92 lines
4.0 KiB
Markdown
92 lines
4.0 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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## Derived hyperopt classes
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Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
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Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
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```python
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class MyAwesomeHyperOpt(IHyperOpt):
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...
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# Uses default stoploss dimension
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class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
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@staticmethod
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def stoploss_space() -> List[Dimension]:
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# Override boundaries for stoploss
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return [
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Real(-0.33, -0.01, name='stoploss'),
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]
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```
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and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
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```
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$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
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or
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$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
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```
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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 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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*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_percent'].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 result
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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_percent, profit_abs, open_date, open_rate, open_fee, close_date, close_rate, close_fee, amount, trade_duration, open_at_end, sell_reason`
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* `trade_count`: Amount of trades (identical to `len(results)`)
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* `min_date`: Start date of the hyperopting TimeFrame
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* `min_date`: End date of the hyperopting TimeFrame
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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 iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
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!!! Note
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Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
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