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222 lines
11 KiB
Markdown
222 lines
11 KiB
Markdown
# Advanced Hyperopt
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This page explains some advanced Hyperopt topics that may require higher
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coding skills and Python knowledge than creation of an ordinal hyperoptimization
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class.
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## Creating and using a custom loss function
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To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
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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.
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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).
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``` python
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from datetime import datetime
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from typing import Any, Dict
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from pandas import DataFrame
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from freqtrade.constants import Config
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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TARGET_TRADES = 600
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EXPECTED_MAX_PROFIT = 3.0
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MAX_ACCEPTED_TRADE_DURATION = 300
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class SuperDuperHyperOptLoss(IHyperOptLoss):
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"""
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Defines the default loss function for hyperopt
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"""
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@staticmethod
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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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config: Config, processed: Dict[str, DataFrame],
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backtest_stats: Dict[str, Any],
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*args, **kwargs) -> float:
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"""
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Objective function, returns smaller number for better results
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This is the legacy algorithm (used until now in freqtrade).
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Weights are distributed as follows:
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* 0.4 to trade duration
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* 0.25: Avoiding trade loss
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* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
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"""
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total_profit = results['profit_ratio'].sum()
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trade_duration = results['trade_duration'].mean()
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trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
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profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
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duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
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result = trade_loss + profit_loss + duration_loss
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return result
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```
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Currently, the arguments are:
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* `results`: DataFrame containing the resulting trades.
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The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
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`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`
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* `trade_count`: Amount of trades (identical to `len(results)`)
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* `min_date`: Start date of the timerange used
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* `min_date`: End date of the timerange used
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* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
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* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
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* `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`.
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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.
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!!! Note
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This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
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!!! Note "`*args` and `**kwargs`"
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Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface in the future.
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## Overriding pre-defined spaces
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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:
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```python
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from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
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class MyAwesomeStrategy(IStrategy):
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class HyperOpt:
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# Define a custom stoploss space.
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def stoploss_space():
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return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]
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# Define custom ROI space
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def roi_space() -> List[Dimension]:
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return [
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Integer(10, 120, name='roi_t1'),
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Integer(10, 60, name='roi_t2'),
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Integer(10, 40, name='roi_t3'),
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SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
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SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
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SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
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]
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def generate_roi_table(params: Dict) -> Dict[int, float]:
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roi_table = {}
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roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
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roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
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roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
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return roi_table
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def trailing_space() -> List[Dimension]:
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# All parameters here are mandatory, you can only modify their type or the range.
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return [
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# Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
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Categorical([True], name='trailing_stop'),
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SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
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# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
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# so this intermediate parameter is used as the value of the difference between
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# them. The value of the 'trailing_stop_positive_offset' is constructed in the
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# generate_trailing_params() method.
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# This is similar to the hyperspace dimensions used for constructing the ROI tables.
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SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
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Categorical([True, False], name='trailing_only_offset_is_reached'),
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]
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```
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!!! Note
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All overrides are optional and can be mixed/matched as necessary.
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### Dynamic parameters
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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.
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``` python
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class MyAwesomeStrategy(IStrategy):
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def bot_start(self, **kwargs) -> None:
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self.buy_adx = IntParameter(20, 30, default=30, optimize=True)
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# ...
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```
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!!! Warning
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Parameters created this way will not show up in the `list-strategies` parameter count.
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### Overriding Base estimator
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You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
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```python
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class MyAwesomeStrategy(IStrategy):
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class HyperOpt:
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def generate_estimator(dimensions: List['Dimension'], **kwargs):
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return "RF"
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```
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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]`".
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Some research will be necessary to find additional Regressors.
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Example for `ExtraTreesRegressor` ("ET") with additional parameters:
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```python
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class MyAwesomeStrategy(IStrategy):
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class HyperOpt:
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def generate_estimator(dimensions: List['Dimension'], **kwargs):
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from skopt.learning import ExtraTreesRegressor
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# Corresponds to "ET" - but allows additional parameters.
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return ExtraTreesRegressor(n_estimators=100)
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```
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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:
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```python
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class MyAwesomeStrategy(IStrategy):
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class HyperOpt:
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def generate_estimator(dimensions: List['Dimension'], **kwargs):
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from skopt.utils import cook_estimator
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from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
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kernel_bounds = (0.0001, 10000)
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kernel = (
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ConstantKernel(1.0, kernel_bounds) *
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Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
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)
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kernel += (
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ConstantKernel(1.0, kernel_bounds) *
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Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
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)
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return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
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```
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!!! Note
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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.
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If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
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## Space options
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For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
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* `Categorical` - Pick from a list of categories (e.g. `Categorical(['a', 'b', 'c'], name="cat")`)
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* `Integer` - Pick from a range of whole numbers (e.g. `Integer(1, 10, name='rsi')`)
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* `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*.
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* `Real` - Pick from a range of decimal numbers with full precision (e.g. `Real(0.1, 0.5, name='adx')`
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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.
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``` python
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from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
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```
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!!! Hint "SKDecimal vs. Real"
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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.
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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]`).
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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]`).
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