From 3e06cd8b3a38231dc967ec7b3a7f6405995ad536 Mon Sep 17 00:00:00 2001
From: Florian Merz <florian.p.m@gmail.com>
Date: Tue, 16 Feb 2021 10:11:33 +0100
Subject: [PATCH 1/2] pass data and config to loss function

---
 freqtrade/optimize/hyperopt.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index d0cdceaeb..83bdbad17 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -546,10 +546,11 @@ class Hyperopt:
 
         )
         return self._get_results_dict(backtesting_results, min_date, max_date,
-                                      params_dict, params_details)
+                                      params_dict, params_details,
+                                      processed=processed)
 
     def _get_results_dict(self, backtesting_results, min_date, max_date,
-                          params_dict, params_details):
+                          params_dict, params_details, processed: Dict):
         results_metrics = self._calculate_results_metrics(backtesting_results)
         results_explanation = self._format_results_explanation_string(results_metrics)
 
@@ -563,7 +564,8 @@ class Hyperopt:
         loss: float = MAX_LOSS
         if trade_count >= self.config['hyperopt_min_trades']:
             loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
-                                       min_date=min_date.datetime, max_date=max_date.datetime)
+                                       min_date=min_date.datetime, max_date=max_date.datetime,
+                                       config=self.config, processed=processed)
         return {
             'loss': loss,
             'params_dict': params_dict,

From 009a447d8a59f791b230537fa6e0d1f8a923245c Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Tue, 16 Feb 2021 19:51:09 +0100
Subject: [PATCH 2/2] Adjust documentation for new parameter in loss functions

---
 docs/advanced-hyperopt.md                   | 7 +++++++
 freqtrade/optimize/hyperopt.py              | 2 +-
 freqtrade/templates/sample_hyperopt_loss.py | 2 ++
 3 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/docs/advanced-hyperopt.md b/docs/advanced-hyperopt.md
index bead18038..50d1946aa 100644
--- a/docs/advanced-hyperopt.md
+++ b/docs/advanced-hyperopt.md
@@ -40,6 +40,11 @@ For the sample below, you then need to add the command line parameter `--hyperop
 A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
 
 ``` python
+from datetime import datetime
+from typing import Dict
+
+from pandas import DataFrame
+
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 TARGET_TRADES = 600
@@ -54,6 +59,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
+                               processed: Dict[str, DataFrame],
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results
@@ -81,6 +87,7 @@ Currently, the arguments are:
 * `trade_count`: Amount of trades (identical to `len(results)`)
 * `min_date`: Start date of the timerange used
 * `min_date`: End date of the timerange used
+* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
 
 This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
 
diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index 83bdbad17..eee0f13b3 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -550,7 +550,7 @@ class Hyperopt:
                                       processed=processed)
 
     def _get_results_dict(self, backtesting_results, min_date, max_date,
-                          params_dict, params_details, processed: Dict):
+                          params_dict, params_details, processed: Dict[str, DataFrame]):
         results_metrics = self._calculate_results_metrics(backtesting_results)
         results_explanation = self._format_results_explanation_string(results_metrics)
 
diff --git a/freqtrade/templates/sample_hyperopt_loss.py b/freqtrade/templates/sample_hyperopt_loss.py
index a2b28f948..389c811f8 100644
--- a/freqtrade/templates/sample_hyperopt_loss.py
+++ b/freqtrade/templates/sample_hyperopt_loss.py
@@ -1,5 +1,6 @@
 from datetime import datetime
 from math import exp
+from typing import Dict
 
 from pandas import DataFrame
 
@@ -35,6 +36,7 @@ class SampleHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
+                               processed: Dict[str, DataFrame],
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results