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
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
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.
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
Please keep the arguments
**kwargs in the interface to allow us to extend this interface later.