From d299b18666362e7f7dd9dbb718db54d94d6aa72b Mon Sep 17 00:00:00 2001
From: Masoud Azizi <mablue92@gmail.com>
Date: Tue, 18 May 2021 22:09:58 +0430
Subject: [PATCH] zeus hyperoptable strategy is up

---
 user_data/strategies/Zeus.py | 149 +++++++++++++++++++++++++++++++++++
 1 file changed, 149 insertions(+)
 create mode 100644 user_data/strategies/Zeus.py

diff --git a/user_data/strategies/Zeus.py b/user_data/strategies/Zeus.py
new file mode 100644
index 0000000..64dd4c5
--- /dev/null
+++ b/user_data/strategies/Zeus.py
@@ -0,0 +1,149 @@
+# Zeus Strategy: First Generation of GodStra Strategy with maximum
+# AVG/MID profit in USDT
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi trailing --strategy Zeus
+# --- Do not remove these libs ---
+import logging
+from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter
+
+from numpy.lib import math
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+# import talib.abstract as ta
+import pandas as pd
+import ta
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+import numpy as np
+
+
+class Zeus(IStrategy):
+
+    # 53/167:    167 trades. 96/66/5 Wins/Draws/Losses. Avg profit   3.00%. Median profit   2.70%. Total profit  0.16479843 BTC ( 164.80Σ%). Avg duration 22:04:00 min. Objective: -63.49577
+
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_cat": "<R",
+        "buy_real": 0.0889,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_cat": "=R",
+        "sell_real": 0.979,
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.336,
+        "134": 0.113,
+        "759": 0.027,
+        "1049": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.245
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.35
+    trailing_stop_positive_offset = 0.375
+    trailing_only_offset_is_reached = False
+
+    buy_real = DecimalParameter(
+        0.001, 0.999, decimals=4, default=0.11908, space='buy')
+    buy_cat = CategoricalParameter(
+        [">R", "=R", "<R"], default='<R', space='buy')
+    sell_real = DecimalParameter(
+        0.001, 0.999, decimals=4, default=0.59608, space='sell')
+    sell_cat = CategoricalParameter(
+        [">R", "=R", "<R"], default='>R', space='sell')
+
+    # Buy hypers
+    timeframe = '4h'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Add all ta features
+        # Clean NaN values
+        dataframe = dropna(dataframe)
+
+        dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
+            dataframe['high'],
+            dataframe['low'],
+            window1=9,
+            window2=26,
+            visual=False,
+            fillna=False
+        )
+        KST = ta.trend.KSTIndicator(
+            close=dataframe['close'],
+            roc1=10,
+            roc2=15,
+            roc3=20,
+            roc4=30,
+            window1=10,
+            window2=10,
+            window3=10,
+            window4=15,
+            nsig=9,
+            fillna=False
+        )
+
+        dataframe['trend_kst_diff'] = KST.kst_diff()
+
+        # Normalization
+        tib = dataframe['trend_ichimoku_base']
+        dataframe['trend_ichimoku_base'] = (
+            tib-tib.min())/(tib.max()-tib.min())
+        tkd = dataframe['trend_kst_diff']
+        dataframe['trend_kst_diff'] = (tkd-tkd.min())/(tkd.max()-tkd.min())
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = []
+        IND = 'trend_ichimoku_base'
+        REAL = self.buy_real.value
+        OPR = self.buy_cat.value
+        DFIND = dataframe[IND]
+        # print(DFIND.mean())
+        if OPR == ">R":
+            conditions.append(DFIND > REAL)
+        elif OPR == "=R":
+            conditions.append(np.isclose(DFIND, REAL))
+        elif OPR == "<R":
+            conditions.append(DFIND < REAL)
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = []
+        IND = 'trend_kst_diff'
+        REAL = self.sell_real.value
+        OPR = self.sell_cat.value
+        DFIND = dataframe[IND]
+        # print(DFIND.mean())
+
+        if OPR == ">R":
+            conditions.append(DFIND > REAL)
+        elif OPR == "=R":
+            conditions.append(np.isclose(DFIND, REAL))
+        elif OPR == "<R":
+            conditions.append(DFIND < REAL)
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'sell'] = 1
+
+        return dataframe