes smaller for better backtest
    results.
    """

    @staticmethod
    def hyperopt_loss_function(
        results: DataFrame,
        trade_count: int,
        min_date: datetime,
        max_date: datetime,
        config: Config,
        processed: dict[str, DataFrame],
        *args,
        **kwargs,
    ) -> float:
        """
        Objective function, returns smaller number for better results
        """
        total_profit = results["profit_ratio"].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