# 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 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],
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_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 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, 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.`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.

### 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():
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():
from skopt.learning import ExtraTreesRegressor
# Corresponds to "ET" - but allows additional parameters.
return ExtraTreesRegressor(n_estimators=100)
```

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]`

).