473 lines
22 KiB
Python
473 lines
22 KiB
Python
# 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_1(IStrategy):
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# Buy hyperspace params:
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buy_params = {
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"buy_min_horizon": 63,
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"buy_min_max_n": 0.11,
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"buy_rsi": 30,
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"decalage": 2,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_b_RSI": 85,
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"sell_percent": 0.08,
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"sell_profit_percent": 0.2,
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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": 1,
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"protection_percent_buy_lost": 24,
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"protection_start_buying_rsi_1d": 5,
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"protection_stop_buying_rsi_1d": 66,
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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 = -0.208
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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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trades = list()
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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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decalage = IntParameter(1, 12, default=6, space='buy')
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# adx_1d_limit = IntParameter(15, 45, default=18, 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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previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_previous_last_candle = dataframe.iloc[-3].squeeze()
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if self.stop_buying.get(pair, None) is None:
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print("enable buying tag", pair)
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self.stop_buying[pair] = False
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if ((last_candle['rsi5'] >= self.protection_stop_buying_rsi_1d.value) | (last_candle['close'] >= last_candle['bb_upperband'])) \
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& (self.stop_buying[pair] is False):
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logger.info("1 - Disable buying %s date %s", pair, last_candle['date'])
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self.stop_buying[pair] = True
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if self.stop_buying[pair] is True:
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if ((last_candle['rsi5'] <= self.protection_start_buying_rsi_1d.value) & (last_candle['percent5'] >= 0.005)):
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logger.info("2 - Enable buying %s date %s", pair, last_candle['date'])
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self.stop_buying[pair] = False
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logger.info("Buy ==> %s ", pair + " " + str(current_time) + "---------------------")
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if self.stop_buying[pair]:
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allow_to_buy = False
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logger.info("3 - cancel buying %s date %s", pair, str(last_candle['date']) + " " + str(last_candle['rsi5']))
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else:
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logger.info("3 - accept buying %s date %s", pair, str(last_candle['date']) + " " + str(last_candle['rsi5']))
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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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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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# else:
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# logger.info("No 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(previous_last_candle['rsi5']) + " " + str(last_candle['rsi5'])
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# )
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return None
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# def informative_pairs(self):
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# # get access to all pairs available in whitelist.
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# pairs = self.dp.current_whitelist()
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# informative_pairs = [(pair, '4h') for pair in pairs]
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# # informative_pairs += [(pair, '4h') for pair in pairs]
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# informative_pairs += [(pair, '1h') for pair in pairs]
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#
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# return informative_pairs
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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['bb_lower_var_5'] = (dataframe['bb_lowerband'] - dataframe['min50']).rolling(5).var()
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# dataframe['bb_lower_5'] = 100 * ((dataframe['bb_lowerband'].rolling(5).mean() / dataframe['bb_lowerband']) - 1)
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# dataframe['bb_lower_width_5'] = (dataframe['bb_lowerband'] * (1 + dataframe['bb_width'] / self.buy_bb_width_n.value))
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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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# dataframe['stop_buying'] = (dataframe['rsi5'] >= self.protection_stop_buying_rsi_1d.value) \
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# & (dataframe['close'] >= dataframe['bb_upperband'])
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# ################### INFORMATIVE 1D
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# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
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# informative["rsi"] = talib.RSI(informative)
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# informative["rsi3"] = talib.RSI(informative, 3)
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# # informative["mrsi3"] = informative["rsi"].rolling(3).mean()
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# # informative["max3"] = talib.MAX(informative['close'], timeperiod=3)
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# # informative["min3"] = talib.MIN(informative['close'], timeperiod=3)
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# # informative['pct_change_1'] = informative['close'].pct_change(1)
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# # informative['pct_change_3'] = informative['close'].pct_change(3)
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# # informative['pct_change_5'] = informative['close'].pct_change(5)
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# informative['sma3'] = talib.SMA(informative, timeperiod=3)
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# informative['sma5'] = talib.SMA(informative, timeperiod=5)
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# informative['sma10'] = talib.SMA(informative, timeperiod=10)
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# # informative['adx'] = talib.ADX(informative)
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#
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# bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
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# informative['bb_lowerband'] = bollinger['lower']
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# informative['bb_middleband'] = bollinger['mid']
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# informative['bb_upperband'] = bollinger['upper']
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# informative["bb_percent"] = (
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# (informative["close"] - informative["bb_lowerband"]) /
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# (informative["bb_upperband"] - informative["bb_lowerband"])
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# )
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#
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# # informative["bb_width"] = (
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# # (informative["bb_upperband"] - informative["bb_lowerband"]) / informative["bb_middleband"]
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# # )
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#
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# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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# ######################## INFORMATIVE 4h
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# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="4h")
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# informative["rsi"] = talib.RSI(informative)
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# informative['pct_change_1'] = informative['close'].pct_change(1)
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# informative['pct_change_3'] = informative['close'].pct_change(3)
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# informative['pct_change_5'] = informative['close'].pct_change(5)
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# informative['sma3'] = talib.SMA(informative, timeperiod=3)
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# informative['sma5'] = talib.SMA(informative, timeperiod=5)
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# informative['sma10'] = talib.SMA(informative, timeperiod=10)
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# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "4h", ffill=True)
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######################## INFORMATIVE 1h
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# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
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# informative["rsi"] = talib.RSI(informative)
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# informative["rsi3"] = talib.RSI(informative, 3)
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# informative["mrsi3"] = informative["rsi"].rolling(3).mean()
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# informative['pct_change_1'] = informative['close'].pct_change(1)
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# informative['pct_change_3'] = informative['close'].pct_change(3)
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# informative['pct_change_5'] = informative['close'].pct_change(5)
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# informative['sma3'] = talib.SMA(informative, timeperiod=3)
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# informative['sma5'] = talib.SMA(informative, timeperiod=5)
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# informative['sma10'] = talib.SMA(informative, timeperiod=10)
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# informative['adx'] = talib.ADX(informative)
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# bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
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# informative['bb_lowerband'] = bollinger['lower']
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# informative['bb_middleband'] = bollinger['mid']
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# informative['bb_upperband'] = bollinger['upper']
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#
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# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
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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['close'] < dataframe['min_n'] * self.buy_min_max_coef.value)
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& (dataframe['min_n'].shift(self.decalage.value) == dataframe['min_n'])
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# & (dataframe['pct_change_1_1d'] > 0)
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& (dataframe['min_max_n'] >= self.buy_min_max_n.value)
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# & (dataframe['percent'] > -0.005)
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# & (dataframe['close'] <= dataframe['close_1d'])
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# & (dataframe['close_1d'] <= dataframe['sma3_1d'])
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# & (dataframe['close_1d'] <= dataframe['sma5_1d'])
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# & (dataframe['close_1d'] <= dataframe['sma10_1d'])
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# & (dataframe['adx_1d'] > self.adx_1d_limit.value)
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# & (dataframe['rsi3_1d'].shift(288) <= dataframe['rsi3_1d'])
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), ['buy', 'buy_tag']] = (1, 'buy_adx_inf')
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return dataframe
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# def bot_loop_start(self, **kwargs) -> None:
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# pairs = self.dp.current_whitelist()
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# print("Calcul des pairs informatives")
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# for pairname in pairs:
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# self.stop_buying[pairname] = True
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|
# print("Fin Calcul des pairs informatives")
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
# dataframe.loc[
|
|
# (
|
|
# (dataframe['percent10'] < -0.05)
|
|
# ), ['sell', 'sell_tag']] = (1, 'sell_percent10')
|
|
# dataframe.loc[
|
|
# (
|
|
# (dataframe['percent'] < -0.02)
|
|
# ), ['sell', 'sell_tag']] = (1, 'sell_percent')
|
|
|
|
# print("dans sell" + metadata['pair'])
|
|
return dataframe
|
|
|
|
# def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
|
# current_rate: float, current_profit: float, min_stake: float,
|
|
# max_stake: float, **kwargs):
|
|
# dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
|
# if (len(dataframe) < 1):
|
|
# return None
|
|
#
|
|
# if (self.stop_buying.get(trade.pair, None) == None):
|
|
# # logger.info("----------- %s ", trade.pair + " Init stop buying " + str(current_profit) + " " + str(current_time) + "---------------------")
|
|
# self.stop_buying[trade.pair] = False
|
|
#
|
|
# if (self.stop_buying[trade.pair] == True):
|
|
# # logger.info("----------- %s ", trade.pair + " Canceled " + str(current_profit) + " " + str(current_time) + "---------------------")
|
|
# return None
|
|
# last_candle = dataframe.iloc[-1].squeeze()
|
|
# previous_last_candle = dataframe.iloc[-2].squeeze()
|
|
# previous_previous_last_candle = dataframe.iloc[-3].squeeze()
|
|
# last_candle_6 = dataframe.iloc[-7].squeeze()
|
|
#
|
|
# # last_candle_5 = dataframe.iloc[-3].squeeze()
|
|
# # last_candle_decalage = dataframe.iloc[- self.buy_min_max_decalage.value].squeeze()
|
|
# # print(last_candle['buy'])
|
|
#
|
|
# condition = (
|
|
# (previous_last_candle['rsi5'] < self.buy_rsi.value)
|
|
# & (previous_last_candle['rsi5'] < last_candle['rsi5'])
|
|
# & (previous_last_candle['rsi5'] < previous_previous_last_candle['rsi5'])
|
|
# & (last_candle_6['min_n'] == last_candle['min_n'])
|
|
# & (last_candle['min_max_n'] >= self.buy_min_max_n.value)
|
|
# # & (last_candle['close'] < last_candle['min_n'] * self.buy_min_max_coef.value)
|
|
# # & (last_candle['close'] <= last_candle['close_1d'])
|
|
# # & (last_candle['close_1d'] <= last_candle['sma3_1d'])
|
|
# # & (last_candle['close_1d'] <= last_candle['sma5_1d'])
|
|
# # & (last_candle['close_1d'] <= last_candle['sma10_1d'])
|
|
# # & (dataframe['close'] < dataframe['min_n'] * self.buy_min_max_coef.value)
|
|
# )
|
|
# if not (condition):
|
|
# return None
|
|
#
|
|
# # min_d = min(last_candle['sma3_4h'], last_candle['close_1d'])
|
|
#
|
|
# filled_buys = trade.select_filled_orders('buy')
|
|
# count_of_buys = len(filled_buys)
|
|
# # days = (current_time - trade.open_date_utc).days
|
|
# # minutes = (current_time - trade.open_date_utc).seconds / 60
|
|
# # condition = condition & ((last_candle['min50'] == last_candle_5['min50']) & (last_candle['close'] <= last_candle['close_1h']))
|
|
# p = self.protection_percent_buy_lost.value
|
|
# percents = [p, p * 2, p * 3, p * 4, p * 5, p * 6, p * 7, p * 8, p * 9]
|
|
#
|
|
# if (0 < count_of_buys <= self.protection_nb_buy_lost.value) \
|
|
# & (current_profit < - (percents[count_of_buys - 1] / 100)) & (condition):
|
|
# try:
|
|
# p = self.config['stake_amount']
|
|
# factors = [p, p, p, p, 2 * p, 4 * p, 5 * p, 6 * p]
|
|
#
|
|
# stake_amount = factors[count_of_buys - 1]# filled_buys[0].cost
|
|
# # This then calculates current safety order size
|
|
# # logger.info("----------- %s ", trade.pair + " " + str(current_profit) + " " + str(count_of_buys) + " " + str(stake_amount) +
|
|
# # " " + str(current_time) + "---------------------")
|
|
# # print("-----------" + trade.pair + " " + str(current_profit) + " " + str(count_of_buys) + " " + str(stake_amount) +
|
|
# # " " + str(current_time) + "---------------------")
|
|
# return stake_amount
|
|
# except Exception as exception:
|
|
# print(exception)
|
|
# return None
|
|
# return None
|