Remove CalmarDaily hyperopt loss

docs/hyperopt.md

@@ -116,8 +116,7 @@ optional arguments:
                         ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
                         SharpeHyperOptLoss, SharpeHyperOptLossDaily,
                         SortinoHyperOptLoss, SortinoHyperOptLossDaily,
-                        CalmarHyperOptLoss, CalmarHyperOptLossDaily,
-                        MaxDrawDownHyperOptLoss
+                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss
   --disable-param-export
                         Disable automatic hyperopt parameter export.
   --ignore-missing-spaces, --ignore-unparameterized-spaces
@@ -526,7 +525,6 @@ Currently, the following loss functions are builtin:
 * `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
 * `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown.
 * `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
-* `CalmarHyperOptLossDaily` Optimizes Calmar Ratio calculated on **daily** trade returns relative to max drawdown.
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 

freqtrade/constants.py

@@ -25,7 +25,7 @@ ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
                          'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
-                         'CalmarHyperOptLoss', 'CalmarHyperOptLossDaily',
+                         'CalmarHyperOptLoss',
                          'MaxDrawDownHyperOptLoss']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',

freqtrade/optimize/hyperopt_loss_calmar_daily.py

@@ -1,81 +0,0 @@
-"""
-CalmarHyperOptLossDaily
-
-This module defines the alternative HyperOptLoss class which can be used for
-Hyperoptimization.
-"""
-from datetime import datetime
-from math import sqrt as msqrt
-from typing import Any, Dict
-
-from pandas import DataFrame, date_range
-
-from freqtrade.optimize.hyperopt import IHyperOptLoss
-
-
-class CalmarHyperOptLossDaily(IHyperOptLoss):
-    """
-    Defines the loss function for hyperopt.
-
-    This implementation uses the Calmar Ratio calculation.
-    """
-
-    @staticmethod
-    def hyperopt_loss_function(
-        results: DataFrame,
-        trade_count: int,
-        min_date: datetime,
-        max_date: datetime,
-        config: Dict,
-        processed: Dict[str, DataFrame],
-        backtest_stats: Dict[str, Any],
-        *args,
-        **kwargs
-    ) -> float:
-        """
-        Objective function, returns smaller number for more optimal results.
-
-        Uses Calmar Ratio calculation.
-        """
-        resample_freq = "1D"
-        slippage_per_trade_ratio = 0.0005
-        days_in_year = 365
-
-        # create the index within the min_date and end max_date
-        t_index = date_range(
-            start=min_date, end=max_date, freq=resample_freq, normalize=True
-        )
-
-        # apply slippage per trade to profit_total
-        results.loc[:, "profit_ratio_after_slippage"] = (
-            results["profit_ratio"] - slippage_per_trade_ratio
-        )
-
-        sum_daily = (
-            results.resample(resample_freq, on="close_date")
-            .agg({"profit_ratio_after_slippage": sum})
-            .reindex(t_index)
-            .fillna(0)
-        )
-
-        total_profit = sum_daily["profit_ratio_after_slippage"]
-        expected_returns_mean = total_profit.mean() * 100
-
-        # calculate max drawdown
-        try:
-            high_val = total_profit.max()
-            low_val = total_profit.min()
-            max_drawdown = (high_val - low_val) / high_val
-
-        except (ValueError, ZeroDivisionError):
-            max_drawdown = 0
-
-        if max_drawdown != 0:
-            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(days_in_year)
-        else:
-            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
-            calmar_ratio = -20.0
-
-        # print(t_index, sum_daily, total_profit)
-        # print(expected_returns_mean, max_drawdown, calmar_ratio)
-        return -calmar_ratio

tests/optimize/test_hyperoptloss.py

@@ -5,7 +5,6 @@ import pytest
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
-from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
 
 
@@ -86,7 +85,6 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
     "SharpeHyperOptLoss",
     "SharpeHyperOptLossDaily",
     "MaxDrawDownHyperOptLoss",
-    "CalmarHyperOptLossDaily",
     "CalmarHyperOptLoss",
 
 ])