Masoud AziziandGitHub
01a44d08f2
# 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",
134 lines
3.6 KiB
Python
134 lines
3.6 KiB
Python
# Heracles Strategy: Strongest Son of GodStra
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# ( With just 1 Genome! its a bacteria :D )
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
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# {
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# "method": "AgeFilter",
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# "min_days_listed": 100
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# },
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# ######################################################################
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# Optimal config settings:
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# "max_open_trades": 100,
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# "stake_amount": "unlimited",
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# --- Do not remove these libs ---
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import logging
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from numpy.lib import math
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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# import talib.abstract as ta
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import pandas as pd
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import ta
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from ta.utils import dropna
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from functools import reduce
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import numpy as np
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class Heracles(IStrategy):
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# 65/600: 2275 trades. 1438/7/830 W/D/L.
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# Avg profit 3.10%. Median profit 3.06%.
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# Total profit 113171 USDT ( 7062 Σ%).
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# Avg duration 345 min. Objective: -23.0
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# Buy hyperspace params:
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buy_params = {
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'buy-cross-0': 'volatility_kcw',
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'buy-indicator-0': 'volatility_dcp',
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'buy-oper-0': '<',
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}
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# Sell hyperspace params:
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sell_params = {
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'sell-cross-0': 'trend_macd_signal',
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'sell-indicator-0': 'trend_ema_fast',
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'sell-oper-0': '=',
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}
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# ROI table:
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minimal_roi = {
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"0": 0.32836,
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"1629": 0.17896,
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"6302": 0.05372,
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"10744": 0
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}
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# Stoploss:
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stoploss = -0.04655
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.02444
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trailing_stop_positive_offset = 0.04406
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trailing_only_offset_is_reached = True
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# Buy hypers
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timeframe = '12h'
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Add all ta features
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dataframe = dropna(dataframe)
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dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
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dataframe['high'],
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dataframe['low'],
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dataframe['close'],
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window=20,
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window_atr=10,
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fillna=False,
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original_version=True
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)
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dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
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dataframe['high'],
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dataframe['low'],
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dataframe['close'],
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window=10,
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offset=0,
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fillna=False
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)
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dataframe['trend_macd_signal'] = ta.trend.macd_signal(
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dataframe['close'],
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window_slow=26,
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window_fast=12,
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window_sign=9,
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fillna=False
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)
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dataframe['trend_ema_fast'] = ta.trend.EMAIndicator(
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close=dataframe['close'], window=12, fillna=False
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).ema_indicator()
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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IND = self.buy_params['buy-indicator-0']
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CRS = self.buy_params['buy-cross-0']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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dataframe.loc[
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(DFIND < DFCRS),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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IND = self.sell_params['sell-indicator-0']
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CRS = self.sell_params['sell-cross-0']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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dataframe.loc[
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(qtpylib.crossed_below(DFIND, DFCRS)),
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'sell'] = 1
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return dataframe
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