feat: add starting_balance as argument to hyperopt_loss_function

freqtrade/optimize/hyperopt/hyperopt_optimizer.py

@@ -28,6 +28,7 @@ from freqtrade.optimize.hyperopt_loss.hyperopt_loss_interface import IHyperOptLo
 from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer, HyperoptTools
 from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
+from freqtrade.util.dry_run_wallet import get_dry_run_wallet
 
 
 # Suppress scikit-learn FutureWarnings from skopt
@@ -363,6 +364,7 @@ class HyperOptimizer:
                 config=self.config,
                 processed=processed,
                 backtest_stats=strat_stats,
+                starting_balance=get_dry_run_wallet(self.config),
             )
         return {
             "loss": loss,

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

@@ -12,7 +12,6 @@ from pandas import DataFrame
 from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_calmar
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from freqtrade.util import get_dry_run_wallet
 
 
 class CalmarHyperOptLoss(IHyperOptLoss):
@@ -29,6 +28,7 @@ class CalmarHyperOptLoss(IHyperOptLoss):
         min_date: datetime,
         max_date: datetime,
         config: Config,
+        starting_balance: float,
         *args,
         **kwargs,
     ) -> float:
@@ -37,7 +37,6 @@ class CalmarHyperOptLoss(IHyperOptLoss):
 
         Uses Calmar Ratio calculation.
         """
-        starting_balance = get_dry_run_wallet(config)
         calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, max_drawdown, calmar_ratio)
         return -calmar_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_interface.py

@@ -31,6 +31,7 @@ class IHyperOptLoss(ABC):
         config: Config,
         processed: dict[str, DataFrame],
         backtest_stats: dict[str, Any],
+        starting_balance: float,
         **kwargs,
     ) -> float:
         """

freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py

@@ -10,7 +10,6 @@ from pandas import DataFrame
 from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_underwater
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from freqtrade.util import get_dry_run_wallet
 
 
 class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
@@ -22,7 +21,9 @@ class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(results: DataFrame, config: Config, *args, **kwargs) -> float:
+    def hyperopt_loss_function(
+        results: DataFrame, config: Config, starting_balance: float, *args, **kwargs
+    ) -> float:
         """
         Objective function.
 
@@ -32,7 +33,7 @@ class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
         total_profit = results["profit_abs"].sum()
         try:
             drawdown_df = calculate_underwater(
-                results, value_col="profit_abs", starting_balance=get_dry_run_wallet(config)
+                results, value_col="profit_abs", starting_balance=starting_balance
             )
             max_drawdown = abs(min(drawdown_df["drawdown"]))
             relative_drawdown = max(drawdown_df["drawdown_relative"])

freqtrade/optimize/hyperopt_loss/hyperopt_loss_multi_metric.py

@@ -36,7 +36,6 @@ from pandas import DataFrame
 from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from freqtrade.util import get_dry_run_wallet
 
 
 # smaller numbers penalize drawdowns more severely
@@ -59,6 +58,7 @@ class MultiMetricHyperOptLoss(IHyperOptLoss):
         results: DataFrame,
         trade_count: int,
         config: Config,
+        starting_balance: float,
         **kwargs,
     ) -> float:
         total_profit = results["profit_abs"].sum()
@@ -84,7 +84,7 @@ class MultiMetricHyperOptLoss(IHyperOptLoss):
         # Calculate drawdown
         try:
             drawdown = calculate_max_drawdown(
-                results, starting_balance=get_dry_run_wallet(config), value_col="profit_abs"
+                results, starting_balance=starting_balance, value_col="profit_abs"
             )
             relative_account_drawdown = drawdown.relative_account_drawdown
         except ValueError:

freqtrade/optimize/hyperopt_loss/hyperopt_loss_profit_drawdown.py

@@ -13,7 +13,6 @@ from pandas import DataFrame
 from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from freqtrade.util import get_dry_run_wallet
 
 
 # smaller numbers penalize drawdowns more severely
@@ -22,12 +21,14 @@ DRAWDOWN_MULT = 0.075
 
 class ProfitDrawDownHyperOptLoss(IHyperOptLoss):
     @staticmethod
-    def hyperopt_loss_function(results: DataFrame, config: Config, *args, **kwargs) -> float:
+    def hyperopt_loss_function(
+        results: DataFrame, config: Config, starting_balance: float, *args, **kwargs
+    ) -> float:
         total_profit = results["profit_abs"].sum()
 
         try:
             drawdown = calculate_max_drawdown(
-                results, starting_balance=get_dry_run_wallet(config), value_col="profit_abs"
+                results, starting_balance=starting_balance, value_col="profit_abs"
             )
             relative_account_drawdown = drawdown.relative_account_drawdown
         except ValueError:

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py

@@ -9,10 +9,8 @@ from datetime import datetime
 
 from pandas import DataFrame
 
-from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_sharpe
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from freqtrade.util.dry_run_wallet import get_dry_run_wallet
 
 
 class SharpeHyperOptLoss(IHyperOptLoss):
@@ -25,10 +23,9 @@ class SharpeHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(
         results: DataFrame,
-        trade_count: int,
         min_date: datetime,
         max_date: datetime,
-        config: Config,
+        starting_balance: float,
         *args,
         **kwargs,
     ) -> float:
@@ -37,7 +34,6 @@ class SharpeHyperOptLoss(IHyperOptLoss):
 
         Uses Sharpe Ratio calculation.
         """
-        starting_balance = get_dry_run_wallet(config)
         sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, up_stdev, sharp_ratio)
         return -sharp_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py

@@ -9,10 +9,8 @@ from datetime import datetime
 
 from pandas import DataFrame
 
-from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_sortino
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from freqtrade.util import get_dry_run_wallet
 
 
 class SortinoHyperOptLoss(IHyperOptLoss):
@@ -25,10 +23,9 @@ class SortinoHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(
         results: DataFrame,
-        trade_count: int,
         min_date: datetime,
         max_date: datetime,
-        config: Config,
+        starting_balance: float,
         *args,
         **kwargs,
     ) -> float:
@@ -37,7 +34,6 @@ class SortinoHyperOptLoss(IHyperOptLoss):
 
         Uses Sortino Ratio calculation.
         """
-        starting_balance = get_dry_run_wallet(config)
         sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, down_stdev, sortino_ratio)
         return -sortino_ratio

tests/optimize/test_hyperoptloss.py

@@ -116,6 +116,7 @@ def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunct
         config=default_conf,
         processed=None,
         backtest_stats={"profit_total": hyperopt_results["profit_abs"].sum()},
+        starting_balance=default_conf["dry_run_wallet"],
     )
     over = hl.hyperopt_loss_function(
         results_over,