2019-07-23 15:51:24 +00:00
|
|
|
"""
|
|
|
|
OnlyProfitHyperOptLoss
|
|
|
|
|
|
|
|
This module defines the alternative HyperOptLoss class which can be used for
|
|
|
|
Hyperoptimization.
|
|
|
|
"""
|
|
|
|
from pandas import DataFrame
|
|
|
|
|
|
|
|
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
|
|
|
|
|
|
|
|
|
|
|
# This is assumed to be expected avg profit * expected trade count.
|
|
|
|
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
|
|
|
|
# expected max profit = 3.85
|
2019-07-25 05:17:41 +00:00
|
|
|
#
|
|
|
|
# Note, this is ratio. 3.85 stated above means 385Σ%, 3.0 means 300Σ%.
|
|
|
|
#
|
|
|
|
# In this implementation it's only used in calculation of the resulting value
|
|
|
|
# of the objective function as a normalization coefficient and does not
|
|
|
|
# represent any limit for profits as in the Freqtrade legacy default loss function.
|
2019-07-23 15:51:24 +00:00
|
|
|
EXPECTED_MAX_PROFIT = 3.0
|
|
|
|
|
|
|
|
|
|
|
|
class OnlyProfitHyperOptLoss(IHyperOptLoss):
|
|
|
|
"""
|
|
|
|
Defines the loss function for hyperopt.
|
|
|
|
|
|
|
|
This implementation takes only profit into account.
|
|
|
|
"""
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
|
|
|
*args, **kwargs) -> float:
|
|
|
|
"""
|
|
|
|
Objective function, returns smaller number for better results.
|
|
|
|
"""
|
|
|
|
total_profit = results.profit_percent.sum()
|
2019-07-25 05:17:41 +00:00
|
|
|
return 1 - total_profit / EXPECTED_MAX_PROFIT
|