From aaac23a96a0b7ef5964b2d3943b694685f504bf8 Mon Sep 17 00:00:00 2001
From: OtenMoten <32872932+OtenMoten@users.noreply.github.com>
Date: Mon, 22 Feb 2021 12:17:46 +0100
Subject: [PATCH] Added Hyper-Optimization "Swing-High-To-Sky"

I like to share my newest hyperopt with you. I though about how cool it would be to know what's the perfect timeperiod for CCI indicator. In a strategy you do something like this: dataframe['cci'] = ta.CCI(timeperiod=14)

You would do this by hand for each timeperiod which is very annoying. Therefore, I created this hyperopt to looking for the perfect timeperiod for the CCI indicator. Please review this pull request very critical and share your minds.

Since the last two months (from 1st Jan 2021 until now) this strategy in BTC/USDT 30m chart had worked very very well. After two months I now optimize this strategy again.
---
 user_data/hyperopts/HO-SwingHighToSky.py | 124 +++++++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 user_data/hyperopts/HO-SwingHighToSky.py

diff --git a/user_data/hyperopts/HO-SwingHighToSky.py b/user_data/hyperopts/HO-SwingHighToSky.py
new file mode 100644
index 0000000..3807ac9
--- /dev/null
+++ b/user_data/hyperopts/HO-SwingHighToSky.py
@@ -0,0 +1,124 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+from pandas import DataFrame
+from typing import Dict, Any, Callable, List
+from functools import reduce
+
+import numpy as np
+from skopt.space import Categorical, Dimension, Integer, Real
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+__author__      = "Kevin Ossenbrück"
+__copyright__   = "Free For Use"
+__credits__     = ["Bloom Trading, Mohsen Hassan"]
+__license__     = "MIT"
+__version__     = "1.0"
+__maintainer__  = "Kevin Ossenbrück"
+__email__       = "kevin.ossenbrueck@pm.de"
+__status__      = "Live"
+
+cciTimeMin = 10
+cciTimeMax = 100
+cciValueMin = -400
+cciValueMax = 400
+cciTimeRange = range(cciTimeMin, cciTimeMax)
+
+class_name = 'HOSwingHighToSky'
+class HOSwingHighToSky(IHyperOpt):
+
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        
+        for cciTime in cciTimeRange:
+        
+            cciName = "cci-" + str(cciTime) 
+            dataframe[cciName] = ta.CCI(dataframe, timeperiod = cciTime)
+            
+        return dataframe
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+   
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+            conditions = []
+                
+            # TRIGGERS & GUARDS
+            if 'trigger' in params:
+            
+                for cciTime in cciTimeRange:
+                
+                    cciName = "cci-" + str(cciTime)
+            
+                    if params['trigger'] == cciName:
+                        conditions.append(dataframe[cciName] < params["buy-cci-value"])
+                        conditions.append(dataframe['macd'] > dataframe['macdsignal'])
+                        conditions.append(dataframe['volume'] > 0)
+                
+            if conditions:
+                dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        
+        buyTriggerList = []
+        
+        for cciTime in cciTimeRange:
+        
+            cciName = "cci-" + str(cciTime)
+            buyTriggerList.append(cciName)
+        
+        return [
+            Integer(cciValueMin, cciValueMax, name='buy-cci-value'),
+            Categorical(buyTriggerList, name='trigger')
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+           
+            conditions = []
+                 
+            # TRIGGERS & GUARDS
+            if 'sell-trigger' in params:
+            
+                for cciTime in cciTimeRange:
+                
+                    cciName = "cci-" + str(cciTime)
+            
+                    if params['sell-trigger'] == cciName:
+                        conditions.append(dataframe[cciName] > params["sell-cci-value"])
+                        conditions.append(dataframe['macd'] < dataframe['macdsignal'])
+                
+            if conditions:
+                dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+       
+        sellTriggerList = []
+        
+        for cciTime in cciTimeRange:
+        
+            cciName = "cci-" + str(cciTime)
+            sellTriggerList.append(cciName)
+            
+        return [
+            Integer(cciValueMin, cciValueMax, name='sell-cci-value'),
+            Categorical(sellTriggerList, name='sell-trigger')
+        ]
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