Advanced Hyperopt

This page explains some advanced Hyperopt topics that may require higher coding skills and Python knowledge than creation of an ordinal hyperoptimization class.

Creating and using a custom loss function

To use a custom loss function class, make sure that the function hyperopt_loss_function is defined in your custom hyperopt loss class. For the sample below, you then need to add the command line parameter --hyperopt-loss SuperDuperHyperOptLoss to your hyperopt call so this function is being used.

A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in userdata/hyperopts.

from freqtrade.optimize.hyperopt import IHyperOptLoss

TARGET_TRADES = 600
EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300

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

    @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 better results
        This is the legacy algorithm (used until now in freqtrade).
        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

Currently, the arguments are:

• results: DataFrame containing the result
  The following columns are available in results (corresponds to the output-file of backtesting when used with --export trades):
  pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason
• trade_count: Amount of trades (identical to len(results))
• min_date: Start date of the hyperopting TimeFrame
• min_date: End date of the hyperopting TimeFrame

This function needs to return a floating point number (float). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.

Note

This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.

Note

Please keep the arguments *args and **kwargs in the interface to allow us to extend this interface later.