44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
"""
|
|
MaxDrawDownRelativeHyperOptLoss
|
|
|
|
This module defines the alternative HyperOptLoss class which can be used for
|
|
Hyperoptimization.
|
|
"""
|
|
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.data.metrics import calculate_underwater
|
|
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
|
|
|
|
|
class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
|
|
"""
|
|
Defines the loss function for hyperopt.
|
|
|
|
This implementation optimizes for max draw down and profit
|
|
Less max drawdown more profit -> Lower return value
|
|
"""
|
|
|
|
@staticmethod
|
|
def hyperopt_loss_function(
|
|
results: DataFrame, starting_balance: float, *args, **kwargs
|
|
) -> float:
|
|
"""
|
|
Objective function.
|
|
|
|
Uses profit ratio weighted max_drawdown when drawdown is available.
|
|
Otherwise directly optimizes profit ratio.
|
|
"""
|
|
total_profit = results["profit_abs"].sum()
|
|
try:
|
|
drawdown_df = calculate_underwater(
|
|
results, value_col="profit_abs", starting_balance=starting_balance
|
|
)
|
|
max_drawdown = abs(min(drawdown_df["drawdown"]))
|
|
relative_drawdown = max(drawdown_df["drawdown_relative"])
|
|
if max_drawdown == 0:
|
|
return -total_profit
|
|
return -total_profit / max_drawdown / relative_drawdown
|
|
except (Exception, ValueError):
|
|
return -total_profit
|