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chore: remove unused arguments in loss functions

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Matthias
2024-12-07 15:51:37 +01:00
parent ebae0a7248
commit 98e0a5f101
4 changed files with 2 additions and 9 deletions
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3
freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
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@@ -9,7 +9,6 @@ from datetime import datetime
from pandas import DataFrame from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_calmar from freqtrade.data.metrics import calculate_calmar
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
@@ -24,10 +23,8 @@ class CalmarHyperOptLoss(IHyperOptLoss):
@staticmethod @staticmethod
def hyperopt_loss_function( def hyperopt_loss_function(
results: DataFrame, results: DataFrame,
trade_count: int,
min_date: datetime, min_date: datetime,
max_date: datetime, max_date: datetime,
config: Config,
starting_balance: float, starting_balance: float,
*args, *args,
**kwargs, **kwargs,

3
freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py
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@@ -7,7 +7,6 @@ Hyperoptimization.
from pandas import DataFrame from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_underwater from freqtrade.data.metrics import calculate_underwater
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
@@ -22,7 +21,7 @@ class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
@staticmethod @staticmethod
def hyperopt_loss_function( def hyperopt_loss_function(
results: DataFrame, config: Config, starting_balance: float, *args, **kwargs results: DataFrame, starting_balance: float, *args, **kwargs
) -> float: ) -> float:
""" """
Objective function. Objective function.

2
freqtrade/optimize/hyperopt_loss/hyperopt_loss_multi_metric.py
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@@ -33,7 +33,6 @@ TARGET_TRADE_AMOUNT variable sets the minimum number of trades required to avoid
import numpy as np import numpy as np
from pandas import DataFrame from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
@@ -57,7 +56,6 @@ class MultiMetricHyperOptLoss(IHyperOptLoss):
def hyperopt_loss_function( def hyperopt_loss_function(
results: DataFrame, results: DataFrame,
trade_count: int, trade_count: int,
config: Config,
starting_balance: float, starting_balance: float,
**kwargs, **kwargs,
) -> float: ) -> float:

3
freqtrade/optimize/hyperopt_loss/hyperopt_loss_profit_drawdown.py
View File

@@ -10,7 +10,6 @@ individual needs.
from pandas import DataFrame from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_max_drawdown from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
@@ -22,7 +21,7 @@ DRAWDOWN_MULT = 0.075
class ProfitDrawDownHyperOptLoss(IHyperOptLoss): class ProfitDrawDownHyperOptLoss(IHyperOptLoss):
@staticmethod @staticmethod
def hyperopt_loss_function( def hyperopt_loss_function(
results: DataFrame, config: Config, starting_balance: float, *args, **kwargs results: DataFrame, starting_balance: float, *args, **kwargs
) -> float: ) -> float:
total_profit = results["profit_abs"].sum() total_profit = results["profit_abs"].sum()