stable/docs/advanced-hyperopt.md

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.

Derived hyperopt classes

Custom hyperopt classes can be derived in the same way it can be done for strategies.

Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:

class MyAwesomeHyperOpt(IHyperOpt):
    ...
    # Uses default stoploss dimension

class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
    @staticmethod
    def stoploss_space() -> List[Dimension]:
        # Override boundaries for stoploss
        return [
            Real(-0.33, -0.01, name='stoploss'),
        ]

and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:

$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...

Sharing methods with your strategy

Hyperopt classes provide access to the Strategy via the strategy class attribute. This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.

from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import freqtrade.vendor.qtpylib.indicators as qtpylib

class MyAwesomeStrategy(IStrategy):

    buy_params = {
        'rsi-value': 30,
        'adx-value': 35,
    }

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return self.buy_strategy_generator(self.buy_params, dataframe, metadata)

    @staticmethod
    def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['rsi'], params['rsi-value']) &
                dataframe['adx'] > params['adx-value']) &
                dataframe['volume'] > 0
            )
            , 'buy'] = 1
        return dataframe

class MyAwesomeHyperOpt(IHyperOpt):
    ...
    @staticmethod
    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
        """
        Define the buy strategy parameters to be used by Hyperopt.
        """
        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
            # Call strategy's buy strategy generator
            return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)

        return populate_buy_trend

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 datetime import datetime
from typing import Dict

from pandas import DataFrame

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,
                               config: Dict, processed: Dict[str, DataFrame],
                               *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_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

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_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs
• trade_count: Amount of trades (identical to len(results))
• min_date: Start date of the timerange used
• min_date: End date of the timerange used
• config: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
• processed: Dict of Dataframes with the pair as keys containing the data used for backtesting.

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.