adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update hyperopt-loss to use resolver

Browse Source
This commit is contained in:
Matthias
2019-07-16 06:27:23 +02:00
parent 7d62bb8c53
commit d23179e25c
9 changed files with 177 additions and 120 deletions
Download Patch File Download Diff File Expand all files Collapse all files

39
docs/hyperopt.md
View File

@@ -153,31 +153,40 @@ add it to the `populate_indicators()` method in `hyperopt.py`.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a function, which get's closer to 0 for increasing values.
Each hyperparameter tuning requires a target. This is usually defined as a loss function, which get's closer to 0 for increasing values.
By default, freqtrade uses a loss function we call `legacy` - since it's been with freqtrade since the beginning and optimizes for short trade duration.
This can be configured by using the `--loss <value>` argument.
Possible options are:
* `legacy` - The default option, optimizing for short trades and few losses.
* `sharpe` - using the sharpe-ratio to determine the quality of results
* `custom` - Custom defined loss-function [see next section](#using-a-custom-loss-function)
FreqTrade uses a default loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
A different version this can be used by using the `--hyperopt-loss <Class-name>` argument.
This class should be in it's own file within the `user_data/hyperopts/` directory.
### Using a custom loss function
To use a custom loss function, make sure that the function `hyperopt_loss_custom` is defined in your custom hyperopt class.
You then need to add the command line parameter `--loss custom` to your hyperopt call so this fuction is being used.
To use a custom loss Class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt class.
For the sample below, you then need to add the command line parameter `--hyperoptloss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
A sample of this can be found below.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation.
``` python
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_custom(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results
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
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()