From 24baad7884b2f076f5ec0b4b12f98e20beb7b0f5 Mon Sep 17 00:00:00 2001
From: Robert Roman <robertroman7@gmail.com>
Date: Sat, 25 Sep 2021 16:28:36 -0500
Subject: [PATCH] Add Calmar Ratio Daily

This hyper opt loss calculates the daily Calmar ratio.
---
 .../optimize/hyperopt_loss_calmar_daily.py    | 79 +++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 freqtrade/optimize/hyperopt_loss_calmar_daily.py

diff --git a/freqtrade/optimize/hyperopt_loss_calmar_daily.py b/freqtrade/optimize/hyperopt_loss_calmar_daily.py
new file mode 100644
index 000000000..c7651a72a
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss_calmar_daily.py
@@ -0,0 +1,79 @@
+"""
+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,
+        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