From 3845d5518641263e8bb3a6864d8043d34f29c9b8 Mon Sep 17 00:00:00 2001
From: Robert Roman <robertroman7@gmail.com>
Date: Tue, 21 Sep 2021 20:04:23 -0500
Subject: [PATCH] a new hyperopt loss created that uses calmar ratio

This is a new hyperopt loss file that uses the Calmar Ratio.

Calmar Ratio = average annual rate of return / maximum drawdown
---
 freqtrade/optimize/hyperopt_loss_calmar.py | 52 ++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 freqtrade/optimize/hyperopt_loss_calmar.py

diff --git a/freqtrade/optimize/hyperopt_loss_calmar.py b/freqtrade/optimize/hyperopt_loss_calmar.py
new file mode 100644
index 000000000..c6211cb2b
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss_calmar.py
@@ -0,0 +1,52 @@
+"""
+CalmarHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+import numpy as np
+from pandas import DataFrame
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+from freqtrade.data.btanalysis import calculate_max_drawdown
+
+
+class CalmarHyperOptLoss(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,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Calmar Ratio calculation.
+        """
+        total_profit = results["profit_ratio"]
+        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
+
+        # calculate max drawdown
+        try:
+            _, _, _, high_val, low_val = calculate_max_drawdown(results)
+            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)
+        else:
+            calmar_ratio = -20.
+
+        # print(calmar_ratio)
+        return -calmar_ratio