From 3b99c84b0a879f9fbf5dae40a35a6a196c5b95d4 Mon Sep 17 00:00:00 2001
From: Robert Roman <robertroman7@gmail.com>
Date: Thu, 23 Sep 2021 21:31:33 -0500
Subject: [PATCH] resolved the total profit issue

I resolved the total profit issue and locally ran flak8 and isort
---
 freqtrade/optimize/hyperopt_loss_calmar.py | 32 +++++++++++++++-------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/freqtrade/optimize/hyperopt_loss_calmar.py b/freqtrade/optimize/hyperopt_loss_calmar.py
index b2a819444..45a7cd7db 100644
--- a/freqtrade/optimize/hyperopt_loss_calmar.py
+++ b/freqtrade/optimize/hyperopt_loss_calmar.py
@@ -5,11 +5,13 @@ 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
 
-import numpy as np
 from freqtrade.data.btanalysis import calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-from pandas import DataFrame
 
 
 class CalmarHyperOptLoss(IHyperOptLoss):
@@ -20,31 +22,41 @@ class CalmarHyperOptLoss(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(results: DataFrame, trade_count: int,
-                               min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        backtest_stats: Dict[str, Any],
+        *args,
+        **kwargs
+    ) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Calmar Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
+        total_profit = backtest_stats["profit_total"]
         days_period = (max_date - min_date).days
 
         # adding slippage of 0.1% per trade
         total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
+        expected_returns_mean = total_profit.sum() / days_period * 100
 
         # calculate max drawdown
         try:
-            _,_,_,high_val,low_val = calculate_max_drawdown(results)
+            _, _, _, high_val, low_val = calculate_max_drawdown(
+                results, value_col="profit_abs"
+            )
             max_drawdown = (high_val - low_val) / high_val
         except ValueError:
             max_drawdown = 0
 
         if max_drawdown != 0:
-            calmar_ratio = expected_returns_mean / max_drawdown * np.sqrt(365)
+            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
         else:
-            calmar_ratio = -20.
+            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+            calmar_ratio = -20.0
 
+        # print(expected_returns_mean, max_drawdown, calmar_ratio)
         return -calmar_ratio