64 lines
3.0 KiB
Markdown
64 lines
3.0 KiB
Markdown
|
# Advanced Hyperopt
|
||
|
|
|
||
|
|
This page explains some advanced Hyperopt issues 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 fuction 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 [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
|
||
|
|
|
||
|
|
``` python
|
||
|
|
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,
|
||
|
|
*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_percent.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 result
|
||
|
|
The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
|
||
|
|
`pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
|
||
|
|
* `trade_count`: Amount of trades (identical to `len(results)`)
|
||
|
|
* `min_date`: Start date of the hyperopting TimeFrame
|
||
|
|
* `min_date`: End date of the hyperopting TimeFrame
|
||
|
|
|
||
|
|
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 iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
|
||
|
|
|
||
|
|
!!! Note
|
||
|
|
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
|