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Jérôme Delacotte
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Zeus_8d_2.py
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Zeus_8d_2.py
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# Zeus Strategy: First Generation of GodStra Strategy with maximum
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# AVG/MID profit in USDT
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
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# --- Do not remove these libs ---
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from datetime import timedelta, datetime
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from typing import Optional
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from freqtrade import data
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from freqtrade.persistence import Trade
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from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter
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from numpy.lib import math
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from freqtrade.strategy.interface import IStrategy
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import pandas
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from pandas import DataFrame
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import time
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import logging
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import calendar
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from freqtrade.loggers import setup_logging
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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# --------------------------------
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# Add your lib to import here
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import ta
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from functools import reduce
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import numpy as np
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import talib.abstract as talib
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from random import shuffle
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logger = logging.getLogger(__name__)
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class Zeus_8d_2(IStrategy):
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# Buy hyperspace params:
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buy_params = {
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"buy_min_horizon": 23,
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"buy_min_max_n": 0.16,
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"buy_rsi": 10,
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"decalage": 8,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_b_RSI": 75,
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"sell_percent": 0.1,
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"sell_profit_percent": 0.1,
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}
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# Protection hyperspace params:
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protection_params = {
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"protection_nb_buy_lost": 3,
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"protection_percent_buy_lost": 17,
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"protection_start_buying_rsi_1d": 17,
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"protection_stop_buying_rsi_1d": 51,
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}
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# ROI table: # value loaded from strategy
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minimal_roi = {
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"0": 10
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}
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# Stoploss:
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stoploss = -1.0 # value loaded from strategy
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# Trailing stop:
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trailing_stop = False # value loaded from strategy
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trailing_stop_positive = None # value loaded from strategy
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trailing_stop_positive_offset = 0.0 # value loaded from strategy
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trailing_only_offset_is_reached = False # value loaded from strategy
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# Buy hypers
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timeframe = '4h'
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stop_buying = {}
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# DCA config
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# position_adjustment_enable = True
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plot_config = {
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"main_plot": {
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"min200": {
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"color": "#86c932"
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},
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"min50": {
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"color": "white"
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},
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# "max200": {
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# "color": "yellow"
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# },
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"sma3_1d": {
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"color": "pink"
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},
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"sma5_1d": {
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"color": "blue"
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},
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"sma10_1d": {
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"color": "orange"
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},
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"close_1d": {
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"color": "#73e233",
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},
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"low": {
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"color": "cyan",
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},
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"bb_lowerband": {
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"color": "#da59a6"},
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"bb_upperband": {
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"color": "#da59a6",
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}
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},
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"subplots": {
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# "Ind": {
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# "trend_ichimoku_base": {
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# "color": "#dd1384"
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# },
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# "trend_kst_diff": {
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# "color": "#850678"
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# }
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# },
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# "BB": {
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# "bb_width": {
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# "color": "white"
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# },
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# "bb_lower_5": {
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# "color": "yellow"
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# }
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# },
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"Rsi": {
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"rsi_1d": {
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"color": "pink"
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},
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# "rsi_1h": {
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# "color": "green"
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# },
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"rsi5": {
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"color": "yellow"
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},
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"rsi3_1d": {
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"color": "red"
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}
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},
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# "Percent": {
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# "pct_change_1_1d": {
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# "color": "green"
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# },
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# "pct_change_3_1d": {
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# "color": "orange"
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# },
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# "pct_change_5_1d": {
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# "color": "red"
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# }
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# }
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}
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}
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buy_min_horizon = IntParameter(1, 100, default=7, space='buy')
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buy_rsi = IntParameter(1, 30, default=12, space='buy')
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buy_min_max_n = DecimalParameter(0.01, 0.2, decimals=2, default=0.05, space='buy')
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buy_percent = DecimalParameter(1.005, 1.02, decimals=3, default=1.01, space='buy')
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decalage = IntParameter(1, 12, default=6, space='buy')
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sell_b_RSI = IntParameter(60, 98, default=60, space='sell')
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sell_profit_percent = DecimalParameter(0.1, 1.5, decimals=1, default=0.8, space='sell')
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sell_percent = DecimalParameter(0.01, 0.30, decimals=2, default=0.05, space='sell')
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# protection_percent_buy_lost = IntParameter(1, 30, default=3, space='protection')
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# protection_nb_buy_lost = IntParameter(1, 3, default=3, space='protection')
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protection_stop_buying_rsi_1d = IntParameter(50, 100, default=76, space='protection')
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protection_start_buying_rsi_1d = IntParameter(1, 50, default=30, space='protection')
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# def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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# current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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# allow_to_buy = True
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# # info_previous_last_candle = informative.iloc[-2].squeeze()
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# # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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# # last_candle = dataframe.iloc[-1].squeeze()
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#
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# return allow_to_buy
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# def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
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# current_profit: float, **kwargs):
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# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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# last_candle = dataframe.iloc[-1].squeeze()
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# previous_last_candle = dataframe.iloc[-2].squeeze()
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# previous_previous_last_candle = dataframe.iloc[-3].squeeze()
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#
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# if (current_profit > self.buy_min_max_n.value * self.sell_profit_percent.value):
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# # \
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# # & (previous_last_candle['rsi5'] > self.sell_b_RSI.value) \
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# # & (previous_last_candle['rsi5'] > last_candle['rsi5']) \
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# # & (previous_last_candle['rsi5'] > previous_previous_last_candle['rsi5']):
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# logger.info("Sell ==> %s ", pair + " " + str(current_time) + " " + str(current_profit)
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# + " " + str(self.buy_min_max_n.value * self.sell_profit_percent.value)
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# + " " + str(previous_previous_last_candle['rsi5']) + " " + str(
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# previous_last_candle['rsi5']) + " " + str(last_candle['rsi5'])
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# )
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# return 'profit_1' # + str(self.sell_percent.value)
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#
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# return None
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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['pct_change'] = dataframe['close'].pct_change(5)
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dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=self.buy_min_horizon.value)
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# dataframe['min10'] = talib.MIN(dataframe['close'], timeperiod=10)
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# dataframe['min20'] = talib.MIN(dataframe['close'], timeperiod=20)
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# dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50)
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dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
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# dataframe['min200_1'] = dataframe['min200'] * 1.005
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# dataframe['moy200_12'] = dataframe['min200'].rolling(12).mean()
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dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
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dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
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dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200']
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dataframe['min_n'] = talib.MIN(dataframe['close'], timeperiod=self.buy_min_horizon.value)
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dataframe['max_n'] = talib.MAX(dataframe['close'], timeperiod=self.buy_min_horizon.value)
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dataframe['min_max_n'] = (dataframe['max_n'] - dataframe['min_n']) / dataframe['min_n']
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dataframe['rsi'] = talib.RSI(dataframe)
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dataframe['rsi5'] = talib.RSI(dataframe, timeperiod=5)
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dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
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# dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
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# dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
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# dataframe['sma50'] = talib.SMA(dataframe, timeperiod=50)
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# dataframe['sma100'] = talib.SMA(dataframe, timeperiod=100)
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dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
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dataframe["percent5"] = dataframe["percent"].rolling(5).sum()
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# dataframe["percent3"] = dataframe["percent"].rolling(3).sum()
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dataframe["percent10"] = dataframe["percent"].rolling(10).sum()
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# dataframe["percent20"] = dataframe["percent"].rolling(20).sum()
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# dataframe["percent50"] = dataframe["percent"].rolling(50).sum()
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# dataframe['percent_lost_n'] = dataframe["percent"].rolling(self.protection_lost_candles.value).sum()
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# dataframe["volume10"] = dataframe["volume"].rolling(10).mean()
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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dataframe['distance_min'] = (dataframe['close'] - dataframe['min']) / dataframe['close']
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dataframe['min1.1'] = 1.01 * dataframe['min']
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dataframe['normal'] = 100 * (dataframe['close'] / dataframe['close'].rolling(200).mean())
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dataframe['min_max_close'] = (
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(dataframe['max200'] - dataframe['close']) / (dataframe['close'] - dataframe['min200']))
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['rsi5'] < self.protection_stop_buying_rsi_1d.value)
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& (dataframe['rsi5'] > self.protection_start_buying_rsi_1d.value)
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# & (dataframe['rsi5'].shift(1) < self.buy_rsi.value)
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# & (dataframe['rsi5'].shift(1) < dataframe['rsi5'])
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# & (dataframe['rsi5'].shift(1) < dataframe['rsi5'].shift(2))
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& (dataframe['min_n'].shift(self.decalage.value) == dataframe['min_n'])
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& (dataframe['min_max_n'] >= self.buy_min_max_n.value)
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& (dataframe['close'] <= dataframe['min_n'] * self.buy_percent.value)
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), ['buy', 'buy_tag']] = (1, 'buy_adx_inf')
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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return dataframe
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