976 lines
44 KiB
Python
976 lines
44 KiB
Python
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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 freqtrade.persistence import Trade
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from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
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IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
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import pandas as pd
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import numpy as np
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from pandas import DataFrame
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from typing import Optional, Union, Tuple
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from scipy.special import binom
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import logging
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import configparser
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from technical import pivots_points
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# --------------------------------
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# Add your lib to import here test git
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import ta
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import talib.abstract as talib
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import requests
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from datetime import timezone, timedelta
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.pipeline import make_pipeline
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logger = logging.getLogger(__name__)
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from tabulate import tabulate
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def pprint_df(dframe):
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print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False))
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def normalize(df):
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df = (df - df.min()) / (df.max() - df.min())
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return df
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class Zeus_11(IStrategy):
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levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
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# ROI table:
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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 # 0.256
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# Custom stoploss
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use_custom_stoploss = False
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# Buy hypers
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timeframe = '5m'
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columns_logged = False
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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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"max50": {
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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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"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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"Rsi": {
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"rsi": {
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"color": "pink"
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}
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},
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"Percent": {
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"max_min": {
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"color": "#74effc"
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}
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}
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}
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}
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# 20 20 40 60 100 160 260 420
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# 50 50 100 300 500
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# fibo = [1, 1, 2, 3, 5, 8, 13, 21]
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# my fibo
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# 50 50 50 100 100 150 200 250 350 450 600 1050
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fibo = [1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21]
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baisse = [1, 2, 3, 5, 7, 10, 14, 19, 26, 35, 47, 63, 84]
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# Ma suite 1 1 1 2 2 3 4 5 7 9 12 16 21
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# Mise 50 50 50 100 100 150 200 250 350 450 600 800 1050
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# Somme Mises 50 100 150 250 350 500 700 950 1300 1750 2350 3150 4200
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# baisse 1 2 3 5 7 10 14 19 26 35 47 63 84
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trades = list()
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max_profit_pairs = {}
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pairs = {
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pair: {
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"first_buy": 0,
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"last_max": 0,
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"trade_info": {},
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"max_touch": 0.0,
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"last_sell": 0.0,
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"last_buy": 0.0,
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'count_of_buys': 0,
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'current_profit': 0,
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'expected_profit': 0,
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"last_candle": {},
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"last_trade": None,
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"last_count_of_buys": 0,
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'base_stake_amount': 0,
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'stop_buy': False
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}
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for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
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"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
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}
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# def min_max_scaling(self, series: pd.Series) -> pd.Series:
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# """Normaliser les données en les ramenant entre 0 et 100."""
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# return 100 * (series - series.min()) / (series.max() - series.min())
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#
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# def z_score_scaling(self, series: pd.Series) -> pd.Series:
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# """Normaliser les données en utilisant Z-Score Scaling."""
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# return (series - series.mean()) / series.std()
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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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# count_buys = 0
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# trade = self.getTrade(pair)
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# if trade:
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# filled_buys = trade.select_filled_orders('buy')
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# count_buys = len(filled_buys)
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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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# last_candle_12 = dataframe.iloc[-13].squeeze()
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# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
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allow_to_buy = True # (rate <= float(limit)) | (entry_tag == 'force_entry')
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self.trades = list()
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dispo = round(self.wallets.get_available_stake_amount())
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self.pairs[pair]['first_buy'] = rate
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self.pairs[pair]['last_buy'] = rate
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self.pairs[pair]['max_touch'] = last_candle['close']
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self.pairs[pair]['last_candle'] = last_candle
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self.pairs[pair]['count_of_buys'] = 1
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self.pairs[pair]['current_profit'] = 0
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print(
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f"|{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
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)
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stake_amount = self.adjust_stake_amount(pair, last_candle)
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action="START BUY",
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pair=pair,
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rate=rate,
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dispo=dispo,
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profit=0,
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trade_type=entry_tag,
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buys=1,
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stake=round(stake_amount, 2)
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)
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return allow_to_buy
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
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time_in_force: str,
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exit_reason: str, current_time, **kwargs, ) -> bool:
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# allow_to_sell = (minutes > 30)
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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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allow_to_sell = (last_candle['percent'] < 0)
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if allow_to_sell:
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self.pairs[pair]['last_count_of_buys'] = self.pairs[pair]['count_of_buys']
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self.pairs[pair]['last_sell'] = rate
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self.pairs[pair]['last_trade'] = trade
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self.pairs[pair]['last_candle'] = last_candle
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self.trades = list()
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dispo= round(self.wallets.get_available_stake_amount())
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# print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action="Sell",
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pair=pair,
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trade_type=exit_reason,
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rate=last_candle['close'],
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dispo=dispo,
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profit=round(trade.calc_profit(rate, amount), 2)
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)
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self.pairs[pair]['max_touch'] = 0
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self.pairs[pair]['last_buy'] = 0
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# else:
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# print('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
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return (allow_to_sell) | (exit_reason == 'force_exit')
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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proposed_stake: float, min_stake: float, max_stake: float,
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**kwargs) -> float:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
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current_candle = dataframe.iloc[-1].squeeze()
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adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle)
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# print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
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# Use default stake amount.
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return adjusted_stake_amount
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def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **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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before_last_candle = dataframe.iloc[-2].squeeze()
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#count_of_buys = trade.nr_of_successful_entries
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max_touch_before = self.pairs[pair]['max_touch']
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self.pairs[pair]['last_max'] = max(last_candle['haclose'], self.pairs[pair]['last_max'])
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last_lost = (last_candle['close'] - max_touch_before) / max_touch_before
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count_of_buys = trade.nr_of_successful_entries
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self.pairs[pair]['count_of_buys'] = count_of_buys
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self.pairs[pair]['current_profit'] = current_profit
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expected_profit = self.expectedProfit(pair, last_candle)
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if (last_candle['rsi_1d'] > 50) & (last_candle['percent12'] < 0.0):
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if (last_candle['percent3'] < 0.0) & (current_profit > last_candle['min_max200'] / 3):
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self.trades = list()
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return 'mx_' + str(count_of_buys)
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if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
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self.trades = list()
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return 'profit_' + str(count_of_buys)
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if (current_profit >= expected_profit) & (last_candle['percent'] < 0.0) \
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and ((last_candle['rsi'] >= 75) or before_last_candle['rsi'] >= 75):
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self.trades = list()
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return 'rsi_' + str(count_of_buys)
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self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch'])
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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, '1d') for pair in pairs]
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informative_pairs += [(pair, '1h') for pair in pairs]
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return informative_pairs
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def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
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last_candle=None):
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# Afficher les colonnes une seule fois
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if self.config.get('runmode') == 'hyperopt':
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return
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if self.columns_logged % 30 == 0:
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# print(
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# f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
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# )
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print(
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f"| {'Date':<16} | {'Action':<10} | {'Pair':<5} | {'Trade Type':<18} | {'Rate':>12} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>5} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>12} | {'Buys':>5} | {'Stake':>10} |"
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)
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print(
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f"|{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
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)
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self.columns_logged += 1
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date = str(date)[:16] if date else "-"
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limit = None
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# if buys is not None:
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# limit = round(last_rate * (1 - self.fibo[buys] / 100), 4)
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rsi = ''
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rsi_pct = ''
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# if last_candle is not None:
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# if (not np.isnan(last_candle['rsi_1d'])) and (not np.isnan(last_candle['rsi_1h'])):
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# rsi = str(int(last_candle['rsi_1d'])) + " " + str(int(last_candle['rsi_1h']))
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# if (not np.isnan(last_candle['rsi_pct_1d'])) and (not np.isnan(last_candle['rsi_pct_1h'])):
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# rsi_pct = str(int(10000 * last_candle['bb_mid_pct_1d'])) + " " + str(
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# int(last_candle['rsi_pct_1d'])) + " " + str(int(last_candle['rsi_pct_1h']))
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# first_rate = self.percent_threshold.value
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# last_rate = self.threshold.value
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# action = self.color_line(action, action)
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sma5_1d = ''
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sma5_1h = ''
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sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
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last_lost = round((last_candle['haclose'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
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max_touch = '' #round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
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pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) # round(100 * self.pairs[pair]['current_profit'], 1)
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if trade_type is not None:
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if np.isnan(last_candle['rsi_1d']):
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string = ' '
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else:
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string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_diff_1d']))
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trade_type = trade_type \
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+ " " + string \
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+ " " + str(int(last_candle['rsi_1h'])) \
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+ " " + str(int(last_candle['rsi_diff_1h']))
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print(
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f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} | {rate or '-':>12} | {dispo or '-':>6} | {profit or '-':>8} | {pct_max or '-':>5} | {max_touch or '-':>11} | {last_lost or '-':>12} | {round(self.pairs[pair]['last_max'], 2) or '-':>12} | {buys or '-':>5} | {stake or '-':>10} |"
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)
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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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pair = metadata['pair']
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['haopen'] = heikinashi['open']
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dataframe['haclose'] = heikinashi['close']
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dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
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dataframe['close_02'] = dataframe['haclose'] * 1.02
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dataframe['pct_change'] = dataframe['close'].pct_change(5)
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dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200)
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dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
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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['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
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dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
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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['max200_diff'] = (dataframe['max200'] - dataframe['close']) / dataframe['close']
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dataframe['max50_diff'] = (dataframe['max50'] - dataframe['close']) / dataframe['close']
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dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
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dataframe['sma5_pct'] = (dataframe['sma5'] - dataframe['sma5']) / dataframe['sma5']
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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["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
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dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
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dataframe["percent5"] = (dataframe["close"] - dataframe["open"].shift(5)) / dataframe["open"].shift(5)
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dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
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dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
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dataframe["percent48"] = (dataframe["close"] - dataframe["open"].shift(48)) / dataframe["open"].shift(48)
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# print(metadata['pair'])
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dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
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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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# Normalization
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dataframe['average_line'] = dataframe['close'].mean()
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dataframe['average_line_50'] = talib.MIDPOINT(dataframe['close'], timeperiod=50)
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dataframe['average_line_288'] = talib.MIDPOINT(dataframe['close'], timeperiod=288)
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# Sort the close prices to find the 4 lowest values
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sorted_close_prices = dataframe['close'].tail(576).sort_values()
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lowest_4 = sorted_close_prices.head(20)
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dataframe['lowest_4_average'] = lowest_4.mean()
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# Propagate this mean value across the entire dataframe
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# dataframe['lowest_4_average'] = dataframe['lowest_4_average'].iloc[0]
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# # Sort the close prices to find the 4 highest values
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sorted_close_prices = dataframe['close'].tail(288).sort_values(ascending=False)
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highest_4 = sorted_close_prices.head(20)
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# # Calculate the mean of the 4 highest values
|
|
dataframe['highest_4_average'] = highest_4.mean()
|
|
|
|
# # Propagate this mean value across the entire dataframe
|
|
# dataframe['highest_4_average'] = dataframe['highest_4_average'].iloc[0]
|
|
|
|
# dataframe['pct_average'] = (dataframe['highest_4_average'] - dataframe['close']) / dataframe['lowest_4_average']
|
|
# dataframe['highest_4_average_1'] = dataframe['highest_4_average'] * 0.99
|
|
# dataframe['highest_4_average_2'] = dataframe['highest_4_average'] * 0.98
|
|
# dataframe['highest_4_average_3'] = dataframe['highest_4_average'] * 0.97
|
|
# dataframe['highest_4_average_4'] = dataframe['highest_4_average'] * 0.96
|
|
# dataframe['highest_4_average_5'] = dataframe['highest_4_average'] * 0.95
|
|
|
|
# Compter les baisses consécutives
|
|
dataframe['down'] = dataframe['hapercent'] <= 0.001
|
|
dataframe['up'] = dataframe['hapercent'] >= -0.001
|
|
dataframe['down_count'] = - dataframe['down'].astype(int) * (
|
|
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
|
|
dataframe['up_count'] = dataframe['up'].astype(int) * (
|
|
dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
|
|
dataframe['down_tag'] = (dataframe['down_count'] < -7)
|
|
dataframe['up_tag'] = (dataframe['up_count'] > 7)
|
|
# Créer une colonne vide
|
|
dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
|
|
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
|
|
|
|
# Normaliser les données de 'close'
|
|
# normalized_close = self.min_max_scaling(dataframe['close'])
|
|
################### INFORMATIVE 1h
|
|
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
|
# x_percent = 0.01
|
|
# n_hours = 6
|
|
# n_candles = n_hours * 60 # metadata["timeframe"] # Convertir en bougies
|
|
#
|
|
# informative["max_profit"] = dataframe["informative"].rolling(n_candles).max()
|
|
# informative["profit_hit"] = dataframe["informative"] >= informative["close"] * (1 + x_percent)
|
|
#
|
|
informative['rsi'] = talib.RSI(informative['close'], timeperiod=7)
|
|
informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1)
|
|
|
|
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
|
informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
|
|
|
################### INFORMATIVE 1d
|
|
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
|
informative['rsi'] = talib.RSI(informative['close'], timeperiod=7)
|
|
informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1)
|
|
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
|
informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
|
|
sorted_close_prices = informative['close'].tail(365).sort_values()
|
|
lowest_4 = sorted_close_prices.head(4)
|
|
informative['lowest_4'] = lowest_4.mean()
|
|
|
|
sorted_close_prices = informative['close'].tail(365).sort_values(ascending=False)
|
|
highest_4 = sorted_close_prices.head(4)
|
|
informative['highest_4'] = highest_4.mean()
|
|
|
|
last_14_days = informative.tail(14)
|
|
|
|
# Récupérer le minimum et le maximum de la colonne 'close' des 14 derniers jours
|
|
min_14_days = last_14_days['close'].min()
|
|
max_14_days = last_14_days['close'].max()
|
|
informative['lowest'] = min_14_days
|
|
informative['highest'] = max_14_days
|
|
informative['pct_min_max'] = (max_14_days - min_14_days) / min_14_days
|
|
informative['mid_min_max'] = min_14_days + (max_14_days - min_14_days) / 2
|
|
informative['middle'] = informative['lowest_4'] + (informative['highest_4'] - informative['lowest_4']) / 2
|
|
informative['mid_min_max_0.98'] = informative['mid_min_max'] * 0.98
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
|
|
|
dataframe['count_buys'] = 0
|
|
|
|
dataframe['last_price'] = dataframe['close']
|
|
dataframe['first_price'] = dataframe['close']
|
|
dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
|
dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01
|
|
dataframe['amount'] = 0
|
|
dataframe['limit'] = dataframe['close']
|
|
count_buys = 0
|
|
if self.dp:
|
|
if self.dp.runmode.value in ('live', 'dry_run'):
|
|
self.getOpenTrades()
|
|
|
|
for trade in self.trades:
|
|
if trade.pair != pair:
|
|
continue
|
|
print(trade)
|
|
filled_buys = trade.select_filled_orders('buy')
|
|
dataframe['count_buys'] = len(filled_buys)
|
|
count = 0
|
|
amount = 0
|
|
for buy in filled_buys:
|
|
if count == 0:
|
|
dataframe['first_price'] = buy.price
|
|
dataframe['close01'] = buy.price * 1.01
|
|
|
|
# Order(id=2396, trade=1019, order_id=29870026652, side=buy, filled=0.00078, price=63921.01,
|
|
# status=closed, date=2024-08-26 02:20:11)
|
|
dataframe['last_price'] = buy.price
|
|
self.pairs[trade.pair]['last_buy'] = buy.price
|
|
print(buy)
|
|
count = count + 1
|
|
amount += buy.price * buy.filled
|
|
dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
|
count_buys = count
|
|
dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
|
|
dataframe['amount'] = amount
|
|
print(f"amount= {amount}")
|
|
|
|
# # trades = Trade.get_trades([Trade.is_open is False]).all()
|
|
# trades = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
|
|
# if trades:
|
|
# trade = trades[-1]
|
|
# print('closed trade pair is : ')
|
|
# print(trade)
|
|
# dataframe['expected_profit'] = (1 + self.expectedProfit(pair, dataframe.iloc[-1])) * dataframe[
|
|
# 'last_price']
|
|
# dataframe['lbp'] = dataframe['last_price']
|
|
# dataframe['lbp_3'] = dataframe['lbp'] * 0.97 # 3
|
|
# dataframe['lbp_6'] = dataframe['lbp'] * 0.94 # 6
|
|
# dataframe['lbp_9'] = dataframe['lbp'] * 0.90 # 10
|
|
# dataframe['lbp_12'] = dataframe['lbp'] * 0.85 # 15
|
|
# dataframe['lbp_20'] = dataframe['lbp'] * 0.8 # 20
|
|
# dataframe['fbp'] = trade.open_rate
|
|
# # else:
|
|
# # last_trade = self.get_trades(pair=pair).order_by('-close_date').first()
|
|
# # filled_buys = last_trade.select_filled_orders('buy')
|
|
# # print(last_trade)
|
|
# # for buy in filled_buys:
|
|
# # print(filled_buys)
|
|
|
|
#dataframe['buy_level'] = dataframe['lowest_4_average'] * (1 - self.levels[count_buys] / 100)
|
|
dataframe['buy_level'] = dataframe['max50'] * 0.99 #(1 - self.levels[count_buys] / 100)
|
|
# ----------------------------------------------------------
|
|
# Calcul de la variation entre deux bougies successives
|
|
dataframe['price_change'] = dataframe['close'].diff()
|
|
|
|
# Marquer les bougies en baisse
|
|
dataframe['is_down'] = dataframe['price_change'] < 0
|
|
|
|
# Identifier les blocs consécutifs de baisses
|
|
# dataframe['drop_id'] = (dataframe['is_down'] != dataframe['is_down'].shift(1)).cumsum()
|
|
dataframe['drop_id'] = np.where(dataframe['is_down'],
|
|
(dataframe['is_down'] != dataframe['is_down'].shift(12)).cumsum(), np.nan)
|
|
|
|
# Identifier uniquement les blocs de baisse
|
|
dataframe['drop_id'] = dataframe['drop_id'].where(dataframe['is_down'])
|
|
# # Grouper par les chutes détectées
|
|
# drop_info = dataframe.groupby('drop_id').agg(
|
|
# start=('close', 'first'), # Prix au début de la chute
|
|
# end=('close', 'last'), # Prix à la fin de la chute
|
|
# start_index=('close', 'idxmin'), # Début de la chute (index)
|
|
# end_index=('close', 'idxmax'), # Fin de la chute (index)
|
|
# )
|
|
#
|
|
# # Calcul de l'ampleur de la chute en %
|
|
# drop_info['drop_amplitude_pct'] = ((drop_info['end'] - drop_info['start']) / drop_info['start']) * 100
|
|
# # Filtrer les chutes avec une amplitude supérieure à 3%
|
|
# drop_info = drop_info[drop_info['drop_amplitude_pct'] < -3]
|
|
|
|
# **************
|
|
|
|
# # Identifier le prix de début et de fin de chaque chute
|
|
# drop_stats = dataframe.groupby('drop_id').agg(
|
|
# start_price=('close', 'first'), # Prix au début de la chute
|
|
# end_price=('close', 'last'), # Prix à la fin de la chute
|
|
# )
|
|
|
|
|
|
return dataframe
|
|
|
|
def getOpenTrades(self):
|
|
# if len(self.trades) == 0:
|
|
print('search open trades')
|
|
self.trades = Trade.get_open_trades()
|
|
return self.trades
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
pair = metadata['pair']
|
|
|
|
#expected_profit = self.expectedProfit(pair, dataframe.iloc[-1])
|
|
#last_candle = dataframe.iloc[-1].squeeze()
|
|
|
|
# print("---------------" + pair + "----------------")
|
|
# print('adjust stake amount ' + str(self.adjust_stake_amount(pair, dataframe.iloc[-1])))
|
|
# # print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
|
|
# print('calcul expected_profit ' + str(expected_profit))
|
|
|
|
# buy_level = dataframe['buy_level'] # self.get_buy_level(pair, dataframe)
|
|
|
|
dataframe.loc[
|
|
(
|
|
(dataframe['rsi_1h'] < 70)
|
|
& (dataframe['rsi_diff_1h'] > -5)
|
|
# (dataframe['down_count'].shift(1) < - 6)
|
|
# & (dataframe['down_count'] == 0)
|
|
# & (dataframe['down_pct'].shift(1) <= -0.5)
|
|
), ['enter_long', 'enter_tag']] = (1, 'down')
|
|
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
|
|
|
# for i in range(len(dataframe) - 48):
|
|
# last_candle = dataframe.iloc[i]
|
|
# if last_candle['enter_long'] is not None:
|
|
# if last_candle['enter_long'] == 1:
|
|
# futur_candle = dataframe.iloc[i + 48]
|
|
# sma5pct_1h = last_candle['sma5_pct_1h']
|
|
# sma5pct_1d = last_candle['sma5_pct_1d']
|
|
# i = i + 48
|
|
# print(f"{i} ===> ;{sma5pct_1d:.2f};{sma5pct_1h:.2f};{100 * futur_candle['percent48']:.1f}")
|
|
|
|
# print(dataframe.columns)
|
|
#
|
|
# colonnes = [
|
|
# 'hapercent', 'close_02', 'pct_change', 'max200_diff',
|
|
# 'max50_diff', 'sma5_pct', 'percent', 'percent3',
|
|
# 'percent5', 'percent12', 'percent24', 'percent48', 'rsi',
|
|
# 'bb_percent', 'down_count',
|
|
# 'up_count', 'down_pct', 'up_pct', 'volume_1h', 'rsi_1h',
|
|
# 'sma5_pct_1h', 'volume_1d', 'rsi_1d', 'sma5_pct_1d',
|
|
# 'pct_min_max_1d']
|
|
#
|
|
# exclude_cols = ['date', 'enter_tag', 'close', 'open', 'low', 'high', 'haclose', 'haopen', 'halow', 'hahigh'
|
|
# , 'date_1h', 'close_1h', 'open_1h', 'low_1h', 'high_1h', 'haclose_1h', 'haopen_1h', 'halow_1h', 'hahigh_1h'
|
|
# , 'date_1d', 'close_1d', 'open_1d', 'low_1d', 'high_1d', 'haclose_1d', 'haopen_1d', 'halow_1d', 'hahigh_1d']
|
|
# for column in colonnes:
|
|
# for column2 in colonnes:
|
|
# print('===============================================')
|
|
# print(f"Colonne 1: {column} Colonne 2: {column2}")
|
|
# list_1 = []
|
|
# list_2 = []
|
|
# data = []
|
|
# key_1 = column
|
|
# key_2 = column2
|
|
# futur = 'percent48'
|
|
#
|
|
# for i in range(200, len(dataframe) - 48):
|
|
# last_candle = dataframe.iloc[i]
|
|
# if last_candle['enter_long'] is not None and last_candle['enter_long'] == 1:
|
|
# futur_candle = dataframe.iloc[i + 48]
|
|
# val_1 = last_candle[key_1]
|
|
# val_2 = last_candle[key_2]
|
|
# if not np.isnan(val_1) and not np.isnan(val_2):
|
|
# value = 100 * futur_candle[futur]
|
|
# list_1.append(val_2)
|
|
# list_2.append(val_1)
|
|
# data.append(value)
|
|
# i += 48 # skip to avoid overlapping trades
|
|
#
|
|
# # Tes données sous forme de listes
|
|
# x = np.array(list_1) # axe X
|
|
# y = np.array(list_2) # axe Y
|
|
# z = np.array(data) # valeur à afficher (performance future)
|
|
# # print(len(list_2), len(list_2), len(data))
|
|
# # print(f"Min/max H1: {min(list_1):.5f}, {max(list_1):.5f}")
|
|
# # print(f"Min/max 1D: {min(list_2):.5f}, {max(list_2):.5f}")
|
|
# # print(f"Min/max Data: {min(data):.5f}, {max(data):.5f}")
|
|
# # Fusionner X et Y comme variables indépendantes
|
|
# XY = np.column_stack((x, y))
|
|
# # Modèle
|
|
# model = LinearRegression()
|
|
# model.fit(XY, z)
|
|
# # Coefficients
|
|
# a, b = model.coef_
|
|
# c = model.intercept_
|
|
# r_squared = model.score(XY, z)
|
|
# print(f"Coefficient de détermination R² : {r_squared:.4f}")
|
|
# print(f"Équation estimée : Z = {a:.4f} * X + {b:.4f} * Y + {c:.4f}")
|
|
|
|
# degree = 2 # Pour inclure X², Y², XY
|
|
# poly_model = make_pipeline(PolynomialFeatures(degree), LinearRegression())
|
|
# poly_model.fit(XY, z)
|
|
#
|
|
# # Pour afficher les coefficients :
|
|
# linreg = poly_model.named_steps['linearregression']
|
|
# print("Coefficients:", linreg.coef_)
|
|
# print("Intercept:", linreg.intercept_)
|
|
#
|
|
#
|
|
# # Données factices
|
|
# # x = np.random.uniform(-2, 2, 500)
|
|
# # y = np.random.uniform(-2, 2, 500)
|
|
# # z = np.sin(x) * np.cos(y) * 10 # variation factice
|
|
#
|
|
# # Discrétisation (binning)
|
|
# xbins = np.linspace(min(x), max(x), 20)
|
|
# ybins = np.linspace(min(y), max(y), 20)
|
|
#
|
|
# # Création des bins 2D
|
|
# H, xedges, yedges = np.histogram2d(x, y, bins=[xbins, ybins], weights=z)
|
|
# counts, _, _ = np.histogram2d(x, y, bins=[xbins, ybins]) # pour normaliser
|
|
#
|
|
# # Moyenne dans chaque bin (évite division par 0)
|
|
# H_avg = np.divide(H, counts, out=np.zeros_like(H), where=counts != 0)
|
|
#
|
|
# # Préparer coordonnées pour le graphique
|
|
# xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1], indexing="ij")
|
|
# xpos = xpos.ravel()
|
|
# ypos = ypos.ravel()
|
|
# zpos = np.zeros_like(xpos)
|
|
#
|
|
# dx = dy = (xedges[1] - xedges[0]) * 0.9
|
|
# dz = H_avg.ravel()
|
|
#
|
|
# # Affichage
|
|
# fig = plt.figure(figsize=(12, 8))
|
|
# ax = fig.add_subplot(111, projection='3d')
|
|
# colors = plt.cm.RdYlGn((dz - dz.min()) / (dz.max() - dz.min() + 1e-5)) # Normalisation
|
|
#
|
|
# ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=colors, shade=True)
|
|
#
|
|
# ax.set_xlabel(f"{key_1}")
|
|
# ax.set_ylabel(f"{key_2}")
|
|
# ax.set_zlabel('Perf. moyenne sur 48 bougies')
|
|
# ax.set_title('Performance 48 bougies (%)')
|
|
# plt.show()
|
|
|
|
# plt.figure(figsize=(10, 8))
|
|
# scatter = plt.scatter(
|
|
# list_1,
|
|
# list_2,
|
|
# c=data, # La couleur selon la performance future
|
|
# cmap='RdYlGn', # Dégradé rouge -> jaune -> vert
|
|
# alpha=0.8,
|
|
# edgecolors='k'
|
|
# )
|
|
# plt.xlabel(f"{key_1}")
|
|
# plt.ylabel(f"{key_2}")
|
|
# plt.title(f"Performance future")
|
|
# plt.colorbar(scatter, label="Performance 48 bougies (%)")
|
|
# plt.grid(True)
|
|
# plt.show()
|
|
|
|
# plt.figure(figsize=(10, 6))
|
|
# plt.scatter(list_1, data, c='blue', alpha=0.6)
|
|
# plt.xlabel("SMA5 % sur 1 jour")
|
|
# plt.ylabel("Variation du prix après 48 bougies (%)")
|
|
# plt.title("Lien entre variation SMA5 1j et performance 48h")
|
|
# plt.grid(True)
|
|
# plt.show()
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
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): # ne rien faire si ordre deja en cours
|
|
if trade.has_open_orders:
|
|
#print("has open orders : true")
|
|
return None
|
|
if (self.wallets.get_available_stake_amount() < 50): # or trade.stake_amount >= max_stake:
|
|
#print("wallet too low")
|
|
return 0
|
|
|
|
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
|
last_candle = dataframe.iloc[-1].squeeze()
|
|
last_candle_3 = dataframe.iloc[-4].squeeze()
|
|
# prépare les données
|
|
count_of_buys = trade.nr_of_successful_entries
|
|
current_time = current_time.astimezone(timezone.utc)
|
|
open_date = trade.open_date.astimezone(timezone.utc)
|
|
dispo = round(self.wallets.get_available_stake_amount())
|
|
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
|
|
|
if (len(dataframe) < 1):
|
|
#print("dataframe empty")
|
|
return None
|
|
pair = trade.pair
|
|
if pair not in ('BTC/USDC', 'XRP/USDC', 'BTC/USDT', 'XRP/USDT'):
|
|
print(f"{pair} not in allowed pairs list")
|
|
return None
|
|
max_buys = 20
|
|
|
|
# filled_buys = trade.select_filled_orders('buy')
|
|
# count_of_buys = len(filled_buys)
|
|
if count_of_buys >= max_buys:
|
|
#print(f"count_of_buys {count_of_buys} > {max_buys} max buys")
|
|
return None
|
|
|
|
# if 'buy' in last_candle:
|
|
# condition = (last_candle['buy'] == 1)
|
|
# else:
|
|
# condition = False
|
|
# self.protection_nb_buy_lost.value
|
|
# limit = last_candle['limit']
|
|
stake_amount = self.config['stake_amount'] + 50 * self.fibo[count_of_buys]
|
|
|
|
# current_time_utc = current_time.astimezone(timezone.utc)
|
|
# open_date = trade.open_date.astimezone(timezone.utc)
|
|
# days_since_open = (current_time_utc - open_date).days
|
|
pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4)
|
|
|
|
# if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
|
|
if (
|
|
(
|
|
last_candle['enter_long'] == 1)
|
|
or (last_candle['percent48'] < - 0.03 and last_candle['rsi_diff_1h'] > -5)
|
|
) \
|
|
and (pct_max < -0.012 - (count_of_buys * 0.001)):
|
|
try:
|
|
|
|
# This then calculates current safety order size
|
|
# stake_amount = stake_amount * pow(1.5, count_of_buys)
|
|
# print(
|
|
# f"Adjust {current_time} price={trade.pair} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
|
|
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
|
self.log_trade(
|
|
last_candle=last_candle,
|
|
date=current_time,
|
|
action="Loss -",
|
|
dispo=dispo,
|
|
pair=trade.pair,
|
|
rate=current_rate,
|
|
trade_type=trade_type,
|
|
profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
|
|
buys=trade.nr_of_successful_entries + 1,
|
|
stake=round(stake_amount, 2)
|
|
)
|
|
self.pairs[trade.pair]['last_buy'] = current_rate
|
|
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
|
self.pairs[trade.pair]['last_candle'] = last_candle
|
|
return stake_amount
|
|
except Exception as exception:
|
|
print(exception)
|
|
return None
|
|
pcte=-0.012 - (count_of_buys * 0.001)
|
|
|
|
if not self.dp.runmode.value in ('backtest', 'hyperopt'):
|
|
logger.error(f"adjust_trade_position {trade.pair} tag={last_candle['enter_long']} pct48={last_candle['percent48']:.1f} pctmax={pct_max:.4f} pcte={pcte:.4f}")
|
|
return None
|
|
|
|
def adjust_stake_amount(self, pair: str, dataframe: DataFrame):
|
|
# Calculer le minimum des 14 derniers jours
|
|
current_price = dataframe['close']
|
|
|
|
# trade = self.getTrade(pair)
|
|
# if trade:
|
|
# current_price = trade.open_rate
|
|
base_stake_amount = self.config['stake_amount'] #.get('stake_amount', 50) # Montant de base configuré
|
|
|
|
# Calculer le max des 14 derniers jours
|
|
min_14_days_4 = dataframe['lowest_4_1d']
|
|
max_14_days_4 = dataframe['highest_4_1d']
|
|
percent_4 = 1 - (current_price - min_14_days_4) / (max_14_days_4 - min_14_days_4)
|
|
factor_4 = 1 / ((current_price - min_14_days_4) / (max_14_days_4 - min_14_days_4))
|
|
max_min_4 = max_14_days_4 / min_14_days_4
|
|
|
|
# min_14_days = dataframe['lowest_1d']
|
|
# max_14_days = dataframe['highest_1d']
|
|
# percent = 1 - (current_price - min_14_days) / (max_14_days - min_14_days)
|
|
# factor = 1 / ((current_price - min_14_days) / (max_14_days - min_14_days))
|
|
# max_min = max_14_days / min_14_days
|
|
# Stack amount ajusté price=2473.47 min_max=0.15058074985054215 percent=0.8379141364642171 amount=20.0
|
|
|
|
adjusted_stake_amount = max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
|
# if pair in ('BTC/USDT', 'ETH/USDT'):
|
|
# if percent_4 > 0.5:
|
|
# adjusted_stake_amount = 300
|
|
|
|
# adjusted_stake_amount_2 = max(base_stake_amount / 2.5, min(75, base_stake_amount * percent))
|
|
|
|
# print(
|
|
# f"Stack amount ajusté price={current_price} max_min={max_min_4:.4f} min_14={min_14_days_4:.4f} max_14={max_14_days_4:.4f} factor={factor_4:.4f} percent={percent_4:.4f} amount={adjusted_stake_amount:.4f}")
|
|
# print(f"Stack amount ajusté price={current_price} max_min={max_min:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} factor={factor:.4f} percent={percent:.4f} amount={adjusted_stake_amount_2:.4f}")
|
|
|
|
return adjusted_stake_amount
|
|
|
|
# def adjust_exit_price(self, dataframe: DataFrame):
|
|
# # Calculer le max des 14 derniers jours
|
|
# min_14_days = dataframe['lowest_1d']
|
|
# max_14_days = dataframe['highest_1d']
|
|
# entry_price = dataframe['fbp']
|
|
# current_price = dataframe['close']
|
|
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
|
# exit_price = (1 + percent) * entry_price
|
|
#
|
|
# print(f"Exit price ajusté price={current_price:.4f} max_14={max_14_days:.4f} exit_price={exit_price:.4f}")
|
|
#
|
|
# return exit_price
|
|
|
|
# def adjust_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
|
# current_rate: float, current_profit: float, **kwargs) -> float:
|
|
# dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
|
# # print(dataframe)
|
|
# last_candle = dataframe.iloc[-1].squeeze()
|
|
#
|
|
# # Utiliser l'ATR pour ajuster le stoploss
|
|
# atr_stoploss = current_rate - (last_candle['atr'] * 1.5) # Stoploss à 1.5x l'ATR
|
|
#
|
|
# # Retourner le stoploss dynamique en pourcentage du prix actuel
|
|
# return (atr_stoploss / current_rate) - 1
|
|
|
|
def expectedProfit(self, pair: str, last_candle):
|
|
|
|
current_price = last_candle['last_price'] # dataframe['close']
|
|
|
|
# trade = self.getTrade(pair)
|
|
# if trade:
|
|
# current_price = trade.open_rate
|
|
|
|
# Calculer le max des 14 derniers jours
|
|
min_14_days = last_candle['lowest_1d']
|
|
max_14_days = last_candle['highest_1d']
|
|
percent = (max_14_days - current_price) / (min_14_days)
|
|
|
|
min_max = last_candle['pct_min_max_1d'] # (max_14_days - min_14_days) / min_14_days
|
|
expected_profit = min(0.1, max(0.01, last_candle['min_max200'] * 0.5 + self.pairs[pair]['count_of_buys'] * 0.0005))
|
|
|
|
return expected_profit
|
|
|
|
# def adjust_exit_price(self, dataframe: DataFrame):
|
|
# # Calculer le max des 14 derniers jours
|
|
# min_14_days = dataframe['lowest_1d']
|
|
# max_14_days = dataframe['highest_1d']
|
|
# entry_price = dataframe['fbp']
|
|
# current_price = dataframe['close']
|
|
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
|
# exit_price = (1 + percent) * entry_price
|
|
#
|
|
# print(f"Exit price ajusté price={current_price} max_14={max_14_days} exit_price={exit_price}")
|
|
#
|
|
# return exit_price
|
|
|
|
# def adjust_entry_price(self, dataframe: DataFrame):
|
|
# # Calculer le max des 14 derniers jours
|
|
# min_14_days = dataframe['lowest_1d']
|
|
# max_14_days = dataframe['highest_1d']
|
|
# current_price = dataframe['close']
|
|
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
|
# entry_price = (1 + percent) * entry_price
|
|
#
|
|
# print(f"Entry price ajusté price={current_price} max_14={max_14_days} exit_price={entry_price}")
|
|
#
|
|
# return entry_price
|
|
|
|
# def adjust_stake_amount(self, dataframe: DataFrame):
|
|
# # Calculer le minimum des 14 derniers jours
|
|
# middle = dataframe['middle_1d']
|
|
#
|
|
# # Récupérer la dernière cotation actuelle (peut être le dernier point de la série)
|
|
# current_price = dataframe['close']
|
|
#
|
|
# # Calculer l'écart entre la cotation actuelle et le minimum des 14 derniers jours
|
|
# difference = middle - current_price
|
|
# # Ajuster la stake_amount en fonction de l'écart
|
|
# # Par exemple, augmenter la stake_amount proportionnellement à l'écart
|
|
# base_stake_amount = self.config.get('stake_amount', 100) # Montant de base configuré
|
|
#
|
|
# multiplier = 1 - (difference / current_price) # Exemple de logique d'ajustement
|
|
#
|
|
# adjusted_stake_amount = max(base_stake_amount / 2.5, base_stake_amount * multiplier)
|
|
#
|
|
# # difference = 346.07000000000016
|
|
# # price = 2641.75
|
|
# # min_14 = 2295.68
|
|
# # amount = 56.5500141951358
|
|
#
|
|
# print(f"Stack amount ajusté difference={difference} price={current_price} middle={middle} multiplier={multiplier} amount={adjusted_stake_amount}")
|
|
#
|
|
# return adjusted_stake_amount
|
|
|
|
def calculateUpDownPct(self, dataframe, key):
|
|
down_pct_values = np.full(len(dataframe), np.nan)
|
|
# Remplir la colonne avec les bons calculs
|
|
for i in range(len(dataframe)):
|
|
shift_value = abs(int(dataframe[key].iloc[i])) # Récupérer le shift actuel
|
|
if i - shift_value > 1: # Vérifier que le shift ne dépasse pas l'index
|
|
down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
|
|
dataframe['close'].iloc[i - shift_value]
|
|
return down_pct_values
|
|
|
|
|