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 datetime import datetime
from typing import Any, Dict

from pandas import DataFrame

from freqtrade.optimize.hyperopt import IHyperOptLoss

EXPECTED_MAX_PROFIT = 3.0

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],
backtest_stats: Dict[str, Any],
*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_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 resulting trades. 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, exit_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.
• backtest_stats: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in generate_strategy_stats() in optimize_reports.py.

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 epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.

*args and **kwargs

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

Overriding pre-defined spaces¶

To override a pre-defined space (roi_space, generate_roi_table, stoploss_space, trailing_space), define a nested class called Hyperopt and define the required spaces as follows:

class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
def stoploss_space():
return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]

# Define custom ROI space
def roi_space() -> List[Dimension]:
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]


Note

All overrides are optional and can be mixed/matched as necessary.

Dynamic parameters¶

Parameters can also be defined dynamically, but must be available to the instance once the * bot_start() callback has been called.

class MyAwesomeStrategy(IStrategy):

def bot_start(self, **kwargs) -> None:

# ...


Warning

Parameters created this way will not show up in the list-strategies parameter count.

Overriding Base estimator¶

You can define your own estimator for Hyperopt by implementing generate_estimator() in the Hyperopt subclass.

class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
return "RF"


Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the scikit-optimize documentation), or "an instance of a class that inherits from RegressorMixin (from sklearn) and where the predict method has an optional return_std argument, which returns std(Y | x) along with E[Y | x]".

Some research will be necessary to find additional Regressors.

Example for ExtraTreesRegressor ("ET") with additional parameters:

class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.learning import ExtraTreesRegressor
# Corresponds to "ET" - but allows additional parameters.
return ExtraTreesRegressor(n_estimators=100)


The dimensions parameter is the list of skopt.space.Dimension objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the skopt.learning.GaussianProcessRegressor estimator. Here's an example:

class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.utils import cook_estimator
from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
kernel_bounds = (0.0001, 10000)
kernel = (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
)
kernel += (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
)

return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)


Note

While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used. If you're unsure about this, best use one of the Defaults ("ET" has proven to be the most versatile) without further parameters.

Space options¶

For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:

• Categorical - Pick from a list of categories (e.g. Categorical(['a', 'b', 'c'], name="cat"))
• Integer - Pick from a range of whole numbers (e.g. Integer(1, 10, name='rsi'))
• SKDecimal - Pick from a range of decimal numbers with limited precision (e.g. SKDecimal(0.1, 0.5, decimals=3, name='adx')). Available only with freqtrade.
• Real - Pick from a range of decimal numbers with full precision (e.g. Real(0.1, 0.5, name='adx')

You can import all of these from freqtrade.optimize.space, although Categorical, Integer and Real are only aliases for their corresponding scikit-optimize Spaces. SKDecimal is provided by freqtrade for faster optimizations.

from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa


SKDecimal vs. Real

We recommend to use SKDecimal instead of the Real space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.

Assuming the definition of a rather small space (SKDecimal(0.10, 0.15, decimals=2, name='xxx')) - SKDecimal will have 5 possibilities ([0.10, 0.11, 0.12, 0.13, 0.14, 0.15]).

A corresponding real space Real(0.10, 0.15 name='xxx') on the other hand has an almost unlimited number of possibilities ([0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]).