# 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](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py). ``` python from datetime import datetime from typing import Any, Dict from pandas import DataFrame from freqtrade.constants import Config 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: Config, processed: Dict[str, DataFrame], backtest_stats: Dict[str, Any], **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, 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. !!! Note "`*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`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows: ```python from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal 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'), ] def generate_roi_table(params: Dict) -> Dict[int, float]: roi_table = {} roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2'] roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1'] roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0 return roi_table def trailing_space() -> List[Dimension]: # All parameters here are mandatory, you can only modify their type or the range. return [ # Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times. Categorical([True], name='trailing_stop'), SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'), # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive', # so this intermediate parameter is used as the value of the difference between # them. The value of the 'trailing_stop_positive_offset' is constructed in the # generate_trailing_params() method. # This is similar to the hyperspace dimensions used for constructing the ROI tables. SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'), Categorical([True, False], name='trailing_only_offset_is_reached'), ] # Define a custom max_open_trades space def max_open_trades_space(self) -> List[Dimension]: return [ Integer(-1, 10, name='max_open_trades'), ] ``` !!! 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](strategy-callbacks.md#bot-start) has been called. ``` python class MyAwesomeStrategy(IStrategy): def bot_start(self, **kwargs) -> None: self.buy_adx = IntParameter(20, 30, default=30, optimize=True) # ... ``` !!! 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. ```python 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](https://scikit-optimize.github.io/)), 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: ```python 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: ```python 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. ``` python from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa ``` !!! Hint "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]`).