From e65893aa70a239675873b183963230971a5fb29f Mon Sep 17 00:00:00 2001
From: Masoud Azizi <mablue92@gmail.com>
Date: Tue, 13 Apr 2021 21:14:37 +0430
Subject: [PATCH] GodStra Strategy + hyperopt file

this is a genetic algorithm Strategy that makes a dna for using as strategy from GoDs genes!
---
 user_data/strategies/GodStra.py | 167 ++++++++++++++++++++++++++++++++
 1 file changed, 167 insertions(+)
 create mode 100644 user_data/strategies/GodStra.py

diff --git a/user_data/strategies/GodStra.py b/user_data/strategies/GodStra.py
new file mode 100644
index 0000000..bf11c70
--- /dev/null
+++ b/user_data/strategies/GodStra.py
@@ -0,0 +1,167 @@
+# GodStra Strategy
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
+#   {
+#       "method": "AgeFilter",
+#       "min_days_listed": 30
+#   },
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json
+
+# --- Do not remove these libs ---
+import logging
+
+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 talib.abstract as ta
+from ta import add_all_ta_features
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+import numpy as np
+
+
+class GodStra(IStrategy):
+    # 5/66:      9 trades. 8/0/1 Wins/Draws/Losses. Avg profit  21.83%. Median profit  35.52%. Total profit  1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
+    # +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
+    # |   Best |   Epoch |   Trades |    Win Draw Loss |   Avg profit |                        Profit |   Avg duration |   Objective |
+    # |--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
+    # | * Best |   1/500 |       11 |      2    1    8 |        5.22% |  280.74230393 USDT   (57.40%) |      2,421.8 m |    -2.85206 |
+    # | * Best |   2/500 |       10 |      7    0    3 |       18.76% |  983.46414442 USDT  (187.58%) |        360.0 m |    -4.32665 |
+    # | * Best |   5/500 |        9 |      8    0    1 |       21.83% | 1,060.11476586 USDT  (196.50%) |      3,440.0 m |     -7.0696 |
+
+    # Buy hyperspace params:
+    buy_params = {
+        'buy-cross-0': 'volatility_kcc',
+        'buy-indicator-0': 'trend_ichimoku_base',
+        'buy-int-0': 42,
+        'buy-oper-0': '<R',
+        'buy-real-0': 0.06295
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        'sell-cross-0': 'volume_mfi',
+        'sell-indicator-0': 'trend_kst_diff',
+        'sell-int-0': 98,
+        'sell-oper-0': '=R',
+        'sell-real-0': 0.8779
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.3556,
+        "4818": 0.21275,
+        "6395": 0.09024,
+        "22372": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.34549
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.22673
+    trailing_stop_positive_offset = 0.2684
+    trailing_only_offset_is_reached = True
+    # Buy hypers
+    timeframe = '12h'
+    print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Add all ta features
+        dataframe = dropna(dataframe)
+        dataframe = add_all_ta_features(
+            dataframe, open="open", high="high", low="low", close="close", volume="volume", fillna=True)
+        # dataframe.to_csv("df.csv", index=True)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = list()
+        # /5: Cuz We have 5 Group of variables inside buy_param
+        for i in range(int(len(self.buy_params)/5)):
+
+            OPR = self.buy_params[f'buy-oper-{i}']
+            IND = self.buy_params[f'buy-indicator-{i}']
+            CRS = self.buy_params[f'buy-cross-{i}']
+            INT = self.buy_params[f'buy-int-{i}']
+            REAL = self.buy_params[f'buy-real-{i}']
+            DFIND = dataframe[IND]
+            DFCRS = dataframe[CRS]
+
+            if OPR == ">":
+                conditions.append(DFIND > DFCRS)
+            elif OPR == "=":
+                conditions.append(np.isclose(DFIND, DFCRS))
+            elif OPR == "<":
+                conditions.append(DFIND < DFCRS)
+            elif OPR == "CA":
+                conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
+            elif OPR == "CB":
+                conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
+            elif OPR == ">I":
+                conditions.append(DFIND > INT)
+            elif OPR == "=I":
+                conditions.append(DFIND == INT)
+            elif OPR == "<I":
+                conditions.append(DFIND < INT)
+            elif OPR == ">R":
+                conditions.append(DFIND > REAL)
+            elif OPR == "=R":
+                conditions.append(np.isclose(DFIND, REAL))
+            elif OPR == "<R":
+                conditions.append(DFIND < REAL)
+
+        print(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 = list()
+        for i in range(int(len(self.sell_params)/5)):
+            OPR = self.sell_params[f'sell-oper-{i}']
+            IND = self.sell_params[f'sell-indicator-{i}']
+            CRS = self.sell_params[f'sell-cross-{i}']
+            INT = self.sell_params[f'sell-int-{i}']
+            REAL = self.sell_params[f'sell-real-{i}']
+            DFIND = dataframe[IND]
+            DFCRS = dataframe[CRS]
+
+            if OPR == ">":
+                conditions.append(DFIND > DFCRS)
+            elif OPR == "=":
+                conditions.append(np.isclose(DFIND, DFCRS))
+            elif OPR == "<":
+                conditions.append(DFIND < DFCRS)
+            elif OPR == "CA":
+                conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
+            elif OPR == "CB":
+                conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
+            elif OPR == ">I":
+                conditions.append(DFIND > INT)
+            elif OPR == "=I":
+                conditions.append(DFIND == INT)
+            elif OPR == "<I":
+                conditions.append(DFIND < INT)
+            elif OPR == ">R":
+                conditions.append(DFIND > REAL)
+            elif OPR == "=R":
+                conditions.append(np.isclose(DFIND, REAL))
+            elif OPR == "<R":
+                conditions.append(DFIND < REAL)
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
+            'sell'] = 1
+
+        return dataframe