From 2f64b4e56f4afc1073efdce52a0afc6464949d0a Mon Sep 17 00:00:00 2001
From: Masoud Azizi <mablue92@gmail.com>
Date: Thu, 15 Apr 2021 11:23:44 +0430
Subject: [PATCH] Delete Heracles.py

---
 user_data/strategies/Heracles.py | 134 -------------------------------
 1 file changed, 134 deletions(-)
 delete mode 100644 user_data/strategies/Heracles.py

diff --git a/user_data/strategies/Heracles.py b/user_data/strategies/Heracles.py
deleted file mode 100644
index b6986d0..0000000
--- a/user_data/strategies/Heracles.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Heracles Strategy: Strongest Son of GodStra
-# ( With just 1 Genome! its a bacteria :D )
-# Author: @Mablue (Masoud Azizi)
-# github: https://github.com/mablue/
-# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
-#   {
-#       "method": "AgeFilter",
-#       "min_days_listed": 100
-#   },
-# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
-# ######################################################################
-# Optimal config settings:
-# "max_open_trades": 100,
-# "stake_amount": "unlimited",
-
-# --- 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 ta
-from ta.utils import dropna
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-from functools import reduce
-import numpy as np
-
-
-class Heracles(IStrategy):
-    # 65/600:   2275 trades. 1438/7/830 W/D/L.
-    # Avg profit   3.10%. Median profit   3.06%.
-    # Total profit  113171 USDT ( 7062 Σ%).
-    # Avg duration 345 min. Objective: -23.0
-
-    # Buy hyperspace params:
-    buy_params = {
-        'buy-cross-0': 'volatility_kcw',
-        'buy-indicator-0': 'volatility_dcp',
-        'buy-oper-0': '<',
-    }
-
-    # Sell hyperspace params:
-    sell_params = {
-        'sell-cross-0': 'trend_macd_signal',
-        'sell-indicator-0': 'trend_ema_fast',
-        'sell-oper-0': '=',
-    }
-
-    # ROI table:
-    minimal_roi = {
-        "0": 0.32836,
-        "1629": 0.17896,
-        "6302": 0.05372,
-        "10744": 0
-    }
-
-    # Stoploss:
-    stoploss = -0.04655
-
-    # Trailing stop:
-    trailing_stop = True
-    trailing_stop_positive = 0.02444
-    trailing_stop_positive_offset = 0.04406
-    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['volatility_kcw'] = ta.volatility.keltner_channel_wband(
-            dataframe['high'],
-            dataframe['low'],
-            dataframe['close'],
-            window=20,
-            window_atr=10,
-            fillna=False,
-            original_version=True
-        )
-        dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
-            dataframe['high'],
-            dataframe['low'],
-            dataframe['close'],
-            window=10,
-            offset=0,
-            fillna=False
-        )
-        dataframe['trend_macd_signal'] = ta.trend.macd_signal(
-            dataframe['close'],
-            window_slow=26,
-            window_fast=12,
-            window_sign=9,
-            fillna=False
-        )
-
-        dataframe['trend_ema_fast'] = ta.trend.EMAIndicator(
-            close=dataframe['close'], window=12, fillna=False
-        ).ema_indicator()
-
-        return dataframe
-
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        IND = self.buy_params['buy-indicator-0']
-        CRS = self.buy_params['buy-cross-0']
-        DFIND = dataframe[IND]
-        DFCRS = dataframe[CRS]
-
-        dataframe.loc[
-            (DFIND < DFCRS),
-            'buy'] = 1
-
-        return dataframe
-
-    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        IND = self.sell_params['sell-indicator-0']
-        CRS = self.sell_params['sell-cross-0']
-
-        DFIND = dataframe[IND]
-        DFCRS = dataframe[CRS]
-
-        dataframe.loc[
-            (qtpylib.crossed_below(DFIND, DFCRS)),
-            'sell'] = 1
-
-        return dataframe